INTERNATIONAL CONFERENCE ON WEB RESEARCH | Year:2019 | دوره:5 |Papers Count:89

SOCIAL NETWORKS’ COMMENTS ARE RICH SOURCES OF INFORMATION THAT MAY BE FUSED WITH AUDIO-VISUAL CONTENTS TO IMPROVE EMOTIONAL VIDEO RETRIEVAL SYSTEMS. THE RATIONALE BEHIND THIS FUSION IS THAT DIFFERENT SOURCES CAN COMPLEMENT EACH OTHER SINCE THEY HAVE DIFFERENT NATURES, FORMATS, AND ORIGINS. ALTHOUGH EMOTIONAL INFORMATION EXPRESSED IN USERS’ COMMENTS ON THE WEB IS IN ACCORDANCE WITH THE EMOTIONAL AUDIO-VISUAL CONTENT OF VIDEOS, THEY ARE NOT SYNCHRONIZED. IN ORDER TO ADDRESS THIS PROBLEM, DECISION-LEVEL FUSION IS NEEDED WHEN SUCH ASYNCHRONOUS MODALITIES SHOULD BE FUSED. IN THIS ARTICLE, A NEW DECISION-LEVEL FUSION APPROACH BASED ON ORDERED WEIGHTED AVERAGING (OWA) OPERATORS IS PROPOSED. IN THIS APPROACH, EMOTION IS FIRST DETECTED BASED ON THE AUDIO, VIDEO, AND USERS’ COMMENTS AND THEN, INDIVIDUAL DECISIONS ARE FUSED USING THE OWA METHOD. THE PROPOSED METHOD IS EVALUATED ON THE MUSIC VIDEOS OF THE STANDARD DEAP DATA SET. THE RESULTS OF COMPARING THE PROPOSED METHOD WITH AVERAGE, PRODUCT, SUM, AND DEMPSTER-SHAFER FUSION METHODS SHOW THAT THE PROPOSED OWA-BASED METHOD OUTPERFORMS OTHER METHODS IN DIFFERENT FUSION SETTINGS.

THE PURPOSE OF THIS STUDY IS TO INVESTIGATE THE EFFECTIVE OF SOCIAL NETWORKS ON INFORMATION SEEKING BEHAVIOUR. THE ROLE OF THE SOCIAL NETWORKING IN INFORMATION BEHAVIOR WAS DETERMINED BY REVIEWING OF RELEVANT LITERATURE AND IDENTIFYING ITS CONCEPTS. THE STUDY FOUND THAT INFORMATION BEHAVIOR IN SOCIAL NETWORKS HAVE BEEN INFLUENCED BY INTERMEDIARIES’ SOCIAL MEDIA AND Q&A. THESE FACTORS AFFECT THE INFORMATION NEEDS, STRATEGIES FOR THE SEARCH, USE AND INTERPRETATION OF INFORMATION. THESE FACTORS SHOULD BE CONSIDERED IN INFORMATION RETRIEVAL STUDIES.

RECOMMENDATION SYSTEMS ARE SUCCESSFUL PERSONALIZING TOOLS IN WEB. THEY ARE USED TO PROVIDE CONTENT AND SERVICES TAILORED TO INDIVIDUALS BASED ON KNOWLEDGE ABOUT THEIR PREFERENCES AND BEHAVIORS IN A SYSTEM WITH MANY OPTIONS. RECOMMENDATION SYSTEM IS A NECESSARY TOOL FOR BIG INTERNET SHOPPING SUCH AS DIGIKALA. ONE OF THE MOST IMPORTANT RECOMMENDATION METHOD IS MATRIX FACTORIZATION METHOD. IN MATRIX FACTORIZATION METHOD, THE LATENT FEATURES OF USERS AND ITEMS ARE DETERMINED IN SUCH A WAY THAT THE INNER PRODUCT OF THE LATENT FEATURES OF A USER WITH THE LATENT FEATURE OF AN ITEM IS EQUAL TO THAT USER'S RATING ON THAT ITEM. THE PERFORMANCE OF THIS METHOD DEPENDS ON THE INITIAL VALUES OF THE LATENT FEATURES OF ITEMS WHICH IS USUALLY SET RANDOMLY. IN THIS PAPER, WE PROPOSE A METHOD TO DETERMINE THE INITIAL VALUES OF ITEM LATENT FEATURES. OUR PROPOSED INITIAL VALUE IS AN APPROXIMATE SOLUTION OF OUR PROPOSED MATHEMATICAL MODEL WHICH IS THE SIMPLE VERSION OF MATRIX FACTORIZATION MODEL. EXPERIMENTAL RESULTS ON THE DATASETS MOVIELENS100K, ML-LATEST-SMALL, DOUBAN, EACHMOVIE SHOWS THAT THE PERFORMANCE OF OUR PROPOSED METHOD IS BETTER THAN THE TRADITIONAL METHODS.

TWITTER IS AMONG THE MOST USED MICROBLOGGING AND ONLINE SOCIAL NETWORKING SERVICES. IN TWITTER, A NAME, PHRASE, OR TOPIC THAT IS MENTIONED AT A GREATER RATE THAN OTHERS IS CALLED A "TRENDING TOPIC" OR SIMPLY “ TREND” . TWITTER TRENDS HAS SHOWN THEIR POWERFUL ABILITY IN MANY PUBLIC EVENTS, ELECTIONS AND MARKET CHANGES. NEVERTHELESS, THERE HAVE BEEN VERY FEW WORKS FOCUSING ON UNDERSTANDING THE DYNAMICS OF THESE TRENDING TOPICS. IN THIS ARTICLE, WE THOROUGHLY EXAMINED THE TWITTER’ S TRENDING TOPICS OF 2018. TO THIS END, WE ACCESSED TWITTER’ S TRENDS API FOR THE FULL YEAR OF 2018, AND DEVISED SIX CRITERIA TO EVALUATE OUR DATASET. THESE SIX CRITERIA ARE: LEXICAL ANALYSIS, TIME TO REACH, TREND REOCCURRENCE, TRENDING TIME, TWEETS COUNT, AND LANGUAGE ANALYSIS. IN ADDITION TO PROVIDING GENERAL STATISTICS AND TOP TRENDING TOPICS REGARDING EACH CRITERION, WE COMPUTED SEVERAL DISTRIBUTIONS THAT EXPLAIN THIS BULK OF DATA.

ONLINE STORES AND E-COMMERCE PLATFORMS HAVE BECOME INCREASINGLY POPULAR IN RECENT YEARS, AND A REASONABLE APPROACH TO COMPARE THE AVAILABLE PRODUCTS IS TO USE COMMENTS OR FEEDBACKS WRITTEN BY OTHER ONLINE USERS FOR EACH PRODUCT. THEREFORE, THESE PLATFORMS CAN BE A GREAT OPPORTUNITY FOR SPAMMERS TO PROMOTE OR DEMOTE THEIR TARGET PRODUCTS WITH FAKE REVIEWS. SO FAR, THERE IS PLENTY OF STUDIES DONE WITH THE PURPOSE OF DISTINGUISHING SPAM REVIEWS OR SPAMMERS FROM GENUINE ONES, BUT IT SHOULD NOT BE NEGLECTED THAT OFTEN SPAMMERS WORK IN COLLUSION WITH EACH OTHER TO CONTROL THE RATING SCORE OF A PRODUCT MORE NATURALLY. HENCE, THIS ARTICLE FOCUSES ON THE LATTER ASPECT I. E., REVIEW SPAMMER GROUP DETECTION. IN MOST OF THE PREVIOUS WORKS, FREQUENT ITEM SET MINING (FIM) IS APPLIED IN THE EARLY STAGE TO FIND CANDIDATE GROUPS AND THEN AN UNSUPERVISED RANKING PROCEDURE IS DONE BASED ON SOME PREDEFINED FEATURES. ALTHOUGH, FIM METHODS MOSTLY SUFFER FROM THRESHOLD SETTING, I. E., USING LOW SUPPORT VALUES CAUSES INEFFICIENCY AND HIGH SUPPORT VALUES IGNORE SOME USEFUL PATTERNS. FURTHERMORE, INSTEAD OF UNSUPERVISED METHODS, SEMI-SUPERVISED ONES WHICH DON’ T NEED MANY LABELED DATA, CAN IMPROVE THE ACCURACY OF DETECTION GREATLY. IN THIS ARTICLE, WE TACKLE THE ABOVE-MENTIONED CHALLENGES TAKING ADVANTAGE OF SOME LABELED INSTANCES IN A HETEROGENEOUS INFORMATION NETWORK (HIN). USING A HIN CAN PRESERVE THE SEMANTICS BETWEEN DIFFERENT KINDS OF NODES IN THE NETWORK. ALSO, WE EXTRACT CANDIDATE GROUPS USING SPAMMER BEHAVIORS AND THEIR RELATIONS WHICH MAKES IT A ROBUST APPROACH WHEN SPAMMERS DECIDE TO BE MORE INTELLIGENT. EXPERIMENTS ON A REALLIFE YELP DATASET SHOW THE EFFICIENCY OF OUR APPROACH.

COMBINATION OF UTILITY COMPUTING AND WORLD WIDE WEB IS BASE OF THE CLOUD COMPUTING. THE INTRINSIC FEATURES OF CLOUD COMPUTING HAVE PRODUCED MANY COMPETITIVE AND COMPUTING BENEFITS. THE DEVELOPMENT OF CLOUD COMPUTING AND THE EXPANSION OF SERVICE PROVIDERS IN THIS AREA HAS LED TO INCREASE IN INVESTMENT IN CLOUD COMPUTING. LARGE NUMBER OF USERS ON THE ONE HAND AND INCREASING THE NUMBER OF SENSITIVE DATA ON CLOUD ENVIRONMENTS ON THE OTHER HAND, CAUSED A DRAMATIC GROWTH IN THE MOTIVATION OF MALICIOUS ACTIVITIES AND AS A RESULT OF SECURITY CHALLENGES. SOLVING THE CLOUD COMPUTING SECURITY CHALLENGES NEED TO PROPER KNOWLEDGE OF THE SECURITY ISSUES AND THE SCOPE OF THEIR RESOLUTION. SECURITY IS A LONG-TERM PRODUCT OF INTERACTION BETWEEN PEOPLE, PROCESS AND TECHNOLOGY. THIS CATEGORIZATION IS BASED ON POSSIBLE SOLUTIONS TO SECURITY ISSUES. THUS, THE DIVISION OF THREATS IN THESE THREE AREAS CAN HELP THE MANAGERS AND SECURITY SECTORS TO SOLVE THE SECURITY PROBLEMS. HENCE, IN THIS PAPER, ALONG WITH THE COMPREHENSIVE IDENTIFICATION OF CYBER SECURITY CHALLENGES, WE TRY TO ADDRESS THESE THREATS TO CATEGORIES OF PEOPLE, PROCESS, AND TECHNOLOGIES, IN ORDER TO FIND COST EFFECTIVE, EFFICIENT AND FEASIBLE SECURITY SOLUTIONS BASED ON THIS BASIS.

THE INTERNET OF THINGS (IOT) IS A CONCEPT THAT ALLOWS THE NETWORKING OF VARIOUS OBJECTS OF EVERYDAY LIFE AND COMMUNICATIONS ON THE INTERNET WITHOUT HUMAN INTERACTION. THE IOT USE A SPECIALLY-DESIGNED IPV6 PROTOCOL FOR LOW-POWER AND LOSSY NETWORKS (LLN) CALLED 6LOWPAN AND ALSO FOR ROUTING, A PROTOCOL CALLED ROUTING OVER LOW-POWER AND LOSSY NETWORKS (RPL) IS USED AT THESE NETWORKS. DUE TO THE RESOURCE-CONSTRAINED NATURE OF RPL NETWORKS, THEY MAY BE EXPOSED TO A VARIETY OF INTERNAL ATTACKS. NEIGHBOR ATTACK AND DIS ATTACK ARE THE SPECIFIC INTERNAL ATTACKS AT THIS PROTOCOL. THIS PAPER PRESENTS AN ANOMALYBASED LIGHTWEIGHT INTRUSION DETECTION SYSTEM (IDS) BASED ON THRESHOLD VALUES FOR DETECTING ATTACKS ON THE RPL PROTOCOL. THE RESULTS OF THE SIMULATION USING COOJA SHOW THAT THE PROPOSED MODEL HAS A VERY HIGH TRUE POSITIVE RATE (TPR) AND IN SOME CASES, IT CAN BE 100%, WHILE THE FALSE POSITIVE RATE (FPR) IS VERY LOW. THE RESULTS SHOW THAT THE PROPOSED MODEL IS FULLY EFFECTIVE IN DETECTING ATTACKS AND APPLICABLE TO LARGE-SCALE NETWORKS.

AUTOMATICALLY CAPTURING THE MAIN POINTS FROM A SINGLE DOCUMENT OR MULTIPLE DOCUMENTS IS A CHALLENGING REQUIREMENT. EXTRACTIVE TEXT SUMMARIZATION WHICH REFERS TO PROVIDING A BRIEF SUMMARY EXTRACTS SIGNIFICANT SENTENCES FROM TEXT, DEALS WITH SEVERAL ISSUES. RECENTLY A CONSIDERABLE AMOUNT OF WORK HAS CONSIDERED LEARNING APPROACHES AS TEXT SUMMARIZATION SOLUTIONS. INTENSIVE RESEARCHES HAVE SURVEYED DIFFERENT STRATEGIES FOR TEXT SUMMARIZATION. THIS PAPER INFLUENCED BY THE MERIT PERFORMANCE OF LEARNING METHODS FOR THIS TASK, ANALYTICALLY REVIEWED CURRENT ALGORITHMS. IN THIS PAPER WE SUGGEST A FRAMEWORK CALLED “ ELTS” INCLUDING CLASSIFICATION OF EXISTING LEARNING BASED ALGORITHM, INTRODUCING SEVERAL CRITERIA IN ORDER TO MAKE COMPARISON BETWEEN CURRENT MODELS AND AN ANALYSIS BASED ON THESE CRITERIA. WE OFFER ELTS WITH THE AIM TO ENHANCE FUTURE RESEARCH WHICH ATTEMPTS TO A) SOLVE CURRENT METHODS DEFECTS, B) EMPLOY EXISTING STRATEGIES ACCORDING TO THEIR REQUIREMENTS OR C) MAKE ANALYTICAL COMPARISON BETWEEN CURRENT AND FUTURE WORK.

CONSIDERING THE IMPORTANCE OF SOFTWARE SYSTEMS IN HUMAN LIFE, THEIR QUALITY ASSURANCE IS VERY IMPORTANT. FAULT LOCALIZATION IS ONE OF THE SOFTWARE TESTING STEPS, IT TRIES TO FIND THE EXACT LOCATION OF FAULT IN CODE. MOST OF AUTOMATIC FAULT LOCALIZATION TECHNIQUES USE COVERAGE INFORMATION AND RESULTS OF TEST CASES TO CALCULATE THE PROGRAM ENTITIES SUSPICIOUSNESS BY SIMILARITY COEFFICIENTS. THE SIMILARITY COEFFICIENTS DESIGNED BASED ON THE INSIGHT AND UNDERSTANDING OF DEVELOPERS FROM SOFTWARE SYSTEM AND THEY DO NOT HAVE THE SAME PERFORMANCE IN DIFFERENT SCENARIOS. TO OVERCOME WITH THIS PROBLEM, WE USE THE BACK PROPAGATION NEURAL NETWORK AND INVESTIGATE THE EFFECT OF WEIGHTED THE NEURAL NETWORK TO ACCURACY OF LOCATING FAULTS IN SOFTWARE PROGRAMS, BECAUSE THE BACK PROPAGATION NEURAL NETWORK IS SENSITIVE TO WEIGHT AND BY THE INITIAL PROPER WEIGHTS TO THE INPUT LAYER NEURONS CONNECTIONS, THE SEARCH SPACE TO ACHIEVE OPTIMAL WEIGHT IS DECREASING AND NETWORK ACCURACY IMPROVES. WE ANALYZE THE EFFECTIVENESS OF THE PROPOSED METHOD WITH RANDOMLY WEIGHTING THE INPUT LAYER NEURONS AND SOME BASIC AND EFFICIENT SIMILARITY COEFFICIENTS ON SIEMENS SUITE BENCHMARK. THE RESULTS SHOW THAT PROPOSED METHOD HAS A SATISFACTORY PERFORMANCE FOR THE SOFTWARE FAULT LOCALIZATION PROCESS.

THE AIM OF THIS PAPER IS TO REVIEW THE STANDARDS IN THE BLOCKCHAIN TECHNOLOGY. IN THIS REGARD, SITUATION OF BLOCKCHAIN STANDARDIZATION IN FOUR INTERNATIONAL INSTITUTIONS INCLUDING ISO, W3C, ITU AND IEEE AS WELL AS NIST AND ECS ACTIVITIES WAS REVIEWED AND FOUND THAT DESPITE THE ONGOING EFFORTS OF THESE INSTITUTIONS, THERE IS NOT ANY APPROVED STANDARDS IN BLOCKCHAIN DOMAIN. HOWEVER, THESE INSTITUTIONS DETERMINE SOME IMPORTANT ISSUES SUCH AS TERMINOLOGY, PLATFORM, ARCHITECTURE, SECURITY, INTEROPERABILITY, ETC. AND ARE DOING SOME PROJECTS AND ACTIVITIES TO STANDARDIZE THE BLOCKCHAIN.

FIRST OF ALL WE ASSUME METHODS OF PROCESS AND OPERATION IN PLANTS ARE A KIND OF OPTIMIZED PROCESSING INFORMATION SYSTEM. IT IS A CORRECT ASSUMPTION TILL NOW BECAUSE THERE IS NO BETTER SUBSTITUTION FOR IT. WHAT NATURE AUTOMATICALLY AND GRADUALLY HAS MADE IT FOR US, THERE IS A LOT OF COMPLEX PROCESSING IN PLANTS PRODUCTION SYSTEM. IF PROCESS MINING BEING DONE ALONG-SIDE COMPLEX PRODUCTION SYSTEM OF PLANTS, A GOOD FRAMEWORK WILL BE ACCESSED THAT CAN BE APPLICABLE FOR OUR WEB MINING PROCESSES. IN A RESEARCH BASED ON THE ABOVE ASSUMPTION WE WILL DISCUSS HOW PLANTS TRANSFORM SOIL MINERALS AND OTHER ENVIRONMENTAL NEEDED THINGS TO USEFUL PRODUCTS. BASED ON MINING THESE COMPLEX PROCESSES WE INTRODUCE A COMPLETE AND COMPREHENSIVE FRAMEWORK THAT IS HELPFUL FOR SOME WEB MINING APPLICATIONS. TWO MAIN ASPECTS THAT CAN BE DERIVED FROM PROPOSED FRAMEWORK ARE: FIRSTLY, A GOOD MODEL FOR GRADUALLY GROWTH UNTIL BECAME MATURE CAN BE GAINED ALONGSIDE LACK AND SHORTAGE DETECTION OVER MODEL IN PARALLEL; SECONDLY, SOME TECHNICAL AGRICULTURAL USE, LIKE DIGITAL, ROBOTIC AND ACCURATE AGRICULTURE AND SO ON CAN BE ACHIEVED.

WITH THE ADVENT OF WEB 4. 0 AND DEVELOPMENT OF INTELLIGENT AGENTS' APPLICATION IN THIS CONTEXT, ELECTRONIC COMMERCE FACES NEW CAPABILITIES AND REQUIREMENTS. ONE OF THESE REQUIREMENTS IS THE DESIGN OF PROTOCOL AND APPROPRIATE NEGOTIATION STRATEGIES. IN THE REAL WORLD, NEGOTIATION IS CONDUCTED WITH INCOMPLETE INFORMATION ABOUT OPPONENT, AND ACHIEVEMENTS OF PARTICIPANTS DEPEND ON THEIR ABILITY TO REVEAL INFORMATION IN SUCH A WAY THAT FACILITATES REACHING AN AGREEMENT WITHOUT RISK OF LOSING PERSONAL PROFIT. IN THIS PAPER, A BUYER AGENT IS DESIGNED, WHICH HAS AUTO NEGOTIATION ABILITY WITH OPPONENT AND OBTAINS INFORMATION ON OPPONENT IN TERMS OF A STRATEGY BY USING A MACHINE LEARNING METHOD. KNOWING THE OPPONENT’ S STRATEGY ALLOWS THE AGENT TO INCREASE ITS PROFIT. BY USING BAYESIAN LEARNING, THIS AGENT LEARNS OPPONENT’ S STRATEGY DURING NEGOTIATION. EXPERIMENTAL RESULTS SHOW THAT BAYESIAN LEARNING METHOD INCREASES THE EFFICIENCY OF NEGOTIATION WHICH IS MEASURED AND EVALUATED WITH PARAMETERS SUCH AS AVERAGE BUYER UTILITY AND AVERAGE SELLER UTILITY. AVERAGE BUYER UTILITY AND AVERAGE SELLER UTILITY HAVE INCREASED FROM 90% TO 94% AND 27% TO 31% RESPECTIVELY.

INFORMATION RETRIEVAL IS PERFORMED EVERY DAY IN AN OBVIOUS WAY OVER THE WEB, TYPICALLY UNDER A SEARCH ENGINE. HOWEVER, CLASSIC MODELS OF IR DON’ T CONSIDER THE SOCIAL DIMENSION OF THE WEB. THEREFORE, CLASSIC MODELS OF IR AND EVEN THE IR PARADIGM SHOULD BE ADAPTED TO THE SOCIALIZATION OF THE WEB, IN ORDER TO FULLY LEVERAGE THE SOCIAL CONTEXT THAT SURROUND WEB PAGES AND USERS. BASED ON THE PROPOSED METHOD FOR THE SAME QUERY, DIFFERENT USERS ACCORDING TO THE SPECIFICATIONS AND USER INTEREST WILL OBTAIN DIFFERENT EXPANDED QUERIES. IN THIS RESEARCH, A PERSONALIZED SOCIAL QUERY EXPANSION FRAMEWORK IS PROPOSED TO PROVIDES A USER-DEPENDENT QUERY EXPANSION BASED ON THE CONSTRUCTED SOCIAL KNOWLEDGE AND FOLKSONOMY. IN THIS METHOD, THE USER INTEREST RATE IS CONSIDERED IN ADDITION TO THE SEMANTIC SIMILARITY FOR EXPANDING THE USER'S QUERY. THE PERFORMANCE OF THE PROPOSED METHOD IS COMPARED WITH THE TAGRANK ALGORITHM, NEIGHBORHOOD BASED APPROACH AND WITH THE EXPANDED SEMANTIC SEARCH QUERY EXPANSION BASED ON MEAN AVERAGE PRECISION AND MEAN RECIPROCAL RANK. THE RESULTS SHOW THAT IN THE PROPOSED METHOD, WE OBTAIN AN IMPROVEMENT OF ALMOST 13. 85% OF THE MEAN AVERAGE PRECISION AND 18. 12% OF THE MEAN RECIPROCAL RANK THAN THE PREVIOUS APPROACHES OF QUERY EXPANSION.

ELECTRONIC COMMERCE HAS AFFECTED ALL ASPECTS OF LIFE; IT IS THE RESULT OF TECHNOLOGICAL DEVELOPMENT AND IS STILL IN PROGRESS. THIS DEVELOPMENT HAS SOME CONSEQUENCES AND RESULTS FOR PEOPLE. BREACH OF PERSONAL DATA AND PRIVACY IS ONE OF THEM. CYBERSPACE HAS TARGETED ALL ASPECTS OF PERSONAL DATA SUCH AS GENETIC, IDENTITY, PHYSIOLOGICAL, BEHAVIORAL AND RELIGIOUS BELIEFS INFORMATION TO ACHIEVE THEIR POLITICAL, ECONOMIC, AND SOCIAL GOALS. THIS ARTICLE AIMS TO ASSESS THE LEGAL PROTECTION OF PRIVACY IN ACCORDANCE WITH GDPR AND IRANIAN LAWS. THAT IS, WHETHER THESE RULES CAN PREVENT THE VIOLATION OF PRIVACY ON THE WEB, OR WHETHER IN THE DIGITAL AGE, IT IS THE END OF PRIVACY AND PERSONAL DATA PROTECTION. THIS IS A MAJOR ISSUE THAT REQUIRES CAREFUL ANALYSIS AND EVALUATION. THIS ARTICLE HAS BEEN DEDICATED TO THE TOPIC OF PRIVACY AND PERSONAL DATA VIOLATION IN CYBERSPACE, AND TO EXAMINE VARIOUS ASPECTS OF PRIVACY AND ITS SUPPORT IN A DESCRIPTIVE-ANALYTIC METHOD, AND ULTIMATELY OFFER NECESSARY SOLUTIONS TO FURTHER PROTECT THIS PRIVACY. THE RESULT OF THIS STUDY WILL BE DETERMINED BY REVIEWING GDPR LAWS AND THE IRANIAN LEGAL SYSTEM, TO SEE WHETHER IT IS POSSIBLE TO PROTECT PRIVACY AND PERSONAL DATA ON THE WIDER WEB, AND IF IT CAN BE SAID THAT THROUGH ADOPTION OF LAWS THAT ARE IN LINE WITH TECHNOLOGICAL ADVANCES, IT IS POSSIBLE FOR PRIVACY TO EXPERIENCE A NEW ERA.

DUE TO THE EXPANSION OF SOCIAL NETWORKS AND MEDIA SUCH AS TWEETER, FACEBOOK, LINKEDIN, AND DIFFERENT WEBLOGS, AND THE GREAT INCREASE IN INFORMATION SHARING AND COMMENTS, WHICH TYPICALLY ARE IN THE FORM OF TEXT DATA, BIG ENOUGH TO BE RECOGNIZED AS BIG DATA., AND WITH RESPECT TO THE IMPORTANCE OF THESE DATA FOR THE ANALYSIS OF CUSTOMERS’ PRIORITIES, NEEDS AND THEIR ATTITUDES TOWARD DIFFERENT PRODUCTS, FINDING AND EXTRACTING DATA FROM THEIR COMMENTS, ARE THE PRIMARY GOALS OF THIS RESEARCH. TO SERVE THIS PURPOSE, THIS RESEARCH HAS USED DEEP LEARNING APPROACH, AND MULTILAYER NEURAL NETWORK METHODS IN ORDER TO EXTRACT THE POLARITY OF CUSTOMERS’ OPINIONS AND COMMENTS IN TWO DOMAINS OF PRODUCTS/SERVICES RANGING FROM RESTAURANT TO LAPTOP. THE FINDINGS OF THIS STUDY INDICATE THAT THE PROPOSED MODEL USING THE POTENCIES OF THE LONG SHORT-TERM-MEMORY NETWORKS, IS ABLE TO DETERMINE THE COMMENTS' POLARITY WITH 85 % AND 84. 62 % PRECISION FOR RESTAURANT AND LAPTOP DOMAINS RESPECTIVELY, IN SUCH A WAY THAT THE RESULTS ARE RELATIVELY MORE ACCURATE THAN THE RESULTS OF OTHER METHODS.

TWITTER HAS BEEN SO POPULAR WITH PROVIDING A PLATFORM FOR SHARING EMOTIONS AND OPINIONS. BECAUSE OF VAST APPLICATION IN TWO AREA OF SOCIAL AND POLITICAL SCIENCE AND BUSINESS, IT GETS MORE ATTENTION IN OPINION MINING IN SOCIAL MEDIAS. OPINION MINING CAN BE STUDIED FROM TWO PERSPECTIVE, ONE FROM EMOTIONS ABOUT A SPECIFIC ENTITY AND THE OTHER FROM GENERAL EMOTION OF A TWEET. IN TWITTER THE FOCUS IS ON SECOND POINT OF VIEW. STUDIES OF GENERAL EMOTIONS OF A TWEET IN PERSIAN ARE DONE BY THREE APPROACHES, SUPERVISED LEARNING, LEXICON BASED OR A COMBINATION OF BOTH. IN THIS WORK WE TRY TO RECOMMEND A NOVEL APPROACH BASED ON LINKING FOR IMPROVE OPINION CLASSIFICATION IN PERSIAN. THE RESULTS SHOW LANGUAGE INDEPENDENT ALGORITHMS HAVE BETTER PERFORMANCE DUE TO COMPLEXITY AND LACK OF PRE-PROCESSING TOOLS. THE PRECISION FOR POSITIVE OPINIONS IS 98. 87% AND FOR NEGATIVES IS 97. 89%, ALSO RECALL FOR POSITIVE OPINIONS IS 99. 24% AND FOR NEGATIVES IS 96. 84%. IN THIS WORK, THE LANGUAGE INDEPENDENT ALGORITHM IS COMPARED TO TWO LEXICON BASED ALGORITHMS. THE RESULTS SHOW IMPROVEMENT IN PRECISION AND RECALL COMPARE TO LEXICON BASED METHODS.

THE BLOCKCHAIN IS A TECHNOLOGY TO OFFER DIGITAL LEDGERS THAT OPERATE IN A DISTRIBUTED MANNER WITHOUT CENTRAL AUTHORITY. THIS TECHNOLOGY HAS UNIQUE CAPABILITIES THAT HAVE A HIGH POTENTIAL TO MAKE CHANGES IN DIFFERENT AREAS. ONE OF THE AREAS AFFECTED BY THE BLOCKCHAIN IS EDUCATIONAL AND RESEARCH AREA. BY EMERGING A NEW TECHNOLOGY, DIFFERENT UNIVERSITIES AND RESEARCH CENTERS CONTRIBUTE TO DEVELOP THE NEW TECHNOLOGY. IN THIS ARTICLE, UNIVERSITIES AND RESEARCH CENTERS IN THE WORLD THAT ARE ACTIVE IN THE CONTEXT OF THE BLOCKCHAIN ARE STUDIED IN TERMS OF CONCENTRATION OF ACTIVITIES ON VARIOUS AREAS ASSOCIATED WITH THE BLOCKCHAIN. THE ACTIVITIES IN THIS FIELD CAN BE GROUPED INTO TWO BROAD CATEGORIES OF RESEARCH & DEVELOPMENT AND EDUCATION. THE ACTIVITIES IN EACH OF THE TWO GROUPS ARE ANALYZED ON THE BASIS OF THEIR FOCUS ON EACH AREA RELATED TO THE BLOCKCHAIN WITH THE AIM OF EXTRACTING THE KEY AREAS OF ACADEMIC ACTIVITY AND RESEARCH CENTERS AROUND THE WORLD. THE RESULTS OF THIS RESEARCH CAN BE AN EFFECTIVE GUIDE FOR UNIVERSITIES AND RESEARCH CENTERS IN IRAN TO DETERMINE THE PATH AHEAD IN THE BLOCKCHAIN DOMAIN.

DATA INTEGRATION IS A WIDELY-USED TECHNIQUE IN WIRELESS SENSOR NETWORKS. SECURITY-AIMED GOALS CONSISTING OF GETTING ASSURANCE ABOUT UNIVERSALITY AND CORRECTNESS OF DATA IN DATA INTEGRATION, WHILE SENSORS ARE DEPLOYED IN AN ENEMY ENVIRONMENT ARE TRULY VITAL. IN THIS ARTICLE, WE HAVE INDICATED THAT USAGE OF THE BLOOM FILTERS CAN SATISFY OUR SECURITY GOALS. FURTHERMORE, THIS TECHNIQUE, IN COMPARISON WITH EXISTING METHODS, PRODUCES FEWER OVERLOADS.

WITH THE SPREAD OF CYBERSPACE, ESPECIALLY THE INCREASE IN PEOPLE'S USE OF MESSENGERS, THE SPREAD OF NEWS AND RUMORS ARE INCREASING DAY BY DAY. THE IMPACT OF THESE RUMORS IN THE COMMUNITY HAS GROWN SO HIGH THAT SEVERAL LARGE PROJECTS HAVE RECENTLY BEEN DEFINED TO IDENTIFY FAKE NEWS. IN THIS ARTICLE, WE HAVE DESIGNED INTELLIGENT SYSTEMS THAT DETECT AND RESPOND TO FAKE NEWS AND RUMORS IN CYBERSPACE. TO DO THIS, WE USE TEXT SIMILARITY TECHNIQUES AND IMAGE PROCESSING. THE TEXT OF A RUMOR DIFFERS FROM OTHER TEXTS AND INCLUDES SEVERAL SPECIFIC KEYWORDS THAT THE USE OF SIMILARITY-MATCHING ALGORITHMS DOES NOT OPTIMIZE FOR THAT PARTICULAR ANSWER. FOR TEXTS, A WEIGHT-CORRELATION MODEL HAS BEEN PROPOSED THAT GIVES BETTER RESULTS THAN THE EXISTING ONES, AND FOR IMAGES, AN ALGORITHM IS CHOSEN THAT RESPONDS BETTER TO THE MATCHING OF IMAGE NEWS REPORTS.

PEER-TO-PEER SYSTEMS ARE WELL-KNOWN PATTERNS OF DISTRIBUTED SYSTEMS WHICH CREATED SEVERAL REVELATIONS IN DESIGNING ULTRA-SCALABLE SYSTEMS. RECENTLY, THESE TYPES OF SYSTEMS ARE EVOLVED TO COGNITIVE PEER-TO-PEER NETWORKS. BECAUSE OF DISTRIBUTED NATURE OF INTERNET OF THINGS (IOT), PEER-TO-PEER SYSTEMS SUCH AS BLOCKCHAIN CAN BE USED TO DESIGN MANAGEMENT MECHANISMS IN IOT. THEREFORE, WE NEED AN ARCHITECTURE WHICH FACILITATES THE UTILIZATION OF PEER-TO-PEER SYSTEMS IN IOT BASED SYSTEMS. TO THE BEST OF OUR KNOWLEDGE, THERE IS NO GENERAL ARCHITECTURE BASED ON COGNITIVE PEER-TO-PEER NETWORKS. THEREFORE, IN THIS PAPER, WE PROPOSE AN ARCHITECTURE FOR MANAGING IOT BASED ON COGNITIVE PEER-TO-PEER NETWORKS. IN ORDER TO STUDY THE POTENTIAL OF THE PROPOSED ARCHITECTURE, WE SUGGEST AN ADAPTIVE SEARCH MECHANISM AND ALSO A TOPOLOGY MANAGEMENT ALGORITHM UTILIZING THE PROPOSED ARCHITECTURE. THE RESULTS SHOW THE EFFICIENCY OF THE SUGGESTED ALGORITHMS. THE PROPOSED ARCHITECTURE MAY FACILITATE A NEW EVOLUTION FROM PEER-TO-PEER SYSTEMS TO THING-TO-THING SYSTEMS.

INTERNET OF THINGS (IOT) OFFERS SOME ADVANCED VERTICAL SERVICES THROUGH DATA CAPTURE AND PROCESSING. TO PROVIDE THE SERVICES TO THE END USER, SOME OTHER SERVICES SUCH AS DATA ANALYTICS, DEVICE MANAGEMENT, AND CONNECTION MANAGEMENT SHOULD BE DELIVERED IN THE IOT ECOSYSTEM. IOT PLATFORM IS THE ELEMENT, WHICH DELIVERS THE CENTRAL PROCESS AND MANAGEMENT, AND THE VERTICAL SERVICES TO THE END USERS, BY PROVIDING SOME TOOLS, AND COMPUTATION FOR DEVICE MANAGEMENT AND DATA LIFECYCLE MANAGEMENT (FROM SENSORS NETWORKS TO THE END USERS). ALTHOUGH THERE ARE LOTS OF IOT PLATFORM PRODUCTS IN THE MARKET, THERE IS NOT ANY UNIQUE, PRECISE, OR STANDARD DEFINITION WITH THE DETAILED DESCRIPTION OF IOT PLATFORM, WHICH INCLUDES VARIOUS DEFINITIONS AND FUNCTIONALITIES OF IOT PLATFORM FROM SCIENTIFIC AND MARKET PERSPECTIVE, ON BOTH CLOUD AND FOG COMPUTING RESOURCES. IN THIS PAPER, A NOVEL, COMPREHENSIVE DEFINITION FOR IOT PLATFORM AND ITS ATTRIBUTES IN CLOUD AND FOG LAYER IS PROPOSED, WHICH IS EXTRACTED FROM SCIENTIFIC DEFINITIONS IN ACADEMIC PAPERS, THE DEFINITION, AND FEATURES FOR COMMERCIAL PRODUCTS PROVIDED BY IOT LEADER COMPANIES, AS WELL AS THE DESCRIPTION OF IOT PLATFORM IN SOME OPEN SOURCE PROJECTS.

DUPLICATE BUG REPORT DETECTION IS ONE OF THE MAJOR PROBLEMS IN SOFTWARE TRIAGE SYSTEMS LIKE BUGZILLA TO DEAL WITH END USER REQUESTS. USER REQUEST CONTAINS SOME CATEGORICAL AND ESPECIALLY TEXTUAL FIELDS WHICH NEED FEATURE EXTRACTION FOR DUPLICATE DETECTION. CONTEXTUAL AND TOPICAL FEATURES ARE ACQUIRED USING CALCULATING COSINE SIMILARITY BETWEEN TERM FREQUENCY OR INVERSE DOCUMENT FREQUENCY OR BM25F TECHNIQUE FROM A PAIR OF BUG REPORTS AGAINST SOME TOPICS. THIS RESEARCH PROPOSES THE INDIVIDUAL MANHATTAN DISTANCE SIMILARITY APPROACH INSTEAD OF COSINE DISTANCE SIMILARITY FOR EVERY TOPIC IN CONTEXTUAL FEATURES TO EXPAND THE FEATURE DIMENSION WHICH CAN INCREASE THE ACCURACY OF THE DUPLICATE BUG REPORT DETECTION PROCESS. THE FOUR FAMOUS DATASETS OF BUG REPORTS HAVE USED FOR EVALUATION OF THE PROPOSED METHOD INCLUDING ANDROID, ECLIPSE, MOZILLA, AND OPEN OFFICE WHICH THE EXPERIMENTAL RESULTS INDICATE PERFORMANCE IMPROVEMENT FOR FOUR CONTEXTUAL FEATURES INCLUDING GENERAL, CRYPTOGRAPHY, NETWORK, AND JAVA TOPICS.

AIM: ACCESS TO THE REQUIRED INFORMATION IN ALL AVAILABLE SCIENTIFIC DISCIPLINES IS ONE OF THE MOST IMPORTANT FACTORS IN THE SURVIVAL OF THAT FIELD. IN THE ARCHITECTURE FIELD, THE TYPE OF INFORMATION FORMAT DIFFERS FROM OTHER DISCIPLINES. IN THIS FIELD, IMAGES ARE IMPORTANT FOR PERFORMING ARCHITECTURAL ACTIVITIES AS WELL AS TRAINING. THE PURPOSE OF THIS STUDY WAS TO IDENTIFY THE BEHAVIOR OF IMAGE IN THE ARCHITECTURE OF SHAHID BEHESHTI UNIVERSITY. THEREFORE BY REVIEWING RELATED RETRIEVAL TEXTS, INFORMATION BEHAVIOR, IMAGE RETRIEVAL, AND ARCHITECTURE, THE DISCOVERY OF THE IMPORTANCE OF THE IMAGE IN THIS FIELD AND THE FEATURES OF IMAGE RETRIEVAL AND IMAGE RETRIEVAL SOLUTIONS WERE ADDRESSED. METHOD: THE PRESENT STUDY IS AN APPLIED TARGET AND DESCRIPTIVE SURVEY METHOD. THE STATISTICAL POPULATION OF THE STUDY CONSISTS OF TWO GROUPS OF STUDENTS AND PROFESSORS IN ARCHITECTURE OF SHAHID BEHESHTI UNIVERSITY. THE NUMBER OF PROFESSORS AND STUDENTS IN THE FIELD OF ARCHITECTURE WAS 1262, AND IT WAS NOT POSSIBLE TO EXAMINE ALL OF THE GROUP MEMBERS, SO THEY WERE SAMPLED FROM THE COMMUNITY. TO DETERMINE THE SAMPLE SIZE, THE COCHRAN FORMULA WAS USED AND THE SAMPLE SIZE IN THIS FORMULA WAS 296, BUT SLIGHTLY MORE THAN THE SAMPLE SIZE, 300 QUESTIONNAIRES WERE DISTRIBUTED. AFTER COLLECTING THE QUESTIONNAIRES AND ANALYZING THE RESPONSES, SPSS SOFTWARE WAS USED TO IDENTIFY THE CHARACTERISTICS OF THE BEHAVIOR OF THE SPECIALISTS IN THE FIELD OF ARCHITECTURE. RESULTS: RESULTS: THE RESULTS SHOWED THAT THE ARCHITECTS MAINLY USED IMAGES FOR IDENTIFYING CREATIVE AND AND THE USE OF DETAILS OF ARCHITECTURAL STRUCTURES. IN ORDER TO IDENTIFY THE NEED FOR IMAGES, THEY ARE REQUIRED MAINLY USED SOCIAL NETWORKS, SPECIALIZED SEARCH ENGINES, SPECIALIZED IMAGE DATABASES AND CONSULT WITH THEIR PROFESSIONALS. THE TYPE OF IMAGE CONTENT THEY USED WAS MOSTLY PHOTOS, MAPS AND CHARTS. FIND THEM IN ENGINES AND IMAGE DATABASES BY LIMITING THE SIZE OF THE IMAGE AND FOLLOWING RELATED LINKS AS LONG AS THE IMAGE WAS TAKEN. ONE OF THE MAJOR OBSTACLES IN FINDING IMAGES FOR ARCHITECTS WAS THE LACK OF FAMILIARITY WITH THE WAY THEY WERE SEARCHED. CREATIVITY, PROXIMITY TO THE SUBJECT, THE CREDIBILITY AND QUALITY OF THE IMAGES WERE THE CRITERIA FOR SELECTING CONTENT. EASY TO USE AND EASY TO NAVIGATE WAS THE MAIN CRITERION FOR SELECTING THE CONTENT DELIVERY SITE. THE AMOUNT OF LIBRARY UTILIZATION AND ITS SERVICES BY ARCHITECTURAL EXPERTS WERE AS ACCEPTABLE AS THE USE OF SEARCH ENGINES AND THE INTERNET, WHICH REQUIRES THE PLANNING AND DEVELOPMENT OF SERVICES FOR THE BENEFIT OF LIBRARY ARCHITECTS.

WITH THE GROWING USE OF COMPUTER NETWORKS AND THE TRANSFER OF PERSONAL AND ORGANIZATIONAL ASSETS IN CYBERSPACE, SECURING SPACE HAS BECOME VITAL. SECURITY OF THE INFORMATION EXCHANGE IS THE RESULT OF ACHIEVING CONFIDENTIALITY, AVAILABILITY AND INTEGRITY, WHICH WE CONSIDER TO BE A SECURITY TRIANGLE. ALL OF THE MECHANISMS AND TOOLS USED TO MAINTAIN SECURITY ARE DUE TO THE NEED FOR THESE THREE FACTORS IN INFORMATION EXCHANGE ENVIRONMENTS. AUTHENTICATION OF ENTITIES BEFORE ACCESS TO ASSETS IS THE MOST COMMON METHOD FOR MAINTAINING CONFIDENTIALITY IMPLEMENTED BY VARIOUS FUNCTIONS. THE WEAKNESS IN THE DESIGN OR IMPLEMENTATION OF THESE FUNCTIONS CAN LEAD TO VULNERABILITY AND INCREASE THE POSSIBILITY OF UNAUTHORIZED EXPLOITATION. THE PURPOSE OF THIS PAPER IS TO ANALYZE THE AUTHENTICATION FUNCTIONS AND EVALUATE THESE FUNCTIONS AGAINST COMMON ATTACKS.

NOWADAYS, PHISHING IS ONE OF THE MOST USUAL WEB THREATS WITH REGARDS TO THE SIGNIFICANT GROWTH OF THE WORLD WIDE WEB IN VOLUME OVER TIME. PHISHING ATTACKERS ALWAYS USE NEW (ZERO-DAY) AND SOPHISTICATED TECHNIQUES TO DECEIVE ONLINE CUSTOMERS. HENCE, IT IS NECESSARY THAT THE ANTI-PHISHING SYSTEM BE REAL-TIME AND FAST AND ALSO LEVERAGES FROM AN INTELLIGENT PHISHING DETECTION SOLUTION. HERE, WE DEVELOP A RELIABLE DETECTION SYSTEM WHICH CAN ADAPTIVELY MATCH THE CHANGING ENVIRONMENT AND PHISHING WEBSITES. OUR METHOD IS AN ONLINE AND FEATURE-RICH MACHINE LEARNING TECHNIQUE TO DISCRIMINATE THE PHISHING AND LEGITIMATE WEBSITES. SINCE THE PROPOSED APPROACH EXTRACTS DIFFERENT TYPES OF DISCRIMINATIVE FEATURES FROM URLS AND WEBPAGES SOURCE CODE, IT IS AN ENTIRELY CLIENT-SIDE SOLUTION AND DOES NOT REQUIRE ANY SERVICE FROM THE THIRD-PARTY. THE EXPERIMENTAL RESULTS HIGHLIGHT THE ROBUSTNESS AND COMPETITIVENESS OF OUR ANTI-PHISHING SYSTEM TO DISTINGUISH THE PHISHING AND LEGITIMATE WEBSITES.

FROM THE VERY EARLY DAYS, AND DUE TO ITS UNPRECEDENTED OPPORTUNITIES AND ADVANTAGEOUS FACILITIES, THE WEB HAS BEEN REVOLUTIONIZING ALMOST EVERY ASPECTS OF HUMAN LIFE, TECHNOLOGY, ETC. AS A DYNAMIC PHENOMENON, THE WEB ITSELF HAS EXPERIENCED SEVERAL MAJOR EVOLUTION STAGES. SUCH FUNDAMENTAL CHANGES NEVER THOUGHT TO ABOLISH PREDECESSOR TECHNOLOGIES AND CONCEPTS BUT TAKING THEM TO A NEW LEVEL OF FUNCTIONALITY. IN FACT, THESE PARADIGM SHIFTS ARE NECESSARY AND INEVITABLE IN ORDER TO TAKE ADVANTAGES OF STATE OF THE ART TECHNOLOGIES AND APPROACHES. IN THIS REGARD, SOCIAL WEB, SEMANTIC WEB AND SIMILAR NOMENCLATURES AIMED TO EMPHASIZE THE KEY IDEA THAT BEST REPRESENTS THE LATEST ACHIEVEMENTS AND POSSIBLY FUTURE DIRECTIONS. THANKS TO MANY YEARS OF RESEARCH AND DEVELOPMENT, THE CURRENT STATE (VERSION) OF THE WEB PUTS FORWARD SOME OUTSTANDING OPPORTUNITIES BOTH TO MAKE THE MOST OF AVAILABLE RESOURCES/CAPABILITIES AND ESTABLISH THE FOUNDATION OF THE FUTURE WEB. SPECIFICALLY, CONVERGENCE OF HUMAN INTELLIGENCE (OF WEB USERS), MACHINE INTELLIGENCE (OF WEB AGENTS) AND INTELLIGENCE OF THINGS (THROUGH WEB OF THINGS) TOWARD SHAPING WEB OF INTELLIGENCE (WOI) CAN BE REGARDED AS A BREAKTHROUGH IN THE FIELD. THE MAJOR CONTRIBUTION OF THIS WORK IS PROPOSING A WEBBASED CONCEPTUAL MODEL FOR INTELLIGENCE HARVESTING (I. E. WEB OF INTELLIGENCE) THROUGH WHICH HUMANS, MACHINES AND THINGS COULD COLLABORATE TO SOLVE LARGE SCALE INTELLIGENCE-INTENSIVE PROBLEMS IN A MORE EFFICIENT AND EFFECTIVE WAY. THE RATIONALE BEHIND THIS IDEA, ITS BUILDING BLOCKS AND RELATED CONSIDERATIONS ARE DELINEATED IN THIS PAPER. MOREOVER, A NOVEL APPLICATION BASED ON THE PROPOSED CONCEPT, ENTITLED COMPREHENSIVE INTELLIGENT SEARCH, IS INTRODUCED.

NUMBER OF DEVICES USING INTERNET IS INCREASING EVERY DAY AND THIS FACT MAKES THE NEED TO IMPROVE THE INTERNET OF THINGS PROTOCOL MORE THAN EVER. DATA PROTECTION AND PRIVACY IS ONE OF THE KEY CHALLENGES IN THE INTERNET OF THINGS TECHNOLOGY. DANGERS INVOLVED IN THE CENTRALIZED TECHNOLOGY OF BLOCKCHAIN SYSTEM HAVE LED TO THE IDEA OF USING TANGLE, WHICH IS A DECENTRALIZED SYSTEM. THE MAIN PURPOSE OF THIS NEW TECHNOLOGY IS TO IMPROVE THE PROBLEMS AND LIMITATIONS OF BLOCKCHAIN SUCH AS HIGH COST AND TIME TO CONFIRM A TRANSACTION. IN THIS NEW ARCHITECTURE, EVERY NODE IS INVOLVED IN MAINTAINING NETWORK SECURITY. THIS WAY, IF A TRANSACTION IS CREATED, THEN IT SHOULD SELECT AND CONFIRM TWO UNCONFIRMED TRANSACTIONS ISSUED BEFORE. A WALKING ALGORITHM IS NEEDED FOR THIS SELECTION. WALKING ALGORITHMS PRESENTED IN LITERATURE THUS FAR ARE EITHER WEIGHTED OR UNWEIGHTED. AN UNWEIGHTED RANDOM WALK ALGORITHM CAN APPROVE TRANSACTIONS NEARLY PROPORTIONAL TO THE TIME OF THEIR ARRIVALS WHILE A WEIGHTED ALGORITHM CAN BETTER DEFEND AGAINST LAZY AND MALICIOUS TRANSACTIONS. IN THIS PAPER, A NEW RANDOM WALK ALGORITHM IS PRESENTED THAT HAS THE BENEFITS OF BOTH ALGORITHMS AT THE SAME TIME. THE IDEA IS TO ADAPT THE WEIGHT VALUE TO THE CURRENT SITUATION OF TRANSACTIONS. NUMERICAL RESULTS HAVE SHOWN THE SUPERIORITY OF THE PROPOSED ALGORITHM IN COMPARISON TO THE EXISTING ALGORITHMS IN PROVIDING A BALANCE BETWEEN TIMELINESS OF APPROVING TRANSACTIONS AND PROTECTING AGAINST MALICIOUS ACTIVITIES.

SERVICE-ORIENTED ARCHITECTURE HAS BEEN WIDELY ACCEPTED AND USED AS A FLEXIBLE SOLUTION FOR DESIGN OF DISTRIBUTED SERVICES. VARIOUS REASONS INCLUDING ANTI-PATTERNS HAVE CAUSED MANY QUALITATIVE PROBLEMS IN DESIGN OF SYSTEMS IN THIS ARCHITECTURE. IN THIS PAPER, AN AUTOMATED APPROACH IS PROPOSED TO DETECT SERVICE ORIENTED ARCHITECTURE ANTI-PATTERNS BASED ON REST TECHNOLOGY. IN THIS APPROACH, THESE QUALITATIVE PROBLEMS ARE IDENTIFIED USING GENETIC PROGRAMMING AND A SET OF CRITERIA RELATED TO THE REST TECHNOLOGY. THE RESULTS OBTAINED FROM THE REVIEW OF TRUE ANTI-PATTERNS SHOWS THAT THE PROPOSED APPROACH HAS AN AVERAGE RECALL AND PRECISION RATE OF 75%.

THE CREATIONS OF HIGH-QUALITY SOFTWARE PLAY AN IMPORTANT ROLE IN THE CURRENT STATE OF THE ENTERPRISE SYSTEMS. A HIGH-QUALITY SOFTWARE SHOULD CONTAIN CERTAIN FEATURES INCLUDING FLEXIBILITY, MAINTAINABILITY, AND A WELLDESIGNED STRUCTURE. CORRECTLY ADHERING TO THE OBJECT-ORIENTED PRINCIPLES IS ONE OF THE METHODS THAT MAKE THE CODE MORE FLEXIBLE. DEVELOPERS USUALLY TRY TO LEVERAGE THESE PRINCIPLES, BUT MANY TIMES NEGLECTING THEM DUE TO THE LACK OF TIME AND THE EXTRA COSTS INVOLVED. THEREFORE, SOMETIMES THEY CREATE CONFUSING, COMPLEX, AND PROBLEMATIC STRUCTURES IN THE CODE KNOWN AS CODE SMELLS. IN THIS PAPER, WE WILL INTRODUCE AN AUTOMATED METHOD FOR IDENTIFYING A SERIES OF CODE SMELLS IN THE JAVA PROGRAMS. THEN, BY USING ARTIFICIAL INTELLIGENCE METHODS SUCH AS FUZZY-GENETIC ALGORITHMS, WE WILL PERFORM THE AUTOMATIC REFACTORING. IN THE PROPOSED METHOD, WE HAVE ALSO USED A GRAPH-MODEL AS THE CORE REPRESENTATION MODEL ALONG THE CORRESPONDING MEASURES SUCH AS LOAD-CENTRALITY, DEGREE-CENTRALITY, BETWEENNESS-CENTRALITY, AND CLOSENESS-CENTRALITY TO IDENTIFY THE CODE SMELLS IN THE PROGRAMS. THEN, WE COMBINE THE APPLIED FUZZY SYSTEM AND THE GENETIC ALGORITHM (I. E. FUZZY-GENETIC) TO REFACTOR THE CODE WITH THE HELP OF GRAPHICAL FEATURES. FINALLY, WE TEST OUR METHOD ON JUNIT AND GANTTPROJECT AND EVALUATE OUR RESULTS AGAINST THE IPLASMA TOOL. THIS APPROACH IDENTIFIES ON AVERAGE MORE THAN 86. 36 % OF THE CLASSES SIMILAR TO IPLASMA. THE PROPOSED METHOD ALSO REFACTORS OVER 93. 5 % OF THE CLASSES CORRECTLY.

MAJOR PART OF INTERNET USERS ARE DEVICES WHICH ARE CONNECTED TO EACH OTHER ON THE INTERNET AND ARE EXCHANGING DATA WITH INTERNET BROKERS TO RECEIVE REQUESTED SERVICES. MANAGING AND ACCOUNTING WELL TO IOT REQUESTS NEEDS MAXIMUM PROCESSING POWER, SPEED IN DATA TRANSFER AND PROPER COMBINING SERVICES IN MINIMUM TIME. THIS MANY DEVICES IN IOT, MADE SOLVING PROBLEMS IN THIS AREA TO USE ABILITIES AND FACILITIES OF CLOUD ENVIRONMENT. HENCE COMBINING SERVICES IN CLOUD ENVIRONMENT IS PAID ATTENTION RECENTLY. IN THIS RESEARCH WE WANT TO GIVE AN ALGORITHM WITH APPROACH OF IMPROVING FACTORS PROPOUNDED IN THE PROBLEM COMBINING SERVICE COMPOSITION PROBLEM LIKE NUMBER OF CLOUDS INVOLVED IN GIVING SERVICES, NUMBER OF SERVICES STUDIED BEFORE FULFILLING USERS REQUESTS AND LOAD BALANCE BETWEEN CLOUDS. IN THIS PAPER WE USE THE FACTOR, SIMILARITY MEASURE, TO FIND THE MOST SUITABLE CLOUD AND COMPOSITION PLAN IN EACH PHASE WHICH IN ADDITION TO IMPROVING QOS METRICS PROPOUNDED IN PREVIOUS PAPERS, IT CAUSED IMPROVING QOS METRIC OF LOAD BALANCING BETWEEN CLOUDS, PREVENTION OF FORMATION OF BOTTLENECK IN CLOUDS ENTRANCE, DECREASING THE PROBABILITY OF TEMPORARILY FAILING OF ANY OF CLOUDS AND CONSEQUENTLY INCREASING THE USERS’ SATISFACTION.

DUE TO THE ABUNDANCE OF COMPUTING RESOURCES IN VEHICLES, THE IDEA OF AGGREGATING AND UTILIZING THESE RESOURCES TO CREATE A CLOUD COMPUTING ENVIRONMENT IS JUSTIFIABLE. CARS ON THE ROAD OR PARKED CARS CAN BE USED TO MANAGE COMPUTING AND STORAGE TASKS LIKE CONVENTIONAL CLOUDS. DUE TO THE WIDE VARIETY OF RESOURCES AS WELL AS THE MOBILITY OF VEHICLES, THE APPROPRIATE SERVICE ALLOCATION AND SERVICE AVAILABILITY ARE AMONG THE MOST IMPORTANT CHALLENGES IN THIS AREA. ON THE OTHER HAND, THE MOBILITY OF THE SERVICE REQUESTER/PROVIDER LEADS TO A TEMPORARY CLOUD, WHICH MAKES IT DIFFICULT TO SET UP A SERVICE LEVEL AGREEMENT. IN THIS PAPER, WE PROPOSE AN APPROACH TO RESOLVE THE CHALLENGES OF APPROPRIATE SERVICE ALLOCATION AND SERVICE PRICING WITH THE AIM OF SETTING UP A SERVICE LEVEL AGREEMENT. IN THIS APPROACH, A METHOD IS AVAILABLE TO DIAGNOSE SERVICE REQUESTER/PROVIDER MOBILITY IN ORDER TO INCREASE THE AVAILABILITY OF SERVICES. SIMULATION RESULTS IN DIFFERENT SCENARIOS INDICATE IMPROVED AVAILABILITY AND SERVICE DELIVERY COMPARED TO THE OTHER RELATED APPROACH. ALSO, WITH THE INCREASE IN THE NUMBER OF VEHICLES, THE PROPOSED APPROACH PROVIDES GOOD SCALABILITY.

POPULARITY OF ONLINE SOCIAL NETWORK SERVICES MAKES IT A SUITABLE PLATFORM FOR RAPID INFORMATION DIFFUSION RANGING FROM POSITIVE TO NEGATIVES INFORMATION. ALTHOUGH THE POSITIVE DIFFUSED INFORMATION MAY WELCOMED BY PEOPLE, THE NEGATIVE INFORMATION SUCH AS RUMOR, HATE AND MISINFORMATION CONTENT SHOULD BE BLOCKED. HOWEVER, BLOCKING INAPPROPRIATE, UNWANTED AND CONTAMINATION DIFFUSION ARE NOT TRIVIAL. IN PARTICULAR, IN THIS PAPER, WE STUDY THE NOTION OF COMPETING NEGATIVE AND POSITIVE CAMPAIGNS IN A SOCIAL NETWORK BY ADDRESSING THE INFLUENCE BLOCKING MAXIMIZATION (IBM) PROBLEM TO MINIMIZE THE BAD EFFECT OF MISINFORMATION. IBM PROBLEM CAN BE DEFINED AS FINDING A SUBSET OF NODES TO PROMOTE THE POSITIVE INFLUENCE UNDER MULTICAMPAIGN INDEPENDENT CASCADE MODEL AS DIFFUSION MODEL TO MINIMIZE THE NUMBER OF NODES THAT ADOPT THE NEGATIVE INFLUENCE AT THE END OF BOTH PROPAGATION PROCESSES. IN THIS REGARD, WE PROPOSED A COMMUNITY BASED ALGORITHM CALLED FC_IBM ALGORITHM USING FUZZY CLUSTERING AND CENTRALITY MEASURES FOR FINDING A GOOD CANDIDATE SUBSET OF NODES FOR DIFFUSION OF POSITIVE INFORMATION IN ORDER TO MINIMIZING THE IBM PROBLEM. THE EXPERIMENTAL RESULTS ON WELL-KNOWN NETWORK DATASETS SHOWED THAT THE PROPOSED ALGORITHM NOT ONLY OUTPERFORMS THE BASELINE ALGORITHMS WITH RESPECT TO EFFICIENCY BUT ALSO WITH RESPECT TO THE FINAL NUMBER OF POSITIVE NODES.

THE INTENSITY OF COMPETITION IN FINANCIAL MARKETS AND THE PERCEIVED IMPORTANCE OF PROTECTING THE CUSTOMERS OF ORGANIZATIONS IN ORDER TO CREATE COMPETITIVE ADVANTAGE IS DEPENDENT ON THE GRADUAL MOVE TOWARDS THE CREATION AND MAINTENANCE OF INNOVATIVE LONG-TERM RELATIONSHIPS WITH CUSTOMERS. ACCORDING TO RESEARCHERS, ONE OF THE BEST APPROACHES THAT FINANCIAL SERVICES COMPANIES AND FINANCIAL SERVICES ARE PURSUING IS ELECTRONIC BANKING AND ITS BENEFITS. IN THIS RESEARCH, THE IMPACT OF SERVICE QUALITY, SYSTEM AND INFORMATION ON VERIFICATION, CONFIRMATION OF SATISFACTION AND SATISFACTION WITH THE CONTINUOUS USE OF ELECTRONIC BANKING HAS BEEN INVESTIGATED. THIS STUDY WAS CONDUCTED USING A DESCRIPTIVE-CORRELATIONAL STUDY DESIGN. FOR THIS PURPOSE, THE CUSTOMERS OF THE BANK OF HOUSING IN TEHRAN ARE THE STATISTICAL POPULATION THAT THE NUMBER OF CUSTOMERS IN THE HOUSING BANK IN TEHRAN IS 384 AND USING A QUESTIONNAIRE TO COLLECT CUSTOMER VIEWS. THE RELIABILITY OF ALL VARIABLES IS WITHIN ACCEPTABLE RANGE. THE LEAST SQUARES AND PLS-GRAPH SOFTWARE WERE USED TO TEST THE MODEL. THE RESULTS OF THE RESEARCH SHOWED THAT THE QUALITY OF ELECTRONIC SERVICES AS A STRONG DETERMINANT OF CUSTOMER'S APPROVAL AND SATISFACTION AND ULTIMATELY LEADS TO THE CONTINUED USE OF E-BANKING.

A RECOMMENDER SYSTEM HELPS ITS USER TO REACH THE TARGET WITHIN A MASSIVE AMOUNT OF INFORMATION BY PROPOSING SUGGESTIONS. THE RECOMMENDER SYSTEMS TRY TO IDENTIFY AND SUGGEST THE MOST APPROPRIATE AND CLOSEST THING TO THE USER'S INTERESTS BY GUESSING THE USER'S WAY OF THINKING THROUGH INFORMATION AVAILABLE ABOUT THE BEHAVIOR AND OPINIONS OF THIS USER OR SIMILAR USERS. COLLABORATIVE FILTERING IS ONE OF THE MOST EFFICIENT METHODS USED IN MANY RECOMMENDER SYSTEMS. THIS METHOD SEEKS TO FIND LATENT FEATURES FOR OPTIONS AND USERS IN ORDER TO OBTAIN THE LATENT FEATURES OF THE USER'S INTEREST IN THE ITEM THEY ARE LOOKING FOR. THE PRIMITIVE COLLABORATIVE FILTERING METHODS CAPTURE THE LATENT FEATURES OF THE USERS AND OPTIONS USING THE SCORES MATRIX, BUT THESE METHODS HAVE PROBLEMS SUCH AS THE COLD START AND THE SPARSITY OF THE SCORE MATRIX. IN THIS PAPER, WE INVESTIGATE FOR IMPROVING THE EFFICIENCY OF COLLABORATIVE FILTERING-BASED RECOMMENDER SYSTEMS USING A DEEP LEARNING APPROACH. IN RECENT YEARS, THE USE OF AVAILABLE EXTRA INFORMATION HAS BEEN CONSIDERED ALONG THE SCORE MATRIX IN ORDER TO OBTAIN HIDDEN ATTRIBUTES. DEEP LEARNING MODELS, ON THE OTHER HAND, HAVE A HIGH ABILITY TO LEARN REPRESENTATIONS APPROPRIATELY, ESPECIALLY WHEN DEALING WITH MASSIVE RAW DATA. ACCORDINGLY, BY FOCUSING ON THE DOMAIN OF FILM RECOMMENDATION AND EXPLOITING USER-TAGGING INFORMATION ALONGSIDE USER-RATING INFORMATION, A MODEL FOR A RECOMMENDATION SYSTEM BASED ON A LONG SHORT-TERM MEMORY NETWORK HAS PROVIDED. DUE TO THE FACT THAT LABELS PROVIDE AUXILIARY INFORMATION TO REFLECT THE PREFERENCES OF USERS AND ITEMS, THE EFFECTIVENESS OF RECOMMENDER SYSTEMS HAS IMPROVED. THE EXPERIMENTAL RESULTS FROM THE EXPERIMENTS ON THE ACTUAL MOVIELENS-20M DATASET FOR THE MEAN SQUARED ERROR MEASURE OF 0. 86 AND FOR THE MEAN ABSOLUTE ERROR MEASURE OF 0. 88, REPRESENTING A GOOD IMPROVEMENT OVER THE OTHER METHODS.

ONE OF THE IMPORTANT APPROACHES TO IDENTIFY RESEARCH COLLABORATIONS OF ACADEMIC ORGANIZATIONS IS ANALYZING CO-AUTHORSHIP PATTERNS IN THE PUBLISHED ARTICLES. THE PURPOSE OF THE PRESENT STUDY IS TO IDENTIFY THE CO-AUTHORSHIP PATTERNS IN THE FERDOWSI UNIVERSITY OF MASHHAD AS WELL AS EXTRACTING THE COLLABORATIVE INSTITUTIONS ACROSS THE COUNTRY. IN THIS PAPER, THE CO-AUTHORSHIP PATTERNS OF ARTICLES ARE IDENTIFIED FOR BOTH JOURNALS AND CONFERENCES, BY USING THE RESEARCH DATABASE OF THE FERDOWSI UNIVERSITY OF MASHHAD. THESE PATTERNS ARE ACHIEVED USING FREQUENT PATTERNS ALGORITHMS OF APRIORI AND é CLAT. THE RESULTS OF THE ANALYSIS INDICATE THAT THE MOST CO-AUTHORSHIP PATTERN IS BETWEEN THE PROFESSORS OF THE FACULTY OF SCIENCE IN THE FIELD OF GEOLOGY AND THEN THE FACULTY OF AGRICULTURE IN THE FIELD OF ANIMAL SCIENCES, AND AT THE NATIONAL LEVEL, THE HIGHEST DEGREE OF INTER-UNIVERSITY COLLABORATION IS WITH MASHHAD UNIVERSITY OF MEDICAL SCIENCES, TEHRAN UNIVERSITY AND TEHRAN UNIVERSITY OF MEDICAL SCIENCES, RESPECTIVELY.

COMPUTATIONAL FEATURE RECOGNITION IS AN ESSENTIAL COMPONENT FOR INTELLIGENT SYSTEMS TO SENSE THE OBJECTS AND ENVIRONMENTS. THIS PAPER PROPOSES A NOVEL CONCEPTUAL MODEL, NAMED AMBIANCE SIGNAL PROCESSING (AMSIP), TO IDENTIFY OBJECTS’ FEATURES WHEN THEY ARE NOT DIRECTLY ACCESSIBLE BY SENSORS. AMSIP ANALYZES THE SURROUNDING AND AMBIANCE OF OBJECTS/SUBJECTS COLLABORATIVELY TO RECOGNIZE THE OBJECT’ S FEATURES INSTEAD OF CONCENTRATING ON EACH INDIVIDUAL AND ACCESSIBLE OBJECT. TO VALIDATE THE PROPOSED MODEL, THIS STUDY RUNS AN EXPERIMENT WITH 50 PARTICIPANTS, WHOSE EMOTIONAL STATE VARIATIONS ARE ESTIMATED BY MEASURING THE SURROUNDINGS FEATURES AND THE EMOTIONS OF OTHER PEOPLE IN THE SAME ENVIRONMENT. THE RESULTS OF A T-TEST ON THE DATA COLLECTED FROM THIS EXPERIMENT SHOWED THAT USERS’ EMOTIONS WERE BEING CHANGED IN A COURSE OF TIME DURING THE EXPERIMENT; HOWEVER, AMSIP COULD ESTIMATE SUBJECTS’ EMOTIONS RELIABLY ACCORDING TO THE ENVIRONMENTAL CHARACTERISTICS AND SIMILAR PATTERNS. TO EVALUATE THE RELIABILITY AND EFFICIENCY OF THIS MODEL, A COLLABORATIVE AFFECTIVE COMPUTING SYSTEM WAS IMPLEMENTED USING KEYBOARD KEYSTROKE DYNAMICS AND MOUSE INTERACTIONS OF THE USERS WHOSE EMOTIONS WERE AFFECTED BY DIFFERENT TYPES OF MUSIC. IN COMPARISON WITH OTHER CONVENTIONAL TECHNIQUES (EXPLICIT ACCESS), THE PREDICTION WAS RELIABLE. ALTHOUGH THE DEVELOPED MODEL SACRIFICES A MINOR ACCURACY, IT EARNS THE SUPERIORITY OF UNCOVERING BLIND KNOWLEDGE ABOUT THE SUBJECTS OUT OF THE SENSORS’ DIRECT ACCESS.

COMMUNITY STRUCTURE DETECTION IN SOCIAL NETWORKS HAS BECOME A BIG CHALLENGE. VARIOUS METHODS IN THE LITERATURE HAVE BEEN PRESENTED TO SOLVE THIS CHALLENGE. RECENTLY, SEVERAL METHODS HAVE ALSO BEEN PROPOSED TO SOLVE THIS CHALLENGE BASED ON A MAPPING-REDUCTION MODEL, IN WHICH DATA AND ALGORITHMS ARE DIVIDED BETWEEN DIFFERENT PROCESS NODES SO THAT THE COMPLEXITY OF TIME AND MEMORY OF COMMUNITY DETECTION IN LARGE SOCIAL NETWORKS IS REDUCED. IN THIS PAPER, A MAPPING-REDUCTION MODEL IS FIRST PROPOSED TO DETECT THE STRUCTURE OF COMMUNITIES. THEN THE PROPOSED FRAMEWORK IS REWRITTEN ACCORDING TO A NEW MECHANISM CALLED DISTRIBUTED CACHE MEMORY; DISTRIBUTED CACHE MEMORY CAN STORE DIFFERENT VALUES ASSOCIATED WITH DIFFERENT KEYS AND, IF NECESSARY, PUT THEM AT DIFFERENT COMPUTATIONAL NODES. FINALLY, THE PROPOSED REWRITTEN FRAMEWORK HAS BEEN IMPLEMENTED USING SPARK TOOLS AND ITS IMPLEMENTATION RESULTS HAVE BEEN REPORTED ON SEVERAL MAJOR SOCIAL NETWORKS. THE PERFORMED EXPERIMENTS SHOW THE EFFECTIVENESS OF THE PROPOSED FRAMEWORK BY VARYING THE VALUES OF VARIOUS PARAMETERS.

XML KEYWORD SEARCH, DUE TO ITS IMPORTANCE AND APPLICABILITY, IS KNOWN AS A WELL-ATTENTIONED TOPIC IN THE CONTEXT OF XML (QUERY) PROCESSING. OVER THE YEARS, MANY DIFFERENT APPROACHES HAVE BEEN PROPOSED IN ORDER TO PERFORM THE PROCESS IN AN EFFECTIVE WAY. THE ANALYSIS OF THE LITERATURE IN THIS FIELD ADDRESSES THE LACK OF BENEFITING USERS’ POTENTIAL AND CAPABILITIES TO IMPROVE THE PROCESS. HOWEVER, THERE ARE SOME USER-CENTRIC STUDIES THAT INCORPORATED USERS IN THE PROCESS IN SOME WAY; TO THE BEST OF THE AUTHORS' KNOWLEDGE, THERE IS NOT ANY FOCUSED RESEARCH WORK THE RESPECTED LITERATURE THAT LEVERAGES HUMAN COMPUTATION ABILITY IN THIS REGARD. ON THE OTHER SIDE, TAKING ADVANTAGES OF HUMANS COMPUTING AND PROCESSING ABILITIES IS CURRENTLY KNOWN AS A WELL-EXPERIENCED STRATEGY TO SOLVE COMPUTING PROBLEMS IN DIFFERENT CONTEXTS. SPECIFICALLY, IN THE FIELD OF DATABASE AND QUERY PROCESSING THERE ARE SEVERAL SEMINAL WORKS THAT LEVERAGED USERS' PARTICIPATION IN ORDER TO IMPROVE THE PROCESS. FOLLOWING SUCH AN IDEA, IN THIS PAPER A CROWDSOURCING-BASED XML KEYWORD SEARCHING APPROACH IS PROPOSED. IN ADDITION TO EXPLAINING THE CONCEPTUAL ARCHITECTURE AND DETAILS OF DIFFERENT PARTS OF THE PROPOSED APPROACH, THE EFFICIENCY OF THE INTRODUCED APPROACH IS PROVED THROUGH CONDUCTING SOME EXPERIMENTS. THE AUTHORS BELIEVE THAT THE PRESENTED SOLUTION, AS THE FIRST STEP, CAN PROVIDE A ROADMAP FOR THE FUTURE WORKS IN THE FIELD.

DEMAND MANAGEMENT AND SALES FORECASTING FORM THE FOUNDATION FOR ALL PLANNING AND DELIVERING PROCESSES. THE BEGINNING OF PROCESS HAS STARTED BY SELECTING CORRECT FEATURES. CORRELATION ANALYSIS IS AN EXTENSIVELY USED TECHNIQUE THAT IDENTIFIES INTERESTING RELATIONSHIPS IN DATA. EACH PRODUCT CATEGORY HAS OWN EFFECTIVE FEATURES AND THESE RELATIONSHIPS HELP US REALIZE THE RELEVANCE OF ATTRIBUTES WITH RESPECT TO THE TARGET CLASS TO BE PREDICTED. IN THIS STUDY, PARAMETERS HAVE CHOSEN BY VISUALIZED PEARSON AND PCA METHODS. THE RESULT SHOWS 8 PARAMETERS ARE GOOD TO USE IN FORECASTING OUT OF 23 PARAMETERS.

THERE HAVE BEEN MANY PREVIOUS WORKS TO DETERMINE THE DETERMINANTS OF HOUSING PRICES. ALL OF THESE WORKS RELIED ON A RELATIVELY SMALL SET OF DATA, MOSTLY COLLECTED WITH THE HELP OF REAL ESTATE AGENCIES. IN THIS WORK, WE USED WEB MINING METHODS TO GENERATE A BIG, ORGANIZED DATASET FROM A POPULAR NATIONAL BROKERAGE WEBSITE. THE DATASET CONTAINS STRUCTURAL CHARACTERISTICS OF MORE THAN 139, 000 APARTMENTS, ALONGSIDE THEIR LOCATION AND PRICE. WE PROVIDED OUR FULL DATASET FOR THE ARTICLE, SO THAT OTHER RESEARCHERS CAN REPRODUCE OUR RESULTS OR CONDUCT FURTHER ANALYSES. USING THIS DATASET, WE ANALYZED HOUSING PRICES OF TEHRAN IN ORDER TO IDENTIFY ITS MAJOR DETERMINANTS. TO THIS AIM, WE EXAMINE THE DYNAMICS OF HOUSING PRICES AT THE DISTRICT LEVELS OF TEHRAN USING HEDONIC PRICE MODEL. OUR RESULTS HIGHLIGHT A NUMBER OF POINTS, INCLUDING: BASE AREA OF AN APARTMENT IS POSITIVELY CORRELATED WITH PRICE PER SQUARE METER (R=0. 89), SHOWING A TWO-FOLDED IMPACT ON THE OVERALL PRICE. AIR QUALITY IS IN A POSITIVE, AND FLOOR LEVEL IS IN NEGATIVE CORRELATION WITH HOUSING PRICES.

ENTITY RESOLUTION REFERS TO THE PROCESS OF IDENTIFYING AND INTEGRATING RECORDS BELONGING TO UNIQUE ENTITIES. THE STANDARD METHODS ARE USING A RULE-BASED OR MACHINE LEARNING MODELS TO COMPARE AND ASSIGN A POINT, TO INDICATE THE STATUS OF MATCHING OR NON-MATCHING THE PAIR OF RECORDS. HOWEVER, A COMPREHENSIVE COMPARISON ACROSS ALL THE RECORDS PAIRS LEADS TO A SECOND-ORDER MATCHING COMPLEXITY. THEREFORE BLOCKING METHODS ARE USING BEFORE THE MATCHING, TO GROUP THE SAME ENTITIES INTO SMALL BLOCKS. THEN THE MATCHING OPERATION IS DONE COMPREHENSIVELY. SEVERAL BLOCKING METHODS PROVIDED TO EFFICIENTLY BLOCK THE INPUT DATA INTO MANAGEABLE GROUPS, INCLUDING THE TOKEN BLOCKING, THAT HOLDS RECORDS WITH A SIMILAR TOKEN IN THE SAME BLOCK. MOST OF THE PREVIOUS METHODS DID NOT TAKE ANY SEMANTIC CRITERIA INTO ACCOUNT. IN THIS PAPER, WE PROPOSE A NEW METHOD, CALLED DEEP BLOCK THAT USES DEEP LEARNING FOR THE TASK OF BLOCKING IN ENTITY RESOLUTION. DEEP BLOCK COMBINES SYNTACTIC AND SEMANTIC SIMILARITIES TO CALCULATE THE SIMILARITY BETWEEN RECORDS. WE HAVE EVALUATED THE DEEP BLOCK OVER THE REAL-WORLD DATASET AND COMPARED IT WITH THE EXISTING BLOCKING TECHNIQUE (TOKEN BLOCKING). OUR EXPERIMENTAL STUDY SHOWS THAT THE COMBINATION OF SEMANTIC AND SYNTACTIC SIMILARITY CAN CONSIDERABLY IMPROVE THE QUALITY OF BLOCKING. THE RESULTS SHOW THAT DEEP BLOCK OUTPERFORMS THE TOKEN BLOCKING METHOD IN AVERAGE 58. 5% WITH RESPECT TO PRECISION AND RECALL.

IN RECENT YEARS, SOCIAL MEDIA DATA HAS EXPONENTIALLY INCREASED, WHICH CAN BE ENUMERATED AS ONE OF THE LARGEST DATA REPOSITORIES IN THE WORLD. A LARGE PORTION OF THIS SOCIAL MEDIA DATA IS NATURAL LANGUAGE TEXT. HOWEVER, THE NATURAL LANGUAGE IS HIGHLY AMBIGUOUS, SPECIFICALLY WITH RESPECT TO THE FREQUENT OCCURRENCES OF ENTITIES, WHICH ARE ADDRESSED BY POLYSEMOUS WORDS OR PHRASES. ENTITY LINKING IS THE TASK OF LINKING THE ENTITY MENTIONS IN THE TEXT TO THEIR CORRESPONDING ENTITIES IN A KNOWLEDGE BASE. MOST OF THE ENTITY LINKING SYSTEMS BEGIN WITH SEARCHING FOR CANDIDATE ENTITIES, AND THEN DISAMBIGUATE THEM TO, FINALLY, CHOOSE THE BEST CANDIDATE. UNFORTUNATELY, DUE TO THE LACK OF A KNOWLEDGE GRAPH, THIS TASK HAD NOT BEEN ABLE TO BE COVERED IN THE PERSIAN LANGUAGE. FORTUNATELY, RECENTLY FARSBASE HAS BEEN INTRODUCED AS A PERSIAN KNOWLEDGE GRAPH WITH ALMOST HALF A MILLION ENTITIES. CORRESPONDINGLY, IN THIS PAPER, WE PROPOSE AN UNSUPERVISED PERSIAN ENTITY LINKING SYSTEM, USING CONTEXT-DEPENDENT AND CONTEXT-INDEPENDENT FEATURES. FOR THIS PURPOSE, WE ALSO PUBLISH THE FIRST ENTITY LINKING CORPUS ON THE PERSIAN LANGUAGE, COMPOSED OF SOCIAL MEDIA TEXTS ON A NUMBER OF POPULAR PERSIAN CHANNELS, IN THE TELEGRAM SOCIAL NETWORK. THE RESULTS PROVE THE HIGHLY EFFICIENT PERFORMANCE OF THE PROPOSED METHOD, WHICH IS COMPARABLE WITH THE CORRESPONDING STATE OF THE ART IN THE ENGLISH LANGUAGE.

USING ONLINE COMMUNITIES IS ONE OF THE GROWING AREAS FOR INTERNET USERS. IN THIS WAY, MILLIONS OF USERS CAN BE CONNECTED INDEFINITELY. IN ADDITION TO THE ROLE OF COMMUNICATION, THIS TECHNOLOGY IS A TREASURE OF USERS' PERSPECTIVES, KNOWLEDGE AND EMOTIONS. SUCH MASSIVE CONTENT CAN HAVE MANY APPLICATIONS IN MARKETING, ELEARNING AND E-COMMERCE. IN SPITE OF THE HIGH NUMBER OF POSTS CREATED IN ONLINE COMMUNITIES, INFORMATION DISSEMINATION FACES MANY PROBLEMS. THE INFORMATION OVERLOAD PROBLEM IN ONLINE COMMUNITIES HAS MADE IT DIFFICULT FOR USERS TO GET THE RIGHT INFORMATION WITH THEIR FEATURES AND INTERESTS. MODELING THE LIFE CYCLE OF A POST PROVIDES AN OPPORTUNITY TO EXAMINE HOW INFORMATION IS DISSEMINATED AMONG USERS. IN THESE COMMUNITIES, EACH POST AFTER CREATION IS REPOSTED AND TRANSMITTED BY USERS. THIS ACTION CONTINUES UNTIL A POST IS DROWNED, THAT IS, WHEN ANOTHER POST IS NOT REINSTATED. IN THIS PAPER, THE METHOD OF MODELING THE LIFE CYCLE OF POSTS IN ONLINE COMMUNITIES IS PRESENTED. THIS METHOD IS CAPABLE OF IDENTIFYING THE POSSIBILITY OF AN EPIDEMIC OF A POST DYNAMICALLY AT ANY TIME. FOR THIS PURPOSE, A LEARNING METHOD BASED ON LEARNING AUTOMATA HAS BEEN USED. THE EVALUATIONS SHOW THAT THIS METHOD IS EFFICIENT IN THREE EVALUATION DATASETS.

SOCIAL NETWORKS ALLOW USERS TO EASILY EXPRESS THEIR OPINIONS AND GET INFORMED ABOUT THE OPINIONS OF OTHERS ON A SPECIFIC TOPIC. THE HIGH VOLUME AND THE LACK OF PROPER STRUCTURE FOR THE TEXT OF THE OPINIONS PRESENTED ON THE WEB SITE MADE IT DIFFICULT TO USE HIDDEN KNOWLEDGE WITHIN THEM. THEREFORE, IT IS IMPORTANT TO PROVIDE METHODS THAT CAN PROVIDE THIS KNOWLEDGE IN A SUMMARIZED AND STRUCTURED WAY. SENTIMENT ANALYSIS IS A FIELD OF NATURAL LANGUAGE PROCESSING RESEARCH AND A POWERFUL TOOL AND SMART PROCESS FOR EXTRACTING EMOTIONS OF USERS. IN THIS RESEARCH, WE COMPARE THE ARTICLES THAT USED THE METHODS OF MACHINE LEARNING AND A HYBRID METHOD, AS WELL AS THE LEVEL OF CLASSIFICATION OF SENTENCE AND DOCUMENT.

USER-GENERATED-CONTENT (UGC) IN THE FORM OF ONLINE REVIEWS CAN BE AN INVALUABLE SOURCE OF INFORMATION FOR BOTH CUSTOMERS AND BUSINESSES. SENTIMENT ANALYSIS AND OPINION MINING TOOLS AND TECHNIQUES HAVE BEEN PROPOSED IN THE LITERATURE TO EXTRACT KNOWLEDGE FROM ONLINE REVIEWS. ASPECT-BASED OPINION MINING WHICH HAS GAINED GROWING ATTENTION MAINLY HAS TWO TASKS INCLUDING ASPECT EXTRACTION AND SENTIMENT POLARITY DETECTION. ONCE AN ASPECT-BASED OPINION MINING TASK HAS BEEN ACCOMPLISHED; A BAG OF SENTIMENTS WILL BE ACHIEVED. IN MANY CASES, IT IS NECESSARY TO OBTAIN AN OVERALL SENTIMENT ABOUT A TYPICAL ASPECT. IN THIS STUDY, WE HAVE PROPOSED A SENTIMENT AGGREGATION SYSTEM BASED ON WEIGHTED SELECTIVE AGGREGATED MAJORITY OWA (WSAM-OWA). WSAM-OWA CONSIDERS BOTH THE MAJORITY AND THE DEGREE OF IMPORTANCE OF INFORMATION SOURCE IN THE PROCESS OF AGGREGATION. THE PROPOSED SYSTEM EXPLOITS THE HELPFULNESS RATING OF REVIEWS IN DETERMINING THE RELIABILITY AND CREDIBILITY OF EACH SENTIMENT. A CASE STUDY WAS CONDUCTED TO ILLUSTRATES THE USEFULNESS OF THE PROPOSED SYSTEM. THE RESULTS OF THIS STUDY DEMONSTRATED THAT THE PROPOSED SENTIMENT AGGREGATION SYSTEM COULD BE INCORPORATED IN OPINION MINING SYSTEMS.

WEB OF THINGS (WOT) ALLOWS USERS TO CREATE AND SHARE CONTENTS THROUGH A NETWORK. HOWEVER, THE SOCIAL WEB OF THINGS (SWOT) IS A NEW CONCEPT MERGING THE WOT AND SOCIAL CAPABILITIES OF THE MODERN INTERNET. THESE DEVICES ARE INTERCONNECTED PHYSICAL, SOCIAL, REAL-TIME, SEMANTIC FOR BETTER COMMUNICATION BY THE WEB. SWOT DESCRIBES APPROACHES, SOFTWARE ARCHITECTURAL STYLES AND PROGRAMMING DESIGNS THAT PERMIT REAL-WORLD OBJECTS TO BE PART OF THE WORLD COMMUNICATION; HOWEVER, INFORMATION PRIVACY IS MISTREATED SINCE INFORMATION IS ACCESSED IN UNCERTAINTY WAYS BY NON-AUTHORITATIVE INDIVIDUALS. IN THIS PAPER, WE REVIEW COMPLEX PRIVACY ISSUES IN SWOT WITH SUGGESTING STATE OF THE ART SOLUTIONS TO OVERCOME AVOIDABLE SCENARIOS BASING ON THE CURRENT SURVEYS, MODELS, AND ARCHITECTURES, ENLIGHTENMENT ON THE DRAWBACK’ S TECHNOLOGY USERS. WE JUSTIFY THAT DURING DEVICES AND PEOPLE INTERACTION TO THE SWOT THROUGH CREDENTIAL ASSESSMENT FOR ACCESS, CONTENT SHARING THROUGHOUT COMMUNICATION AND CONTROLLING PHYSICAL THINGS WILL BE THE MAIN PRIVACY ISSUES.

TODAY, SOCIAL NETWORKS HAVE PROVIDED A SUITABLE PLATFORM FOR SOCIAL RELATIONSHIP. AMONG ONLINE SOCIAL NETWORKS, TWITTER HAS BECOME A POPULAR PLATFORM FOR INFORMATION DIFFUSION AROUND THE WORLD. DUE TO POPULARITY OF TWITTER, IT HAS BEEN TARGETED BY SPAMMERS AND MALICIOUS ACTIVITIES. IN THIS REGARD, SEVERAL STUDIES HAVE BEEN CONDUCTED USING MACHINE LEARNING TECHNIQUES BY RESEARCHERS TO REACH PROMISING RESULTS. IN RECENT YEARS, ENSEMBLE LEARNING ALGORITHMS HAVE BEEN PRESENTED AS ONE OF THE MODERN MACHINE LEARNING TECHNIQUES, DUE TO ITS HIGH ACCURACY, FOR DATA MINING. IN THIS PAPER, WE PROPOSE A DATA MINING FRAMEWORK USING ENSEMBLE LEARNING FOR SPAM DETECTION IN TWITTER. IN THE PROPOSED METHOD, AFTER DATA COLLECTION, PREPROCESSING, FEATURE EXTRACTION AND FEATURE SELECTION, THE CLASSIFICATION IS CONDUCTED BY ENSEMBLE LEARNING USING THE DECISION TREE, K-NEAREST NEIGHBOR AND NAï VE BAYES. THE SIMULATION RESULTS ARE COMPARED WITH OTHER CLASSIFICATION ALGORITHMS.

RECENTLY, MOBILE DEVICES HAVE BEEN INTRODUCED IN VARIOUS WIRELESS SENSOR NETWORKS (WSNS) APPLICATIONS IN ORDER TO INCREASE DATA COLLECTION EFFICIENCY. MOBILITY AND RESOURCES OPTIMIZED MANAGEMENT ARE STILL OPEN CHALLENGING ISSUES FOR THE SUCCESS AND PROLIFERATION OF THE INTERNET OF MOBILE THINGS. ROUTING PLAYS AN IMPORTANT ROLE IN THE OVERALL ARCHITECTURE OF THE IOT. THE EXISTING ROUTING PROTOCOL RPL HAS A VERY LOW REACTIVITY TO MOBILITY MAKING IT INEFFICIENT AND OPEN TO FURTHER RESEARCH IMPROVEMENTS. IN THIS PAPER, THE RPL PROTOCOL HAS BEEN IMPROVED TO SUPPORT MOBILITY ON THE NETWORK. WE CREATED A CONTINUOUS CONNECTION BY REDUCING THE NUMBER OF HAND-OFFS USING THE HOP COUNT METRIC. THIS LED TO THE REDUCED NETWORK OVERHEAD AND THE INCREASED DATA DELIVERY RATE. TO SIMULATE THE PROPOSED PROTOCOL, THE CONTIKI 6LOWPAN/RPL STACK HAS BEEN USED. OBTAINED SIMULATION RESULTS SHOW THAT PROPOSED PROTOCOL OUTPERFORMS BOTH THE RPL AND THE MRPL PROTOCOLS IN TERMS OF PACKET DELIVERY RATE, DELAY, NETWORK OVERHEAD AND ENERGY CONSUMPTION.

IN THIS PAPER, WE DESCRIBE OUR PROPOSED METHODOLOGY FOR CONSTRUCTING AN ONTOLOGY OF NATURAL LANGUAGE PROCESSING (NLP). WE USE A SEMI-AUTOMATIC METHOD; A COMBINATION OF RULE-BASED AND MACHINE LEARNING TECHNIQUES; TO CONSTRUCT AND POPULATE AN ONTOLOGY WITH BILINGUAL (ENGLISH-PERSIAN) CONCEPT LABELS (LEXICON) AND EVALUATE IT MANUALLY. THIS METHODOLOGY RESULTS IN A COMPLETE ONTOLOGY IN THE NATURAL LANGUAGE PROCESSING DOMAIN WITH 887 CONCEPTS, 88 RELATIONS, AND 71 FEATURES. THE BUILT ONTOLOGY IS POPULATED WITH NEAR 36000 NLP RELATED PAPERS AND 32000 AUTHORS, AND ABOUT 201000 "IS_RELATED_TO", 83500 "IS_AUTHOR_OF", AND 29000 "PRESENTED_IN" RELATIONS. THE INSTANTIATION IS DONE TO ENABLE APPLICATIONS FIND EXPERTS, PUBLICATIONS AND INSTITUTIONS RELATED TO VARIOUS TOPICS IN NLP FIELD.

QUALITY-OF-SERVICE (QOS)-AWARE WEB SERVICE COMPOSITION IS VERY IMPORTANT IN INTEGRATING INDIVIDUAL SERVICES WITH RESPECT TO THE FUNCTIONAL AND NONFUNCTIONAL REQUIREMENTS. DESPITE THE LARGE NUMBER OF CANDIDATE SERVICES, AUTOMATION OF THE COMBINATION IS ESSENTIAL IN ORDER TO PROVIDE A GOOD COMBINATION OF SERVICE. ALTHOUGH MANY OF THE EXISTING METHODS OFFER A SOLUTION THAT IS OPTIMAL, MOST OF THEM HAVE LITTLE FLEXIBILITY. IN SOME CASES, THE COMPOUND SERVICE COMPONENTS FAIL, SO THE COMBINATION ALGORITHM HAS TO RUN AGAIN TO FIND ANOTHER OPTIMAL SOLUTION. ALSO IN MANY SITUATIONS, USERS PREFER TO HAVE SEVERAL ALTERNATIVE SOLUTIONS. THEREFORE, PROVIDING A TOP-K SERVICE COMPOSITION ACCORDING TO THEIR QOS IS MORE DESIRABLE. BECAUSE WEB SERVICES ARE UNRELIABLE, ALSO SINCE PROVIDING THE TRANSACTIONAL SUPPORT IN EXECUTION OF A SERVICE COMPOSITION IS AN IMPORTANT DESIGN REQUIREMENT; SO IN THIS RESEARCH, A FAULT MANAGEMENT PROCEDURE IS IMPLEMENTED TO ENSURE THE TRANSACTION EXECUTION OF THE SERVICE COMBINATIONS. THIS PROCEDURE IN THE EVENT OF SERVICE FAILURE UNDO THE IMPACT OF THIS SERVICE BY CALLING THE EQUIVALENT SERVICE. THE PROPOSED METHOD ENCOUNTERS THESE THREE ISSUES: 1) THE SEMANTIC SELECTION OF SERVICES; 2) QOS-AWARE WEB SERVICE COMPOSITION WITH THE TOP-K SOLUTION; AND 3) FAULTHANDLING/ RECOVERY PROCEDURE. IN THE PROPOSED METHOD, ONTOLOGY CONCEPT RANKING ALGORITHM IS USED IN SERVICE SELECTION, AND TOP-K METHOD IS EMPLOYED TO SOLVE SERVICE COMBINATION. ERROR HANDLING PROCEDURE WILL THEN BE REVIEWED AND DESIGNED TO ENSURE THE TRANSACTIONAL EXECUTION OF THE SERVICE COMPOSITION. THE EVALUATION RESULTS SHOW THAT THE PROPOSED METHOD NOT ONLY FINDS OPTIMAL SOLUTIONS BUT ALSO CAN PROVIDE ALTERNATIVE SOLUTIONS WITH OPTIMAL QOS.

IN THE FIELD OF GEOGRAPHIC INFORMATION SCIENCE SPATIOTEMPORAL INFORMATION EXTRACTION FROM WEB PAGES, ESPECIALLY UNSTRUCTURED DOCUMENTS, IS ONE OF THE GROWING AREAS OF THE RESEARCH. ABUNDANT NEWS IS PUBLISHING EVERY HOUR ON THE WEB, WHICH CONTAINS VALUABLE SPATIOTEMPORAL INFORMATION FOR ITS USERS. IT IS CUMBERSOME AND TIME-CONSUMING TO SEARCH AMONG UNSTRUCTURED TEXTS AND FIND EVENTS OF INTEREST. IN THIS WORK, WE WILL SHOW YOU HOW TO EXTRACT SPATIOTEMPORAL AND SEMANTIC ENTITIES AND RELATIONSHIPS REPRESENTING IN CULTURAL EVENT NEWS REPORTS AND SEARCH WITHIN INFORMATION. NATURAL LANGUAGE PROCESSING (NLP) AND AUTOMATIC ONTOLOGY POPULATION ARE TIGHTLY COUPLED, AND TOGETHER THEY MAKE IT POSSIBLE TO HAVE WEB DOCUMENTS SEMANTICALLY SO THAT NOT ONLY CAN MACHINES COMPREHEND THE WEB DOCUMENTS, BUT ALSO AS A RESULT, USERS ARE ABLE TO FIND THE IDEAL INFORMATION WITH EASE. A SPATIOTEMPORAL SEMANTIC SEARCH ENGINE ENABLES US TO ANSWER, WHERE AND WHEN AN EVENT WILL TAKE PLACE.

REQUIREMENTS ENGINEERING IS AN IMPORTANT PROCESS IN SOFTWARE ENGINEERING. AN IMPORTANT TASK IN THE AREA IS TO SELECT AND OPTIMIZE THE REQUIREMENTS BY CONSIDERING REQUIREMENT INTERACTION MANAGEMENT. ACTUALLY, THE MAIN GOAL OF THIS AREA IS TO MAKE BEST AND MOST OPTIMAL CHOICES AMONG ALL THE POSSIBLE REQUIREMENTS, TAKING INTO ACCOUNT THE DEPENDENCIES BETWEEN REQUIREMENTS. IN THIS WAY, THERE ARE CHALLENGES AND THREATS AT ALL STAGES, INCLUDING REQUIREMENTS ENGINEERING, SEARCH-BASED SOFTWARE ENGINEERING, REQUIREMENTS INTERACTIVE MANAGEMENT, AND SELECTION AND OPTIMIZATION OF REQUIREMENTS. THE IDENTIFICATION AND CLASSIFICATION OF CHALLENGES HELP TO BETTER UNDERSTAND THE PROBLEM AND FIND BETTER SOLUTIONS. WE INTEND TO EXAMINE AND CLASSIFY THE MAIN CHALLENGES IN THE PAPERS IN THIS AREA. OUR GOAL IN THIS ARTICLE IS TO CLASSIFY THE CHALLENGES IN THIS AREA FROM A NEW PERSPECTIVE.

REDUCED OUTPUT QUALITY AND BEING UNAWARE OF CONTENT ARE AMONG MAJOR ISSUES WITH TRADITIONAL IMAGE COMPRESSION TECHNIQUES. SUCH ISSUES CAUSE SOME CRITICAL PROBLEMS WHEN IT COMES TO QUALITY-INTENSIVE APPLICATIONS, INCLUDING OBJECT/FACE DETECTION AND RECOGNITION, WEB-BASED IMAGE VIEWERS AND MANAGEMENT SYSTEMS, ETC. ON THE OTHER SIDE, EFFICIENCY OF WEB-BASED IMAGE SEARCH ENGINES AND RETRIEVAL SYSTEMS IN TERMS OF USER EXPERIENCE AND USABILITY COULD BE AFFECTED. IN ORDER TO COPE WITH THESE CHALLENGES, A NOVEL IMAGE COMPRESSION METHOD IS PROPOSED THAT TAKES ADVANTAGES OF COLLECTIVE HUMAN COGNITIVE INTELLIGENCE TO DETECT THE SALIENT OBJECT(S) BASED ON THE RECOGNIZED KEY CONCEPT(S). THEN, OTHER LESS-IMPORTANT REGIONS/OBJECTS WILL BE SUBJECT TO THE SAFE COMPRESSION. SUCH AN APPROACH, BESIDES PRESERVING SEMANTIC ASPECTS OF THE IMAGES THAT WILL RESULT IN SMART (CONCEPT-AWARE) COMPRESSION, COULD PROVIDE SOME CROWDSOURCED LABELS FOR MORE EFFICIENT INDEXING AND ANNOTATING OF IMAGES. IN THIS REGARD, TWO BIRDS COULD BE BEATEN WITH ONE STONE: COMPRESSING WEB IMAGES WITH RESPECT TO THEIR CONTENT/CONCEPT AND ANNOTATING THEM WITH CROWD-SUGGESTED LABELS. THE EXPERIMENTAL RESULTS AS WELL AS USER ACCEPTANCE EVALUATION PROVED THE EFFICACY OF THE INTRODUCED METHOD.

WEB SEARCH ENGINES ARE ESSENTIAL TOOLS FOR FINDING INFORMATION NEED FROM THE WORLD WIDE WEB. SO USERS' BEHAVIOR ANALYSIS HAS BECOME ONE OF THE MOST IMPORTANT RESEARCH TOPICS. IN THIS PAPER, WE STUDY INFORMATION GOALS AND PATTERNS OF PERSIAN USER 'S SEARCH FOR HEALTH INFORMATION ON THE WEB, REPORTING RESULTS OF A LARGE-SCALE LOG-BASED STUDY. WE FIRST CLASSIFY USER QUERIES INTO THREE CLASSES: SYMPTOMS, CAUSES AND REMEDY AND THEN ANALYZE THE CHANGE OF SEARCH FOCUS ALONG A SESSION. FINALLY, WE SHOW THAT IT IS POSSIBLE TO BUILD A CLASSIFIER TO PREDICT WHAT IS THE NEXT FOCUS OF A USER IN A HEALTH SESSION. OUR EXPERIMENTAL RESULTS SHOW THAT HEALTH-RELATED SEARCHES HAVE SPECIFIC ATTRIBUTES WHICH CAN BE USED BY SEARCH ENGINES TO INCREASE THE QUALITY OF THE SEARCH RESULTS. WE ALSO FIND THAT PERSIAN USERS’ BEHAVIOR CHANGES WITH REGARD TO THE OCCURRENCE TIME OF HEALTH-RELATED EVENTS SUCH AS WORLD AIDS DAY. THESE EVENTS TRIGGERING THE USER’ S ATTENTION AND EVENTUALLY LEADING TO RAPID INCREASES IN THE FREQUENCY OF THEIR RELATED QUERIES.

NOWADAYS, DISTRIBUTED DATA CENTERS (DCS) ARE VERY COMMON, BECAUSE OF PROVIDING MORE ENERGY EFFICIENCY, HIGHER SYSTEM AVAILABILITY AS WELL AS FLEXIBILITY. IN A DISTRIBUTED CLOUD, EACH LOCAL DC RESPONDS TO THE SPECIFIC PORTION OF THE INCOMING LOAD WHICH DISTRIBUTED BASED ON DIFFERENT GEOGRAPHICALLY LOAD BALANCING (GLB) POLICIES. TAKING ADVANTAGES OF DIVERSITY IN CONDITIONS AMONG DISTRIBUTED DCS, GLB HAS THE PROMISE OF SIGNIFICANT MONETARY SAVING. ONE OF THE BASIC ASSESSMENT METRICS AMONG DCS IS POWER USAGE EFFECTIVENESS (PUE). CONVENTIONALLY, PUE IS CONSIDERED AS A CONSTANT VALUE WHICH IS THE RATIO OF THE TOTAL POWER REQUIRED TO OPERATE AND COOL THE DC VS. THE POWER DRAWN BY THE IT EQUIPMENT. HOWEVER, IN MODERN DCS EQUIPPED WITH FREE COOLING AND RENEWABLE ENERGY (RE), THE COOLING POWER (AS A BIG PORTION OF TOTAL POWER) VARIES WITH THE CHANGES IN ENVIRONMENTAL CONDITIONS AS WELL AS WITH THE DYNAMICALLY CHANGES IN IT POWER. THEREBY, PUE IS NOT ONLY NOT FIXED BUT ALSO VARIES OVER TIME AS WELL AS IT LOAD. IN ADDITION, WHILE GREEN ENERGY IS INCREASINGLY USED IN DCS, THE CONVENTIONAL PUE IS INCAPABLE OF DESCRIBING THE GREEN POWER EFFICIENCY. THIS PAPER CONSIDERS THE GREEN PUE AS A POWER EFFICIENCY MEASUREMENT IN DCS, WHICH IS A FUNCTION OF TIME AS WELL AS IT LOAD. WE SHOW THAT GREEN PUE CAN EFFECTIVELY REFLECT THE CHANGES IN POWER EFFICIENCY (INCLUDING THE GREEN POWER EFFICIENCY) CAUSED BY CHANGES IN DC'S CONDITIONS. USING GREEN PUE AS AN ASSESSMENT CRITERION, WE PROPOSE A COST-EFFECTIVE GLB APPROACH. RESULTS SHOW THAT THE PROPOSED GLB REACHES %22-%26 COST SAVING, COMPARED TO PRIOR WORKS.

TODAY, DATA STORAGE AND PROCESSING PLAY A MAJOR ROLE IN ALL AREAS, INCLUDING MEDICAL TOPICS. EVERY DAY, A GREAT DEAL OF INFORMATION AND HEALTH RECORDS WITH A SECRET NATURE ARE PRODUCED IN A DISTRIBUTED MANNER SO PROVIDING CONSENSUS, STORAGE, INTEGRITY, VALIDITY AND SHARING IT AMONG DIFFERENT SOURCES, HAS MANY CHALLENGES. ON THE OTHER HAND, BLOCK CHAIN TECHNOLOGY PROVIDES US A PLACE FOR STORAGE AND SHARING DISTRIBUTED DATA, WHICH CAN GUARANTEE THE INTEGRITY OF THE DATA IN AN INTEGRATED NETWORK OF MEMBER NODES. DUE TO THESE FEATURES, THE USE OF THIS TECHNOLOGY IS CURRENTLY SPREADING IN A VARIETY OF FIELDS, INCLUDING MEDICINE, TO OVERCOME THE CHALLENGES POSED. BECAUSE OF THE BLOCK CHAIN TECHNOLOGY IS NEW AND A LITTLE AMBIGUOUS TODAY AND ALSO THE IMPORTANCE OF PRIVACY IN HEALTH DATA ISSUE, THIS PAPER ATTEMPTS TO PRESENT A VARIETY OF OPTIONS FOR SETTING UP THE BLOCK CHAIN SYSTEM WITH AN ELECTRONIC HEALTH RECORD APPROACH AND DESCRIBING ITS DIMENSIONS. IN THE END, BASED ON THE STUDY AND THE POLICY ON HOW TO STORE AND PROCESS INFORMATION, THE APPROPRIATE BLOCK CHAIN OPTION IS SUGGESTED FOR IMPLEMENTATION.

THE DEVELOPMENT OF SOCIAL NETWORKS, IN PARTICULAR TWITTER, HAS TRANSFORMED SOCIAL RELATIONSHIPS AND THE WAY SOCIAL INTERACTION IS CREATED AND COORDINATED. TWITTER IS USED TO MOBILIZE PEOPLE IN NATURAL DISASTERS, CONTAGIOUS DISEASES, AND MORE RECENTLY AS A COMMUNICATIONS PLATFORM WHEN SOCIAL AND POLITICAL MOVEMENT. GIVEN THE VAST AMOUNT OF INFORMATION SENT DAILY BY USERS ON THIS SOCIAL NETWORK, DISTINGUISHING USEFUL INFORMATION FROM INVALUABLE ONE AND DETERMINING WHO PUBLISHES THIS INFORMATION AND ITS IMPACT LEVEL THEY ARE VERY IMPORTANT. FOR THIS REASON, IN THIS ARTICLE, WE EXAMINE DIFFERENT METHODS FOR DETERMINING THE ROLE OF INDIVIDUALS WHEN TWEETING, TO STUDY THE ROLE OF NETWORKS AND SOCIAL MEDIA IN THE PRODUCTION AND DISSEMINATION OF IMPORTANT MESSAGES AND NEWS, AS WELL AS METHODS FINDING INFLUENTIAL PEOPLE AND ULTIMATELY INTRODUCING THE BEST WAY TO IDENTIFY AND MEASURE KEY PEOPLE AND THEIR ROLES.

THE RAPID GROWTH OF INFORMATION ON THE INTERNET AND THE LOSS OF USERS IN THE COMPLEX STRUCTURE OF INFORMATION, INCREASINGLY RAISES THE IMPORTANCE OF CONCEPTS SUCH AS RECOMMENDATION SYSTEMS. THIS RAPID GROWTH OF USERS AND ITEMS, ESPECIALLY ON THEIR COMMERCIAL SITES, HAS BEEN A MAJOR CHALLENGE FOR THESE SYSTEMS, WHICH GREATLY AFFECTED THEIR ACCURACY AND SCALABILITY. AS THE SCALABILITY OF A RECOMMENDATION SYSTEM HAS A HIGHER PRIORITY THAN OTHER FACTORS LIKE THE ACCURACY OF THE BIG SITES. IN THIS PAPER, A HIGHLY ACCURATE SYSTEM HAS BECOME SCALABLE BY USING THE MAPREDUCE ALGORITHM ON THE HADOOP FRAMEWORK. THE EVALUATION RESULTS SHOW THE SCALABILITY OF THE PROPOSED METHOD IN DIFFERENT DATASETS SIZE.

BLOCKCHAIN IS A DECENTRALIZED DISTRIBUTED LEDGER TO RECORDS ALL TRANSACTIONS MADE IN THE NETWORK. THE UNIQUE FEATURES AND CAPABILITIES OF THIS TECHNOLOGY AND ITS IMPACT IN DIFFERENT AREAS HAVE ENCOURAGED MANY USERS TO TAKE ADVANTAGE OF THIS TECHNOLOGY. THE EXISTENCE OF DIVERSE IMPLEMENTATIONS OF BLOCKCHAIN MAKES CHALLENGE FOR USERS TO SELECT APPROPRIATE BLOCKCHAIN PLATFORM. ACCORDINGLY, THIS PAPER, ALONG WITH INTRODUCING THE BLOCKCHAIN FEATURES, AIMS TO PROVIDE A HIGHLEVEL CLASSIFICATION OF THE BLOCKCHAIN PLATFORMS BASED ON A SET OF QUESTIONS THAT MAY BE IMPORTANT FOR THE END USER. THE PURPOSE OF THIS CLASSIFICATION IS PROVIDING AN APPROPRIATE VIEW FOR DETERMINING THE BLOCKCHAIN PLATFORM MATCHED WITH THE REQUIREMENTS OF THE END-USER. BASED ON THE PROPOSED CLASSIFICATION, A SET OF BLOCKCHAIN PLATFORMS HAVE BEEN EVALUATED IN ORDER TO PROVIDE AN EXHAUSTIVE VIEW ON THE PROPOSED CLASSIFICATION. THE OBTAINED RESULTS FROM THIS EVALUATION EMPOWER THE END USERS, REGARDLESS OF THEIR EXPERTISE, TO SELECT THE APPROPRIATE PLATFORM.

OPINION FORMATION MODELS DESCRIBE THE OPINION DYNAMICS OF INTERACTING PEOPLE. SOCIAL MEDIA ARE DRASTICALLY INCREASING AND HAVE BECOME ONE OF THE MOST CRITICAL MEDIA FOR PEOPLE INTERACTIONS. ACCORDING TO PSYCHOLOGICAL RESEARCHES, ONE’ S EMOTION DIFFUSES ACROSS INTERACTING PEOPLE. FURTHERMORE, EMOTION AFFECTS PEOPLE’ S OPINION. THE EMOTION CONTAGION ALSO HAPPENS THROUGH SOCIAL MEDIA VIA THE USERS’ POSTS AND AFFECTS THE READERS. THEREFORE, EMOTION IS AN ESSENTIAL ELEMENT IN OPINION FORMATION MODELS IN A SOCIAL NETWORK WHICH HAS ATTRACTED LITTLE ATTENTION. IN THIS PAPER, WE SHOW HOW CONSIDERING EMOTION IN OPINION FORMATION MODEL FOR ONLINE SOCIAL NETWORKS IMPROVES THE MODEL. WE HAVE USED A DATASET CONTAINING SOME DEBATES FROM THE CREATEDEBATE. COM WEBSITE. TWO CLASSIFIERS, WITH AND WITHOUT CONSIDERING EMOTIONS, HAVE BEEN IMPLEMENTED BASED ON THE SOCIAL IMPACT MODEL OF OPINION FORMATION TO PREDICT THE STANCES OF THE USERS’ NEXT POST IN THE DATASET AND THE RESULTS HAVE BEEN COMPARED WITH THE DATASET. THE EXPERIMENT RESULTS LEAD US TO CONCLUDE THAT CONSIDERING EMOTIONS IMPROVES THE ACCURACY AND PRECISION OF THE SOCIAL IMPACT MODEL OF OPINION FORMATION IN SOCIAL MEDIA.

SIMULATION FRAMEWORKS ARE ONE OF THE MOST IMPORTANT TOOLS FOR ANSWERING WHAT-IF QUESTIONS IN A WIDE-RANGE OF CONTEXTS FOR THE RESEARCHERS. THEREFORE, A LOT OF RESEARCH ON SIMULATION TOOLKITS AND THEIR ARCHITECTURE AND ADVANTAGES HAVE BEEN PERFORMED. THERE ARE MANY TYPES OF SIMULATION MODELS WITH DIFFERENT FEATURES. SELECTING THE MOST SUITABLE SIMULATION TOOLKIT DEPENDS ON SPECIFICATIONS OF THE SYSTEM WHICH WE WANT TO SIMULATE. NOWADAYS, A MAJOR BREAKTHROUGH IN THE FIELD OF COMPUTER SIMULATIONS IS THE POSSIBILITY TO CONSIDER OBJECTS WITH DIFFERENT ATTRIBUTES WITHIN THE SIMULATION FRAMEWORK. SUCH CONSTRUCTION IS REFERRED TO AS AGENTBASED SIMULATION. IN THE TOOLKITS THAT SUPPORT THIS APPROACH, WE CAN CREATE AUTONOMOUS, SELF-CONTAINED AND SELF-DIRECTED AGENTS THAT INTERACT WITH EACH OTHER AS WELL AS THEIR ENVIRONMENT, DEFINING THEIR BEHAVIORS AND GOALS. AGENT-BASED SIMULATION COMPARED TO TRADITIONAL APPROACHES HAS MANY ADVANTAGES BEING USED IN THE APPROPRIATE CONTEXT. CURRENT AGENT-BASED FRAMEWORKS OFFER LOTS OF FEATURES AND FUNCTIONS TO THEIR USERS. BUT, USUALLY THEY FACE A SERIOUS PROBLEM IN LARGE-SCALE SIMULATION MODELS. MOST OF THE TIME, THEY CANNOT RESPOND TO THE USERS WITHIN THE TIME LIMITS WHEN THE NUMBER OF AGENTS AND THEIR INSTRUCTIONS ARE MASSIVE AND THERE IS NO WAY TO EXECUTE THESE SIMULATIONS IN A DISTRIBUTED MANNER. IN THIS PAPER, WE AIM TO DESIGN AND IMPLEMENT A DISTRIBUTED AGENTBASED SIMULATION FRAMEWORK BY IMPLEMENTING EVERY AGENT IN A MICRO-SERVICE, LEVERAGING THE POWER OF THE NEWLY INTRODUCED MICRO-SERVICE ARCHITECTURE. BY USING THIS APPROACH, WE CAN GAIN THE ADVANTAGES OF BOTH THE MICRO-SERVICE AND DISTRIBUTED AGENT-BASED SIMULATIONS ARCHITECTURES. THIS NEW FRAMEWORK WILL ALLOW US TO CREATE AND EXECUTE SIMULATION MODELS IN A MORE FLEXIBLE, REUSABLE AND EXTENDABLE MANNER WHILE BEING ABLE TO RUN OVER A DISTRIBUTED SYSTEM.

WITH THE DEVELOPMENT OF NEW TECHNOLOGIES IN THE WORLD, GOVERNMENTS INTEND TO MAKE A COMMUNICATIONS WITH PEOPLE AND BUSINESS WITH THE HELP OF SUCH TECHNOLOGIES. ELECTRONIC GOVERNMENT (E-GOVERNMENT) IS DEFINED AS UTILIZING INFORMATION TECHNOLOGIES SUCH AS ELECTRONIC NETWORKS, INTERNET AND MOBILE PHONES BY ORGANIZATIONS AND STATE INSTITUTIONS IN ORDER TO MAKING WIDE COMMUNICATION BETWEEN CITIZENS, BUSINESS AND DIFFERENT STATE INSTITUTIONS. DEVELOPMENT OF E-GOVERNMENT STARTS WITH MAKING WEBSITE IN ORDER TO SHARE INFORMATION WITH USERS AND IS CONSIDERED AS THE MAIN INFRASTRUCTURE FOR FURTHER DEVELOPMENT. WEBSITE ASSESSMENT IS CONSIDERED AS A WAY FOR IMPROVING SERVICE QUALITY. DIFFERENT INTERNATIONAL RESEARCHES HAVE INTRODUCED VARIOUS INDEXES FOR WEBSITE ASSESSMENT, THEY ONLY SEE SOME DIMENSIONS OF WEBSITE IN THEIR RESEARCH. IN THIS PAPER, THE MOST IMPORTANT INDEXES FOR WEBSITE QUALITY ASSESSMENT BASED ON ACCURATE REVIEW OF PREVIOUS STUDIES ARE “ WEB DESIGN” , “ NAVIGATION” , SERVICES” , “ MAINTENANCE AND SUPPORT” , “ CITIZENS PARTICIPATION” , “ INFORMATION QUALITY” , “ PRIVACY AND SECURITY” , “ RESPONSIVENESS” , “ USABILITY” . CONSIDERING MENTIONED INDEXES IN DESIGNING THE WEBSITE FACILITATES USER INTERACTION WITH THE E-GOVERNMENT WEBSITES.

SINCE THE NUMBER OF FACIAL IMAGES HAS GROWN IN SOCIAL NETWORKS, SUCH AS FACEBOOK AND INSTAGRAM, FACE RECOGNITION HAS BEEN RECOGNIZED AS ONE OF THE IMPORTANT BRANCHES OF IMAGE PROCESSING RESEARCH AREA, AND LARGE DATABASES OF FACE IMAGES HAVE BEEN CREATED. AS A RESULT, THE RESPONSE TIME OF THE FACE RECOGNITION SYSTEM IS RECOGNIZED AS A CHALLENGE. FORTUNATELY, DIMENSION REDUCTION TECHNIQUES HELP TO REDUCE THE NUMBER OF COMPUTATIONS SIGNIFICANTLY, WHICH DIRECTLY EFFECTS ON SYSTEM RESPONSE TIME. AS MANY FACIAL FEATURES DO NOT INCLUDE IMPORTANT INFORMATION, WHICH IS REQUIRED FOR GETTING A BETTER RESULT FROM THE FACE RECOGNITION SYSTEMS, THESE TECHNIQUES ARE APPLICABLE FOR FACIAL IMAGES, AS WELL. LOCAL FEATURE HASHING (LFH) IS A HASHBASED ALGORITHM THAT HAS BEEN USED FOR FACE RECOGNITION. IT HAS SHOWN NOTABLE COMPUTATIONAL IMPROVEMENTS OVER NAIVE SEARCH AND CAN OVERCOME SOME CHALLENGES, INCLUDING RECOGNITION OF POSE, FACIAL EXPRESSION, ILLUMINATION, AND PARTIAL OCCLUSION PARAMETERS. WITH THE AIM OF IMPROVING THE TIME THAT IT TAKES TO RUN THE LFH ALGORITHM, WE HAVE TESTED SEVERAL VERSIONS OF LOCALITY-SENSITIVE HASHING (LSH) ALGORITHM. THE RESULTS SHOWED THAT SOME OF THESE ALGORITHMS IMPROVE THE RESPONSE TIME OF THE LFH ALGORITHM. IN COMPARISON WITH THE PREVIOUSLY CONDUCTED RESEARCH, THE NUMBER OF INPUT IMAGES HAS BEEN INCREASED IN OUR TESTS. BESIDES, THE NUMBER OF EXTRACTED KEY-POINT VECTORS HAVE BEEN DECREASED, AND THE ACCURACY HAS NOT BEEN DECREASED. IN ADDITION, OUR ALGORITHM IS ABLE TO OVERCOME THE CHALLENGE OF THE EXISTENCE OF FOREIGN OBJECTS, SUCH AS GLASS. AMONG ALL DIFFERENT HASH VERSIONS THAT FOR THE FIRST TIME USED FOR FACE RECOGNITION, SOME OF THEM ARE NOT RECOMMENDED FOR THESE SYSTEMS AND SEVERAL FUNCTIONS CAN PROVIDE MINIMUM RESPONSE TIME, RATHER THAN PREVIOUS HASHBASED ALGORITHMS.

INDIVIDUALS MAY CHANGE THEIR OPINION IN EFFECT OF A WIDE RANGE OF FACTORS LIKE INTERACTION WITH PEER GROUPS, GOVERNMENTAL POLICIES AND PERSONAL INTENTIONS. WORKS IN THIS AREA MAINLY FOCUS ON INDIVIDUALS IN SOCIAL NETWORK AND THEIR INTERACTIONS WHILE NEGLECT OTHER FACTORS. IN THIS PAPER WE HAVE INTRODUCED AN OPINION FORMATION MODEL THAT CONSIDER THE INTERNAL TENDENCY AS A PERSONAL FEATURE OF INDIVIDUALS IN SOCIAL NETWORK. IN THIS MODEL AGENTS MAY TRUST, DISTRUST OR BE NEUTRAL TO THEIR NEIGHBORS. THEY MODIFY THEIR OPINION BASED ON THE OPINION OF THEIR NEIGHBORS, TRUST/DISTRUST TO THEM WHILE CONSIDERING THE INTERNAL TENDENCY. THE RESULTS OF SIMULATION SHOW THAT THIS MODEL CAN PREDICT THE OPINION OF SOCIAL NETWORK ESPECIALLY WHEN THE AVERAGE OF NODAL DEGREE AND CLUSTERING COEFFICIENT ARE HIGH ENOUGH. SINCE THIS MODEL CAN PREDICT THE PREFERENCES OF INDIVIDUALS IN MARKET, IT CAN BE USED TO DEFINE MARKETING AND PRODUCTION STRATEGY.

IN THE RECENT YEARS, MANY SOLUTIONS HAVE BEEN PROPOSED TO DELIVER MULTIMEDIA OVER THE BEST-EFFORT INTERNET. THE OBJECTIVE OF THESE APPROACHES IS TO ENSURE HIGH QUALITY OF SERVICE (QOS) AND QUALITY OF EXPERIENCE (QOE) FOR MULTIMEDIA DELIVERY IN THE PRESENCE OF BANDWIDTH FLUCTUATIONS MADE BY FACTORS LIKE SIGNAL PROPAGATION AND NETWORK CONGESTION. HOWEVER, MANY OF THESE TECHNIQUES FACES SEVERE LIMITATIONS SUCH AS NETWORK BOTTLENECK AND CONGESTION DUE TO THE LACK OF GLOBAL NETWORK VIEW. THE SEPARATION OF CONTROL LOGIC AND DATA PLANE IN SOFTWARE DEFINED NETWORKING (SDN) ARCHITECTURE CONTRIBUTES TO THE SMARTNESS, FLEXIBILITY, AND CONTROLLABILITY OF COMPUTER NETWORKS AND PROVIDES NEW OPPORTUNITIES FOR GUARANTEEING HIGHER QOE FOR MULTIMEDIA VIEWERS. IN THIS SURVEY, WE PRESENT A TAXONOMY OF THE STATE-OF-ART SDN-BASED SOLUTIONS FOR MULTIMEDIA COMMUNICATIONS AND WE SHED LIGHT ON THE RESEARCH GAPS IN THIS AREA.

OVER THE LAST DECADES, A MULTITUDE OF SEMANTIC RELATEDNESS MEASURES HAVE BEEN PROPOSED. DESPITE AN EXTENSIVE AMOUNT OF WORK DEDICATED TO THIS AREA OF RESEARCH, THE UNDERSTANDING OF THEIR FOUNDATION IS STILL LIMITED IN REAL-WORLD APPLICATIONS. IN THIS PAPER, A UNIFYING APPROACH REPRESENTING TOPIC-BASED MODELS IS PROPOSED AND FROM WHICH THE STATE-OF-THEART SEMANTIC RELATEDNESS MEASURES ARE DIVIDED INTO TWO DISTINCT TYPES OF TOPIC-BASED AND ONTOLOGY-BASED MODELS. REGARDLESS OF EXTENSIVE RESEARCHES IN THE FIELD OF ONTOLOGY-BASED MODELS, TOPICBASED MODELS HAVE NOT BEEN TAKEN INTO ACCOUNT CONSIDERABLY. CONSEQUENTLY, THE UNIFIED APPROACH IS ABLE TO HIGHLIGHT EQUIVALENCES AMONG THESE MODELS AND PROPOSE BRIDGES BETWEEN THEIR THEORETICAL BASES. ON THE OTHER HAND, PRESENTING A COMPREHENSIVE UNIFYING APPROACH OF TOPIC-BASED MODELS INDUCES READERS TO HAVE A COMMON UNDERSTANDING OF THEM DESPITE THE DIFFERENCES AND COMPLEXITIES BETWEEN THEIR ARCHITECTURE AND CONFIGURATION DETAILS. IN ORDER TO EVALUATE TOPIC-BASED MODELS IN COMPARISON TO ONTOLOGY-BASED MODELS, COMPREHENSIVE EXPERIMENTS IN THE APPLICATION OF SEMANTIC RELATEDNESS OF GEOGRAPHIC PHRASES HAVE BEEN APPLIED. EMPIRICAL RESULTS HAVE DEMONSTRATED THAT NOT ONLY TOPIC-BASED MODELS IN COMPARISON TO ONTOLOGY-BASED MODELS CONFRONT WITH FEWER RESTRICTIONS IN THE REAL WORLD, BUT ALSO THEIR PERFORMANCE IN COMPUTING SEMANTIC RELATEDNESS OF GEOGRAPHIC PHRASES IS SIGNIFICANTLY SUPERIOR TO ONTOLOGY-BASED MODELS.

COMMUNITY DETECTION IS ONE OF THE MOST IMPORTANT TASKS IN SOCIAL NETWORKS ANALYSIS. THIS PROBLEM BECOMES MORE CHALLENGING WHEN THE STRUCTURE OF THE NETWORK CHANGES DURING THE TIME. IT IS VERY IMPORTANT TO UPDATE THE STRUCTURES OF THE COMMUNITY IN A DYNAMIC NETWORK WITHOUT TIME-CONSUMING PROCEDURES. THIS PAPER SUGGESTS A HYBRID EVOLUTIONARY ALGORITHM FOR ONLINE COMMUNITY DETECTION. THE PROPOSED ALGORITHM CALLED MEMETIC BASED ONLINE COMMUNITY DETECTION (MBOC) IS BASED ON A MEMETIC ALGORITHM WITH NEW GENETIC OPERATORS AND A NOVEL STOCHASTIC LOCAL SEARCH TO ASSIGN NEW NODES TO COMMUNITIES AND ANOTHER LOCAL SEARCH CALLED DENSE SEARCH TO MODIFY COMMUNITIES AFTER NEW ASSIGNMENTS. THE METHOD IS EVALUATED OVER SEVERAL WELL-KNOWN BENCHMARK NETWORKS. THE RESULTS SHOW THAT THE PROPOSED APPROACH OUTPERFORMS THE PREVIOUS METHODS IN MOST CASES.

IN THIS PAPER WE USE DISTANT SUPERVISION FOR THE TASK OF RELATION EXTRACTION FROM A LARGE CORPUS IN THE PERSIAN LANGUAGE. THERE ARE SUPERVISED AND UNSUPERVISED METHODS FOR RELATION EXTRACTION FROM TEXT. IN SUPERVISED RELATION EXTRACTION WE USE HAND LABELED CORPORA. THIS METHOD SUFFERS FROM DOMAIN DEPENDENCIES AND THE DIFFICULTIES OF LABELING THE TEXT. IN UNSUPERVISED METHOD, WE USE LARGE CORPORA WITHOUT HAVING TO LABEL THEM BUT RELATIONS EXTRACTED USING THIS METHOD CANNOT BE USED TO POPULATE KNOWLEDGE BASES. DISTANT SUPERVISION TAKES ADVANTAGE OF LARGE CORPORA WITHOUT SUFFERING FROM DOMAIN DEPENDENCIES AND CAN POPULATE KNOWLEDGE BASES. FOR OUR EXPERIMENT WE USE FARSBASE, A KNOWLEDGE BASE CONTAINING MILLIONS OF RELATION INSTANCES, AND ALIGN ENTITIES IN 630000 PERSIAN WIKIPEDIA ARTICLES TO THESE RELATION INSTANCES AND CREATE A DISTANTLY SUPERVISED DATASET. WE THEN EXTRACT NEW RELATION INSTANCES USING PIECEWISE CONVOLUTIONAL NEURAL NETWORKS AND COMPARE THE RESULTS WITH THE BASELINE MODEL THAT USES MANUALLY EXTRACTED FEATURES.

THESE DAYS, BASED ON THE RECENT DEVELOPMENT IN IOT TECHNOLOGIES, A LARGE NUMBER OF INNOVATIVE HEALTHCARE APPLICATIONS SHOULD BE CREATED. HEALTH INTERNET OF THINGS (HIOT) IS A NOVEL TECHNOLOGY THAT USED TO CONNECT MANY DIVER’ S MEDICAL SENSORS TO IOT DEVICES. THE HIOT FACILITATES REMOTE DATA COLLECTION AND MONITORING IN HEALTHCARE FIELDS. THIS PAPER IS A REVIEW STUDY THAT ADDRESSES THE IMPACT OF HIOT ON HEALTHCARE APPLICATIONS. IN THIS REGARDS, WE INVESTIGATED THE MOST IMPORTANT TECHNOLOGIES USED IN HIOT AND DESCRIBE THE MAIN STRENGTHS, WEAKNESSES, OPPORTUNITIES, AND THREATS OF HIOT-BASED APPLICATIONS. BASED ON THE RESULTS, THE POTENTIAL STRENGTHS AND OPPORTUNITIES OF THE HIOT TECHNOLOGY IS VERY DIFFERENT AND WIDESPREAD; HOWEVER, IT HAS SOME SIGNIFICANT WEAKNESSES AND THREATS INCLUDING COMPLEXITY OF HANDLING A HUGE NUMBER OF HETEROGENEOUS OBJECTS, ACHIEVING SCALABILITY, RELIABILITY, EFFICIENCY, AVAILABILITY, SECURITY, AND INTEROPERABILITY WITH IOT SYSTEMS ACROSS HEALTHCARE APPLICATIONS.

THE SCIENTIFIC OUTPUT OF A FIELD AND ITS RESEARCH ACHIEVEMENTS AT THE NATIONAL AND INTERNATIONAL LEVELS ARE AMONG THE IMPORTANT INDICATORS THAT DETERMINE THE STATUS AND DIRECTION OF THE MOVEMENT OF THAT AREA. ONE OF THE KNOWN METHODS IN THIS FIELD IS TO INVESTIGATE THE SCIENTIFIC OUTPUT INDEXED BY RESEARCHERS IN THAT FIELD AT INTERNATIONALLY RELIABLE DATABASES. THEREFORE, CONSIDERING THE IMPORTANCE OF SEMANTIC WEB AND ITS CLOSE RELATIONSHIP WITH OTHER WEB-BASED RESEARCHES, HUMANCOMPUTER INTERACTION, DATA STORAGE AND RETRIEVAL, AND. . ., THE PRESENT STUDY AIMS TO ANALYZING THE SCIENTIFIC OUTPUT OF RESEARCHERS IN THE SEMANTIC WEB DOMAIN OF THE SCOPUS CITATION DATABASE IN THE GAP BETWEEN 1996 AND 2018 WAS DESIGNED AND IMPLEMENTED. THE PRESENT STUDY IS AN APPLIED RESEARCH METHODOLOGY. THE RESEARCH COMMUNITY INCLUDES ALL SCIENTIFIC PRODUCTIONS (10357 RECORDS) OF THE WORLD'S SCHOLARS IN THE DOMAIN OF SEMANTIC WEB. THE RESULTS ARE PRESENTED USING EXCEL ANALYSIS SOFTWARE AND IN THE FORM OF TABLES AND CHARTS. THE FINDINGS OF THIS RESEARCH SHOWED A GENERAL VIEW OF THE STATE OF SCIENTIFIC PRODUCTION OF SEMANTIC WEB DOMAIN AT THE NATIONAL AND INTERNATIONAL LEVEL DURING THE YEARS, AND IN PARTICULAR, REVEALED THE POSITION OF IRANIAN RESEARCHERS IN THE PROCESS OF PRODUCING SCIENCE IN THIS FIELD. LEADING RESULTS CAN BE USEFUL FOR WEBMASTER MANAGERS AND POLICY MAKERS AT UNIVERSITIES AND OTHER RELATED ORGANIZATION.

SENTIMENT ANALYSIS OF ONLINE REVIEWS HAS ATTRACTED AN INCREASING ATTENTION FROM BOTH ACADEMIA AND INDUSTRY. ALTHOUGH ONLINE REVIEWS ARE VALUABLE SOURCES OF INFORMATION FOR DETECTING PUBLIC OPINION TOWARDS DIFFERENT ASPECTS OF PRODUCTS, THEY MAY BE WRITTEN BY SPAMMERS WITH DIFFERENT PURPOSES. IN ORDER TO DETECT SUCH SPAM REVIEWS, SEVERAL METHODS HAVE BEEN PROPOSED FOR ENGLISH LANGUAGE BUT NO STUDY HAS BEEN REPORTED ON PERSIAN SPAM DETECTION SO FAR. IN THE CURRENT STUDY, PERSIAN REVIEWS OF CELL-PHONES ARE INVESTIGATED TO FIND SPAM TYPE 1 AND TYPE 2 WHICH ARE FAKE REVIEWS AND REVIEWS ONLY WRITTEN ABOUT BRANDS, RESPECTIVELY. IN THE PROPOSED FRAMEWORK A LABELED DATASET, SPAMPER, IS FIRST CREATED USING A MAJORITY VOTING ON THE ANSWERS OF 11 QUESTIONS PREVIOUSLY DESIGNED FOR SPAM DETECTION BY HUMAN ANNOTATORS. THEN SEVERAL PREPROCESSING STEPS FOR PERSIAN LANGUAGE ARE PERFORMED TO REFINE THE TRAINING DATA. FINALLY REVIEW-BASED AND METADATA FEATURES ARE EXTRACTED. THE OBTAINED RESULTS ON 3000 REVIEWS OF SPAMPER SHOWS THAT THE HIGHEST ACCURACY IS OBTAINED USING THE DECISION TREE WITH 0. 78 F1-MEASURE. MOREOVER, THE RESULTS REVEAL THAT SVM FOR UNBALANCED DATA AND DECISION TREE FOR BALANCED DATA ACHIEVE BETTER PERFORMANCE WHEN THEY ARE TRAINED ON THE COMBINATION OF METADATA AND REVIEW-BASED FEATURES.

IN THIS PAPER, WE PROPOSE NEW ALGORITHMS TO IMPROVE THE PERFORMANCE OF RECOMMENDER SYSTEMS, BASED ON HIERARCHICAL BLOOM FILTERS. SINCE BLOOM FILTERS CAN MAKE A TRADEOFF BETWEEN SPACE AND TIME, PROPOSING A NEW HIERARCHICAL BLOOM FILTER CAUSES A REMARKABLE REDUCTION IN SPACE AND TIME COMPLEXITY OF RECOMMENDER SYSTEMS. SPACE REDUCTION IS DUE TO HASHING ITEMS IN A BLOOM FILTER TO MANAGE THE SPARSITY OF INPUT VECTORS. TIME REDUCTION IS DUE TO THE STRUCTURE OF HIERARCHICAL BLOOM FILTER. TO INCREASE THE ACCURACY OF THE RECOMMENDER SYSTEMS WE USE PROBABILISTIC VERSION OF HIERARCHICAL BLOOM FILTER. THE STRUCTURE OF HIERARCHICAL BLOOM FILTER IS B+ TREE OF ORDER D. PROPOSED ALGORITHMS NOT ONLY DECREASE THE TIME COMPLEXITY BUT ALSO HAVE NO SIGNIFICANT EFFECT ON ACCURACY.

MANY STUDIES HAVE SHOWN THAT POOR QUALITY SEARCH WITHIN ENTERPRISES CAUSED SIGNIFICANT ECONOMIC PENALTIES. ONE OF THE MAIN REASONS FOR POOR QUALITY OF ENTERPRISE SEARCH ENGINES IS THAT MOST OF THEM USE THE BAG-OF-WORDS MODEL, WHICH CONSIDERS THE FREQUENCY OF WORDS BUT DISREGARDS THEIR ORDER. FOR SATISFYING USERS INFORMATION NEEDS, SEARCH ENGINES NEED TO UNDERSTAND THE UNDERLYING MEANING OF THE QUERY. THUS WE NEED MORE COMPLEX RETRIEVAL MODELS THAN BAG-OF-WORDS. ACCORDING TO VARIOUS STUDIES, MOST OF THE QUERIES IN SEARCH ENGINES MENTION OR TARGET SPECIFIC ENTITIES. BEING ABLE TO IDENTIFY ENTITIES IN USER QUERIES IS A KEY STEP TOWARDS UNDERSTANDING USERS INFORMATION NEEDS AND IS A MAJOR HELP TOWARDS RETRIEVING RELEVANT DOCUMENTS. LEVERAGING ENTITIES IN DOCUMENT RETRIEVAL HAVE BEEN USED FOR MANY YEARS TO IMPROVE RESULTS IN WEB SEARCH ENGINES, BUT THERE ARE FEW STUDIES THAT USE THESE TECHNIQUES IN ENTERPRISE SEARCH. THAT ASIDE, MANY OF THESE STUDIES SUFFER FROM LOW EFFICIENCY. THIS STUDY REPRESENTS AN ENTITY LINKING APPROACH WHICH IS CUSTOMIZED FOR ENTERPRISE SEARCH. EVALUATIONS SHOW THAT IN ADDITION TO HIGH PRECISION, OUR APPROACH ALSO HAS HIGH EFFICIENCY.

DATA ABOUT THE BEHAVIOR OF WEB USERS WHILE BROWSING THE RESULTS RETURNED BY WEB SEARCH ENGINES ENTITLED “ CLICK-THROUGH DATA” , IS A SOURCE OF IMPORTANT INFORMATION, WHICH IMPROVES THE FUNCTIONALITY OF WEB SEARCH ENGINES. MEANWHILE, ALMOST NONE OF THE RELEASED AND PUBLICLY AVAILABLE BENCHMARK DATASETS DEVOTED TO THE TASK OF LEARNING TO RANK, INCLUDE EXPLICIT CLICK-THROUGH DATA. AS A RESULT, MOST OF THE PROPOSED LEARNING TO RANK ALGORITHMS, IN PRACTICE HAVE NOT USED SUCH ENRICHED SOURCE OF DATA IN THEIR RANKING PROCESS. BESIDES, THESE DATASETS PRESENT A LARGE VOLUME OF FEATURES FOR THE TASK OF LEARNING TO RANK WHICH IN TURN BRINGS OUT NOTICEABLE COMPUTATIONAL COSTS FOR THE PROPOSED ALGORITHMS, AND MAKES THEM INEFFECTIVE IN REAL-WORLD SITUATIONS. TO ADDRESS THESE PROBLEMS, A NOVEL LEARNING TO RANK METHOD, QRC-RANK, HAS PROPOSED THE CONCEPT OF “ CLICK-THROUGH FEATURES” . IN THIS RESEARCH, THE FUNCTIONALITY OF THESE RANKING TECHNIQUES IS STUDIED IN THE RETRIEVAL OF THE PERSIAN CONTENTS BY APPLYING ON DOTIR BENCHMARK DATASET. THESE ANALYZES CLEARLY DEMONSTRATE SIGNIFICANT IMPROVEMENTS IN COMPARISON WITH WELL-KNOWN RANKING METHODS BASED ON THE EVALUATION CRITERIA. SPECIFICALLY, SUCH AN IMPROVEMENT IS MORE NOTICEABLE ON THE TOP OF RANKED LISTS, WHICH USUALLY ATTRACT USERS’ ATTENTIONS MORE THAN OTHER PARTS OF THESE LISTS.

NOWADAYS, INTERNET OF THINGS IS ONE OF THE HOTTEST TOPICS IN ICT AND ONE OF THE SIX EMERGING TECHNOLOGIES IN THE WORLD. IN GENERAL, THINGS ARE REFERRED TO NETWORK-ENABLED AND RESOURCE-CONSTRAINED DEVICES THAT ARE HETEROGENEOUS FROM DIFFERENT ASPECTS. SO, INTEROPERABILITY (COOPERATIVE BEHAVIOR) IS ONE OF THE MAJOR CHALLENGES IN IOT. THERE ARE THING-TO-THING (T2T) AND THING-TO-GATEWAY (T2G) COMMUNICATIONS BETWEEN DIFFERENT THINGS. THESE COMMUNICATIONS CAN BE INTRA-DOMAIN OR INTER-DOMAIN (MULTI-DOMAIN). INDEED, THE IOT CAN BE CONSIDERED AS A SET OF MULTI-DOMAIN NETWORKS THAT CO-EXIST IN THE SAME GEOGRAPHICAL LOCATIONS, BUT RUN BY DIFFERENT AUTHORITIES. CRITERIA OF THIS HETEROGENEITY IS DEFINED BASED ON THE PREPROCESSING COSTS FOR THE THINGS IN EACH DOMAIN. ADDITIONALLY, THESE THINGS OFTEN HAVE A DIRECT RELATIONSHIP, THEY USUALLY HAVE BUILT-IN SECURITY MECHANISMS. ON THE OTHER HAND, WE ASSUME THAT EACH THING IS CAPABLE TO MAKE MALICIOUS PACKETS AND PROPAGATE THEM IN NETWORK. IN OTHER WORDS, WE ARE INTERESTED IN INVESTIGATING INTEROPERABILITY IN MULTI-DOMAIN INTERACTIONS. THERE ARE SOME TRADEOFFS BETWEEN ENERGY CONSUMPTION, LIFETIME AND SECURITY LEVEL OF DOMAINS IN THESE SITUATIONS WE WILL PROPOSE A NON-COOPERATIVE GAME THEORETIC MODEL, WHICH IS A REPEATED AND COMPLETE INFORMATION GAME. THEORY. WE WILL FIND THE EFFECTIVE FACTORS ON COOPERATIVE BEHAVIOR, INCLUDING NUMBER OF THINGS PER DOMAIN, PATH LOSS EXPONENT AND COMMUNICATION RANGE. IN DEFINED SETTINGS, WE WILL SHOW THE AVERAGE LIFETIME OF EACH DOMAIN IF THE THINGS COOPERATE, ALSO FORWARD PACKETS FROM OTHER DOMAINS.

TWITTER IS ONE OF THE MOST POPULAR SOCIAL NETWORKS NOWADAYS. PEOPLE SHARE THEIR IDEAS, COMMENTS AND FEELINGS ABOUT DIFFERENT SUBJECTS OF THEIR DAILY LIFE IN TWITTER. BECAUSE OF HUGE AMOUNT OF TWEET, TWITTER IS A SUITABLE DATABASE FOR BIG DATA AND THERE ARE MANY RESEARCH ON IT. ON THE OTHER HAND SENTIMENT ANALYSIS IN SOCIAL MEDIA IS VERY POPULAR AMONG RESEARCHERS RECENTLY. MOREOVER IT’ S RESULTS USE IN ECONOMICAL, SOCIAL AND POLITICAL SUBJECTS. IN THIS RESEARCH, WE ARE GOING TO REPRESENT AN APPROACH TO COLLECT DATA FROM TWITTER FIRST, THEN STORE AND ANALYZE DATA USING HADOOP FRAMEWORK AND A HYBRID MODEL THAT USE BAYES THEOREM AND DICTIONARY OF WORDS FOR SENTIMENT ANALYSIS. ACCORDING TO THE RESULTS OF EXPERIMENT ON DATA, THE ACCURACY OF THE PROPOSED APPROACH HAS REACHED TO 70 PERCENT.

THE CURRENT PAPER AIMS TO CONCEPTUALIZE SELF-EFFICACY AS AN IMPORTANT PSYCHOLOGICAL CONSTRUCT WHICH PUT FORTH MORE THAN 30 YEARS AGO BY THE MOST IMPORTANT AND HIGH CITATION LIVED PSYCHOLOGIST ALBERT BANDURA. THE CONCEPT HAS BEEN TESTED AND APPROVED BY MANY OF RESEARCHERS IN DIFFERENT DISCIPLINES AND RECENTLY IN WEB ENVIRONMENT. UNFORTUNATELY, THE CONCEPT HAS NOT YET BEEN CONSIDERED IN PERSIAN LITERATURE WHILE DIFFERENT INSTRUCTIONAL AND APPLICATION CONTEXTS OF THE WEB ARE RELATED AND CONDITIONED TO HUMAN BEHAVIOR. SUCH AREAS OF SUBJECTS AS INSTRUCTING INFORMATION SEARCHING SKILLS, ADAPTATION AND APPLICATION OF TECHNOLOGY, EDUCATION AND RESEARCH, ORGANIZATION AND RETRIEVAL OF INFORMATION ARE DEPENDENT TO USERS’ BEHAVIORS LIKE SELF-EFFICACY IN WEB ENVIRONMENT. SOME OF THE MOST IMPORTANT AND RELATED TYPES OF WEB SELF-EFFICACY LIKE IT SELF-EFFICACY, COMPUTER SELF-EFFICACY, INTERNET SELF-EFFICACY, ACADEMIC SELF-EFFICACY, RESEARCH SELF-EFFICACY, INFORMATION LITERACY SELF-EFFICACY AND WEB INFORMATION SEARCHING SELF-EFFICACY ARE INTRODUCED IN THIS PAPER.

AS THE UNDERLYING INFRASTRUCTURE OF THE INTERNET BECOMES MORE ADVANCED AND HIGH-SPEED INTERNET ACCESS BECOMES MORE PREVALENT, THE USAGE OF WEB-BASED APPLICATIONS INCREASES. WEB APPLICATIONS PROVIDE USERS WITH A WIDE RANGE OF SERVICES AND PROVIDING A PLATFORM FOR SHARING CYBER SECURITY INFORMATION IS ONE OF THOSE. STUDIES HAVE SHOWN CYBER ACTORS MOSTLY UTILIZE THE SAME TECHNIQUES, TACTICS AND PROCEDURES (TTPS) TO ATTACK DIFFERENT COMPANIES AND PLATFORMS. THEREFORE, SHARING THREAT DATA, BEST PRACTICES AND COUNTERMEASURES CAN HELP EVERY PARTICIPANT IN THE SHARING PROCESS TO IDENTIFY POTENTIAL THREATS, MITIGATE THEIR IMPACTS OR PREVENT FARREACHING ATTACKS FROM HAPPENING. ESTABLISHING A WEB-BASED INFORMATION SHARING CENTER HAVE BEEN PROVED TO BE BENEFICIAL AS A COMMUNICATION CHANNEL FOR ASSISTING FIRMS AND ORGANIZATIONS TO PROMOTE THEIR OWN CYBER OR PHYSICAL SECURITY. ACCORDINGLY, IN THIS PAPER WE LOOK AT INFORMATION SHARING CHALLENGES AND DIFFERENT TRUST MODELS; WE ALSO PRESENT REQUIRED TECHNICAL METHODS, POLICIES AND REGULATIONS TO OVERCOME THE INTRODUCED CHALLENGES BY CAREFULLY STUDYING ESTABLISHED RULES AND REGULATIONS IN THE U. S. AND EUROPEAN UNION. THE PROPOSED TECHNICAL METHODS, POLICIES AND REGULATIONS ARE BENEFICIAL FOR IMPLEMENTING A TRUSTFUL WEB-BASED INFORMATION SHARING CENTER. FINALLY, FUTURE WORK IS OUTLINED.

WEB TABLES ARE WORTHY SOURCES OF RELATIONAL INFORMATION. THE NUMBER OF HIGH-QUALITY TABLES WITH USEFUL RELATIONAL INFORMATION IS RAPIDLY INCREASING TO HUNDREDS OF MILLIONS. SOME SEARCH ENGINES USUALLY IGNORE MEANINGS OF ENTITIES AND RELATIONSHIPS IN INDEXING THUS THEY HAVE POOR PERFORMANCE IN TABULAR DATA TO A SUITABLE FIELD OF RESEARCH IS THE TRANSFORMATION OF WEB TABLES INTO MACHINE-READABLE KNOWLEDGE. WE FIRST STUDY OVERVIEW OF THE USE OF WEB TABLES IN DIFFERENT DOMAINS THEN FOCUS ON UNDERSTANDING KNOWLEDGE OF WEB TABLES. THE RESULTS INDICATE THAT BY COMBINING OLD INFORMATION EXTRACTION TECHNIQUES, AND TABLE FEATURES AND GENERAL INFERENCE MODELS CAN EXTRACT KNOWLEDGE FROM WEB TABLES.

BLOCKCHAIN TECHNOLOGY HAS ATTRACTED ATTENTION FROM THE INDUSTRY AND THE UNIVERSITY. THE BLOCKCHAIN IS A PEER-TOPEER INFORMATION NETWORK. IN FACT, IT SHARES A FINANCIAL OFFICE SYSTEM AND IS A DECENTRALIZED COMPUTING MODEL WHICH MAINTAINS RECORDS OF DIGITAL TRANSACTIONS THROUGH DISTRIBUTED OFFICES OR RESOURCES AND REPLACES TRADITIONAL DATABASES CONTROLLED BY MEDIATORS SUCH AS BANKS AND GOVERNMENTS. THE CURRENT SUPPLY CHAIN IS A LINEAR ECONOMIC MODEL THAT MEETS THE SUPPLY NEEDS DIRECTLY OR INDIRECTLY. BUT THIS MODEL HAS PROBLEMS SUCH AS THE RELATIONSHIP BETWEEN THE SUPPLY CHAIN MEMBERS. THIS ARTICLE UNLOCKS THE MYSTIQUE OF BLOCKCHAIN TECHNOLOGY AND DISCUSSES WAYS TO LEVERAGE BLOCKCHAIN TECHNOLOGY TO ENHANCE SUPPLY CHAIN RESILIENCE IN TIMES OF INCREASED RISKS AND UNCERTAINTY. WE EXAMINE HOW THE BLOCKCHAIN TECHNOLOGY CAN BE EFFECTIVE IN SUPPLY CHAIN PERFORMANCE AND, BY CREATING A ROTATING ECONOMY, SOLVES MANY DISADVANTAGES OF THE CURRENT SUPPLY CHAIN.

THERE ARE SEVERAL TOOLS TO ACCESS INFORMATION ON A SOCIAL NETWORK. ONE IMPORTANT AND USEFUL TOOL TO ACCESS THE INFORMATION IS TAG CLOUD. THE TASK OF THIS TOOL IS CHOOSING A LIMITED NUMBER OF TAGS IN A SOCIAL NETWORK AND OTHER SOCIAL WEBSITES. SEVERAL APPROACHES HAVE BEEN PROPOSED FOR TAG SELECTION. THESE APPROACHES ARE BASED ON CONTENT OF TAGS, WHERE NONE OF THEM CONSIDER DIFFERENT PREFERENCES OF THE USERS. IN THIS PAPER, A TAG CHOOSING APPROACH FOR TAG CLOUD IS PROPOSED WHICH TAKES INTO ACCOUNT THE USERS PREFERENCES. MOREOVER, WE PROPOSE TWO NEW METRICS NAMELY USER COVERAGE AND USER OVERLAP. FINALLY, WE EXPERIMENT WITH THE PROPOSED APPROACH AND THE PREVIOUS APPROACHES ON THE DELICIOUS DATASET. RESULTS SHOWED THAT USER COVERAGE AND USER OVERLAP METRICS IN THE PROPOSED APPROACH WERE BETTER THAN THE PREVIOUS APPROACHES. IN FACT, THE PROPOSED METHOD COVER MORE USERS WITH LOW OVERLAP.

AUTOMATIC EXTRACTIVE TEXT SUMMARIZATION IS THE PROCESS OF CONDENSING TEXTUAL INFORMATION WHILE PRESERVING THE IMPORTANT CONCEPTS. THE PROPOSED METHOD AFTER PERFORMING PREPROCESSING ON INPUT PERSIAN NEWS ARTICLES GENERATES A FEATURE VECTOR OF SALIENT SENTENCES FROM A COMBINATION OF STATISTICAL, SEMANTIC AND HEURISTIC METHODS AND THAT ARE SCORED AND CONCATENATED ACCORDINGLY. THE SCORING OF THE SALIENT FEATURES IS BASED ON THE ARTICLE’ S TITLE, PROPER NOUNS, PRONOUNS, SENTENCE LENGTH, KEYWORDS, TOPIC WORDS, SENTENCE POSITION, ENGLISH WORDS, AND QUOTATIONS. EXPERIMENTAL RESULTS ON MEASUREMENTS INCLUDING RECALL, F-MEASURE, ROUGE-N ARE PRESENTED AND COMPARED TO OTHER PERSIAN SUMMARIZERS AND SHOWN TO PROVIDE HIGHER PERFORMANCE.

THE TEHRAN MUNICIPALITY NEWSLETTER, AS A TYPE OF NEWSLETTER IN THE ORGANIZATIONAL PORTAL, HAS BEEN AT THE ORGANIZATIONAL LEVEL HIGHLIGHTING EMPLOYEES AND EVEN EXTERNAL USERS IN RECENT YEARS. THIS PAPER HAS BEEN CARRIED OUT ACCORDING TO THE CONCEPT OF "ENTERPRISE INFORMATION PORTAL AND KNOWLEDGE MANAGEMENT" AROUND THE INTRANET AND ORGANIZATIONAL PORTAL AND BASED ON THE EXTRACTED DATA FROM A MONTH'S NEWSLETTER OF THE TEHRAN MUNICIPALITY. HERE ARE TWO ISSUES THAT FOCUS ON THE MUNICIPALITY'S NEWSLETTER: FIRST, ANALYZING THE CONTENT OF THE NEWS ARTICLES IN THE FORM OF MEANING GROUPING OF NEWS ARTICLES WITH TEXT MINING TECHNIQUES AND CALCULATION OF TERM FREQUENCY. THE SECOND ISSUE IS THE STUDY OF THE STATISTICS OF THE SYSTEM COUNTERS FROM THE NUMBER OF AUDIENCES OF EACH CATEGORY OF NEWS, WHICH INDICATES THE HIGH TENDENCY OF THE AUDIENCE TO THE INTRA-ORGANIZATIONAL NEWS RELATED TO HUMAN RESOURCES. FINALLY, WITH THESE TWO STAGES OF DATA ANALYSIS, IT HAS BEEN ATTEMPTED, THROUGH THE CATEGORIZATION OF NEWS TEXTS AND THE IDENTIFICATION OF TERM FREQUENCY OF EACH CATEGORY, TO DISCOVERY A STRATEGY FOR THE ORGANIZATION TO FIND KEYWORDS BASED ON AUDIENCE SIZE, THROUGH THE SELECTION OF APPROPRIATE WORDS IN THE MANAGEMENT SYSTEM THE CONTENT (CMS) WILL PROVIDE A SEARCH ENGINE OPTIMIZATION FOR THE TEHRAN MUNICIPALITY PORTAL.

NAMED ENTITY RECOGNITION (NER) IS ONE OF THE MOST IMPORTANT PREREQUISITES FOR MANY NATURAL LANGUAGE PROCESSING TASKS. DESPITE THE MANY RECENT RESEARCH STUDIES ON THE PERSIAN NER, THE PROPOSED SOLUTIONS ARE NOT STILL AS ACCURATE AS ITS STATE OF THE ART IN ENGLISH. FURTHERMORE, TO THE BEST OF OUR KNOWLEDGE, ALL OF THE PUBLIC CORPORA FOR THE PERSIAN NER (INCLUDING PARSNER AND ARMANPERSONER) ARE BASED ON BIJANKHAN CORPUS, WHICH IS CREATED FROM THE CONTENT OF HAMSHAHRI NEWSPAPER IN 2004. CORRESPONDINGLY, ALL OF INDUSTRIAL NER MODELS IN THE PERSIAN, ARE SPECIAL-PURPOSE FOR THE NEWS DATA AND NOT FLEXIBLE ENOUGH TO BE RUN ON THE OTHER APPLICATIONS, SUCH AS SOCIAL MEDIA TEXTS. IN THIS PAPER, WE INTRODUCE PARSNER-SOCIAL, A CORPUS WHICH IS CRAFTED FROM THE SOCIAL MEDIA DATA AND A METHOD BASED ON CONDITIONAL RANDOM FIELDS FOR NAMED ENTITY RECOGNITION. THE METHOD IS COMPARED WITH THE STATE-OF-THE-ART NER METHODS IN THE PERSIAN LANGUAGE AND THE RESULTS PROVE THE SUPERIORITY OF THE PROPOSED METHOD TO THEM.

TODAYS ANALYZING WEB WEAKNESSES AND VULNERABILITIES IN ORDER TO FIND SECURITY ATTACKS HAS BECOME MORE URGENT. IN CASE THERE IS A COMMUNICATION CONTRARY TO THE SYSTEM SECURITY POLICIES, A COVERT CHANNEL HAS BEEN CREATED. THE ATTACKER CAN EASILY DISCLOSURE INFORMATION FROM THE VICTIM’ S SYSTEM WITH JUST ONE PUBLIC ACCESS PERMISSION. COVERT TIMING CHANNELS, UNLIKE COVERT STORAGE CHANNELS, DO NOT HAVE MEMORY STORAGE AND THEY DRAW LESS ATTENTION. DIFFERENT METHODS HAVE BEEN PROPOSED FOR THEIR IDENTIFICATION, WHICH GENERALLY BENEFIT FROM THE SHAPE OF TRAFFIC AND THE CHANNEL’ S REGULARITY. IN THIS ARTICLE, AN ENTROPYBASED DETECTION METHOD IS DESIGNED AND IMPLEMENTED. THE ATTACKER CAN ADJUST THE AMOUNT OF CHANNEL ENTROPY BY CONTROLLING MEASURES SUCH AS CHANGING THE CHANNEL’ S LEVEL OR CREATING NOISE ON THE CHANNEL TO PROTECT FROM THE ANALYST’ S DETECTION. AS A RESULT, THE ENTROPY THRESHOLD IS NOT ALWAYS CONSTANT FOR DETECTION. BY COMPARING THE ENTROPY FROM DIFFERENT LEVELS OF THE CHANNEL AND THE ANALYST, WE CONCLUDE THAT THE ANALYST MUST INVESTIGATE TRAFFIC AT ALL POSSIBLE LEVELS.

THE PREDICTION OF CHANGES IN STOCK MARKET VALUE IS ONE OF THE INTERESTING TOPICS FOR ANY INVESTOR OR FINANCIAL RESEARCHER, BECAUSE A TIME INVESTMENT IS THE MOST PROFITABLE, WHICH IMPLIES THE LOWEST RISK. CHANGES IN STOCK VALUES ARE BASED ON VARIOUS REASONS, INCLUDING POLITICAL, ECONOMIC, AND COMMUNITY-BASED THOUGHTS. ON THE OTHER HAND, WITH THE ADVENT OF TECHNOLOGY AND PUBLIC ACCESS TO THE INTERNET AND SOCIAL MEDIA, IT IS A GOOD PLACE TO EXPRESS THE THOUGHTS AND FEELINGS OF PEOPLE AROUND THE VARIOUS EVENTS OF THE COMMUNITY. THEREFORE, IN THE PROCESS OF IDENTIFYING THE VIEWS EXPRESSED, EFFECTIVE BEHAVIORS CAN BE APPLIED TO ECONOMIC AND FINANCIAL DECISIONS. THIS ARTICLE IS DEVOTED TO THE DESIGN AND EVALUATION OF A METHOD THAT CAN SOMEWHAT PREDICT CHANGES IN THE VALUE OF THE SHARES OF FIVE GLOBAL COMPANIES BASED ON EXTRACTION DATA FROM SOCIAL NETWORKS. THIS INFORMATION IS TRANSMITTED THROUGH THE MICROBLOGGING OF TWITTER AND THE REGISTERED CHANGES IN THE STOCK INDEX OF FIVE COMPANIES OVER A TEN-YEAR PERIOD IN A TIME AND PLACE. TWEETS ARE CATEGORIZED AND EVALUATED BASED ON MACHINE LEARNING ALGORITHMS SUCH AS SVM, NAï VE BAYES. THEN, BASED ON THE RESULTS OF THE GROUPING AND USING THE REGRESSION RELATIONSHIP, THE FUTURE VALUE OF THE STOCK IS ESTIMATED AND EXPRESSED. THE RESULTS INDICATE A LOW ERROR RATE BETWEEN THE ACTUAL STOCK VALUE AND THE APPROXIMATE VALUE PREDICTED BY THE MODEL.

HOSPITAL INFORMATION SYSTEM (HIS) PLAYS A DOMINANT ROLE IN IMPROVING HEALTH PROCESSES AND IN THE PROVISION OF PATIENT CARE SERVICES. THIS STUDY SEEKS POTENTIAL ORGANIZATIONAL FACTORS INFLUENCING ADOPTION OF HIS IN MALAYSIAN PUBLIC HOSPITALS. THIS STUDY INTEGRATES THE TECHNOLOGY ORGANIZATION ENVIRONMENT (TOE) FRAMEWORK AND ABSORPTIVE CAPACITY THEORY (ACT) IN PROPOSING A NEW THEORETICAL FRAMEWORK FOR HIS ADOPTION IN MALAYSIA. SPECIFICALLY, WE EXAMINED THE ORGANIZATIONAL DIMENSION IN THE PROPOSED FRAMEWORK AND INVESTIGATED MODERATING ROLE OF HOSPITAL SIZE IN THE ADOPTION OF HIS. THIS RESEARCH APPLIED A QUANTITATIVE APPROACH AND CONDUCTED A NATIONWIDE SURVEY INVOLVING 137 PUBLIC HOSPITALS, WITH DIFFERENT HOSPITAL SIZE. A SURVEY METHOD USING A WEB-BASED QUESTIONNAIRE WAS CONDUCTED TO EXAMINE THE EFFECTS OF THE IDENTIFIED FACTORS ON HIS ADOPTION. THE LOGISTIC REGRESSION METHOD IS THEN DEPLOYED FOR TESTING THE RESEARCH HYPOTHESES. REGARDING THE HOSPITAL SIZE AS THE ONLY MODERATOR OF THIS STUDY, WE FOUND THAT EFFECTS OF THE SIGNIFICANT FACTORS ON HIS ADOPTION ARE THE SAME IN DIFFERENT HOSPITAL SIZES. THIS ANALYTICAL STUDY APPROACHES TO THE TOE FRAMEWORK AND EXTENDS IT BY TAKING INTO ACCOUNT THE ABSORPTIVE CAPACITY OF HOSPITAL AS THE IMPORTANT COMPONENT OF THE INTERNAL ORGANIZATION. THEREFORE, THE STUDY AND ITS FINDINGS ARE TO PROVIDE MORE INSIGHT INTO THE PROCESS OF SUCCESSFUL ADOPTION OF HIS FOR PUBLIC HOSPITALS OF MALAYSIA.

IMAGE AUTHENTICATION TECHNIQUE IS ONE OF THE IMPORTANT METHODS FOR A LARGE NUMBER OF MULTIMEDIA APPLICATIONS. WHEN A DIGITAL IMAGE IS PASSED OVER NON-SECURE CHANNELS SUCH AS THE INTERNET, IT MAY BE CHANGED AND MANIPULATED. FOR SOME IMPORTANT IMAGES SUCH AS MILITARY AND MEDICAL IMAGES, THESE MANIPULATIONS ARE VERY HARMFUL AND SUCH IMAGES SHOULD BE PROTECTED AGAINST THEM. THERE ARE SEVERAL WAYS SUCH AS FRAGILE AND SEMI-FRAGILE WATERMARKING TO AUTHENTICATE IMAGES FROM MALICIOUS ATTACKS. THIS PAPER PRESENTS A FRAGILE WATERMARKING ALGORITHM FOR IMAGE AUTHENTICATION BY USING QR FACTORIZATION AND FOURIER TRANSFORM (FT). BY APPLYING FOURIER TRANSFORM TO HOST IMAGE, FREQUENCY DOMAIN WHICH CAUSES VISUAL QUALITY IN WATERMARKING IS ACHIEVED. AFTER APPLYING FT, IT IS FACTORIZED BY QR DECOMPOSITION. QR FACTORIZATION IS ALSO APPLIED TO WATERMARK IMAGE. AFTER FACTORIZING BOTH IMAGES, A COEFFICIENT OF THE UPPER TRIANGULAR MATRIX R FROM WATERMARK IMAGE IS EMBEDDED TO THE UPPER TRIANGULAR MATRIX R FROM HOST IMAGE. SO A SIGN OF THE WATERMARK IMAGE IS HIDDEN IN THE HOST IMAGE. THIS METHOD IS A FRAGILE WATERMARKING AND IT IS SENSITIVE TO A LITTLE ATTACK. SO IF AN IMAGE IS ATTACKED OVER THE INTERNET, THE WATERMARK IMAGE CAN NOT BE EXTRACTED AND IT MEANS THAT IT HAS BEEN ATTACKED AND IT HELPS US TO RECOGNIZE IF AN IMAGE IS CHANGED AFTER BEING TRANSMITTED OVER THE INTERNET. THE EXPERIMENTAL RESULTS SHOW THAT THIS METHOD IS SENSITIVE TO EVERY WEAK ATTACK AND EXTRACTION PART CAN NOT EXTRACT WATERMARK IMAGE IF IT HAS BEEN ATTACKED.

THE BLOCKCHAIN IS AN EMERGING TECHNOLOGY WITH A CORE OF A DISTRIBUTED LEDGER OF CONNECTED BLOCKS CONSISTING OF IMMUTABLE TRANSACTIONS WITH DECENTRALIZED MANAGEMENT. THE IMPORTANCE OF BLOCKCHAIN TECHNOLOGY AND ITS IMPACT IN DIFFERENT DOMAINS HIGHLIGHTS THE NEED FOR UNDERSTANDING AND EVALUATION OF THIS TECHNOLOGY. UNDERSTANDING AND EVALUATING TECHNOLOGY FROM DIFFERENT ASPECTS REQUIRES IDENTIFYING THE CHARACTERISTICS AND SPECIFIC CRITERIA OF THAT TECHNOLOGY. IN THIS REGARD, IN THIS PAPER, AFTER DISCUSSING THE SUBJECT LITERATURE, THE CONCEPT OF THE BLOCKCHAIN PLATFORM IS DESCRIBED AND A NUMBER OF SELECTED BLOCKCHAIN PLATFORMS ARE EXAMINED. BESIDES, THE HIGH-LEVEL BENCHMARKING OF THE PLATFORM IS EVALUATED FROM TWO ASPECTS OF THE EXPECTED FEATURES OF THE PLATFORMS AND THEIR ARCHITECTURAL COMPONENTS. FINALLY, DUE TO THE IMPORTANCE AND SIGNIFICANCE OF THE CRITERIA AND THEIR FREQUENCY, A SET OF EFFECTIVE CRITERIA IS CHOSEN AND ACCORDINGLY, SELECTED PLATFORMS OF THE BLOCKCHAIN ARE ANALYZED.