INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP) | Year:2015 | دوره:1 |Papers Count:60

ONE OF THE WAYS TO ENHANCE THE INFORMATION RETRIEVAL PERFORMANCE IS QUERY EXPANSION (QE) WHICH MEANS ADDING SOME TERMS TO THE QUERY IN ORDER TO REDUCE MISMATCH BETWEEN INFORMATION NEEDS AND RETRIEVED DOCUMENTS. IN THIS WAY “QUERY DRIFT” OCCURRING FOR AMBIGUOUS QUERIES IS A COMMON PROBLEM. SPECIAL CASE OF THIS PROBLEM IS “OUTWEIGHTING” THAT USUALLY OCCURS FOR LONG QUERIES, THAT IS, SOME AUGMENTED WORDS STRONGLY RELATED TO AN INDIVIDUAL QUERY WORDS BUT NOT TO THE ALL. IN THIS PAPER WE PROPOSE A NEW METHOD FOR QE TO REDUCE THE EFFECTS OF DISAMBIGUATED QUERY TERMS AND DECREASE QUERY DRIFTING. IN PROPOSED METHOD FOR WORD OUTWEIGHTING ELIMINATION, QUERY TERMS ARE GROUPED BASED ON THEIR SEMANTIC RELATIONSHIPS. FOR EACH GROUP, CANDIDATES ARE FETCHED FROM WORDNET THAT RELATES TO THE ALL OF WORDS GROUP. THEN BY USING RECURSIVE STRUCTURE OF HOPFIELD NETWORK WORDS WITH THE MOST RELATIONSHIP WITH OTHER WORDS ARE SELECTED. MOREOVER, THE TERM SEMANTIC NETWORK HAS USED TO OVERCOME SOME OF THE SHORTCOMINGS OF WORDNET. EVALUATION RESULTS ON CACM AND CERC TEST COLLECTIONS SHOW THAT THE PROPOSED METHOD IS EFFECTIVE AND IMPROVE 4% AND 12% OF MEAN AVERAGE PRECISION RESPECTIVELY. ...

OPTIMIZATION CAN BE DEFINED AS THE ACT OF GETTING THE BEST RESULT UNDER GIVEN CIRCUMSTANCES. EVOLUTIONARY ALGORITHMS ARE WIDELY USED FOR SOLVING OPTIMIZATION PROBLEMS. ONE OF THESE EVOLUTIONARY ALGORITHMS IS PARTICLE SWARM OPTIMIZATION (PSO). DIFFERENT KINDS OF PSO SUCH AS ADAPTIVE PARTICLE SWARM OPTIMIZATION (APSO), HAVE BEEN PRESENTED TO IMPROVE THE ORIGINAL PSO AND ELIMINATE ITS DISADVANTAGES. ALTHOUGH APSO CAN OVERCOME THE PROBLEM OF PREMATURE CONVERGENCE AND ACCELERATE THE CONVERGENCE SPEED AT THE SAME TIME, IT IS COMPUTATIONALLY INTENSIVE BECAUSE OF ITS NESTED LOOPS. THE GOAL OF THIS PAPER IS HIGH PERFORMANCE IMPLEMENTATION OF APSO ALGORITHM BASED ON GPU. IN ORDER TO ANALYZE THIS ALGORITHM AND EVALUATE ITS COMPUTATIONAL TIME, WE HAVE IMPLEMENTED APSO ON BOTH CPU AND GPU. DIFFERENT PARALLELISMS SUCH AS LOOP-LEVEL PARALLELISM HAVE BEEN EXPLOITED AND WE HAVE ACHIEVED SIGNIFICANT SPEEDUP UP TO 152X COMPARED TO CPU BASED IMPLEMENTATION. ...

LOCAL BINARY PATTERN (LBP) IS A SIMPLE AND COMPUTATIONALLY EFFICIENT TEXTURE DESCRIPTOR WHICH HAS BEEN ATTRACTING MANY ATTENTIONS SINCE ITS INTRODUCTION; DUE TO THE EXTENSIVE RESEARCH DONE IN THIS REGARD, DIVERSE VARIANTS OF LBP HAVE BEEN INTRODUCED IN RECENT YEARS. WHILE ORIGINAL FORM OF THIS OPERATOR ENCODES STRUCTURES LIKE SPOTS, EDGES, AND CORNERS IN FORM OF A BINARY CODE, A MORE RECENT TYPE OF LBP CALLED HIGH ORDER DIRECTIONAL DERIVATIVE LBP (DLBP) REVEALS SOME ALTERNATIVE STRUCTURES SUCH AS CONVEXITIES AND CONCAVITIES. EVEN THOUGH THESE STRUCTURES ARE IMPORTANT FEATURES IN THE IMAGES, ANOTHER SIGNIFICANT CONSIDERATION IS THE RELATIONSHIP BETWEEN THEM. FOR INSTANCE, THERE IS A HIGH PROBABILITY THAT AN EDGE STRUCTURE BE PRESENT NEAR ANOTHER ONE. IN THIS PAPER, WE HAVE INTRODUCED A NOVEL TEXTURE DESCRIPTOR NAMED HJDLBP (HIGH ORDER JOINT DLBP) WHICH IS ABLE TO ENCODE RELATIONSHIPS BETWEEN MICRO PATTERNS IN ADDITION TO THE PREVALENT STRUCTURES. TO EVALUATE THE PROPOSED DESCRIPTOR, WE HAVE CONSIDERED TWO RENOWNED JAFFE AND YALE FACIAL IMAGE DATABASES AND THEN EXPLOITED THE PROPOSED TEXTURE DESCRIPTOR FOR FACE RECOGNITION ISSUES. THE EXPERIMENTS ARE IMPLEMENTED IN SOFTWARE IN THE FOLLOWING MANNER: AS A FIRST STEP, THE FACE PART OF EACH IMAGE IS SEGMENTED FROM ITS BACKGROUND USING VIOLA AND JONES ALGORITHM. AFTERWARD, THE MICRO PATTERNS AND RELATIONSHIPS BETWEEN THEM ARE EXTRACTED FROM RECTANGULARLY PARTITIONED FACE IMAGES; AND THEIR HISTOGRAMS ARE CONSTRUCTED AS WELL. FINALLY, A GROUP OF SVMS ARE TRAINED FOR CLASSIFICATION. WE HAVE COMPARED OBTAINED RESULTS USING THE NEW OPERATOR WITH THE RESULTS ATTAINED WHEN CONVENTIONAL LBP AND HIGH ORDER DLBP ARE APPLIED FOR FEATURE EXTRACTION FROM IMAGE BLOCKS. THE COMPARATIVE RESULTS SHOW THE EFFICACY OF THE PROPOSED OPERATOR AS A TEXTURE DESCRIPTOR. ...

THE WAYS IN WHICH AN INNOVATION (E.G., NEW BEHAVIOUR, IDEA, TECHNOLOGY, PRODUCT) DIFFUSES AMONG PEOPLE CAN DETERMINE ITS SUCCESS OR FAILURE. IN THIS PAPER, WE ADDRESS THE PROBLEM OF DIFFUSION OF INNOVATIONS OVER MULTIPLEX SOCIAL NETWORKS WHERE THE NEIGHBOURS OF A PERSON BELONG TO ONE OR MULTIPLE NETWORKS (OR LAYERS) SUCH AS FRIENDS, FAMILIES, OR COLLEAGUES. TO THIS END, WE GENERALISE ONE OF THE BASIC GAMETHEORETIC DIFFUSION MODELS, CALLED NETWORKED COORDINATION GAME, FOR MULTIPLEX NETWORKS. WE PRESENT ANALYTICAL RESULTS FOR THIS EXTENDED MODEL AND VALIDATE THEM THROUGH A SIMULATION STUDY, FINDING AMONG OTHER PROPERTIES A LOWER BOUND FOR THE SUCCESS OF AN INNOVATION. WHILE SIMPLE AND LEADING TO INTUITIVELY UNDERSTANDABLE RESULTS, TO THE BEST OF OUR KNOWLEDGE THIS IS THE FIRST EXTENSION OF A GAME-THEORETIC INNOVATION DIFFUSION MODEL FOR MULTIPLEX NETWORKS AND AS SUCH IT PROVIDES A BASIC FRAMEWORK TO STUDY MORE SOPHISTICATED INNOVATION DYNAMICS. ...

THE GOAL OF THE PRESENT RESEARCH IS TO DESPECKLE SAR IMAGES, WHICH IS CRITICAL FOR SEGMENTATION AND TARGET RECOGNITION IN SATELLITE SAR IMAGES. WHEN A DESPECKLING ALGORITHM IS APPLIED TO A SAR IMAGE, IMPORTANT INFORMATION SUCH AS THE EDGES, CORNERS, TEXTURES, AND OBJECT PARTS WILL DEGRADE. CURVELET TRANSFORM IS A RECENTLY PROPOSED FORM OF MULTI-SCALE ANALYSIS THAT ACHIEVES BETTER PERFORMANCE OF WAVELET AND GABOR TRANSFORMS IN EDGE AND CURVE DETECTION. THIS IS A GEOMETRIC TRANSFORM THAT IS USEFUL FOR SAR IMAGE PROCESSING. FOR UNSUPERVISED TEXTURE IMAGES, SEGMENTATION IS DIFFERENT AND DISTINCT FROM THE TEXTURES, SO THE TEXTURES AT THE BOUNDARY NOISES WILL DISAPPEAR. CURVELET TRANSFORM HAS PRODUCED GOOD RESULTS IN THE DETECTION OF CURVED EDGES WITH HIGHER ACCURACY IN FINDING THE ORIENTATION THAN WAVELET TRANSFORMS. THE PRESENT STUDY USES FAST DISCRETE CURVELET TRANSFORM (FDCT) BASED ON WRESTING AND USES UNSUPERVISED ADAPTIVE THRESHOLD LEARNING TO DEVELOP A NEW DESPECKLING ALGORITHM FOR SAR IMAGES. IN THE PROPOSED ALGORITHM, EACH SEGMENT OF THE SAR IMAGE CAN BE LEARNED FOR SELECTION OF ITS ADAPTIVE THRESHOLD. SIMULATION RESULTS DEMONSTRATE THAT THE PROPOSED ALGORITHM PERFORMS BETTER THAN SIMILAR METHODS. ...

     IN THIS PAPER NEW IDEAS ARE PRESENTED FOR RESOLVING THE ISSUES OF TWO PAST SELF-ORGANIZED CRITICALITY (SOC) EVOLUTIONARY ALGORITHMS (EAS). THE CONCEPT OF SOC WAS FIRST DEVELOPED FOR MODELING MASS EXTINCTION AND IMPLEMENTED BY MEANS OF SAND PILE MODEL IN EAS. THESE TYPES OF EAS ARE ESPECIALLY EMPLOYED WHEN THE OPTIMIZATION PROBLEMS ARE MULTIMODAL IN WHICH PRESERVING THE DIVERSITY OF SOLUTIONS IS A CRUCIAL TASK. THEREFORE ANALYZING THE PROBLEMS OF SOC BASED EAS IS WORTHWHILE FOR MAKING A PROGRESS IN THE FIELD OF MULTIMODAL OPTIMIZATION. CONSEQUENTLY, AFTER AN EXACT INSPECTION OF PAST RESEARCH STUDIES, THE MAJOR SHORTCOMINGS OF PREVIOUSLY DEVELOPED ALGORITHMS ARE ADDRESSED WHICH ARE TWOFOLD: FIRSTLY, THE LACK OF AVALANCHES IN EARLY GENERATIONS, AND SECONDLY, THE NUMBER OF AVALANCHES OCCURRED IN A POPULATION IS OUT OF PROPORTION IN TERMS OF POPULATION SIZE. IN ORDER TO RESOLVE THESE PROBLEMS, SOME SOLUTIONS ARE PROPOSED IN THIS STUDY. THE IMPACT OF THESE MODIFICATIONS ARE EXAMINED AND ILLUSTRATED BY MEANS OF SEVERAL BENCHMARK OPTIMIZATION PROBLEMS EXTRACTED FROM PAST RESEARCH STUDIES. MODIFIED ALGORITHM IS COMPARED AND CONTRASTED AGAINST PREVIOUSLY DEVELOPED SOC BASED ALGORITHMS AND CLASSICAL GENETIC ALGORITHM (CGA). RESULTS APPARENTLY SHOW THE EFFECTIVENESS OF ELIMINATING ADDRESSED DEFICIENCIES IN TERMS OF ACCURACY AND ESCAPING FROM LOCAL OPTIMA. ...

TRAFFIC IS AN ISSUE THAT MANY BIG CITIES ARE CONFRONTED WITH BECAUSE OF EVER-INCREASING POPULATION GROWTH. IN THIS PAPER WE PROPOSE A TWO PHASE TRAFFIC LIGHT CONTROL SYSTEM BASED ON FUZZY Q-LEARNING FOR AN ISOLATED 4-WAY INTERSECTION. THE STATES AND ACTIONS OF THE Q-LEARNING VARIABLES IS SET BY A FUZZY ALGORITHM WHICH CAN BE LEARNED THROUGH ENVIRONMENTAL INTERACTIONS AND TAKING ADVANTAGE OF FUZZY LOGIC. THE PROPOSED ALGORITHM WAS SIMULATED FOR A PERIOD OF ONE HOUR FOR EACH OF 14 DIFFERENT TRAFFIC CONDITIONS. COMPARISON WITH OTHER METHODS WAS CARRIED OUT ON THE 14 TRAFFIC CONDITIONS. THE RESULTS SHOWED THAT THE PROPOSED ALGORITHMS DECREASE THE TOTAL WAITING TIME AND THE MEAN OF QUEUE LENGTH. ...

IN THIS PAPER, A FEATURE SELECTION (FS) METHOD BASED ON HESITANT FUZZY SETS (HFS) IS PROPOSED. THE RANKING VALUE OF THREE FILTER METHODS (I.E. FISHER, RELIEF, INFORMATION GAIN) FOR EACH FEATURE ARE CONSIDERED AS HESITANT FUZZY ELEMENTS (HFE) OF THAT FEATURE WITH RESPECT TO CLASS RELEVANCY, THEN HESITANT CORRELATION MATRIX OF FEATURES IS CALCULATED. AFTER THAT THREE SIMILARITY MEASURES ARE CONSIDERED TO EVALUATE THE SECOND HESITANT CORRELATION MATRIX OF FEATURES. THE FIRST CORRELATION MATRIX REPRESENTS THE CORRELATION OF FEATURES WITH RESPECT TO THEIR RELEVANCY TO THE CLASS. THE SECOND CORRELATION MATRIX PRESENTS THE CORRELATION BASED ON REDUNDANCY OF FEATURES AMONG THEMSELVES. ONE HESITANT FUZZY SETS CLUSTERING ALGORITHM (HFSCA) IS RUN ON THESE MATRIXES. FINALLY THE INTERSECTION OF CLUSTERS IS CONSIDERD AS A FEATURES SUBSET WHICH CONTAINS THE HIGHLY RELEVANCE AND LOWLY REDUNDANT FEATURES. THE EXPERIMENTAL RESULTS CONFIRM THE ABILITY OF OUR PROPOSED METHOD IN BOTH NUMBER OF SELECTED FEATURES AND ACCURACY COMPARING TO THE OTHER ONES. ...

THIS PAPER PROPOSE A NEW APPROACH TO PREDICT SPREADING BEHAVIOR OF CONVENTIONS. CONVENTIONS IN OUR CASE ARE VERBAL I.E. PHRASES USED BY MANY PEOPLE FOR A NEW PURPOSE REGARDING A SOCIAL ISSUE. WE STUDY USAGE OF SOME CONVENTIONS IN TWITTER POPULARIZED AMONG PERSIAN SPEAKING USERS. WE SHOW THAT THE NUMBER OF TWEETS THAT CONTAIN A CONVENTION PHRASE IN A PERIOD HAS A BELL SHAPED CURVE. WE USE THE LATENT SPACE MODEL TO CALCULATE THE DISTANCE MATRIX FOR A CONVENTION IN ORDER TO UNDERSTAND ITS SPREADING BEHAVIOR. WE FIRST CALCULATE THE DISTANCE MATRICES OF THE CONVENTIONS AND UTILIZE THEM TO ESTIMATE THE DISTANCE MATRIX FOR NEW CONVENTIONS. ...

GROWTH OF SOCIAL NETWORK ANALYSIS AS AN ACADEMIC FIELD HAS COINCIDED WITH AN EXPLOSION IN POPULAR INTEREST IN SOCIAL NETWORKS. EXPERT FINDING IS ONE OF THE MOST IMPORTANT SUBJECTS FOR MINING FROM SOCIAL NETWORKS SEARCHING FOR THE RIGHT PERSONS WITH THE APPROPRIATE SKILLS AND KNOWLEDGE. THE RLS ALGORITHM EXPLOITED Q-LEARNING AND REFERRALS TO FIND EXPERTS IN SOCIAL NETWORK TO SEARCH EXPERT IN SOCIAL NETWORK. COMPARISON OF RLS WITH SIMPLE SEARCH ALGORITHM, REFERRAL ALGORITHM AND SNPAGERANK SHOWS INCREASE IN BOTH PRECISION AND RECALL. RLS LEARNS TO FIND NEW EXPERTS AS OLD EXPERTS SUBSTITUTE THEIR ROLE WITH NEW ONES DUE TO CHANGES IN SOCIAL NETWORK ENVIRONMENT.

A SOLUTION FOR SEPARATING SPEECH FROM MUSIC SIGNAL AS A SINGLE CHANNEL SOURCE SEPARATION IS NON-NEGATIVE MATRIX FACTORIZATION (NMF). IN THIS APPROACH SPECTROGRAM OF EACH SOURCE SIGNAL IS FACTORIZED AS MULTIPLICATION OF TWO MATRICES WHICH ARE KNOWN AS BASIS AND WEIGHT MATRICES. TO ACHIEVE PROPER ESTIMATION OF SIGNAL SPECTROGRAM, WEIGHT AND BASIS MATRICES ARE UPDATED ITERATIVELY. TO ESTIMATE DISTANCE BETWEEN SIGNAL AND ITS ESTIMATION A COST FUNCTION IS USED USUALLY. DIFFERENT COST FUNCTIONS HAVE BEEN INTRODUCED BASED ON KULLBACK-LEIBLER (KL) AND ITAKURA-SAITO (IS) DIVERGENCES. IS DIVERGENCE IS SCALE-INVARIANT AND SO IT IS SUITABLE FOR THE CONDITIONS IN WHICH THE COEFFICIENTS OF SIGNAL HAVE A LARGE DYNAMIC RANGE, FOR EXAMPLE IN MUSIC SHORT-TERM SPECTRA. BASED ON THIS IS PROPERTY, IN THIS PAPER, WE PROPOSE TO USE IS DIVERGENCE AS COST FUNCTION OF NMF IN THE TRAINING STAGE FOR MUSIC AND ON THE OTHER HAND WE SUGGEST TO USE KL DIVERGENCE AS NMF COST FUNCTION IN THE TRAINING STAGE FOR SPEECH. MOREOVER, IN THE DECOMPOSITION STAGE, WE PROPOSE TO USE A LINEAR COMBINATION OF THESE TWO DIVERGENCES IN ADDITION TO A REGULARIZATION TERM WHICH CONSIDERS TEMPORAL CONTINUITY INFORMATION AS A PRIOR KNOWLEDGE. EXPERIMENTAL RESULTS ON ONE HOUR OF SPEECH AND MUSIC, SHOWS A GOOD TRADE-OFF BETWEEN SIGNAL TO INFERENCE RATIO (SIR) OF SPEECH AND MUSIC IN COMPARISON TO CONVENTIONAL NMF METHODS. ...

THIS PAPER PRESENTS A ROTATION-INVARIANT TEXTURE DESCRIPTOR, WHICH IS ROBUST TO NOISE. IN THE PROPOSED METHOD, A GIVEN GRAY-SCALE TEXTURE IMAGE IS FIRST FILTERED BY A SET OF GABOR WAVELETS FILTERS. THE FILTERS ARE DESIGNED SUCH THAT THEIR HALFPEAK MAGNITUDE SUPPORT IN THE FREQUENCY SPECTRUM TOUCH EACH OTHER WITH NO OVERLAP TO REDUCE REDUNDANT INFORMATION. AFTER THAT A NUMBER OF LOCAL BINARY PATTERNS CALLED “LOCAL GABOR WAVELETS BINARY PATTERNS” (LGWBPS) ARE COMPUTED BASED ON THE OBTAINED GABOR WAVELETS FILTERS RESPONSES VIA GLOBAL MEASURES. THE HISTOGRAM OF THE COMPUTED LGWBPS IS THEN USED AS A TEXTURE FEATURE VECTOR. EXTENSIVE EXPERIMENTS WERE CONDUCTED ON THE WELLKNOWN OUTEX, AND CURET DATABASES IN THE PRESENCE OF DIFFERENT LEVELS OF GAUSSION NOISE. EXPERIMENTAL RESULTS INDICATE THAT THE PROPOSED METHOD CAN BE UTILIZED AS A SUITABLE NOISE-ROBUST AND ROTATION-INVARIANT TEXTURE DESCRIPTOR FOR TEXTURE CLASSIFICATION. ...

A NOVEL CONTENT-BASED IMAGE RETRIEVAL (CBIR) SCHEMA WITH WAVELET AND COLOR FEATURES FOLLOWED BY ANT COLONY OPTIMIZATION (ACO) FEATURE SELECTION HAS BEEN PROPOSED IN THIS PAPER. A NEW FEATURE EXTRACTION SCHEMA INCLUDING TEXTURE FEATURES FROM WAVELET TRANSFORMATION AND COLOR FEATURES IN RGB AND HSV DOMAIN IS PROPOSED AS REPRESENTATIVE FEATURE VECTOR FOR IMAGES IN DATABASE. ALSO, APPROPRIATE SIMILARITY MEASURE FOR EACH FEATURE IS PRESENTED. RETRIEVING RESULTS ARE SO SENSITIVE TO IMAGE FEATURES USED IN CONTENT-BASED IMAGE RETRIEVAL. WE ADDRESS THIS PROBLEM WITH SELECTION OF MOST RELEVANT FEATURES AMONG COMPLETE FEATURE SET BY ANT COLONY OPTIMIZATION BASED FEATURE SELECTION. TO EVALUATE THE PERFORMANCE OF OUR PROPOSED CBIR SCHEMA, IT HAS BEEN COMPARED WITH OLDER PROPOSED SYSTEMS, RESULTS SHOW THAT THE PRECISION AND RECALL OF OUR PROPOSED SCHEMA ARE HIGHER THAN OLDER ONES FOR THE MAJORITY OF IMAGE CATEGORIES. ...

COMMUNITY DETECTION IN SOCIAL NETWORKS IS USUALLY DONE BASED ON THE DENSITY OF CONNECTIONS BETWEEN GROUPS OF NODES. HOWEVER, THESE LINKS DO NOT NECESSARILY REPRESENT AN ACTUAL FRIENDSHIP ESPECIALLY IN ONLINE SOCIAL NETWORKS. THERE ARE USERS WITH DECLARED FRIENDSHIP CONNECTIONS BUT WITHOUT ACTUAL COMMUNICATION AND NO COMMON INTERESTS. MOST OF THE WORKS IN THIS AREA CAN BE DIVIDED INTO TWO GROUPS: TOPOLOGY-BASED AND TOPIC-BASED. THE FORMER USUALLY LEADS TO COMMUNITIES EACH CONTAINING DIVERSE TOPICS, AND THE LATTER LEADS TO COMMUNITIES EACH WITH A CONSISTENT TOPIC BUT WITH DIVERSE STRUCTURE. IN THIS PAPER, WE MEASURE THE SIMILARITY BETWEEN USERS USING TOPIC MODELS TO GENERATE VIRTUAL LINKS FOR USERS WITH COMMON INTERESTS. MOREOVER, IN ORDER TO REDUCE THE EFFECT OF USELESS LINKS BETWEEN USERS, WE WEIGHT THE NETWORK BY MEASURING SIMILARITY OF USERS’ TOPICS, SO WE COULD GENERATE CONFORMING COMMUNITIES, WHICH CONTAIN ONLY ONE TOPIC OR A GROUP OF CONSISTENT TOPICS. THE TEST RESULTS ON ENRON EMAIL DATASET HAVE SHOWN THE SUPERIOR PERFORMANCE OF OUR PROPOSED METHOD IN THE TASK OF COMMUNITY DETECTION. ...

LINEAR COMBINATION OF ENDMEMBERS ACCORDING TO THEIR ABUNDANCE FRACTIONS AT PIXEL LEVEL IS AS THE RESULT OF LOW SPATIAL RESOLUTION OF HYPERSPECTRAL SENSORS. SPECTRAL UNMIXING PROBLEM IS DESCRIBED BY DECOMPOSING THESE MEDLEY PIXELS INTO A SET OF ENDMEMBERS AND THEIR ABUNDANCE FRACTIONS. MOST OF ENDMEMBER EXTRACTION TECHNIQUES ARE DESIGNED ON THE BASIS OF SPECTRAL FEATURE OF IMAGES SUCH AS OSP. ALSO SSPP IS IMPLIED WHICH CONSIDERS SPATIAL CONTENT OF IMAGE PIXELS BESIDES SPECTRAL INFORMATION. WE PROPOSE A SELFGOVERNING MODULE PRIOR THE SPECTRAL BASED ENDMEMBER EXTRACTION ALGORITHMS TO ACHIEVE SUPERIOR PERFORMANCE OF RMSE AND SAD-BASED ERRORS BY CREATING A NEW SYNTHETIC IMAGE USING HYDRA TOOL AND USGS LIBRARY WITH VARIOUS VALUES OF SNR IN ORDER TO EVALUATE OUR METHOD WITH OSP AND SSPP+OSP. EXPERIMENTAL RESULTS IN COMPARISON WITH THE MENTIONED METHODS SHOW THAT THE PROPOSED METHOD CAN UNMIX DATA MORE EFFECTIVELY.

COMMUNITY DETECTION IN SOCIAL NETWORK IS A SIGNIFICANT ISSUE IN THE STUDY OF THE STRUCTURE OF A NETWORK AND UNDERSTANDING ITS CHARACTERISTICS. A COMMUNITY IS A SIGNIFICANT STRUCTURE FORMED BY NODES WITH MORE CONNECTIONS BETWEEN THEM. IN RECENT YEARS, SEVERAL ALGORITHMS HAVE BEEN PRESENTED FOR COMMUNITY DETECTION IN SOCIAL NETWORKS AMONG THEM LABEL PROPAGATION ALGORITHM IS ONE OF THE FASTEST ALGORITHMS, BUT DUE TO THE RANDOMNESS OF THE ALGORITHM ITS PERFORMANCE IS NOT SUITABLE. IN THIS PAPER, WE PROPOSE AN IMPROVED LABEL PROPAGATION ALGORITHM CALLED MEMORY-BASED LABEL PROPAGATION ALGORITHM (MLPA) FOR FINDING COMMUNITY STRUCTURE IN SOCIAL NETWORKS. IN THE PROPOSED ALGORITHM, A SIMPLE MEMORY ELEMENT IS DESIGNED FOR EACH NODE OF GRAPH AND THIS ELEMENT STORE THE MOST FREQUENT COMMON ADOPTION OF LABELS ITERATIVELY. OUR EXPERIMENTS ON THE STANDARD SOCIAL NETWORK DATASETS SHOW A RELATIVE IMPROVEMENT IN COMPARISON WITH OTHER COMMUNITY DETECTION ALGORITHMS. ...

DATA CLUSTERING IS A CRUCIAL TECHNIQUE IN DATA MINING THAT IS USED IN MANY APPLICATIONS. IN THIS PAPER, A NEW CLUSTERING ALGORITHM BASED ON GRAVITATIONAL SEARCH ALGORITHM (GSA) AND GENETIC OPERATORS IS PROPOSED. THE LOCAL SEARCH SOLUTION IS UTILIZED THROW THE GLOBAL SEARCH TO AVOID GETTING STOCK IN LOCAL OPTIMA. THE GSA IS A NEW APPROACH TO SOLVE OPTIMIZATION PROBLEM THAT INSPIRED BY NEWTONIAN LAW OF GRAVITY. WE COMPARED THE PERFORMANCES OF THE PROPOSED METHOD WITH SOME WELL-KNOWN CLUSTERING ALGORITHMS ON FIVE BENCHMARK DATASET FROM UCI MACHINE LEARNING REPOSITORY. THE EXPERIMENTAL RESULTS SHOW THAT OUR APPROACH OUTPERFORMS OTHER ALGORITHMS AND HAS BETTER SOLUTION IN ALL DATASETS. ...

COPYRIGHT PROTECTION OF DIGITAL MEDIA BECOMES A MAJOR PROBLEM IN INTERNET DUE TO ITS NATURE WHICH ALLOWS GROSS DUPLICATION, ALTERATION OR EVEN STOLEN OF DATA. SEVERAL WATERMARKING METHODS ARE REPORTED IN RECENT YEARS TO PROTECT COPYRIGHT OF DIGITAL IMAGES. A GOOD WATERMARKING ALGORITHM MUST SATISFY IMPERCEPTIBILITY, ROBUSTNESS AND HIGH DATA HIDING CAPACITY BUT WITHOUT COMPROMISING FOR ANY OF THEM. IN THIS PAPER A NOVEL BLIND WATERMARKING SCHEME IN HADAMARD TRANSFORM DOMAIN FOR DIGITAL IMAGE IS PROPOSED. FOR EMBEDDING WATERMARK BITS WE MODIFY THE DHT COEFFICIENT OF TWO ADJACENT BLOCKS IN SAME POSITION. WATERMARK IS A STRING OF CHARACTERS. THIS ALGORITHM OFFERS ADVANTAGES OF SIMPLER IMPLEMENTATION, LOW COMPUTATION COST AND HIGH RESILIENCY UNDER COMPRESSION. THE EXPERIMENTAL RESULTS AND PERFORMANCE ANALYSIS SHOWS GOOD ROBUSTNESS UNDER JPEG IMAGE COMPRESSION AND OTHER COMMON IMAGE PROCESSING OPERATIONS LIKE CROPPING, ROTATION, NOISE ADDITION AND FILTERING. ...

THIS PAPER INTRODUCES AN APPROACH TO DETECT REFERENCE POINT FOR FINGERPRINT IMAGES. REFERENCE POINT EXTRACTION IS A KEY COMPONENT IN AUTOMATIC FINGERPRINT IDENTIFICATION SYSTEM (AFIS). A NEW METHOD WAS PROPOSED FOR FINGERPRINT REFERENCE POINT EXTRACTION, BASED ON FIELD FLOW CURVE AND CLUSTERING. POINTS OF CURVATURE IN THE FLOW CURVES, ALONG WITH THEIR CURVATURE VALUES, ARE USED IN OUR REFERENCE POINT DETECTION. BECAUSE WE USE FLOW CURVE INSTEAD OF RIDGE FOR REFERENCE POINT DETECTION, OUR METHOD IS ROBUST TO NOISE AND HAS A GOOD RESULT ON FINGERPRINT IMAGE WITH LOW QUALITY. ALSO OUR METHOD CAN DETECT A REFERENCE POINT FOR AN ARCH TYPE FINGERPRINT WHICH IS HARD FOR OTHER METHODS TO DETECT IT. THE EXPERIMENTS OF REFERENCE POINT DETECTION ARE CONDUCTED ON FVC 2002 AND FVC 2004. EXPERIMENTAL RESULTS SHOW THAT OUR ALGORITHM IS ACCURATE AND ROBUST, GIVING BETTER RESULTS THAN COMPETING APPROACHES. ...

JAVASCRIPT CODE OBFUSCATION HAS BECOME A MAJOR TECHNIQUE USED BY MALWARE WRITERS TO EVADE STATIC ANALYSIS TECHNIQUES. OVER THE PAST YEARS, A NUMBER OF DYNAMIC ANALYSIS TECHNIQUES HAVE BEEN PROPOSED TO DETECT OBFUSCATED MALICIOUS JAVASCRIPT CODE AT RUNTIME. HOWEVER, BECAUSE OF THEIR RUNTIME OVERHEADS, THESE TECHNIQUES ARE SLOW AND THUS NOT WIDELY USED IN PRACTICE. ON THE OTHER HAND, SINCE A LARGE QUANTITY OF BENIGN JAVASCRIPT CODE IS OBFUSCATED TO PROTECT INTELLECTUAL PROPERTY, IT IS NOT EFFECTIVE TO USE THE INTRINSIC FEATURES OF OBFUSCATED JAVASCRIPT CODE FOR STATIC ANALYSIS PURPOSES. THEREFORE, WE ARE FORCED TO DISTINGUISH BETWEEN OBFUSCATED AND NON-OBFUSCATED JAVASCRIPT CODE SO THAT WE CAN DEVISE AN EFFICIENT AND EFFECTIVE ANALYSIS TECHNIQUE TO DETECT MALICIOUS JAVASCRIPT CODE. IN THIS PAPER, WE ADDRESS THIS ISSUE BY PRESENTING JSOBFUS DETECTOR, A NOVEL ONE-CLASS CLASSIFIER ENSEMBLE TO DETECT OBFUSCATED JAVASCRIPT CODE. TO CONSTRUCT THE CLASSIFIER ENSEMBLE, WE APPLY A BINARY PARTICLE SWARM OPTIMIZATION (PSO) ALGORITHM, CALLED PARTICLE PRUNER, ON AN INITIAL ENSEMBLE OF ONE-CLASS SVM CLASSIFIERS TO FIND A SUB-ENSEMBLE WHOSE MEMBERS ARE BOTH ACCURATE AND HAVE DIVERSITY IN THEIR OUTPUTS. WE EVALUATE JSOBFUSDETECTOR USING A DATASET OF OBFUSCATED AND NON-OBFUSCATED JAVASCRIPT CODE. THE EXPERIMENTAL RESULTS SHOW THAT JSOBFUSDETECTOR CAN ACHIEVE ABOUT 97% PRECISION, 91% RECALL, AND 94% F-MEASURE. ...

IT HAS BECOME CRITICAL TO PROTECT BIOMETRIC TEMPLATES IN THE CURRENT BIOMETRIC COMMUNITY. FUZZY VAULT IS A PROMISING AND APPLICABLE SCHEME, WHICH CAN PROTECT BIOMETRIC TEMPLATES. ALSO IT CAN PERFORM SECURE KEY MANAGEMENT. ALIGNMENT OF THE TEMPLATE BIOMETRIC SAMPLE AND THE QUERY BIOMETRIC SAMPLE IN THE ENCRYPTED DOMAIN REMAINS A CHALLENGING TASK. IN THIS PAPER, WE PROPOSE AN ALIGNMENT-FREE CRYPTOSYSTEM BASED ON MULTIPLE FUZZY VAULTS. IN PROPOSED METHOD, IN REGISTRATION PHASE, MULTIPLE VAULTS CONSTRUCT FOR ONE FINGERPRINT AND IN VERIFICATION PHASE, IF AT LEAST TWO OF THE VAULTS DECODED SUCCESSFULLY BY THE QUERY FINGERPRINT, THE SECRET WILL BE RECOVERED. EXPERIMENTS ON FVC2002-DB2A AND FVC2002-DB1A ARE CONDUCTED TO SHOW THE PROMISING PERFORMANCE OF THE PROPOSED FINGERPRINT CRYPTOSYSTEM. ...

 AUTOMATIC ANALYSIS IN SOCCER SCENES IS STILL A DIFFICULT TASK IN THE ABSENCE OF SOCCER FIELD INFORMATION. THE FIRST AND MOST IMPORTANT STEP IN ALMOST ALL ANALYSIS, IS SOCCER FIELD LINE RECOGNITION AND HOMOGRAPHY EXTRACTION. THE AIM OF THIS PAPER IS INTRODUCING A NOVEL APPROACH FOR AUTOMATIC DETECTION AND RECOGNITION OF SOCCER FIELD LINES AND ARCS BY MINIMAL INFORMATION. A SIMPLE CAMERA MODEL AND PERSPECTIVE MAP IS ASSUMED TO REDUCE UNKNOWN PARAMETERS. AN ACCURATE METHOD IS UTILIZED FOR DETECTING LINE PIXELS. THE SIDE OF PLAYFIELD AREA IS DETERMINED BASED ON THE ORIENTATION OF LINES AND ARCS. BASED ON THE DETECTED PLAYFIELD AREA SIDE, AN INITIAL PERSPECTIVE MAP IS OBTAINED. AN OPTIMIZATION ALGORITHM THEN ADJUSTS THE PARAMETERS OF PERSPECTIVE TRANSFORM AND CAMERA. THE PROPOSED METHOD NEEDS ONLY SOME MINIMAL INFORMATION IN THEORY AND PRACTICE. IT IS APPLIED TO SOME TYPICAL SOCCER VIDEOS. THE ACHIEVED RESULTS DEMONSTRATE ITS ROBUSTNESS AND ACCURACY. ...

THE SPARSITY CONCEPT HAS BEEN WIDELY USED IN IMAGE PROCESSING APPLICATIONS. IN THIS PAPER, AN APPROACH FOR SUPER-RESOLUTION HAS BEEN PROPOSED WHICH USES SPARSE TRANSFORM. THIS APPROACH HAS MIXED THE INPAINTING CONCEPT WITH ZOOMING VIA A SPARSE REPRESENTATION. A DICTIONARY IS BEING TRAINED FROM A LOW-RESOLUTION IMAGE AND THEN A ZOOMED VERSION OF THIS LOW RESOLUTION IMAGE WILL USE THAT DICTIONARY IN A FEW ITERATIONS TO FILL THE UNDEFINED IMAGE PIXELS. EXPERIMENTAL RESULTS CONFIRM THE STRENGTH OF THIS ALGORITHM AGAINST THE OTHER INTERPOLATION ALGORITHMS.

THIS PAPER REPRESENTS A STATISTICALLY FRAMEWORK FOR A PERSIAN SPOKEN DIALOGUE SYSTEM. THE FRAMEWORK IS BASED ON THE PARTIALLY OBSERVABLE MARKOV DECISION PROCESS (POMDP). A BAYESIAN NETWORK IS USED TO REPRESENT THE STATES OF THE POMDP MODEL. IT IS SHOWN THAT BAYESIAN APPROACHES CAN IMPROVE THE SPOKEN DIALOGUE SYSTEM PERFORMANCE BY HANDLING UNCERTAINTIES. ALSO NATURAL ACTOR CRITIC (NAC) ALGORITHM IS USED FOR LEARNING IN SPOKEN DIALOGUE SYSTEM AND FINALLY A FRAMEWORK FOR COLLECTING TRAINING DATA IS PROPOSED. WE COMPARE THE SYSTEM WITH A HANDCRAFTED SPOKEN DIALOGUE SYSTEM TO SHOW THE EFFICIENCY OF THE PROPOSED FRAMEWORK. ...

IN THIS PAPER, FUZZY C-MEANS CLUSTERING AND ROTATION FOREST (RF) ARE COMBINED TO CONSTRUCT A HIGH PERFORMANCE CLASSIFIER FOR IMBALANCED DATA CLASSIFICATION. DATA SAMPLES ARE CLUSTERED VIA FUZZY CLUSTERING AND THEN FUZZY MEMBERSHIP FUNCTION MATRIX IS ADDED INTO DATA SAMPLES. THEREFORE, CLUSTERS MEMBERSHIPS OF SAMPLES ARE UTILIZED AS NEW FEATURES THAT ARE ADDED INTO THE ORIGINAL FEATURES. AFTER THAT, RF IS UTILIZED FOR CLASSIFICATION WHERE THE NEW SET OF FEATURES AS WELL AS THE ORIGINAL ONES ARE TAKEN INTO ACCOUNT IN THE FEATURE SUBSPACING PHASE. THE PROPOSED ALGORITHM UTILIZES SMOTE OVERSAMPLING ALGORITHM FOR BALANCING DATA SAMPLES. THE OBTAINED RESULTS CONFIRM THAT OUR PROPOSED METHOD OUTPERFORMS THE OTHER WELL-KNOWN BAGGING ALGORITHMS. ...

   IN THE RECENT DECADES, MANY INTRUSION DETECTION SYSTEMS (IDSS) HAVE BEEN PROPOSED TO ENHANCE THE SECURITY OF NETWORKS. A CLASS OF IDSS IS BASED ON CLUSTERING OF NETWORK TRAFFIC INTO NORMAL AND ABNORMAL ACCORDING TO SOME FEATURES OF THE CONNECTIONS. THE SELECTED DISTANCE FUNCTION TO MEASURE THE SIMILARITY AND DISSIMILARITY OF SESSIONS' FEATURES AFFECT THE PERFORMANCE OF CLUSTERING BASED IDSS. THE MOST POPULAR DISTANCE METRIC, WHICH IS USED IN DESIGNING THESE IDSS IS THE EUCLIDEAN DISTANCE FUNCTION. IN THIS PAPER, WE ARGUE THAT MORE APPROPRIATE DISTANCE FUNCTIONS CAN BE DEPLOYED FOR IDSS. WE PROPOSE A METHOD OF LEARNING AN APPROPRIATE DISTANCE FUNCTION ACCORDING TO A SET OF SUPERVISION INFORMATION. THIS METRIC IS DERIVED BY SOLVING A SEMI-DEFINITE OPTIMIZATION PROBLEM, WHICH ATTEMPTS TO DECREASE THE DISTANCE BETWEEN THE SIMILAR, AND INCREASES THE DISTANCES BETWEEN THE DISSIMILAR FEATURE VECTORS. THE EVALUATION OF THIS SCHEME OVER KYOTO2006+ DATASET SHOWS THAT THE NEW DISTANCE METRIC, CAN IMPROVE THE PERFORMANCE OF A SUPPORT VECTOR MACHINE (SVM) CLUSTERING BASED IDS IN TERMS OF NORMAL DETECTION AND FALSE POSITIVE RATES. ...

PARTICLE SWARM OPTIMIZATION (PSO) IS A PRACTICAL AND EFFECTIVE OPTIMIZATION APPROACH THAT HAS BEEN APPLIED RECENTLY FOR DATA CLUSTERING IN MANY APPLICATIONS. WHILE VARIOUS NON-EVOLUTIONARY OPTIMIZATION AND CLUSTERING ALGORITHMS HAVE BEEN APPLIED FOR CLUSTERING MULTIVARIATE TIME SERIES IN SOME APPLICATIONS SUCH AS CUSTOMER SEGMENTATION, THEY USUALLY PROVIDE POOR RESULTS DUE TO THEIR DEPENDENCY ON THE INITIAL VALUES AND THEIR POOR PERFORMANCE IN MANIPULATING MULTIPLE OBJECTIVES. IN THIS PAPER, A PARTICLE SWARM OPTIMIZATION ALGORITHM IS PROPOSED FOR CLUSTERING MULTIVARIATE TIME SERIES DATA. SINCE THE TIME SERIES DATA SOMETIMES DO NOT HAVE THE SAME LENGTH AND THEY USUALLY HAVE MISSING DATA, THE REGULAR EUCLIDEAN DISTANCE AND DYNAMIC TIME WARPING CAN NOT BE APPLIED FOR SUCH DATA TO MEASURE THE SIMILARITY. THEREFORE, A HYBRID SIMILARITY MEASURE BASED ON PRINCIPAL COMPONENT ANALYSIS AND MAHALANOBIS DISTANCE IS APPLIED IN ORDER TO HANDLE SUCH LIMITATIONS. THE COMPARISON BETWEEN THE RESULTS OF THE PROPOSED METHOD WITH THE SIMILAR ONES IN THE LITERATURE SHOWS THE SUPERIORITY OF THE PROPOSED METHOD. ...

IN THIS PAPER, A NEW METHOD FOR COLOR IMAGE SEGMENTATION IS PRESENTED. THIS METHOD IS BASED ON HISTOGRAM THRESHOLDING AND CORRELATION BETWEEN THE DIFFERENCE OF COLOR COMPONENTS. HENCE, NEARLY ALL HISTOGRAM THRESHOLDING METHODS WORK ONLY IN ONE OR TWO DIMENSIONS OF GRAY SCALE HISTOGRAM, NEIGHBORHOOD, PROBABILITY FUNCTION OR ENTROPY. THE PROPOSED METHOD WILL TRY TO USE COLOR COMPONENTS AS THE MAIN FEATURES OF SEGMENTATION BY FINDING THE CORRELATION BETWEEN THE PEAKS OF HISTOGRAM IN EACH COLOR COMPONENT. IT WILL HELP US TO FIND MAIN COLOR COMPONENTS OF EACH OBJECT AND THE BACKGROUND OF IMAGE. WHILE, WE HAVE MAIN COLOR COMPONENTS; IT WILL BE EASY TO USE PARALLEL PROCESSING TO SEGMENT ENTIRE IMAGE AT ONCE WITHOUT USING ANY NEIGHBORHOOD WINDOW OR LOSING ANY DATA IN COLOR SPACE TRANSFORM INTO GRAY SCALE. WITH THESE BENEFITS, A FAST AND ACCURATE METHOD BASED ON ADAPTIVE HISTOGRAM THRESHOLDING IS PRESENTED IN THIS PAPER FOR SEGMENTATION OF COLOR IMAGES. THE EXPERIMENTAL RESULTS ON BENCHMARK DATASETS DEMONSTRATE THE EFFICIENCY OF THE PROPOSED METHOD. ...

A NOVEL VIDEO WATERMARKING ALGORITHM BASED ON WAVELET TRANSFORM AND CHAOTIC MAPS IS HERE INTRODUCED. WE APPLY THE TWO DIMENSIONAL WAVELET TRANSFORM ON I-FRAMES AND THEN INSERT THE CHAOTIC WATERMARK INTO PART OF THE SUB-BAND COEFFICIENTS. SINCE CHAOTIC MAPS ARE SENSITIVE TO INITIAL VALUES, INITIAL VALUES OF THE CHAOTIC MAPS AND THEIR CHAOTIC PARAMETERS ARE EXPLOITED AS SECRET KEYS IN OUR ALGORITHM. RESULTS ARE PRESENTED TO REVEAL THE USEFULNESS OF THE ALGORITHM. COMPARISONS ARE MADE WITH THE LATEST VIDEO WATERMARKING SCHEMES. ...

A COMPREHENSIVE DATABASE OF FARSI PRINTED TEXTS IS AN ESSENTIAL RESOURCE FOR RESEARCH IN THIS AREA. ALTHOUGH THERE ARE SOME ARABIC PRINTED DATABASES, BUT THOSE DATABASES DO NOT HAVE ALL THE NECESSARY FEATURES FOR FARSI OR ARABIC TEXT RECOGNITION RESEARCH. IN THIS PAPER, WE INTRODUCE A COMPREHENSIVE FARSI PRINTED TEXT DATABASE CALLED AUT-PFT. THE PURPOSE OF THIS DATABASE IS TO PROVIDE A LARGE-SCALE, REAL WORLD, MULTI FONT AND MULTI SIZE CORPUS FOR TRAINING FARSI OR ARABIC TEXT RECOGNITION SYSTEMS. THIS DATABASE IS MADE UP OF 10000 GENERATED WORDS. 127 UNIQUE GLYPHS ARE USED IN THESE WORDS IN A WAY THAT APPEARANCE DISTRIBUTION OF GLYPHS IS APPROXIMATELY UNIFORM. THESE WORDS ARE GENERATED WITH 10 WIDELY USED FARSI FONTS AND 4 DIFFERENT FONT SIZES. IN ORDER TO HAVE REAL WORLD NOISE IN THIS DATABASE, ALL GENERATED IMAGES WERE PRINTED AND SCANNED. GROUND TRUTH DATA ARE ALSO PROVIDED FOR THIS DATABASE AND UNLIKE OTHER DATABASES, DETAILED INFORMATION ABOUT DOCUMENT TEXT IS PROVIDED AT GLYPH LEVEL. ...

 IN THIS PAPER, THE ROLE OF NOUNS IN REDUCING THE CONCEPTUAL DENSITY OF CONTEXTS HAS BEEN EXAMINED. A NEW METHOD IS PROPOSED TO IDENTIFY AND PRUNE NOUNS WITH NEGATIVE IMPACT ON CONCEPTUAL DENSITY OF CONTEXTS. IN THE PROPOSED METHOD, A FITNESS FUNCTION IS OFFERED; A FITNESS DEGREE IS ASSIGNED TO UNAMBIGUOUS NOUNS SENSE WITHIN THE CONTEXT. USING THE MEAN FITNESS DEGREE OF UNAMBIGUOUS NOUNS’ SENSE, A THRESHOLD IS PRODUCED FOR THAT CONTEXT. THIS THRESHOLD IS THEN USED AS A MEASURE TO PRUNE THE SENSE OF NOUNS WITH LOWER FITNESS DEGREE THAT REDUCES THE CONCEPTUAL DENSITY OF THE CONTEXT. FINALLY, BY IMPLEMENTING THIS METHOD ON THE CONTEXTS PRODUCED BY CONCEPTUAL DENSITY METHOD, ALL CONTEXTS WILL BE OPTIMIZED SIGNIFICANTLY; THIS SIGNIFICANTLY INCREASES THE ACCURACY OF DISAMBIGUATION. ...

THE SECURITY OF CLOUD COMPUTING IS THE MOST IMPORTANT CONCERNS THAT MAY DELAY ITS WELL-KNOWN ADOPTION. AUTHENTICATION IS THE CENTRAL PART OF SECURITY IN CLOUD COMPUTING MODEL, AIMING TO GUARANTEE THAT STORED DATA ARE PERMITTED TO BE ACCESSED ONLY BY VALID/CERTIFIED USERS. THERE ARE SEVERAL AUTHENTICATION SCHEMES THAT BASED ON USERNAME/PASSWORD, BUT THEY ARE CONSIDERED WEAK METHODS OF CLOUD AUTHENTICATION. IN THE OTHER SIDE, IMAGE’S DIGITIZATION BECOMES HIGHLY VULNERABLE TO MALICIOUS ATTACKS OVER CLOUD COMPUTING. IN THIS PAPER, WE PROPOSE TWO-FACTOR AUTHENTICATION SCHEME BASED ON IMAGE PARTIAL ENCRYPTION TO OVERCOME ABOVE AFOREMENTIONED ISSUES AND DRAWBACKS OF AUTHENTICATION SCHEMES. ADDITIONALLY, WE USE A FAST PARTIAL IMAGE ENCRYPTION SCHEME USING CANNY’S EDGE DETECTION WITH SYMMETRIC ENCRYPTION IS DONE AS A SECOND FACTOR. IN THIS SCHEME, THE EDGE PIXELS OF IMAGE ARE ENCRYPTED USING THE STREAM CIPHER AS IT HOLDS MOST OF THE IMAGE’S DATA AND THEN WE APPLIED THIS WAY TO AUTHENTICATE VALID USERS. THE RESULTS OF SECURITY ANALYSIS AND EXPERIMENTAL RESULTS VIEW THAT THE PROPOSED SCHEME SUPPORTS AN EFFICIENT AND SECURE SCHEME FOR REAL-TIME IMAGE ENCRYPTION AND TRANSMISSION. ...

WITH RAPID PROLIFERATION OF AFFORDABLE VIDEO CAPTURING DEVICES AND STATE-OF-THE-ART VIDEO EDITING SOFTWARE TOOLS, IT IS NOW EASIER THAN EVER TO MANIPULATE VIDEO CONTENTS. IN THIS PAPER A PASSIVE METHOD FOR COPY/MOVE VIDEO FORGERY DETECTION IN MPEG VIDEOS IS PROPOSED. THE METHOD FIRST DIVIDES EACH VIDEO FRAME INTO SUSPICIOUS AND APPARENTLY INNOCENT PARTS. SUBSEQUENTLY, AN OPTICAL FLOW COEFFICIENT IS COMPUTED FROM EACH PART. FORGERIES ARE LOCATED WHEN AN UNUSUAL TREND IN THE OPTICAL FLOW COEFFICIENT OF THE SUSPICIOUS OBJECT IS DETECTED. EXPERIMENTS ON A SET OF FORGED AND ORIGINAL SEQUENCES VALIDATE THE JUSTIFICATIONS MADE BY THE PROPOSED METHOD. ...

THIS PAPER INVESTIGATES THE SYNCHRONIZATION OF TWO DIFFERENT FRACTIONAL-ORDER CHAOTIC SYSTEMS. BASED ON SOME PROPERTIES ON FRACTIONAL CALCULUS AND THE STABILITY THEOREM OF FRACTIONAL-ORDER SYSTEMS, WE PROPOSE A NEW METHOD TO ACHIEVE THE SYNCHRONIZATION IN SUCH CASES. THE ANALYTICAL CONDITIONS FOR SYNCHRONIZATION OF THESE DIFFERENT FRACTIONAL-ORDER SYSTEMS ARE DERIVED BY UTILIZING LAPLACE TRANSFORM. THE FRACTIONAL OPERATORS ARE INTRODUCED INTO THE CONTROLLER TO TRANSFORM PROBLEM OF SYNCHRONIZATION OF DIFFERENT FRACTIONAL-ORDER CHAOTIC SYSTEMS INTO SYNCHRONIZATION BETWEEN FRACTIONAL-ORDER CHAOTIC SYSTEMS WITH IDENTICAL ORDERS, AND NONLINEAR FEEDBACK CONTROL IS PROPOSED BASED ON THE CONCEPT OF ACTIVE CONTROL TECHNIQUE. WE PRESENT AN EXAMPLE THAT ILLUSTRATE THE EFFECTIVENESS AND APPLICATION OF PROPOSED METHOD.

 THE SHORTAGE OF THE ANNOTATED TRAINING DATA IS STILL AN IMPORTANT CHALLENGE TO BUILDING MANY NATURAL LANGUAGE PROCESS (NLP) TASKS SUCH AS NAMED ENTITY RECOGNITION. NER REQUIRES A LARGE AMOUNT OF TRAINING DATA WITH A HIGH DEGREE OF HUMAN SUPERVISION WHEREAS THERE IS NOT ENOUGH LABELED DATA FOR EVERY LANGUAGE. IN THIS PAPER, WE USE AN UNLABELED BILINGUAL CORPORA TO EXTRACT USEFUL FEATURES FROM TRANSFERRING INFORMATION FROM RESOURCE-RICH LANGUAGE TOWARD RESOURCE-POOR LANGUAGE AND BY USING THESE FEATURES AND A SMALL TRAINING DATA, MAKE A NER SUPERVISED MODEL. THEN WE UTILIZE A GRAPH-BASED SEMI-SUPERVISED LEARNING METHOD THAT TRAINS A CRF-BASED SUPERVISED CLASSIFIER USING THAT LABELED DATA AND USES HIGH-CONFIDENCE PREDICTIONS ON THE UNLABELED DATA TO EXPAND THE TRAINING SET AND IMPROVE EFFICIENCY OF NER MODEL WITH THE NEW TRAINING SET.

TEXT MINING IS A FIELD THAT IS CONSIDERED AS AN EXTENSION OF DATA MINING IN GENERAL, ALSO KNOWN AS KNOWLEDGE DISCOVERY IN DATABASES. IN THE CONTEXT OF TEXT MINING, DOCUMENT CLUSTERING IS AN UNSUPERVISED LEARNING METHOD FOR AUTOMATICALLY SEGREGATING SIMILAR DOCUMENTS OF A CORPUS INTO THE SAME GROUP, CALLED CLUSTER, AND DISSIMILAR DOCUMENTS TO DIFFERENT GROUPS. WHILE HUNDREDS OF CLUSTERING ALGORITHMS EXIST, IT IS DIFFICULT TO FIND A SINGLE CLUSTERING ALGORITHM THAT CAN HANDLE ALL TYPES OF CLUSTER SHAPES AND SIZES, OR EVEN DECIDE WHICH ALGORITHM WOULD BE THE BEST ONE FOR A PARTICULAR DATA SET. EACH ALGORITHM HAS ITS OWN APPROACH FOR ESTIMATING THE NUMBER OF CLUSTERS, IMPOSING A STRUCTURE ON THE DATA, AND VALIDATING THE RESULTING CLUSTERS. THE IDEA OF COMBINING DIFFERENT CLUSTERING EMERGED AS AN APPROACH TO OVERCOME THE WEAKNESS OF SINGLE ALGORITHMS AND FURTHER IMPROVE THEIR PERFORMANCES. ON THE OTHER HAND, INSPIRED BY THE GRAVITATIONAL LAW, DIFFERENT CLUSTERING ALGORITHMS HAVE BEEN INTRODUCED THAT EACH ONE ATTEMPTED TO CLUSTER COMPLEX DATASETS. GRAVITATIONAL ENSEMBLE CLUSTERING (GEC) IS AN ENSEMBLE METHOD THAT EMPLOYS BOTH THE CONCEPTS OF GRAVITATIONAL CLUSTERING AND ENSEMBLE CLUSTERING TO REACH A BETTER CLUSTERING RESULT. THIS PAPER REPRESENTS AN APPLICATION OF GEC TO THE PROBLEM OF DOCUMENT CLUSTERING. THE PROPOSED METHOD USES A MODIFICATION OF THE ORIGINAL GEC ALGORITHM. THIS MODIFICATION TRIES TO PRODUCE A MORE VARIED CLUSTERING ENSEMBLE USING NEW PARAMETER SETTING. COMPUTATIONAL EXPERIMENTS WERE CONDUCTED TO TEST THE PERFORMANCE OF THE GEC APPROACH USING DOCUMENT DATASETS. PROMISING RESULTS OF THE PRESENTED METHOD WERE OBTAINED IN COMPARISON WITH COMPETING ALGORITHMS. ...

FEATURE SELECTION IS THE PROCESS OF CHOOSING A SUBSET OF RELEVANT AS WELL AS IRREDUNDANT FEATURES FROM A BIGGER SET. IN OTHER WORDS, IT REMOVES REDUNDANT AND IRRELEVANT FEATURES FROM ORIGINAL SET. IN THIS PAPER, A NEW ALGORITHM WHICH IS CALLED BIDIRECTIONAL ANT COLONY OPTIMIZATION FEATURE SELECTION (BDACOFS) BASED ON ANT COLONY OPTIMIZATION (ACO) ALGORITHM AND INSPIRED FROM ACOFS (A RECENTLY PROPOSED FEATURE SELECTION METHOD) IS PRESENTED. IN THE PROPOSED ALGORITHM, PROBLEM IS MODELED BY A CIRCULAR GRAPH IN WHICH EVERY NODE HAS ONLY TWO ARCS TO ITS SUBSEQUENT NODE. ONE OF ARCS REPRESENTS SELECTING AND ANOTHER IMPLIES DESELECTING THE NEXT NODE. IN ADDITION, HEURISTIC DESIRABILITY OF EVERY NODE'S SELECTION IS CALCULATED ACCORDING TO TWO FACTORS; ONE IS RELATED TO DISCRIMINATION ABILITY OF FEATURES AND SECOND ONE IS RELATED TO MUTUAL INFORMATION AMONG FEATURES. THE PROPOSED ALGORITHM HAS BEEN TESTED AGAINST SOME WELL-KNOWN DATASETS AND ITS PERFORMANCE HAS BEEN COMPARED TO SOME WELL-KNOWN ALGORITHMS. THE RESULT INDICATES THAT PROPOSED ALGORITHM BY ADDING MUTUAL STATISTICAL INFORMATION TO ITS HEURISTIC DESIRABILITY COULD REMOVE MORE REDUNDANT FEATURES THAN ORIGINAL ACOFS. MEANWHILE IT KEEPS CLASSIFICATION ACCURACY AS HIGHLY AS THE ORIGINAL ACOFS.

IN THIS WORK WE PROPOSED A NOVEL SUPER PIXEL BASED SEGMENTATION APPROACH TO SOLVE ENERGY MINIMIZATION PROBLEM WHICH CAN BE USED TO DEAL WITH INDOOR SCENE LABELING PROBLEM. WE USED RANGE DATA BESIDE COLOR IMAGE CAPTURED FROM KINECT SENSOR. THIS SENSOR ENABLES US TO USE 3D FEATURES OF STRUCTURE LIKE NORMAL VECTOR AND 2D COLOR FEATURES. WE EXTRACTED THE REGION OF SCENE AS SUPER PIXEL BASED ON THE BOTH COLOR AND DIRECTION CHANGE; AND, CONSEQUENTLY, WE CONSTRUCTED OUR GRAPHICAL MODEL ON THESE REGIONS AND APPLY MARKOV RANDOM FIELD INFERENCE TO ASSIGN EFFICIENT LABELS TO THEM. OUR EVALUATION ON 30 SCENES OF CHALLENGING NYU V1 DATASET SHOWS THAT OUR PROPOSED METHOD REACHED HIGHER VALUES OF “CORRECT DETECTION” AND LOWER RATE OF “MISSED INSTANCES” AND “NOISE INSTANCES” CRITERIA ACCORDING TO HOOVER EVALUATION METHOD. ...

IMAGE REGISTRATION IS AN IMPORTANT TASK IN MEDICAL IMAGE PROCESSING. ASSUMING SPATIALLY STATIONARY INTENSITY RELATION AMONG IMAGES, CONVENTIONAL AREA BASED ALGORITHMS SUCH AS CC (CORRELATION COEFFICIENTS) AND MI (MUTUAL INFORMATION), SHOW WEAKER RESULTS ALONGSIDE SPATIALLY VARYING INTENSITY DISTORTION. IN THIS RESEARCH, A STRUCTURAL REPRESENTATION OF IMAGES IS INTRODUCED. IT ALLOWS US TO USE SIMPLER SIMILARITY METRICS IN MULTIMODAL IMAGES WHICH ARE ALSO ROBUST AGAINST THE MENTIONED DISTORTION FIELD. THE EFFICIENCY OF THIS PRESENTATION IN NON-RIGID IMAGE REGISTRATION IN THE PRESENCE OF SPATIAL VARYING DISTORTION FIELD IS EXAMINED. EXPERIMENTAL RESULTS ON SYNTHETIC AND REAL-WORLD DATA SETS DEMONSTRATE THE EFFECTIVENESS OF THE PROPOSED METHOD FOR IMAGE REGISTRATION TASKS.

DIGITAL INFINITE IMPULSE RESPONSE (IIR) FILTER HAVE BECOME THE TARGET OF GROWING INTEREST, BECAUSE THEY OFTEN PROVIDE A MUCH BETTER PERFORMANCE AND LESS COMPUTATIONAL COST THAN FINITE IMPULSE RESPONSE (FIR) FILTERS. SINCE THE PROBLEM OF ERROR SURFACE OF DESIGNING DIGITAL IIR FILTERS IS GENERALLY NONLINEAR AND MULTIMODAL, GLOBAL OPTIMIZATION TECHNIQUES ARE REQUIRED IN ORDER TO AVOID LOCAL MINIMA. IN THIS PAPER, AN EVOLUTIONARY METHOD BASED ON IMPERIALIST COMPETITIVE ALGORITHM (ICA) HAS BEEN PROPOSED TO DESIGN DIGITAL IIR FILTERS. BY ADDING A STEP TO THE STANDARD ICA ALGORITHM, ITS PERFORMANCE HAS BEEN IMPROVED IN SEARCHING SOLUTION SPACE AND CONVERGENCE TO THE GLOBAL MINIMA. SIMULATION RESULTS SHOW THE EFFICIENCY OF THE PROPOSED METHOD TO DESIGN DIGITAL IIR FILTER. ...

     THE PROBLEM OF SMALL TARGET DETECTION IN INFRARED IMAGES IS ONE OF THE MOST IMPORTANT AREAS OF RESEARCH IN PASSIVE DEFENSE SYSTEMS. THIS DETECTION METHOD IS CLASSIFIED IN THE ELECTRO OPTIC SYSTEMS GROUP. GENERALLY, THE CHALLENGES OF THE FIELD ARE DIVIDED INTO TWO PARTS: DETECTION AND TRACKING. 1) DUE TO LONG DETECTION DISTANCE, THE AMPLITUDE OF TARGET SIGNAL COMPARED WITH HEAVY BACKGROUND CLUTTER IS WEAK. ON THE OTHER HAND, TARGETS APPEAR WITH FEW PIXELS, SO THAT THERE IS NO OBVIOUS AND USABLE STRUCTURAL AND CONTEXTUAL INFORMATION. 2) ANOTHER CHALLENGE IN TRACKING SMALL TARGETS IS PARTIAL OBSTRUCTION OR CLOSENESS OF BACKGROUND'S BRIGHTNESS LEVEL TO BRIGHTNESS LEVEL OF THE DESIRED TARGET (FADING). IN THIS PAPER, FIRST BACKGROUND IS REMOVED BY SUBTRACTING ROW MEAN, THEN THE TARGET ARE TRACKING USING MORPHOLOGICAL FILTERING, THRESHOLDING THE IDENTIFIED TARGETS AND FINALLY BY KALMAN FILTER. ...

IT IS AN INTERESTING PROBLEM IN A SOCIAL SYSTEM INVESTIGATING HOW TO AFFECT A LARGE NUMBER OF PEOPLE BY JUST INVESTING ON A MINORITY OF THEM. THIS PROBLEM, I.E., INFLUENCE MAXIMIZATION, IS CALLED "MAXIMIZING PRODUCT ADOPTION" IN MARKETING APPLICATIONS. IN THIS PAPER, WE FIRST PROPOSE A MULTI-AGENT FRAMEWORK CALLED MAFIM TO BE USED FOR MAXIMIZING PRODUCT ADOPTION IN DYNAMIC SOCIAL NETWORKS. MAFIM CONSISTS OF TWO TYPES OF AGENTS: MODELING AGENTS AND SOLUTION PROVIDER AGENTS. THESE AGENTS VIEW A DYNAMIC SOCIAL NETWORK AS CONSECUTIVE STATIC NETWORK SNAPSHOTS AND REGARDING THAT, CHOOSE A BUDGET ASSIGNMENT POLICY SO THAT EACH SNAPSHOT OBTAINS ITS SHARE FROM THE BUDGET DEFINED BY THE SALES MANAGER. BASED ON MAFIM, WE PRESENT MASPEL, A SINGLE PRODUCT MODEL WHICH TAKES NETWORK COMMUNITIES, THEIR JUDGMENTS ON EACH OTHER AND THEIR PROFITABILITIES INTO ACCOUNT. MASPEL MAKES USE OF A SPECIFIC BUDGET ASSIGNMENT POLICY IN WHICH BUDGETS ARE ASSIGNED TO ADVERTISEMENT CAMPAIGNS IN A PROGRESSIVELY DECREASING MANNER. WE APPLIED OUR MODEL ON SEVERAL REAL AND SYNTHETIC DYNAMIC SOCIAL NETWORKS THEN EVALUATED THE EFFECTIVENESS OF DIFFERENT CAMPAIGN LENGTHS. OUR RESULTS SHOW THAT IT IS MORE EFFECTIVE TO LAUNCH MANY SHORT-LIVED CAMPAIGNS INSTEAD OF FEW LONG-LIVED ONES. IT WAS ALSO OBSERVED THAT BETWEENNESS HAS THE BEST PERFORMANCE AMONG CENTRALITY-BASED HEURISTICS IN LEADING THE MAJORITY TOWARDS LIKING THE ADVERTISED PRODUCT.

SKIN LESION SEGMENTATION IS ONE OF THE MOST IMPORTANT STEPS IN AUTOMATED EARLY SKIN CANCER DETECTION, SINCE THE ACCURACY OF THE FOLLOWING STEPS SIGNIFICANTLY DEPENDS ON IT. IN THIS PAPER, A TWO-STAGE APPROACH BASED ON MEAN SHIFT AND SPECTRAL GRAPH PARTITIONING ALGORITHMS IS PROPOSED. THIS METHOD EFFECTIVELY EXTRACTS LESION BORDERS. MOREOVER, A DISTINCTIVE ADVANTAGE OF THIS APPROACH IS EXTRACTING THE REGION OF INTEREST LEVELS THAT IS NOT ADDRESSED IN PERVIOUS STATE OF THE ART METHODS. IN THE FIRST STAGE, THE IMAGE IS SEGMENTED TO REGIONS USING MEAN SHIFT ALGORITHM. IN THE SECOND STAGE, A GRAPH-BASED REPRESENTATION IS USED TO DEMONSTRATE THE STRUCTURE OF THE EXTRACTED REGIONS AND THEIR RELATIONSHIPS. AFTERWARDS A CLUSTERING PROCESS IS APPLIED, CONSIDERING THE NEIGHBORHOOD SYSTEM AND ANALYZING THE COLOR AND TEXTURE DISTANCE BETWEEN REGIONS. THE PROPOSED METHOD IS APPLIED TO 170 DERMOSCOPIC IMAGES AND EVALUATED WITH TWO DIFFERENT METRICS. THIS EVALUATION HAS PERFORMED BY MEANS OF THE SEGMENTATION RESULTS PROVIDED BY AN EXPERIENCED DERMATOLOGIST AS THE GROUND TRUTH. EXPERIMENTS DEMONSTRATE THAT IN THIS METHOD, CHALLENGING FEATURES OF SKIN LESIONS ARE HANDLED AS MIGHT BE EXPECTED WHEN COMPARED TO FIVE STATE OF THE ART METHODS. ...

DELAY SENSITIVE APPLICATIONS NEED TO OVERCOME THE SERVICE PROBLEMS IN DYNAMIC ENVIRONMENTS WITH RESPECT TO BOTH THE MULTIMEDIA SOURCE DATA (E.G., VARIABLE BIT-RATE) AND THE WIRELESS CHANNELS (E.G., FADING CHANNEL). THIS PAPER CONSIDERS THE PROBLEM OF POINT TO POINT TRANSMISSION OF SCALABLE VIDEO CODING OVER A FADING CHANNEL. WE FORMULATE THE RATE ADAPTATION CHALLENGE OF WLAN MULTIMEDIA NETWORKS AS A MARKOV DECISION PROCESS AND RESOLVE THIS PROBLEM ONLINE BASED ON REINFORCEMENT LEARNING. THE BUFFER STATE, CHANNEL STATE, AND VIDEO STATE WERE CONSIDERED AS A JOINT STATE OF SYSTEM TO MAXIMIZE THE AVERAGE QUALITY OF SERVICE UNDER DELAY CONSTRAINTS. TO IMPROVE THE CONVERGENCE SPEED OF LEARNING, SYSTEM’S UNDERLYING DYNAMICS WERE PARTITIONED INTO A PRIORI KNOWN AND A PRIORI UNKNOWN COMPONENTS. THE PROPOSED LEARNING ALGORITHM EXPLOITS KNOWN INFORMATION ABOUT THE SYSTEM, SO THAT LESS INFORMATION NEEDS TO BE LEARNED COMPARED WITH THAT IN CONVENTIONAL REINFORCEMENT LEARNING ALGORITHMS. ...

 VIDEO INPAINTING METHODS HAS A LARGE NUMBER OF APPLICATIONS AND SOME OF THESE ALGORITHMS ARE SPECIALIZED FOR SPECIFIC APPLICATIONS SUCH AS LOGO REMOVAL. THERE ARE ONLY A FEW GENERAL VIDEO INPAINTING ALGORITHMS MOST OF WHICH ARE VERY TIME-CONSUMING. THIS PROBLEM MAKES THESE ALGORITHMS UNSUITABLE FOR FAST VIDEO INPAINTING. IN THIS PAPER, A FAST SIMPLE LOGO REMOVAL ALGORITHM HAS BEEN PROPOSED WHICH USES FRAMES OF EACH VIDEO SHOT FOR LOGO REMOVAL AND REMOVES LOGO FROM VIDEO AFTER A FEW ITERATIONS. A MORE ACCURATE NON-CASUAL VERSION OF OUR ALGORITHM IS ALSO PROPOSED WHICH USES BOTH THE INFORMATION OF PREVIOUS AND NEXT FRAMES. THE QUALITY OF THE INPAINTED VIDEO IS ALSO COMPARABLE WITH WELL-KNOWN VIDEO INPAINTING ALGORITHMS. ...

IN THE PRESENT PAPER THE IDENTIFICATION AND ESTIMATION PROBLEM OF A FRACTIONAL ORDER STATE SPACE SYSTEM WILL BE ADDRESSED. THIS PAPER PRESENTS A FRACTIONAL ORDER INFORMATION FILTER AND ALSO A HIERARCHICAL IDENTIFICATION ALGORITHM TO IDENTIFY AND ESTIMATE PARAMETERS AND STATES OF A FRACTIONAL ORDER SYSTEM. THEN, MERGING THIS ALGORITHM WITH FRACTIONAL ORDER INFORMATION FILTER, A NOVEL IDENTIFICATION METHOD BASED ON HIERARCHICAL IDENTIFICATION THEORY IS INTRODUCED TO REDUCE THE COMPUTATIONAL COMPLEXITY. FINALLY, THE APPLICABILITY AND PERFORMANCE OF THIS PLATFORM ON AN EXEMPLARY SYSTEM IS EXAMINED. ...

THIS PAPER PRESENTS A NEW METHOD FOR COUNTERATTACK DETECTION USING ESTIMATED CAMERA MOTION AND EVALUATES SOME CLASSIFICATION METHODS TO DETECT THIS EVENT. TO THIS END, VIDEO IS PARTITIONED TO SHOTS AND VIEW TYPE OF EACH SHOT IS RECOGNIZED FIRST. THEN, RELATIVE PAN OF THE CAMERA DURING FAR-VIEW AND MEDIUM-VIEW SHOTS IS ESTIMATED. AFTER WEIGHTING OF PAN VALUE OF EACH FRAME ACCORDING TO THE TYPE OF SHOTS, THE VIDEO IS PARTITIONED TO MOTION SEGMENTS. THEN, MOTION SEGMENTS ARE REFINED TO ACHIEVE BETTER RESULTS. FINALLY, THE FEATURES EXTRACTED FROM CONSECUTIVE MOTION SEGMENTS ARE INVESTIGATED FOR COUNTERATTACK DETECTION. WE PROPOSE TWO METHODS FOR COUNTERATTACK DETECTION: (1) RULE-BASED (HEURISTIC RULES) AND (2) SVM-BASED. EXPERIMENTS SHOW THAT THE SVM CLASSIFIER WITH LINEAR OR RBF KERNEL RESULTS IN THE BEST RESULTS. ...

IN THIS PAPER, POWER ALLOCATION AND TARGET ASSIGNMENT IS CONSIDERED AS A PROMISING WAY TO OBTAIN LOW PROBABILITY OF INTERCEPTION (LPI) IN THE RADAR NETWORK. SPATIAL DIVERSITY IN THE NETTED RADARS GIVES US A FLEXIBILITY TO CONTROL POWER INTELLIGENTLY AND RADAR ASSIGNMENT DYNAMICALLY IN SUCH A WAY THAT NOT ONLY DETECTION PERFORMANCES SATISFIED BUT LPI CHARACTERISTICS OF THE NETWORK ARE OPTIMIZED. WE FORMULATE THIS PROBLEM AS A NON-CONVEX AND NONLINEAR OPTIMIZATION PROBLEM ASSOCIATED WITH DETECTION PERFORMANCE CONSTRAINTS. THE OPTIMUM SOLUTION OF GENERAL PROBLEM IS COMPLICATED AND CANNOT BE SOLVED MATHEMATICALLY. WE RELAXED PROBLEM TO MORE TRACTABLE FORM FOR THE NETWORKS WITH LOW COMPLEXITY IN WHICH COMBINATION OF RADAR'S INFORMATION CANNOT BE HANDLED. IN THE CONSIDERED SCENARIO, FOR EACH TARGET ONLY ONE RADAR IS ASSIGNED AND EACH ASSIGNED RADAR CAN ONLY TRANSMIT ONE TARGET'S INFORMATION. WE PROPOSE A SIMPLE FRAMEWORK TO OBTAIN OPTIMUM POWER ALLOCATION AND RADAR ASSIGNMENT STRATEGY DUE TO SPATIAL DIVERSITY OF NETTED RADARS. THE FRAMEWORK HAS LOWER COMPLEXITY COMPARED WITH OPTIMUM EXHAUSTIVE SEARCH ALGORITHM AND SIMULATION RESULTS SHOW EFFECTIVENESS OF PROPOSED ALGORITHM IN SATISFACTION OF DETECTION PERFORMANCES AND IMPROVEMENT OF LPI SPECIFICATION OF RADAR NETWORK. ...

WITH THE GROWING PRESENCE OF COMPUTERS INEVERYDAY LIFE, COMMUNICATION IMPROVEMENT BETWEEN HUMAN AND MACHINES IS INEVITABLE. TALKING FACES ARE THE FACES WHOSE MOVEMENTS ARE SYNCHRONIZED TO SPEECH. THEY HAVE AN EFFECTIVE ROLE IN MANY APPLICATIONS. LIP IS THE MOST IMPORTANT PART OF A TALKING FACE. THE MAIN GOAL OF THIS PROJECT IS IMPLEMENTING A NATURAL AND HUMAN-LIKE LIP MOVEMENT SYNTHESIS SYSTEM FOR THE FARSI LANGUAGE. FOR THIS PURPOSE, A COMPREHENSIVE AUDIO VISUAL DATABASE CALLED SFAVD1 WAS DESIGNED AND USED. AFTER EXTRACTING THE SUFFICIENT FEATURES AND DESIGNING A PARALLEL HIDDEN MARKOV MODEL, THE SPEECH DRIVEN LIP MOVEMENT SEQUENCE GENERATOR SYSTEM FOR FARSI INPUT SPEECH WAS IMPLEMENTED. TO REMOVE DISCONTINUITIES BETWEEN LIP FRAMES PRODUCED BY THE SYSTEM, A MORPHING ALGORITHM WAS USED. THE PROPOSED SYSTEM IS UNIQUE FOR FARSI, AND THE EVALUATIONS HAVE SHOWN ITS ACCEPTABLE QUALITY. 

NANOMEDICINE IS AN INTERDISCIPLINARY RESEARCH AREA THAT AIMS AT PREVENTION, DIAGNOSIS AND TREATMENT OF COMPLEX DISEASES BY THE NANOSCALE OPERATORS TO REDUCE SIDE EFFECTS AND INCREASE THE CURE RATE. SIMPLICITY AND LIMITED FUNCTIONALITY OF THESE PARTICLES, AS WELL AS THE DECENTRALIZED COMPUTING AND THE UNCERTAIN DYNAMICS OF THE HUMAN BODY ENVIRONMENT ARE SOME OF MAJOR CHALLENGES IN THIS AREA. IN THIS PAPER, WE PROPOSE THAT EQUIPPING THE NANO-AGENTS WITH LEARNING ABILITY PROVIDES HIGH ROBUSTNESS AGAINST THE UNCERTAINTIES AND CHANGING DYNAMICS OF THE HUMAN BODY. IN PARTICULAR, WE PROPOSE A SWARM OF LEARNING NANO-AGENTS FOR THE TREATMENT OF ATHEROSCLEROSIS. THE SWARM LEARNS TO APPROXIMATE THE DESIRABLE DRUG RELEASE FUNCTION THAT CHANGES IN TIME ACCORDING TO THE ENVIRONMENTAL CONDITIONS OF THE DISEASE LOCATION. FOR THIS PURPOSE, WE USE RADIAL BASIS FUNCTION NEURON STRUCTURES THAT CAN ADAPT WITH HUMAN BODY. EXPERIMENTAL RESULTS SHOW THE EFFECTIVENESS OF THE PROPOSED METHOD IN TERMS OF DISEASE CONTROL TIME AND DRUG RELEASE RATE, AS WELL AS ROBUSTNESS AGAINST POSSIBLE DISTURBANCES. ...

THE RECENT METEORIC SIGNIFICANT DEVELOPMENTS IN THEBIOLOGICAL AND MEDICAL SCIENCES HAVE BEEN THE CULMINATION OF SUBSTANTIAL EFFORTS DEVOTED TO PRECISELY MODELING THE BEHAVIOR OF BIOLOGICAL SYSTEMS AND THEIR RESPONSES TO VARIOUS STIMULI. THE COMPLICATED INTERACTIONS WITHIN VARIED COMPONENTS OF BIOLOGICAL SYSTEMS AS WELL AS WITH THEIR ENVIRONMENTS MAKE THEM EXTREMELY COMPLEX NONLINEAR SYSTEMS. THE RESULTS OF SEVERAL CONTEMPORARY RELEVANT INVESTIGATIONS HAVE MANIFESTED THEIR CHAOTIC BEHAVIORAL PATTERNS. WITH THE AIM OF MODELING THIS SPECIFIC BEHAVIOR OF BIOSYSTEMS, WE EMPLOY A PARTICULAR MULTILAYER FEED-FORWARD NEURAL NETWORK. THE DISTINCTIVE FEATURE OF OUR MODELING METHOD, WHICH MAKES IT DOMINANT WITHIN THE MODELING TECHNIQUES, IS TRAINING THE SELECT NEURAL NETWORK WITH THE CHAOTIC MAP EXTRACTED FROM THE UNDER-STUDY TIME SERIES. OUR RESULTS, WHICH ARE BRIEFLY REPRESENTED IN THIS PAPER, CONFIRM THAT THE SPECIFIED NEURAL NETWORK DOES POSSESS THE POTENTIALITY TO MODEL THE CHAOTIC DYNAMICS OF BIOLOGICAL SYSTEMS, EVEN IN THE PRESENCE OF NOISE. IN PURSUANCE OF EVALUATING OUR MODEL, WE ASSESS AND MODEL THE CHAOTIC RESPONSE OF THE BRAIN TO THE FLICKER LIGHT THROUGH SOME RECORDED ELECTRORETINOGRAM DATA. ...

OWING TO PERSONAL COMPUTERS BEING APPLIED INMANY FIELDS, MOST INFORMATION IS TRANSMITTED WITH DIGITAL FORMAT SUCH AS TEXT DOCUMENTS, AUDIO, IMAGES AND VIDEOS. WITH THE RAPID GROWTH OF THE INTERNET AND THE DEVELOPMENT OF MULTIMEDIA NETWORK SYSTEM , ILLEGAL COPYING, TAMPERING, MODIFYING AND COPYRIGHT PROTECTION HAVE BECOME VERY IMPORTANT ISSUES AND THAT IS A CHALLENGING TASK TO PROTECT COPYRIGHT OF AN INDIVIDUAL IS CREATION. SO, THERE IS GREAT NEED OF PROHIBITING SUCH ILLEGAL COPYRIGHT OF DIGITAL MEDIA. DIGITAL WATERMARKING PROVIDES A VIABLE SOLUTION TO PROTECT COPYRIGHT AND ACKNOWLEDGE THE OWNERSHIP OF AN INTELLECTUAL PROPERTY. IN THIS PAPER, WE PROPOSE A METHOD OF NON-BLIND TRANSFORM DOMAIN WATERMARKING BASED ON DWT-DCT. IT PERFORMS 3 LEVEL DWT OF ORIGINAL (COVER) IMAGE AND THEN WATERMARK IMAGE WOULD BE EMBEDDED IN THE MIDDLE-COEFFICIENTS OF DCT TRANSFORMED OF SUBBANDS( LL3) OF COVER IMAGE. THE PARAMETERS USED TO TEST THE ROBUSTNESS AND TRANSPARENCY OF THE PROPOSED ALGORITHM IS THE PEAK SIGNAL TO NOISE RATIO (PSNR) AND SIMILARITY RATE (SR). EXPERIMENTAL RESULTS SHOW THAT COMBINING THE TWO TRANSFORMS IMPROVED THE PERFORMANCE OF THE WATERMARKING ALGORITHM. MOREOVER, THIS METHOD PROVIDES HIGH IMPERCEPTIBILITY AS WELL AS HIGH ROBUSTNESS AGAINST DIFFERENT ATTACKS SUCH AS JPEG COMPRESSION, GAUSSIAN NOISE, SALT & PEPPER NOISE, ETC. ...

SIGNATURE VERIFICATION TECHNIQUES EMPLOY VARIOUS SPECIFICATIONS OF A SIGNATURE. FEATURE EXTRACTION AND FEATURE SELECTION HAVE AN ENORMOUS EFFECT ON ACCURACY OF SIGNATURE VERIFICATION. FEATURE EXTRACTION IS A DIFFICULT PHASE OF SIGNATURE VERIFICATION SYSTEMS DUE TO DIFFERENT SHAPES OF SIGNATURES AND DIFFERENT SITUATIONS OF SAMPLING. THIS PAPER PRESENTS A METHOD BASED ON FEATURE LEARNING, IN WHICH A SPARSE AUTOENCODER TRIES TO LEARN FEATURES OF SIGNATURES. THEN LEARNED FEATURES HAVE BEEN EMPLOYED TO PRESENT USERS’ SIGNATURES. FINALLY, USERS’ SIGNATURES HAVE BEEN CLASSIFIED USING ONE-CLASS CLASSIFIERS. THE PROPOSED METHOD IS SIGNATURE SHAPE INDEPENDENT THANKS TO LEARNING FEATURES FROM USERS’ SIGNATURES USING AUTOENCODER. VERIFICATION PROCESS OF PROPOSED SYSTEM IS EVALUATED ON SVC2004 SIGNATURE DATABASE, WHICH CONTAINS GENUINE AND SKILLED FORGERY SIGNATURES. THE EXPERIMENTAL RESULTS INDICATE ERROR REDUCTION AND ACCURACY ENHANCEMENT. ...

IN THIS PAPER, A NOVEL APPROACH FOR AUTOMATIC SPEAKER WEIGHT ESTIMATION FROM SPONTANEOUS TELEPHONE SPEECH SIGNALS IS PROPOSED. IN THIS METHOD, EACH UTTERANCE IS MODELED USING THE I-VECTOR FRAMEWORK WHICH IS BASED ON THE FACTOR ANALYSIS ON GAUSSIAN MIXTURE MODEL (GMM) MEAN SUPERVECTORS, AND THE NON-NEGATIVE FACTOR ANALYSIS (NFA) FRAMEWORK WHICH IS BASED ON A CONSTRAINED FACTOR ANALYSIS ON GMM WEIGHTS. THEN, THE AVAILABLE INFORMATION IN BOTH GAUSSIAN MEANS AND GAUSSIAN WEIGHTS IS EXPLOITED THROUGH A FEATURE-LEVEL FUSION OF THE I-VECTORS AND THE NFA VECTORS. FINALLY, A LEAST-SQUARES SUPPORT VECTOR REGRESSION (LS-SVR) IS EMPLOYED TO ESTIMATE THE WEIGHT OF SPEAKERS FROM GIVEN UTTERANCES. THE PROPOSED APPROACH IS EVALUATED ON THE TELEPHONE SPEECH SIGNALS OF NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY (NIST) 2008 AND 2010 SPEAKER RECOGNITION EVALUATION (SRE) CORPORA. EXPERIMENTAL RESULTS OVER 2339 UTTERANCES SHOW THAT THE CORRELATION COEFFICIENTS BETWEEN ACTUAL AND ESTIMATED WEIGHTS OF MALE AND FEMALE SPEAKERS ARE 0.56 AND 0.49, RESPECTIVELY, WHICH INDICATE THE EFFECTIVENESS OF THE PROPOSED METHOD IN SPEAKER WEIGHT ESTIMATION.

IN THIS PAPER A PARTICLE SWARM CLASSIFIER IS PROPOSED TO CLASSIFY FUZZY DATA SETS. THIS CLASSIFIER IS ABLE TO FIND THE DECISION HYPERPLANES BETWEEN DIFFERENT CLASSES WITH FUZZY SAMPLES. THE PERFORMANCE OF THE PROPOSED CLASSIFIER HAS BEEN TESTED ON VARIOUS FUZZY DATA SETS. THE EXPERIMENTAL RESULTS SHOW THAT OUR PROPOSED CLASSIFIER IS ABLE TO CLASSIFY FUZZY DATA SETS AS OTHER COMMON UNCERTAIN DATA CLASSIFIERS. ALSO THE RESULTS OBTAINED FROM CLASSIFYING SOME CRISP DATA SETS SHOW THE POWERFULNESS OF OUR PROPOSED CLASSIFIER FOR CRISP DATA SETS IS SAME AS THE TRADITIONAL PARTICLE SWARM CLASSIFIER. ...

THIS PAPER PRESENTS A NEW AND EFFICIENT APPROACH FOR SINGLE-IMAGE SUPER-RESOLUTION BASED ON SPARSE SIGNAL RECOVERY. THIS APPROACH USES A CO-OCCURRENCE TRAINED DICTIONARY OF IMAGE PATCHES THAT OBTAINED FROM A SET OF OBSERVED LOW- AND HIGH-RESOLUTION IMAGES. THE LINEAR COMBINATION OF THE DICTIONARY PATCHES CAN RECOVER EVERY PATCH, THEN EACH PATCH THAT USED ON THE LOW-RESOLUTION IMAGE, CAN BE RECOVERED BY THE DICTIONARY PATCHES. SINCE THE RECOVERED PATCH IS A LINEAR COMBINATION OF SOME PATCHES, THE NOISE OF EVERY PATCH, AGGREGATED IN THE RECOVERED PATCH, THEN WE PREFER A LINEAR COMBINATION WHICH IS MORE SPARSE RATHER THAN OTHER COMBINATIONS. SO THE SPARSE REPRESENTATION OF PATCHES CAN FILTER THE NOISE IN THE SOLUTION. RECENTLY THIS APPROACH HAS BEEN USED IN SINGLE IMAGE SUPER-RESOLUTION PROBLEM. THESE METHODS CALCULATE THE SPARSE REPRESENTATION OF EVERY PATCHES SEPARATELY AND SET IT TO THE RECOVERED HIGH-RESOLUTION IMAGE. SO THE COMPLEXITY OF SUCH METHODS ARE VERY HIGH AND FOR SUITABLE SOLUTION THE PARAMETERS OF ALGORITHM MUST BE ESTIMATED, THEREFORE, THIS PROCESS(RECOVER ALL PATCH WITH AN ITERATIVE ALGORITHM AND PARAMETER ESTIMATION FOR EACH ITERATE) IS VERY TIME CONSUMING. THIS PAPER PRESENTS AN INTEGRATED METHOD FOR RECOVERING A LOW-RESOLUTION IMAGE BASED ON SPARSE REPRESENTATION OF PATCHES WITH ONE STEP AND RECOVER WHOLE IMAGE TOGETHER. ...

VENTRICULAR TACHYARRHYTHMIA PARTICULARLY VENTRICULAR TACHYCARDIA (VT) AND VENTRICULAR FIBRILLATION (VF) ARE THE MAIN CAUSES OF SUDDEN CARDIAC DEATH IN THE WORLD. A RELIABLE PREDICTOR OF AN IMMINENT EPISODE OF VENTRICULAR TACHYCARDIA THAT COULD BE INCORPORATED IN AN IMPLANTABLE DEFIBRILLATOR CAPABLE OF PREVENTIVE THERAPY WOULD HAVE IMPORTANT CLINICAL UTILITIES. AS VARIABILITY OF T WAVE, ST SEGMENT AND QT INTERVAL ARE INDICATORS OF CARDIAC INSTABILITY, THESE CHANGES CAN LEAD US TO DEVELOP ACCURATE PREDICTOR FOR VT. IN THIS STUDY, WE PRESENT AN ALGORITHM THAT PREDICTS VT USING MORPHOLOGICAL FEATURES OF ELECTRICAL SIGNAL OF VENTRICLES ACTIVITY OBTAINED FROM ELECTROCARDIOGRAM (ECG). CHANGES IN T WAVE, ST SEGMENT, QT INTERVAL AND NUMBERS OF PREMATURE VENTRICULAR COMPLEXES(PVCS) ARE CONSIDERED AS EFFECTIVE INDICATORS OF VT. CLASSIFICATION OF SELECTED FEATURES BY A SUPPORT VECTOR MACHINE (SVM) CAN IDENTIFY HIDDEN PATTERNS IN ECG SIGNALS BEFORE VT OCCURRENCE. EVALUATION OF THIS ALGORITHM ON 40 RECODS OF VT PATIENT AND 40 CONTROL RECORDS SHOWS THAT THE PROPOSED ALGORITHM CAN REACH SENSITIVITY OF 88% AND SPECIFICITY OF 100% IN VT PREDICTION.

 THIS PAPER, DESCRIBES A SOLUTION FOR TRACKING MULTIPLE SOCCER PLAYERS, SIMULTANEOUSLY, IN SOCCER GROUND. IT ADAPTS KALMAN FILTER FOR TRACKING MULTIPLE PLAYERS. ADAPTING KALMAN FILTER IS DIVIDED TO FOUR MAIN TASKS. THE FIRST TASK IS DEFINING THE STATE VECTOR FOR MULTIPLE OBJECT TRACKING. THE SECOND TASK IS DETERMINING A MOTION MODEL FOR ESTIMATING THE POSITION OF SOCCER PLAYERS IN THE NEXT FRAME. THE THIRD TASK IS DEFINING AN OBSERVATION METHOD FOR DETECTING SOCCER PLAYERS IN EACH FRAME. FINALLY, THE FOURTH TASK IS TUNING THE MEASUREMENT NOISE COVARIANCE AND ESTIMATING NOISE COVARIANCE. IN THE THIRD TASK, A NOVEL OBSERVATION METHOD FOR DETECTING SOCCER PLAYERS IS PROPOSED. THIS METHOD DIVIDES THE PLAYER BODY INTO THREE PARTS AND CALCULATES THE HISTOGRAM OF EACH PART, SEPARATELY. ALSO, AN ALGORITHM FOR UPDATING THE REFERENCE OBJECT PATCH IS INTRODUCED IN OBSERVATION METHOD. EACH TASK IS DISCUSSED IN DETAIL AND THE PROMISING PERFORMANCE OF THE PROPOSED METHOD FOR TRACKING SOCCER PLAYERS WHEN RUN ON THE AZADI DATASET IS SHOWN. ...

RADIO-FREQUENCY IDENTIFICATION (RFID) ARE BECOMING A PART OF OUR EVERYDAY LIFE WITH A WIDE RANGE OFAPPLICATIONS SUCH AS LABELING PRODUCTS AND SUPPLY CHAIN MANAGEMENT AND ETC. THESE SMART AND TINY DEVICES HAVE EXTREMELY CONSTRAINED RESOURCES IN TERMS OF AREA, COMPUTATIONAL ABILITIES, MEMORY, AND POWER. AT THE SAME TIME, SECURITY AND PRIVACY ISSUES REMAIN AS AN IMPORTANT PROBLEM, THUS WITH THE LARGE DEPLOYMENT OF LOW RESOURCE DEVICES, INCREASING NEED TO PROVIDE SECURITY AND PRIVACY AMONG SUCH DEVICES, HAS ARISEN. RESOURCE-EFFICIENT CRYPTOGRAPHIC INCIPIENT BECOME BASIC FOR REALIZING BOTH SECURITY AND EFFICIENCY IN CONSTRAINED ENVIRONMENTS AND EMBEDDED SYSTEMS LIKE RFID TAGS AND SENSOR NODES. AMONG THOSE PRIMITIVES, LIGHTWEIGHT BLOCK CIPHER PLAYS A SIGNIFICANT ROLE AS A BUILDING BLOCK FOR SECURITY SYSTEMS. IN 2014 MANOJ KUMAR ET AL PROPOSED A NEW LIGHTWEIGHT BLOCK CIPHER NAMED AS FEW, WHICH ARE SUITABLE FOR EXTREMELY CONSTRAINED ENVIRONMENTS AND EMBEDDED SYSTEMS. IN THIS PAPER, WE SIMULATE AND SYNTHESIZE THE FEW BLOCK CIPHER. IMPLEMENTATION RESULTS OF THE FEW CRYPTOGRAPHY ALGORITHM ON A FPGA ARE PRESENTED. THE DESIGN TARGET IS EFFICIENCY OF AREA AND COST. ...

LINK PREDICTION HAS RECENTLY RECEIVED GREAT ATTENTION BY MANY RESEARCHERS AS AN EFFECTIVE TECHNIQUE IN SOCIAL NETWORK ANALYSIS TO UNDERSTAND THE RELATIONS BETWEEN NODES. IN THIS PAPER A METHOD IS PROPOSED FOR FRIEND RECOMMENDATION IN SOCIAL NETWORKS USING BAYESIAN NETWORKS. THE BAYESIAN NETWORK IS A RELIABLE MODEL TO UNDERSTAND THE RELATIONS BETWEEN VARIABLES AND HAS BEEN USED IN MANY AREAS FOR PREDICTION. THIS METHOD WITH CONSIDERING EFFECTIVE FEATURES ON CREATING FRIENDSHIPS, SUGGESTS FRIENDS TO USERS ACCURATELY. FIRST, THE GOAL IS TO FIND ATTRIBUTES AND SIMILARITIES THAT HAVE THE MOST EFFECT ON CREATING A FRIENDSHIP. AFTER THAT FRIENDS WITH MOST COMMON SIMILARITIES WILL BE SUGGESTED TO EACH OTHER. THE RESULTS OF THE PROPOSED METHOD ARE COMPARED WITH THOSE OBTAINED FROM DIFFERENT ALGORITHMS LIKE FRIEND OF FRIEND AND IT IS FOUND THAT THE METHOD USED IN THIS PAPER SIGNIFICANTLY IMPROVES THE ACCURACY OF FRIEND SUGGESTION DUE TO INCLUSION OF SEVERAL FEATURES. ...