The doctrine that the content of the conclusion of a deductively valid argument is included in the content of its premises, taken jointly, is a familiar one. It has important consequences for the question of what value valid arguments possess, since it indicates the poverty of three traditional answers: that arguments may and should be used as instruments of persuasion, that they may and should be used as instruments of justification; and that they may and should be used to advance knowledge. The truth is, however, that in each of these cases the argument has only a managerial role and, if there is any work done, it is the premises that do it. It will be maintained that this point has little force against the critical rationalist answer, which I shall defend, that the principal purpose of deductive reasoning from an assemblage of premises is the exploration of their content, facilitating their criticism and rejection. That said, the main aim of the present paper is not to promote critical rationalism but to consider some published objections to the doctrine that a statement asserts every statement that is validly deducible from it. The alleged counterexamples to be considered fall roughly into two groups: statements that emerge with time from a rich mathematical or empirical theory, but were originally unformulated and are deducible from the theory only in a non-trivial way (Frederick 2011, 2014; Williamson 2012); and statements, notably disjunctions, that are easily formulated and are deducible from a theory in a trivial way (Schurz & Weingartner 1987; Mura 1990, 2008; Gemes 1994; Yablo 2014). Each of these counterexamples will be evaluated and dismissed.

Persian language is one of the most widely used languages in the Web environment. Hence, the Persian Web includes invaluable information that is required to be retrieved effectively. Similar to other languages, ranking algorithms for the Persian Web content, deal with different challenges, such as applicability issues in real-world situations as well as the lack of user modeling. CF-Rank, as a recently proposed learning to rank data, aims to deal with such issues by the classifier fusion idea. CF-Rank generates a few click-through features, which provide a compact representation of a given primitive dataset. By constructing the primitive classifiers on each category of click-through features and aggregating their decisions by the use of information fusion techniques, CF-Rank has become a successful ranking algorithm in English datasets. In this paper, CF-Rank is customized for the Persian Web content. Evaluation results of this algorithm on the dotIR dataset indicate that the customized CF-Rank outperforms baseline rankings. Especially, the improvement is more noticeable at the top of ranked lists, which are observed most of the time by the Web users. According to the NDCG@1 and MAP evaluation criteria, comparing the CF-Rank with the preeminent baseline algorithm on the dotIR dataset indicates an improvement of 30 percent and 16. 5 percent, respectively.

The ever-increasing evolution of the Internet and the growing number of the resources accessible through this medium has turned the issue of resource evaluation of the internet-based information into a controversial and cogent issue. By nature, the internet milieu is uncontrollable. It concomitantly contains both verifiable and unverifiable information. Thus it behooves the internet users to develop resource evaluation and critical thinking skills in order to discern appropriate quality resources. Through review and comparison of the available checklists, the present paper endeavors to offer criteria for Website evaluation.

Recent improvements in Web standards and technologies enable the attackers to hide and obfuscate infectious codes with new methods and thus escaping the security filters. In this paper, we study the application of machine learning techniques in detecting malicious Web pages. In order to detect malicious Web pages, we propose and analyze a novel set of features including HTML, JavaScript (jQuery library) and XSS attacks. The proposed features are evaluated on a data set that is gathered by a crawler from malicious Web domains, IP and address black lists. For the purpose of evaluation, we use a number of machine learning algorithms. Experimental results show that using the proposed set of features, the C4.5-Tree algorithm offers the best performance with 97: 61% accuracy, and F1-measure has 96: 75% accuracy. We also rank the quality of the features. Experimental results suggest that nine of the proposed features are among the twenty best discriminative features.

With the increased confidence in the use of the Internet and the World Wide Web, the number of electronic commerce (e-commerce) transactions is growing rapidly. Therefore, finding useful patterns and rules of users’ behaviors has become the critical issue for e-commerce and can be used to tailor e-commerce services in order to successfully meet the customers’ needs. This paper proposes an approach to integrate Web content mining into Web usage mining. The textual content of Web pages is captured through extraction of frequent word sequences, which are combined with Web server log files to discover useful information and association rules about users’ behaviors. The results of this approach can be used to facilitate better recommendation, Web personalization, Web construction, Website organization, and Web user profiling.

The rapid growth of Web spam in the World Wide Web has motivated researchers to propose algorithms for combating Web spam. Despite using these techniques, the search engines do not perform well in detecting Persian spam Websites. In this paper, we analyze the effectiveness of many previously proposed content-based features on detecting Persian spam Websites, and also present a number of new content-based features. As another approach, we explain and examine our Bag-Of-Spam-Words (BOSW) method to do Web spam detection. In this method, we represent each document as a vector of specific words selected from a spam corpus. Finally, we apply a number of feature selection methods and use various kinds of classification algorithms to classify the Persian Websites. For this purpose, we have created a dataset of Persian hosts. Our results show that using the BOSW method with the SVM classifier has the best performance in detecting Persian spam Websites.