Rasim M. Alguliyev

PDLK: Plagiarism detection using linguistic knowledge

Abstract Plagiarism is described as the reuse of someone elses previous ideas, work or even words without sufficient attribution to the source. This paper presents a method to detect external plagiarism using the integration of semantic relations between words and their syntactic composition. The problem with the available methods is that they fail to capture the meaning in comparison between a source document sentence and a suspicious document sentence, when two sentences have same surface text (the words are the same) or they are a paraphrase of each other. Therefore it causes inaccurate or unnecessary matching results. However, this method can improve the performance of plagiarism detection because it is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. It is executed by computing the semantic and syntactic similarity of the sentence-to-sentence. Besides, the proposed method expands the words in sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed in a different wording. This method is also capable to identify various kinds of plagiarism such as the exact copied text, paraphrasing, transformation of sentences and changing of word structure in the sentences. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11. The experimental results also displayed that the proposed method demonstrates better performance as compared to other existing techniques on PAN-PC-10 and PAN-PC-11 datasets.

An unsupervised approach to generating generic summaries of documents

We model document summarization as a quadratic Boolean programming problem.We create a modified differential evolution to solve the optimization problem.Experimental study shows that the model improves the summarization results. We present an optimization-based unsupervised approach to automatic document summarization. In the proposed approach, text summarization is modeled as a Boolean programming problem. This model generally attempts to optimize three properties, namely, (1) relevance: summary should contain informative textual units that are relevant to the user; (2) redundancy: summaries should not contain multiple textual units that convey the same information; and (3) length: summary is bounded in length. The approach proposed in this paper is applicable to both tasks: single- and multi-document summarization. In both tasks, documents are split into sentences in preprocessing. We select some salient sentences from document(s) to generate a summary. Finally, the summary is generated by threading all the selected sentences in the order that they appear in the original document(s). We implemented our model on multi-document summarization task. When comparing our methods to several existing summarization methods on an open DUC2005 and DUC2007 data sets, we found that our method improves the summarization results significantly. This is because, first, when extracting summary sentences, this method not only focuses on the relevance scores of sentences to the whole sentence collection, but also the topic representative of sentences. Second, when generating a summary, this method also deals with the problem of repetition of information. The methods were evaluated using ROUGE-1, ROUGE-2 and ROUGE-SU4 metrics. In this paper, we also demonstrate that the summarization result depends on the similarity measure. Results of the experiment showed that combination of symmetric and asymmetric similarity measures yields better result than their use separately.

Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method.

Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method.

Query-based multi-documents summarization using linguistic knowledge and content word expansion

In this paper, a query-based summarization method, which uses a combination of semantic relations between words and their syntactic composition, to extract meaningful sentences from document sets is introduced. The problem with current statistical methods is that they fail to capture the meaning when comparing a sentence and a user query; hence there is often a conflict between the extracted sentences and users’ requirements. However, this particular method can improve the quality of document summaries because it is able to avoid extracting a sentence whose similarity with the query is high but whose meaning is different. The method is executed by computing the semantic and syntactic similarity of the sentence-to-sentence and sentence-to-query. To reduce redundancy in summary, this method uses the greedy algorithm to impose diversity penalty on the sentences. In addition, the proposed method expands the words in both the query and the sentences to tackle the problem of information limit. It bridges the lexical gaps for semantically similar contexts that are expressed using different wording. The experimental results display that the proposed method is able to improve performance compared with the participating systems in DUC 2006. The experimental results also showed that the proposed method demonstrates better performance as compared to other existing techniques on DUC 2005 and DUC 2006 datasets.

Big Data: Big Promises for Information Security

Big Data is related to technologies for collecting, processing, analyzing and extracting useful knowledge from very large volumes of structured and unstructured data generated by different sources at high speed. Big Data creates critical information security and privacy problems, at the same time Big Data analytics promises significant opportunities for prevention and detection of advanced cyber-attacks using correlated internal and external security data. We must address several challenges to realize true potential of Big Data for information security. The paper analyzes Big Data applications for information security problems, and defines research directions on Big Data analytics for security intelligence.

A Model for Text Summarization

Text summarization is a process for creating a concise version of documents preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic cluster and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

Cyber-physical systems and their security issues

Abstract The creation of cyber-physical systems posed new challenges for people. Ensuring the information security of cyber-physical systems is one of the most complex problems in a wide range of defenses against cyber-attacks. The aim of this paper is to analyse and classify existing research papers on the security of cyber-physical systems. Philosophical issues of cyber-physical systems are raised. Their influence on the aspects of peoples lives is investigated. The principle of cyber-physical system operation is described. The main difficulties and solutions in the estimation of the consequences of cyber-attacks, attacks modeling and detection and the development of security architecture are noted. The main types of attacks and threats against cyber-physical systems are analysed. A tree of attacks on cyber-physical systems is proposed. The future research directions are shown.

Anomaly Detection in Big Data based on Clustering

Selection of the right tool for anomaly (outlier) detection in Big data is an urgent task. In this paper algorithms for data clustering and outlier detection that take into account the compactness and separation of clusters are provided. We consider the features of their use in this capacity. Numerical experiments on real data of different sizes demonstrate the effectiveness of the proposed algorithms.

A linguistic treatment for automatic external plagiarism detection

Plagiarism is the unauthorized use of the ideas, presentation of someone elses words or work as your own. This paper presents an External Plagiarism Detection System (EPDS), which employs a combination of the Semantic Role Labeling (SRL) technique, the semantic and syntactic information. Most of the available methods fail to capture the meaning in the comparison between a source document sentence and a suspicious document sentence when two sentences have same surface text. Therefore, it leads to incorrect or even unnecessary matching results. However, the proposed method is able to avoid selecting the source text sentence whose similarity with suspicious text sentence is high but its meaning is different. On the other hand, an author may change the sentence from: active to passive and vice versa; hence, the method also employed the SRL technique to tackle the aforementioned challenge. Furthermore, the method used the content word expansion approach to bridge the lexical gaps and identify the similar ideas that are expressed using different wording. The proposed method is able to detect different types of plagiarism such as the exact verbatim copying, paraphrasing, transformation of sentences, changing of word structure. As a result, the experimental results have displayed that the proposed method is able to improve the performance compared with the participating systems in PAN-PC-11 and other existing techniques.

Modifications to the journal impact factor

New versions of journal impact factor are proposed for comparing them with JCR IF. They focus on the journal self-citation and the number of citing sources. The proposed versions are grouped into: 1) IFs penalized by self-citations, 2) IF encouraged by the number of citing sources and 3) IFs combining the penalized IFs and encouraged IF. This study evaluates the impact of journal self-citations and distribution of citations among sources on JCR IF. The study indicates that self-citations have little impact on the values of IF and the IF rankings, whether or not journal self-citations are included. The proposed indicators have been evaluated for the 30 journals in Computer Science field indexed in JCR 2013. The Spearman’s ρ correlation between the JCR IF and IFs penalized by self-citations is in the range of 0.76–0.96 and the Kendall’s τ correlation is in the range of 0.57–0.86. The study also indicates that compared to the IFs penalized by self-citations, the IF encouraged (EIF) by the number of citing sources correlated moderate with the JCR IF, the Spearman’s ρ correlation is 0.73 and the Kendall’s τ correlation is 0.59. Experiment results showed that the JCR IF moderately correlated with the combined IFs, Spearman’s ρ correlation is in the range of 0.69–0.72 and Kendall’s τ correlation is in the range of 0.55–0.58. We also showed that penalization strategy of self-citation can influence on the result. For example, IF linearly penalized (LPIF) by self-citations highly correlated with the JCR IF (Spearman’s ρ correlation is 0.99 and Kendall’s τ correlation is 0.93), and IF non-linearly penalized (nLPIF) by self-citations moderately correlated with the JCR IF (Spearman’s ρ correlation is 0.76 and Kendall’s τ correlation is 0.57). Finally, we concluded that IFs with and without penalization of self-citations lowly correlated with the number of articles and the number of citing sources.