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Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

Influence maximization is an important research area in social network analysis where researchers are concerned with detecting influential nodes. The detection of influential nodes is of great interest in several disciplines including computer science, opinion propagation, political movements, or economics, where systems are often modeled as graphs. The Influence Maximization problem is proved NP-hard. This computational complexity is justified by two main factors. The first factor is about the important size of social networks. Modern social networks like TikTok and Facebook have reached an unprecedented number of users. Dynamic social networks, whose topology or/and informational content is able to evolve, represent the second factor. Maximizing influence in such networks remains a significant task. In this light, several methods have been proposed. Being motivated by this fact, we provide in this paper a detailed survey of influence maximization approaches. Our main concern is to provide a taxonomy of existing models in both static and dynamic networks. In addition, we provide a comparison of the state-of-the-art approaches according to a clear categorization. New trends for detecting influential nodes are also discussed. We provide then some challenges as well as future directions. • Taxonomy of existing IM models in both static and dynamic networks is provided. • A comparison of existing approaches according to a clear categorization is presented. • New trends for detecting influential nodes are also discussed. [ABSTRACT FROM AUTHOR]

• A novel SMO inspired by mimicking behavior to solve optimization problems is presented. • SMO includes a mimic operator to simulate search in the response space. • SMO does not require control operator respect to other Meta heuristic methods. • SMO solve optimization problems with minimum population size. • SMO is compared with 14 well-known and state of the art optimization algorithms. Increase in complexity of real world problems has provided an area to explore efficient methods to solve computer science problems. Meta-heuristic methods based on evolutionary computations and swarm intelligence are instances of techniques inspired by nature. This paper presents a novel social mimic optimization (SMO) algorithm inspired by mimicking behavior to solve optimization problems. The proposed algorithm is evaluated using 23 test functions. Obtained results are compared with 14 known optimization algorithms including Whale optimization algorithm (WOA), Grasshopper optimization algorithm (GOA), Particle Swarm Optimization (PSO), Stochastic fractal search (SFS), Grey Wolf Optimizer (GWO), Optics Inspired Optimization (OIO), League Championship Algorithm (LCA), Wind Driven Optimization (WDO), Harmony search (HS), Firefly Algorithm (FA), Artificial Bee Colony (ABC), Biogeography Based Optimization (BBO), Bat Algorithm (BA), and Teaching Learning Based Optimization (TLBO). Obtained results indicate higher capability of the SMO algorithm in solving high-dimensional decision variables. Furthermore, SMO is used to solve two classic engineering design problems. Three important features of SMO are simple implementation, solving optimization problems with minimum population size and not requiring control parameters. Results of various evaluations show superiority of the proposed method in finding the optimal solution with minimum function evaluations. This superiority is achieved based on reducing number of initial population. The proposed method can be applied to applications like automatic evolution of robotics, automatic control of machines and innovation of machines in finding better solutions with less cost. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a novel algorithm for numerical optimization, called Simple Adaptive Climbing (SAC). SAC is a simple efficient single-point approach that does not require a careful fine-tunning of its two parameters. SAC algorithm shares many similarities with local optimization heuristics, such as random walk, gradient descent, and hill-climbing. SAC has a restarting mechanism, and a powerful adaptive mutation process that resembles the one used in Differential Evolution. The algorithms SAC is capable of performing global unconstrained optimization efficiently in high dimensional test functions. This paper shows results on 15 well-known unconstrained problems. Test results confirm that SAC is competitive against state-of-the-art approaches such as micro-Particle Swarm Optimization, CMA-ES or Simple Adaptive Differential Evolution. [Copyright &y& Elsevier]

Abstract: Mobile or SMS spam is a real and growing problem primarily due to the availability of very cheap bulk pre-pay SMS packages and the fact that SMS engenders higher response rates as it is a trusted and personal service. SMS spam filtering is a relatively new task which inherits many issues and solutions from email spam filtering. However it poses its own specific challenges. This paper motivates work on filtering SMS spam and reviews recent developments in SMS spam filtering. The paper also discusses the issues with data collection and availability for furthering research in this area, analyses a large corpus of SMS spam, and provides some initial benchmark results. [Copyright &y& Elsevier]

Abstract: Mobile communication has become the major choice of personal communication today. With the rapid proliferation of wireless network technologies, mobile users can communicate through more and more different wireless networks. In such a heterogeneous wireless network environment, traditional wisdom is to ensure mobile users always best connected (ABC) to one of their nearby available networks. However, the ABC strategy may be sub-optimal as the cost and quality of multimedia communication depends not only on the access network of the caller (source) but also on that of the callee (destination). Thus, intelligently selecting a best network connection, or a source and destination access network pair, for users is necessary in next generation multimedia communication, in which various media types, such as voice, video and data, are related and jointly contribute to the communication quality and cost. This paper utilized Analytic Network Process (ANP) to model the problem of call setup strategy for multimedia communication. ANP is powerful in representing complex networked decision process. In the paper, we illustrated, by an example, how ANP sets up calls that fulfill the requirement of users. [Copyright &y& Elsevier]

Abstract: Corporate fraud these days represents a huge cost to our economy. In the paper we address one specific type of corporate fraud, internal transaction fraud. Given the omnipresence of stored history logs, the field of process mining rises as an adequate answer to mitigating internal transaction fraud. Process mining diagnoses processes by mining event logs. This way we can expose opportunities to commit fraud in the followed process. In this paper we report on an application of process mining at a case company. The procurement process was selected as example for internal transaction fraud mitigation. The results confirm the contribution process mining can provide to business practice. [Copyright &y& Elsevier]

Abstract: Features play a fundamental role in sentiment classification. How to effectively select different types of features to improve sentiment classification performance is the primary topic of this paper. Ngram features are commonly employed in text classification tasks; in this paper, sentiment-words, substrings, substring-groups, and key-substring-groups, which have never been considered in sentiment classification area before, are also extracted as features. The extracted features are then compared and analyzed. To demonstrate generality, we use two authoritative Chinese data sets in different domains to conduct our experiments. Our statistical analysis of the experimental results indicate the following: (1) different types of features possess different discriminative capabilities in Chinese sentiment classification; (2) character bigram features perform the best among the Ngram features; (3) substring-group features have greater potential to improve the performance of sentiment classification by combining substrings of different lengths; (4) sentiment words or phrases extracted from existing sentiment lexicons are not effective for sentiment classification; (5) effective features are usually at varying lengths rather than fixed lengths. [Copyright &y& Elsevier]

Abstract: Knowledge intensive service processes should be managed in a human-oriented way since human workers who naturally undertake complex operations of an intellectual nature in the processes are the most valuable resources for service-providing companies. For the human-oriented management, a role-based approach has been widely applied. The foremost step in the role-based human process management is to assign relevant roles to appropriate humans. The role assignment task is also highly dependent on the ability of humans to consider information about roles and humans. In order to manage the role assignment in a human-oriented way, this paper presents two fundamental principles upon which the assignment task is carried out. First, roles should not be assigned until they need to be activated; second, the authority for the role assignment should be distributed to participants in the processes. In order to support the principles, the information should be explicitly and formally described to enable all the participants to easily understand and utilize it. Consequently, this paper proposes a methodology for managing the role assignment in a human-oriented way by adopting an ontological approach which can precisely describe the information. From the proposed methodology, human workers can be notified which roles are triggered to be activated and wait to be assigned. Moreover, they can be provided appropriate candidate humans when carrying out the assignment task. The contribution of this paper is to suggest a role assignment methodology that not only lightens the assignment workload by providing the capability of identifying a small set of appropriate candidates but also enables the efficient assignment by streamlining the assignment task. [Copyright &y& Elsevier]

Abstract: In this paper we discuss the redesign of a support management system deployed in a small and medium sized enterprise (SME) in the UK. The original system was not fulfilling its needs as it had not captured work practices in a way that was recognizable to the users. The advantages of the redesign included: improved usefulness; improved efficiency and productivity; reduced learning time; improved usability; and increased acceptance among users. The system is used to support complex and distributed cooperative activities taking place in an SME. We evaluated the current system and analysed work practices using a user-centred design and evaluation philosophy. In this paper we discuss how user needs are incorporated into the enhanced design of the support management system. The user-centred design techniques used in this research include interviews, questionnaires, observations and user tests. We present comparative evaluation results that show significant improvement in performance of user tasks using the redesigned support management system. The contribution of this paper is the presentation of a case study to show how a user-centred design and evaluation philosophy can lead to better requirements capture resulting in systems that more accurately capture the users’ conceptual models. [ABSTRACT FROM AUTHOR]

Abstract: The paper develops and discusses a theoretical model for collaborative design systems based on motivated learning agents, however, with a novel self-development module to help the system improve itself. Self-Development Agent based on previous work is an intelligent agent, which not only receives information from sensors in the environment, but also gives valuable suggestions that could help to improve the system. Two case studies with different system setups are described in detail to help better understand this model. The theoretical model is not limited to the specific systems described in this paper, but could be adapted to other collaboration systems as well. [ABSTRACT FROM AUTHOR]

This paper presents a novel clustering method this is called extension genetic algorithm (EGA). The new method is a combination of extension theory and genetic algorithm (GA). In the past, we used the extension method in some clustering problems. With the method, we had to rely on experiences to set rules on classical domain and weight, which caused to increase two tedious and complicated steps in clustering processes. In order to improve this defect, the paper uses the EGA to find the best parameter of classical domain. Through the simulations, we prove that this new method can eliminate try and error adjustment of modeling parameters and increase the accuracy of clustering problems. Experimental results from three different examples, including two benchmark data sets and one practical application, verify the effectiveness and applicability of the proposed work. [Copyright &y& Elsevier]

• Fileless malware has no signature because it does not leverage executable files. • We suggest a memory-based approach for detecting and analyzing fileless malware. • This proposed method offers useful insight for the experts working in this field. • The proposed methodʼs applicability was demonstrated using a real case study sample. The rapid advancements in cyber-attack strategies are in parallel with the measures for detection, analysis, and prevention. Attackers have recently developed fileless malware that can simply bypass existing security mechanisms. Researchers publish reports to help discover fileless malware and to better understand the threatʼs scope to counteract it. However, with the lack of studies on fileless malware regarding the classification and the scale of the threat, they have not been thoroughly analyzed. As a result, in this research, we explored the most recent advancements in fileless malware prevention and detection and highlighted future research challenges. We also propose an analytical approach based on the attack strategies and attributes of the selected sample. Our method simplifies feature extraction and reduces processing load. Furthermore, compared to the static analysis we do not need for decompression and unpacking for the analysis. We applied the proposed method on a real case example. It has been seen that information about fileless malware detection, working mechanism, attack method and attacker named "Kovter" can be accessed. Our approach is advantageous and can be applied as a new technique for fileless malware detection to protect systems from cyber threats. This paper also presents an insight to the fileless malware threat and provides a basic review of the methods and techniques used in the detection and analysis of fileless malware attacks. [ABSTRACT FROM AUTHOR]

Remediation actions are performed in scenarios in which consequences of a problem should be promptly mitigated when its cause takes too long to be addressed or is unknown. Such scenarios are recurrent in the real world, including in the context of computer science. Existing approaches that address these scenarios are application-specific. Nevertheless, the reasoning about remediation actions as well as cause identification and resolution, in order to address problems permanently, can be abstracted in such a way that they can be incorporated to autonomous software components, often referred to as agents. They can thus autonomously deal with these scenarios, which we refer to as critical cause-effect situations . In this paper, we propose a domain-independent extension to the belief-desire-intention (BDI) architecture that provides such agents with this automated reasoning. Our work provides an extensible solution to this recurrent problem-solving strategy and allows agents to flexibly deal with resource-constrained scenarios. This solution removes the need for manually implementing the coordination of actions performed by agents, using causal models to capture the knowledge required to carry out this task. Therefore, it not only allows the development of systems with remediative behaviour, but also enables the reduction of development effort by means of a reusable infrastructure that can be used in several different domains. Our approach was evaluated based on an existing solution in the network resilience domain, which showed that our extended agent can autonomously address a network challenge, with a reduction in the development effort and no impact in agent performance. [ABSTRACT FROM AUTHOR]

Activity recognition has been a hot topic for decades, from the scientific research to the development of off-the-shelf commercial products. Since people perform the activities differently, to avoid overfitting, building a general model with activity data of various users is required before the deployment for personal use. However, annotating a large amount of activity data is expensive and time-consuming. In this paper, we build a general model for activity recognition with a limited amount of labelled data. We combine Latent Dirichlet Allocation (LDA) and AdaBoost to jointly train a general activity model with partially labelled data. After that, when AdaBoost is used for online prediction, we combine it with graphical models (such as HMM and CRF) to exploit the temporal information in human activities to smooth out the accidental misclassifications. Experiments with publicly available datasets show that we are able to obtain the accuracy of more than 90% with 1% labelled data. [ABSTRACT FROM AUTHOR]

Many real-world networks have a topological structure characterized by cohesive groups of vertices. To perform the task of identifying such subsets of vertices, community detection in networks has aroused the interest of researchers and practitioners alike. In spite of the existence of various efficient community detection algorithms in the literature, most of them uses global information about the network, not applicable to distributed networks. This paper proposes a genetic-based algorithm to detect communities in directed networks based on local information to generate the offspring. The major difference between the proposed strategy and those found in the literature is the way of exploiting target regions of interest in the solution space. This step is directly influenced by the crossover operator that depends largely on the individual representation. In the introduced strategy, GA-LP, the individual is locally stored in the vertices as labels, what brings more flexibility in the system to be adapted to address applications that involve, for example, dynamic networks. In computational experiments, the proposed strategy showed an outstanding performance, being fast, achieving the best results on average in the networks tested. [ABSTRACT FROM AUTHOR]

• A comprehensive survey/review paper on computer-aided diagnosis (CAD) in medicine of 251 publications. • Referenced publications had to pass criteria to determine subject relevance and quality of publication. • It bridges a crucial gap that currently exists in the literature regarding an overview of CAD systems in medicine. • It provides a venue for people with different backgrounds to understand pertinent aspects in medicine and in computer science. • Provides a foundation for accelerating research in this vital field in the interface of medicine and computer science. Computer-aided diagnosis (CAD) in medicine is the result of a large amount of effort expended in the interface of medicine and computer science. As some CAD systems in medicine try to emulate the diagnostic decision-making process of medical experts, they can be considered as expert systems in medicine. Furthermore, CAD systems in medicine may process clinical data that can be complex and/or massive in size. They do so in order to infer new knowledge from data and use that knowledge to improve their diagnostic performance over time. Therefore, such systems can also be viewed as intelligent systems because they use a feedback mechanism to improve their performance over time. The main aim of the literature survey described in this paper is to provide a comprehensive overview of past and current CAD developments. This survey/review can be of significant value to researchers and professionals in medicine and computer science. There are already some reviews about specific aspects of CAD in medicine. However, this paper focuses on the entire spectrum of the capabilities of CAD systems in medicine. It also identifies the key developments that have led to today's state-of-the-art in this area. It presents an extensive and systematic literature review of CAD in medicine, based on 251 carefully selected publications. While medicine and computer science have advanced dramatically in recent years, each area has also become profoundly more complex. This paper advocates that in order to further develop and improve CAD, it is required to have well-coordinated work among researchers and professionals in these two constituent fields. Finally, this survey helps to highlight areas where there are opportunities to make significant new contributions. This may profoundly impact future research in medicine and in select areas of computer science. [ABSTRACT FROM AUTHOR]

A detailed and extensive empirical study of empty prediction of multi-label classification is conducted in this paper and to the best of our knowledge this work is the first empirical study of this problem. Total 8 state of the art multi-label classification methods, BR, CC, CLR, HOMER, RAkEL, ECC, MLkNN, and BRkNN, are compared on 11 datasets. The empirical results clearly answer the two research questions, (1) whether empty prediction problems happen in commonly used state of the art multi-label classification methods and what their empty prediction rates (EPR) on different test sets are and (2) what multi-label classification methods are with overall highest/lowest EPRs. Specifically, it is empirically shown that every method considered all made empty predictions on different datasets. In addition, several thresholding methods which in theory can solve empty prediction are compared. The clear answers to the two research questions and the experimental findings are the main contributions of this work to multi-label classification. [ABSTRACT FROM AUTHOR]

In this paper we describe a tool that can be effectively used to evaluate student learning outcomes using concept maps and Markov chain analysis. The main purpose of this tool is to advance the use of artificial intelligence techniques by using concept maps and Markov chains in evaluating a student’s understanding of a particular topic of study using concept maps. The method used in the tool makes use of XML parsing to perform the required evaluation. For the purpose of experimenting this tool we have taken into consideration concept maps developed by students enrolled in two different courses in Computer Science. The result of this experimentation is also discussed. [ABSTRACT FROM AUTHOR]

QUALIFLEX (QUALItative FLEXible multiple criteria method) is a very useful outranking method to deal simultaneously with the cardinal and ordinal information in decision making process. The purpose of this paper is to develop a hesitant fuzzy QUALIFLEX with a signed distance-based comparison method for handling multi-criteria decision-making problems in which both the assessments of alternatives on criteria and the weights of criteria are expressed by hesitant fuzzy elements (HFEs). We propose a novel concept of hesitancy index for the HFE to measure the degree of hesitancy of the decision-maker or the decision organization. By taking their hesitancy indices into account, we present a signed distance-based method to compare the magnitude of HFEs. Using the signed distance-based comparison approach, we define the concordance/discordance index, the weighted concordance/discordance index and the comprehensive concordance/discordance index. By investigating all possible permutations of alternatives with respect to the level of concordance/discordance of the complete preference order, the optimal ranking orders of alternatives can be obtained. An application study of the proposed method on green supplier selection is conducted. The study indicates that the proposed method does not require the complicated computation procedures but still yields a reasonable and credible solution. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier. [Copyright &y& Elsevier]

Abstract: This article presents an overview, analysis and benchmark of the best-known artificial intelligence (AI) conferences, including the Mexican International Conference on Artificial Intelligence (MICAI) conference series, and describes how MICAI has contributed to both the growth of artificial intelligence (AI) research in Mexico and the advancement of AI research worldwide. Among the prestigious AI conferences examined are the IJCAI, AAAI, ECAI, IBERAMIA, AAJCAI and PRICAI. Features analyzed include number of papers, acceptance rate and the h index as a measure of the scientific impact. The MICAI has been held in Mexico since 2000, when the National Meeting on AI, held by the Mexican Society for Artificial Intelligence (SMIA) since 1983, and the International Symposium on Artificial Intelligence (ISAI), organized by Tecnológico de Monterrey (ITESM) since 1988, merged into a single conference. Conference trends and future developments are also explained. [Copyright &y& Elsevier]

Highlights: [•] A new method for environmental impact assessment is proposed. [•] The proposed method is more effective by considering various types of uncertainty. [•] A new representation of uncertain information, called D numbers, is presented. [•] This paper provides a new framework for the environmental impact assessment. [ABSTRACT FROM AUTHOR]

Abstract: Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field. [Copyright &y& Elsevier]

Abstract: After having recalled some well-known shortcomings linked with the Semantic Web approach to the creation of (application oriented) systems of “rules” – e.g., limited expressiveness, adoption of an Open World Assumption (OWA) paradigm, absence of variables in the original definition of OWL – this paper examines the technical solutions successfully used for implementing advanced reasoning systems according to the NKRL’s methodology. NKRL (Narrative Knowledge Representation Language) is a conceptual meta-model and a Computer Science environment expressly created to deal, in an ‘intelligent’ and complete way, with complex and content-rich non-fictional ‘narrative’ data sources. These last include corporate memory documents, news stories, normative and legal texts, medical records, surveillance videos, actuality photos for newspapers and magazines, etc. In this context, we will expound first the need for distinguishing between “plain/static” and “structured/dynamic” knowledge and for introducing appropriate (and different) knowledge representation structures for these two types of knowledge. In a structured/dynamic context, we will then show how the introduction of “functional roles” – associated with the possibility of making use of n-ary structures – allows us to build up highly ‘expressive’ rules whose “atoms” can directly represent complex situations, actions, etc. without being restricted to the use of binary clauses. In an NKRL context, “functional roles” are primitive symbols interpreted as “relations” – like “subject”, “object”, “source”, “beneficiary”, etc. – that link a semantic predicate with its arguments within an n-ary conceptual formula. Functional roles contrast then with the “semantic roles” that are equated to ordinary concepts like “student”, to be inserted into the “non-sortal” (no direct instances) branch of a traditional ontology. [Copyright &y& Elsevier]

Abstract: The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu’s method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed. [Copyright &y& Elsevier]

Abstract: Railway scheduling has been a significant issue in the railway industry. Over the last few years, numerous approaches and tools have been developed to compute railway scheduling. However, robust solutions are necessary to absorb short disruptions. In this paper, we present the robustness problem from the point of view of railway operators and we propose analytical and simulation methods to measure robustness in a single railway line. In the analytical approach, we have developed some formulas to measure robustness based on the study of railway line infrastructure topology and buffer times. In the simulation approach, we have developed a software tool to assess the robustness for a given schedule. These methods have been inserted in MOM (More information can be found at the MOM web page http://www.dsic.upv.es/users/ia/gps/MOM), which is a project in collaboration with the Spanish Railway Infrastructure Manager (ADIF). [Copyright &y& Elsevier]

Abstract: Regression via classification (RvC) is a method in which a regression problem is converted into a classification problem. A discretization process is used to covert continuous target value to classes. The discretized data can be used with classifiers as a classification problem. In this paper, we use a discretization method, Extreme Randomized Discretization (ERD), in which bin boundaries are created randomly to create ensembles. We present two ensemble methods for RvC problems. We show theoretically that the proposed ensembles for RvC perform better than RvC with the equal-width discretization method. We also show the superiority of the proposed ensemble methods experimentally. Experimental results suggest that the proposed ensembles perform competitively to the method developed specifically for regression problems. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new method for fuzzy risk analysis based on the proposed new fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. First, we present a fuzzy ranking method for ranking generalized fuzzy numbers with different left heights and right heights. The proposed method considers the areas of the positive side, the areas of the negative side and the centroid values of generalized fuzzy numbers as the factors for calculating the ranking scores of generalized fuzzy numbers with different left heights and right heights. It can overcome the drawbacks of the existing fuzzy ranking methods. Then, we propose a new method for fuzzy risk analysis based on the proposed fuzzy ranking method, where the evaluating values are represented by generalized fuzzy numbers. The proposed fuzzy risk analysis method provides us with a useful way for fuzzy risk analysis based on generalized fuzzy numbers with different left heights and right heights. [Copyright &y& Elsevier]

Abstract: Text retrieval has received a lot of attention in computer science. In the text retrieval field, the most widely-adopted similarity technique is using vector space models (VSM) to evaluate the weight of terms and using Cosine, Jaccard or Dice to measure the similarity between the query and the texts. However, these similarity techniques do not consider the effect of the sequence of the information. In this paper, we propose an integrated text retrieval (ITR) mechanism that takes the advantage of both VSM and longest common subsequence (LCS) algorithm. The key idea of the ITR mechanism is to use LCS to re-evaluate the weight of terms, so that the sequence and weight relationships between the query and the texts can be considered simultaneously. The results of mathematical analysis show that the ITR mechanism can increase the similarity on Jaccard and Dice similarity measurements when a sequential relationship exists between the query and the texts. [Copyright &y& Elsevier]

Abstract: In recent years, many studies have been conducted to deal with automatic construction of domain-oriented sentiment lexicon. However, most of the attempts rely on only the relationship between sentiment words, failing to uncover the mutual relationship between the words and the documents, as well as ignoring the useful knowledge of some existed domains (or “old domain”). This paper proposes a random walk algorithm to construct domain-oriented sentiment lexicon by simultaneously utilizing sentiment words and documents from both old domain and target domain (or “new domain”). The approach simulates a random walk on the graphs that reflect four kinds of relationships (the relationship between words, the relationship from words to documents, the relationship between documents, the relationship from documents to words) between documents and words. Experimental results indicate that the proposed algorithm could dramatically improve the performance of automatic construction of domain-oriented sentiment lexicon. [Copyright &y& Elsevier]

Abstract: Over the past decade, an increasing number of efficient algorithms have been proposed to mine frequent patterns by satisfying the minimum support threshold. Generally, determining an appropriate value for minimum support threshold is extremely difficult. This is because the appropriate value depends on the type of application and expectation of the user. Moreover, in some real-time applications such as web mining and e-business, finding new correlations between patterns by changing the minimum support threshold is needed. Since rerunning mining algorithms from scratch is very costly and time-consuming, researchers have introduced interactive mining of frequent patterns. Recently, a few efficient interactive mining algorithms have been proposed, which are able to capture the content of transaction database to eliminate possibility of the database rescanning. In this paper, we propose a new method based on prime number and its characteristics mainly for interactive mining of frequent patterns. Our method isolates the mining model from the mining process such that once the mining model is constructed; it can be frequently used by mining process with various minimum support thresholds. During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. The experimental results verify the efficiency of our method for interactive mining of frequent patterns. [Copyright &y& Elsevier]

Abstract: In this paper, we present a new method for students’ learning achievement evaluation by automatically generating the importance degrees of the attributes of questions. The proposed method considers the “accuracy rate”, the “time rate”, the “importance” and the “complexity” for evaluating students’ learning achievement. First, the proposed method transforms the attributes “accuracy rate” and “time rate” into the “effect of accuracy rate” and the “effect of time rate”, respectively. Then, it generates the weights of the attributes “effect of accuracy rate”, “effect of time rate”, “importance” and “complexity”, respectively. Then, it generates the importance degrees of the attributes of questions based on the weights of the attributes. Then, it calculates the learning achievement indices of the students having the same total score. Finally, it determines the new ranking order of the students having the same original total score based on the learning achievement indices of the students. The proposed method is simpler than due to the fact that the proposed method is based on simple arithmetic calculations rather than the complicated fuzzy reasoning method presented in . The time complexity of the proposed method is O(mn), where m denotes the number of questions and n denotes the number of students. [Copyright &y& Elsevier]

Abstract: Radio-frequency identification (RFID) uses non-contact data access mechanism to read or write tag data through the radio frequency transmitted by the readers. Currently, the RFID systems are applied in different areas. However, the data access rate of a RFID system could be interfered by the environment furnishings, barriers, deployment method, and number of readers. The system stability heavily depends on the experiences of the deployment engineers especially for the active readers. This paper proposes an intelligent deployment system for RFID readers. The system uses the furnished simulation of environment and the best deployment of readers to construct an automatic deployment system for the RFID readers. The former constructs a fuzzy interference model of barriers according to the materials and inference degree of the furnishings. The latter uses the reader cluster and genetic algorithm to establish the best deployment plan of readers. The experimental results show that the system can reduce the interference problem of reader deployment as well as improve the reliability and stability of the RFID systems. [Copyright &y& Elsevier]

Abstract: Cross-efficiency evaluation is an effective approach to ranking decision making units (DMUs) that utilize multiple inputs to produce multiple outputs. Its models can usually be developed in a way that is either aggressive or benevolent to other DMUs, depending upon the decision maker (DM)’s subjective preference to the two extreme cases. This paper proposes several new data envelopment analysis (DEA) models for cross-efficiency evaluation by introducing a virtual ideal DMU (IDMU) and a virtual anti-ideal DMU (ADMU). The new DEA models determine input and output weights from the point of view of distance from IDMU or ADMU without the need to be aggressive or benevolent to any DMUs. As a result, the cross-efficiencies measured by these new DEA models are neutral and more logical. Numerical examples are provided to illustrate the potential applications of these new DEA models and their effectiveness in ranking DMUs. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a co-evolutionary immune algorithm for the multi-criteria decision making (MCDM) model, and use the model to solve the large scale garment matching problem. Size fitting problem is a main obstacle to large scale garment sales and online sales because it is difficult to find the fit garments by the general size information. This study regards the fit garment matching problem as a MCDM model with the constraints of size satisfaction. An immune co-evolutionary algorithm is used to search the fit garments from the candidate garments in the stock. The garments in the stock are taken as antibodies and the customer request as antigen. The concepts of ideal garment and loose garment are virtual garment to model the customer request. Two affinity measures including dominance affinity and distance affinity are defined to represent the similarity of antibody to antibody and that of antibody to antigen. Correspondingly, the fit garments are chosen by two methods: the Pareto optimal garments by the MCDM solving algorithm, and the optimal garments with the minimal distance affinity to the ideal garments. An evaluation model on garments is proposed to evaluate the fit garments by the affinity measures. Based on the experiment data from the effective study of detail factors of female trousers, the proposed model and algorithm demonstrate to be a feasible and effective attempt aiming at a valuable problem and provide the key tool for garment store sale system or online garment order system to support accurate garment size matching. [Copyright &y& Elsevier]

Abstract: One critical problem in the operations of chemical processes is the occurrence of abnormal events. Therefore, an effective process monitoring methodology that can help detect, diagnose and predict abnormal events becomes potentially very useful. For the purpose of knowledge representation of chemical abnormality, a specified type of timed fuzzy Petri net (tFPN) approach is explicitly introduced in this paper. The dominant feature of tFPN metrics can be recognized from the fact that a timing factor is assigned to each transition, as well as a degree of reliability is associated with each place, which allows accurately representing the dynamic nature of fuzzy knowledge pertaining to abnormal events. Following a procedure towards abnormal event monitoring, two efficient algorithms in terms of abnormality prognostication and diagnosis are exploited by means of reachability analysis of tFPN. The benefits of derived techniques and solutions are illustrated through a case study consisting in a polypropylene reactor. [Copyright &y& Elsevier]

Abstract: Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. It can discover clusters with arbitrary shapes and only requires two input parameters. DBSCAN has been proved to be very effective for analyzing large and complex spatial databases. However, DBSCAN needs large volume of memory support and often has difficulties with high-dimensional data and clusters of very different densities. So, partitioning-based DBSCAN algorithm (PDBSCAN) was proposed to solve these problems. But PDBSCAN will get poor result when the density of data is non-uniform. Meanwhile, to some extent, DBSCAN and PDBSCAN are both sensitive to the initial parameters. In this paper, we propose a new hybrid algorithm based on PDBSCAN. We use modified ant clustering algorithm (ACA) and design a new partitioning algorithm based on ‘point density’ (PD) in data preprocessing phase. We name the new hybrid algorithm PACA-DBSCAN. The performance of PACA-DBSCAN is compared with DBSCAN and PDBSCAN on five data sets. Experimental results indicate the superiority of PACA-DBSCAN algorithm. [Copyright &y& Elsevier]

Abstract: Transfer learning aims to solve the problem that the training data from a source domain and the test data from a target domain follow different distributions. The feature-based method and the case-based method have been widely used in transfer learning. In this paper we propose a knowledge transfer method based on feature representation mapping from the source domain to the target domain. We first construct a new feature subspace, then build a feature representation mapping function and re-weight the source domain and the target domain data to minimize the distance between different distributions. As a result, with the new feature representations in this subspace, we can apply standard machine learning methods to train classifier models in the source domain for use in the target domain. Importantly, different from many previously proposed methods, we combine the feature-based method and the case-based method to construct the knowledge transfer model for solving text classification problems. The experimental results show that our algorithm greatly improves the classification performance over the traditional learning algorithms. [Copyright &y& Elsevier]

Abstract: Vertex coloring problem is a combinatorial optimization problem in which a color is assigned to each vertex of the graph such that no two adjacent vertices have the same color. Cellular learning automata (CLA) is an effective probabilistic learning model combining cellular automata and learning automata. Irregular cellular learning automata (ICLA) is a generalization of cellular learning automata in which the restriction of rectangular grid structure in traditional CLA is removed. In this paper, an ICLA-based algorithm is proposed for finding a near optimal solution of the vertex coloring problem. The proposed coloring algorithm is a fully distributed algorithm in which each vertex chooses its optimal color based solely on the colors selected by its adjacent vertices. The time complexity of the proposed algorithm is computed for finding a optimal solution of the vertex coloring problem in an arbitrary graph. To show the superiority of our proposed algorithm over the existing methods, simulation experiments have been conducted. The obtained results show that the proposed algorithm outperforms the others in terms of the required number of colors and running time of algorithm. [Copyright &y& Elsevier]

Abstract: Large amounts of high-dimensional data are distributed with the application of networks. Distributed clustering has become an increasingly important task due to variety of real-life constrains, including bandwidth and security aspects. Many distributed clustering algorithm have been proposed, but most of them have high transmission cost and poor clustering quality. In this paper, we propose a scalable local density-based distributed clustering algorithm which can easily fit high-dimensional data sets by this method such as density attractor distance and noise factor. In order to keep a lower transmission cost, we determine suitably low factor noises to send to the server. Furthermore, Test data sets, CMC data sets and KDD-CUP-99 are used for experimental evaluation to validate the performance practically. The experimental results and theoretical analysis show that the efficiency and quality for clustering of the proposed algorithm are superior to the other distributed clustering algorithm. [Copyright &y& Elsevier]

Abstract: Outlier detection has important applications in many fields in which the data can contain high dimensions. However, finding the intentional knowledge of outliers will become inefficient and even infeasible in high dimensional space. In this paper, we introduced the concept of rough set and used it as the model of outlier detection and analysis system to realize outlying reduction. Furthermore, by defining outlying partition similarity, we can mine the outliers in the key knowledge attribute subspace rather than in the full dimensional attribute space of dataset. An effective method for finding the key knowledge attribute subspace was proposed. It first finds all outliers in the full attribute space and then, calculates KAS for corresponding projection of each outlier. Finally, the key knowledge attribute subspace can be identified by the value of outlying partition similarity. The experimental results show that our method can be efficiently used in high dimensional dataset to identify outlier. [Copyright &y& Elsevier]

Abstract: This paper presents the hybrid harmony search algorithm with swarm intelligence (HHS) to solve the dynamic economic load dispatch problem. Harmony Search (HS) is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work is an attempt to hybridize the HS algorithm with the powerful population based algorithm PSO for a better convergence of the proposed algorithm. The main aim of dynamic economic load dispatch problem is to find out the optimal generation schedule of the generators corresponding to the most economical operating point of the system over the considered timing horizon. The proposed algorithm also takes care of different constraints like power balance, ramp rate limits and generation limits by using penalty function method. Simulations were performed over various standard test systems with 5 units, 10 units and 30 units and a comparative study is carried out with other recently reported results. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques. [Copyright &y& Elsevier]

Abstract: Frequent-itemset mining only considers the frequency of occurrence of the items but does not reflect any other factors, such as price or profit. Utility mining is an extension of frequent-itemset mining, considering cost, profit or other measures from user preference. Traditionally, the utility of an itemset is the summation of the utilities of the itemset in all the transactions regardless of its length. The average utility measure is thus adopted in this paper to reveal a better utility effect of combining several items than the original utility measure. It is defined as the total utility of an itemset divided by its number of items within it. The average-utility itemsets, as well as the original utility itemsets, does not have the “downward-closure” property. A mining algorithm is then proposed to efficiently find the high average-utility itemsets. It uses the summation of the maximal utility among the items in each transaction with the target itemset as the upper bound to overestimate the actual average utilities of the itemset and processes it in two phases. As expected, the mined high average-utility itemsets in the proposed way will be fewer than the high utility itemsets under the same threshold. The proposed approach can thus be executed under a larger threshold than the original, thus with a more significant and relevant criterion. Experimental results also show the performance of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: The support vector clustering (SVC) algorithm consists of two main phases: SVC training and cluster assignment. The former requires calculating Lagrange multipliers and the latter requires calculating adjacency matrix, which may cause a high computational burden for cluster analysis. To overcome these difficulties, in this paper, we present an improved SVC algorithm. In SVC training phase, an entropy-based algorithm for the problem of calculating Lagrange multipliers is proposed by means of Lagrangian duality and the Jaynes’ maximum entropy principle, which evidently reduces the time of calculating Lagrange multipliers. In cluster assignment phase, the kernel matrix is used to preliminarily classify the data points before calculating adjacency matrix, which effectively reduces the computing scale of adjacency matrix. As a result, a lot of computational savings can be achieved in the improved algorithm by exploiting the special structure in SVC problem. Validity and performance of the proposed algorithm are demonstrated by numerical experiments. [Copyright &y& Elsevier]

Abstract: Last few years have witnessed the development of various multi-objective evolutionary algorithms since they allow the generation of the overall Pareto front for multi-objective optimizations. With the problems in the real-world becoming more and more complex, however, no reported work in the literature focuses on the high-dimensional objective optimizations (HOPs). In this paper, we propose an evolutionary algorithm named HOEA (high-dimensional objective evolutionary algorithm) for HOPs. By adopting the concept of nonlinear definition for optimizing object, HOPs can be solved by HOEA, while the well-known multi-objective evolutionary algorithms work poorly on HOPs. We further analyze the nonlinear dynamic properties of HOEA on the basis of martingale theoretical framework. The theoretical results indicate that this new algorithm is indeed capable of achieving convergence. We also conduct experiments on HOEA with two representative test instances. The experimental results either confirm our theoretical results or show that the proposed algorithm is efficient and effective for HOPs. [Copyright &y& Elsevier]

Abstract: This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems. [Copyright &y& Elsevier]

Abstract: This paper presents an error detection methodology to enable fault detection inspired on recent immune theory. The fault detection problem is a challenging problem due to processes increasing complexity and agility necessary to avoid malfunction or accidents. The key challenge is determining the difference between normal and potential harmful activities. A promising solution is emerging in the form of Artificial Immune System (AIS). In this article, Natural Killer (NK) immune cells mechanisms inspired an AIS. The AIS proposed uses recent biological mechanism such as: NK activation and education machinery. DAMADICS benchmark was applied to compare the proposed AIS performance to others fault detection algorithms. The results show that the novel approach developed provides better detection rate and false alarms tradeoff when compared to other methods in literature. [Copyright &y& Elsevier]