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In this paper, we present a system for automatic evaluation of the quality of a question paper. Question paper plays a major role in educational assessment. The quality of a question paper is crucial to fulfilling the purpose of the assessment. In many education sectors, question papers are prepared manually. A prior analysis of a question paper might help in finding the errors in the question paper, and better achieving the goals of the assessment. In this experiment, we focus on higher education in the technical domain. First, we conducted a student survey to identify the key factors that affect the quality of a question paper. The top factors we identified are question relevance, question difficulty, and time requirement. We explored the strategies to handle these factors and implemented them. We employ various concepts and techniques for the implementation. The system finally assigns a numerical quality score against these factors. The system is evaluated using a set of question papers collected from various sources. The experimental results show that the proposed system is quite promising.

PurposeThis study develops an integrated zero waste discharge planning approach for improving resource efficiency in a pulp-and-paper manufacturing firm.Design/methodology/approachThe objectives of this study are to (1) identify the environmental, technical and social metrics in resource efficiency; (2) utilize fuzzy multi-objective programming and the hybrid particle swarm optimization algorithm to solve the fuzzy problem; and (3) develop an assessment for resource efficiency improvement in an industrial case study.FindingsThe findings demonstrate the superiority of hybrid particle swarm optimization algorithm in generating optimal results for a pulp-and-paper manufacturing firm.Practical implicationsThe findings demonstrate the superiority of hybrid particle swarm optimization algorithm in generating optimal results for a pulp-and-paper manufacturing firm.Originality/valueResource efficiency is a multi-objective problem in an uncertain environment. In particular, zero waste discharge planning involves minimizing the total cost and maximizing the waste material recovery rate, wastewater reuse, and waste heat recovery.

In society, there is widespread and growing acceptance of the value of cooperation between industry and universities being collaborative Research and Development (R&D) domain and industrial policy the fuel of knowledge-based economies. In this paper, different domains of cooperation between the University and Industry as well as their generated benefits for each partner are identified. These benefits have been assigned to each domain of cooperation, showing a large number of benefits associated to collaborative R&D. Leading us to make a statement - collaborative R&D is the key cooperation domain for sustainable long-term university-industry partnerships. Suggestions are given about how to continue this research study.

The recycling of waste paper has been an effective way to achieve the environmental-friendly growth of papermaking industry. Focusing on the mixed-pulping process which has been generally employed, to ensure the required properties of the deinking pulp (DIP) and minimize the purchase cost of waste paper, an intelligent model scheduling the mixing ratio of waste paper was developed in the study. Primarily, driven by the field data of mixing ratio of waste paper and DIP properties, the prediction models of DIP properties were developed by SVM, GA-SVM, and BP-NN algorithms. Subsequently, based on the best prediction model, the scheduling model for the mixing ratio of waste paper was set up by NSGAII algorithm. The experimental results showed that, based on the obtained best BP-NN prediction model of DIP properties, the developed NSGAII scheduling model for the ratio of waste paper could achieve excellent scheduling results under the rigorous constraints and multi-objective. Compared with the previous study, the developed prediction and scheduling models in this study reduced the purchase cost of waste paper by 2.16%, and improved the acceptable proportion of DIP property from 56.07% to 100%.

Abstract writing is a very important since it is the piece of formal writing which should attract the attention of the reader and make him/her read the rest of the paper. Thus, it must be clear, concise, coherent, and powerful enough to attract the reader and provide him/her with the key information about the content of a research paper. All university students have to write an abstract in English in their qualification papers. However, non-native students of English usually have difficulties in writing an abstract in English. For example, the Czech students lack formal writing skills in English since the skill of formal writing is not developed enough in their native language, i.e. Czech. Therefore, the aim of this article is to discuss the use of an off-line scaffolding tool, which can help students develop an abstract in English, as well as to demonstrate how this tool can be incorporated in the whole Course of Practical English taught at the Faculty of Informatics and Management of the University of Hradec Kralove in the Czech Republic. The application was developed for students as support to a face-to-face class instruction, which proved to be insufficient in teaching students how to write an abstract in English. The application reflects the structure and sentence functions of the abstract and provides students with a model abstract, as well as with additional formulations. Overall, the application was designed with the purpose to enhance student’s skill of writing an abstract in English, as well as to lead students to successfully complete the task by making the application user-friendly - easy-to-use, accessible and transferrable as far as the knowledge is concerned. The application aims to enhance students’ autonomous learning by tailoring the content to their specific needs and making it accessible anytime and from anywhere.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

International audience; This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

When conducting a search for research papers, the search should return comprehensive results related to the user’s query. In general, a user inputs a Boolean query that reflects the information need, and the search engine ranks the research papers based on the query. However, it is difficult to anticipate all possible terms that authors of relevant papers might have used. Moreover, general query-based ranking methods emphasize how to rank the relevant documents at the top of the results, but require some means of guaranteeing the comprehensiveness of the results. Therefore, two ranking methods that consider the comprehensiveness of relevant papers are proposed. The first uses a topic-based Boolean query search. This search converts every word in the abstract set and query into a topic via topic analysis by Latent Dirichlet Allocation (LDA) and conducts a search at the topic level. The topic assigned to synonyms of a search term is expected to be the same as that assigned to the search term. Each paper is ranked based on the number of times it is matched with each topic-based Boolean query search executed for various LDA parameter settings. The second is a hybrid method that emphasizes better results from our topic-based ranking result and a general query-based ranking result. This method is based on the observation that the paper sets retrieved by our method and by a general ranking method will be different. Through experiments using the NTCIR-1 and -2 datasets, the effectiveness of our topic-based and hybrid methods are demonstrated.

With the increasing advance of computer and information technologies, numerous research papers have been published online as well as offline, and as new research fields have been continuingly created, users have a lot of trouble in finding and categorizing their interesting research papers. In order to overcome the limitations, this paper proposes a research paper classification system that can cluster research papers into the meaningful class in which papers are very likely to have similar subjects. The proposed system extracts representative keywords from the abstracts of each paper and topics by Latent Dirichlet allocation (LDA) scheme. Then, the K-means clustering algorithm is applied to classify the whole papers into research papers with similar subjects, based on the Term frequency-inverse document frequency (TF-IDF) values of each paper.

Continuous production of an integrated pulp and paper mill depends on the availability of paper machines. Reliability and maintainability are essential for accomplishing the availability of paper machines. This study investigates the reliability, availability and maintainability (RAM) characteristics of a 120 tonnes per day paper machine from a paper mill in southern region of India. The paper machine is divided into five subsystems for failure analysis. The data was analyzed for trend and serial correlation tests for validating the inference of independent and identical distribution of failure and repair information before each sub system is best fitted to the theoretical probability distributions. Reliability and maintainability of the paper machine has been evaluated at various times which are useful for establishing the preventive maintenance schedules for improving availability of the paper machine. Wire part is found to be more critical subsystem from all points of view and require improvement by 4% and 1.5% in maintainability and availability point of view. The results of the analysis are presented to maintenance department for their active consideration.Keywords: Paper machine, reliability, maintainability, availability, mean time between failures (MTBF), mean time to repair(MTTR), independent and identically distributed(IID)

Cyber-physical systems theory offers a powerful framework for modeling, analyzing, and designing real engineering systems integrating communication, control, and computation functionalities (the cyber part) within a natural and/or man-made system governed by the laws of physics (the physical part). New methodological developments in cyber-physical systems theory are required by traditional application domains such as manufacturing, transportation, and energy systems, which are currently experiencing significant and – to some extent – revolutionary changes to address the needs of our modern society. The goal of this position paper is to provide the cyber-physical systems community, and especially young researchers, a clear view on what are research directions worth pursuing motivated by the challenges posed by modern applications.

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

Prediction and detection of skin diseases have always been a challenging and critical task for health care professionals. There are many clinics and skin practitioners who are offering their services at exorbitant costs. At the same time, skin condition prevails in our country in every part. In the current scenario majority of the skincare practitioners are using traditional methods to diagnose the disease which may take a considerable amount of time. Skin Diseases are serious problems in recent times as it is a matter of environmental factors, socioeconomic factors, lack of full diet, etc. This paper is a comparative study about understanding various skin diseases related to normal skin issues as well as cosmetology. A comparison between oily, dry, and normal skin is also projected in this study. A survey of different papers is done on basis of technologies used, results with accuracy, ethical conduct, number of diseases diagnosed, datasets, etc are done.

The metal casting industry, Nitikan is a micro business whose production process is carried out manually. The equipment used is simple and without considering the user's health. This can be seen in the product finishing process that uses a sanding machine. The equipment is not designed according to the user's posture so the operator bends in doing work movements, of course this is very risky to experience lower back pain injuries. This study aims to analyze the work activities of the finishing process using the Rapid Entire Body Assessment (REBA) and Rapid Upper Limb Assessment (RULA) approach and design work tools that can be applied to improve work posture. The results of data processing and analysis obtained a Rapid Entire Body Assessment score on a sandpaper machine single head of 5 and a Rapid Upper Limb Assessment score of 4, the score indicates that the work position has the potential to experience a risk of low back pain, so there must be an improvement in work posture. Work posture improvement is done by designing a tool in the form of a sandpaper machine that takes into account ergonomic dimensions to reduce the risk of lower back injury. The final result after the improvement is obtained Rapid Entire Body Assessment score of 3 and Rapid Upper Limb Assessment score of 4. This shows that the risk of lower back injuries can be minimized.

This paper describes approaches to the use of wearables and related monitoring and diagnostic tools to glean and present data to patients and their caregivers in the form of Personal Health Dashboard. Raw data from devices such as fitness watches, blood glucose and blood pressure monitors can be analyzed by machine learning algorithms ensembles over the cloud or at the edge, and the resulting patterns can be presented to allow for actionable responses. The advent of low-cost computational, sensing, monitoring and diagnostic tools allow for cost-effective solutions that can have wide adaptability. The paper draws from prior and ongoing research conducted by the team to present these positions in the advancement of healthcare.

Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles' files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%.

Aiming at the problem of easy tampering and difficult integrity authentication of paper text documents, this paper proposes a robust content authentication method for printed documents based on text watermarking scheme resisting print-and-scan attack. Firstly, an authentication watermark signal sequence related to content of text document is generated based on the Logistic chaotic map model; then, the authentication watermark signal sequence is embedded into printed paper document by using a robust text watermarking scheme; finally, the watermark information is extracted from scanned image of paper document, and compared with the authentication watermark information calculated in real time by the text document content obtained by OCR technology, thereby performing content integrity authentication of the paper text documents. Experimental results show that our method can achieve the robust content integrity authentication of paper text documents, and can also accurately locate the tampering position. In addition, the document after embedding the watermark information has a good visual effect, and the text watermarking scheme has a large information capacity.

Nowadays, recommender system has become one of the main tools to search for users’ interested papers. Since one paper often contains only a part of keywords that a user is interested in, recommender system returns a set of papers that satisfy the user’s need of keywords. Besides, to satisfy the users’ requirements of further research on a certain domain, the recommended papers must be correlated. However, each paper of an existing paper citation network hardly has cited relationships with others, so the correlated links among papers are very sparse. In addition, while a mass of research approaches have been put forward in terms of link prediction to address the network sparsity problems, these approaches have no relationship with the effect of self-citations and the potential correlations among papers (i.e., these correlated relationships are not included in the paper citation network as their published time is close). Therefore, we propose a link prediction approach that combines time, keywords, and authors’ information and optimizes the existing paper citation network. Finally, a number of experiments are performed on the real-world Hep-Th datasets. The experimental results demonstrate the feasibility of our proposal and achieve good performance.

Internet of Things (IoT) devices are increasingly being found in civilian and military contexts, ranging from smart cities and smart grids to Internet-of-Medical-Things, Internet-of-Vehicles, Internet-of-Military-Things, Internet-of-Battlefield-Things, etc. In this paper, we survey articles presenting IoT security solutions published in English since January 2016. We make a number of observations, including the lack of publicly available IoT datasets that can be used by the research and practitioner communities. Given the potentially sensitive nature of IoT datasets, there is a need to develop a standard for sharing IoT datasets among the research and practitioner communities and other relevant stakeholders. Thus, we posit the potential for blockchain technology in facilitating secure sharing of IoT datasets (e.g., using blockchain to ensure the integrity of shared datasets) and securing IoT systems, before presenting two conceptual blockchain-based approaches. We then conclude this paper with nine potential research questions. Keywords: Blockchain, Blockchain security, Collaborative security, Internet-of-military things, IoT dataset, IoT self-healing, IoT security, Intrusion-prevention system, Predictive IoT security, Predictive security

Proportional–Integral–Derivative controller technique continues to provide the easiest and effective solutions to most of the industrial applications in recent years. However PID controller is poorly tuned in practice compared to most other tuning methods and is complicated with poor performance. This research presents a multi objective optimization approach involving Genetic Algorithm, Evolutionary Programming, Particle Swarm Optimization and Bacterial foraging optimization. The proposed multi objective optimization algorithm is used to tune the PID controller parameters and their performances have been compared with the conventional methodologies like Ziegler Nichols method. The results proved that the use of multi objective optimization approach based controller tuning improves the performance of process in terms of time domain specifications and performance index, set point tracking and regulatory changes and also provides stability. This paper describes the various multi objective optimization algorithms and its implementation to tune the PID Controller used in paper machine DCS as real time processing of a Pulp and paper industry processes.

The security of critical infrastructures is of paramount importance nowadays due to the growing complexity of components and applications. This paper collects the contributions to the industry dissemination session within the 14th International Conference on Critical Information Infrastructures Security (CRITIS 2019). As such, it provides an overview of recent practical experience reports in the field of critical infrastructure protection (CIP), involving major industry players. The set of cases reported in this paper includes the usage of serious gaming for training infrastructure operators, integrated safety and security management in the chemical/process industry, risks related to the cyber-economy for energy suppliers, smart troubleshooting in the Internet of Things (IoT), as well as intrusion detection in power distribution Supervisory Control And Data Acquisition (SCADA). The session has been organized to stimulate an open scientific discussion about industry challenges, open issues and future opportunities in CIP research.

Electryo is a paper-based voting protocol that implements the Selene mechanism for individual verifiability. This short paper aims to provide the first formal model of Electryo, with security proofs for vote-privacy and individual verifiability. In general, voting protocols are complex constructs, involving advanced cryptographic primitives and strong security guarantees, posing a serious challenge when wanting to analyse and prove security with formal verification tools. Here we choose to use the \({{\,\mathrm{\textsc {Tamarin}}\,}}\) prover since it is one of the more advanced tools and is able to handle many of the primitives we encounter in the design and analysis of voting protocols.

Success management can bring valuable contributions to improve project results and stakeholders’ relationships. However, the success management process must be explicitly integrated into project management approaches, guides and methodologies (such as PMBOK, PRINCE2 or PM2), which is not currently happening. The purpose of this position paper is to discuss the need and importance for this integration and to present a first proposal on how this can be operationalized. To support the position, the methodology of the European Union PM2 was used. The result of the integration demonstrates that it is possible to advance management and raise the level of project success.

PurposeThe industry is relying on the preventive maintenance techniques that can minimize failures and provide industrial plants with effective equipment, but in many companies the maintenance tasks are performed very frequently and not as per plan and do not take into consideration the conditions of the plant and equipments. The failure of each and every component needs to be studied in order to choose the best maintenance strategy. This paper presents a fuzzy VIKOR (Multicriteria Optimization and Compromise Solution) technique which is used in developing a comprehensive approach for maintenance strategy selection in the Deinking plant of the paper industry to choose the appropriate maintenance strategy thereby reducing the unnecessary cost incurred on the maintenance.Design/methodology/approachIn this paper, the Fuzzy VIKOR based methodology was applied for determining the maintenance criticality index of the deinking plant of the paper industry. The effect of failure of components were evaluated by three maintenance experts on five performance criteria that is chance of failure, chance of non-detection, downtime length, severity, spare part criticality. The components were ranked according to the maintenance criticality index and thereby implementing the appropriate maintenance strategy.FindingsThe Fuzzy VIKOR technique was applied to calculate the ranking of various components of paper industry based on the views and judgment of three maintenance experts. The proposed technique suggested the appropriate maintenance strategy for various components taking into consideration the maintenance criticality index of the components.Originality/valueThe proposed technique will help the maintenance managers to solve a discrete problem with non-commensurable and conflicting criteria. The study will help the industries to reduce the unnecessary maintenance tasks and thereby reduce the maintenance cost. This will help the maintenance practitioners in choosing the best and most effective strategy for the organization with regard to the market and company situation especially in the changing business requirement of Industry 4.0.

Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation. Refereed/Peer-reviewed

We present a mixed reality-based assistive system for shading paper sketches. Given a paper sketch made by an artist, our interface helps inexperienced users to shade it appropriately. Initially, using a simple Delaunay-triangulation based inflation algorithm, an approximate depth map is computed. The system then highlights areas (to assist shading) based on a rendering of the 2.5-dimensional inflated model of the input contour. With the help of a mixed reality system, we project the highlighted areas back to aid users. The hints given by the system are used for shading and are smudged appropriately to apply an artistic shading to the sketch. The user is given flexibility at various levels to simulate conditions such as height and light position. Experiments show that the proposed system aids novice users in creating sketches with impressive shading.

At present, the tissue paper manufacturing is mostly based on the dry crepe technology. During the last decade, the manufacturers have introduced new tissue machines concepts that increase the softness, bulk, and absorption capacity. Such machines produce a strong regular three-dimensional (3D) structure to the sheet before the Yankee cylinder. At present, the quality of the 3D structure is not evaluated, or it is evaluated only subjectively at the mill. This is mostly because of the difficulties to separate reliably the regular 3D pattern from other variations. This paper introduces a frequency analysis based method which separates the surface profile variances in tissue paper to the creping, to the regular 3D pattern and to the residual variation. The 3D surface profiles and their variances were determined online with the photometric stereo method. We show that the introduced analysis method evaluates the variance portions reliably and the results are consistent with the visual perception of the 3D surfaces. In one particular product, the regular 3D pattern explains 74 % of total surface variance; the creping explains 10 % and residual variations 16 %. Furthermore, the creping and residual variances are quite stable over time whereas the variance of the regular 3D pattern fluctuates significantly.

Purpose Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers. Design/methodology/approach This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords. Findings Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich. Originality/value This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

PurposeHow to provide the most useful papers for searchers is a key issue for academic search engines. A lot of research has been carried out to address this problem. However, when evaluating the effectiveness of an academic search engine, most of the previous investigations assume that the only concern of the user is the relevancy of the paper to the query. The authors believe that the usefulness of a paper is determined not only by its relevance to the query but also by other aspects including its publication age and impact in the research community. This is vital, especially when a large number of papers are relevant to the query.Design/methodology/approachThis paper proposes a group of metrics to measure the usefulness of a ranked list of papers. When defining these metrics, three factors, including relevance, publication age and impact, are considered at the same time. To accommodate this, the authors propose a framework to rank papers by a combination of their relevance, publication age and impact scores.FindingsThe framework is evaluated with the ACL (Association for Computational Linguistics Anthology Network) dataset. It demonstrates that the proposed ranking algorithm is effective for improving usefulness when two or three aspects of academic papers are considered at the same time, while the relevance of the retrieved papers is slightly down compared with the relevance-only retrieval.Originality/valueTo the best of the authors’ knowledge, the proposed multi-objective academic search framework is the first of its kind that is proposed and evaluated with a group of new evaluation metrics.

In this paper, we analyze the intelligent subdesign of the simulated marking system through an in-depth study of it. This paper proposes a correlation analysis-based quantification of N-element sense values and a rationality enhancement-based scoring fitting algorithm for English essays. This paper also extracts word features, sentence features, and chapter structure features in essays to fit English composition scores. Since not all students can complete the essays according to the topic requirements, a triage scoring model is used to separate the normal essays from the low-scoring essays. Statistically, it was found that the essay scores also showed a certain normal distribution. The standard support vector regression algorithm is prone to data skewing problems, so this paper addresses this problem by using a rationality enhancement method that gives a corresponding penalty factor according to the distribution of the dataset. The results show that the English essay scoring fitting algorithm proposed in this paper can well improve the prediction accuracy of some data and solve the problem of skewed data where the scores show a normal distribution. This paper designs and implements an online mock examination system that incorporates an intelligent scoring system for essays, enabling it to meet the needs of teachers and students for online examinations and intelligent scoring.

Basic repair and maintenance tasks of industrial machines are usually performed by local service personnel at the location of the component to be maintained. Complex maintenance tasks require an expert who travels to the customer to solve a specific problem. This process is time and cost intensive. Augmented Reality (AR) Remote Assistance offers means to support newcomers on site by guiding personnel through the maintenance process with novel visualizations. This paper describes an Augmented Reality based remote assistance tool for industrial purposes. We present the technology and our implementation approach, as well as the challenges and limitations we have encountered. The potential of the tool was evaluated and analyzed in an industry oriented use case for repairing an industrial PC. The results were compared with a maintenance procedure that was carried out using standard paper repair instructions. Our approach enabled the users to perform complex tasks faster and with fewer errors. Overall, the error rate decreased significantly due to the AR-based remote support. We also found that paper-based instructions are still more efficient in solving simple tasks.

Marked mix-nets were introduced by Pereira and Rivest as a mechanism to allow very efficient mixing that ensures privacy but at the cost of not guaranteeing integrity. This is useful in a number of e-voting schemes such as STAR-Vote and Selene. However, the proposed marked mix-net construction comes with no proof of security and, as we show in this paper, does not provide privacy even in the presence of a single corrupt authority. Fortunately, the attack that we present is easy to prevent and we show several possible ways to address it. Finally while the original marked mix-net paper worked with ElGamal, we identify conditions that the adopted encryption scheme should satisfy in order to be appropriate for a marked mix-net. This opens the possibility of building marked mix-nets based on intractability assumptions which are believed to hold in the presence of a quantum computer.

© 2013 IEEE. With the rapid growth of scientific publications, it is hard for researchers to acquire appropriate papers that meet their expectations. Recommendation system for scientific articles is an essential technology to overcome this problem. In this paper, we propose a novel low-rank and sparse matrix factorization-based paper recommendation (LSMFPRec) method for authors. The proposed method seamlessly combines low-rank and sparse matrix factorization method with fine-grained paper and author affinity matrixes that are extracted from heterogeneous scientific network. Thus, it can effectively alleviate the sparsity and cold start problems that exist in traditional matrix factorization based collaborative filtering methods. Moreover, LSMFPRec can significantly reduce the error propagated from intermediate outputs. In addition, the proposed method essentially captures the low-rank and sparse characteristics that exist in scientific rating activities; therefore, it can generate more reasonable predicted ratings for influential and uninfluential papers. The effectiveness of the proposed LSMFPRec is demonstrated by the recommendation evaluation conducted on the AAN and CiteULike data sets.

With the rapid growth of scientific publications, research paper recommendation which suggests relevant research papers to users can bring great benefits to researchers. In this paper, we focus on the problem of recommending conference papers to the conference attendees. While most of the related existing methods depend on the content-based filtering, we propose a unified conference paper recommendation method named $CPRec$ , which exploits both the contents and the authorship information of the papers. In particular, besides the contents, we exploit the relationships between a user and the authors of a paper for recommendation. In our method, we extract several features for a user-paper pair from the citation network, the coauthor network, and the contents, respectively. In addition, we derive a user’s pairwise preference towards the conference papers from the user’s bookmarked papers in each conference. Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user’s preference towards a paper based on the extracted features. We conduct a recommendation performance evaluation using real-world data and the experimental results demonstrate the effectiveness of our proposed method.

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context. info:eu-repo/semantics/publishedVersion

Educational evaluation helps to improve the learning and teaching process. The learning process can be assessed by the examination system. In the examination system, the learners have to answer a set of questions. The learning assessment ensures improved performance in the educational system. The success of the learning assessment is highly dependent on the listening and understanding ability of each student. The assessment of the question paper can be at two levels—cognitive level assessment and difficulty level. The cognitive level assessment is purely based on Bloom’s Taxonomy and the difficulty level assessment is purely based on the examination marks of each student. This article presents a technical method called Question Paper’s Difficulty Level (QPDL) that helps to identify the difficulty level of each question in the question paper. The QPDL method is incorporate in the implementation of ‘QAUDIT’ web application to assess the difficulty level of the question paper systematically. The systematic analysis to identify the difficulty level of the question paper is made for 239 question papers. Each question paper can be recognized based on three difficulty levels such as High, Moderate and Low.

Finding relevant papers is a non-trivial problem for scholars due to the tremendous amount of academic information in the era of scholarly big data. Scientific paper recommendation systems have been developed to solve such problem by recommending relevant papers to scholars. However, previous paper recommendations calculate paper similarity based on hand-engineered features which are inflexible. To address this problem, we develop a scientific paper recommendation system, namely VOPRec, by vector representation learning of paper in citation networks. VOPRec takes advantages of recent research in both text and network representation learning for unsupervised feature design. In VOPRec, the text information is represented with word embedding to find papers of similar research interest. Then, the structural identity is converted into vectors to find papers of similar network topology. After bridging text information and structural identity with the citation network, vector representation of paper can be learned with network embedding. Finally, top- $Q$ Q recommendation list is generated based on the similarity calculated with paper vectors. Through the APS data set, we show that VOPRec outperforms state-of-the-art paper recommendation baselines measured by precision, recall, F1, and NDCG.

With the arrival of the Internet and social media, at the same time as masses of humans have benefitted from the full-size reassets of records available, there was an full-size boom with inside the upward push of cyber-crimes, mainly targeted closer to women. According to a 2019 file with inside the Economics Times, India has witnessed a 457% upward push in cybercrime with inside the 5 years span among 2011 and 2016. Most speculate that that is because of effect of social media inclusive of Facebook, Instagram and Twitter on our day by day lives. While those simply assist in growing a legitimate social network, advent of consumer debts in those websites normally desires simply an email-id. A actual lifestyles man or woman can create more than one fake IDs and for this reason impostors can effortlessly be made. Unlike the actual international state of affairs in which more than one policies and guidelines are imposed to become aware of oneself in a completely unique manner (as an instance at the same time as issuing one’s passport or driver’s license), with inside the digital international of social media, admission does now no longer require this kind of checks. In this paper, we study the one-of-a-kind debts of Instagram, specifically and try and verify an account as fake or actual the use of Machine Learning strategies specifically Logistic Regression and Random Forest Algorithm.

When it comes to leaked credentials and credit card information, we observe the development and use of anti-fingerprinting browsers by malicious actors. These tools are carefully designed to evade detection, often by mimicking the browsing environment of the victim whose credentials were stolen. Even though these tools are popular in the underground markets, they have not received enough attention by researchers. In this paper, we report on the first evaluation of four underground, commercial, and research anti-fingerprinting browsers and highlight their high success rate in bypassing browser fingerprinting. Despite their success against well-known fingerprinting methods and libraries, we show that even slightest variation in the simulated fingerprint compared to the real ones can give away the presence of anti-fingerprinting tools. As a result, we provide techniques and fingerprint-based signatures that can be used to detect the current generation of anti-fingerprinting browsers.

This paper describes a user identification method conducted across online social networks (OSNs) using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names. This latter method encounters the problem of decreased identification accuracy if a user registers different display names across OSNs. Our proposed method aims at addressing this issue by utilizing information on friends’ locations. We convert location information extracted from an OSN to that of such geographic administrative units as country, state and city, and calculate the weighted occurrence frequency of a location pair on the basis of distance between the pair. This is based on the hypothesis that a friend list relatively rarely includes an account pair whose locations are extremely distant from one another. We calculate a similarity score between two accounts’ weighted occurrence frequencies for each geographic administrative unit. Finally, we identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit. Evaluation experiments show that our proposed method improves performance over that of a conventional method (accuracy: 83.1%$\rightarrow$91.2%).

Several applications can benefit from recording information about the places a mobile entity visits and the length of time it spends there (e.g., shoppers, employees, buses, movable equipment, autonomous robots, etc.). In this paper, we present our approach to recording spatio-temporal information in a secure and inviolable way using a Distributed Ledger Technology. So far in this work, we observe that, although sending data to the blockchain is a heavy process, there are ways that this transaction time does not disrupt the flow of new data such as adding these data to a queue. However, this makes the sending process overall longer. We intend to continue this study by testing different blockchain services and comparing their performances.

This work develops a multi-objective linear programming model for a multi-echelon, multi-product, multi-period supply chain planning. This model considers all dimensions of sustainable development paradigm simultaneously and expands six objective functions for making the model realistic. To solve this model and large-scale sustainable supply chain management, an improved version of the augmented e-constraint method (AUGMECON2) is used. Computational results illustrate the proposed model admitted various progresses in all the three pillars of sustainable development; namely remarkable progress, mildly progress and irregular one, respectively. In addition, important managerial insights are provided. Trade-off interactions between multi-objectives are perceived by the obtained Pareto solutions that represents the cost of being sustainable from the point of optimizing the social factors and environmental impacts. An application to the paper industry is presented and discussed.

We demonstrate that XOR Arbiter PUFs with an even number of arbiter chains have inherently biased responses, even if all arbiter chains are perfectly unbiased. This rebukes the believe that XOR Arbiter PUFs are, like Arbiter PUFs, unbiased when ideally implemented and proves that independently manufactured Arbiter PUFs are not statistically independent.

Recommendation approaches generally fail to recommend newly-published papers as relevant, owing to the lack of prior information about the said papers and, more particularly, problems associated with cold starts. It would appear, to all intents and purposes, that researchers currently interact more on social networks than they normally would in academic circles, and relationships of a purely academic nature have witnessed a paradigm shift, in keeping with this new trend. In existing paper recommendation methods, the social interaction factor has yet to play a pivotal role. The authors propose a social network-based research paper recommendation method, that alleviates cold start problems by incorporating users' social interaction, as well as topical relevancy, among assorted papers in the Mendeley academic social network using a novel approach, random walk Ergodic Markov Chain. The system yields improved results after cold start alleviation, compared with the existing system.

Efficient methods for solving economic load dispatch (ELD) problems have been provided to build effective multi-objective evolutionary algorithms (MOEAs). ELD is a dynamic bi-objective optimization problem that is strictly limited. The MOEA applications to solve the ELD problems have been published in various publications since the 1990s. This paper explores the state of the art of this direction-related research. It addresses subjects such as standard MOEAs, traditional ELD problems, dynamic ELD problems, wind power integration ELD problems, electric car integration ELD problems and micro-grid ELD problems. Furthermore, several potential directions are also provided for future study.

The paper Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009 received the 2020 SIGEVO Impact Award for ten-year impact, which was announce at the ACM GECCO 2020 conference. The work compares the performance of 31 algorithms that had been benchmarked with the Comparing Continuous Optimizer platform (COCO) for the Black-Box-Optimization Benchmarking (BBOB) GECCO workshop in 2009.

Speech driven facial animation can be regarded as a speech-to-face translation. Speech driven facial motion synthesis involves Speech analysis and face modeling. This method makes use of still image of a person and speech signals to produce an animation of a talking character. Our method makes use of GAN classifier to obtain better lip synchronizing with audio. GAN methodology also helps to obtain realistic facial expressions thereby making a talking character more effective. Factors such as lip-syncing accuracy, sharpness, and ability to create high -quality faces and natural blinks are taken into consideration by this system. GANs are mainly used in case of image generation as adversarial loss generates sharper and more depictive images. Along with images, GANs can also handle videos easily.

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.

Our proposed method focuses on offline friends who are acquainted with a target user in the real world. We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to distinguish between online friends of a target user who are also offline friends and online friends who are acquainted with the target user only in cyberspace. By utilizing the probability of friendship as a function of the geographical distance between users’ home locations, our proposed method achieves an estimation accuracy 1.7 percentage point better than that of a conventional method. We have also been able to verify the effectiveness of an optimization method that narrows down candidate areas for a target user’s home location.

The present work presents a hybrid identification of recursive least squares with a metaheuristic called radial movement optimization (RMO) applied to the joints of a cylindrical robotic manipulator. The main contribution of the research is to consider the covariance matrix with RMO. At the end a comparative analysis will be made with some classic methods. The results of the identifications were tested with the RMSE. A covariance matrix has also been generated from manipulator identifications.