Reviewerly/exHarmony huggingface dataset:
Knowledge-Based Approach to Detect Potentially Risky Websites

Nowadays, fraudulent and malicious websites are emerging as a harmful and very common problem on the Internet. It causes huge money losses and irreparable damage for both companies and particulars. To face this situation, governments have approved multiple law projects. This way, the legality on the Internet is being enforced and sanctions to those offenders who develop illegal or malicious activities are being imposed. However, governments still need a way to simplify the classification of websites into risky or non-risky, since most of this work is manual. This paper presents the DOmains Classifier based on RIsky Websites (DOCRIW) framework to detect domains that contain possible fraud or malicious content. It is based on two main components. The first component is a previously built knowledge base containing information from risky websites. The second one complements the system with a binary classifier able to label a website (as risky or not) considering just its domain. The system makes use of web information sources and includes host-based variables. It also applies similarity measures, supervised learning algorithms and optimization methods to enhance its performance. The presented work is experimental, rendering promising outcomes.
Using Eye-Tracking Data to Compare Differences in Code Comprehension and Code Perceptions between Expert and Novice Programmers

Previous research has examined how eye-tracking metrics can serve as a proxy for directly measuring the amount of cognitive effort and processing required for comprehending computer code.We conducted a pilot study comprising expert (n = 10) and novice (n = 10) computer programmers to examine group differences in code comprehension abilities and perceptions.Programmers were asked to read two pieces of computer code, rate the code on various attributes, and then describe what the code does.Results indicate that experts and novices significantly differ in terms of their fixation counts made during the task, such that experts had more fixations than novices.This was counter to our hypothesis that experts would have fewer fixations than novices.We found no evidence that experts and novices differed in their average fixation durations, trustworthiness and performance perceptions, or willingness to reuse the code.
Detection and Recognition of Moving Video Objects: Kalman Filtering with Deep Learning

Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications.This paper presents an approach for object recognition in videos by combining Kalman filter with CNN.Kalman filter is first applied for detection, removing the background and then cropping object.Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks.After detection and cropping the moving object, a CNN model will predict the category of object.The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers.The first layer, which is the input image, is of 100 * 100 size.The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling.The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames.In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms.
White-Collar Cybercrime: Evaluating the Redefinition of a Criminological Artifact

White-Collar Cybercrime: Evaluating the Redefinition of a Criminological Artifact Christopher Hamerton Abstract This paper explores the cause and effect of cybercrime from the perspective of what has been termed white-collar cybercrime, providing a layered analysis of established theoretical models and typologies and evaluating these to determine where white-collar cybercrime might fit within the evolving discipline of cybercriminology and wider interdisciplinary social sphere. White-collar crime itself offers the rare example of a criminological theory that has the attributes of an artifact - establishing a distinct criminal offence type within law and criminal justice and entering mainstream knowledge and terminology within half a century of inception. Despite this, white-collar cybercrime is a relatively new concept for cyber criminological analysis and is currently a rarity within the academic literature. Thus, the piece primarily seeks to compliment and expand recent scholarship in offering further critical evaluation of an important emergent model. This is done in terms of its history, evolution, characteristics, position within social change theory, and via examination of some of the many policy, practice and security challenges that appear inherent to the modern networked workplace. Full Text: PDF DOI: 10.15640/jlcj.v8n2a6
Movie Recommender System

This paper focuses upon EDA on IMDB data and building of a recommendation system using Content and Collaborative based recommender system based on K nearest neighbours and NLP techniques.
Automated handwriting analysis based on pattern recognition: a survey

&lt;span&gt;Handwriting analysis has wide scopes include recruitment, medical diagnosis, forensic, psychology, and human-computer interaction. Computerized handwriting analysis makes it easy to recognize human personality and can help graphologists to understand and identify it. The features of handwriting use as input to classify a person’s personality traits. This paper discusses a pattern recognition point of view, in which different stages are described. The stages of study are data collection and pre-processing technique, feature extraction with associated personality characteristics, and the classification model. Therefore, the purpose of this paper is to present a review of the methods and their achievements used in various stages of a pattern recognition system. &lt;/span&gt;
Influence of Innovative Technologies on the Ecosystem of Additional Professional Education

In recent years, digital technologies have been rapidly developing in the field of additional professional education, which offer greater freedom of choice of educational programs, reduce the time and financial costs of listeners, and increase the speed of information transfer. In the context of the COVID-19 coronavirus pandemic, interest in e-learning forcedly increased, since classes in the classroom form familiar to students of additional professional education programs became impossible for a while. The question arises of how, in the absence of prohibitions and restrictions, the classical and electronic forms of education should be combined. The authors draw attention to the ecosystem approach in the field of continuing professional education, in which the key role is assigned to the customer of the educational service. This article is a logical continuation of a series of scientific publications by the authors devoted to innovative ecosystems, including in education. The authors highlight the key elements of the innovative ecosystem of additional professional education, determine the role and significance of each of them, give specific examples of the implementation of the ecosystem approach in the field of additional professional education.
Application-based Evaluation of Automatic Terminology Extraction

The aim of this paper is to evaluate performance of several automatic term extraction methods which can be easily utilized by translators themselves.The experiments are conducted on German newspaper articles in the domain of politics on the topic of Brexit.However, they can be easily replicated on any other topic or language as long as it is supported by all three tools used.The paper first provides an extensive introduction into the field of automatic terminology extraction.Next, selected terminology extraction methods are assessed using precision with respect to the gold standard compiled on the same corpus.Moreover, the corpus has been completely annotated to allow for the calculation of recall.The effects of using five cut-off points are examined in order to find an optimal value which should be used in translation practice.
Security Bug Report Usage for Software Vulnerability Research: A Systematic Mapping Study

Context: Security bug reports are reports from bug tracking systems that include descriptions and resolutions of security vulnerabilities that occur in software projects. Researchers use security bug reports to conduct research related to software vulnerabilities. A mapping study of publications that use security bug reports can inform researchers on (i) the research topics that have been investigated, and (ii) potential research avenues in the field of software vulnerabilities. Objective: The objective of this paper is to help researchers identify research gaps related to software vulnerabilities by conducting a systematic mapping study of research publications that use security bug reports. Method: We perform a systematic mapping study of research that use security bug reports for software vulnerability research by searching five scholar databases: (i) IEEE Xplore, (ii) ACM Digital Library, (iii) ScienceDirect, (iv)Wiley Online Library, and (v) Springer Link. From the five scholar databases, we select 46 publications that use security bug reports by systematically applying inclusion and exclusion criteria. Using qualitative analysis, we identify research topics investigated in our collected set of publications. Results: We identify three research topics that are investigated in our set of 46 publications. The three topics are: (i) vulnerability classification; (ii) vulnerability report summarization; and (iii) vulnerability dataset construction. Of the studied 46 publications, 42 publications focus on vulnerability classification. Conclusion: Findings from our mapping study can be leveraged to identify research opportunities in the domains of software vulnerability classification and automated vulnerability repair techniques.
Leading FAIR Adoption Across the Institution: A Collaboration Between an Academic Library and a Technology Provider

Universities strive to foster knowledge sharing and greater research productivity. Some recognize that this requires research output to be findable, accessible, interoperable and reusable. But current tools do not yet allow a comprehensive adoption of these FAIR principles for making research openly and globally accessible to generate new knowledge. To address this gap, diverse stakeholders are collaborating to build effective research data management [RDM] solutions for institutional research output [publications and data] that benefit researchers, institutions, and developers. This paper illustrates a university-industry collaboration between a private U.S. university (Drexel University) and a global commercial vendor (Ex Libris, a ProQuest company). The authors examine how an emerging technology infrastructure for Research Data Management will enable librarians to help institutions adopt the FAIR principles at scale. They highlight an approach for collaborative product development that aims not to change researcher habits or add to librarians’ workloads. Their first year working together confirms factors recognized as contributing to successful collaborations, such as aligning goals, building understanding of each other’s organizations, and sustaining honest engagement. Though FAIR offers a simple articulation to help build campus infrastructure and change culture, its implementation will vary between different groups of researchers. Libraries and technology providers have a mutual interest in collaborating to address RDM challenges, but must recognize that collaboration takes time, perseverance, and flexibility to effect change. Librarians, researchers, and administrators from such campus offices as Research, Compliance, IT, Legal, and Graduate Studies will benefit from key lessons raised by this case study.
Blackbox Testing Model Boundary Value Of Mapping Taxonomy Applications and Data Analysis of Art and Artworks

The classification of artistic expertise in an area on products and actors of art greatly affects the progress of artistic life. One method of classifying cultural data is the taxonomic method. In the taxonomic method, an art product can be categorized into several domains. For example, the product of Kawung (Indonesian) batik cloth can be included in the domains of fashion, philosophy, and fine arts. An example from the taxonomy of art actors, for example, an artist can have various expertise in music, dance, fine arts, or others. The source of information used to classify this research is the big data of art actors in Malang, Indonesia. Big data is obtained from art actors directly who provide input from the instrument about the suitability of the art field with the expertise possessed by each of them. Individual artists generally have more than one artistic skill which can be classified taxonomically and ranked using fuzzy clustering. The purpose of ranking with fuzzy clustering is to determine the weight of artistic skills starting from the level just done to the most proficient to do. To achieve accurate weighing results, a taxonomy application for mapping and data analysis of artists and works of art was created. This research discusses functional testing (black-box testing) of the taxonomy application of mapping and data analysis on web-based artists and artworks.
Image Compression Based on Arithmetic Coding Algorithm

The past years have seen a rapid development in the area of image compression techniques, mainly due to the need of fast and efficient techniques for storage and transmission of data among individuals. Compression is the process of representing the data in a compact form rather than in its original or incompact form. In this paper, integer implementation of Arithmetic Coding (AC) and Discreet Cosine Transform (DCT) were applied to colored images. The DCT was applied using the YCbCr color model. The transformed image was then quantized with the standard quantization tables for luminance and chrominance. The quantized coefficients were scanned by zigzag scan and the output was encoded using AC. The results showed a decent compression ratio with high image quality.
The investigation of the mediating role of innovation and organizational intelligence on the relationship between knowledge management and financial performance of the organizations active in the e-banking industry

Knowledge management is essential for activities such as problem-solving, dynamic learning and decision making, and can improve organizational performance and in particular the financial performance of the organizations by enabling the organization to perform smarter. The main purpose of this study was to investigate the impact of knowledge management on the financial performance of the organizations considering the mediating role of innovation and organizational intelligence in the e-banking industry. The statistical population of the study consisted of senior executives of 31 public and private banks active in the e-banking industry of Iran (n=54). Library and field research (questionnaire) was used to collect research data. In this study, Kolmogorov-Smirnov and Spearman-Bartlett tests were used to analyze the data to determine the normality of the research data and to determine the adequacy of the sample size (using SPSS software). Then, structural equation modeling was used to analyze the conceptual model and to test the hypotheses (using Smart PLS software). The hypotheses test results showed that knowledge management has a positive and significant impact on the financial performance of the organizations active in the e-banking industry and the innovation and organizational intelligence influence the relationship between knowledge management and the financial performance of the organizations. Thus, the mediating role of these two variables (innovation and intelligence) was confirmed.
Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences

Finding good correspondences is a critical prerequisite in many feature based tasks. Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier and estimates relative pose through classified correspondences. First, we analyze that due to the imbalance in the number of correct and wrong correspondences, the loss function has a great impact on the classification results. Thus, we propose a new Guided Loss that can directly use evaluation criterion (Fn-measure) as guidance to dynamically adjust the objective function during training. We theoretically prove that the perfect negative correlation between the Guided Loss and Fn-measure, so that the network is always trained towards the direction of increasing Fn-measure to maximize it. We then propose a hybrid attention block to extract feature, which integrates the Bayesian attentive context normalization (BACN) and channel-wise attention (CA). BACN can mine the prior information to better exploit global context and CA can capture complex channel context to enhance the channel awareness of the network. Finally, based on our Guided Loss and hybrid attention block, a cascade network is designed to gradually optimize the result for more superior performance. Experiments have shown that our network achieves the state-of-the-art performance on benchmark datasets. Our code will be available in https://github.com/wenbingtao/GLHA.
A citation recommendation method based on context correlation

Researchers need to formulate their achievements as research papers. Representative references are essential to high-quality papers. Academic citation recommendation refers to providing the recommendation of citations for the author of papers when they write. With the help of citation recommendation, researchers can improve the efficiency of writing academic papers and reduce the omission of important related literature. To achieve this goal, some methods were proposed. Many of them used citation networks to learn the representation of papers and chose references, they tended to ignore the content properties of papers. There are also some methods used partial properties to recommend citation. But their performance can be further improved. In this paper, we propose a citation recommendation method based on context correlation. We use two neural network models to learn the representations of papers and their references, then calculate the context similarity of them. Besides, we also introduce the publishing time and authority of papers, two key properties of papers for citation evaluation. In the experiment section, we compare our method with other methods and evaluate the performance of different properties choice in our method, it shows that our method outperforms some baselines and the combination of the dimensions including time, authority and context performs better.
Beyond deep fakes: Conceptual framework, applications, and research agenda for neural rendering of realistic digital faces

Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces.NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data.NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someone's face for deception or satire.In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes.We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases.We conclude with research ideas for studying the potential applications and implications of NR-based digital characters.
Virtual Visitation: Conceptualization and Metrics for Digital Twinning of Large Interactive Social Events

The COVID-19 pandemic has accelerated a move towards digital engagement in many parts of life.At the same time, it has halted large public gatherings such as music, sports or arts events.In this paper we discuss opportunities for such large-scale social events to create digital experiences that mirror, mimic or enhance traditional experiences, with new forms of digital twinning.Originating from the world of manufacturing, and popularized by the Industry 4.0 initiative, digital twins refer to the creation of digital representations of physical entities.In the context of large social events digital twins denote digital spaces inhabitable by visitors with their own digital twins in the form of avatars.In this paper we investigate how event coordinators can account for different kinds of social engagements in digital twinning spaces.We develop a framework for user interactions along two dimensions: interactions with the space itself, and social interactions between virtual visitors (avatars).Accounting for virtual visitations is more challenging but offers unique opportunities compared to traditional attendance.We present four metrics, Views, Visits, Active Visits, and Interactive Visits, and we discuss considerations for implementing advanced digital social events.
Real-time Motion Planning Based on Layered Cost Map for AGV Navigation

A motion planning method that integrates global planning and local control is proposed in this paper, aiming to enable Automated Guided Vehicle (AGV) to navigate automatically in two-dimensional environment. The layered cost maps are utilized and divided into global cost map and local cost map to respectively provide environment information for the global planning and local control. Specifically, the global planning is based on the path planning algorithm to establish a global optimal path on the global cost map. Under the guidance of global path, local control generates an optimal collision free trajectory with temporal information on the local cost map, which can avoid contact with surrounding obstacles. The local control operates in real-time and consider temporal information, thereby it can directly generate commands for the underlying robot motion controller. Both the simulation and experimental results are presented in support of the feasibility and effectiveness of the proposed motion planning.
Face Recognition Comparative Analysis Using Different Machine Learning Approaches

The problem of a facial biometrics system was discussed in this research, in which different classifiers were used within the framework of face recognition.Different similarity measures exist to solve the performance of facial recognition problems.Here, four machine learning approaches were considered, namely, K-nearest neighbor (KNN), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Principal Component Analysis (PCA).The usefulness of multiple classification systems was also seen and evaluated in terms of their ability to correctly classify a face.A combination of multiple algorithms such as PCA+1NN, LDA+1NN, PCA+ LDA+1NN, SVM, and SVM+PCA was used.All of them performed with exceptional values of above 90% but PCA+LDA+1N scored the highest average accuracy, i.e. 98%.