Reviewerly/exHarmony huggingface dataset:
Getting Formal Ontologies Closer to Final Users Through Knowledge Graph Visualization: Interpretation and Misinterpretation

Knowledge Graphs are extensively adopted in a variety of disciplines to support knowledge integration, visualization, unification, analysis and sharing at different levels. On the other side, Ontology has gained a significant popularity within machine-processable environments, where it is extensively used to formally define knowledge structures. Additionally, the progressive development of the Semantic Web has further contributed to a consolidation at a conceptual level and to the consequent standardisation of languages as part of the Web technology. This work focuses on customizable visualization/interaction, looking at Knowledge Graphs resulting from formal ontologies. While the proposed approach in itself is considered to be scalable via customization, the current implementation of the research prototype assumes detailed visualizations for relatively small data sets with a progressive detail decreasing when the amount of information increases. Finally, issues related to possible misinterpretations of ontology-based knowledge graphs from a final user perspective are briefly discussed.
A Study of Online Electronic Voting System Based Blockchain

As the offline paper voting, which has been conducted until recently, continues to be a problem in terms of security and reliability, the interest in online electronic voting with increased safety and convenience is increasing. Several countries around the world are now adopting and activating online electronic voting. However, existing electronic voting has not been officially introduced in most countries due to interdependence and procedural security flaws. If 100% of these interdependencies and security can be trusted, the high-cost paper voting method will gradually decrease. Therefore, in this paper, blockchain technology is applied to compensate for the problem of electronic voting. This system can verify and hold blocks independently using a P2P method without a central authority to prevent falsification with reliability. In addition, by removing temporal and spatial constraints, the voter turnout can be increased and voting is possible directly on smartphones and PCs.
Real-Time and Robust Visual Tracking with Scene-Perceptual Memory

Aerial visual tracking is one of the research hotspots in computer vision. However, the main-stream trackers for UAV still have two shortcomings: (1) the accuracy-speed trade-off ofcorrelation filter tracker. (2) object occlusion and camera movement also seriously restrictthe application of aerial tracking. To address these problems, and inspired by the start-of-artAutoTrack tracker, we propose an aerial correlation filtering tracker based on scene-perceptualmemory, fast-AutoTrack. Firstly, to perceive and judge tracking anomalies, a confidence score isdesigned by perceiving and remembering the changing trend of the confidence and the localhistorical confidence. Furthermore, several search regions are predicted based on the localobject motion trend and the Spatio-temporal context information for object re-detection. Onthree typical aerial tracking datasets UAVDT, UAV123@10fps, and DTB70, Fast-AutoTrackrun 71.4% faster than AutoTrack with almost equal accuracy and show favorable accuracy-speedtrade-off.
Dynamics in the Development of Digital Competence of Bulgarian Teachers

The current case study presents a short overview of the development and dynamics of the teachers’ digital competence in the last two decades. The content analysis has identified a lot of legislative documents at a national level, related to the pre-service and in-service teachers’ professional development, including in the field of ICT. At the same time, there is a need for quality assurance of the training in ICT at the university level and during the continuous teachers’ professional development. This could be achieved by top-level regulation, recommendation, or standards on assessing teacher-specific digital competence before they enter into the teaching profession. The quality of continuous teachers’ qualifications should also be investigated to ensure the development of teachers’ digital competence and a smooth transition from face-to-face to distance learning, if needed. It is recommended for the educational institutions to conduct a training need assessment before training with the pre-service and in-service teachers so that to propose more individualized learning paths and to ensure the development of every teacher's professional digital competence.
Strategies for Addressing Prosopagnosia as a Potential Solution to Facial Deepfake Detection

The detection of deep fakes simulating human faces for potentially nefarious purposes is an ongoing and evolving topic of interest. Research in prosopagnosia, or face-blindness, have shown that specific parts of the face, and their movement, provide more clues for identification to subjects with the condition. This paper outlines studies in the area of detecting and addressing the effects of prosopagnosia. For the first time, we suggest that the findings of these studies could be applied to the detection of deep fake faces, drawing a link between the facial features and movements most useful in combating the effects of prosopagnosia, with the features most productive for analysis in deep fake facial detection.
IntStream: Towards Flexible, Expressive, and Scalable Network Telemetry

Due to the complexity of the network structure and the high growth of the transmission speed, the measurement and management of the network are facing serious challenges. The traditional bottom-up network telemetry methods are no longer applicable to complex network scenarios. To bridge this gap, we propose IntStream, a flexible, expressive and scalable network telemetry framework to allow network operators to measure and analyze network through passive stream processing and active probing. However, there are three key challenges to building an intent-based telemetry system: (1) The diversity of network data sources. (2) The complexity of the measurement tasks. (3) The low overhead requirements of the telemetry system. IntStream introduces a lightweight component to extract and parse data from various data sources and divides the data stream processing into local and global stages. IntStream provides a set of rich expressive primitives to support operators to write telemetry tasks based on intent. By performing part of the telemetry task on the local stage, the transmission overhead of intermediate data can be effectively reduced. The evaluation results conducted on a large campus network show that IntStream can support a wide range of telemetry tasks while reducing the intermediate data transmission overhead by 99.31% on average.
Digital Competencies of Czech Pre-service Teachers: Review Study

The aim of this chapter is to explore the level of digital competency (DC) among students of the faculties of education in Czech Republic based on previous research. For this purpose, a systematic analysis of the literature (peer-reviewed articles, proceedings from conferences) indexed in repositories and scientific databases such as Google Scholar, EBSCO, Scopus, and Web of Science was used. Based on data from 2001–2021 on Czech students, these being future educators, it was noted that (1) the occurrence of studies regarding to digital competence is very rare in Czech Republic; (2) there is problem in core definition in this region of digital competency concept. The concept of digital literacy is often used as synonym of digital competence; (3) the authors published not only in scientific journals but also in proceedings of conferences; (4) the part of studies is regarding to analysis of text; (5) studies are conducted in different institutions; (6) the level of digital competence is most often described as good; and (7) part of the studies had got comparative character, mainly with neighbored countries and the level of DC is similar.
Forensic Profiling of "Cryptocurrency" Criminals

The fraud triangle is well-known and has been applied for many years in explaining a variety of white-collar crimes. Equally, the Dark Triad of psychological traits has been applied in definitions of individuals who exhibit a range of criminal activities. In this paper, the concept of financial fraud, including Ponzi schemes, is analysed against the types of individuals acting in this deception. First, it is proposed that a Ponzi triangle consisting of three separate classes of actors is required for a successful pyramid scheme or other fraudulent investment schemes. The various parties include the initial promoter, which derives from individuals who actively seek out fraud. Next, average individuals fall within the 10-80-10 definition of individuals who act dishonestly if given the opportunity and believe they wonâ€™t get caught form the majority of deceived people. Finally, it is noted that ambivalence must exist with regard to either regulators or law enforcement. The regulators either fail to act because of ignorance of the problem or fear of acting and disrupting a novel industry.
The False Dichotomy of Usability and Aesthetics in the Field of HCI Design

The profession of Human-Computer Interface (HCI) Design has a history of considering usability and aesthetics as separate aspects of a design. The dichotomy has perpetuated a common split in the profession between the subspecialities of Interaction Design and Visual Design, or a split between who designs the logic and who designs the look. This split creates more opportunity for diminished synergy between the logic and the visual design. The concept of "wrapping wireframes in a visual design" reflects a narrow view of aesthetics as focused on branding, style, and visual appeal [1]. Aesthetics plays a role in everything we experience, including usability, and this broad view is more in line with designing a whole user experience [2–4]. What is ignored by not considering usability and aesthetics as fundamentally integrated might be the deep harmony of logical order, the simple elegance of minimal options, or the perfect marriage of shape and behavior. Three reasons for the dichotomy of usability and aesthetics in the HCI profession might be: 1.) Using computers is much more complicated than using non-computer products. 2.) The HCI field has been historically filled with people from mostly science-based backgrounds that don't value aesthetics. 3.) HCI designers lack the power to assert aesthetic priorities in the way that designers can in Architecture or Industrial Design due to business and engineering priorities. Now that computation is escaping the limitations of the GUI, gaining varied physical embodiments, mobility, intelligence, even personalities, it is time embrace a broad view of aesthetics.
A Novel Framework for Detection of Face Mask Using R-CNN During This COVID-19

In recent times when we are in a pandemic and people all over the world are being advised to wear masks, some people are not used to it and avoid or refuse to wear masks. AI helps to identify whether people wearing a mask or not wearing a mask in public places would be helpful for our safety. If implemented correctly, the mask detector could help ensure our safety. We have achieved an accuracy of around 93% with a lower error rate.
A BERT-Based Model for Question Answering on Construction Incident Reports

Construction sites are among the most hazardous workplaces. To reduce accidents, it is required to identify risky situations beforehand, and to describe which countermeasures to put in place. In this paper, we investigate possible techniques to support the identification of risky activities and potential hazards associated with those activities. More precisely, we propose a method for classifying injury narratives based on different attributes, such as work activity, injury type, and injury severity. We formulate our problem as a Question Answering (QA) task by fine-tuning BERT sentence-pair classification model, and we achieve state-of-the-art results on a dataset obtained from the Occupational Safety and Health Administration (OSHA). In addition, we propose a method for identifying potential hazardous items using a model-agnostic technique.
Sparsha-Ar an Android Application for the Visualization of Aero Engine Components’ Assemblies and Sub-Assemblies Using Augmented Reality Environment

In this project we are applying the concept of Augmented Reality (AR) to display the Aero engine components’ assemblies and sub assemblies. We are displaying those components in Android Smartphone in more attractive way by enabling the user to interact with those components’ using Android Smartphone and Windows PC. Also, as part of the project we have made it possible to display the information of respective model along with voice read out complete description of model simultaneously.
Coded Hate Speech Detection via Contextual Information

Hate speech on online social media seriously affects the experience of common users. Many online social media platforms deploy automatic hate speech detection programs to filter out hateful content. To evade detection, coded words have been used to represent the targeted groups in hate speech. For example, on Twitter, “Google” is used to indicate African-Americans, and “Skittles” is used to indicate Muslim. As a result, it would be difficult to determine whether a hateful text including “Google” targets African-Americans or the search engine. In this paper, we develop a coded hate speech detection framework, called CODE, to detect hate speech by judging whether coded words like Google or Skittles are used in the coded meaning or not. Based on a proposed two-layer structure, CODE is able to detect the hateful texts with observed coded words as well as newly emerged coded words. Experimental results on a Twitter dataset show the effectiveness of our approach.
Towards Open Zero-Shot Learning

In Generalized Zero-Shot Learning (GZSL), unseen categories (for which no visual data are available at training time) can be predicted by leveraging their class embeddings (e.g., a list of attributes describing them) together with a complementary pool of seen classes (paired with both visual data and class embeddings). Despite GZSL is arguably challenging, we posit that knowing in advance the class embeddings, especially for unseen categories, is an actual limit of the applicability of GZSL towards real-world scenarios. To relax this assumption, we propose Open Zero-Shot Learning (OZSL) as the problem of recognizing seen and unseen classes (as in GZSL) while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided. We formalize the OZSL problem introducing evaluation protocols, error metrics and benchmark datasets. We also tackle the OZSL problem by proposing and evaluating the idea of performing unknown feature generation.
Paradigmatic Shifts in Education: Causes, Effects, and Risks

The main purpose of this work is to substantiate the essential and semantic, structural and content, and functional understanding and explanation of the paradigm and the paradigm shift in education; also, to identify the deterministic foundations of the paradigmatic shift, the parameters of its manifestation, and the potential of riskiness in modern education. An approach to bringing the practice of paradigmalization of education to a common denominator has been substantiated. Based on the understanding of education as an area of functioning of a pedagogical science and the sphere of educational practice, two types of paradigms are determined. They are pedagogical and educational, as well as the corresponding types of paradigmatic shifts in education. It is shown that the basis of paradigmatic shifts in education is radically changing the conditions of personal life and aggravated contradictions in society in the process of its cultural, scientific, informational, political, and economic development. The paradigmatic niches in education as an area of practice (its cognitive, value-worldview, and technological professional-pedagogical bases) are identified, concerning which, first of all, shifts are recorded. It is substantiated that at different stages of the development of society, individual paradigmatic niches can act as the dominant basis that subordinates the entire educational process and the institution of education as a whole. Based on empirical data (analysis of changes in value judgments about education and knowledge, education, teacher, family, and school), it is shown that in modern education, the dominant paradigmatic niche that configures the shift is the value and worldview base of teachers, students, and their parents. Possible risks for the institution of education are revealed due to the effects of a paradigmatic shift in the absence or lack of their scientific reflection, and also without taking their consequences into account.
Synthesis for image analysis across modalities

Inter-modality imaging problems are ubiquitous in medical imaging. The acquisition of multiple modalities – or different variations of the same modality – is frequent in many clinical applications and research problems. Inter-modality image analysis techniques have been developed to cope with the differences between modalities, but they do not always perform as well as their intra-modality counterparts. Image synthesis can be used to bridge this gap, by making one of the two modalities resemble the other, and using intra-modality methods. It is often the case that the error in the synthesis is outweighed by the improvement in performance given by switching from intra- to inter-modality analysis. In this chapter, we present a survey of methods based on this idea, with a focus on two of the most representative problems in medical image analysis, registration and segmentation.
Trustworthiness Models to Categorize and Prioritize Code for Security Improvement

The exploitation of software security vulnerabilities can have severe consequences. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing costs and shortening the time to market. In this paper, we propose an approach that uses quality evidencesÂ to feed a set of trustworthiness models, which allow characterizing code from a security perspective. In practice, the goal is to identify the code units that are more prone to be vulnerable, thus helping developers to improve their code. A clustering-based approach is used to categorize the code units based on the combination of the scores provided by several trustworthiness models and taking into account the criticality of the code. To instantiate our proposal, we use a dataset of software metrics (e.g., CountLine, Cyclomatic Complexity, Coupling Between Objects) for files and functions of the Linux Kernel and Mozilla Firefox projects, and a set of machine learning algorithms to build the trustworthiness models. Results show that code that is more prone to be vulnerable can be effectively distinguished, thus demonstrating the applicability and usefulness of the proposed approach in diverse scenarios.
A New Approach for Acute Lymphocytic Leukemia Identification Using Transfer Learning

Leukemia a blood cancer, especially acute lymphocytic leukemia (ALL) is one of the most deadly diseases that affects adults and children, causing early deaths. Computerized software was then used to minimize chances of recommending improper medications. To prevent the deadly effects of leukemic disease, it become necessary to develop an automated and robust classification system. Existing methods related to leukemia classification were based on several segmentation techniques. In this paper, a fully automated classification system is proposed based on a pretrained transfer learning deep networks like AlexNet, and VGG16 architecture is applied. Out of which, AlexNet is used as classifier, whereas VGG16 is used as classifier and modified as feature extractor, where the feature vector extracted from VGG16 is given to support vector machine (SVM) classifier as input. Experimental comparison analyzes that our approach outperformed the performance against literature work. Experiments were performed on an ALL-IDB dataset which verified that the VGG performance is better than the AlexNet due to 100% accuracy of classification.
Metric Spaces

The classical $$\varepsilon $$ - $$\delta $$ definition of continuity makes sense once there is a concept of ‘distance’. So, it is natural to begin a study of topology with a look at spaces with a distance function: metric spaces.
A New Fitness Function for Evolutionary Music Composition

In this paper we propose a new fitness function for Evolutionary Computation purposes, based on a weighted by neighborhood average distance between two sequences of points within any metric space. We will apply this fitness function to the field of Computer-Assisted Composition focusing on the problem of thematic bridging, consisting in the evolutionary creation of a soft set of transitions between two given different melodies, the initial and the final one. Several self-adaptive strategies will be used to perform the search. A symbolic melody will be genotypically mapped into a sequence of genes, each of then containing the information of duration, frequency and time distance to following note. We will test the implementation of the fitness function by means of two experiments, showing some of the intermediate melodies generated in a successful run, and benchmarking every experiment with performance indicators for any of the three distinct evolutionary strategies implemented. The results prove this novel fitness function to be a quick and suitable way for individual evaluation in genetic algorithms.
Enhancing Clustering Performance Using Topic Modeling-Based Dimensionality Reduction

Mainly in the present times, the description of the services and their working procedure have been established in natural text language. We have obtained service groups based on their similarities to reduce search space and time in service innovation. Major topic models such as LSA, LDA, and CTM policies have not been able to show effective performance due to the short description and limited description of services in text form, the reduction or absence of words that occur. To solve the issues created by brief text, the Dirichlet Multinomial Mixer model (DMM) with features representation using the Gibbs algorithm has been developed to reduce dimensionality in clustering and enhance performance. The launch results prove that DMM-Gibbs can give better results than all other methods with agglomerative or K-means clustering methods by sampling. Evaluations with internal and external criteria were used to calculate clustering performance based on these two values. Using this standard model, the dimensionality can be reduced to 93.13% and better clustering performance can be achieved.