Reviewerly/exHarmony huggingface dataset:
Violence Detection in Surveillance Video using Computer Vision Techniques

The aim of the project is to come up with alarm just in case of abnormal activities and to help human operators and for offline review. A challenge is to detect/develop intelligent video systems capable of mechanically analyzing and investigate violence within the scene. Such capability could also be extraordinarily helpful in some videos police investigation eventualities like in prisons, medical specialty centers or perhaps in sensitive areas within the town.I.
Development of Decision Support System for Quality Management of University Education

The subject of the study is the process of studying at University.The purpose of the research is the development of an information system, which could record all the events related to the process of obtaining education for a particular student at all its stages.The article describes the main information systems out both Russian and foreign.The analysis of the considered systems allows concluding that none of the proposed products allows for an in-depth analysis and forecasting of a possible option for the development of the educational process.A number of works on the issues of building a competency model of a graduate were studied, including mathematical models and approaches to evaluate learning outcomes.Based on the research of developments in this area, we can conclude that the available information systems make it possible to solve individual problems associated with the support of the educational process, there is also no methodology for the design and synthesis of such ISs, taking into account their integration into existing ISs at universities.The paper proposes the use of system analysis methods in order to: obtaining the structure of an IS for analysis and management of the competence-building process; the study of the design object, methods and means of its interaction with the environment; identification of functional subsystems implementing individual tasks, their structural elements that implement individual information processing and analysis algorithms; identification of functional relationships in the form of interacting input and output information flows of system elements; determining the order and logic of their interaction in the implementation of individual tasks of the information system.The article proposes to use the developed information system for analysis and management to identify common system properties and the main laws of information processing in assessing the competence of graduates taking into account professionally significant personal qualities.
Create Your Rational Cybersecurity Success Plan

This has been Rational Cybersecurity for Business: The Security Leaders’ Guide to Business Alignment. I’ve made this guidance as detailed and specific as possible because all too often, we get only platitudes or generalizations on the topic. We can’t afford that anymore. Misalignment between security and the business has a corrosive effect on any security effort it touches. And as organizations transform into digital businesses, they fall under increasing IT-related risk and regulation. Aligning security to business leaders and business processes is exponentially more important now.
A request allocation model for processing data in federated cloud computing

Purpose As the storage and processing requirement of digital information is increasing on the cloud, it is very difficult for the single cloud provider (CP) to meet the resource requirement. Multiple providers form a federation for the execution of users’ requests. For the federated cloud, this paper aims to address the issue distribution of users’ request for resources and revenue among the providers by offering fair and stable distribution models for the federated cloud. Design/methodology/approach This paper uses cooperative game (CG)-theoretical models, i.e. Shapley–Shubik power index (SSPI) and Banzhaf power index (BPI) for distribution. Performance is analysed using variance and monotonicity using a case study. Findings Numerical analysis is done using two scenarios. Monotonicity is evaluated. Results show that SSPI performs better as compared to BPI in terms of fairness accuracy and the framework provide the fair distribution of revenue among providers in the federated cloud. Research limitations/implications The proposed framework works efficiently under the specific defined conditions. Social implications Paper provides the fair distribution. It assist the centralised cloud exchange in managing the users’ request in such a way every CPs, in the federated cloud will get an equal chance of serving the users’ request. The framework also provides the stable federation. Proposed work provides less rejection rate of users’ request. Finally, it assists the providers in increasing their profits in the federation. Originality/value This paper presents a CG theoretic-based framework for the distribution of resources required and revenue. The framework analysed the performance of distribution models by considering the variance and monotonicity for multiple users’ requests.
Study of Convolutional Neural Networks for Global Parametric Motion Estimation on Log-Polar Imagery

The problem of motion estimation from images has been widely studied in the past. Although many mature solutions exist, there are still open issues and challenges to be addressed. For instance, in spite of the well-known performance of convolutional neural networks (CNNs) in many computer vision problems, only very recent work has started to explore CNNs to learning to estimate motion, as an alternative to manually-designed algorithms. These few initial efforts, however, have focused on conventional Cartesian images, while other imaging models have not been studied. This work explores the yet unknown role of CNNs in estimating global parametric motion in log-polar images. Despite its favourable properties, estimating some motion components in this model has proven particularly challenging with past approaches. It is therefore highly important to understand how CNNs behave when their input are log-polar images, since they involve a complex mapping in the motion model, a polar image geometry, and space-variant resolution. To this end, a CNN is considered in this work for regressing the motion parameters. Experiments on existing image datasets using synthetic image deformations reveal that, interestingly, standard CNNs can successfully learn to estimate global parametric motion on log-polar images with accuracies comparable to or better than with Cartesian images.
KNOWLEDGE DISTILLATION USING GANS FOR FAST OBJECT DETECTION

Abstract. In this paper, we propose a new method for knowledge distilling based on generative adversarial networks. Discriminator CNNs is used as an adaptive knowledge distilling loss. In experiments, single shot multibox detector SSD based on MobileNet v2 and ShuffleNet v1 are used as student networks. Our tests showed AP and mAP improvement of more than 3% on PascalVOC and 1% on MS Coco datasets compared with the baseline algorithm without any architecture or dataset changes. The proposed approach is general and can be used not only with SSD but also with any type of object detection algorithms.
PORTING ARDUPILOT TO ESP32: TOWARDS A UNIVERSAL OPEN-SOURCE ARCHITECTURE FOR AGILE AND EASILY REPLICABLE MULTI-DOMAINS MAPPING ROBOTS

Abstract. In this article, we are interested in the implementation of an open-source low-level architecture (critical system) adapted to agile and easily replicable close-range remote sensing robots operating in multiple evolution domains. After reviewing the existing autopilots responding to these needs, we discuss the available hardware solutions and their limits. Then, we propose an original solution (software and hardware) that we developed to obtain a universal low-level architecture for all our exploration robots, whatever their environment of evolution, and the steps needed to make it run on our chosen family of micro-controllers: the ESP32. Finally, we present the operational results obtained on our different platforms (land, surface, submarine and air), their limits and the envisaged perspectives.
Educate to Keep It Great: The Long-Term Successes of a Virtual Great Barrier Reef Education Program

The Great Barrier Reef (GBR) is under pressure from climate change and other threats, and part of the solution is to raise awareness of the GBR and its threats beyond a local audience (Great Barrier Reef Marine Park Authority, 2019).Reef HQ Aquarium (the national education centre for the Great Barrier Reef Marine Park Authority) implemented a virtual marine education program focussed on the GBR in 1998.The program uses videoconferencing technology to connect real-time marine biology experts to participants around the world.We report on > ten years of participant trends for this program to assess whether the program is a mechanism for increasing community educational outreach.Since 2005, the program increased the Aquarium's community and educational outreach by 4.8% by connecting an additional 83 388 participants from 19 countries worldwide.While the videoconference technology has evolved overtime, the program content remains a unique, interactive virtual experience that has proven to be an effective way for an informal learning institute to increase their outreach on a national and international level, particularly as there has been a transition to online learning and educational programs due the recent global Coronavirus pandemic.
Multi-Domain Recognition of Hand-Drawn Diagrams Using Hierarchical Parsing

This paper presents an approach for the recognition of multi-domain hand-drawn diagrams, which exploits Sketch Grammars (SkGs) to model the symbols’ shape and the abstract syntax of diagrammatic notations. The recognition systems automatically generated from SkGs process the input sketches according to the following phases: the user’ strokes are first segmented and interpreted as primitive shapes, then by exploiting the domain context they are clustered into symbols of the domain and, finally, an interpretation of whole diagram is given. The main contribution of this paper is an efficient model of parsing suitable for both interactive and non-interactive sketch-based interfaces, configurable to different domains, and able to exploit contextual information for improving recognition accuracy and solving interpretation ambiguities. The proposed approach was evaluated in the domain of UML class diagrams obtaining good results in terms of recognition accuracy and usability.
Improving Students’ Achievement in Writing Descriptive Paragraph through Teaching Prewriting Stages

This study was aimed to find out whether or not Teaching Prewriting Stages can improve students’ ability in writing descriptive paragraphs, to describe the role of prewriting stages in helping the students to write descriptive paragraphs. This study was conducted by using Classroom Action Research. It was done through 1) planning, 2) action, 3) observation, and 4) reflection. The population and the sample was the ninth grade students of SMP Swasta BUDISATRYA at 2018/2019 academic year. This research applied quantitative and qualitative method. Quantitative data were taken from the students' writing descriptive paragraph test. Qualitative data were taken from observation, interview, and diary notes. The result of the research shows that there was some improvement of students’ ability in writing descriptive paragraphs through teaching prewriting stages. It can be proved from the students' score which improved from cycle 1 to cycle 2. The improvement can be seen that in pre test the means score was 33.52. In the first cycle, meeting 1 the means score was 37.35, in meeting 2 was 42.64 and in meeting 3 was 52.64. In the second cycle, meeting 1 the means score was 64,41, in meeting 2 was 80 and in meeting 3 was 91.17. The improvement also can be seen from the percentage of the students’ achievement in writing descriptive paragraph by using prewriting stage; in pre test, no one of the students got 75 points. In the first cycle, in meeting 1, and meeting 2, there was also no one student got 75 point, but in meeting 3 there was 23,52% (8 students) got 75 points. It means there was an improvement about 23.52 % . In the second cycle, in meeting 1 there was 29.41% (10 students) got 75 points, it means that there was an improvement about 5.89%. In meeting 2 there was 79.41% (27 students) got 75 points, it means that there was an improvement about 50%. In meeting 3 there was 100% (34 students) got up 75 points. It means that prewriting stages helped the students to write descriptive paragraphs through brainstorming, listing supporting point, making a simple outline to write event, place or object related to the topic. So, the students’ achievement in writing descriptive paragraph is significantly improved by applying prewriting stage.
OBJECT DETECTION IN UAV-BORNE THERMAL IMAGES USING BOUNDARY-AWARE SALIENCY MAPS

Abstract. In this paper, we propose a method of object detection based on thermal images acquired from unmanned aerial vehicles (UAV). Compared with visible images, thermal images have lower requirements for illumination conditions, but they have some problems, such as blurred edges and low contrast. To address these problems, we propose to use the saliency map of thermal images for image enhancement as the attention mechanism of the object detector. In the paper, the YOLOv3 network is trained as a detection benchmark and BASNet is used to generate saliency maps from the thermal images. We fuse the thermal images with their corresponding saliency maps through the pixel-level weighted fusion method. Experiment results tested on real data have shown that the proposed method could realize the task of object detection in UAV-borne thermal images. The statistical results show that the average precisions (AP) of pedestrians and vehicles are increased by 4.5% and 2.6% respectively, compared with the benchmark of the YOLOv3 model trained on only the thermal images. The proposed model provides reliable technical support for the application of thermal images with UAV platforms.
Combating political hate speech online

Chapter 12 explains the most important, and common, methods of combating political hatred online. First and foremost, the analysis is dedicated to understanding the network, its nature and its dangers, evaluating whether new regulatory work is really necessary. Subsequently, the study tries to understand how the positive side of the network could be used to make the most of its power against hatred. Furthermore, the chapter addresses the very current problem of institutional tones taken by online politicians, and whether law enforcement activity should also take in ‘education’ of the politician and their presence on the net. The final section discusses the topics of self-regulation, the obligation for providers to remove content and the political choice between control of speech or freedom in the fight against hatred.
Expressing (Un)certainty through Modal Verbs in Advance Fee Fraud Emails

Advanced fee fraud is a common form of online financial scam on the Internet. Previous researchers have attempted a broad study of the nature and narrative features of this scam.Â The current study attempts a linguistic analysis of 200 samples of advanced fee scam emails and examines specifically how modal auxiliary verbs are used to express certainty or uncertainty in the scam. For instance, modal verbs are commonly used to direct, promise, threaten and even serve politeness functions in scam emails. Deontic modal verbs such as â€œwillâ€ is found to be the most frequent verb in the data and is manipulatively deployed at speaker-oriented level of discourse to express commitment on the part of the speaker and impose some obligation such as ensuring the interaction proposed in the letters remain secret.Â Findings from the study reveal that the manipulative use of modal verbs contributes to the success of the proposed scam.
Pedestrian Re-identification Model based on Joint learning Method

A pedestrian re-identification(Re-ID) model based on joint learning method is proposed. This paper proposed a two-branch model including global feature branch and global feature branch. The global feature branch uses the spatial pyramid pooling model to average the local feature informations of the image and increase the global information of different receptive fields. The local feature branch uses the dynamical distance optimization algorithm without additional information. Experimental results on public datasets show that the proposed model can effectively learn global and local features for Re-ID and outperforms existing basic person re-identification methods.
SUPPLY CHAIN INTEGRATION AND SUPPLY CHAIN PERFORMANCE: THE ROLE OF KNOWLEDGE SHARING AS A MEDIATOR

The purpose of this paper is to examine the mediating role of knowledge sharing in the relationship between supply chain integration and supply chain performance. This study used a survey questionnaire for a sample size of 277 managers from various sectors in the manufacturing industry in Jordan. This study utilised PLS Structural Equation Modeling for testing the hypothesis. The finding indicates a significant positive relationship between supply chain integration and supply chain performance and that knowledge sharing plays partial mediation in this relationship. This study provides an important implication on the role of knowledge sharing. The performance of the supply chain can be maximised if the supply chain partners share knowledge among them. This will expedite the process of delivering the products to the customers. Awareness among partners on the importance to share and utilise knowledge better should be raised. The study contributes to the research on supply chain management by advancing the understanding of the role of knowledge sharing that can increase the performance of the supply chain partners. Keywords: Supply chain integration, supply chain management, knowledge sharing, supply chain performance, supplier integration.
Analysis of DNA Image Encryption Effect by Logistic&amp;#x2013;Sine System Combined with Fractional Chaos Stability Theory

Abstract To explore the DNA image encryption method based on the Logistic‐sine system and the fractional-order chaos stability theory, a fractional-order fuzzy differential equation is first introduced to construct a chaotic synchronization system. Then the green, blue, and red primary color matrix is established to design new DNA image encryption, and the encryption process is explained. Next, a data encryption algorithm and an advanced encryption algorithm are introduced to perform simulation experiments on the MATLAB 2014 software platform. It is found that the images encrypted by the new algorithm all exhibit striped snowflakes, and after decryption, it is almost the same as the original image. The histogram of the image encrypted by the new algorithm is flat, which is very different from the original image histogram. The average pixel change rate of the image encrypted by the new algorithm is 99.6267%, and the average change intensity reaches 33.5183%. The average information entropy of the image encrypted by the new algorithm is 7.9624, which is close to the upper limit of 8. The calculation time and occupied space of the new algorithm are less than those of the data encryption algorithm and the advanced encryption algorithm. This result shows that the DNA image encryption algorithm based on the Logistic‐sine system and the fractional-order chaos stability theory has excellent performance and can provide a certain theoretical basis for research in the field of digital image encryption.
The impact of fake news on the real estate market

Abstract We are living in an extremely globalized and digitalized world where social media represent the core of our existence whether for personal or professional reasons. Social media impact on business in general caught the attention of researchers (Grizane &amp; Jurgelane, 2017) during the last years, especially to measure the impact of their use in business growth, increasing sales and/or customer loyalty. There are more and more studies that focus on the harmful effect of fake news on specific markets, news that may appear easily and are difficult to control. Fake news can affect both the customers and/ or the business depending on the context. The purpose of this study is to identify how are the fake news appearing in a world where access to information is so simple and everybody can check the truthfulness of information and what kind of barriers are used to protect the business from being affected and the customers from being misinformed. Data for this paper are gathered using in-depth interviews with entrepreneurs from real estate businesses. The results show that the level of increasing fake news is affecting the trust building process. Recommendations are put forward for organizations to analyze more user behavior and use it for the decision-making process in order to limit the negative effects of fake news. Also, customers need to learn to identify quality brand communication, pay attention to details and be very precautions while taking decisions based on news that may be fake.
A CONCEPTUAL UNDERSTANDING OF THE IMPACT OF EMPLOYEE TRAINING PROGRAMS ON JOB PERFORMANCE AND JOB SATISFACTION IN THE TELECOMMUNICATION COMPANIES IN MALAYSIA

&lt;p&gt;Presently, many companies are confronted with tribulations while operating in a tempestuous business atmosphere; this is combined with the global pandemic, which is Covid-19, that has deteriorated the potency of the businesses. The telecommunication companies in Malaysia were also affected during these conditions. Therefore, a vital stratagem for the telecommunication firms to attain competitive advantage is through training programs conducted for all its employees throughout the various sections in the organization in order to beat this present obstacle. The objective of this research is to uncover if training influences job performance and job satisfaction which are two critical factors that guarantee the survival and growth of the telecommunication firms. Therefore, to realize the aim of this study, the survey technique is applied to cultivate a detailed profile which will be distributed to employees of three leading telecommunication cooperates in Malaysia. This study will use quantitative methods to generate empirical results and substantiations that responded to the research questions. The literature section scrutinized the areas of training, job performance, and job satisfaction to fill the gap and to determine the influence of training programs on job performance and satisfaction of employees. Therefore, the findings of this study could perhaps aid other corporations in Malaysia to instil continuous and well-planned training programs and not to take for granted the significance of training to improve job performance and satisfaction during the times of environmental turbulence. Thus, this could ensure that competitive strength is unceasingly maintained in the telecommunication firms in Malaysia.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/edu_01/0646/a.php" alt="Hit counter" /&gt;&lt;/p&gt;