Reviewerly/exHarmony huggingface dataset:
INDIVIDUAL PREFERENCES IN SECURITY RISK DECISION MAKING: AN EXPLORATORY STUDY UNDER SECURITY PROFESSIONALS

Risk assessments in the (cyber) security domain are often, if not always, based on subjective expert judgement.For the first time, to the best of our knowledge, the individual preferences of professionals from the security domain are studied.In on online survey they are asked to mention, rate and rank their preferences when assessing a security risk.The survey setup allows to differentiate between easily accessible or "on top of mind" attributes and guided or stimulated attributes.The security professionals are also challenged to both non-compensatory and compensatory decision making on the relevance of the attributes.The results of this explorative study indicate a clear difference and shift in the individual perceived relevance of attributes in these different settings.Another remarkable finding of this study is the predominant focus on impact attributes by the respondents and the less significant position of likelihood or probability.The majority of professionals seem to ignore likelihood in their security risk assessment.This might be due to so called probability neglect as introduced by other scholars.the security in organisations and society is depending on the assessment and judgement of these professionals, understanding their preferences and the influence of cognitive biases is paramount.This study contributes to this body of knowledge and might raise attention to this important topic in both the academic and professional security domain.
Crystal: A Privacy-Preserving Distributed Reputation Management

This paper investigates the situation in which exists the unshared Internet in specific areas while users in there need instant advice from others nearby. Hence, a peer-to-peer network is necessary and established by connecting all neighbouring mobile devices so that they can exchange questions and recommendations. However, not all received recommendations are reliable as users may be unknown to each other. Therefore, the trustworthiness of advice is evaluated based on the advisor's reputation score. The reputation score is locally stored in the user’s mobile device. It is not completely guaranteed that the reputation score is trustful if its owner uses it for a wrong intention. In addition, another privacy problem is about honestly auditing the reputation score on the advising user by the questioning user. Therefore, this work proposes a security model, namely Crystal, for securely managing distributed reputation scores and for preserving user privacy. Crystal ensures that the reputation score can be verified, computed and audited in a secret way. Another significant point is that the device in the peer-to-peer network has limits in physical resources such as bandwidth, power and memory. For this issue, Crystal applies lightweight Elliptic Curve Cryptographic algorithms so that Crystal consumes less the physical resources of devices. The experimental results prove that our proposed model performance is promising.
Use mean field theory to train a 200-layer vanilla GAN

Training a deep generative adversarial network (GAN) with hundreds or even thousands of layers is difficult. The backpropagation depth of generator is deeper than discriminator, leading it to occur vanishing/exploding gradients easily. This paper proposes a method to train deep vanilla GAN based on mean field theory. By adjusting the parameter variances and activation of the GAN, a 200-layer vanilla GAN can be trained steadily without adding any batch normalization layers or residual blocks. We demonstrate that deep GAN is very sensitive to the parameter variances $\sigma _w^2$ , $\sigma _b^2$ in the initialization scheme, and explain why hard tanh is more suitable than relu as an activation in a deep vanilla GAN. Experiments on the MNIST and Fashion-MNIST data sets validate that our method trains a deep vanilla GAN well and can produce high-quality images.
Sistem Pakar Mendiagnosa Penyakit Bawang Merah Dengan Metode Depthfirst Search Backward Chaining

There were 12 disease data obtained from the agriculture and horticulture department of Deli Serdang, namely earthworms, thrips pests, leaf caterpillars, armyworms, aphids, root namhodes, purple spots, powdery mildew, stem neck rot, anthracnose, fusarium wilt, rot. poor, dead shoots (phytopthora porri foister) and there are 30 Symptoms of disease in onion plants An expert system is a computer program that provides expert advice (decisions, recommendations or problem solving) as if people had been consulted. The backward chaining method is the opposite of forward chaining which starts with a hypothesis (an object) and asks for information to convince or ignore. Backward chaining inference engines are often called Object-Driven/Goal-Driven. In this study, a web-based expert system was designed using the backward chaining method which aims to detect the type of disease in shallot plants by paying attention to the symptoms experienced in shallot plants. By using the backward chaining method, the value of disease certainty in shallot plants will be obtained from the expert system designed. The test results concluded that the design of an expert system for diagnosing diseases in shallots using the backward chaining method was as expected.
COOPERATION MANAGEMENT MODEL: A CASE STUDY AT THE FACULTY OF ECONOMICS UNIVERSITAS NEGERI MEDAN

The purpose of this paper is to explore the collaboration that has been implemented in the curriculum as a cooperation management model to support campus performance indicators. Cooperation management uses the management functions introduced by Robbins et al., namely planning, organizing, operating, coordinating and evaluating. The case studies used in this research involve all study programs (N = 9) at the Faculty of Economics Universitas Negeri Medan, Indonesia. The data were collected using a questionnaire consisting of items focused on a differential semantic scale and qualitative responses to all items submitted at the time of the interview. This paper also reveals the dominant functions of cooperation management performed in the study program, namely: organization (8.00), planning (7.87), coordination (7.75), action (7.56), respectively evaluation (6.96). The function of the actuating and evaluation components is a severe concern for campus leaders. Flexibility and commitment to fund management support is essential for existing collaborations and plans for cooperation on strategic objectives. The implications of the study's findings - the model of industrial cooperation to strengthen collaboration between the two sides will serve for the future.
Exploring Frontiers in Electronic Evidence: The Philippine Experience

This paper is the first formal empirical study on electronic evidence (EE) since the promulgation of Rules on Electronic Evidence (REE) by the Philippine Supreme Court in 2001. A basic qualitative research design was employed in the study. A total of 29 litigation lawyers and 13 trial judges from the Province of Cavite and the National Capital Region were the participants in the study. A qualitative survey questionnaire was used to gather data from the participants through Google Docs. Results showed that EE was more commonly presented in criminal cases, a few in civil cases, and none in quasi-judicial and administrative cases. Text messages were found to be the most frequently presented EE at trial. Results further revealed three major themes such as (1) admissibility of EE, (2) authentication of EE, and (3) suggestions on the implementation of REE. The vagueness of REE provisions on the admissibility of electronic evidence results in varying interpretations of judges, which ultimately affect their appreciation of the evidence presented at a trial. The present empirical study would indeed contribute to the scholarly discourse of electronic evidence showcasing the Philippine experience.
Deep Learning-based Video Summarization

With the development of communication technology, many different kinds of media transmission have become popular.Among various media, video is the most popular media these days.However, users need to spend much time watching the whole video content.Due to the characteristics of video media, many users tend to playback video content quickly or even stop watching in the middle.Some websites provide summary images by capturing only important frames of video content, which is called a video summary.Users can shorten the viewing time by only watching the summary results.In particular, it is highly useful because content such as news articles or speeches can be delivered and utilized quickly.Since video summarization is a labor-intensive task, there is an increasing demand for research on automation techniques.In this paper, an automated process to solve the temporary problem of existing video summary techniques is proposed.The proposed method improves the existing video summarization methods that have been performed manually through human labor by developing artificial intelligence technology that can effectively perform content delivery using video summary automation.In the preprocessing process, the information transfer unit is partitioned using optical flow.In the following process, CNN (Convolutional Neural Network) is used as an in-depth learning method for feature extraction.The results show the efficiency of the proposed algorithm, and some future work will be given in the end.
An approach to Fuzzy clustering of the iris petals by using Ac-means Analysis

This paper proposes a definition of a fuzzy partition element based on the homomorphism between type-1 fuzzy sets and the three-valued Kleene algebra. A new clustering method based on the C-means algorithm, using the defined partition, is presented in this paper, which will be validated with the traditional iris clustering problem by measuring its petals.
Skeleton-Based Attention Mask for Pedestrian Attribute Recognition Network

This paper presents an extended model for a pedestrian attribute recognition network utilizing skeleton data as a soft attention model to extract a local feature corresponding to a specific attribute. This technique helped keep valuable information surrounding the target area and handle the variation of human posture. The attention masks were designed to focus on the partial and the whole-body regions. This research utilized an augmented layer for data augmentation inside the network to reduce over-fitting errors. Our network was evaluated in two datasets (RAP and PETA) with various backbone networks (ResNet-50, Inception V3, and Inception-ResNet V2). The experimental result shows that our network improves overall classification performance with a mean accuracy of about 2-3% in the same backbone network, especially local attributes and various human postures.
A Multi-objective Discrete PSO Algorithm for Simulation Components Deployment in the Cloud and Edge Environment

The booming cloud computing, together with edge computing, lays the computing resource foundation for larger and more granular simulation. To achieve better execution of the simulation in cloud and edge environments, it is necessary to assign simulation components to cloud computing centers and edge computing nodes appropriately. This is an optimization problem and a particle swarm optimization(PSO) algorithm is proposed for this purpose. However, deployment problems are a kind of discrete optimization problem and PSO algorithm was originally designed to solve continuous optimization problems, thus, various discretization methods had been proposed to modify original PSO algorithm to handle discrete problems. Unsatisfactorily, many discrete PSO algorithms suffer from random movements of particles due to improper use of the guidance information obtained from the global best particle and personal best particle. To counter this challenge, we proposed a selection-based discrete PSO algorithm(SPSO) and conducted simulation experiments on MATLAB platform. The results showed that SPSO got better performance in simulation components allocation problems compared with NSGA-II and other pure discrete PSO algorithms.
SYNTHESIS TRENDS OF FORECASTING USING INDUCTIVE MODELING METHODS

Modern development of computer technology and the possibility of implementing calculations in parallel allow to solve increasingly large-scale problems of numerical modeling. The development of multiprocessor computing and parallel computing makes it important to solve problems of optimization analysis. The optimization analysis is based on the mass solution of inverse problems when the defining parameters of the considered class of problems change in certain ranges. Thus, calculations of not only direct problems where it is necessary to model the phenomenon at the known initial data, but also calculations of inverse problems where it is necessary to define on what defining parameters there is this or that phenomenon become more and more demanded. This formulation requires multiple solutions of direct problems and solving the problem of optimization analysis and construction of predictive trends. Sets of multidimensional parametric data in the paper are considered as numerical solutions of the optimization problem. The construction of predictive trends is implemented on the basis of the group method of data handling as a direction of induction modeling. The methodology of visualization of results of calculation of parametric functions is realized. The scheme of Data Mining with application of methods of visualization by means of the Matlab software environment is described
The Belief-Desire-Intention Architecture of Sincere Software Agent Environment

Software agent is autonomous technology that helps a lot the human being in performing the task. The capability of agent to take actions on behalf of human is one of the reasons why researchers or developers of autonomous systems adapt the human characteristics either in the form of physical or behaviours. Sincerity is one of ethical human behaviour that can be instilled in software agent environment system. To instil the ethical behaviour such as sincerity, the belief-desire-intention (BDI) architecture should be designed. This paper explains the BDI architecture for sincere software agent environment system during performing task.
Classification of Genuinity in Job Posting Using Machine Learning

Abstract: To avoid fraudulent Job postings on the internet, we target to minimize the number of such frauds through the Machine Learning approach to predict the chances of a job being fake so that the candidate can stay alert and make informed decisions if required. The model will use NLP to analyze the sentiments and pattern in the job posting and TF-IDF vectorizer for feature extraction. In this model, we are going to use Synthetic Minority Oversampling Technique (SMOTE) to balance the data and for classification, we used Random Forest to predict output with high accuracy, even for the large dataset it runs efficiently, and it enhances the accuracy of the model and prevents the overfitting issue. The final model will take in any relevant job posting data and produce a result determining whether the job is real or fake. Keywords: Natural Language Processing (NLP), Term Frequency-Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Random Forest.
The Author’s Role in the New Media Art: Composers’ Experiments

The paper analyzes the concepts of authorship reflected in the literary works of French structuralists, the V. Martynov’s idea of “the end of the era of composers”, as well as the L. Manovych’s models of authorship in the culture of new media. On the one hand, these ideas are based on the paradigm of postmodernism; on the other hand, they reflect the impact of digital technology on artistic processes, which in turn leads to the formation of new models of creation and, consequently, authorship. As a result of the analysis of art projects of the last decades, in particular of the network project Global Mix, the interactive piano play Wiki Piano Net by Alexander Schubert, the performance of the international group Totem Kit-fit, the interactive sound installation Cave of Sounds, the AIVA program — Artificial intelligence Virtual Artist, etc., two directions for the use of digital technologies in artistic processes were identified. The first implies involvement of digital technologies as a tool to automate certain processes or to expand the means of expression. Within the second trend, technology becomes a prominent formative factor, often determining the end result with minimal author involvement. In the art of the second half of the twentieth and early twenty-first century, there were significant changes in the mecha-nisms of creativity and aesthetic guidelines reflected in the perception of the author’s status.
Retraction: Modeling and development of a fundamentally new way of landing a quadcopter on inclined surfaces (J. Phys.: Conf. Ser 2061 012109)

This article has been retracted by IOP Publishing following an allegation that this article substantially overlaps with [1]. IOP Publishing has investigated in line with the COPE guidelines, and agree that while the reference is listed in the article, the overlap is substantial and is unethical. Consequently, this paper has been retracted by IOP Publishing. Citations to this work should be redirected to [1]. The authors agree to this retraction. [1]. John Bass and Alexis Lussier Desbiens, (2020), 'Improving Multirotor Landing Performance on Inclined Surfaces Using Reverse Thrust', IEEE Robotics and Automation Letters, Volume: 5, Issue: 4, https://ieeexplore.ieee.org/abstract/document/9143402 Retraction published: 23 December 2021
A Verifiable Electronic Voting System with Homomorphic Tallying using Elliptic-curve Cryptography

In this paper, the use of elliptic-curve cryptography (ECC) to build an end-to-end verifiable electronic voting system with homomorphic tallying is discussed.Verifiability is an important property of electronic voting systems, allowing any interested person to independently check that all votes were correctly recorded and counted.On the other hand, homomorphic tallying allows the votes to be counted without having to be individually decrypted, which reinforces ballot secrecy, another crucial security requirement for voting systems.After the encrypted votes are counted, only the final tally is decrypted in order to reveal the election result.Ballots are encrypted using elliptic-curve cryptography, which has been proven to offer high security levels, while keeping smaller key sizes, in comparison to other well known cryptographic primitives.
Structured expert judgment using the Classical Method

have not yet been made public.The experts are scored on their performance in assessing the calibration questions.Validation is achieved by assessing the statistical accuracy of an expert's assessments together with how much information they provide.These two quantitative measures of performance are used to calculate performancebased weights.
Dead voices and lost sounds: an imagined sonic history

A project description of a sound art installation and interactive performance presented as part of the Up Close Festival in New York City in the winter of 2019/2020. The article is authored by the creator and director of the piece, Adrienne Kapstein. Created for an all-age audience, the piece was a unique, relational and socially engaged experience that merged sound art, live performance, illusion, technology, and audience participation. Designed to be completed in partnership with members of the community it sought to serve, the piece invited participation from every audience member through multiple and varied means of engagement.
The Role of Family Disintegration in Piracy of Electronic Games “A Field Study”

Purpose: Recognizing the technological effects of family disintegration.&#x0D; Method: Human field: a sample of the dangerous electronic games players, including "4" players who are still alive, and "5" players who committed suicide. Methods and Tools: The Case study method, Ethnographic method, Descriptive approach, and Interview. The research type is Analytic, and the theoretical framework is Postmodernism Theory.&#x0D; Originality: The researcher tries to provide a comprehensive view of how electronic games piracy on their players and pushes them to suicide, in the presence of the family disintegration element.&#x0D; Findings: family disintegration was the main reason for children’s addiction to electronic games. Thus, electronic games were like escaping from reality and living in imagination, and spending free time. Also, electronic games were a means that absorbed the negative charge and feelings of anger among the children instead of the family. There are many types of piracy on players: (programming for the mind, charging with negative thoughts, threatening to kill parents, an emotional challenge to the teenager, blackmail and intimidation, or with talismans).&#x0D; Conclusion: a person can control another, to the extent that this other person allows this person to control him. Do not allow a game administrator to control you, activate Cybersecurity.
Supplemental Material: Eocene thickening without extra heat in a collisional orogenic belt: A record from Eocene metamorphism in mafic dike swarms within the Tethyan Himalaya, southern Tibet

Table S1: EPMA data of minerals compositions for garnet amphibolite; Table S2: Zircon U-Pb SHRIMP data for garnet amphibolite T0526; Table S3: Major and trace element compositions of garnet amphibolites.
DEONTOLOGICAL COMPONENT OF EVALUATION-ANALYTICAL COMPETENCE OF A TEACHER

With the approval of teachers’ professional standard there is a need for a qualitatively different teacher training, which allows to combine the fundamentals of professional basic knowledge with innovative thinking and practice-oriented research approach to the implementation of a new evaluation format. Modernization of the system of education in Ukraine has radically changed the «portrait» of the modern teacher and the requirements for his work. There is a demand for professionals who not only know their subject well, but also have a flexible professional self-awareness, emotional intelligence and communication skills focused on creativity, innovation, entrepreneurship. There is a need to create a route map for the development of teachers` professionalism in Ukraine. The analysis of the teacher’s professional standard encourages to more detailed definition and comprehension of the value sphere of teacher`s professional activity. It is here, in our opinion, that the achievements of a relatively new field of scientific knowledge − pedagogical deontology − should be used. The purpose of the article is to identify and determine the place of the deontological component within the teacher`s evaluative-analytical competence in the formulation of teacher`s professional standard. Based on the comparative analysis of literature sources, the author's conclusions are formulated. Motivational-value (personal) component of the structure of evaluative-analytical competence clearly reveals the personal traits of the teacher, which gives the opportunity to reveal students` motives and aspirations. This gives grounds to define the motivational-value (personal) component as a deontological component of the teacher’s evaluative-analytical competence. It should be a trigger for fair and motivating assessment that enables the student to feel valued and find meaning in learning. The teacher forms certain models of professional behavior, which are a manifestation of the formation of his evaluative-analytical competence.