Reviewerly/exHarmony huggingface dataset:
Simulation of Heterogeneous Network Task Scheduling Based on Improved Grey Evolutionary Algorithm

Aiming at the inefficiency and local optimization problems of current heterogeneous network task scheduling methods, this paper constructs a heterogeneous network task scheduling method based on improved gray evolution and conducts experimental simulations. This article first uses the degree of relevance as a metric to construct a geometric series, based on the definition of gray relevance, uses weights to express the importance of its indicators, and then reconstructs its weights based on subjective characteristics, objective characteristics, and associated characteristic indicators. Finally, the composite weights are solved according to the relative proportional coefficients to realize the task scheduling of heterogeneous networks. It can be seen from the simulation results that the above method can meet the ideal conditions of each index
Implementing ECC on Data Link Layer of the OSI Reference Model

The Internet, a rapidly expanding communication infrastructure, poses significant cybersecurity challenges. A few techniques have been developed to provide security in the OSI model's application, presentation, and network layers. Instead of using Media Access Control (MAC), this paper recommends using public key Elliptic Curve Cryptography to serve the Data Link Layer. On a microFourQ-MSP-IAR Embedded Workbench IDE-MSP430 7.12.4, a prototype of the architecture was implemented. When the cost of key generation was compared to the curve, it was shown that they are directly related. The cost of key generation time rises in proportion to the bit length of the curve. Because of the large number of connected devices that may be exposed due to a lack of effective security in the data link layer, significant concerns arise in terms of network privacy, governance, and security. As a result, this research suggests using a public key cryptosystem based on an elliptic curve to secure the data link layer for safe data communication in Internet-connected devices.
An Improved Method to Remove Salt and Pepper Noise in Noisy Images

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).
The Effect of Institutional Quality and Macroeconomic Indicators on Financial Markets: A Research on the Emerging Markets

This study aims to determine the institutional and macroeconomic factors affecting the development of the stock markets included in the Morgan Stanley Capital International Emerging Markets Index (MSCIEF) for 2002 to 2017. In this context, the data of 25 emerging markets are analyzed with panel data models in the study. According to the findings, political stability and the absence of violence and voice and accountability variables have positive effects on the development of those stock markets. Likewise, among macroeconomic variables, the annual gross domestic product (GDP) growth rate, domestic credits to the private sector, and domestic savings affect the development of those stock markets positively.
The educational environment for forming secure base behavior in cyberspace of future professionals in economics and management

The article deals with the problem of training modern specialists in the fields of knowledge 05 «Social and behavioral sciences» and 07 «Management and administration» for secure base behavior in cyberspace. The technique of creating a network learning environment with the possibilities of step-by-step virtualization, the use of cloud technologies and distance learning has been described. Issues of providing students with specialized and professional knowledge and skills in information security and cyber security are considered. Based on the results of research in the psycho-pedagogical literature and our own 10 years of experience, we have determined the amount of theoretical and practical knowledge for the formation of skills and abilities in information protection of cyberspace. The list of requirements for professionally significant characteristics of the economics and management specialists in the field of information protection has been determined. The article substantiates the need to use a modern networked learning environment with wide use of open educational resources, mobile applications, cloud technologies to increase the level of competence of students in information and cyber security. Comparative analysis of open information educational space resources, which can be used as elements of the academic discipline and used for independent work of students, has been made. The criteria for selecting open learning resources has been defined and the methodology for their use has been proposed. The organization forms of the educational process, modern methods and teaching tools for the network learning environment of a higher education institution have been described. The ways of implementation of the proposed methodology step-by-step creation of a learning environment at the Taras Shevchenko National University of Kyiv and Borys Grinchenko Kyiv University have been presented on the example of academic disciplines «Information Security», «Information Protection», «Cybersecurity». It is proved that the use of innovative training technologies, open learning resources of leading educational institutions, cloud and mobile services from data protection allows training professionals with practical information security skills that are in demand in the labor market.
Product Recognition: Survey

Product recognition systems have attracted widespread attention because they help customers to obtain the product relevant information very fast (e.g., product’s price, the nearest store selling this product, consumer recommendation products, etc.) With the recent growth of the space, this is the first a survey of product recognition systems. In addition to a survey of the few existing works, we discuss opportunities and open problems.The objective of this article is to provide new researchers a brief and comprehensible review of product recognition approaches. We discuss product recognition on a mobile device systems that use the camera-phones. Then, clothing recognition methods rely on the estimated human pose. Grocery products are recognized using local features or a perexemplar multi label image classification approach. We also present the datasets that have been used in product recognition applications. Finally, we provide researchers open problems and advice.
Wi-Piga: A Personnel-independentMethod For Actions Recognition Based On Wi-Fi

In the process of action recognition, due to the difference of channel state information (CSD collected by different experimenters, the identification method can not be Personnel independent. To address this issue, activity recognition method for combining convolutional neural network(CNN) and generative adversarial network(GAN) is put forward. The CSI data of yoga movements are collected, and the CSI data are preprocessed by second-order Butterworth and principal component analysis (PCA) at first. The high-level motion features which are personnel-independent are extracted by the fusion method, and then these motions are classified and recognized At the same time, whether the action is standardis judged. The experimental results show that this method can identify yoga movements successfully and it has nothing to do with people, which is of great significance for family sports guidance and sports experience.
Quantum Algorithms for Variants of Average-Case Lattice Problems via Filtering

We show polynomial-time quantum algorithms for the following problems: (*) Short integer solution (SIS) problem under the infinity norm, where the public matrix is very wide, the modulus is a polynomially large prime, and the bound of infinity norm is set to be half of the modulus minus a constant. (*) Extrapolated dihedral coset problem (EDCP) with certain parameters. (*) Learning with errors (LWE) problem given LWE-like quantum states with polynomially large moduli and certain error distributions, including bounded uniform distributions and Laplace distributions. The SIS, EDCP, and LWE problems in their standard forms are as hard as solving lattice problems in the worst case. However, the variants that we can solve are not in the parameter regimes known to be as hard as solving worst-case lattice problems. Still, no classical or quantum polynomial-time algorithms were known for those variants. Our algorithms for variants of SIS and EDCP use the existing quantum reductions from those problems to LWE, or more precisely, to the problem of solving LWE given LWE-like quantum states. Our main contributions are introducing a filtering technique and solving LWE given LWE-like quantum states with interesting parameters.
The Importance of Integrating Core Concepts of News Literacy: Rethinking the News Literacy Course in Taiwan

The Asian Hong Kong Youth Internet Literacy Survey shows that as high as 48.1% of Hong Kong middle school students read news, they have never found that the information they read or forwarded is false (Hong Kong Federation of Youth Groups, 2016). In Taiwan, which is also a Chinese-speaking region, a youth media literacy survey also found that 76% of youths do not verify and criticize the news they read (Golden Car Foundation, 2020). A large number and various types of fake information and fake news will cause harm to society (Dell, 2019). According to the 2020 News Literacy Survey of Taiwan's Parent-Child World Magazine, 14% of students said they had never doubted when they saw questionable news or messages, and 25% of students said they doubted but never verified it. The main channel for students to check the news is to check on the Internet, and 31% of students choose this channel. (Yahui, C., 2020). Taipei City is an extremely advanced city in the development of digital networks and traditional media in Asia. It has a free news environment, and there are more than ten television stations with­­­­ 24-hour television news media. Therefore, in the past two decades, domestic media educators and the Ministry of Education have been committed to Promoting news literacy, Taipei City became one of the first international cities to promote media literacy. The mission of media literacy is to: specula1te on media and its practical purpose, cultivate students and citizens to learn independently, and critical thinking and problem-solving abilities (Ministry of Education, 2002). However, with the continuous increase of news information, it is very important to improve news literacy skills. At present, in addition to a complete media literacy framework, Taiwan still needs more comprehensive journalism education and reading skills courses. This article will use qualitative research to analyze and discuss the global media literacy education and Taiwan news literacy education-related materials, to achieve the two goals of exploration and discovery, redefine the core definition of Taiwanese news literacy, and apply it to Taiwan's youth news. Suggestions for rethinking the literacy course will help improve Taiwan's youth journalism literacy.
SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning.

Distributed deep learning frameworks, such as split learning, have recently been proposed to enable a group of participants to collaboratively train a deep neural network without sharing their raw data. Split learning in particular achieves this goal by dividing a neural network between a client and a server so that the client computes the initial set of layers, and the server computes the rest. However, this method introduces a unique attack vector for a malicious server attempting to steal the client's private data: the server can direct the client model towards learning a task of its choice. With a concrete example already proposed, such training-hijacking attacks present a significant risk for the data privacy of split learning clients. In this paper, we propose SplitGuard, a method by which a split learning client can detect whether it is being targeted by a training-hijacking attack or not. We experimentally evaluate its effectiveness, and discuss in detail various points related to its use. We conclude that SplitGuard can effectively detect training-hijacking attacks while minimizing the amount of information recovered by the adversaries.
LibGuides: DBA - Management and Instruction: APA Citation Style Guide

This LibGuide is intended to provide a library research home base for students enrolled in the Benson School of Business DBA program for Management and Instruction.
Performance Analysis Of Load Balancing Algorithms Based On Swarm Intelligence In Cloud Computing

The demand for storage and data retrieval over the internet changes the perception of technology. Cloud computing facilitates on-demand services based on users’ requirements. The pool of cloud computing services is based on various factors such as resource allocation, virtualization and management of user requests. The computing process provides a job scheduler for the processing of user requests. The nature of the job schedular is static and dynamic for the allocation of resources. The static job allocation is the conventional approach to handle the user requests. On the other hand, dynamic job schedulers play an essential role in capacity improvements in cloud computing—the dynamic scheduling process extent in the form of cloud load balancing. The ways of load balancing in cloud computing transform the storage mechanism and allocation process. Various research scholars and developers proposed dynamic load balancing algorithms based on swarm intelligence. The dynamic characteristics of swarm intelligence influence the performance of cloud computing. This paper analyzes the performance of various swarm algorithms applied for load balancing. The foremost swarm intelligence-based algorithms are particle swarm optimization, ant colony optimization, glowworm optimization and derived swarm intelligence algorithms. The process of analysis use cloud sim simulator and test different user groups with the different data center. The performance of algorithms indicates the utilization of swarm intelligence in cloud computing environments for load balancing.
91. DISTINGUISHING DIFFERENT PSYCHIATRIC DISORDERS USING POLYGENIC PREDICTION

Estimating the number of latent topics is a prerequisite to using topic modeling algorithms such as LDA. A number of methods for this purpose are compared, including the de facto approach based on perplexity. Results from synthetic data show that a combination of methods yields better estimates than any single method, and in particular it provides an indicator of the reliability of the estimated number of topics, something no single method can do. In this paper, we extend the findings based on synthetic data over real data and introduce a technique, “Cosine inter-intra topics”, to assess the validity of the ground truth of real data. Moreover, we measure the reliability of estimated number of topics through the variance of methods. Results corroborate the ones from synthetic data, suggesting the approach generalizes to real text corpus.
BRITISH COUNCIL PODCAST APPS TO IMPROVE STUDENTS’ LISTENING COMPREHENSION

This study investigated the effectiveness of British Council Podcast Apps to improve students’ listening comprehension of students in senior high school. For that purpose, this study carried out a quasi-experimental research design by applying pre-test and post-test. There were two classes consisting 72 students (36 students are in control group; 36 students in experimental) of senior high school. This study took place in SMA Negeri 9 Semarang, Indonesia. In collecting the data, an adopted listening comprehension test was used in pre-test and post-test. SPSS 23 was then used to analyze the mean, standard deviation and t-test calculation. The result showed that British Council Podcast Apps is effective to improve the students’ listening comprehension. This study implies that utilizing technology in listening class is important to support the students achieve the maximum goal of learning as well as the improvement of their listening comprehension.
Distance learning as an educational technology: development prospects and challenges of today

The objective of the research was to discuss distance education as a current and necessary object for contemporary education. The following research methods were used in the article: literature study, analysis, synthesis, methods of theoretical generalization. According to the solution research hypothesis, the following main tasks were: to define the concept of "distance learning"; to reveal the advantages and disadvantages of distance learning in education. It is concluded that: although there are disagreements about distance education as a tool in presential school; its use is a path of no return. Teachers and technicians must be equipped to update and use them in a common way in their daily lives.
Digital Technologies In Mathematical Education

The article discusses the issues of digitalization and integration of mathematics education. The use of information technologies in teaching mathematical disciplines on the basis of practice-oriented tasks has been substantiated. The integrative presentation of information is disclosed due to its compression, concentration, generalization, as an effective tool for rationalizing memorization and understanding. Integration of mathematical education obliges to consider objects and phenomena of reality in their interconnection and interdependence. In these conditions, the view on intra- and intersubjective connections changes. In a number of cases, mathematics should become not a source, but a consumer of the knowledge offered in the classroom in general professional disciplines, relying on ideas formed in the study of these disciplines. In the work, the issues of digitalization and integration of mathematical education are revealed on the basis of a practice-oriented problem in probability theory and mathematical statistics. An algorithm for studying the parameters of the binomial distribution using the MS Excel spreadsheet is presented. A study was carried out and it was shown that for a large number of trials n, the binomial distribution, even with p significantly different from 0.5, is fairly accurately approximated by the normal distribution. The use of information technologies in teaching mathematical disciplines contributes to an increase in the level of mathematical training of future specialists of the EMERCOM of Russia, as well as the formation and development of professional motivation.
Pelatihan Penggunaan Media Edmodo Dalam Pembelajaran di SMK PGRI Bandar Lampung

Training on the Use of Edmodo Media in Learning at SMK PGRI Bandar Lampung aims to provide knowledge and skills on how a teacher can use Edmodo media in learning. This activity was carried out because the teacher's knowledge was still limited about online learning and using internet-based learning media in online teaching and learning activities, namely Edmodo. To overcome these problems, the solution offered is to provide training on the use of Edmodo media in learning for teachers at SMK PGRI Bandar Lampung, especially in order to make it easier for teachers to carry out teaching and learning activities. This service activity is in the form of training on steps to use Edmodo media that can be applied to online learning. After monitoring and evaluation were carried out by the STKIP PGRI Bandar Lampung lecturer, it was seen that the results of the training that had been carried out showed that the teachers of SMK PGRI Bandar Lampung were able to use Edmodo media in learning well.
Systematization of Knowledge of Used Cryptographic Functions In Block Chain

Blockchain has been proposed to be a disruptive technology with possible for applications in various industries. The fundamentals of blockchain are cryptography and cryptographic ideas that deliver reliable and secure decentralized solutions. Although many recent papers study the use-cases of blockchain in different industrial areas, such as finance, health care, legal relations, IoT, information security, and consensus building systems, only few studies scrutinize the cryptographic concepts used in blockchain. To the best of our information, there is no Systematization of Knowledge (SoK) that gives a complete picture of the existing cryptographic concepts which have been deployed or have the potential to be deployed in blockchain. This paper concerns the analysis and systematize all cryptographic concepts which are already used in blockchain. However, there is a clear lack of a systematic framework with which different systems can be analysed and compared against each other. Such a framework can be used to assess blockchains’ viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms Finally, we conclude the survey by presenting a list of possible directions for future research. Also, we give a list of cryptographic concepts which have not yet been applied but have big potentials to improve the current blockchain solutions. We also include possible instantiations of these cryptographic concepts in the blockchain domain.
What Happened in Social Media during the 2020 BLM Movement? An Analysis of Deleted and Suspended Users in Twitter.

After George Floyd's death in May 2020, the volume of discussion in social media increased dramatically. A series of protests followed this tragic event, called as the 2020 BlackLivesMatter (BLM) movement. People participated in the discussion for several reasons; including protesting and advocacy, as well as spamming and misinformation spread. Eventually, many user accounts are deleted by their owners or suspended due to violating the rules of social media platforms. In this study, we analyze what happened in Twitter before and after the event triggers. We create a novel dataset that includes approximately 500k users sharing 20m tweets, half of whom actively participated in the 2020 BLM discussion, but some of them were deleted or suspended later. We have the following research questions: What differences exist (i) between the users who did participate and not participate in the BLM discussion, and (ii) between old and new users who participated in the discussion? And, (iii) why are users deleted and suspended? To find answers, we conduct several experiments supported by statistical tests; including lexical analysis, spamming, negative language, hate speech, and misinformation spread.