Reviewerly/exHarmony huggingface dataset:
Adaptive Optimization Swarm Algorithm Ensemble Model Applied to the Classification of Unbalanced Data

In order to solve the problem that the hyper-parameters of the existing random forest-based classification prediction model depend on empirical settings, which leads to unsatisfactory model performance.We propose a based on adaptive particle swarm optimization algorithm random forest model to optimize data classification and an adaptive particle swarm algorithm for optimizing hyper-parameters in the random forest to ensure that the model can better predict unbalanced data.Aiming at the premature convergence problem in the particle swarm optimization algorithm, the population is adaptively divided according to the fitness of the population, and an adaptive update strategy is introduced to enhance the ability of particles to jump out of the local optimum.The main steps of the model are as follows: Normalize the data set, initialize the model on the training set, and then use the particle swarm optimization algorithm to optimize the modeling process to establish a classification model.Experimental results show that our proposed algorithm is better than traditional algorithms, especially in terms of F1-Measure and ACC evaluation standards.The results of the six-keel imbalanced data set demonstrate the advantages of our proposed algorithm.
The Impact of Earning Management and Social and Environmental Costs Disclosure on Financial Performance: An Empirical Study in Jordan

This research aimed to discover the moderating impact of earnings management between social and environmental costs disclosure on financial performance. To realize this purpose, this research employed a quantitative method by using primary data collected from the Amman Stock Exchange. This study used the questionnaire to collect the data from a sample of 127 companies. The Smart Partial Least Squares (PLS) is used to test the data. The results of the study show that the social and environmental costs disclosure positively and significantly impacted the financial performance of the firms. This was consistent with agency, legitimacy, and stakeholders' theories. This means that more disclosure about the cost of social and environmental information could create opportunities for firms. Also, earnings management was found to have a significant and positive effect on the relationship between social cost disclosure and the financial performance of the firms. And earnings management was found to have a significant and positive effect on the relationship between environmental cost disclosure and financial performance of the firms.
Deployment of Open and Distance Learning to Persevere Teaching and Learning during COVID-19 Pandemic in Malaysia

Education is essential to an individual and definitely to a country in a large scale as it contributes to human development. However, when COVID-19 pandemic struck the world and the number of cases had rose sharply in 2020, it affected every sector including education sector.
Unity3D-based Simulation for Operations Management Teaching

Business is still male-dominated in the twenty-first-century despite women being contributing to every field of day-to-day life. In developing countries, women's participation in economic activities is even worse. Women entrepreneurs face unique challenges in developing countries to grow and sustain their growth. The purpose of this study is to identify and rank the major challenges that obstruct women entrepreneurâs growth in developing countries like Pakistan. A Fuzzy Technique for Order Performance by Similarity to Ideal Solution (FTOPSIS) is proposed. Thus, using simulation based fuzzy TOPSIS technique, the decision criteria are quantitatively evaluated. As a result, the approach generates decision-making knowledge, and thereafter, the model can be adopted and at the same time if necessary, modified by decision makers. To demonstrate the applicability of the proposed approach, data was collected through questioner from women entrepreneurs in Pakistan and the results analyzed.
Decision Support Systems in Determining the Product Marketing Strategy of PT. Universal Gloves with Multi-Objective Optimization Method On The Basic Of Ratio Analysis (MOORA)

Multi-Objective Optimization based on Ratio Analysis or often referred to as the MOORA method is a method that is often used as a decision-making method that uses optimization techniques to solve complex problems that have many criteria or multi-criteria. The MOORA method can separate various factors by separating the subjective part of the factors into several decision-making attributes by forming a decision matrix. The decision matrix will be normalized and optimized for each criterion to produce a preference value that can be used as a decision making. In this study, decision making was carried out using the MOORA method on the right product marketing strategy based on various existing criteria to determine the strategy that the company will implement in marketing its products so that the company has a competitive advantage to win the market from competitors. The results of the analysis show that the differentiation strategy has a higher preference value, namely 53.243164 and the cost leadership strategy with a value of 48.123638, and the market segmentation strategies with a value of 46.540075.
Deep Learning Approaches to Pedestrian Detection: State of the Art

Pedestrian or human detection and classification is a significant and challenging problem in the field of computer vision. In recent years, researchers have employed deep learning frameworks and models for pedestrian detection and classification. Nevertheless, these frameworks have their shortcomings. Obtaining straightforward comparisons and choosing a relevant deep learning framework is a wearisome task for researchers. To address these shortcomings, we present a dogmatic analysis of a state-of-the-art deep learning framework adopted for pedestrian detection along with real-time solicitudes and challenges. In this chapter, our contributions comprise 1) a well-organized comparison of an advanced deep learning framework utilized for pedestrian detection, 2) a comparison of resembled benchmark pedestrian datasets, 3) a unique pedestrian database that embraces students’ behavior in educational institutions, and 4) a proposed case study of a scale invariant approach of pedestrian detection. Our investigation will provide a new research direction in the area of pedestrian detection for practitioners and research scholars.
Prof. Momchil Dobrev and Prof. Mariola Garibova-Dobreva THE LAW of ENERGY-INFORMATION GENE ADAPTATION - proof for Prof. Momchil Dobrev and Prof. Mariola Garibova-Dobreva for Energy-information formation of matter, Theory of ENERGY INFORMATION GENETICS, Theory of ENERGY INFORMATION MEDICINE, Theory of Energy-information psychology, Theory of energy information psychiatry as part of Theory of the Universe

Prof. Momchil Dobrev and Prof. Mariola Garibova-Dobreva THE LAW of ENERGY-INFORMATION GENE ADAPTATION - proof for Prof. Momchil Dobrev and Prof. Mariola Garibova-Dobreva for Energy-information formation of matter, Theory of ENERGY INFORMATION GENETICS, Theory of ENERGY INFORMATION MEDICINE, Theory of Energy-information psychology, Theory of energy information psychiatry as part of Theory of the Universe
Long Term Memory Effect in Selected Cryptocurrencies

The worth of digital currencies is increasing due to its proposed advantages and profits. Though decentralized, these digital currencies can be bought with digital wallets using cryptocurrency platform. Efficient Market Hypothesis (EMH) suggests fundamentals for understanding of financial markets however the opponents believe that this theory is incompetent in explaining the functioning of the markets. EMH is not a perfect model nevertheless it provides a concrete base for the analysis of capital markets. EMH’s weak version is utilized for this study. This research compares three top cryptocurrencies- Bitcoin, Ethereum and Litecoin to analyze their long-range memory effect to check the market efficiency and also to estimate the volatility for further investments in different cryptocurrencies. Generalized Hurst exponent methodology is applied to examine long range memory in selected cryptocurrencies market. Daily data from 17th September 2015 till 17th October 2018 is used in this study. It was found that: (i) Long memory exists in the selected cryptocurrencies; (ii) Ethereum market is more persistent than Bitcoin and Litecoin as its Hurst exponent is more than the other cryptocurrencies. These findings can be a source of assistance for the policy makers and investors while making prudent decisions regarding investment in emerging cryptocurrencies market.
Efficient Augmented Reality on Low-Power Embedded Systems

In this paper we propose a development technique for low-power devices with limited computing capacity to obtain efficient, high-performance and non-CPU-invasive Augmented Reality (AR) applications. The paper will discuss how to exploit both the available hardware and software resources. Many boards on the market are equipped with CPUs with low computing power together with GPUs for 2D/3D graphics and multimedia. The paper analyses the strengths of these architectures and how to exploit them. The Operating System (O.S.) also provides features that allow greater control over the system (e.g., avoid wasting resources) and its performance. The techniques proposed are then used, as an example, in the development of an AR application for remote assistance.
Research on ASP. NET AJAX Framework and Its Application in Web Development

Web page requests is the common way for applications to implement human-computer interaction, but full page refreshing of the page reduces the effects of users’experience, increases the pressure of servers and slows down the speed of program response. Partial update pages with AJAX asynchronous transfer technology can increase the efficiency of program interactions and give user a friendly program interface. Microsoft’ s ASP. NET AJAX framework can greatly reduce the difficulty of programmers in developing AJAX applications. This paper introduces AJAX engine principle and ASP. NET AJAX framework firstly, and then describes ASP. NET AJAX configuration in the Web site, at the end we imported AJAX application in the design of online examination system in order to achieve asynchronous partial update , therefore a friendly user interactive interface is brought about.
Analysis of Students’ Online Interactions in the Covid Era from the Perspective of Anomaly Detection

During the pandemic, most of the teaching has been done online. The lack of face-to-face interaction has many undesirable effects, including students being less focused, not receiving feedback on how they are approaching the current topic/task, and an increased risk of cheating. It is expected that those students with similarly graded assignments/exams would have similar interactions during online teaching sessions. The opposite is an anomaly, for better or worse. It is possible to find out if assignments/exams are legitimate by using anti-plagiarism tools or by carefully examining submissions, but it is time-consuming and only protects against one type of fraud. In this paper, we propose to apply anomaly detection techniques to the students’ interactions to reduce the number of assignments/exams that need to be checked against fraud.
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks

In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement learning (RL) is a promising approach for acquiring short-horizon skills autonomously. However, the focus of RL algorithms has largely been on the success of those individual skills, more so than learning and grounding a large repertoire of skills that can be sequenced to complete extended multi-stage tasks. The latter demands robustness and persistence, as errors in skills can compound over time, and may require the robot to have a number of primitive skills in its repertoire, rather than just one. To this end, we introduce EMBR, a model-based RL method for learning primitive skills that are suitable for completing long-horizon visuomotor tasks. EMBR learns and plans using a learned model, critic, and success classifier, where the success classifier serves both as a reward function for RL and as a grounding mechanism to continuously detect if the robot should retry a skill when unsuccessful or under perturbations. Further, the learned model is task-agnostic and trained using data from all skills, enabling the robot to efficiently learn a number of distinct primitives. These visuomotor primitive skills and their associated pre- and post-conditions can then be directly combined with off-the-shelf symbolic planners to complete long-horizon tasks. On a Franka Emika robot arm, we find that EMBR enables the robot to complete three long-horizon visuomotor tasks at 85% success rate, such as organizing an office desk, a file cabinet, and drawers, which require sequencing up to 12 skills, involve 14 unique learned primitives, and demand generalization to novel objects.
How social media is affecting the youth

Social networking sites gives a platform for discussion on burning problem that has been unnoted in today’s scenario this analysis is conducted to see the impact of social networking sites within the dynamical frame of mind of the youth. The objective of this analysis is to investigate’ the influence of social media on the youth social life.1- To assess the beneficial and preferred from of social media for youth2-to evaluate the attitude of youth towards social media and measure the spending time on social media.Utilization of social media is an integral part of Indian youth today. Over utilization of social media, has captured the attention of youth entirely. The dependency of youths on the social media has reached at such level that, without social media, every young person cannot think about the direction of their growth. Dependency of youth on social media is now leading to addiction. Findings show that the majority of the respondents show the agreement with these influences of social media and the respondents opine that Instagaram as their favorite social media form and then they like twitter as second popular form of social media. Negative use of social media promotes unethical pictures video clips and images among youth ant-religious post and links create hatred among the peoples of a different communities of social media is deteriorating the relationship among the countries. In this social media is playing a major role to create political awareness among the youth.
Prototype Cataloging Interface Based on the IFLA Library Reference Model (LRM). Part 2: Usability Evaluation

This paper reports on usability evaluation that was carried out to evaluate the LRM-based prototype interface, presented in Part 1. A combination of focus group, Wizard of Oz method, and think-aloud protocol was used. The study was conducted in May 2019 with seven Slovenian catalogers from Maribor Public Library. Although participants had some difficulties understanding the LRM model, the user interface proved to be quite easy to use, quick to understand, and transparent. The functionality of the proposed prototype proved to be adequate, since the catalogers successfully and independently completed all the tasks without major problems and errors.
Least resolved trees for two-colored best match graphs

2-colored best match graphs (2-BMGs) form a subclass of sink-free bi-transitive graphs that appears in phylogenetic combinatorics. There, 2-BMGs describe evolutionarily most closely related genes between a pair of species. They are explained by a unique least resolved tree (LRT). Introducing the concept of support vertices we derive an $O(|V|+|E|\log^2|V|)$-time algorithm to recognize 2-BMGs and to construct its LRT. The approach can be extended to also recognize binary-explainable 2-BMGs with the same complexity. An empirical comparison emphasizes the efficiency of the new algorithm.
Combating Cyber Crime

The present paper has proposed a model to show that a synergy of technology and legal procedure can work as deterrent to cybercrime. The fraudulent activities the model considers are of two types. The first one is the case when the processed information gets diverted to reach someone else rather than reaching its targeted destination, thereby creating an incidence of data theft and hacking. The second one is a case of already hacked situation. The model has established that in the present-day context, synergic efforts of technology and legal provisioning can emerge as an effective option to ensure cyber security to some significant extent. The application of the proposed model in practical field is also illustrated with the analysis of a case of cybercrime.
Assessing sediment pollution by applying some geochemical indices for Al-Wind River banks/ East of Iraq

15 sediment samples were collected; 8 samples from the eastern bank, and 7 samples from the western bank of Al-Wind River in Diyala governorate to assess the sediment pollution in some trace elements such as Fe, Ni, Cd, Zr, Zn and Cu in addition to some oxides such as Al2O3, CaO, Na2O and K2O to find the effect of anthropogenic pollution and the industrial production on the sediment closed especially Naftkhana by using some geochemical pollution indices such as: geoaccumulation factor (I-geo), enrichment factor (EF),contamination factor (CF), pollution loud index (PLI) and to evaluate the degree of weathering by Applying the Chemical Index of Alteration (CIA)in both banks of Al-Wind River. The results of general contamination factors indicate that the eastern bank of Al-Wind River especially that closed by Naftkhana area having quite more concentrations of trace element and oxides from the western bank. The results of I-geo presents unpolluted sediment with Pb and Fe and slightly polluted with Zn, Cu, and Ni in both banks, while the western bank in some locations recorded of moderately polluted sediments with Ni and slightly to moderately polluted sediments with Cu and Zn and reach moderately severely polluted in the eastern bank. The contamination factor (CF) for Ni and Zr classified as class 2 which indicate moderately contamination to severely pollute in both banks, while Fe, Pb, Cu and Zn conbankred unpolluted in both banks. Enrichment factor (EF) for Fe, Pb, Zr and Cu are conbankred deficiency to low enrichment the range of natural variability, while Ni record high values reached to 6.4 indication of anthropogenic inputs. Pollution load Index (PLI) is perfection and classified as class 0 in both banks. Chemical index of alteration (CIA) reflecting the chemical weathering intensity especially for the western bank.
An Approach to Avoid Meet in the Middle Attack in 2 DES

Cryptography is evolving with new and better algorithms. The existing DES provides security but is vulnerable on account of its smaller key. An attacker can easily break through DES with a minimal number of operations. 2 DES or double DES uses a longer key but is highly vulnerable due to Meet in the middle attack, which reduces the brute force time by almost a factor of 2. In this paper, we introduce a better algorithm that incorporates the best features from DES and 2 DES. The new algorithm is essentially a modified version of DES. It is operational over blocks of size 128 bits. The key used is of 112 bits and thus ensuring better security over DES. We use single encryption, unlike 2 DES that uses double encryption, ensuring edge over Meet in the Middle attack. The algorithm makes use of the Feistel cipher algorithm. The plaintext is divided into blocks of size 128 bits. Each block is further divided into two sub-blocks say L and R 64 bits each. We divide these L and R blocks again, say L0 and R0 (from L), and L’0 and R’0 (from R). In the next step, we perform Feistel cipher over L0 and R’0 on one hand and L’0 and R0 on the other. Multiple divisions and inter Feistel between left and right blocks ensures better mixing and provides a better avalanche effect. Everything combined ensures better security of the plaintext involved.
Setting Up Your Python Environment, Easily

This chapter introduces the Python programming language to geologists. To do so, it first describes the main features of Python, and subsequently defines the programming paradigms supported in Python. Finally, it provides a step-by-step guide to prepare a Python environment for scientific computing.
Evaluation of Artificial Intelligence-Based Denoising Methods for Global Illumination

As the demand for high-quality rendering for mixed reality, videogame, and simulation has increased, global illumination has been actively researched. Monte Carlo path tracing can realize global illumination and produce photorealistic scenes that include critical effects such as color bleeding, caustics, multiple light, and shadows. If the sampling rate is insufficient, however, the rendered results have a large amount of noise. The most successful approach to eliminating or reducing Monte Carlo noise uses a feature-based filter. It exploits the scene characteristics such as a position within a world coordinate and a shading normal. In general, the techniques are based on the denoised pixel or sample and are computationally expensive. However, the main challenge for all of them is to find the appropriate weights for every feature while preserving the details of the scene. In this paper, we compare the recent algorithms for removing Monte Carlo noise in terms of their performance and quality. We also describe their advantages and disadvantages. As far as we know, this study is the first in the world to compare the artificial intelligence-based denoising methods for Monte Carlo rendering.
User Experience Analysis of binus.ac.id Website with The Usability Testing Perspective (A Case Study Approach)

Nowadays student registration can be done via online as well as offline. Which is why university website is important and need to be clear, so the potential student or parent can acquire correct and complete information about the university. The author using BINUS university website as an object for this research. This research will use two online accessibility tools for gathering information such as quality assurance for the component of the website and load speed when user click the website. The goal for this research is to find any missing spot on the website, which is been ignore from the initiate of the website deployment. So, from the result of this research, it can give the website developer suggestion to reconstruct the website by improving the quality of user experience and this research become the lesson learned also for the researcher in area of user experience.
Middleware for Healthcare Systems: A Systematic Mapping

The improvement of technological applications focused on the health context is growing. Different types of applications, such as Health Information Systems (HIS), Electronic Health Records (EHR), and e-Health and m-Health applications, are being developed. With the increasing use of technological solutions in this context, there is a need to integrate the data collected, stored, and processed in these systems. However, the heterogeneity of the data is a factor that makes this integration difficult. To mitigate this problem, middleware appears as an option. This paper aims to understand the current state and future trends of middleware for healthcare applications. To do so, we carried out systematic mapping of the literature on middleware for healthcare systems. This Systematic Mapping initially collected 1162 works, which after the execution of its stages, 34 works were selected for careful reading and, as a result, we present answers to the defined research questions, collected from a careful reading of the selected works. Finally, we concluded that, although there are several middleware solutions in the literature, there is no standard among these solutions, and they end up being limited to solving the problem in a specific context in the health area.