Reviewerly/exHarmony huggingface dataset:
Price competition and blockchain technology adoption strategies of agents on the digital platform

The rise of digital platforms intensifies the price competition among agents. Agents often use low price strategies to attract consumers. However, the low-price strategy is often filled with false information and consumers perceive the non-truthfulness of the price information. Then, consumers' trust in agents gradually decreases, which inhibits the growth of online shopping. Blockchain is seen as a solution to the trust crisis between agents and consumers. Our research is based on two competing agents selling the same type of goods on the same platform. We discuss agents' blockchain technology application strategies in three scenarios, which are defined by whether agents choose to apply blockchain technology to improve consumer trust. The results show that the application of blockchain technology is beneficial to agents only when consumer trust is low. Furthermore, the YN strategy is regarded as a possible equilibrium strategy, which depends on the blockchain application cost and consumer trust. Some extended cases are discussed for post-blockchain consumer welfare, cost-sharing contracts, dishonesty penalties, and variable blockchain costs, and the results show that the analysis in this manuscript is robust. Our findings have important practical significance for promoting the application of blockchain technology and alleviating the problem of price information asymmetry in platform shopping.
The effects of applying an augmented reality English teaching system on students’ STEAM learning perceptions and technology acceptance

With the inherently interdisciplinary and hands-on nature of the STEAM system, this study changes the way the English language is taught, no longer confining it to monotonous tests and dull textbooks. Teaching via Augmented Reality (AR) technology tends to pique students' interest, making them more willing to participate in learning. This study used AR technology in conjunction with gaming, where the participants were split into pairs, which promoted friendly competition among themselves, and, in turn, drove them to learn more. Moreover, the ability to track their own performance was added to the system, as well as an in-depth explanation for every question. The teacher used our teaching aids system in the lessons to make the teaching process more interesting and enjoyable. There were two English course experiments, each lasting for 4 weeks. The goal of the first experiment was to gain a general understanding of how students felt about the lessons and how well they could operate the system. After analyzing the results, some improvements for problems that arose were made in the second experiment. The goal of the second experiment was to find out how students felt about the lesson content and teaching aids and to compare the results of the two experiments. To help us find potential oversights and check how participants felt about this approach, surveys regarding STEAM learning and overall ease of use were administered. The results showed that AR English learning was widely considered helpful by the students, and also, our system was easy to operate, and competing with peers really motivated them to do better.
The Impact of FDI Inﬂows on Poverty Reduction: Empirical Evidence from Egypt

Foreign direct investment (FDI) is a major driver of international economic integration. With the right policy framework, FDI can provide financial stability, promote economic development and enhance the well-being of societies. It is generally considered by many international institutions, politicians and economists, as a factor promoting the economic growth of the recipient/ host country, as well as solving the economic problems of developing countries. This can be achieved through allowing the host country to; improve its competitive position; transfer technology and knowledge between economies; promote its products on a larger scale in international markets. In addition to all these benefits, FDI is considered as an important source of capital for the host country.&#x0D; &#x0D; In the light of this, this paper aims to determine the impact of FDI on poverty in Egypt during the period of 1961 to 2018 using Autoregressive distributive lag model (ARDL) Since there is no single variable that can capture poverty in Egypt, three variables have been used as proxy to poverty which are Household Consumption (POV1), Infant Mortality rate (POV2), and Life Expectancy at birth (POV3). After combining the results, some policy recommendations are proposed to enhance the impact of FDI on poverty reduction in Egypt which in turn affects economic growth.
Modelling Organizational Commitment, Employee Motivation, Employee Participation and Employee Performance: A Conceptual Model

The UAE banking sector is experiencing a decline in profitability and performance. If the trend is not curtailed, it could aggravate unemployment, loss of market, declined growth of entrepreneurship and a recessed economy. Despite many factors contributing to superior organizational performance, employee performance has been deemed as the most crucial. Therefore, this paper proposes a conceptual model that theorizes the direct effect of organizational commitment on employee motivation and employee performance, the mediating role of employee motivation, as well as the moderating role of employee participation. Using an extensive literature survey, this paper presents a logic-based and empirically based conceptual discussion that employee performance, employee motivation, organizational commitment, and employee participation are significant drivers of performance. The model extends the knowledge base on organizational commitment, employee motivation, participation and performance, and offers some insights for the stakeholders, managers in the banking sectors, and policymakers on what could be positive drivers of enhanced employee performance. Future studies could empirically validate the model in the context of the UAE banking sector to provide solid evidence on how the model could enhance the employee performance of the UAE banks.
Learning Machine Learning with a Game

AIs playing strategic games have always fascinated humans. Specifically, the reinforcement learning technique Alpha Zero (D.Silver, 2016) has gained much attention for its capability to play Go, which was hard to crack problem for AI for a long time. Additionally, we see the rise of explainable AI (xAI), which tries to address the problem that many modern AI decision techniques are black-box approaches and incomprehensible to humans. Combining a board game AI for the relatively simple game Connect-Four with explanation techniques offers the possibility of learning something about an AI's inner workings and the game itself. This paper explains how to combine an Alpha-Zero-based AI with known explanation techniques used in supervised learning. Additionally, we combine this with known visualization approaches for trees. Alpha-Zero combines a neuronal network and a Monte-Carlo-Search-Tree. The approach we present in this paper focuses on two explanations. The first explanation is a dynamic analysis of the evolving situation, primarily based on the tree aspect, and works with a radial tree representation (Yee et al., 2001). The second explanation is a static analysis that tries to identify the relevant situation elements using the Lime (Local Interpretable Model Agnostic Explanations) approach (Christoforos Anagnostopoulos, 2020). This technique focuses primarily on the neuronal network aspect. The straightforward application of Lime towards the Monte-Carlo-Search-Tree approach would be too compute-intensive for interactive applications. We suggest a modification to accommodate search trees and sacrifice the model agnosticism specifically. We use a weighted Lasso-based approach on the different board constellations analyzed in the search tree by the neuronal network to get a final static explanation of the situation. Finally, we visually interpret the resulting linear weights from the Lasso analysis on the game board. The implementation is done in Python using the PyGame library for visualization and interaction implementation. We implemented the neuronal networks with PyTorch and the Lasso analysis with Scikit Learn. This paper provides implementation details on an experimental approach to learning something about a game and how machines learn to play a game.
A MULTI-CRITERIA GIS BASED ANALYTICAL HIERARCHICAL APPROACH FOR SOLAR PHOTOVOLTAIC FARM SITE SELECTION IN THE KOLKATA METROPOLITAN AREA, INDIA

Abstract. A smart solar city ensures smart energy resilient future, low emissions and sustainable development of cities through networked infrastructure. This study aims to determine suitable locations for solar farm development in Kolkata Metropolitan Area (KMA), India, which may help the decision makers to plan resource allocation for smart renewable energy development efficiently. Numerous criteria were considered while simulating the model, including solar irradiation, distance from power substation, distance from major roads, land cover distribution, mainly canals, vacant land, urban green zones, and agricultural land. Built-up residential spaces and protected zones were however, no considered. The analysis suggests that wetlands, canals, and a major portion of open vacant land in the outskirts of the KMA are most suitable for investment in solar photovoltaics infrastructure. The present study covers different aspects of solar city planning to determine the suitable locations, which when accompanied by careful onsite investigation and meticulous planning will help decision makers and planners to design an efficient solar energy plan for the deployment of necessary infrastructure and technologies in cities.
The Project Win Game™: A Serious Game for Project Management Simulation

Agile project management methodology usage have grown widely in the last decade. Not all types of projects should apply agile methodologies, and the tasks that would typically be executed by a project manager are not (explicitly) addressed in agile methodologies. This paper describes The Project Win Game: a serious game for people to experience the differences in decision-making between traditional/waterfall and agile projects. The game engages an audience in complex decision-making and allows them to experience the effects of their decisions; this allows non-managers to learn project management responsibilities and translate that experience into knowledge. This paper describes the gamification of project management, the development of the game, and the lessons learned through single-player and competitive play. This paper would interest people that need to engage non-managers in management tasks and understand how a board game builds knowledge based on the experience of playing the game.
HUMAN MACHINE INTERFACING VIA PERIPHERAL SENSORY NEURON STIMULATION

Electronics Travels Aids (ETAs) are electronic devices that are created to help the blind navigate their environment. To help the visually impaired people to navigate the environment, this paper seeks to address the following goals: • Detect object in path of travel • Detect motion of object • Detect horizontal • Detect vertical location of object • Detect shape of object Brail Vision consists of a belt of vibro-motor actuators which will be mounted to user’s head, and an android phone as the processing unit and camera. The ETA was mainly developed to enable experimentation with the algorithm proposed which is described in detail in this paper. The proposed system managed to meet 80% of the targets. The paper details how the proposed system was implemented and tested.
An SDE perspective on stochastic convex optimization

We analyze the global and local behavior of gradient-like flows under stochastic errors towards the aim of solving convex optimization problems with noisy gradient input. We first study the unconstrained differentiable convex case, using a stochastic differential equation where the drift term is minus the gradient of the objective function and the diffusion term is either bounded or square-integrable. In this context, under Lipschitz continuity of the gradient, our first main result shows almost sure convergence of the objective and the trajectory process towards a minimizer of the objective function. We also provide a comprehensive complexity analysis by establishing several new pointwise and ergodic convergence rates in expectation for the convex, strongly convex, and (local) {\L}ojasiewicz case. The latter, which involves local analysis, is challenging and requires non-trivial arguments from measure theory. Then, we extend our study to the constrained case and more generally to certain nonsmooth situations. We show that several of our results have natural extensions obtained by replacing the gradient of the objective function by a cocoercive monotone operator. This makes it possible to obtain similar convergence results for optimization problems with an additively "smooth + non-smooth" convex structure. Finally, we consider another extension of our results to non-smooth optimization which is based on the Moreau envelope.
DATA CENTER CORRELATION WITH CARBON EMISSION AND ENVIRONMENTAL IMPACT

For every second, massive data were created on the internet by emails, visited websites, uploaded videos or photos, or information.There are approximately 44 zettabytes of data in the world.For handling this large number of data, data centers are needed.Data centers support all types of computation, data storage, and network and business applications for the enterprise.From the technology, business, and political perspective it is good but data centers impact badly on the environment.Data centers need a large scale of electricity for cooling and running IT equipment, which causes carbon and greenhouse emissions.Reducing carbon and greenhouse emissions, generating renewable energy sources, and controlling e-waste created by data centers are the current barriers for the data centers industry to handle.There is a large amount of data centers across the world most of the data centers run on fossil fuel only a few of them use renewable sources.This paper is about making the data center more efficient and sustainable by reducing carbon emissions.The following review of the literature summarizes the approach of the data center industry to environmental impact and provides direction for future research.
Academic spam mails from “Predatory Journals” in cardiology: a cross-sectional study over 3 months

Abstract Background Numerous online journals are referred to as predatory journals because their efforts are focused primarily on collecting publication fees rather than on content, peer review, or manuscript presentation. Many of these journals send Academic Spam Mails (ASM), requesting manuscript submissions. Purpose/Methods Over a 3-month period, all ASMs addressed to the author were collected and analyzed. A fake publication was created with a commonly used structure (Introduction, Methods, Results and Discussion). The article just repeated the phrase “Please get me off your mailing list” over and over again. This publication was submitted in response to each request to test the control mechanisms of the respective journals. Results During the study period, excluding duplicate requests, 125 ASMs (average 2.1 per business day) were received from a wide variety of journals spanning 38 different subject areas, of which 29/38 (76.4%) were from the medical field. 16/125 (16%) were related to a previously published article but not among the author's most important publications (median h-index rank 22; range 10–138). The majority (73.6%) of journals were listed in Beall's predatory journal list, only an absolute minority (0.8%) were listed in the Directory of Open Access Journals, and 96% were not listed in PubMed. Original papers (94.4%), reviews (88.0%), and case reports (87.2%) were most frequently requested; the publication charge (APC) was a median of $1,199 (range $50–3,586); in about 22% of cases, it was not apparent whether APCs were charged and, if so, how much. For the 125 nonsense publications sent out, there was a response in 16/125 (13%) (6 acknowledgements of receipt, 3 apologies, 2 the journal comments that the publication consisted of only one sentence, and 5 other responses). Interestingly, in one case editorial board members had also created a nonsense publication titled: “Ok we not send mail to you” and sent it to the author. 21.6% of the journals had sent ASM to the author again during the next months. Conclusions Academic spam mails from “predatory” online journals are a common problem. They are recognizable by their appearance in warning lists or non-appearance in directories of reputable open access journals. A placement of a nonsense publication in such a “predatory journal” was not successful. Funding Acknowledgement Type of funding sources: None.
Disclosure of Classified Documents by The Public Officer and Its Effects In Iight of the Saudi Law

This paper investigates the public employee’s disclosure of classified documents and the effects of such disclosure in the Saudi laws, given that classified documents are one of the most important methods of management in facilitating daily work. Besides, classified documents are of major importance with regard to state security and the interests of individuals and their personal lives. The research includes an introduction, a preface, two chapters and a conclusion. The preface demonstrates the concepts of disclosure, public employee, and classified documents. The first chapter of the research deals with the basis of the public employee’s commitment to protection of classified documents in the Saudi laws. The second topic focuses on the effects of the public employee’s disclosure of classified documents and the penalties resulting thereof in the Saudi laws. It was found that classified documents are written documents issued by official departments and similar public institutions that are subject to the authority of the state and its legislations, including information that the employee is notified of divulging or disclosing them. The researcher recommended that the administration should establish a clear mechanism for protecting classified documents, and a classification of the degrees of confidentiality that aligns with the nature of the activity of each department, giving importance for disseminating awareness among employees of the danger of disclosing classified documents, and effects of such disclosure on the security of the state, society and individuals.
Impacts of Societal Broadcasting on Student’s Educational Prominence

Pakistan is third among all major nations in terms of the number of internet users, according to societal and mobile spectators. Social media platforms like WhatsApp, Facebook, YouTube, Twitter, Instagram, and others in Pakistan distract students from their studies. Students utilize social media more frequently than personal email. Even so, safety and privacy are compromised. It offers chances to get in touch with friends, classmates, and individuals who share interests. Today's students should priorities their education and future careers. However, a lot of students rely on the information being available on various forms of community broadcasting. Report also highpoints how commonplace social gathering places are in many students. Social networking sites and societal media have altered the world and brought people closer together than beforehand. Apprentices may take benefit of this, though, and use it to improve their lives and tomorrow. It should be utilized to interconnect, keep in touch, while avoiding time wastage. 200 people made up the study's sample. To identify the many sociocultural media influences on students' education, a questionnaire was created. Gender, education, societal impact, and academic standing are the factors noted.
Fighter Aircraft Detection using CNN and Transfer Learning

In this work, Deep learning techniques such as Convolutional Neural networks (CNN) and Transfer Learning are used to detect and identify Fighter aircraft or jets. A dataset consisting of 21 different aircraft with 20000 images is being processed using the above algorithms. CNN works on the principle of "pooling," which progressively reduces the spatial size of the model to decrease the number of parameters and computations in the network. CNN's are widely used for image detection in different domains, including defense, agriculture, business, face recognition technology, etc. Transfer learning is a machine learning method where a model created for a task is reused as the initial point for a model on a second task. Transfer learning is related to issues such as multi-task learning and concept drift and is not only an area of study in deep learning. The dataset is processed and uses python libraries such as pandas, seaborn, sci-kit- learn, etc., to find any pre-trained patterns and insights. Data is separated into train and test datasets with 80-20 percent of total data, respectively. A model is built using the TensorFlow library for CNN. The metric used is "accuracy." A transfer learning model is also built to compare the accuracy results and adopt the best-fitting one
Instilling Type Knowledge in Language Models via Multi-Task QA

Understanding human language often necessitates understanding entities and their place in a taxonomy of knowledge -- their types. Previous methods to learn entity types rely on training classifiers on datasets with coarse, noisy, and incomplete labels. We introduce a method to instill fine-grained type knowledge in language models with text-to-text pre-training on type-centric questions leveraging knowledge base documents and knowledge graphs. We create the WikiWiki dataset: entities and passages from 10M Wikipedia articles linked to the Wikidata knowledge graph with 41K types. Models trained on WikiWiki achieve state-of-the-art performance in zero-shot dialog state tracking benchmarks, accurately infer entity types in Wikipedia articles, and can discover new types deemed useful by human judges.
Toward Degradation-Robust Voice Conversion

Any-to-any voice conversion technologies convert the vocal timbre of an utterance to any speaker even unseen during training. Although there have been several state-of-the-art any-to-any voice conversion models, they were all based on clean utterances to convert successfully. However, in real-world scenarios, it is difficult to collect clean utterances of a speaker, and they are usually degraded by noises or reverberations. It thus becomes highly desired to understand how these degradations affect voice conversion and build a degradation-robust model. We report in this paper the first comprehensive study on the degradation robustness of any-to-any voice conversion. We show that the performance of state-of-the-art models nowadays was severely hampered given degraded utterances. To this end, we then propose speech enhancement concatenation and denoising training to improve the robustness. In addition to common degradations, we also consider adversarial noises, which alter the model output significantly yet are human-imperceptible. It was shown that both concatenations with off-the-shelf speech enhancement models and denoising training on voice conversion models could improve the robustness, while each of them had pros and cons.