Reviewerly/exHarmony huggingface dataset:
Blockchain Technology in Inclusive Finance Under the Background of Big Data

Big data finance is prone to cause information asymmetry, which enables it to achieve a better integration with blockchain technology. This research mainly discusses the application of blockchain technology in inclusive finance under the background of big data. First, according to the customer’s risk and value, transaction behavior characteristics and preferences, etc., the advanced database system and big data mining and analysis technology are used to make full use of the customer information they have. Then use social network technology and big data technology to realize online collection of customer information and timely processing of transaction data. Finally, in terms of resource allocation, in the GSP financial model based on blockchain technology, the relevant information of the supply and demand parties of funds is directly published on the network and matched by themselves, and the supply and demand parties of funds directly communicate and trade. It can be seen from the comparison of the average value of risk changes after using blockchain technology in the big data scenario: credit risk and operational risk are both lower than 2.5, and these two risks are considered to be overall risk reduction. This research provides active guidance for further improving the inclusive financial service system.
Functional-predictive Model Environments of Students in Graduate School as a Means of Scientific and Scientific-pedagogical Training of Highly Qualified Personnel

Introduction to the problem. The relevance of the analysis of the environment of a graduate student from the position of searching for a pedagogical means of managing his formation as a highly qualified specialist is due to the multidimensional goals and objectives of postgraduate training, which are based on the conditions of real life. The purpose of the article is to identify and justify the functional and predictive model of the environment of students in graduate school as a means of scientific and scientific-pedagogical training of highly qualified personnel. The methodology of the study. Theoretical bases of the description of a model of students in graduate school made presentation on environmental design, as part of a technology of the environmental approach in education, as well as theoretical and practical experience of understanding the way of life as a condition of personality during the educational process. An important role was played by the results of the study of the use of information and communication technologies as a system-forming factor in the training of highly qualified personnel in graduate school, conducted by the author since 2017. Results and conclusion. The environment of students in graduate school is presented as an integral tool that includes the parameters of the possibility, probability and reliability of achieving an educational goal in the environment and with the help of the environment. These parameters are revealed in the course of a sequential solution of nine tasks to describe the environment of students in graduate school, based on the rules of combining parts of the environmental approach in order to realize the educational potential inherent in it. The way of life of a graduate student is considered as a condition for becoming a highly qualified specialist with scientific and scientific-pedagogical training as a result of the interaction of the student with the environment.
Discovering Diverse Multi-Agent Strategic Behavior via Reward Randomization

We propose a simple, general and effective technique, Reward Randomization for discovering diverse strategic policies in complex multi-agent games. Combining reward randomization and policy gradient, we derive a new algorithm, Reward-Randomized Policy Gradient (RPG). RPG is able to discover multiple distinctive human-interpretable strategies in challenging temporal trust dilemmas, including grid-world games and a real-world game Agar.io, where multiple equilibria exist but standard multi-agent policy gradient algorithms always converge to a fixed one with a sub-optimal payoff for every player even using state-of-the-art exploration techniques. Furthermore, with the set of diverse strategies from RPG, we can (1) achieve higher payoffs by fine-tuning the best policy from the set; and (2) obtain an adaptive agent by using this set of strategies as its training opponents. The source code and example videos can be found in our website: https://sites.google.com/view/staghuntrpg.
A survey on Variational Autoencoders from a GreenAI perspective

Variational AutoEncoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to efficiently solve the generation problem for high dimensional data. The key insight of VAEs is to learn the latent distribution of data in such a way that new meaningful samples can be generated from it. This approach led to tremendous research and variations in the architectural design of VAEs, nourishing the recent field of research known as unsupervised representation learning. In this article, we provide a comparative evaluation of some of the most successful, recent variations of VAEs. We particularly focus the analysis on the energetic efficiency of the different models, in the spirit of the so called Green AI, aiming both to reduce the carbon footprint and the financial cost of generative techniques. For each architecture we provide its mathematical formulation, the ideas underlying its design, a detailed model description, a running implementation and quantitative results.
Multi-Robot Path Planning for Persistent Coverage Tasks with Performance Guarantees

Recent advances on the design of autonomous mobile agents have motivated the use of the latter in persistent surveillance tasks with applications in agriculture, information gathering and search and rescue missions. In these tasks the goal is to design agents’ paths so as every point in the area of interest is covered more than once. In literature the persistent surveillance problem has been addressed under two different frameworks: 1) the visitation frequency of a finite set of points (discrete spaces) and 2) the amount of coverage offered by the agents over the area of interest. In the first category agents aim at visiting a finite set of points in the area so as the time elapsed since a point was last visited is minimized over the set. Here, the environment is considered static and no information on the coverage condition of the points is available. In the second category the environment is characterized by a property that changes over time, called coverage level and the goal is to design agents’ paths so as the coverage level is maintained at a desired level over the area. In this approach the error between the actual and desired coverage level of the area is bounded. However, locally no information is available on the frequency at which each point gets covered. The novelty of this thesis lies in the integration of the aforementioned solution approaches so as complete coverage information is provided for a finite set of points. Here, the goal is to design the agents’ paths so as the coverage level of each point is bounded from below by a desired level. The paths are planned over a finite optimization horizon and found as a solution to a Mixed Integer Linear Program (MILP). Two formulations of the problem are proposed and their efficiency is validated in simulation. The recursive feasibility of the centralized problem is also guaranteed when a set of time-varying terminal constraints is introduced to the problem. These constraints force agents to move along a pre-defined set of closed paths designed in a way that the coverage level boundness constraint is always satisfied. A two-step method is proposed for their design that relies on the solution of a Linear Program (LP) when a set of closed paths violating the objective is given, found as a solution to a modified version of the proposed MILP at step 1. Finally, two distributed formulations of the problem are introduced in which agents solve in parallel a local path planning problem. The difference on these methods lies in the accuracy of the information available to the agents for planning. Despite this difference simulation tests show a difference of only 1.5% in the coverage level performance when various sized teams of agents are employed for the task.
Educazione linguistica inclusiva e <i>Language Teacher Cognition</i> Proposta di uno strumento di indagine e alcuni dati da un’indagine in Lombardia

Italy has always been a multilingual country. Over the past fifty years, globalisation has brought a new multilingualism to schools. During approximately the same time span, many scholars and linguists have outlined a vision of language education as a global process, which has gradually found its way into Italian Ministry of Education documents. The implementation of language education, however, is ultimately entrusted to teachers and is therefore conditioned by their ‘language teacher cognition’. This contribution, based on data derived from a larger survey conducted in Lombardy, focuses on teachers’ attitudes to and opinions about pluringual and pluri/intercultural activities.
Water Pollution scrutinize and forecasting Approach based on the application of 3s technique

With the inexorably unmistakable of the water contamination issue and fast improvement of the 3S procedure, the observing and anticipating of water contamination framework join with 3S gets conceivable. Water contamination organize has been produced for a long time, however is constrained to the investigation of patterns as opposed to making precise transient figures. A significant boundary to water contamination determining is the absence of an effective framework for water contamination observing. Conventional water contamination examining is tedious, costly, and must be taken for little sizes. 3S procedure gives another method to screen water contamination monotonously for an enormous zone. Also, it tends to be constant observing and anticipating. The created determining framework is intended to anticipate water contamination factors utilizing distant detecting information as a contribution to instate and refresh water contamination conditions.
Comparative Analysis of Reinforcement Learning Algorithms on TORCS Environment

In this study, reinforcement learning algorithms are compared in TORCS simulation environment. In this simulation environment, the goal is to finish the track as soon as possible by controlling the car. The agent decides actions by using highlevel observations from the environment. For this goal, two reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Deep Q Network (DQN)) are used and the results are compared and analyzed. Since the action space is continuous, DDPG algorithm performed better as expected. However, we were able to show that DQN algorithm also gives comparable results.
EVALUASI PROGRAM PEMBERDAYAAN PEREMPUAN MELALUI PELATIHAN MENJAHIT DI SANGGAR KEGIATAN BELAJAR (SKB) UJUNG PANDANG

This study was aimed to describe evaluation results of: (1) participants’ level of satisfaction, (2) participants’ level of understanding of the material, (3) participants’ post-training level of implementation, (4) improved success of participants due to the training. The research type was evaluation research using Kirkpatrick’s Four Level model, which consisted of: Reaction, learning, behavioral, and result levels. The research samples were 20 former training participants. Data was collected by questionnaire, observation, and unstructured interview. Quantitative data was processed with the help of SPSS 21.0 for Windows. Validity of instrument at reaction, learning, behavioral, and result levels used Corrected Item-Total Correlation. Reliability calculation used Alpha formula. The research result showed that: 1) evaluation of reaction level: the satisfaction to sewing making training in terms of material, tutor, facility, and training method by PAP and PAP was “satisfying” 2) evaluation of learning level: understanding of material of the participants of making training sewing in terms of understanding of training material by PAP and PAN was “Mastering” 3) evaluation of behavioral level: participants’ implementation after training sewing of making in terms of post-training behavioral aspect by PAP and PAN was “implementing” 4) evaluation of result level: improved resulting competence due to training sewing of making by PAP and PAN was “poorly improved”.
Practical Approaches to Linked Data

In this article, Jeannie Hartley presents research regarding linked data and its founder, and discusses how linked data can be used. Some benefits and challenges discovered in the research are discussed, as well as some requirements for the process. Focus is given to Research Description Framework (RDF) and Resource Description and Access (RDA) for use in linked data records. Hartley also offers an examination of some linked data projects. The article then transitions to Heidy Berthoud’s discussion of various linked data projects in process at the Smithsonian Libraries (SIL). She addresses the development of the linked data macro, SIL’s participation in the Program for Cooperative Cataloging’s (PCC) Uniform Resource Identifiers (URIs) in Machine Readable Cataloging (MARC) Pilot Project, and early explorations into Wikidata.
LibGuides: IED-PK | How to Get Copyright Clearance: Identifying Predatory Journals

The purpose of this guide will assist you to get permissions for learning materials and their fair use.
Deep Convolutional Neural Network Ensembles using ECOC

Deep neural networks have enhanced the performance of decision making systems in many applications including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is very high or the performance gain obtained is not very significant. In this paper, we analyse error correcting output coding (ECOC) framework to be used as an ensemble technique for deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a combinatory technique which is shown to achieve the highest classification performance amongst all.
Pengembangan Website Direktorat Jenderal Industri Agro Kementerian Perindustrian

The Directorate General of Agro-Industry is one of the implementing elements of the duties of the Minister of Industry, which has the task of organizing the formulation and implementation of policies. In order to encourage the growth and development of the role of the agro-industry, especially in providing informative information, which is very important in the current era of technology 4.0, publications and information facilities are needed regarding the development of the agro-industry. The design of this study used a descriptive method, using an object-oriented approach, while its development used a prototype model. This study aims to develop a website that is in accordance with several important factors that need to be considered so that website development can touch the quality side of the Ministry of Industry, Directorate General of Agro-industry which is in line with Law Number 14 of 2008 concerning Information Disclosure. This research produces a quality website at the Ministry of Industry, Directorate General of Agro-Industry, and meets the principles of good governance in accordance with Presidential Instruction No. 3 of 2003. So that the transparency and accountability of the information on this website are expected to have an impact on increasing the confidence of both local and foreign investors.
코로나19 환경의 온라인 관광영어수업에 관한 연구

The present study is aimed at investigating online-based Tourism English courses during COVID-19 and suggests teaching and learning activities to...
Implementación de las TIC para fortalecer las competencias básicas en el área de matemáticas

In the current information and knowledge society, the development of our daily activities is unthinkable without the support of the tools that technology gives us; Education is not exempt from this reality, it is increasingly common to observe in classrooms the use of ICT tools that are implemented by teachers to boost the teaching-learning process and thus generate a more active and participatory student in its own learning process, developing a critical reflexive thinking that guarantees meaningful learning. This work represents a proposal for the design and implementation of an online course in mathematics education that contributes to acquire or develop the eight basic competences in the area of ​​mathematics such as: Thinking and reasoning; To argue; Communicate; To model; Pose and solve problems; Represent; Use symbolic, formal and technical language and operations and finally the use of ICT tools and resources.
Few-shot Self-optimization Learning Based on Deep Metric

Due to the lack of labeled images and the effects of noisy pseudo-labels make few-shot learning (FSL) extremely challenging. For this problem, we propose a deep metric self-optimization (DMSO) method based on deep neural networks. Firstly, we construct a siamese network classifier (SNC) that was trained through labeled images. Then, the classifier is used to predict pseudo-labels for unlabeled images. In order to reduce the influence of noise, we create a confidence selection module (CSM) and use it to select pseudo-labeled images with higher confidence. Then, adding pseudo-labeled images to the training set. Finally, we use labeled images and pseudo-label images to jointly retrain the classifier. By the above means, we achieve the purpose of data augmentation and model self-optimization. In this paper, we focus on the one-shot classification task and achieve the state-of-the-art results which exceed those of other few-shot learning models. We evaluate DMSO method on two FSL benchmarks including miniImageNet and tieredImageNet.
Intervensi Team Building Training Untuk Meningkatkan Kualitas Koordinasi dalam Teamwork

This study aims to analyze the effect of team building training on the quality of coordination in teawork in the Indonesian Educating Organization (IM). This research was conducted qualitatively and quantitatively, which was conducted using interviews, observations and distribution of questionnaires taken based on the teamwork quality questionnaire (TWQ) from Hoegl and Gumenden (2001). The TWQ  consisted of six aspects including: (communication, coordination, balance of member contributions, support, effort, and cohesion). The respondents in this study were 30 members of the organization who came from the overall population of the IM Organization team. Implementation of the study, researchers distributed initial questionnaires to measure the quality of coordination in teamwork, there was a low score of coordination scores in teamwork in Indonesian educational organizations (IM). So, the researchers decided to take an action intervention in the form of providing team building training where the teambuilding training intervention was considered able to encourage organizational members to improve the group's internal functions, using t-test data analysis and statistical description (Pre-Post Test). The results of the t-value of -14,498 with a significance of 0,000 (p <0.05), indicate that there is a significant difference between the total score of coordination dimensions on the teamwork before and after the training. This can be seen from the mean score of the total quality of teamwork after training (mean = -0.826) which is greater than before training (mean 0.826). Thus, it can be concluded that the provision of team building training interventions influences coordination among fellow IM team members.
The Influence of Internal Communication on Job Performance: Controlling for Key Worker Characteristics

This study assessed the influence of internal communication on job performance, with relevant lurking variables controlled for as a new way of contributing to academic debate. This study was a correlational design that drew data from employees of University of Education Winneba (UEW). All 274 employees who met some selection criteria participated in this study. A self-reported questionnaire was used to gather data. Descriptive statistics, Pearson’s correlation test, ordinary least squares (OLS) regression analysis, and confirmatory factor analysis (CFA) were used to present findings of the study. The study found that downward, upward, and lateral communication in the university made a positive effect on job performance after controlling for job tenure, education, income, and employee level. Downward communication makes the strongest effect on job performance. It is therefore concluded that increasing the level of effectiveness in terms of downward, upward, and lateral communication in the university can increase the level of job performance.
Alfabetizzazione digitale, scrittura enciclopedica ed educazione linguistica democratica

The text describes two different kinds of literacy: the traditional and the digital one. It then places them in a context of “Linguistic education” ( educazione linguistica) , highlighting the possible benefits of a more significant inclusion of real practices within the educational curricula. This is done by looking at both “vernacular literacy” practices and practices taken from the workplace. The use of Wikipedia in the Italian language classroom is afterwards introduced as an example of such possible integrations.
Traveling Light — A Low-Overhead Approach for SPARQL Query Optimization

SPARQL query processing in triplestores has to deal with many of the same problems as query processing in relational databases, and additional problems due to the schema relaxed nature of RDF. The flexible pattern matching capabilities of SPARQL queries entail performance challenges for complex queries. Most modern query optimizers produce a significant overhead as they use an exhaustive statistics generation and storage approach. Currently, there is no pure online cost-based optimizer for SPARQL queries. In this paper, we explore the hypothesis that just storing selectivity statistics for predicates enables effective optimization of typical queries. Based on this, we introduce a pure online optimizer for triplestores, the Online Join Order Optimizer (OJOO), which learns from query executions. OJOO's overhead in creating and persisting statistics is very low, and it provides an easily extendable storage architecture for statistics. We implemented the OJOO in a main-memory triplestore, PDStore (Parsimonious Data Store), and evaluated its performance experimentally using the Lehigh University Benchmark (LUBM). Our experimental results revealed that the OJOO is competitive, efficient, scalable, and has a negligible runtime overhead.
UiTM Law Newsletter (Jan - Apr 2019)

UiTM Law is more than pleased to officially launch the inaugural issue of its quarterly newsletter. Ever since its establishment, UiTM Law has developed into a strongautonomous and integrated academic community of academic staff and students that have incorporated into a leading university network. Being a faculty consisting ofmembers from wide–ranging expertise, knowledge and skills, there are numerous activities, diverse programmes as well as achievements occurring within our community.We hope to share these exciting activities as well as distinguished achievements through the upcoming issues of our newsletter. Our activities have expanded in depth and breadth in response to the increasing sophistication and internationalization of the education fraternity in Malaysia. Our upcoming issues would also present ourcontinuous improvements and innovations, which would hopefully contribute to further development of UiTM Law.