Reviewerly/exHarmony huggingface dataset:
A Real-time Multiplayer FPS Game using 3D Modeling and AI Machine Learning

AIs have been a key component in the gaming industry throughout its history. Developers have had multiple ways of creating new AI models that best suit their game in order to enhance the playing experience. However, with the increase in the popularity of online multiplayer games, AIs now must compete with the experience of playing with other people.To enhance AI behaviors to match that of a real player, the paper discusses the one solution for creating models that can be used for further AI research. Through utilizing some of the built-in features of Unity as well as Photon Network services, the game Maze Escape combines the multiplayer aspect of FPS games and some simple game AI models to allow them to be compared against each other in order to more easily recreate multiplayer experience using AI bots. Thus, this paper hopes to encourage developers to think about how AIs are not only used to enhance single player experiences, but it can also be used in multiplayer.
Pengaruh Metode Outdoor Learning terhadap Hasil Belajar IPS Siswa Kelas V SD Gugus II Kecamatan Bontoramba Kabupaten Jeneponto

This research is a quantitative research with a quasi-experimental research design.. The data analysis technique is descriptive statistical analysis and inferensial analysis using independent sample t-test. The result obtained in this study are: (a) there are differences in students’social studies learning outcomes between the experimental class and control class learning outcomes are higher than the control class, (b) there is a significant effect using the outdoor learning method on social studies learning outcomes. Student. The conclusion in this study is that there is an effect of using outdoor learning methods on social studies learning outcomes for fifth grade students of SD Gugus II, Bontoramba District, as evidenced by the results of the independent sample t-test with the results of the analysis obtained t count (7.064) &gt; t table (2.0243).
Analysis on Security and Privacy-preserving in Federated Learning

Data privacy breaches during the training and implementation of the model are the main challenges that impede the development of artificial intelligence technologies today. Federated Learning has been an effective tool for the protection of privacy. Federated Learning is a distributive machine learning method that trains a non-destructive learning module based on a local training and passage of parameters from participants, with no required direct access to data source. Federated Learning still holds many pitfalls. This paper first introduces the types of federated learning, including horizontal federated learning, vertical federated learning and federated transfer learning, and then analyses the existing security risks of poisoning attacks, adversarial attacks and privacy leaks, with privacy leaks becoming a security risk that cannot be ignored at this stage. This paper also summarizes the corresponding defence measures, from three aspects: Poison attack defence, Privacy Leak Defence, and Defence against attack, respectively. This paper introduces the defence measures taken against some threats faced by federated learning, and finally gives some future research directions.
Critical Factors in Promoting Sustainable Innovation in Bahrain

Despite many initiatives to help foster innovation over the last decade, Bahrain has yet to realise its full potential, indicated by low levels of published research, patent registrations, and entrepreneurial activities. The COVID-19 pandemic highlighted the importance of sustainable innovation for economic recovery. This study aimed to identify the critical factors needed to stimulate innovation, as well as any barriers. Using a case study approach, emerging findings from an online survey of participants from a range of private and government organizations were explored through in-depth interviews with selected "innovation influencers". It was found that the economic and cultural environments in Bahrain favor a conservative approach to innovation, with the opening of new market opportunities with existing products the most reported form applied. The need for a sustainable economic recovery post-pandemic has raised awareness of the importance of innovation in all its forms. The environmental factors influencing innovation are charted in an ecosystem model, with effective leadership and a national strategy identified as key components. The Innovation Ecosystem derived from this case study of Bahrain has wider applicability as a benchmarking framework. By mapping the local context against framework elements, identified gaps can usefully inform a sustainable innovation strategy at both a national and organizational level.
Production and Development of Wind Energy in Wasit and Al-Mothennah Governorates

Abstract Renewable or alternative energy is a criterion for international progress in investing its natural resources, especially wind speed. Since the uses of wind are witnessing a wide spread, it has become necessary to work on calculating the rate of wind energy density because it is a criterion that determines the extent to which wind turbines are used or installed. The researcher depends on a comparison between two areas with different classification; where Al-Mothennah governorate enjoys a large quantity of winds, while Wasit governorate is experiencing lower rates than its counterpart in Al-Semawah station.
Current research topics of Belarusian scientists through the prism of citation

The article considers a quantitative analysis of the citation of publications by Belarusian authors in subject areas in Scopus in order to present general data for all years and for the last decade. The obtained results were compared to identify possible changes. Subject areas of Belarusian science are outlined; the leading scientific thematic directions are determined both by the number of publications and by the number of citation. Separately, a block is presented concerning the dynamics of citation of publications of the National Academy of Sciences of Belarus, as the main scientific organization of the republic. The relevance of the study is confirmed by its applied nature, the results obtained can be used as input data by specialists in further research, heads of scientific organizations to make managerial decisions in the field of science policy, to determine the most promising areas of research, etc. It is concluded that the most Physics, Materials Science and Chemistry remain the cited subject areas of Belarusian science in Scopus, but over the past decade, in addition to Physics, Medical and Engineering Sciences have taken a leading position. An annual increase in the number of publications of the National Academy of Sciences of Belarus in Scopus was noted, their priority thematic focus both in the number of works and in the number of citations to them is Physics, Materials Science and Engineering. Recommendations for authors are given on how to increase the visibility of the results of research activities.
Policy Implementation of Luwu Regional Library Development Policy

This study aims to measure the implementation of regional library development service policies in Luwu Regency based on 4 indicators, namely: development and arrangement of library service systems, promotion of library services and promotions, development of future library services, and development of integrated service systems with libraries and information centers. This study uses a quantitative method with non-probability sampling technique that is exploratory, the determination of the sample is carried out by purposive sampling involving 194 respondents consisting of: Regional Librarians. Campus/School/Mosque Librarians, Community Reading Park Librarians (TBM), Village Library Librarians, Literacy Activists, and Library Visitors. The results showed that the development and arrangement of the library service system was in good category, namely 55%, service promotion and library promotion was in good category, 57%, future library service development was in good category, which was 52%, and the development of integrated service system with libraries and information centers was in the good category. good that is 56%.
The possibility of using electronic monitoring devices to supervise drug offenders in the Eastern Economic Corridor (EEC)

This research aimed: (1) to investigate best practices, problems, and hindrances related to the redirecting of offenders from the justice system applied by other countries; (2) to explore public attitudes towards the use of electronic monitoring devices for developmental approaches; and (3) to propose suggestive approaches for the supervision of offenders using electronic monitoring devices in the EEC to the Ministry of Justice and the Ministry of Labor.This research applied mixed methods including in-depth interviews with eight key informants.In addition, the researcher collected quantitative data from 1,240 samples available from three provinces: Chonburi, Rayong, and Chachoengsao.The analysis of the quantitative data revealed that the 574 samples in the EEC or 44.11 percent agreed with the application of electronic monitoring devices to the supervision of drug offenders who were drug addicts and dealers possessing heroin or other drugs of less than 20 grams and the employment of drug offenders with minimum wage.The research recommended four approaches for the supervision of defenders using the electronic monitoring system: (1) having offenders work in industrial factories instead of being imprisoned; (2) having offenders who have completed at least one-third imprisonment period wear electronic monitoring devices and allowed to work in an industrial factory instead of imprisonment;(3) carrying on the Correction Industrial Estate Project as piloted in Ban Bueng Temporary Prison, Chonburi, in November 2020; and (4) organizing a one-day working program where imprisoned persons would be allowed to leave for work in the morning and come back in the afternoon.
The effects of differentiated reading instruction on reading comprehension and self-efficacy of Thai undergraduate students

This study examined the effects of differentiated reading instruction (DRI) on the reading comprehension and self-efficacy of undergraduate students.The study indicated their improvement in both areas after the intervention.Thirty-three students attending the course of English for Scientists were chosen for the study.The pre-test in reading comprehension and the self-efficacy questionnaire were administered before the intervention.The scores of the pre-test of reading comprehension were used to divide the students into two groups according to their level of reading proficiency: lower-proficiency level students and higherproficiency level students.The post-test of reading comprehension and that of self-efficacy was administered after the course to measure the students' improvement.The content, process and product were tiered to divide the students into two groups.To differentiate the content and process, the materials were tiered according to each group's reading ability level.Intermediate-level students received the on-level texts while beginner-level students received the simplified texts.Scaffolding was also provided to the students to help their reading according to their proficiency.To differentiate the product or students' performance, the criteria for assessing students' presentation were also adjusted to suit the level of the students.The findings showed a significant improvement in both reading comprehension and self-efficacy, with more significant improvement in the lowerlevel proficiency group.Further research should explore how differentiated instruction functions with learners in different contexts and other language skills.
Structured Matrix Factorization Approach for Image Deblurring

In this chapter a method deblurring an image using two blurred instances is presented. The cases of a blurring function, measurement errors and noise are considered. The proposed algorithm is based on Numerical Linear Algebra methods for computing the Greatest Common Divisor (GCD) of polynomials. Because of the large size of the matrices representing the images, the exploitation of the special structure of the block banded matrices that are used, is of crucial importance. The proposed algorithm takes advantage of this structure and reduces the computational complexity of classical methods per one order.
Image stitching based on attention mechanism

With the rapid development of science and technology, almost all the cameras of smart devices have the function of image stitching. However, the current more popular image stitching still has problems in accuracy. If we need to get more accurate image stitching, the requirements of the algorithm will be higher and more memory will be occupied in the matching process. To solve the above drawbacks, I plan to introduce an attention mechanism to extract feature points by selecting more important feature regions from the overlapping regions of the target object as subjective feature regions, thus reducing the memory occupation at runtime. The experiment uses a binocular camera as the acquisition tool for the target object. By using subjective feature regions to extract feature points to optimize the feature point extraction and matching process, after repeated experiments, compared with the traditional method of using subjective feature regions to extract and match feature points with the traditional method, not only the memory consumption is optimized, but also the accuracy of stitching can be improved by about 10%.
Data flow adaptive clustering method in data center

With the continuous development of power grid informatization technology, the scale of data accumulated by power grid will become larger and the data dimension will be higher and higher. How to efficiently use flow clustering for power grid data mining is an urgent problem that needs to be solved. A data flow adaptive clustering method based on the power grid data middle station. The algorithm uses the high density between micro-clusters to calculate the local density peak of each micro-cluster node to adaptively generate the number of macro-clusters; Perform recursive online update; and remove the computation between edge-intersecting micro-clusters to reduce the amount of computation required to maintain macro-clusters. Theoretical analysis and experimental comparison show that the proposed method has higher performance than current mainstream stream clustering algorithms Quality clustering effect.
The Analysis and Calculation Method of the Protection Principle Diagram Simulation

The protection principle diagram can show the function realization process of the microcomputer protection software module, furthermore, it can be utilized for simulation to repeat protection function details. The paper designs an analysis and calculation method to get the detail procedure in protection action process simulation. Firstly, the paper constructs a schematic diagram model consistent with the actual microcomputer protection equipment. Secondly, the paper proposed the method to calculate the action sequence of each functional graphic element in the relay protection schematic model in real time, and so on, the state change process of each functional graphic element is obtained and recorded. As a result, it is inferenced of the state change sequence of the functional graphic element in the protection schematic diagram. Furthermore, the joint calculation of multiple protection schematic diagrams is achieved through the associated attributes of the graphic elements. The dynamic change process of each link in protection schematic simulation is reproduced by the proposed method for protection training simulation. The engineering practice of the simulation system verifies the effectiveness and real-time performance of the method.
SCPM-CR: A Novel Method for Spatial Co-location Pattern Mining with Coupling Relation Consideration

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located in nearby geographic space. Previous studies of SCPM only concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations in a co-location. Specifically, InterPCI is proposed to capture the inter-features coupling in a pattern, and IntraCAI is designed to capture the congregating behavior of intra-feature objects. Based on the anti-monotone property of InterPCI, a general framework which searches for patterns in a level-wise manner is suggested. To tackle the participating object search problem, a candidate-and-search algorithm with a heuristic backtracking search is proposed, namely CS-HBS. Extensive experiments are conducted to demonstrate the superiority of SCPM-CR, and also to validate the efficiency and scalability of CS-HBS.
Practical Measurement Angular Errors Analysis for Thermal-State Continuous-Variable Quantum Key Distribution

Thermal-state continuous-variable quantum key distribution (CV-QKD) of direct reconciliation with heterodyne detection can improve the robustness of the protocols. However, CV-QKD protocols require outlier detection, optical phase shifters are used to achieve orthogonal differentiation of the modes, but the actual optical phase shifters suffer from measurement angular errors, resulting in the imperfect phase. This kind of error degrades the security of the system. In this paper, we analyze the causes of measurement angular error and its impact on the secret key rate of the thermal-state CV-QKD protocols. We propose a compensation method to compensate for the effects of the measurement angular error and give a more complete secure analysis method under collective attack. The results obtained in this paper further improve the secure analysis under the thermal-state protocol and further enhance the performance of the system using the compensation method.
Student responses to the electronic module of the circulatory system

The use of electronic teaching materials could increase students’ interest in learning. An electronic module was one of the teaching materials. This study aimed to see the feasibility of the electronic module material for thecirculatory system. We used the research and development method. This study’s method had steps that included potential and problems, data collection, product design, design validation, design revision, and product testing. Five validators validated product design. The data analysis used the CVR and CVI values for each criterion in the validation aspect which are course, graphic, contents, and language with a minimum value of 0.99. The study found two important findings. First, from the validator, the CVR values in all elements had a value of 1, and the CVI results each had a value of 1. Hence, the electronic module of the circulatory system material is declared valid and feasible to use. Second, the product trial obtained an overall average score of 88.4% and was very positive. It means that the electronic module could be an alternative teaching material used in the learning process at school or self-study for the student at home.
Special issue on Application-oriented aspects of graphs and graph transformation (ICGT 2020)

Lifted (family-based) static analysis based on abstract interpretation is capable of analyzing all variants of a program family (or any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted domain are tuples, which maintain one property per system variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces.The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and #if directives). In this work, we introduce new symbolic representations of the lifted domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. In the first approach, elements of the new lifted domain are decision trees, in which decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the domains for representing linear constraints and leaf nodes. Furthermore, we propose another alternative approach for efficient lifted analysis. We encode a program family with numerical features as a family with only Boolean features, and then use a BDD lifted domain to analyze the resulting program family.To illustrate the potential of our representations, we have implemented an experimental lifted static analyzer, called SPLNum2Analyzer, for inferring invariants of #if-annotated C programs. The tool implements all three approaches for lifted analysis based on abstract interpretation: tuple-based, decision tree-based, and BDD-based. It uses existing numerical abstract domains (e.g., intervals, octagons, polyhedra) from the APRON library as parameters. An empirical evaluation on benchmarks from SV-COMP and BusyBox yields promising results indicating that our tool can be successfully used for analyzing program families with very large configuration spaces.
Machine learning and deep-learning algorithms for blockchain and IoT-driven smart grids

Over the last few years, the development of a blockchain mechanism for Internet of Things (IoT)-driven applications has emerged as a solitary, troubleshooting as well as a leading mechanism. The distributed database in blockchain technology gives more importance to information security and privacy. It also has a consensus system that assures information security and legitimacy. This mechanism is widely used in the financial economy, IoT, cloud computing, large data, and edge computing. Nonetheless, it introduces novel security concerns, such as majority assault and double-spending, and many more. Hence, there is a need to address these novel security concerns as this technology is being used in many critical and edge-cutting applications. Hence, this chapter introduces mechanisms for the amalgamation of data analytics with blockchain to secure the information. The value of new technologies, machine learning (ML) and deep learning (DL), is highlighted through analytics on securing the information on the blockchain. To improve the accuracy of outcomes, data reliability and exchange are critical. When these two technologies (ML and DL) are combined, they can produce extremely exact outcomes. In the next section, we focus on the broad knowledge of ML and DL. After that, we emphasize ML and DL mechanisms for smart grids that are to be followed by them for blockchain in smart grids.
Digital Teaching Competencies Evaluation of Teachers in Saudi Arabia: A Systematic Review

Given the pivotal role of teachers in facilitating learning, it is necessary that they be equipped with digital competencies to be able to prepare the new generations to work and live in the digital age efficiently.There is a need to assess Digital Teaching Competencies and investigate the training needed in this area.The purpose of this review was to identify the Digital Teaching Competencies of Saudi teachers.Twelve reviewed studies show significant heterogeneity in the theoretical frameworks and tools used.Teachers indicate that they have basic digital skills.However, there has been a lack of a theoretical framework guiding the development of digital competencies in Saudi Arabia which could be a concern in the educational system.