Reviewerly/exHarmony huggingface dataset:
Multiagent Hierarchical Cognition Difference Policy for Multiagent Cooperation

Multiagent cooperation is one of the most attractive research fields in multiagent systems. There are many attempts made by researchers in this field to promote cooperation behavior. However, several issues still exist, such as complex interactions among different groups of agents, redundant communication contents of irrelevant agents, which prevents the learning and convergence of agent cooperation behaviors. To address the limitations above, a novel method called multiagent hierarchical cognition difference policy (MA-HCDP) is proposed in this paper. It includes a hierarchical group network (HGN), a cognition difference network (CDN), and a soft communication network (SCN). HGN is designed to distinguish different underlying information of diverse groups’ observations (including friendly group, enemy group, and object group) and extract different high-dimensional state representations of different groups. CDN is designed based on a variational auto-encoder to allow each agent to choose its neighbors (communication targets) adaptively with its environment cognition difference. SCN is designed to handle the complex interactions among the agents with a soft attention mechanism. The results of simulations demonstrate the superior effectiveness of our method compared with existing methods.
Leveraging The Scalability: A Distributed Cloud For Tomorrow’S Internet Of Autonomous Things

In a cloud computing environment, the dynamic resource distribution is especially important for accomplishment of customer demands in a fast and secure manner. A distributed cloud uses fog and edge computing in its infrastructure to reduce the latencies by offering computation and data storage closer to devices. This paper presents the concept of distributed cloud computing and its base components and propose a three-layered architecture model. The study also discusses several platforms which implement the concept in different application areas, such as education, the automotive sector, manufacturing industry and transport.
Accelerating the training of deep reinforcement learning in autonomous driving

Deep reinforcement learning has been successful in solving common autonomous driving tasks such as lane-keeping by simply using pixel data from the front view camera as input. However, raw pixel data contains a very high-dimensional observation that affects the learning quality of the agent due to the complexity imposed by a 'realistic' urban environment. Ergo, we investigate how compressing the raw pixel data from high-dimensional state to low-dimensional latent space offline using a variational autoencoder can significantly improve the training of a deep reinforcement learning agent. We evaluated our method on a simulated autonomous vehicle in car learning to act and compared our results with many baselines including deep deterministic policy gradient, proximal policy optimization, and soft actorcritic. The result shows that the method greatly accelerates the training time and there was a remarkable improvement in the quality of the deep reinforcement learning agent.
The Role of Deep Learning Method Based on Environmental Geochemical Data in Resource

In recent years, with the continuous development of artificial intelligence, deep learning, as an important method of artificial intelligence learning, has made great progress. At present, deep learning has been successfully applied in many engineering fields. Environmental science itself involves a wide range, among which environmental geochemistry is an important branch. The combination of environmental geochemical problems and deep learning can better study the role of geochemical problems in environmental investigation, and also can use the basic situation of regional environmental elements to predict mineral resources.
Decoding Algorithm by Cooperation Between Hartmann Rudolph Algorithm and a Decoder Based on Syndrome and Hash

In this paper, the authors present a concatenation of Hartmann and Rudolph (HR) partially exploited and a decoder based on hash techniques and syndrome calculation to decode linear block codes. This work consists firstly to use the HR with a reduced number of codewords of the dual code then the HWDec which exploits the output of the HR partially exploited. Researchers have applied the proposed decoder to decode some Bose, Chaudhuri, and Hocquenghem (BCH) and quadratic residue (QR) codes. The simulation and comparison results show that the proposed decoder guarantees very good performances, compared to several competitors, with a much-reduced number of codewords of the dual code. For example, for the BCH(31, 16, 7) code, the good results found are based only on 3.66% of the all codewords of the dual code space, for the same code the reduction rate of the run time varies between 78% and 90% comparing to the use of Hartmann and Rudolph alone. This shows the efficiency, the rapidity, and the reduction of the memory space necessary for the proposed concatenation.
The Advantages of Using Augmented Reality (AR) Technology in Midwifery Education: A Literature Review

Introduction:The Indonesian government has expressed the importance of the implementation of quality assurance systems through national standards of higher education (Permenristekdikti 44/2015) including educational infrastructure standards which educational media is one of them.In the industrial revolution 4.0 era, educational media must be adapted with advancing technology and providing the convenience of students in the learning process.For midwife, the understanding of anatomy and physiology is mandatory.Augmented Reality (AR) was the technology that will be able to bridge between cyberspace and the real world, so it will be facilitate the understanding of students.The aim of this literature review was to discover the benefits of integrated modules for the midwifery education.Methods: This literature review is the first step in a study of the development of integrated AR module.Literature was formed by using criteria that are relevant to the objectives of the study, which contain articles about AR applications and their benefits in health education.Results: A total of 12 articles (2012-2018) that were relevant had been found in accordance with the relevant criteria.The AR application has the benefits of medical students among them 1) increasing the student's knowledge retention, 2) increasing the student's learning motivation.Conclusion: AR integrated modules have been proven having benefits for medical education.For this reason, the research will continue in the next stage, namely the making of AR integrated modules and their application to midwifery education.
Deficit of conceivability: response to Bogdan Faul’s article «Minimal dualism and epistemic approach»

The argument in defense of minimal dualism presented in Bogdan Faul’s article presents the idea that we can conceive consciousness existing only in the introspection without a physical body. From that kind of conceivability follows the possibility of consciousness. And this leads to the falsity of physicalism. I argue that Faul’s argument is not fundamentally different from the ghost argument. Then I consider a step from conceivability to possibility and conclude that no argument of conceivability guarantees the possibility that consciousness is non-physical since we lack the epistemic capacity for such a conclusion. In the last part of this article, I discuss three kinds of conceivability. The classification of these kinds of conceivability demonstrates what kind of conceivability we lack for an argument to be sound, and we cannot have such conceivability
Elderly Fall Detection Based on Improved SSD Algorithm

We propose an improved a single-shot detector (SSD) algorithm to detect falls of the elderly.The VGG16 network part of the SSD network is replaced with the MobilenetV2 network.At the same time, we change the infrastructure of MobilenetV2 network, the three layers that were not downsampled at the end were removed, which can make the model structure simpler and faster to detect.The complete Intersection-over-Union (CIoU) loss function is introduced to get a good regression of the target borders that have different sizes and different proportions.We use Feature Pyramid Network (FPN) for upsampling, it can fuse low-level feature maps with high resolution and high-level feature maps with rich semantic information.For sampling results, we use the Secure Shell (SSH) module to extract different receptive fields, which improves the detection accuracy.Our model ensures that the accuracy of the elderly fall detection remains unchanged, but it greatly improves the detection speed that only takes 10 milliseconds to detect a picture.
The Effect of Computerized Glossary Instructional Program on Deaf Students' Learning English Language Vocabulary and Their Retention in Jordan

In Jordan, deaf learners suffer from great weakness in English language; this is attributed to using the traditional method for implementing modern or more active methods.Therefore, this study aimed at looking into the effect of a computerized glossary instructional program on deaf students to learn English language vocabulary skills and their retention in Jordan.The two questions of the study were: Are there any statistically significant differences at (α=0.05) in student's vocabulary skills that are attributed to the teaching strategy?Are there statistically differences in deaf students' retention of the new vocabulary items attributed to the teaching strategy?The design of the research is quasi-experimental with an experimental group and a control group.Pre-post test was used to collect the data.The delayed is used to measure students' retention.The participants of the study were male and female students from Al Amal School for deaf students in Irbid city.They were randomly divided into a control group (21 students) and an experimental group (22 students).The results of the study showed that the computerized glossary had a significant effect on deaf students' vocabulary skills as well their retention.Thus, the deaf students had the ability to remember the computerized glossary through spelling the new words, reading them, and remembering their meanings in Arabic through using the sign language.In light of the results, it is recommended to integrate the computerized glossary to deaf students in the Jordanian EFL curriculum for teaching English language and vocabulary skills.
MOVING: A User-Centric Platform for Online Literacy Training and Learning

Abstract In this paper, we present an overview of the MOVING platform, a user-driven approach that enables young researchers, decision makers, and public administrators to use machine learning and data mining tools to search, organize, and manage large-scale information sources on the web such as scientific publications, videos of research talks, and social media. In order to provide a concise overview of the platform, we focus on its front end, which is the MOVING web application. By presenting the main components of the web application, we illustrate what functionalities and capabilities the platform offer its end-users, rather than delving into the data analysis and machine learning technologies that make these functionalities possible.
To Be fAIr or Not to Be: Using AI for the Good of Citizens

In the last decade, there has been an explosion in the progress and applications of artificial intelligence (AI) in our society. For the first time, the applications of AI have left the laboratory to reach society in a broad, visible, and relevant way. This fact has raised numerous questions about the potential of AI in the future and its implications in our lives. The benefits of AI do not come alone; they also bring with them responsibilities that if not considered properly can become misuses, intentional or not.
Peningkatan Keterampilan Mengelola Emosi Marah Melalui Strategi Emotional Literacy

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The purpose of this study is to determine the effectiveness of emotinal literacy strategies through group guidance to improve the skills to manage angry emotions in students of SMPN 5 Kendari. The approach used is quantitative with a quasi-experimental design that is pretest-posttest with control group design. The sample selection in this research is purposive sampling technique. Data analysis used the Wilcoxon test to find out the increased skills in managing angry emotions before and after the intervention was given and the Mann Whitney U Test was used to test whether there were differences between the two intervention groups. Based on the results of the analysis showed that emotional literacy strategies through effective group guidance to improve anger emotion management skills in students of SMPN 5 Kendari. &lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt;&lt;em&gt; &lt;/em&gt;Tujuan penelitian ini yaitu untuk mengetahui keefektifan strategi &lt;em&gt;emotional literacy &lt;/em&gt;melalui bimbingan kelompok untuk meningkatkan keterampilan mengelola emosi dengan desain kuasi eksperimen yaitu &lt;em&gt;pretest-posttest with control group design&lt;/em&gt;. Pemilihan sampel dalam penelitian yaitu teknik &lt;em&gt;purposive sampling&lt;/em&gt;. Analisis data menggunakan uji Wilcoxon&lt;em&gt; &lt;/em&gt;untuk mengetahui peningkatan keterampilan mengelola emosi marah sebelum dan setelah diberikan intervensi dan uji Mann Whitney U Test&lt;em&gt; &lt;/em&gt;untuk menguji apakah ada perbedaan antara kedua kelompok intervensi. Berdasarkan hasil analisis menunjukkan bahwa strategi &lt;em&gt;emotional literacy &lt;/em&gt;melalui bimbingan kelompok efektif untuk meningkatkan keterampilan mengelola emosi marah pada siswa SMPN 5 Kendari.
Improvement of Professional Thinking in Future Teachers

The article shows the culture of thought and the importance of pedagogical communication based on the external influence of the subject and its internal psychological conditions, the content of theoretical bases, methodological principles and results of the empirical research. In the course of the study, the pedagogicalpsychological peculiarities of the professional thinking of the students of the direction of pedagogical education were determined. And our developing authorship program served as an important practical motivation to the development of professional thinking of future teachers
Site Selection of Solar Farms Deploying Fuzzy Analytical Hierarchy Process (F-AHP) A KPK Based Study

Solar energy that is produced through solar farms is a freely available, renewable source of energy that reduces greenhouse effect.It holds utmost importance in a developing country with power shortcomings and pollution issues.Choosing the best site for a solar farm amid various location alternatives is a complex multiple criteria decision making (MCDM) problem as numerous conflicting qualitative and quantitative criteria demands to be considered.Based on the literature review the factors selected for the study are Quality of Terrain, Land price, Population, Security, Proximity to Transmission Line, Agricultural Concern and Local Transmission Capacity.One of the most frequently used MCDM methods in literature, the Analytical Hierarchy Process (AHP) is integrated with fuzzy logic, called as F-AHP, is used in this research to identify and evaluate solar farm location in division of Khyber Pakhtunkhwa (KPK).Thana, Chakdara, Harichand, Palai, Pir Khel and Shergarh city are evaluation by Fuzzy AHP on the basis of above-mentioned factors.Shergarh city is selected for most suitable location for the installation of solar panels.
A Novel Method for the Complex Tube System Reconstruction and Measurement

Pipe structures are at the base of the entire industry. In the industry structure, heat and vibration are transmitted in each pipe. The minimum distance between each pipe is significant to the security. The assembly error and the deformation of the pipeline positions after multiple runs are significant problems. The reconstruction of the multi-pipe system is a critical technical difficulty in the complex tube system. In this paper, a new method for the multi-pipes structure inspection is presented. Images of the tube system are acquired from several positions. The photogrammetry technology calculates positions, and the necessary coordination of the structure is reconstructed. A convolution neural network is utilized to detect edges of tube-features. The new algorithm for tube identification and reconstruction is presented to extract the tube feature in the image and reconstruct the 3D parameters of all tubes in a multi-pipes structure. The accuracy of the algorithm is verified by simulation experiments. An actual engine of the aircraft is measured to verify the proposed method.
Contactless Academia – The Case for Automated Essay Scoring (AES) System in COVID 19 Pandemic

Background: The 2019 SAR COV- 2 outbreak ushered and made the term ‘contactless’ a new normal for most businesses as a mitigation measure to risky coronavirus exposure. Similarly, there are several exposure scenarios in higher education where contact poses a threat. One of which is the handling and marking of essay scripts from assignments, task, research outputs and more. An invaluable measure worth considering is the inclusion of ‘Automated Essay Scoring’ (AES) system in the mitigation toolkits for higher institutions of learning.&#x0D; Objectives: We conducted this scoping review to identify the suitability of AES products in higher education and examine the type of methods used to present these products.&#x0D; Methods: This study was undertaken in the form of a scoping review using the Prisma flow sequence of literature search and selection from 6 databases.&#x0D; Findings: Different AES products, literatures and research designs were employed in the investigation of AES products. The outcome of reviewed literatures varied on suitability of AES in scoring essay task in Higher Institution of Learning.&#x0D; Conclusion: There exist substantial case for the use of AES in most literatures amongst few opposing authors; however, in order to achieve contactless interface with human and materials in COVID 19 pandemic, AES should be used with triggers for human raters’ intervention in exceptional cases.
A Reflection on the Effects of Mother-Tongue Hermeneutics on Christianity: An Entrepreneurial Dimension for Propagating the Christian Faith

This study uses the historical method to display the importance of Mother tongue hermeneutics and its contribution to the viability of Christianity, particularly in Ghana. Though the enterprise of translation is primitive Mother tongue hermeneutics is a new development in theological studies. Its focus is to make the Bible easy for everyone to read and understand in his or her own mother tongue. Thus, it is an enterprise that conveys Biblical concepts from the original languages (Hebrew and Greek) of the Judeo-Christian Bible into varied local languages. The historical method helps to trace the historical context of an event of mother tongue hermeneutics and its impact on society. Mother tongue hermeneutics has birthed several indigenous churches as well as many preachers of the gospel today. It makes Christianity in Africa particularly Ghana viable. Keywords: Entrepreneurship, mother-tongue hermeneutics, inculturation, libration.
Weighted Dirichlet Process Mixture Models to Accommodate Complex Sample Designs for Linear and Quantile Regression

Abstract Standard randomization-based inference conditions on the data in the population and makes inference with respect to the repeating sampling properties of the sampling indicators. In some settings these estimators can be quite unstable; Bayesian model-based approaches focus on the posterior predictive distribution of population quantities, potentially providing a better balance between bias correction and efficiency. Previous work in this area has focused on estimation of means and linear and generalized linear regression parameters; these methods do not allow for a general estimation of distributional functions such as quantile or quantile regression parameters. Here we adapt an extended Dirichlet Process Mixture model that allows the DP prior to be a mixture of DP random basis measures that are a function of covariates. These models allow many mixture components when necessary to accommodate the sample design, but can shrink to few components for more efficient estimation when the data allow. We provide an application to the estimation of relationships between serum dioxin levels and age in the US population, either at the mean level (via linear regression) or across the dioxin distribution (via quantile regression) using the National Health and Nutrition Examination Survey.
Psychological Aspects of Designing the Education Process of Law Enforcement Personnel with their Subjective Position in Mind

The article justifies the necessity for studying and taking into account the subjective positions of law enforcement officers in designing their education. This is accomplished using skill- and personality-based approaches. The author's typology of such subjective positions is given. Psychological and educational risks of maladjustment in the process of professional and personal development are discussed along with the special psychological and educational counselling that are needed to handle them. The results of research are given, revealing average and absolute proportions of groups of students of the Academy of Management of the Ministry of Internal Affairs of Russia who were mapped to the four subjective positions that have been singled out. 422 respondents took part in the research, which was questionnaire based. Checking the obtained data with the contingency table and Pearson's chi-squared test confirmed the hypothesis of a relationship between the student's subjective position and the lecturer's role he/she needs. On the whole, the singled out learners' positions and the lecturers' roles are mutually correlated (at a 0.01 significance level 0,21≤r≤0,76). Basing on the theoretical analysis and the empirical data obtained we formulated methodological principles for designing of departmental education systems.
A Deep Conditioning Treatment of Neural Networks.

We study the role of depth in training randomly initialized overparameterized neural networks. We give a general result showing that depth improves trainability of neural networks by improving the conditioning of certain kernel matrices of the input data. This result holds for arbitrary non-linear activation functions under a certain normalization. We provide versions of the result that hold for training just the top layer of the neural network, as well as for training all layers, via the neural tangent kernel. As applications of these general results, we provide a generalization of the results of Das et al. (2019) showing that learnability of deep random neural networks with a large class of non-linear activations degrades exponentially with depth. We also show how benign overfitting can occur in deep neural networks via the results of Bartlett et al. (2019b). We also give experimental evidence that normalized versions of ReLU are a viable alternative to more complex operations like Batch Normalization in training deep neural networks.