Reviewerly/exHarmony huggingface dataset:
Social Portrait of a Student as a Component of Monitoring the Quality of the Organization of the Educational Process

Introduction . Today, one of the criteria for assessing the quality of education is the opinion of students about educational services. Undergraduates enrolled in distance learning programs represent a specific student audience. Features of this category form a social portrait that has the information potential for organizing an effective educational process adapted to the “target audience”. Methodology and sources. In the academic year 2018–2019, the author conducted an empirical case study through an anonymous online survey of graduate students of the correspondence department of the first year of study. Results and discussion . As a result of the survey, respondents have received answers characterizing the peculiarities of their life, sociocultural environment, professional activity and factors of formation of motives of educational activity. The information obtained is presented in tables, which allowed to form a “numerical” outline of the social portrait of an extramural student. The result of an empirical study was a description of the collective image of a student studying in absentia in a magistracy. Active professional activity, focus on success, limited time resources, etc., determined the content of wishes for improving the conditions and process of educational activity. The main adjustments should relate to the effective planning of the educational process, methodological support of classroom and extracurricular activities, providing comfortable conditions for a long stay in an educational institution and communication with fellow students. Conclusion. The social portrait of students contains information about their typical representatives, the sociocultural environment of life. Regular monitoring of the dynamics of socially significant characteristics of students allows you to quickly make adjustments that improve the quality of education.
Training and Assistance Classroom Action Research (CAR) for Teacher MA Manbaul Bayan Lombok Timur

Abstract: This community service aims to provide insight into the knowledge of writing scientific papers in an effort to improve teacher professionalism. Training The methods used in community service activities are the lecture, discussion and question, and answer method. Results of community service activities overall it can be considered quite good, seen from the presence of trainees reaching 90% of the number of MA Manbaul Bayan East Lombok teachers. The implementation procedure in the classroom action research training was rated as well based on positive responses from the participants and the level of mastery of the participants reached 75% of all stages of the implementation of the Classroom Action Research.Keywords: Classroom Action Research, Training and Assistance
Two-Person Multi-strategy Games

Much of what has been developed in previous chapters holds for games with more players and/or pure strategies. Rather than carrying out fairly obvious extensions, we identify attributes that differ from $$2\times 2$$ games. This includes explaining why certain $$2\times 2$$ assertions collapse with more strategies, differences in $$\mathcal G^B$$ features, a mystery associated with the Ultimatum game,GamesUltimatum and the Nash structure of $$k_1\times k_2$$ games (including possible hiding places for NME).
The State of Open, 2019

An analysis and report of a variety of activities at NC State that are advancing open knowledge.
Improving Quality of Service in Cloud Computing

Quality of Service (QoS) has a significant role in the provision of resources within service oriented distributed systems. In Quality of Service, cloud computing creates new challenges for improvements using the concept of virtualization. Currently, Cloud Computing is very emerging technology in every field of data storage and resource distribution over the network. Considering this new emerging technology, for the ease of data accessibility, price, resource use, restoration, response time and number of constraints the quality performance measures need to be upgraded. The paper highlights the research gap in providing a solution to achieve a Quality of Services in Cloud Computing. We also review the issues and challenges arising in cloud computing to guarantee quality.
A Dynamic Motion Planning Framework for Autonomous Driving in Urban Environments

Abstract: we present a framework for robust autonomous driving motion planning system in urban environments which includes trajectory refinement, trajectory interpolation, avoidance of static and dynamic obstacles, and trajectory tracking. Given road centerline, our approach smoother the original line via cubic spline. Fifth order Bezier curve is then used to generate more human-like trajectories that guarantee at least second order continuity and curvature continuity. Dynamic trajectory planning task is decoupled into lateral spatial and longitudinal velocity planning problems. A bunch of candidate trajectory sets are generated and evaluated by an object function which considers kinematic feasibility, trajectory smoothness, driving comfort and collision-checking. Meanwhile, an LQG controller is used to generate longitudinal velocity profile to ensure safety and comfort. After that, spatial and velocity profiles are transformed into commands executed by lateral steering and longitudinal acceleration controllers. This framework is validated within a simulation study and has been deployed on our autonomous vehicle shown in Fig.1 that has travelled thousands of miles in urban environments.
Modeling Algorithmic Skeletons for Automatic Parallelization Using Template Metaprogramming

This article presents a framework for algorithmic skeletons that aims at representing a whole algorithm, both its sequential and possibly parallelizable parts, in order to enable making global decisions about its implementation. With our modeling, a skeleton is described by an algorithmic structure and a data flow graph, built from the composition of bones and other skeletons. We introduce this notion of bones which represents elementary sequential or parallel patterns whose implementation is available (from the library or designed by well-aware developers), whereas skeletons are automatically implemented from their description. The proposed design, implemented with Template Metaprogramming (TMP), able to operate both at compile- and run-time, allows implementing new bones, describing new skeletons, or simply the instantiation of a skeleton by providing muscles in the form of sequential functions.Once a skeleton is instantiated, one can decide to generate either a sequential or a parallel code of the algorithm. To optimize the parallelization process, we propose orchestrators, in the form of C++ templates that can analyze a skeleton at compile-time and tune its execution.A C++ library-based solution is presented, and its mechanisms and usage are illustrated by implementing a GRASPxELS algorithm, a common OR metaheuristic, that enables two levels of parallelism. Performance results are shown to assert that this approach presents negligible run-time overhead.
The formation of infrastructure centers of university science

In the formation and development of the national innovation system, the urgent task is to develop a mechanism for introducing university science into the national innovation system.According to the author of the article, one of the most effective forms of action of this mechanism is the educational cluster.
Rancang Bangun Aplikasi Ujian Sekolah Menggunakan Algorithma Fisher Yates Shuffle

Exams are one way to get results that are useful for evaluating learning and measuring the level of achievement of a teaching goal. The school exam system found at the Bina Jaya Palembang Vocational School is processing question data, student data and value data. In the process of implementing the test, an algorithm that has methods that will produce random permutations can reduce the fraud done by the examinees. The Fisher Yates Shuffle algorithm is an algorithm that produces random permutations from a finite set, in other words scrambling a set. If implemented correctly, the results of this algorithm will not be one-sided so that each permutation has the same possibility. The process carried out by the algorithm is to enter the attribute attributes into the strrat (list of questions that have not been selected), then make a range (number of questions that have not been selected), then do the randomization process, then form a roll (for a question of all the questions) the results of the selected questions are entered into the result (the results of all the questions that have been randomized). From the algorithm process carried out the results of changes in position or sequence of questions are obtained so that it is found that each exam participant who performs the exam at the same time but different questions.
Pedagogical Conditions of Forming Ideological Culture of a Future Music Teacher

The article outlines pedagogical conditions that enable shaping the worldview of the future music teacher most effectively and purposefully. The need for their use ineducational process of the renewed school is stipulated by modern requirements for the activities of a teacher, who should be an active subject of educational process. On the basis of systematic analysis of academic literature, it was determined that the formation of the ideological culture of a music teacher demands special consideration, since it acquires a special significance in their professional activity. The presence of such culture helps to reveal a deep humanistic content, to identify the most significant ideas and views embedded in works of art. The development of pedagogical conditions as the main component of the methodology of forming the students’ ideological culture was based on the poly-artistic, discursive-interpretative, and humane-aesthetic approaches, since they are the ones that contribute most to the formation of the students’ ideological culture in terms of professional training. Thus, the following pedagogical conditions can be distinguished: 1) providing a foundation for musical and aesthetic knowledge of a future music teacher; 2) axiological orientation of ideological beliefs as personal qualities; 3) extrapolation of ideological beliefs in the process of musical and educational activities. The use of each pedagogical condition is accompanied by the use of cognitive research, visual textual, analytical semantic methods, as well as methods of comparison, a reflexive circle, discussing artistic phenomena, stimulating educational and cognitive activity, micro-teaching and role-training.As a result of the applied pedagogical conditions and the corresponding teaching methods, students learn to express their own ideological position related to the ability to find and substantiate professional problems, to see their significance and consonance to the conceptual positions of art pedagogy.
UNIVERSITY WITHOUT WALLS IN THE THIRD CYCLE PROGRAM: USING ONLINE MANAGEMENT

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7426-7432ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1772Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Enabling Data Marketplaces with Multi-Party Computation (MPC): An Exploratory Study investigating the Implication of the Maturation of Multi-Party Computation (MPC) technology to the Architecture and the Threat Landscape of the Data Marketplaces

The emergence of the Data Marketplaces is the latest iteration in the phenomenon of data-driven transformation of the world. Data marketplaces have emerged as a new form of data-driven business models which enable trading of data between the data owners/providers and data consumers by providing the necessary technological and non-technological infrastructure. These features present an alternative to the cumbersome logistics currently involved in searching, buying and selling data; thus, simplify the data supply chains between the data-driven business entities. However, they suffer to take off into mainstream success because of a myriad of reasons. Of all the reasons, 2 of them are focused in this thesis. Firstly, the difficulty involved in architecturally enabling a data marketplace platform as the prospective enabling technologies are still immature. Secondly, the uncertainty associated with the commodification of data which comprises of the intellectual property enforcement of data (data ownership), privacy and confidentiality breach (threats), regulatory ignorance (implication of GDPR), reluctance of businesses from participating because of the previous reasons et cetera. This reason is collectively referred as due to the uncertainty around the threat landscape of the data marketplaces. Multi-Party Computation (MPC) technology provide a solution to these problems. Through its capabilities to preserve the confidentiality of data architecturally and thereby securing the interests of the data actors with respect to the uncertainty of the threat landscape around data, MPC can enable safe and secure data sharing between data actors. This characteristic of MPC can help data marketplaces to overcome their challenges and foster their realisation. However, since MPC cannot handle the scale of real-life application, it is not mature enough yet to be incorporated into real-life data marketplaces. An EU funded project called SafeDEED: Safe Data-Enabled Economic Development, proposes to overcome the scalability issue and intends to achieve the maturation of MPC for real-life application. Building upon this forecast, a research was conducted to investigate the implication of the maturation of MPC technology towards the 2 problems faced by data marketplaces, architectural and threat landscape; and the same is documented in this thesis.
DESIGN OF A VIRTUAL TOOL FOR ESTIMATING VOCABULARY ACQUISITION THROUGH READING

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 7724-7732ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1834Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
IoT Environment and Security Countermeasures in 4th Industrial Revolution

The role of the Internet of Things in the Fourth Industrial Revolution is in the era of collecting data at the end and analyzing big data through technology to analyze the future or behavior. Therefore, due to the nature of the IoT, it is vulnerable to security and requires a lightweight security protocol. The spread of things Internet technology is changing our lives a lot. IT companies all over the world are already focusing on products and services based on things Internet, and they are going to the era of all things internet that can communicate not only with electronic devices but also with common objects. People, people, people and objects, things and things interact without limitation of time and space, collecting, analyzing and applying information. Life becomes more and more smart, but on the other hand, the possibility of leakage of personal information becomes greater. Therefore, this study proposed security threats that threaten the protection of personal information and countermeasures, and suggested countermeasures for building a secure IoT environment suitable for the Fourth Industrial Revolution.
PixelRL: Fully Convolutional Network with Reinforcement Learning for Image Processing

This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the introduction of the deep Q-network, deep RL has been achieving great success. However, the applications of deep reinforcement learning (RL) for image processing are still limited. Therefore, we extend deep RL to pixelRL for various image processing applications. In pixelRL, each pixel has an agent, and the agent changes the pixel value by taking an action. We also propose an effective learning method for pixelRL that significantly improves the performance by considering not only the future states of the own pixel but also those of the neighbor pixels. The proposed method can be applied to some image processing tasks that require pixel-wise manipulations, where deep RL has never been applied. Besides, it is possible to visualize what kind of operation is employed for each pixel at each iteration, which would help us understand why and how such an operation is chosen. We also believe that our technology can enhance the explainability and interpretability of the deep neural networks. In addition, because the operations executed at each pixels are visualized, we can change or modify the operations if necessary. We apply the proposed method to a variety of image processing tasks: image denoising, image restoration, local color enhancement, and saliency-driven image editing. Our experimental results demonstrate that the proposed method achieves comparable or better performance, compared with the state-of-the-art methods based on supervised learning. The source code is available on https://github.com/rfuruta/pixelRL.
Common neural patterns for serial order coding in working memory, number and letter domains: A multi-voxel pattern analysis approach

Location information is processed through two types of spatial processing; categorical and coordinate processing. Categorical spatial relations indicate where an object is relative to another object, without regard to the metric distance between the two objects. Coordinate spatial relations indicate metric distance without regard to relative location. In human behavioral studies, the magnocellular pathway of the lateral geniculate nucleus has been implicated in coordinate spatial processing abilities. Magnocellular pathway cells (type IV) have a center surround organization, such that red light inhibits neuronal firing. In these behavioral studies, red stimuli decreases coordinate spatial processing accuracy. Prior studies also show that there is a lag between the time of visual stimulus presentation and the time of observing a neural response in the magnocellular pathway. We sought to understand whether prior presentation of red stimuli decreases coordinate spatial performance, and also examined its effects on categorical spatial performance. The results indicate that prior presentation of red stimuli decreases accuracy and perceived confidence on both the categorical and coordinate tasks. These results confirm prior findings of the association between magnocellular pathway function and coordinate spatial processing. Implications for categorical task results and associated neural pathways are discussed.
On the relationship between multitask neural networks and multitask Gaussian Processes

Despite the effectiveness of multitask deep neural network (MTDNN), there is a limited theoretical understanding on how the information is shared across different tasks in MTDNN. In this work, we establish a formal connection between MTDNN with infinitely-wide hidden layers and multitask Gaussian Process (GP). We derive multitask GP kernels corresponding to both single-layer and deep multitask Bayesian neural networks (MTBNN) and show that information among different tasks is shared primarily due to correlation across last layer weights of MTBNN and shared hyper-parameters, which is contrary to the popular hypothesis that information is shared because of shared intermediate layer weights. Our construction enables using multitask GP to perform efficient Bayesian inference for the equivalent MTDNN with infinitely-wide hidden layers. Prior work on the connection between deep neural networks and GP for single task settings can be seen as special cases of our construction. We also present an adaptive multitask neural network architecture that corresponds to a multitask GP with more flexible kernels, such as Linear Model of Coregionalization (LMC) and Cross-Coregionalization (CC) kernels. We provide experimental results to further illustrate these ideas on synthetic and real datasets.
Is Big Data Performance Reproducible in Modern Cloud Networks

Performance variability has been acknowledged as a problem for over a decade by cloud practitioners and performance engineers. Yet, our survey of top systems conferences reveals that the research community regularly disregards variability when running experiments in the cloud. Focusing on networks, we assess the impact of variability on cloud-based big-data workloads by gathering traces from mainstream commercial clouds and private research clouds. Our data collection consists of millions of datapoints gathered while transferring over 9 petabytes of data. We characterize the network variability present in our data and show that, even though commercial cloud providers implement mechanisms for quality-of-service enforcement, variability still occurs, and is even exacerbated by such mechanisms and service provider policies. We show how big-data workloads suffer from significant slowdowns and lack predictability and replicability, even when state-of-the-art experimentation techniques are used. We provide guidelines for practitioners to reduce the volatility of big data performance, making experiments more repeatable.
THE INFLUENCE OF COMPUTER AVAILABILITY AT HOME AND AT SCHOOL FOR STUDENTS ON 2019 UNBK RESULTS Safari Safari

The main purpose of this study is to answer the following question. Is there an influence on the availability of computers at home and at school for students on the results of the 2019 UNBK. The population of this study is SMP / MTs students who take the UNBK in 2019. The data in this study took the form of test scores and student responses to questionnaires in 34 provinces throughout Indonesia. The questionnaire was filled in voluntarily on the last day of UNBK, so the number of respondents who filled out the questionnaire was 63902 junior high school students. The number of respondents is a sample in this study. Based on the results of the t-test analysis the following results were obtained. The effect of the availability of computers at home and at school for students on the 2019 UNBK results is proven (P <0.05). This means that there are differences in the availability and unavailability of computers both at home and at school against the 2019 UNBK results for Indonesian, English, Mathematics, and Science subjects. The conclusion is that there are differences in the availability and unavailability of computers both at home and at school against the 2019 UNBK results.
Trafic Intersection Fuzzy Control and Supervision System based on IoT

IoT has the ability to solve the most common problems that we have around us and one of them is urban traffic. This paper aims to implement an intelligent control system based on the fuzzy logic of an intersection by developing a graphical interface of supervision and control through the concept of IoT. The implementation is done through a Raspberry for interfacing and processing, an Arduino and radio frequency (RF) communication modules to connect the intersection to the server. The software solutions are developed in a visual programming environment "Node Red" for the IoT application using Python and JavaScript languages. The test results show the success of the proposed system.