Reviewerly/exHarmony huggingface dataset:
SpecMCTS: Accelerating Monte Carlo Tree Search using Speculative Tree Traversal

Monte Carlo Tree Search (MCTS) algorithms show outstanding strengths in decision-making problems such as the game of Go. However, MCTS requires significant computing loads to evaluate many nodes in the decision tree to make a good decision. Parallelizing MCTS node evaluations is challenging because MCTS is a sequential process that each round of tree traversal depends on the previous node evaluations. In this work, we present SpecMCTS , a new approach for accelerating MCTS by speculatively traversing the search tree. Many MCTS applications, such as AlphaGo Zero, use a deep neural network (DNN) model to evaluate the tree nodes during the search. SpecMCTS uses a pair of DNN models, the speculation model and the main model . The faster (but less accurate) speculation model accelerates the sequential tree search while the more accurate main model improves the decision quality. SpecMCTS accelerates MCTS for the game of Go by up to $2.09\times {}$ on the NVIDIA T4 GPU. This performance improvement can be translated into a better decision quality by performing a larger number of tree traversals within the time limit. For a fixed decision time, SpecMCTS shows stronger gameplay (higher win rate) than the original sequential MCTS and state-of-the-art MCTS parallelization approaches.
Indices of Post-Graduate Students’ Social Mobility

The article presents statistical indicators for analyzing the potential of postgraduate studies and, in general, the field of science and innovation in the context of social (educational and professional) mobility of postgraduate students. Key indicators characterizing the potential of educational mobility are arguably dynamics of the number and age structure of postgraduate students, as well as the dynamics of the number of defended dissertations. Since the sphere of science and innovation is for postgraduate students the labor market (a field of professional mobility as well), the article considers following indicators: share of the “science and innovation” sphere in the country’s GDP; research funding structure; number of research organizations; dynamics and structure of research organizations personnel. Conclusions are made regarding, on the one hand, ambivalent nature of the postgraduate studies as a factor of educational mobility, and on the other hand, the discrepancy between trends and internal dynamics between postgraduate studies as an educational institution and sciences as a sphere of professional mobility. On the basis of the analyzed indicators, a conclusion is also made about a certain limited potential of social (educational and professional) mobility and social maturing of postgraduate students.
Decentralized Asynchronous Non-convex Stochastic Optimization on Directed Graphs

We consider a decentralized stochastic optimization problem over a network of agents, modeled as a directed graph: Agents aim to asynchronously minimize the average of their individual losses (possibly non-convex), each one having access only to a noisy estimate of the gradient of its own function. We propose an asynchronous distributed algorithm for such a class of problems. The algorithm combines stochastic gradients with tracking in an asynchronous push-sum framework and obtains a sublinear convergence rate, matching the rate of the centralized stochastic gradient descent applied to the nonconvex minimization. Our experiments on a non-convex image classification task using convolutional neural network validate the convergence of our proposed algorithm across different number of nodes and graph connectivity percentages.
Cross-domain Slot Filling with Distinct Slot Entity and Type Prediction

Supervised learning approaches have been proven effective in slot filling, but they need massive labeled training data which is expensive and time-consuming in a given domain. Recent models for cross-domain slot filling adopt transfer learning framework to cope with the data scarcity problem. However, these cross-domain slot filling models rely on the same encoder representation in different stages for slot entity task and slot type task, which decrease the performance of both tasks. Besides, these models treat different source domains equally and ignore the shared slot-related information in different domains, which may damage the performance of cross-domain learning. In this paper, we present a pipeline approach for cross-domain slot filling (PCD) by learning distinct contextual representations for slot entity identification and slot type alignment, and fusing slot entity information at the input layer of the slot type alignment model for incorporating global context. Moreover, we also present a simple yet effective instance weighting scheme (\(\mathbf {Iw}\)) to our approach for better capturing the slot entities in the cross-domain setting. Experiments on multiple domains show that our approach achieves state-of-the-art performance in cross-domain slot filling. Ablation analysis and further experiments also prove the effectiveness of each part of our model, especially in the identification of slot entities.
Evaluation of Cooperative UAV-UGV Navigation Strategies with Maneuvering UAVs

This paper presents and evaluates cooperative navigation methods used to reduce navigation solution error growth between members of an unmanned aerial vehicle - unmanned ground vehicle (UAV-UGV) team when Global Position System (GPS) measurements are partially or completely unavailable to the group. A scenario in which two UAVs and one UGV travel in a cooperative team was simulated with a centralized navigation algorithm based on the Extended Kalman Filter (EKF) and with a decentralized navigation algorithm based on the Covariance Intersection (CI) filter. Measurements including relative range, relative range-rate, and relative bearing were made available to the vehicles in different simulation runs to compare their impact on navigation state observability and navigation state estimation accuracy. The UAVs were also guided along varied trajectories of a “spiral" class during different simulation runs to investigate whether estimation accuracy can be improved by varying intervehicle dynamics and geometry. To analyze the observability of the studied scenarios, a condition number test was performed on the observability Gramian matrix. This study indicates that the navigation state observability in cooperative navigation scenarios where a kinematic vehicle model is aided with relative measurements can be improved by the proposed vehicle maneuver. As the rate of the proposed spiral maneuver is increased, this analysis suggests an improvement in observability. This result is further validated in the simulated results which show that with relative bearing only, even low rates of inter-vehicle spiral motion allow for estimates of relative position with less than 3 meters of error. As the spiral rate increases, accurate relative positioning is shown to be possible with only relative range measurements. The loss of inter-vehicle covariance information encountered in the decentralized case resulted in a significant reduction of positioning accuracy that varied depending on vehicle maneuver and available relative measurements. Lastly, an experimental hardware implementation of the simulated scenarios was performed to validate the simulation results. This experiment demonstrated similar results to the simulated scenarios. Relative position error was reduced from over 100 meters to sub-meter accuracy, depending on relative measurement availability. Absolute error was also reduced from over 70 meters (in the IMU-only case) to meter-level accuracy depending on measurement availability.
A Literature Review of Hand-Based Interaction in Virtual Environments Through Computer Vision

Interaction is an essential feature to achieve immersion in virtual reality. So that, virtual reality applications can achieve greater growth and diffusion by improving user interactions like exploration of virtual environments, select and transform virtual objects and text input. Hand-based interaction can provide naturalness, intuition, and creativity to these interaction techniques. In this paper, we present a literature survey of this broad field, including a comparative analysis of several hand-based interaction proposals through computer vision for virtual reality applications, where we considered the common features of the revised proposals. In addition, we propose two classifications, the first categorizes the interaction gestures, and the second group the used methods. Interaction guidelines are proposed to achieve hand gesture recognition with low computational cost.
Crime against Women in Cyber World

Information technology is like two side edge of human development, progress and profit as well as humiliation, suppression and subjugation especially in related to women it get more spiky and spiny. The cyber world is a virtual reality where everyone conceals their own identity. There are several crimes known and an unknown to normal person who is mostly women get victimized. Several criminal like hackers, crackers, and stalkers have institute methods and measures to hamper with internet accounts got access to user’s computer system may stole the important data of the user. If user is women then crimes like harassment through mail, cyber stalking, cyber sex, cyber defamation, photo morphing, non-consensual pornography, child pornography, cyber bullying, cyber grooming, publishing obscene material in electronic form etc. Present article will highlight especially about the nature and category of cyber crimes which may take place against women while using internet. The most important is the kind of measures and technique, skills used by the criminals through which victim may easily trap. It also will discuss about procedure for remedy to the women when they trapped under these kind cybercrime and legal provision and procedural aspect to get redress of this kind of situation. It is very indispensable that what kind key measures will be functional for online safety while using the internet.
KNOWLEDGE MANAGEMENT IN THE QUALITY MANAGEMENT SYSTEM OF THE ORGANIZATION OF ADDITIONAL PROFESSIONAL EDUCATION

Introduction. The paper presents an analysis of the knowledge management system in the organization of additional professional education. The relevance and significance of the study lie in providing grounds for and constructing a knowledge management system and developing a knowledge update management system in terms of the process approach. Description of the knowledge management process. The paper focuses on the development and adoption of a knowledge management system and a knowledge update management system in additional professional education. The main stages of development and adoption of a knowledge management system in the State Autonomous Institution of Continuing Professional Education “Institute for the Development of Education of the Irkutsk Region.” The Institute for the Development of Education of the Irkutsk Region will ensure quality services to external and internal consumers by implementing the knowledge management stages. Organization knowledge management automation. The Institute for the Development of Education of the Irkutsk Region has developed and implemented an automated information system for the effective management of process improvement and knowledge update. Conclusion. The development, implementation, and automation of the knowledge management system and knowledge update according to the proposed algorithm for the implementation of the “Knowledge Management” process will improve the quality of the processes and procedures carried out by the organization. Keywords: knowledge, explicit knowledge, implicit knowledge, individual knowledge, personal knowledge, knowledge management, interested party, process approach.
Random-index PIR and Applications

Private information retrieval (PIR) lets a client retrieve an entry from a database without the server learning which entry was retrieved. Here we study a weaker variant that we call random-index PIR (RPIR), where the retrieved index is an output rather than an input of the protocol, and is chosen at random. RPIR is clearly weaker than PIR, but it suffices for some interesting applications and may be realized more efficiently than full-blown PIR.
Generating Relevant, Correct and Fluent Answers in Natural Answer Generation

This study focuses on natural answer generation that generates a complete answer using given paragraphs. Existing methods of only extracting the answer span loses the naturalness while those of generating it word by word increases the learning difficulty. In this paper, we propose to split the answering process into two branches, which share the same encoder and deeply interact with each other. On one branch, we generate the answer template based on the question through text editing method. On the other branch, we extract the answer span based on the documents. Finally, the answer is composed of the generated answer template and the extracted answer span. Besides, we propose to select the span in candidates to better fit the template with more fluency. Experiments show that our method improves the performance on natural answer generation.
Economic Development Thresholds for a Green Economy in Sub-Saharan Africa

This study investigates how increasing economic development affects the green economy in terms of CO2 emissions, using data from 44 countries in the SSA for the period 2000-2012. The Generalised Method of Moments (GMM) is used for the empirical analysis. The following main findings are established. First, relative to CO2 emissions, enhancing economic growth and population growth engenders a U-shaped pattern whereas increasing inclusive human development shows a Kuznets curve. Second, increasing GDP growth beyond 25% of annual growth is unfavorable for a green economy. Third, a population growth rate of above 3.089% (i.e. annual %) has a positive effect of CO2 emissions. Fourth, an inequality-adjusted human development index (IHDI) of above 0.4969 is beneficial for a green economy because it is associated with a reduction in CO2 emissions. The established critical masses have policy relevance because they are situated within the policy ranges of adopted economic development dynamics.
Robot Trajectories When Approaching a User with a Visual Impairment

Mobile robots have been shown to be helpful in guiding users in complex indoor spaces. While these robots can assist all types of users, current implementations often rely on users visually rendezvousing with the robot, which may be a challenge for people with visual impairments. This paper describes a proof of concept for a robotic system that addresses this kind of short-range rendezvous for users with visual impairments. We propose to use a lattice graph-based Anytime Repairing A* (ARA*) planner as a global planner to discourage the robot from turning in place at its goal position, making its path more human-like and safer. We also interviewed an Orientation & Mobility (O&M) Specialist for their thoughts on our planner. They observed that our planner produces less obtrusive trajectories to the user than the ROS default global planner and recommended that our system should allow the robot to approach the person from the side as opposed to the front as it currently does. In the future, we plan to test our system with users in-person to better validate our assumptions and find additional pain points.
Pengaruh Teknologi Informasi Terhadap Kinerja Organisasi Universitas Kota Padang Dengan Budaya Organisasi Sebagai Variabel Moderasi

Higher education is a higher education organizing unit as an advanced level of secondary education in the education pathway and has a strategic function, namely as a cultural center, the development of science and technology and as a moral force. This situation can be realized if well managed and able to show the resources in an organization that play a role in achieving success. The problem that arises is that universities have experienced fundamental changes that have never happened before, namely problems related to organizational performance, especially universities in the city of Padang, as a whole is not optimal, this is indicated by the implementation of services related to complicated student UKT payments, inadequate qualifications of lecturer workforce the number of student applicants and students accepted is small and the number of published studies and publications is relatively limited due to the system. In this study, the hypothesis is obtained that information technology (X) has a positive and significant effect on organizational performance at the University in Padang City. This can be seen from the significance value (sig.) Of information technology of 0.000 &lt;0.05 and organizational culture (Z) moderates the significant influence of information technology (X) on organizational performance (Y). the real level is 0.05. Data processing using SPSS Ver.25.Keyword: Information technology, Organizational performance, Organizational Culture
Preliminary Identification of Indonesian Digital Heritage Archives (IDHA) as a Digital Preservation for Indonesian Culture

Indonesia is the largest archipelago state in the world. The diversity of Indonesian culture identifies it as a heterogeneous nation. Along with the development of modern civilization, the culture of each ethnic group in Indonesia is difficult to find in the form of staging and visual documentation. If this continues, some of the cultural identity may vanish, especially in the era of disruption and globalization. The possibility of multimedia technology is not just limited to recording digital data such as photos or videos. Information records and saved with high quality and detail on 3D information. Indonesian Digital Heritage Archives designed with not only data of text, audio, photos, and video, but also allows the Recording of motion capture or rotoscoping method with Three-dimensional information. The design of the database was started by mapping the data from Indonesian cultural products and can be built based on the data collected and identified. The existence of digital data is essential for presentation and virtual learning applications for current and future generations.
Analysis of the Drawbacks of English-Chinese Intelligent Machine Translation Based on Deep Learning

Machine translation is an interdisciplinary subject. In terms of subject area, it belongs to an application field of computational linguistics. However, the research of machine translation is based on the three disciplines of linguistics, mathematics and computing technology. Nowadays, the advantage of machine translation is speed, but there are problems such as text grammar errors. Therefore, this article analyzes the disadvantages of English-Chinese intelligent machine translation based on deep learning. First, this article explains the principles of machine translation, and analyzes the shortcomings of English-Chinese intelligent machine translation; then, researches on deep learning algorithms, designs a model for detecting shortcomings of English-Chinese intelligent machine translation, and conducts performance testing on it. The final detection results show that the model can detect long-translated articles, and can well detect sentence grammatical errors caused by machine translation malpractices.
Nonverbal synchrony, media, and emotion

Whether using media or interacting face-to-face, people demonstrate consistent timing between utterances and nonverbal behavior during interaction, and researchers refer to the process as synchrony. While the existence of synchrony has been documented in dozens of experiments, clearly capturing the mechanism of it—or even coming up with a clear definition and measurement of it—has been challenging. In this chapter, we review theory and research which seeks to overcome these challenges, such as experiments that examine the causes and effects of synchrony on psychological constructs such as emotion, with a focus on studies utilizing media. We then discuss possible implications for academic research and media applications.
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Many widely used datasets for graph machine learning tasks have generally been homophilous, where nodes with similar labels connect to each other. Recently, new Graph Neural Networks (GNNs) have been developed that move beyond the homophily regime; however, their evaluation has often been conducted on small graphs with limited application domains. We collect and introduce diverse non-homophilous datasets from a variety of application areas that have up to 384x more nodes and 1398x more edges than prior datasets. We further show that existing scalable graph learning and graph minibatching techniques lead to performance degradation on these non-homophilous datasets, thus highlighting the need for further work on scalable non-homophilous methods. To address these concerns, we introduce LINKX -- a strong simple method that admits straightforward minibatch training and inference. Extensive experimental results with representative simple methods and GNNs across our proposed datasets show that LINKX achieves state-of-the-art performance for learning on non-homophilous graphs. Our codes and data are available at https://github.com/CUAI/Non-Homophily-Large-Scale.
Gradual Typing for Python, Unguarded

Thesis (Ph.D.) - Indiana University, School of Informatics, Computing, and Engineering, 2019
Innovative Educational Practices and Policies through the Application of Geoinformation Systems and Technologies

Modern global education is characterized by an active transition to the use of new geographic information technologies. The new educational policy orients teachers and university professors towards the introduction of modern teaching methods based on the use of geoinformation and communication technologies. Modern educational technologies provide an opportunity for maximum solution of the problems for the development of education and its individualization. The system for retraining and in-service training of teachers (and teachers in pedagogical universities) also does not sufficiently take into account the vital need to master information technology by working teachers. So far, the processes of self-education predominate in the development of such technologies. Information and communication technologies (ICT) are evolving rapidly on a daily basis. Geographic information technologies, such as geographic information systems (GIS), remote sensing (RS) or global positioning systems (GPS) are important parts of ICT. They are used especially in geography education, but are not limited to this. Geographic information technologies provide such new methods and means of information processing that provide high visibility for displaying diverse information and an accessible set of tools for reality analysis. GIS has great potential for information analysis in order to make management decisions in the socio-economic sphere. The publication reveals the problems of higher and secondary education related to modern geoinformation technologies for searching, interpreting and demonstrating various geographical data. Basic guidelines for the development of the implementation of geoinformation technologies in the learning environment are presented.