Reviewerly/exHarmony huggingface dataset:
A Framework for Enhancing the Security of Motorbike Riders in Real Time

With the rise in the population these days the requirement of vehicles especially two-wheelers is increasing which is also leading to an increase in the number of accidents; therefore, we require a robust system to enforce the safety guidelines and ensure that people are following the traffic rules that are made to maintain their security; therefore, we have suggested an approach to detect dodgers using an automated system which detects bike-riders with and without a helmet. To achieve this goal, we have used Faster-RCNN inception v2 model, and have procured 96.08% accuracy in detecting bike-riders without a helmet and an accuracy of 86.7% in detecting bike-riders with helmet and the overall accuracy of 93.37%.
Social and Investment Activities of University as a Subject of the Socioeconomic System in the Context of Provision of National Security

In the conditions of depleted potential of the resource model of development and increase of the crisis phenomena in the life of regions, social security of the Russian society is peculiar for consolidation of the all-Russian space, in which negative factors of interregional differentiation work. The author comes to the conclusion that socio-investment activities of universities as subjects of the region’s socioeconomic system are most optimal for strengthening of social security of the Russian society, as within the educational and project activities it stimulates the preservation of the socio-investment inclinations of the population and confidence in the future, which reduces the risks of social depression in relations with the subjects of regional development and has the role of monopolist in the sphere of the educational and culture policy.
Hand Gesture Recognition Using Machine Learning Techniques: A Systematic Review

Hand gesture recognition framework got extraordinary consideration in the ongoing barely any years due to its complexness applications and the capacity to collaborate with machine effectively through human PC cooperation. Right now overview of ongoing hand gesture recognition frameworks is introduced. Key issues of hand gesture recognition framework are given difficulties of gesture framework. Audit techniques for late stances and gestures recognition framework introduced also. Outline of research aftereffects of hand gesture strategies, databases, and examination between principle gesture recognition stages are likewise given. Favorable circumstances and downsides of the talked about frameworks are clarified at last.
Interactive Virtual Bracket Positioning Method in Orthodontics

In the process of orthodontics, accurate bracket positioning is a necessary condition to quickly obtain the ideal treatment result. We propose a virtual bracket-interaction method based on the Layered Depth-Normal Images (LDNI) and the point set matching. By means of mouse-interaction, the fast movement of the bracket on the tooth surface is realized. Specifically, LDNI is used to complete the sampling of the bottom point of the bracket in space coordinate system. Then the nearest-distance algorithm is used to find the corresponding point on the tooth surface. Finally, the position of the bracket on the tooth surface is determined by point set matching. In addition, in order to help the orthodontist to complete clinical correction, we design a virtual bracket interaction software to realize the positioning operation of the virtual bracket through mouse interaction. The results demonstrates that the software we developed is very helpful for orthodontists to complete bracket positioning.
Emerging Technology System Evolution

This paper introduces a strategy for exploring possibility spaces for creative design of emerging technology systems. These systems are represented as directed acyclic graphs with nodes representing different technologies and data or media types. The naming of system and data components is intended to be populated by a subjective, personal ontology. The source material can be simply modified by a user who wishes to explore a space of possibilities, constrained by specific resources, to generate simple visual descriptions of systems that can be quickly browsed. These dataflow visualizations are a familiar representation to those who use visual programming interfaces for creative media processing. An interactive genetic programming based approach is used with small populations of individual designs. The system is being developed using web browser technology. A minimum functionality proof of concept system has been implemented. Next steps and current challenges are discussed.
Integrating Discrete and Neural Features Via Mixed-Feature Trans-Dimensional Random Field Language Models

There has been a long recognition that discrete features (n-gram features) and neural network based features have complementary strengths for language models (LMs). Improved performance can be obtained by model interpolation, which is, however, a sub-optimal two-step integration of discrete and neural features. The trans-dimensional random field (TRF) framework has the potential advantage of being able to flexibly integrate a richer set of features. However, either discrete or neural features are used alone in previous TRF LMs. This paper develops a mixed-feature TRF LM and demonstrates its advantage in integrating discrete and neural features. Various LMs are trained over PTB and Google one-billion-word datasets, and evaluated in N-best list rescoring experiments for speech recognition. Among all single LMs (i.e. without model interpolation), the mixed-feature TRF LMs perform the best, improving over both discrete TRF LMs and neural TRF LMs alone, and also being significantly better than LSTM LMs. Compared to interpolating two separately trained models with discrete and neural features respectively, the performance of mixed-feature TRF LMs matches the best interpolated model, and with simplified one-step training process and reduced training time.
Diacritic-Level Pronunciation Analysis Using Phonological Features

Speech sound disorders affect 10% of preschool and school-age children, adversely affecting their communication, academic performance, and interaction level. Effective pronunciation training requires prolonged supervised practice and interaction. Unfortunately, many children have limited or no access to a speech-language pathologist. Computer-assisted pronunciation training has the potential for being a highly effective teaching aid; however, to-date such systems remain incapable of identifying pronunciation errors with sufficient accuracy. We propose a system that combines a multi-target architecture with weighted finite-state transducers to first segment and then analyze an utterance in terms of its phonological features. We analyze a corpus of 90 children aged 4–7 and find differences between the typically developing and the speech disordered groups.
Predicting the Size of Candidate Document Set for Implicit Web Search Result Diversification

Implicit result diversification methods exploit the content of the documents in the candidate set, i.e., the initial retrieval results of a query, to obtain a relevant and diverse ranking. As our first contribution, we explore whether recently introduced word embeddings can be exploited for representing documents to improve diversification, and show a positive result. As a second improvement, we propose to automatically predict the size of candidate set on per query basis. Experimental evaluations using our BM25 runs as well as the best-performing ad hoc runs submitted to TREC (2009–2012) show that our approach improves the performance of implicit diversification up to 5.4% wrt. initial ranking.
Towards Eclipse Plug-ins for Automated Data Warehouse Design from an Ontology

The Semantic Web is experiencing a rise in recent years. As a result, ontologies have become ubiquitous in many areas while attracting growing interest from researchers. However, this knowledge representation model escapes to multidimensional analysis due to the lack of tools allowing the transition from the ontological model to the multidimensional model. In this contribution, we present our approach for the implementation of our multi-approaches model for the automatic generation of a Data Warehouse model from an ontology. In this context, we have first developed an Eclipse Plug-in for generating a conceptual model of a relational database from an ontology. Secondly, we have developed another Eclipse Plug-in for generating a Data Warehouse model from a database model. And finally, we have integrated these two plug-ins, based on model-to-model transformation and to build an automated design solution for a Data Warehouse model from an ontology.
PhytoGAN: Unpaired Dead-to-Live Phytoplankton Translation

Detecting phytoplankton that causes red tide is an urgent task. However, the live phytoplankton images are scarce and difficult to obtain. This paper aims to discover a mapping between live and dead phytoplankton. We propose PhytoGAN, a Generative Adversarial Network for the unpaired dead-to-live phytoplankton translation. We design a new PCALoss by extracting the principal component of the image to enhance the contour of the generated image. The addition of PCALoss can significantly improve the integrity of images. The experiments are carried out on the existing phytoplankton dataset. A series of experiments are discussed in the paper to demonstrate the performance of the domain transformation and the proposed loss functions. The experimental results indicate that PhytoGAN can produce more integral images of phytoplankton while completing the domain transformation compared with the existing methods.
Binary Algorithm for Big Data Management and Analytics of MyRA Data

The development of the binary algorithm for MyRA big data analysis and management is discussed in this paper. The algorithm is used in database group at the Universiti of Kuala Lumpur (UniKL) and that is a central data base server service aimed at domain scientists around the all 14 UniKL campuses. The MyRA database project has two main objectives. The first is to create an engine that is mature enough, fast and easy to use that domain researchers can use. The second goal is to create a platform to test new database research concepts inspired by our users' needs. To create the system, we have created a binary algorithm that has the freedom to determine all components of the system design as expected. So, we highlight the binary algorithm for selecting and innovating MyRA Big Data key design and reporting on our experience with the use of binary algorithms in the use of various data sciences.
AI Poker Agent based on Game Theory

Game Theory missions with the mathematical study of decision making via strategic interactions. Games are a standout amidst the most evident territories of advancement in the AI space. The splendid information games like Chess, Jeopardy and Go have been mastered by the AI Systems. However, Imperfect information games like No Limit Texas Hold'em variant of poker is further yet a challenge to the AI Industry. In this paper we highlight the importance of Game Theory and how the concepts of game theory and opponent modeling is equally important in modeling an AI poker agent.
An Event-Based User Experience Evaluation Method for Virtual Reality Applications

With the development and advancement of Virtual Reality (VR) technology in recent years, hardware is no longer the main factor limiting VR into the public life, and the demand for user experience (UX) continues to increase. The development of VR is beginning to reveal a content-oriented trend. Whether the content is attractive enough will greatly affects users' experience and determine the service life of VR applications. Consequently, searching for interaction events that would affect VR UX is critical to improve user stickiness. At present, the researches on VR mainly focus on the improvement of hardware and software, and pay less attention to the content event design. Moreover, there is no unified and clear standard for VR UX evaluation. This paper attempts to find out the relationship between user traits, VR interaction events and user experience through realistic experiments. This paper firstly define four types of VR interaction events, and design a questionnaire for collecting the tester's traits and their subjective evaluation. During the experiment, objective physiological data of the testers and their participation process were recorded. The 80 testers were divided into two groups to experience two types of VR games with constant time. The statistical method and improved Prism algorithm were used to find out the correlation among user traits, type of game interaction events and user experience. The experiment results could provide references for VR designers and developers, and at the same time provide the preliminary study to standardized VR user experience evaluation.
Lightweight, Fast and Secure Data Authentication Algorithm for Satellite Application

Authenticity and integrity of data are two primary and critical information security services, which are generally provided together through data authentication algorithms in many of the modern-day communication applications. Cipher based Message Authentication Code (CMAC) is one of the widely used authentication algorithms and also recommended for space applications. Besides, CMAC has some limitations, such as security vulnerabilities and serial architecture. On the other hand, due to the advent and growth of Space Information Network (SIN) and the trend of miniaturization of satellites, lightweight, fast, high data rate supported algorithms are desired nowadays for satellite applications. This paper presents a new lightweight authentication algorithm which removes the shortcomings of the CMAC algorithm to provide more efficient and secure data authentication. Experimental results validate the performance superiority of the proposed algorithm over the CMAC algorithm.
A Joint Learning Information Extraction Method Based on an Effective Inference Structure

Abstract Over the past few years, natural language processing is getting much attraction from more scholars and institutions. Knowledge graph has been regarded as a crucial role in pushing natural language understanding forward. The task of information extraction is the first step to build a large-scale knowledge graph, which means to identify information from the natural language text and extract it in the form of entity and relation triplets. Some joint learning method have been proposed in this domain recently. In this paper, we inherit the idea of joint learning, use a simple, lightweight but effective structure to solve this task and compare our method with some recent algorithms on the benchmark dataset NYT and WebNLG. Results show that our method can get an improvement in F1 score.
Trace Wringing for Program Trace Privacy

A quantitative approach to optimizing computer systems requires a good understanding of how applications exercise a machine, and real program traces from production environments lead to the clearest understanding. Unfortunately, even the simplest program traces can leak sensitive details about users, their recent activity, or even details of trade secret algorithms. Given the cleverness of attackers working to undo well-intentioned, but ultimately insufficient, anonymization techniques, many organizations have simply decided to cease making traces available. Trace wringing is a new formulation of the problem of sharing traces where one knows a priori how much information the trace is leaking in the worst case. The key idea is to squeeze as much information as possible out of the trace without completely compromising its usefulness for optimization. We demonstrate the utility of a wrung trace through cache simulation and examine the sensitivity of wrung traces to a class of attacks on Advanced Encryption Standard (AES) encryption.
Co-Planar Waveguide Resonator to Mediate Coupling between Superconducting Quantum Bits

This paper proposes a design of a coplanar waveguide resonator (CPWR) to integrate two distant superconducting qubits which is a function required for a quantum information system. In this CPWR, the states of one of the super conducting qubits can be moved to and recovered from the other qubit by microwave resonator photons. The superconducting qubits are relatively straightforward to fabricate from materials such as aluminium using electron beam lithography. The superconducting qubits, which are typically a few hundred micrometres in size, are well suited to perform fast quantum logic gate operations. The CPWR highly couples the two qubits through the tightly confined oscillating magnetic field of the resonator microwave photons. It is shown that, in the proposed CPW resonator structure, the microwave photons can mediate coupling between two distant superconducting qubits (21 mm apart) to realize quantum logic gates.
Policy-guided Monte Carlo: Reinforcement-learning Markov chain dynamics

We introduce policy-guided Monte Carlo (PGMC), a computational framework using reinforcement learning to improve Markov chain Monte Carlo (MCMC) sampling. The methodology is generally applicable, unbiased, and opens up a path to automated discovery of efficient MCMC samplers. After developing a general theory, we demonstrate some of PGMC's prospects on an Ising model on the kagome lattice, including when the model is in its computationally challenging kagome spin ice regime. Here we show that PGMC is able to automatically machine learn efficient MCMC updates without a priori knowledge of the physics at hand.
Parking Area Data Collection and Scenario Extraction for the Purpose of Automatic Parking ADAS Function

Abstract Automatic Parking is the most significant ADAS function that could achieving mass production in the field of unmanned driving. For the purpose of automatic parking function, we’re foundamentally facing the corresponding data collection and data analysis.In view of three classical parking scenarios,this paper is concentrating on three contents including the sensor hardware construction, the perception algorithm development,and scenario data analysis.For the the hardware construction,we focus on the sensor installation,time calibration,and space unification.Based on the front camera and RADAR data fusion,we put great emphasis on the multi-target matching and fusion algorithm. And for the purpose of the scenario extraction and big data analysis work, we are developing automatic big data processing Python code,realizing TB level original data labeled, and data completely processed in short time.The whole technology process and tool chain in this program,have extensively high level scientific research and commercial value
Low-Cost Smart Home Using Sensor Fusion and Autonomous Wheel Chair: Caring Lonely Elders Specially in India

India being a developing country faces lots of obstacles in its societal approach of taking care of elderly persons. More often than not, these obstacles appear in the form of social conditions and economic hardships. In order to mitigate these hindrances, technology should be employed as a tool of survival. In this paper, we report how we have attempted to conceive and construct a device that will help the younger generation to take good care of the older without stressing or compromising their own professional commitments. While designing the device, it was kept in mind that this has to suit Indian economy and social nuances which is an important orientation of this project. Though there is abundant research in the area of smart homes, we are yet to arrive at appropriate solutions for India. This paper is focused on delivering a robust solution that is suitable for and sustainable even in remote areas of the country.
The Issue of Environmental Protection in the Regional Mass Media Space

Modern economic processes require an interdisciplinary research, due to the fact that they entail crucial changes in the economic, social, political spheres, as well as in public mind, personal views and goals. The need to include the media information space analysis in multidisciplinary studies is determined by the increasing influence of the media (which have turned into the open interactive discussion platforms about all spheres of human life) on the public opinion about the economy and the environmentalism of technological progress. The authors studied the Pskov region mass media discourse regarding the situation with the “Titan-Polymer” plant, the planned construction of which caused an ambiguity in society, because of environmental issues as well. Along with political and social forces, regional mass media can play an important role in stabilization of the economic situation. The discourse analysis of mass media identifies the basic patterns for creating meanings in the information space that influence the human acceptance or rejection of new economic objects, and to determine the mechanisms of interaction between the linguistics of the text and the real situation surrounding the object. The content analysis of regional mass media publications took into account the rating scale when highlighting an informational event. The publications analysis, which resulted from the discussions of “Titan-Polymer” plant construction, identified and described the main trends in organizing the regional mass media discourse and the effect on the public opinion transformations, which can be used to stabilize the situations related to greening the technological progress.
Finding Points of Reform in the Fact-finding Survey System

본 연구는 2010년 이후 실시된 예술인 실태조사들 간의 연극인 소득수준 비교를 통해 실태조사 실천과정에서의 개선 방향을 모색한다. 2012년, 2015년, 2018년에 문화체육관광부가 조사한 ‘예술인 실태조사’, 대학로 연극을 대상으로 서울문화재단에서 2013년 실행한 ‘대학로 연극 실태조사’, 전국의 공연단체와 공연시설을 대상으로 문화체육관광부와 예술경영지원센터가 2018년 진행한 ‘공연예술실태조사’, 연극공연예술인을 대상으로 성북구 노동권익센터에서 2018년 발표한 ‘공연예술분야 근로조건 실태조사’ 등 6가지 조사를 중심으로 비교한다. 각 실태조사 결과에 나타난 연극인 총소득의 평균과 분포, 연극 활동에 의한 소득, 연극 내 직무별 소득의 차이 등을 비교 검토하면서 연극인의 소득 수준을 점검하고, 각 보고서에 나타난 결과의 비교를 통해 통계 수치의 실효성을 점검한다. 실태조사 보고서의 비교 검토를 통해, 예술의 분야별 특성이 반영된 심층 실태조사와 직무별 현황조사 실시, 실태조사 표본수의 획기적 증대, 실태조사 결과의 통합분석 시스템 구축, 연극뿐 아니라 영화, 방송까지 아우르는 배우 직군의 독자적인 실태조사 실시 등의 실태조사 개선 방향을 도출하였다.This study aimed at finding points of reform in the fact-finding survey system by comparing the survey reports on the current economic situation of theatrical artists. “The Survey of Artists Situation in 2012, 2015, and 2018 by Ministry of Culture, Sports, and Tourism (MCST),” “The 2013 Survey on the Daehak-ro by Seoul Foundation for Arts and Culture,” “The 2018 Survey on the Performing Groups and Performing Arts Facilities by MCST and Arts Management Supports Centre,” and “The Survey on the Working Conditions of the Theatrical Performances by Seongbuk-gu Labor Rights Center” were compared. Besides, “The average & the distribution of total income of theatrical artists,” “the average & the distribution of income through theatrical activities,” and “the average & the distribution of income created by jobs in theatre” were dealt with, and it was concluded that the economic situation of theatrical artists was poor and there were differences in the income created by jobs in theatre. By comparing the surveys, this study inferred that several aspects of the fact-finding research should be reformed for deriving accurate data on the artists’ condition. Furthermore, it was also concluded that the research should reflect the characteristics of artistic jobs and artistic genres. Increasing the number of research subjects and establishing a platform of integrated survey management were among the suggestions for more effective fact-findings. Finally, in-depth research about actors in different media, such as film, TV, and stage, was suggested for a better understanding of the economic situation of actors.