Reviewerly/exHarmony huggingface dataset:
Neural Discrete Abstraction of High-Dimensional Spaces: A Case Study In Reinforcement Learning

We employ Neural Discrete Representation Learning to map a high-dimensional state space, made up from raw video frames of a Reinforcement Learning agent's interactions with the environment, into a low-dimensional state space made up from learned discrete latent representations. We show experimentally that the discrete latents learned by the encoder of a Vector Quantized Auto-Encoder (VQ-AE) model trained to reconstruct the raw video frames making up the high-dimensional state space, can serve as meaningful abstractions of clusters of correlated frames. A low-dimensional state space can then be successfully constructed, where each individual state is a quantized vector encoding representing a cluster of correlated frames of the high-dimensional state space. Experimental results for a 3D navigation task in a maze environment constructed in Minecraft demonstrate that this discrete mapping can be used in addition to, or in place of, the high-dimensional space to improve the agent's learning performance.
Design of Big Data Algorithm Based on MapReduce

With the widespread application of Internet technology, the utilization rate of similar blogs and social networks has been significantly improved. With the intervention of cloud computing and other technologies, a large amount of data generated during browsing the Internet can be effectively accumulated and processed, which indicates that big data has been integrated into people's daily life. MapReduce, as a parallel programming environment, can effectively deal with big data related problems, and has been applied in large Internet companies such as Google and Amazon Maxdiff, an efficient histogram algorithm based on MapReduce, includes accurate algorithm and approximate algorithm, and proposes two-table equijoin algorithm and multistandard equijoin algorithm for data skew And optimize the efficiency of the connection algorithm when one or more data in the data set appear too much. This paper starts with the efficiency optimization of big data connection algorithm based on MapReduce, and studies the efficiency optimization of equijoin algorithm, data skew connection algorithm and connection algorithm in detail, and then puts forward an algorithm that can effectively improve the program execution efficiency, hoping to provide reference for the follow-up research work.
Evaluating mean time to security failure based on continuous-time Markov chains

In the article, we consider a Markov model of computer attacks, in the framework of which attacks and the system responses are modelled by homogeneous Poisson point processes. We describe a method of solving the corresponding Kolmogorov system of equations by calculating eigenvalues and eigenvectors of some matrix. An important random variable associated with the corresponding Markov chain and called the time to security failure is explored in detail. The comparison of the results obtained and the results of simulation modelling are presented.
An Intelligent Agent Playing Generic Action Games based on Deep Reinforcement Learning with Memory Restrictions

Among the topics that increasingly gained special attention in Computer Science recently, the evolution of Artificial Intelligence has been one of the most prominent subjects, especially when related to games. In this work we developed an intelligent agent with memory restrictions so to investigate its ability to learn playing multiple, different games without the need of being provided with specific details for each of the games. As a measure of quality of the agent, we used the difference between its score and the scores obtained by casual human players. Aiming to address the possibilities of using Deep Learning for General Game Playing in less powerful devices, we explicitly limited the amount of memory available for the agent, apart from the commonly used physical memory limit for most works in the area. For the abstraction of machine learning and image processing stages, we used the Keras and Gym libraries. As a result, we obtained an agent capable of playing multiple games without the need to provide rules in advance, but receiving at each moment only the game video frame, the current score and whether the current state represents an endgame. To assess the agent effectiveness, we submitted it to a set of Atari 2600™ games, where the scores obtained were compared to casual human players and discussed. In the conclusion, we show that promising results were obtained for these games even with memory limitations and finally a few improvements are proposed.
Big Data Issues

Various unimaginable opportunities and applications can be attained by the development of internet-connected automation. The network system with numerous wired or wireless smart sensors is called as IoT. It is showing various enhancement for past few years. Without proper security protection, various attacks and threats like cyberattacks threat causes serious disaster to IoT from the day it was introduced. Hence, IoT security system is improvised by various security and the management techniques. There are six sections in security management of IoT works. IoT security requirement is described intensively. The proposed layered of security management architecture is being defined and explained. Thus, this proposed architecture shows the security management system for IoT network tight security management for a network of the IoT which is elaborately explained with examples and about GDPR. In information security, intrusion recognizable proof is the showing of placing exercises that attempt to deal the protection, respectability, or availability of a benefit.
Sentence Answer Selection for Open Domain Question Answering via Deep Word Matching

Question answering is a challenging task due to the diversity of expression in human language. This work proposes a novel unsupervised approach for sentence answer selection, called Deep Word Matching (DWM), that uses both the string form and distributed representations of words, thereby capturing their latent semantic relatedness. Our method takes advantage of publicly available linguistic resources and word embeddings in order to explore various levels of word similarity and identify matching concepts between a question and the sentence that contains the answer. By evaluating on three large corpora (SQuAD, NewsQA and WikiQA), we show that the proposed method outperforms previously published baselines and is also task independent, eliminating the need for retraining and tuning on a new task. We obtain improvements between 5% and 11% on target baseline for two tasks, and best result on the third task.
A Dynamic Contingency Analysis Visualization Tool

This paper presents a web-browser based visualization tool for displaying extensive dynamic contingency outputs, generated by the Dynamic Contingency Analysis Tool (DCAT). DCAT is an open-platform and publicly available methodology to help develop applications aiming to improve the capabilities of power system planning engineers to assess impact and likelihood of extreme contingencies, and potential cascading events across the systems and interconnections. We have developed this DCAT Visualization Tool as a new feature to the DCAT package to afford a visual situational awareness environment that allows engineers to better comprehend, interpret, and analyze DCAT data and results and to more directly view behaviors and causes and effects against the topology of a power grid. A usability evaluation was conducted to support the effectiveness of this tool. The techniques developed are generic enough that can be readily adapted by other power system applications. This visualization tool represents a vital capability for power system engineers to quickly capture and comprehend critical information.
An Attention-based Neural Model for Popularity Prediction in Social Service

Web based social services like Twitter and Weibo are popular nowadays. A large number of users generate massive text-based information continuously and as a result, many topic popularity prediction models arise. However, most of them simply use forwarding or view count to measure popularity, ignoring semantic relations between posts and the topic. Moreover, few model use the important component of opinion - sentiment, to enhance topic popularity prediction. Therefore, in this paper, we propose a attention-based convolutional network model to generate sentiment-aware time series and predict topic popularity. Our model quantifies popularity more accurately with posts' semantic information and incorporates sentiment into popularity prediction. Concretely, our model is composed of a semantic-aware popularity quantification metric, a sentiment intensity detection neural network, and an Autoregressive convolutional network prediction scheme. We have conducted extensive experiments to prove that our model outperforms the existing popularity prediction models on a real-world Twitter dataset. We find that sentiment time series have the ability to improve popularity prediction accuracy significantly. Furthermore, we find our prediction scheme is superior to other models in utilizing sentiment information.
Notice of Violation of IEEE Publication Principles: Music Staff Elimination using Supervised Pixel Classification

This paper presents an approach for music staff elimination from an image containing music score without disturbing any symbol information. This is one of the most important tracks which improve the realization in optical music recognition system. Depending on intrinsic of music scores staff elimination was performed in literature based on image processing techniques. Here, a problem is modeled as a supervised pixel learning classification task for the elimination of staff lines. In this scenario, front pixel is tagged as symbol or staff. To train the classification algorithms, a pairs of scores with and without stafflines are used. Our proposed methodology is tested with other well-know classification techniques. Moreover, in our experiment certain parameters are set to default setting which is provided by libraries, an attempt of tuning the classification algorithm is not permitted. Our ultimate aim represents that even though by applying the straightforward method, still our method gives competitive results by using significant algorithms. Some of the advantages of using this method over the earlier methods are its high versatility.
Multivariate Prediction of Correct Lane for Autonomous Electric Vehicle Using Deep Learning Models

The target of a self-driving car researches is to build a better autonomous driver. We control the car to be able to drive itself without falling off the track using appropriate accelerating and braking. Using udacity as an open source simulator to depict a real-time environment. With the help of a model trained by deep neural networks, we mimic the driving behavior of a human on the simulator (Behavioral Cloning) [1]. We use Keras and a high-level API that uses TensorFlow as the backend for dataflow programming. Keras provides sequential models for building a linear stack of network layers. CNN models (Neural network layers were optimized in series combinations of Time-Distributed Convolution Layers, Maxpooling, Flatten, Dense, Dropout and so on) are experimented for reaching the best model of control. Finally, we concluded that model 2 performed the best at the end of 60 epochs with the least loss = 0.007.
App coronavírus-SUS

The Ergonomic Criteria for Usability by Christian Bastien and Dominique Scapin are a set of ergonomic standards that graphic user interface must present. Popular in the 90s, the criteria are not aimed at mobile devices, motivating this research to apply these criteria to see their suitability for new scenarios of hardware and software. As a firts step towards revisiting the Ergonomic Criteria, we present results and discussions from an evaluation activity where the criteria were applied to evaluate the Coronavirus-SUS, an application developed to inform Brazilian population about the COVID-19 pandemic situation. Because the application's target audience is the entire Brazilian population, many different issues can be experienced and must be considered. Results reveal several critical problems that may affect the application understanding and usage, and suggest the criteria are able to support usability evaluation in mobile contexts, although a need for revisiting and updating the criteria was identified.
Development of Control Method For An Optimal Quantum Receiver

Abstract We propose a method for optimal displacement controlling of an optimal quantum receiver for registrations a binary coherent signal. An optimal receiver is able to distinguish between two phase-modulated states of a coherent signal. The optimal receiver controlling method can be used later in practice in various physical implementations of the optimal receiver.
A Geometric Measure of Polysemy in Hindi Language

A word referring to two or more different meanings is called polysemous. In this study, we introduce a geometric method to estimate the polysemy of words using the discrete Ollivier-Ricci curvature of a graph of synonyms in Hindi. We show that this approach can effectively measure the polysemy of words and is strongly correlated with theoretical interpretations of polysemy.
Document Image Analysis in Compressed Domain-Limitations, Applications &amp; Challenges

Document image analysis plays a vital role in this digital era. Recent developments in the IT industry have led to the growth of digital data in various fields like medical, government offices, education sector, banks, social media, digital library, and so on. Advancement in the recent technologies has paved their way to convert the traditional offices into paperless offices. Also, the growth of digital libraries, e-governance, and internet based applications has led to the increase in the volume of digital data, which mainly include texts, graphs, images, audio and video as various components in the document image by resulting in the development of complex document images, which are used for archival and transmission on regular basis. This paper proposes an idea for processing the document image in its compressed version by particularly focusing on how content matching and structural analysis can be performed in the compressed representation of document image. This gives an insight on the importance of processing document images in its compressed domain. Due to the exponential growth of data, the data is stored in compressed form. There is an actual need for investigating further research from the perspective of dealing directly with the compressed representation of document images as a remedy to the ever-increasing big data-related challenges. This paper also discusses the various applications of document images and opens up the challenges faced by the researchers in addressing these applications. An overview of the state of the art datasets available in the literature in the area of document image analysis is also addressed.
Design of a Spherical Chaotic Oscillator and its Application in Four Cases of Celestial Mechanics

This paper presents the procedure for the design of a spherical chaotic system based on the movement of two bodies that interact with each other because of the gravitational force exerted by the body of greater mass concerning the body of smaller mass. Establishing that the secondary body presents a chaotic behavior along its orbit, four cases are determined in which this could happen, which leads to creating a project that simulates each case. During the process, the precision of the results is considered to define the most optimal parameters of the project. The three output signals represent the movement of the secondary body and can be verified by the results obtained from the same system by using Matlab programs that simulate the four cases, respectively. Finally, the complete design of the four cases was performed on an FPGA board, using VHDL code.
Internationalization of Vocational Education in the Arctic Council Member-States’ Universities

Nowadays, the potential for international cooperation in the Arctic is concentrated within the framework of such an influential international structure as the Arctic Council, where Russia is presiding over soon in 2021–2023. The article is devoted to international cooperation evaluation of 15 Arctic universities from 7 Arctic Council member-states with a student contingent of at least 3,600 people (2 in the USA, 1 in Iceland, 5 in Russia, 2 in Finland, 2 in Sweden, 2 in Norway, 1 in Canada). It seems possible to evaluate international cooperation potential between Arctic universities through the prism of 10 quantitative indicators, including the number of international students admitted to the university; number of courses taught in English; number of educational programs for international students; a number of foreign scientists invited to the university, etc. We can evaluate both academic mobility within the Arctic universities and vocational education internationalization due to accumulated statistical data.
Low Light Enhancement by Unsupervised Network

This paper focuses on unsupervised low-light enhancement methods for practical applications such as automated driving, surveillance cameras, and so on. This paper proposes an unsupervised method that is independent of paired samples and works well to solve the image darkness problem. Furthermore, to increase the brightness of the image and restore the information hidden in the dark, an auxiliary attention module is incorporated into our model. This proposed module can help generate images with good edges and make the image colors and details richer. The experimental results show that the proposed method works well for both image enhancement and noise control.
Efficient Light Deep Network for Street Scene Parsing

The semantic segmentation is a dense pixel label pre-diction task, which takes quite a lot of resources and computation cost in most of the time. In our approach, we pay attention to balance the speed and better performance which outperforms the state of the art in speed and accuracy for real-time performance. We come up with the idea of new efficient deep backbone that can extract more semantic details, reduce the computation cost and be easy to deploy at the same time. We call our new backbone as Cascaded Mobile Network, which is proved to be very useful. Our proposed model achieves 72.1 mIOU on the CityScapes val, and 69.5 on CamVid. We achieve good balance between speed and accuracy.
Trial Development of a Cyber Risk Visualization System with Function of k-Anonymity and Compatibility with Other Organizations

This paper deals with the trial development of a new cyber risk visualization system. Although numerous organizations have implemented a wide variety of information technology (IT) systems in recent years, the number of cybersecurity incidents demanding rapid and efficient responses continues to increase. Furthermore, while it is desirable for organizations to set appropriate target levels when addressing such risks, the process is difficult because managers are seldom information security specialists. In such situations, management typically sets goals regarding information security, communicates risk factors with the information department, and then provides decision support for concrete measures. However, the fact that they are not security experts often makes it difficult to implement these measures. As a partial countermeasure to such cases, we believe that an approach by which an organization can gain an understanding of similar situations in other organizations, understand and adjust the methods by which they approach related issues, and then adapt those methods to their own use can be effective. However, a primary concern regarding information sharing between organizations is that while one organization may desire to know the status of other organizations, they do not want necessarily want to share similar information related to their own organizations. As a result, active efforts to facilitate such sharing have seen little progress. In this paper, we report on the development of a system that visualizes cyber-risks related to a particular organization, shows similar results for other organizations in the form of average values within a range that allows k-anonymity to be maintained, and then makes comparisons among those results. This makes it difficult for other participating organizations to gain a specific understanding of conditions within a specific organization. We then developed a prototype system using LimeSurvey, which is an easily modifiable open source web application, in order to make our proposed system easy to customize and use. Our experimental results show that this prototype enables comparisons to be made regarding situations among various participating organizations in the same industries while maintaining the anonymity of each individual organization.
Analysis of CNN Models to develop a New Appearance Model for Multiple-Object-Tracking

Multiple-object-tracking, MOT, is a paramount research area in computer vision tasks such as: intelligent surveillance systems, motion analysis, self-driving, etc. Different techniques have been considered for the solution of MOT, however, recent works are aimed to involve deep neural networks. Therefore, this paper presents an analysis of different convolutional neural networks (CNN) reported in the literature for MOT. The aim of this analysis is to determine which CNN models perform better when they are evaluated in the main challenges found in MOT. Then, based on the analysis results, a new robust appearance model generated from a Siamese CNN features is proposed. Findings indicate that CNN with fewer layers perform better than CNN with more layers, which may be to the fact that more trivial features allow a better discrimination among the objects of the same class.
Twitter Scrapping for Profiling Education Staff

Social media (Facebook, Instagram, Twitter, etc.) have been widely used. They have many advantages, especially for business. However, such media sometimes invite negative effects, e.g. decreasing employee performance, conflict in a relationship, crime, etc. Therefore, this study proposes a method to scrap one of the social media, i.e. Twitter for profiling. Gephi application is used for network analysis after scrapping the network using Twecoll, a Python-based scrapping application. A web-based application is also created including the Apache-based server and Python-based script. The result shows that the scrapped account has several groups/communities including the weight of each connection. In addition, the result can be used for group profiling and additional analysis to complete the sentiment analysis based on tweets.