Reviewerly/exHarmony huggingface dataset:
Massive Crime Data Screening Framework based on Downsampling and Compressed Sensing

Massive crime data screening framework based on downsampling and compressed sensing is implemented in this research work. By selecting an appropriate kernel mapping and forming a sparse representation of the signal in the feature space, the least squares method can be used to then obtain an analytical solution, which is called kernel compressed sensing. The paper proposed the novelties as follows. First, compared with the shortcomings of traditional methods, big data has opened up a new direction for the crime prediction. The big data model is applied to the information pattern analysis. Second, random down-sampling algorithm is employed to randomly select some samples of the majority class and removes these samples from the majority class to adjust the balance of the original data set. The model is simulated on the public data set to validate the performance. The simulation compared with the latest model has also proven the satisfactory results.
Online mode as a motivation for library initiatives (on the example of the Russian State Library of Arts)

The article generalizes and systematizes exhibition, educational and project activities of The Russian State Library of Arts (RSLA) in terms of measures related to self-isolation under the threat of coronavirus infection ­(COVID-19) spreading. The necessary and sufficient conditions for effective work in remote mode are considered. Emphasis is made on the organization of work on projects. The conclusion is made on the strategic importance of teamwork, network interaction of employees, and operational coordination of work. It is noted that the library managed to significantly expand its Internet resources. This was reflected in the development and presentation of virtual exhibitions, thematic online lectures and publications, video reviews, virtual excursions, meetings on the Zoom platform, in the reorganization and adaptation of planned events to new conditions. The important organizational steps were: mastering the necessary software products, including video editing, and psychological and pedagogical skills to retain the audience; understanding the need to discuss virtual expositions in working groups and exhibition councils. All this made it possible not only to preserve the old audience, but also to attract a new one for RSLA. A conclusion is also made for the future: when planning events and publications in the web space, it is very important to balance the issues of lectures, reviews, video meetings, modern and retrospective topics, archival recordings and new publications.
P-Cloud - A cost efficient definitive cloud with user satisfaction using non-cloud resources

Today is the age of Cloud Computing Technology in IT Industries. Cloud computing which is built on the Internet has the most robust architecture of computation. In this work, we have proposed a cloud infrastructure. P-Cloud (Personal Cloud) for leasing computing resources appending non-cloud resources such as laptops, PCs, clusters and enterprise servers. Generally, infrastructure resource providers are responsible for providing infrastructure resources, however, the proposed P-Cloud deals with personal resources. P-Cloud permits a huge number of excess computing resources, on the scale of hundreds of millions, to be leased out to cloud users. By sharing of such resources allows resource owners to earn from idle resources, and cloud users to have a cost-efficient auxiliary to vast cloud service providers. In this work, we mainly focus to develop strategies to make relations between the users and resource owners where the two have opposite goals as well as how to fulfill their requirements mainly user satisfaction and system profit. The proposed system makes a platform between the user group and the resource owner group. Finally, the proposed system is implemented in CloudSim Plus.
Real-Time Sign Language Learning System

Abstract Learning sign language can be fun and easy when a suitable learning method is used. Different people would have different interest level in learning sign language, and this also can be affected by their communication skills. With the traditional teaching method, there would be a lot of constraints that could stop the learner from learning effectively. This work proposes a learning platform to address these concerns and facilitating sign language learners in gaining the required knowledge. The developed web application includes online learning materials such as videos, and incorporates interactive elements such as a practice session to increase the efficiency of the learning process. The findings indicate that the Sign Language Learning System could be effective for those who are interested in learning sign language.
Straightforward Working Principles Behind Modern Data Visualization Approaches

From state-of-the-art visualization algorithms, we distill six working principles which are, by hypothesis, sufficient to produce visual projections qualitatively similar to those obtained with these state-of-the-art algorithms. These working principles are presented through the geometrical reasoning of the classical Multidimensional Scaling algorithm, and their effectiveness is illustrated through a novel straightforward algorithm for data visualization. We show, using several datasets originated from various applications, that our algorithm can produce visual projections qualitatively similar to those obtained with these state-of-the-art algorithms. Besides, under the same motivation (of simplification), the problem of visualizing large datasets is tackled through a companion algorithm which is able to embed new input patterns.
RETRACTED: Simulation of swimming sports image recognition based on multi-core processor and dynamic image sampling

Human understanding of human movement is one of the most important areas of research activities. In recent years, the use of RGB-D movement recognition data has raised concerns. With artificial intelligence development, the depth of learning technology has made great success in computer vision. A variety of application scenarios and real-time constraints computer vision applications spurred on computer architecture conducted extensive research. These studies take advantage of image processing algorithms and computer vision functions to maximize and improve efficiency through the hardware's scalability. Growth, programmable computing power, and multi-core architecture's potential to accelerate parallel image processing and computer vision algorithms provide a broad prospect. Conduct a new study to discover the unique attributes of three computer vision algorithms that are widely used and determine the optimal computing architecture for each algorithm. Advantages of parallel processing hardware to provide modern programmable graphics data and the thread of instructions, which is significantly faster than the standard CPU implementation.
Referential Nominal Metaphor Identification in Myanmar Language

Metaphor, one of the figurative usages, becomes a problem in natural language processing (NLP). Identification of metaphor becomes one of the attentive research works in NLP. Metaphor can be classified as nominal metaphor, verbal metaphor and adjective metaphor. In nominal metaphor, two types of nominal metaphor can be found: noun-noun metaphor and referential nominal metaphor. This paper presents the identification of referential nominal metaphor in Myanmar language. Identification of referential nominal metaphor requires to extract the two reference nouns and extracting rules are created for reference nouns extraction. The structure of rules for reference nouns extraction and how to extract the reference nouns using these extracting rules are explained detail in this paper. Myanmar WordNet, wordnet2sql and bilingual dictionary are used to identify the referential nominal metaphor. The experiments are done on the sentences of 6 genres, news, novels, articles, conversational, formal and overall sentences. The precision results for these 6 genres can be found 75% for news, 81% for novels, 83% for articles, 78% for conversational, 81% for formal and 81% for overall respectively. General discussion about the issues and explanations are also described in this paper.
Performance Analysis of Modulation Classification with a Preprocessing

In this paper, we propose a preprocessing method for automatic modulation classification that allows reliable classification of the digital baseband modulation schemes whose signal constellations have symmetry about the in-phase and quadrature axes. The proposed preprocessing is used to generate additional data for modulation classification based on the symmetric property of the signal constellation and provides robustness to noisy environment. Through computer simulations, we validate the modulation classification with the proposed preprocessing in terms of the classification performance.
Homing of a Robot

Homing of a robot is characterized as its travelling through a path to perform an assigned task and returning to its home position or underlying position. The paper centers on the idea of programming a wheeled mobile robot for automating homing by path planning, obstacle avoidance and control. The proposed homing scheme is designed by modifying existing search algorithms. A comprehensive comparison of 4 different algorithms is also executed and presented in the paper. Of which best suited algorithm is modified and applied to a Quanser Qbot 2e experimental platform. The QBot leverages Quanser Rapid Control Prototyping software which can seamlessly integrate with MATLAB/ Simulink software to provide real time communication. The experiment has demonstrated that the suggested algorithm achieves homing accurately, with automation.
Implementation of the System of the Early Diagnostics of Pancreatic Cancer in Clinical Practice

In the paper, we present the results of the pancreatic cancer diagnostic research and the proposal of the implementation of the new decision support system as the team of agents using the example of a decision-making the task for the early diagnostics of pancreatic cancer by a medical consultation in clinical practice.
Challenges for and with Autonomous Vehicles: An Introduction

The deployment of autonomous vehicles has been announced for years. Yet, full autonomous vehicles are not on public roads. Elon MuskElon Musk, speaking at an event during the first half of 2020, stated that his firm will be able to present a fully autonomous vehicle technology by the end of the year. This statement is met with skepticism, especially because several of the challenges that existed have not been solved. Road traffic laws have not been adjusted to face the reality of driving by an autonomous machine. The only way that full autonomous vehicles can hit public roads is through test procedures. There also exists quite some uncertainty on who should be liable for accidents with autonomous vehicles. Accidents may occur, and this is something that adversarial machine learningAdversarial machine learning is showing. Even with the best set of sensors, the interpretation of the sensed environment may be misinterpreted. ConnectivityConnectivity is being suggested as a possible solution to several of the problems autonomous vehicles are facing. Deploying autonomous vehicles will also challenge business organization, as car manufacturersManufacturer may turn their business vehicles into mobility service providers. This may require a different type of organization within the firm.
About proof-search in intuitionistic natural deduction calculus using partial Skolemization

Abstract In this paper, automated proof search in single-conclusion sequential variant of intuitionistic and minimal predicate calculus is considered. In this algorithm, meta-variables and partial Skolemization are used. Theorems of soundness and completeness for the considered algorithm are proved.
대학생의 국제회의통역사에 대한 직업이미지 연구

This study explores the occupational image of conference interpreters as perceived by undergraduates in Seoul, Korea. The study conducted a survey on 212 undergraduates in the Seoul metropolitan area. Quantitative analysis including exploratory factor analysis and descriptive statistical analysis was performed. Five factors such as, power, reward, social reputation, job trait, and expertise, were extracted. The result of the analysis reveals that participants perceive conference interpreting as a well respected, interesting job that requires extensive expertise. It is also perceived as a job that offers self-development opportunities. Furthermore participants believe that conference interpreters can be influential at work. Female students were more likely than their male counterparts to perceive conference interpreting as an interesting and powerful profession enjoying high social status. The job was moderately popular among participants and was regarded as similar to diplomats as both jobs involve using foreign languages to represent a certain culture or country and facilitate communication. The findings indicate that while conference interpreting is a highly professional job it needs to reinforce the power and reward images in order to attract more young talents to this field.
Error Analysis on Parameters Measurement Model of Wheel-Mounted Four-Wheel Alignment

Aiming at the problem that measurement models on different parameters would produce different theoretical errors, this paper proposes an error analysis method on parameters measurement model of wheel-mounted four-wheel alignment. Through the analysis of theoretical errors on different measurement models, we select the one with less error to improve measurement accuracy. The experiment results indicate that under experimental conditions, measurement model II on wheel camber angle, model I on kingpin inclination angle and model II on kingpin caster angle generate the less error respectively. This error analysis method can help select the optimal four-wheel alignment parameters measurement model and the experimental results are consistent with the theoretical analysis.
Mathematical Method for Processing SCADA Information and Diagnostic Flows

This paper addresses the issue of creating and applying mathematical methods for processing and analyzing large flows of diagnostic information. It specifically refers to automating the monitoring of SCADA performance in real time as well as minimizing the time and computational resources involved in the diagnostics of its subsystems. We reviewed and analyzed modern methodologies and mathematical methods used to solve these particular problems. The mathematical method was developed to work with an ordered data structure known as “m-tuples based on Ordered Sets of Arbitrary Cardinality (OSAC)”. It characterizes the operability of SCADA backbone nodes. We also derived a set of functional dependencies between m-tuples based on their location in the ordered data structure. It allowed the mathematical model to be formed under specific conditions so as to minimize computational resources when performing a set of operations on data structure elements. This also included performing automatic SCADA self-diagnostics. Displayed within the illustration of the Boolean graph is an example of one of its operations on the Boolean elements based on the mathematical method developed. We obtained logical conclusions regarding how these mathematical methods influenced the minimization of the computational resources for SCADA subsystem diagnostics. Prerequisites were created for scientifically-based methods and algorithms for automatic self-recovery of SCADA operability in real time after reversible failures.
A Multi-Camera Tracker for Monitoring Pedestrians in Enclosed Environments

Multi-camera pedestrians tracking is a challenging computer vision task. We propose a multi-camera tracker for monitoring pedestrians in an enclosed shopping environment. We assess the performance of the multi-camera tracker in a case study, tracking customers in a food and speciality market hall. Our multi-camera tracker tracks customers' walking between the stalls in the market. The information is useful for market management, visitor safety, and other potential application areas.
Image invariant features and SVM techniques for college level English learning platform

Under the Internet information network environment, College English teaching mode is faced with a new choice and transformation. SPOC and STEAM classes have multiple advantages, which can make up for the lack of a single teaching model and provide new ideas for teaching reform. It is a practical application of statistical method to optimize the model of artificial neural network by machine learning method of statistics. The application of mathematical statistics can solve some related problems of artificial perception. Therefore, artificial neural network has the same simple decision ability and judgment ability as human beings. In this paper, the authors analyze the image invariant features and SVM algorithms application in college English education platform. The results show that this method has a positive effect on learners’ English proficiency and learning effect. Teachers also avoid paying a lot of labor, which is very beneficial to the implementation of innovative teaching. However, compared with the traditional teaching, the phenomenon of student achievement differentiation is very serious, and teaching is facing great pressure. Therefore, improving students’ autonomous learning ability and teachers’ information literacy is still very helpful to improve the teaching effect.
A Fuzzy Fan Speed Controller for Smart Data Processing Device

Thanks to high computing power brought by new processor designs such as neural network processing unit (NPU), machine learning is widely used for data processing in the field of big data. However, smart data processing requires huge amount of computing, and may cause the NPU to quickly enter a working state at full load and the temperature to rapidly increase, which may lead to a crash or damage the circuit. Although reducing the frequency can prevent the temperature from rising, it will cause the loss of NPU performance. Artificial Intelligence (AI) tasks are usually computationally intensive, as a result, a high performance working mode should be provided. An external fan with suitable control algorithm can meet the above demands. However, traditional fan control algorithms usually increase the fan power consumption, which will result in a substantial increase in the total power. In this paper, we propose a fitness model based on fuzzy control to make better tradeoff between cooling efficiency and power consumption under high workload. The experimental results show that the model can effectively prevent the NPU temperature from being too high during the rapidly rising temperature and can reduce fan power consumption by 10%.
Knowledge infrastructure and the role of the university

As open access to research information grows and publisher business models adapt accordingly, knowledge infrastructure has become the new frontier for advocates of open science. This paper argues that the time has come for universities and other knowledge institutions to assume a larger role in mitigating the risks that arise from ongoing consolidation in research infrastructure, including the privatization of community platforms, commercial control of analytics solutions, and other market-driven trends in scientific and scholarly publishing.
Analysis of Gaze Point Distribution Affected by Observation Conditions When Viewing Video Clips

In our day life, the televisions are viewed under the various conditions of each households, and we rarely watch TV from a standard distance with the gaze position used in image quality evaluation. Accordingly, we examined mean gaze point distribution affected by observation conditions (the resolution, screen size, viewing position). Consequently, we found that there were mostly no differences in mean gaze point position and its standard deviation under the different conditions. It was suggested that consumers could choose and buy a display of their preferred resolution and size, and arrange a display with fitting each the room layout.
Design and Implementation of Componentized Big Data Platform

The rapid development of the Internet has produced massive amounts of data, and has promoted the development of various big data components in the big data field, such as Hadoop, Spark and other big data components. However, many big data components face many problems in actual industrial production, make the construction and maintenance cost of the big data platform high. Although there is an automated deployment platform for big data components on the market, it lacks comprehensive management and monitoring functions for the entire big data platform, resulting in users not being able to fully pay attention to all aspects of the big data platform, and the service and resource management is not flexible enough. Therefore, we have designed and implemented an open componentized big data platform, so that users can not only easily install and deploy big data components in the cluster, but also manage the resources and services of big data components conveniently and flexibly. Besides, complete monitoring information also provides the necessary guarantee for the stable running of the big data platform. The open componentized big data platform provides users with one-stop big data services.
Automatic Detection of Chewing and Swallowing Using Hybrid CTC/Attention

Eating behavior is an important parameter representing health status. This paper evaluates the performance of a system with Hybrid CTC/Attention for monitoring eating behavior from the viewpoint of health maintenance. Previous studies have failed to detect the quality of eating behavior. Therefore, we examined the position of chewing (left/right/front) and the possibility of automatic detection of swallowing in order to grasp eating behavior in greater detail. We evaluated the chewing and swallowing of common foods and confirmed the model's stable detection performance.
Saliency Prediction for 360-degree Video

Saliency detection simulates human's perception in locating crucial regions, enabling further processing for many practical applications. Even though saliency prediction for conventional 2D images and videos have been well developed, prediction on 360° contents is still challenging. For each pixel in the equirectangular frame, there will be corresponding surrounding pixels according to their spherical coordinate. Therefore, the conventional convolution method may induce certain inaccuracy in attempt to simulate humans perceive the surrounding environment. This paper proposes a novel spherical convolutional network concentrating on 360° video saliency prediction in which the kernel is defined as a spherical cap. In the process of convolution, instead of using neighboring pixels with regular relationship in the equirectangular projection coordinate, the convolutional patches will be changed to preserve the spherical perspective of the spherical signal. Our model is trained and tested on the dataset including 104 360° videos that comprise dynamic sporty content. The proposed spherical convolutional network is evaluated by Pearson correlation coefficient (CC) and Kullback-Leibler divergence (KLD). Our experiments show the efficiency of our pro-posed spherical convolution method's application in 360° video saliency detection utilizing spherical U-net model. Further analysis on the proposed system have been presented in this study.