Reviewerly/exHarmony huggingface dataset:
The Realization of Moving Target Tracking in Monitoring Video based on Deep Leaming

The purpose of this research is to expand the application field of deep learning, to apply deep learning to target tracking problems, to conduct an in-depth study on the monitoring video detection and recognition methods of suspect targets. In this research, from the perspective of feature extraction, the deep learning algorithm that can better reflect the essential features of objects was deeply understood. Then, the stack noise reduction self-encoder was trained with the offline training method without supervision. On this basis, support vector machine (SVM) classifier was added to improve the accuracy of target tracking, and the trained self-encoder was used to extract object features from the deep network. Finally, the deep learning algorithm of “offline training + online fine-tuning” was used to track the moving target in monitoring video, and the robustness performance of the algorithm in this experiment was compared with other kinds of the experimental algorithm through the visual tracker benchmark (VTB) data set. The results showed that the accuracy rate and success rate of all sequences of target tracking algorithm based on deep learning were more than 60%, and their accuracy rate and success rate were higher than other algorithms. And the accuracy and success rate of inter-robustness evaluation of target tracking algorithm based on deep learning were much higher than other algorithms. The tracking algorithm proposed in this research had a higher success rate and accuracy rate than many excellent algorithms such as direct linear transformation (DLT), circulant structure kernel (CSK) and tracking learning detection (TLD) in one-time pass evaluation (OPE) and time robustness evaluation (TRE), and the performance of video sequence attribute algorithm was obviously higher than other algorithms. Target tracking algorithm based on deep learning had good accuracy, robustness, and tracking effect, so it was worth further exploration and research.
Estimating parameter in two-way analysis of variance when variance between cells is heterogeneous

Abstract Analysis of Variance (ANOVA) is a technique in statistics to test the mean differences of more than two groups in the presence of factors that may affect the mean difference. There are three types of analysis of variance, namely one-way analysis of variance, two-way analysis of variance, and multi-way analysis of variance. In this paper, we will discuss two-way analysis of variance which can be seen also the interaction of two factors. In the two-way analysis of variance, there are assumption that must be met, is observed in the cell or group must be normally distributed, the observations between cells or groups are mutually independent, and the variance between cells or groups is homogeneous. A common problem with two-way analysis of variance is unfulfilled assumptions, one of which variance between cells or groups is heterogeneous. Before determining test statistics for two-way analysis of variance, there are parameters to be estimated. This paper discusses the estimation of parameters for testing the effects of main factors and interaction factors on a two-way analysis of variance when the variance between cells or groups is heterogeneous.
Memorability based image to image translation

This paper presents a memorability based image-to-image translation technique to make an image more memorable while retaining its high-level contents. Conventionally, the image-to-image translation task aims to learn the mapping between images of two different domains using a set of aligned image pairs. However, dataset having such one-to-one mapping is not available for memorability based image-to-image translation. Therefore, the aim of the proposed task is defined to learn the mapping F: I &rarr; I' between two image domains I and I'. Here, I corresponds to input image domain and I' is the unknown image domain containing the modified version of the input images. Also, every image in I' is more memorable than its corresponding image in I. Therefore, the proposed task is achieved by developing a deep learning based method to learn the mapping F: I&rarr; I' using mean-squared error and memorability loss between I and F(I). The experimental results showed that the proposed approach increases the memorability of the given image better than the state-of-the-art image-to-image translation techniques.
Research on Command and Control Network Load Redistribution Algorithm Based on "Triangle" Structure

As an important channel of information transmission in the network, the key edge plays a vital role in the network information flow. In order to solve the problem of load redistribution of cascade failure network, this paper mainly does the following work. Firstly, When cascade failure occurs in the network, the process of cascading failure is analyzed according to the "triangle" position of the failed edge, thereby the load of the failed edge is allocated to the synergy edge or the skip-level edge according to the idle capacity. Secondly, a load redistribution method based on "triangle" structure is proposed. Finally, by randomly deleting edges, the carrying capacity of the network is simulated when the network is attacked. The experimental results show that the load redistribution algorithm proposed in this paper improves the destruction resistance of the command and control network.
The Importance of Researching and Developing the Semantic Web of Things

As an unprecedented technological innovation, the Internet of Things has its inherent contradictions. The Semantic Web of Things is the solution to the internal contradiction of the Internet of Things. As an Intelligent Internet of Things, the Semantic Web of Things transforms grammar matching into semantic matching, which enhances the essence of the Internet of Things. This paper introduces the background and research significance of the Semantic Web of Things and introduces the branch of the current Intelligent Internet of Things. The related definition is given and the semantic relational structured network model of the Semantic Web of Things is proposed. This paper provides a theoretical basis and research basis for further research on Semantic Web of Things.
Hierarchical Classification and Regression with Feature Selection

Previous studies proposed different hierarchical estimation approaches for solving certain specific domain problems. They usually combine two or more estimation models in a hierarchical fashion. Therefore, in our previous work [2], we proposed a hierarchical approach for generic purposes, the Hierarchical Classification and Regression (HCR), that incorporates classification and estimation techniques. The HCR [2] approach significantly outperformed three benchmark flat estimation models. Having seen the potential of the proposed HCR as a generic hierarchical regression scheme, we propose to further improve the HCR by introducing feature selection (FS) techniques to the HCR. In order to thoroughly investigate the effect of FS on the HCR, we examine different numbers of attributes remained after feature selection with respect to datasets of various sizes. The results showed that the HCR with linear regression performed significantly better than the other HCRs while feature selection helped lower the RMSE slightly with only 50% of the original features.
A Real-time AD-Census Stereo Matching based on FPGA

In this paper, we present an environment for the acquisition of a flight image of drone and design an stereo matching accelerator based on FPGA(Field Programmable Gate Array) to acquire depth information at real-time from acquired images. The acquisition of the flight images of drone uses a stereo camera (ZED) and Jetson TX2 board environment. The obtained binocular image can acquire depth information in real time through a stereo matching accelerator. Stereo matching operations have large computational complexity and require hardware acceleration. In this paper, we design an FPGA based stereo matching accelerator. Accelerators implemented AD-Census stereo matching algorithm and Box Filtering cost aggregation function at high speed. Census cost calculation method uses 9×7 filter, and cost aggregation function uses 15×15 filter. In order to reduce the internal memory usage, a cost aggregation function with parallel operation is implemented and a dynamic fixed point operation method is applied in order to reduce the amount of computation block usage. As a result, the 480×270 resolution image on the KU040 FPGA board showed 41fps performance at 100MHz clock operating speed.
Communicative Culture of Modern Students: Problems of Formation and Correction in the Conditions Of Educational Institutions

The article considers the specifics of the communicative culture of modern Russian students, identifies the main characteristic features of its communication practices. The authors prove the importance of the problem of procrastination communication based on social and civic passivity of students, reducing the degree of motivation for socially significant goals, priorities of professional and personal growth. The features of modern domestic educational institutions associated with the lack of development of their corporate culture, the backlog of training of teachers in the aspect of their psychological and pedagogical competencies are determined. The necessity of a comprehensive approach to the formation and correction of communicative culture of students, based on the simultaneity of the processes of creating a high corporate culture of educational institutions, improving the psychological and communication competence of teachers, creating a system of social and educational support of civic activity, professional and creative self-realization of young people.
Hope Speech Detection: A Computational Analysis of the Voice of Peace

The recent Pulwama terror attack (February 14, 2019, Pulwama, Kashmir) triggered a chain of escalating events between India and Pakistan adding another episode to their 70-year-old dispute over Kashmir. The present era of ubiquitious social media has never seen nuclear powers closer to war. In this paper, we analyze this evolving international crisis via a substantial corpus constructed using comments on YouTube videos (921,235 English comments posted by 392,460 users out of 2.04 million overall comments by 791,289 users on 2,890 videos). Our main contributions in the paper are three-fold. First, we present an observation that polyglot word-embeddings reveal precise and accurate language clusters, and subsequently construct a document language-identification technique with negligible annotation requirements. We demonstrate the viability and utility across a variety of data sets involving several low-resource languages. Second, we present an analysis on temporal trends of pro-peace and pro-war intent observing that when tensions between the two nations were at their peak, pro-peace intent in the corpus was at its highest point. Finally, in the context of heated discussions in a politically tense situation where two nations are at the brink of a full-fledged war, we argue the importance of automatic identification of user-generated web content that can diffuse hostility and address this prediction task, dubbed \emph{hope-speech detection}.
Facial Model Deformation Based on Landmarks Using Laplacian

Reconstructing a 3D face model from an unknown skull provides many benefits in the fields of archeology, anthropology, and forensic investigation. Computer systems for 3D facial reconstruction provide a big advantage in reducing time consumption. In this paper, we propose computer-based 3D face reconstruction for Indonesian from CT Scan images using facial model deformation which is limited by knowledge of soft tissue thicknesses. First, we prepare supporting data in the form of general face models and FSTTs for Indonesians. Then we set landmarks on the unknown skulls. By adding FSTT to the skull landmarks, we get landmark coordinates from predicted face. Finally, we performed Laplacian deformation on the face model so that its shape and size matched the landmark coordinates of the predicted face.
Proceedings of 2019 IEEE Region 10 Symposium (TENSYMP) Classification and Prediction of Cardiovascular Autonomic Neuropathy Severity Using Fuzzy Logic-Based Expert System

The present article aims at designing an expert system for diagnosis of Cardiovascular Autonomic Neuropathy (CAN) severity. A data set was synthesized representing the significant characteristics of some study subjects with varying CAN severity and considered in this study. The three-step design procedure consists of dimension reduction of data set using Sammon's non-linear mapping (NLM) followed by entropy-based fuzzy C-means clustering of the mapped data set and finally, designing the expert system. The design parameters of the expert system were optimized using a Particle Swarm Optimization (PSO) off-line, which could later be deployed for prediction of the severity of CAN on-line. The characteristics with notable influence on CAN were utilized by the expert system as inputs and the output in the form of the grade/degree of CAN severity was obtained.
A Novel Algebaric Variety Based Model for High Quality Free-Viewpoint View Synthesis on a Krylov Subspace

This paper presents a new depth-image-based rendering algorithm for free-viewpoint 3DTV applications. The cracks, holes, ghost countors caused by visibility, disocclusion, resampling problems associated with 3D warping lead to serious rendering artifacts in synthesized virtual views. This challenging problem of hole filling is formulated as an algebraic matrix completion problem on a higher dimensional space of monomial features described by a novel variety model. The high-level idea of this work is to exploit the linear or nonlinear structures of the data and interpolate missing values by solving algebraic varieties associated with Hankel matrices as a member of Krylov subspace. The proposed model effectively handles artifacts appear in wide-baseline spatial view interpolation and arbitrary camera movements. Our model has a low runtime and results excel with state-of-the-art methods in quantitative and qualitative evaluation.
A Study of Development and Application on K-SACRAI (Korean Sexual Assault Case Record Abstraction Instrument)

성폭행 피해자에 대한 지원 서비스 모델 개발을 위해서는 정확한 사례 정보가 필수적이다. 따라서 최근 성폭력 및 아동학대 등에 관한 논의에서는 성폭력 피해자를 지원하는 기관의 사례기록 정보를 활용해 성폭력 경험과 특성을 분석해야한다는 논의가 존재한다. 사례기록 정보는 성폭력 사건에 대한 공식 정보와 개입 과정을 포함하는 이점이 있다. 그러나 동시에 사례 기록 정보는 관리 데이터로서 즉각적인 사용이 제한된다. 따라서 사례 관리 정보를 체계적으로 추출할 수 있는 도구를 개발하여 적용할 필요가 있다. 본 연구는 성폭력 피해자를 위한 대표적인 지원단체인 해바라기센터 표준행정시스템에서 사례정보를 추출하는 도구인 K-SACRAI를 개발하여 적용하였다. K-SACRAI는 전문가, 정신과 의사, 변호사, 사회복지 교수들로 구성된 다 학제적인 위원회를 통해 해외 사례 정보 추출 도구의 검토와 수정과정을 거치며 개발되었다. K-SACRAI의 추출 항목 중 일부를 피해자지원기관의 아동, 청소년, 성인사례에 적용한 결과, 이전 논의에서 찾을 수 없었던 다양한 피해특성을 발견할 수 있었다. 가령, 연령대에 따라 동반되는 피해특성의 차이와 서비스 이용에서는 차이가 존재했다. 본 연구에서는 이러한 결과를 바탕으로 실천적, 정책적 함의를 논하였다.Accurate case information is essential for the development of a support service model for victims of sexual assault. Therefore, recent discussions on sexual assault and child maltreatment say that it is necessary to analyze sexual assault experiences and characteristics by utilizing the case record information of the institution supporting victims of sexual assault. The case record information includes official information on sexual violence cases and the process of intervention. At the same time, however, case record information is limited to immediate use as administrative data. Therefore, it is necessary to try to develop and apply tools that can systematically extract case management information. This study developed and applied K-SACRAI, a tool for extracting case information from the standard administrative system of the Sunflower Center, a representative support organization for victims of sexual violence. The K-SACRAI was developed through the review and modification of overseas case information extraction tools by organizing a multidisciplinary panel of experts, psychiatrists, lawyers and social welfare professors. The results of the application of some of K-SACRAI s extractive items to child, adolescent and adult cases of victim support agencies indicated various characteristics that previous discussions had not found. For example, there was a difference between the characteristics of blood damage and the use of services in cases that were accompanied by age groups. Based on these results, this study discussed practical and policy implications.
On the Digital Ecosystem of the Modern University

The process of digital transformation is progressively englobing education, learning and management processes within the system of higher education. This trend at the level of universities has resulted in emergence, evolution and development of the concept of the “digital university” and of more advanced construct of university digital ecosystem. The article considers different aspects of the concept of university digital ecosystem, studying the results of implementation of digital learning and management technology into the practices of world and Russian universities, obstacles and outlook for their further extension, and risks accompanying digital transformation of universities. The article concludes that the process of forming and developing university digital ecosystems should consider complexity of factors accounting for diversified conditions, objectives, tasks and results of building a digital ecosystem. The main conclusion is that concept of digital transformation should be integrated into university development strategy.
An Intelligent Method for Comparing Shapes of Three-Dimensional Objects

The article discusses a method for comparing the shape of three-dimensional objects, which was developed at the Peter the Great St. Petersburg Polytechnic University. The main method is the consecutive double conversion of the original three-dimensional object to a vector of parameters that are invariant to the initial position, rotation, and size of the explored object. At the first step, three-dimensional objects are transformed by a three-dimensional version of the chord method into a matrix of distances between random points on the surface of the object. Then the matrix is converted into a vector that forms a histogram of distances. The resulting vector is summed up with additional indicators and compared in pairs with objects stored in the database using the Siamese neural network, which at the output gives an estimate for the proximity of the three-dimensional objects’ shape. The developed method is the basis of a system for comparing the shape of three-dimensional industrial designs, and is also used in the detection of cancer of the lungs and prostate gland.
Book Review: Evolution of the Learning Brain: Or How You Got to Be So Smart…

BOOK REVIEW article Front. Psychol., 04 February 2020Sec. Evolutionary Psychology Volume 11 - 2020 | https://doi.org/10.3389/fpsyg.2020.00100
WibsonTree: Efficiently Preserving Seller's Privacy in a Decentralized Data Marketplace.

We present a cryptographic primitive called WibsonTree designed to preserve users' privacy by allowing them to demonstrate predicates on their personal attributes, without revealing the values of those attributes. We suppose that there are three types of agents --buyers, sellers and notaries-- who interact in a decentralized privacy-preserving data marketplace (dPDM) such as the Wibson marketplace. We introduce the WibsonTree protocol as an efficient cryptographic primitive that enables the exchange of private information while preserving the seller's privacy. Using our primitive, a data seller can efficiently prove that he/she belongs to the target audience of a buyer's data request, without revealing any additional information.
Teacher training modernization as innovative tendency of education development in the USA

In the modern world, it is impossible for society to work without highly qualified specialists, therefore modern education in all countries of the world is important. Education is the basis for raising the country's economic level, influencing the culture and self-knowledge of the people. The professional activities of American educators are becoming increasingly difficult due to the different ethnic and social backgrounds of students, the diversity of languages, cultures, styles of learning activities, as well as individual preferences and potential. At the same time, there has been a decline in success rates among ethnic minority students. Failure to learn often encourages students of diverse ethnic backgrounds, African Americans, and Mexicans to drop out of school and lose the ability to get a high school diploma. It was revealed that the main causes of the above problems are a high level of poverty among ethnic minorities, unequal access to education, as well as low expectations of teachers from students of other linguistic and cultural backgrounds. The quality of education that students receive directly depends on the personal characteristics and qualifications of the teacher. To better meet the needs of today's students, teacher training requirements and expectations continue to grow, while school funding is reduced. Simply put, today teachers must work as much as possible while giving work, and receive less, which led to a decrease in interest in teaching among young people and a lack of licensed pedagogical specialists. The combination of all these factors is a serious problem for the teaching profession as a whole, not to mention the training of teachers. According to the reform of the American system of primary and secondary education, the broader priority is the training of competent specialists who can inspire young people. realize their potential and transfer to them the skills necessary for success in college and in the workplace. The country needs training programs that train teachers who not only understand and are able to meet the diverse needs of modern youth, but can also teach, evaluate content that 21st century students should learn and use in a multicultural environment
Content-based Image Retrieval System for Locating Building in Syiah Kuala University using Android Platform

Syiah Kuala University (Unsyiah) regularly involved in various activities and held national events that encompassed lots of people from another city. The lack of facilities to provide information about Unsyiah’s building resulted in the difficulty of visitors to quickly and accurately find information about the building. Based on this problem, an Android based application was built by utilizing Content-Based Image Retrieval (CBIR) method, that can use image as the query for the search engine to locate buildings within the Unsyiah’s compound. Image of the building will be used as inputs for the query. The method used for developing the image retrieval application is the Scale Invariant Feature Transform (SIFT). The application was built according to the stages of eXtreme Programming (XP) which involves planning, design, coding, and testing. The application is then tested using the Espresso framework and the System Usability Scale (SUS). The result from SUS test is 79.75 which can be categorized as and thus considered as acceptable and user-friendly. The results of the calculation for Recall, Precision, and F-Measure could reach 100% for some building and just 40% for others, depending on the quality of image used for the query. This method is considered as quite good in terms of recognizing object’s features because the application could still able to recognize and retrieve the image sought by user despite the poor-quality of image used as the query.