Reviewerly/exHarmony huggingface dataset:
Improved Hierarchical Convolutional Features for Robust Visual Object Tracking

The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.
Face Mask Detection and Social Distancing Monitoring

During pandemic COVID-19, the World Health Organization (WHO) reports suggest that the two main routes of transmission of the COVID-19 virus are respiratory droplets and physical contact. Respiratory droplets are generated when an infected person coughs or sneezes. Any person in close contact (within 1 m) with someone who has respiratory symptoms (coughing, sneezing) is at risk of being exposed to potentially infective respiratory droplets. Droplets may also land on surfaces where the virus could remain viable; thus, the immediate environment of an infected individual can serve as a source of transmission (contact transmission). Wearing a medical mask and social distancing is one of the prevention measures that can limit the spread of certain respiratory viral diseases, including COVID-19. World Health Organization has made wearing masks and also social distancing compulsory to protect against this deadly virus. This paper is about we developed a generic Deep Neural Network-Based model for mask detection, tracking using cameras and also we developed second application for social distancing monitoring application with the help of Python and Computer Vision.
Suspend feature for multiple devices of same type in bhyve

The FreeBSD specific hypervisor solution, bhyve, is a mature virtualization solution that allows its users to configure the virtual machines in a robust manner by adding different types of devices. Moreover, the virtual machine state can be suspended for a later use by using the snapshotting mechanism. However, the existing snapshot mechanism can only store the state of one device of each type. This behaviour may be troublesome for entities that want to use the save and restore mechanism for virtual machines with multiple same type devices (e.g., two disks, two network interfaces) since it can lead to an inconsistent virtual machine state. This paper proposes a solution for extending this feature by allowing the saving and restoration of multiple devices of the same type, whenever they are used together
Activity Based Analysis and Prediction Strategy for the Class Room Performance Improvement

Class room assessment is one of the key issues where in individual performance gives a great impact in the overall performance of the class.In general, various activities will help in identifying the student's ability and weak strengths in understanding the subject as well.Though various online learning platforms are present, an overwhelmed usage is occurring due to the pandemic in this period.Nevertheless, very few online resources are giving their utmost use in efficient way.At the same time class room activities are also need to shape up accordingly that supports to the virtual platforms.Few activities like chitchat, polling, and turn around were performed through virtual platform over a class group, to address the complex engineering problems that were identified in Embedded Systems Design, an undergraduate subject.Further a common strategy was found to apply while dealing with that subject.In this process, collected the outcomes of the complete learning activities conducted during the course and using few data-mining techniques the classroom performance has been analysed.For initial analysis, k means clustering was used in which the parameters chosen are like individual marks, total class strength.The outcome of this implementation resulted in identification of clusters in the name of weak, average and best groups along with an accuracy of 46.51%.The analyses were further carried out using other measuring parameters like spontaneity, delays in answering, best answer, etc., have been chosen during the activities and Naïve Bayes Classifier was chosen for predicting the performance improvement and was observed as 70%.Finally, based on the experimental results, classification and regression supplemented by logistic was applied for further evaluation of learning performances and a remarkable accuracy progress of 96.96% is investigated in this study.
Identifying the coefficients of thermal expansion of an electronic device composed of various materials using the virtual fields method

Abstract A method for inverse determination of the coefficients of thermal expansion of an electronic device that is composed of multiple materials, i.e. dissimilar materials, is proposed. The virtual fields method is applied for obtaining the coefficients of thermal expansion. Appropriate virtual displacement fields have been automatically obtained instead of using any arbitrary virtual displacement. This leads to an accurate and effective determination of the coefficient of thermal expansion. Full-field displacement distributions measured by digital image correlation are used as input for the inverse analysis. The validity of the proposed method has been demonstrated by measuring samples made of various materials. Furthermore, the proposed method is applied to samples simulating electronic devices with and without constraints on the boundary. Results show that the coefficients of thermal expansion of an object composed of dissimilar materials are obtained properly. The proposed method is expected to be used for determining the coefficients of thermal expansion of actual electronic devices.
Local invariants of braiding quantum gates—associated link polynomials and entangling power

Abstract For a generic n -qubit system, local invariants under the action of <?CDATA $SL{\left(2,\mathbb{C}\right)}^{\otimes n}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mi>S</mml:mi> <mml:mi>L</mml:mi> <mml:msup> <mml:mrow> <mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mi mathvariant="double-struck">C</mml:mi> </mml:mrow> <mml:mo stretchy="false">)</mml:mo> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mo>⊗</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:msup> </mml:math> characterize non-local properties of entanglement. In general, such properties are not immediately apparent and hard to construct. Here we consider two-qubit Yang–Baxter operators and show that their eigenvalues completely determine the non-local properties of the system. Moreover, we apply the Turaev procedure to these operators and obtain their associated link/knot polynomials. We also compute their entangling power and compare it with that of a generic two-qubit operator.
CULTURAL APPROACH IN THE DEVELOPMENT OF FUTURE TECHNOLOGICAL EDUCATIONAL INDUSTRY SPECIALISTS’ ECOLOGICAL CULTURE

The article is devoted to outlining the possibilities of culturological approach in the formation of ecological culture of teachers of labor education and technology. The basic principles, terminology, significance of the obtained result in the structure of personality are determined. Emphasis is placed on the uniqueness of each of the subjects of educational activities, the importance of individualized approaches. The culturological approach is considered as a set of theoretical and methodological provisions and organizational and pedagogical measures aimed at the gradual mastery of cultural values by students in a personalityoriented environment, the formation of the future specialist as a subject with a high level of values. It has been found that the culturological approach is based on the relationship and interaction of culture and education. Education is considered part of culture and the form of its manifestation, culturally appropriate system and cultural process in which the holistic transformation and develop- ment of personality. The functions inherent in culture, general pedagogical and special principles, important in the organization and implementation of the process of development of ecological culture are outlined. it is proved that culture is a way and means of self-development of an individual, his material and spiritual world. In the development of ecological culture of future teachers of labor training and technology, the culturologi- cal approach is considered as a system of organization of the process of improvement and self-improvement, development and self-development both in professional activity and in personal dimensions
Direct Video Frame Interpolation With Multiple Latent Encoders

We present a simple but effective video interpolation framework that can be applied to various types of videos including conventional videos and 360° videos. Our main idea is to predict the latent feature of an intermediate frame, through the latent feature encoders between encoder and decoder networks, without explicitly computing optical flow or depth maps. The latent feature encoders take latent features of input images and then predict the latent feature of a target image, i.e. an intermediate frame. Afterward, the decoder network reconstructs the target image from the latent feature. The proposed framework consists of fully convolutional networks, and it is therefore end-to-end trainable from scratch without requiring additional information except for consecutive frames. We experimentally verify the superiority of proposed method by comparing it to state-of-the-art methods with various types of datasets. Moreover, an ablation study is carried out to analyze the key components of the proposed method. Our proposed method performs interpolation in latent domain, it is advantageous to apply various video interpolation (e. g. NIR and depth videos) without limiting the type of input data.
FORMATION OF AGRICULTURAL STUDENTS’ SELF-EDUCATIONAL COMPETENCE

The results of the research on the formation of selfeducational competence of agricultural students with the help of interactive computer technologies are considered . High speed of science development quickly makes knowledge acquired by students in high school obsolete and reveals the urgent need for the formation of selfeducational competence. In conditions of widespread computerization and active use of the latest modern technical developments in everyday life, the use of interactive computer technology seems the most effective and promising direction to form selfeducational competence of university students. We have identified four components of selfeducational competence: knowledge; motivation; activity; communication. In accordance with these components we developed criteria, indicators and tools to determine selfeducational competence formation of university students through interactive computer technology. The pedagogical experi- ment took place from 2017-2019 on the basis of Chernyatyn College of Vinnytsia National Agrarian University. The survey covered 87 students of four faculties, 26 people were in the control group and 61 in the experimental group. To standardize the initial results of diagnostic tests and questionnaires we used percentile. After calculating percentiles for each component of self- educational competence, a table of standardization was composed. Four levels of component formation of selfeducational competence were identified: low, below average, above average and high. The purpose of the formative phase of the experiment was the model testing of selfeducational competence formation of university students through interactive computer technology. It can be concluded that students of control and experimental groups have significant differences caused not only by random factors but a certain legitimate reason. The reason was the experimental work performance to implement structural and functional models for selfeducational competence formation of university students through interactive computer technology.
Penerapan Machine Learning dengan Konsep Data Mining Rough Set (Prediksi Tingkat Pemahaman Mahasiswa terhadap Matakuliah)

The Rough Set (RS) method is part of machine learning that analyzes the uncertainty of the dataset used to determine the attributes of important objects (classification). The purpose of this study was to extract information from the rough set using the Rosetta application in predicting cases of students' level of understanding of the course. The attributes used are communication (F1), learning atmosphere (F2), learning media (F3), appearance (F4), and teaching methods (F5). Sources of data obtained from the output of the Journal of Physics: Conference Series, 1255 (1). https://doi.org/10.1088/1742-6596/1255/1/012005. The results of the application of the Rough Set method in determining the prediction of the level of student understanding of the course, produce new knowledge, namely learning outcomes based on the subject. There are 15 Reductions with 90 Generate Rules. But overall, the attributes that affect the level of student understanding of the subject are communication (F1) and learning media (F3)
Distance Learning in Moroccan Higher Education: Students' Attitude towards ENS -Ecole Normale Superieure - Classroom Platform and Effective Recommendations

Distance learning is developing at high speed in Morocco as well as in the rest of the world.With the inability to move from home due to the COVID 19 outbreak, it may seem obvious that it represents a solution and a good way to develop skills and knowledge and to guarantee academic continuity.As we live now in a society with constant social and cultural change, information and communication technologies have been contributing towards the construction of a new kind of society-the information society, where students need to be equipped with skills to do research, analyse and select knowledge from different paths and digital resources [1].So, the purpose of this study is to find out about Moroccan university students' attitudes towards ENS Classroom platform which they have been using during quarantine and how they are discovering distance learning for the first time.The study revealed that although the platform has truly enabled them to overcome the distance that separates students from the university, a set of challenges appeared during their e-learning courses and hindered the best understanding and retention of the course content.The results are significant since they show how unsatisfied ENS students are towards their distance learning experience.The study also suggests a set of recommendations to cater for better academic outcomes.
Human Capabilities and Instrumental Freedoms Relationship: An Empirical Investigation

Purpose of the study: In Development as freedom, Amartya Sen confirmed the crucial "instrumental" role of five kinds of freedoms in the promotion of capabilities and therefore in the process of human development in general. These are political freedoms, economic facilities, transparency guarantees, social opportunities, and protective security. This paper has three empirical objectives: 1) to measure the effect of instrumental freedoms on capabilities, 2) to verify if this effect becomes stronger once instrumental freedoms are interconnected, and 3) to verify whether this relationship is moderated by the level of economic development achieved by each country. Methodology: To achieve these three objectives, this article confronts the second-order construct of "instrumental freedoms" (as an exogenous variable) with five first-order constructs (as endogenous variables). The five endogenous latent variables reflect the capabilities of health, education, housing, employment, and communication and mobility at the level of the sixty countries selected as the analysis samples. The estimation of the hierarchical structural model is done using the partial least squares approach and the repeated indicator method. Main Findings: This study highlights three major results: 1) The existence of a significant effect of instrumental freedoms on the five relevant capabilities selected. 2) When instrumental freedoms interconnect, they reinforce each other and their effect on human capabilities becomes stronger. 3) The multi-group analysis suggests that instrumental freedoms positively and significantly impact human capabilities in the same way in both developed and developing countries. Research implications: Freedom plays a "constitutive" and "instrumental" role in the development process. To provide people with the freedom to live according to their aspirations, public policies must be empowering. In other words, they should improve the instrumental perspective of at least three essential freedoms: political freedoms, economic facilities, and transparency guarantees. Novelty/Originality of this study: Apart from the works which attempted to operationalize Amartya Sen's capability approach, the relationship between instrumental freedoms and human capabilities has not been the subject of empirical studies. This paper is intended as a contribution to this field of investigation.
Robust Domain Adaptation: Representations, Weights and Inductive Bias

Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence of Domain Invariant Representations (IR) has improved drastically the transferability of representations from a labelled source domain to a new and unlabelled target domain. However, a potential pitfall of this approach, namely the presence of label shift, has been brought to light. Some works address this issue with a relaxed version of domain invariance obtained by weighting samples, a strategy often referred to as Importance Sampling. From our point of view, the theoretical aspects of how Importance Sampling and Invariant Representations interact in UDA have not been studied in depth. In the present work, we present a bound of the target risk which incorporates both weights and invariant representations. Our theoretical analysis highlights the role of inductive bias in aligning distributions across domains. We illustrate it on standard benchmarks by proposing a new learning procedure for UDA. We observed empirically that weak inductive bias makes adaptation more robust. The elaboration of stronger inductive bias is a promising direction for new UDA algorithms.
Design and Build Customer Complain Applications for Mobile Based MVVM Architecture Method

Abstract&#x0D; PT. DhanaJaya BogaIndo has more than twenty restaurants spread across Jakarta, Surabaya and Australia. At present complaints regarding services and food served are still being done by providing limited criticism and suggestions during operational hours, so employees in the service section always record complaints and suggestions from customers who come to restaurants are accommodated, then convey related to food, service and the environment dirty one. related to customer complaints. Information on customer complaints can be received directly by management, so actions can be taken quickly.
Automatic Aspect-Based Sentiment Summarization for Visual, Structured, and Textual Summaries

Online reviews are valuable sources of information to help companies to make good decisions for business intelligence. In this study, we propose an Automatic Aspect-based Sentiment Summarization (AAbSS) system that has two components and can generate a summary as an output. The first component is the Aspect-based Knowledge Representation and Selection (AKRS) used to represent reviews based on aspects and their polarities for selecting aspect-based knowledge. To represent and selection knowledge, a set of frequency of polarity opinion strength, a summation of frequency of aspect, and an information of aspect are initiated. The second component is the Summary Format Generation (SFG) used to automatically generate three kinds of formats. In this component (SFG), new representations for visual and structured summaries, and a new way of applied natural language generation for a textual summary are proposed. In the experiments, 13 domains from benchmark datasets of customer reviews, e.g. cell phone, digital camera, etc. are used. The proposed system not only fast generates summaries having good performance when compared to other summaries generated by other systems and easily updated when adding new reviews in the same domain but also does not spend memory capacity to save any raw data.
Online Learning amid the COVID-19 Pandemic: A Case Study of the State Islamic University of Mataram

This research aims to investigate the challenges and opportunities of the use of information technology in an online learning at the State Islamic University of Mataram (UIN Mataram) during the COVID-19 pandemic. This research incorporates a survey to identify the use of information technology among students. The sample used in this research was the first semester students of the State Islamic University of Mataram (UIN Mataram). The research results indicate that 27.3% of students did not have laptops, 61.8% of students experienced difficulties accessing the telecommunication signal in their home town, and 10.9% experienced electricity problems during online learning. The use of Google Classroom and WhatsApp group media is prevalent at the university during the 2020 COVID-19 pandemic.
Enhanced Image Encryption Using Two Chaotic Maps

Information security is an important aspect in various communication areas, multimedia frameworks, medical imaging and militant communications. However, most of them encounter issues such as insufficient robustness or security. Recently, the approach of achieving information security by using chaotic techniques has gained popularity, since they provide ergodic and random generated keys. This paper introduces a combination of two chaotic maps (3D logistic map and Arnold's cat map) that meet the general security requirements of image transmission. First the image is encrypted using Arnold's cat map, which shuffles the image pixels. 3D logistic map is applied to the encrypted image for transformation and permutation purposes. Then the XOR operation for the encrypted image and a chaotic sequence key are used to provide more security after the pixel values have been changed. The performance of the proposed security method was evaluated using MATLAB by analyzing the correlation between adjacent pixels, histogram analysis, and entropy information. The simulation results showed that the proposed method is robust and resilient. It can achieve an average of 7.99 for entropy information, 99.6% for NPCR, and 33.77 % for UCAI.