Reviewerly/exHarmony huggingface dataset:
A Real-time Image Processing Hardware Acceleration Method based on FPGA

Real-time image sensed by the visual sensor usually contains a lot of noise information. Model reasoning, and pattern recognition-oriented CNNs face such thorny issues as excessive computation, poor accuracy and high resource occupancy. Hence, CNN architecture was heterogeneously deployed on the Zynq platform to realize hardware acceleration for the image processing algorithm. MNIST dataset was adopted to train CNN for extracting network parameters on PC terminal under the Caffe framework; the convolutional layer responsible for heavy computational load was deployed onto FPGA for parallel computing to increase system speed; input layer and output layer responsible for a small amount computation were placed on ARM terminal to reduce resource consumption; real-time image acquired by the camera was binarized to highlight image features and improve the recognition accuracy; the hardware acceleration performance of the heterogeneously deployed CNN was verified with numerous experiments on image recognition of handwritten numerals. Experimental results indicated that: CNN hardware accelerator kept an image recognition accuracy up to 99.02% which is largely equivalent to that of client PC; When recognizing a single piece of handwritten numerical sample, under the use of optimized instructions and 100MHz clock frequency, the recognition time of a single image is 0.53s, which is 16 times faster than pure ARM operation; the maximum power consumption of the system is 2.606W, which is far Lower than general-purpose processors.
On the results of the development of the skills of primary school teachers to pedagogically accompany research (project) activities

"This article is a continuation of the theoretical research presented in the previously published works of the authors, which provides the rationale for the conditions that provide initiation and support for the understanding of the value-semantic foundations of pedagogical support of students, the formation, development and maintenance of the subject-subject nature of the activities of teachers and students. This study presents the results of approbation of research work on the development of the skills of primary school teachers to pedagogically accompany the research (project) activities of students. Based on the theoretical model and the concept of development of primary school teachers, providing the formation of skills to pedagogically accompany the research (project) activities of students «presented in a previously published work, the authors have developed and tested a program of advanced training courses for teachers» Pedagogical support of extracurricular research (project) activities of primary students classes «. The experience of organizing courses for the professional development of teachers on the formation of skills to carry out pedagogical support of research (project) activities of students is analyzed. The implementation of the practical part of the advanced training courses for teachers was of a complex design and research nature and was carried out in the form of on-line meetings using distance educational technologies. The approbation was implemented in stages and consisted of the following five stages: 1) preparation and training of tutors for courses of professional and personal development of teachers; 2) conducting the first session of courses on professional and personal development of teachers; 3) the practice of initiating project and research activities of students by course participants; 4) holding the second session of courses on professional and personal development of teachers; 5) organization of the International Summer School «Design and Research for the Future». "
Photonic Machine Intelligence Hardware: From Photonic Memory and Photonic TPU to Optical CNN

Here, I review our latest work on a Photonic TPU (P-TPU), a PIC-based ASIC for vector matrix multiplication acceleration and introduce a programmable multi-level non-volatile photonic random access memory (P-RAM).
Chapter 7. Neuro-Symbolic AI = Neural + Logical + Probabilistic AI

There is a broad consensus that both learning and reasoning are essential to achieve true artificial intelligence. This has put the quest for neural-symbolic artificial intelligence (NeSy) high on the research agenda. In the past decade, neural networks have caused great advances in the field of machine learning. Conversely, the two most prominent frameworks for reasoning are logic and probability. While in the past they were studied by separate communities, a significant number of researchers has been working towards their integration, cf. the area of statistical relational artificial intelligence (StarAI). Generally, NeSy systems integrate logic with neural networks. However, probability theory has already been integrated with both logic (cf. StarAI) and neural networks. It therefore makes sense to consider the integration of logic, neural networks and probabilities. In this chapter, we first consider these three base paradigms separately. Then, we look at the well established integrations, NeSy and StarAI. Next, we consider the integration of all three paradigms as Neural Probabilistic Logic Programming, and exemplify it with the DeepProbLog framework. Finally, we discuss the limitations of the state of the art, and consider future directions based on the parallels between StarAI and NeSy.
SUBJECT AREA “TECHNOLOGY” AND FORMATION OF REGIONAL EDUCATIONAL STRUCTURES

The article considers the role of the subject area “Technology” as the integration basis for the content of general education in the technological training of schoolchildren in the conditions of infrastructure modernization. The authors reveal the relationship between technosphere transformations and secondary vocational education infrastructure, higher education and content tracks in the subject area “Technology”, its role in regional educational systems that ensure the quality of training personnel taking into account the priorities of regional development.
Selectable Heaps and Optimal Lazy Search Trees

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Selectable Heaps and Optimal Lazy Search TreesBryce Sandlund and Lingyi ZhangBryce Sandlund and Lingyi Zhangpp.1962 - 1975Chapter DOI:https://doi.org/10.1137/1.9781611977073.78PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We show the O(log n) time extract minimum function of efficient priority queues can be generalized to the extraction of the k smallest elements in O(k log(n/k)) time1, which we prove optimal for comparison-based priority queues with o(log n) time insertion. We show heap-ordered tree selection (Kaplan et al., SOSA '19) can be applied on the heap-ordered trees of the classic Fibonacci heap and Brodal queue, in O(k log(n/k)) amortized and worst-case time, respectively. We additionally show the deletion of k elements or selection without extraction can be performed on both heaps, also in O(k log(n/k)) time. Surprisingly, all operations are possible with no modifications to the original Fibonacci heap and Brodal queue data structures. We then apply the result to lazy search trees (Sandlund & Wild, FOCS '20), creating a new interval data structure based on selectable heaps. This gives optimal O(B+n) time lazy search tree performance, lowering insertion complexity into a gap Δi from O(log(n/|Δi|) + log log n) to O(log(n/|Δi|)) time. An O(1) time merge operation is also made possible when used as a priority queue, among other situations. If Brodal queues are used, all runtimes of the lazy search tree can be made worst-case. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Computer control in human-machine interaction systems by hand movements

Computers have become popular in recent years. The forms of human-computer interaction are increasingly diverse. In many cases, controlling the computer is not only through the mouse and keyboard, but humans must control the computer through body language and representation. For some people with physical disabilities, controlling the computer through hand movements is essential to help them interact with the computer. The field of simulation also needs these interactive applications. This paper studies a solution to build a hand tracking and gesture recognition system that allows cursor movement and corresponding actions with mouse and keyboard. The research team confirms that the system works stably, accurately and can control the computer instead of a conventional mouse and keyboard through the implementation and evaluation.
Comparative Analysis of Pitching Prediction Algorithms

Abstract Two algorithms are described in the paper; one of them is the Kalman filter, which is based on the use of a pitching mathematical model, and the second uses a neural network in which the model is considered unknown. The results of the algorithms sensitivity analysis to the parameters of the model and its influence on the potential accuracy of prediction are presented. A stationary narrow-band second-order Markov process is used as a model of the ship pitching, which was used to form the input signal of the algorithms. Also, the results of the algorithms simulation in predicting real data are presented.
Long-Term Analysis for Job Characteristics on the Supercomputer

A deep understanding of the job characteristics and their impacts on the high performance computing system is one of the most critical steps for efficiently planning its design, development and optimization. However, frequent and regular characterization studies are insufficient in many HPC systems, which might make the study done by the system researchers inconsistent with the actual system features and application characteristics, and ultimately lead to the failure of the proposed strategy. Our study in this paper tries to bridge the gap by performing long-term analysis for job characteristics on a petascale ARM supercomputer, in this way, we get many meaningful findings and insights, which we believe can benefit the co-design of hardware and applications, and improve performance and experience of the job submitters in the HPC system.
THE INSIDER THREAT MINIMIZATION AND MITIGATION FRAMEWORK

Countering the insider threat is a difficult and daunting task.Organizations concerned with the problem usually train their employees on security-related matters, rules of behavior policies, and the consequences of committing criminal activities.More technically-oriented solutions include enhanced credentialing and access control, and the use of monitoring tools that provide insight into the health and status of systems.This paper addresses the deficiency of widely-used monitoring tools and strategies.It proposes a solution that equips a system with innate self-defense mechanisms that relieve the system from having to rely on human intervention.The paper introduces the Insider Threat Minimization and Mitigation Framework.The framework equips systems with self-defense mechanisms such that a system can instantaneously respond to potential threats and defend itself against users who have unfettered access to it.The framework employs the autonomous demotion of power users' access privileges based on analysis and evaluation of the user's risk level.The paper presents the details of the proposed framework and simulates its effectiveness within a data center environment of mission-critical systems.
Gaussian Filtering Method to Remove Noise in Images

Capturing every moment is not taboo in this era. One way to capture the moment is to use a photo, but the results are often unsatisfactory. Noise, is one of the many causes of unsatisfactory results. Noise is a disturbance caused by digital data storage received by the image data receiver which can interfere with image quality. Noise can be caused by physical (optical) disturbances in the image capturing device, such as dust on the camera lens or due to improper processing. To get rid of this noise, you can use various methods, of which Gaussian Filtering is one of them. In this research, we will implement it using Matlab. The type of file used is a photo that has a jpg format and has noise above 75%. After doing image processing, it shows the results of the image which initially has noise and after the image quality improvement process is carried out, the image quality is clearer and the noise decreases.
Sensor-based skeleton modeling and MVEEs approach of the redundant manipulator for reaction motion

With the wide application of redundant manipulators, sharing a working space with humans and dealing with uncertainty seems an inevitable problem, especially in the dynamic and unstructured domain. How to deal with obstacle avoidance is of particular importance that robots and humans/environments are safe interactions to fulfill the complex cooperating tasks. This paper aimed at solving the problem of multiple points avoidance for the reaction motion based on the skeleton algorithm in unstructured and dynamic environments. A method named “sensor-based skeleton modeling and MVEEs approach of the redundant manipulator for the reaction motion” is proposed. The extraction of skeleton information from image is obtained to calculate the distances of the multiple control points and establish the repulsion in this method. Afterward, the force Jacobian related to the priority weighting factors is calculated and then a reaction force with damping term is established, which is corresponding nominal torque commands. For the redundant manipulator, the joint angles are obtained through torque iteration instead of inverse kinematics to reduce calculation cost. Finally, the method was tested by a 7-DOF manipulator in the ROS framework. The obtained results indicate that the method in this method can realize dynamic obstacle avoidance and time cost reduction.
ПРАКТИКА РАЗВИТИИ МОТИВАЦИИ УЧЕНИКОВ ПРИ ИСПОЛЬЗОВАНИИ ГЕЙМИФИКАЦИИ

Background. The article discusses gamification, which is seen as one of the most effective ways for general secondary education institutions to develop the motivation of primary school students.Methods. In this article were used methods questionnaire, study of documents, pedagogical study.Results. the research of scientists who have conducted scientific research on the possibilities of applying the elements of gamification to education has been analyzed.Conclusion. The article emphasizes the need for the organization of socio-pedagogical activities aimed at the formation of basic competencies in a harmoniously developed person in society. Maqolada ta'lim muassasalarida boshlang'ich sinf o'quvchilarining motivatsiyasini rivojlantirishning eng samarali usullaridan biri sifatida qaraladigan o'yinlashtirish haqida gap boradi. В статье рассматривается геймификация, которая рассматривается как один из наиболее эффективных для общеобразовательных учреждений способов развития мотивации учащихся начальных классов.
A Study on Text Rank Algorithm for Automatic Text Summarization and Question Generation

This paper proposes a new rule-based approach to automated question generation. The proposed approach focuses on the analysis of both sentence syntax and semantic structure. The design and implementation of the proposed approach is also described in detail. Although the primary purpose of a design system is to generate query from sentences, automated evaluation results show that it can also perform great when reading comprehension datasets that focus on question output from paragraphs. With regard to human evaluation, the designed system performs better than all other systems and generates the most natural (human-like) questions. We present a fresh approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems. In our system, we will take data from various sources for a particular topic and summarize it for the convenience of the people, so that they don't have to go through so multiple sites for relevant data.
Penerapan Media Video dengan Model E-Learning (Daring) dalam Upaya Meningkatkan Hasil Belajar IPS Materi Globalisasi dan Nasionalisme pada Siswa Kelas IX MTs Negeri 9 Aceh Timur Tahun Pelajaran 2020/2021

This research is an effort to Improve Social Studies Learning Outcomes through the E-Learning Model with Video Media for Class IX Students at MTs Negeri 9 Aceh Timur. Giving assignments without any learning materials delivered by the teacher causes social studies learning outcomes to be relatively low or still below the KBM, which is 75. This study uses Classroom Action Research (CAR) in the form of implementing an e-learning model with video media with the aim of improving student learning outcomes for Class IX MTs Negeri 9 Aceh Timur which consists of two cycles and each cycle consists of planning, implementing, observing, and reflecting. The research subjects were students of Class IX of MTs Negeri 9 Aceh Timur. The number of students was 29 students, consisting of 10 male students and 19 female students. The results showed that the application of the e-learning model with video media could improve social studies learning outcomes for Class IX students of MTs Negeri 9 Aceh Timur. Judging from the results of the pre-cycle students who completed KBM only 14 students (48.3%) and 15 students (51 ,7%) which has not been completed. In the first cycle there are 24 students (82.8%) who have completed and 5 students (17.2%) have not completed, and in the second cycle there are 26 students (89.7%) who have completed and there are 3 students (10.3 %) unfinished. From the increase in learning outcomes, this classroom action research was stopped in cycle II because it had achieved the classical completeness criteria, namely 85% of the total number of students who had completed learning and were declared successful in improving social studies learning outcomes for Class IX students of MTs Negeri 9 Aceh Timur in the 2020/2021 academic year. .
Enabling Measurements of Qubit Errors at NEXUS

noise sources and how they impact a given quantum system. Often times, T1and T2 are used to quantify noise. In this project, we provide an approach as to how T1 and T2 values are calculated and simulated for quantum systems. In addition, we compare simulated values of T1 and T2 with those of real quantum computer’s measurements. IBMQ Experience, an open source software allowing users to simulate and use real quantum hardware, is used. QuTip, a Python-based toolbox offering quantum simulation tools for open quantum systems, is also used.
Police Officers’ Discourse on Social Networking Sites Surrounding High-Profile Cases

Research on police officers' attitude towards the use of excessive force have not been studied fully.Several studies have focused on their behavior than their attitudes towards excessive force.The on-going pandemic raised renewed interest in the persistent issue of police brutality in the United States.The paper presents online conversations of police officers revealing their opinions towards officers use of excessive force.This paper provides some groundwork for future researchers by examining police officers discourse online.The paper concludes with discussion and implications for law enforcement agencies and the safety of the society.
Research on Intelligent Question Answering algorithm based on machine learning

Aiming at the student consultation faced by the enrollment of experimental classes, a small sampleQuestion Answering System was constructed, which can help students obtain enrollment status in time. First, I collected information and data about a university and experimental class, and designed a text similarity matching algorithm that combines word vectors and corpus tags to apply to the Question Answering System. Based on the word vectors of the neural network training corpus, the corpus is grouped using a clustering algorithm. The keyword extraction algorithm is used to extract the group tags. In the question sentence similarity calculation, the category similarity calculation is first performed, and then the sentence similarity calculation is performed in the category. In the case of a small sample, compared with the traditional sentence vector similarity calculation method, the accuracy rate in the Question Answering System is 64%, which meets the requirements of the Question Answering System in this field.
A Novel Pseudo-Random Number Generator Based on Linear Feedback Shift Register and Its Security Analysis

The random number is an essential mathematical element in computer science, electronic science, and information science, which is widely used in machine learning, artificial intelligence, secret communication, identity verification, channel coding, image processing, etc. At present, the mainstream method of the generation sequences is FPGA-based pseudo-random number generators, which are the m-sequences and are generally and easily recognized and analyzed. We propose a novel pseudo-random number generator based on a linear feedback shift register. By means of the built-in dynamic generation function and data processing matrix, the characteristics of the sequences are hidden to a certain extent, and the randomness of the generated sequence is better enhanced. The experiments demonstrate that the pseudo-random generator has adequate performance and security. This Pseudo-Random Number Generator can be applied in high-level confidential communication in diplomacy and other aspects, encryption of TCP/IP data frames, and encryption of RFID smart cards.
Quality of Remote Learning during Outbreak of COVID 19 Pandemic and its Effect on Nursing Students,Satisfaction

Background: COVID-19 interfere the designed system of education to achieve cognitive, affective, and psychomotor domain of learning and rapid application of remote learning systems is linked to obstacles that can affect students , satisfaction.Aim of the study: This study directed to assess quality of remote learning as perceived by nursing students and its effect on their satisfaction.Subjects and method: A descriptive, correlational research design was applied.The current study was conducted in the faculty of nursing, Tanta University.A convenience sample consisted of 794 undergraduate nursing students was involved in the study.Quality of Remote Learning and Nursing Student's Satisfaction regarding remote Learning scales were utilized to achieve the aim of this study.Results: More than sixty percent of nursing student perceived quality of remote learning as low especially for faculty staff, course structure, information, and system quality.As well as nearly two thirds of nursing student were dissatisfied with remote learning.Conclusion: Quality of remote learning affect nursing student's satisfaction where there was a statistically significant positive correlation between overall quality of remote learning and overall nursing student's satisfaction.Recommendation:Improving faculty staff online experiences is vital by providing continuous training and education opportunities.Also, maintaining powerful online education platform and internet infrastructure is necessary to improve remote learning quality and increase nursing student's satisfaction.