Reviewerly/exHarmony huggingface dataset:
A Fast 3D Multi-Object Tracking Method Based On Motion and Appearance Features

3D-MOT(3D multi-target tracking) is an important direction in the research of autonomous driving and intelligent robots, which can provide a rich and reliable dynamic representation of environment for modules such as path planning. This paper proposes a 3D-MOT method that fuses 3D and 2D input data. This method takes into account the motion characteristics and appearance characteristics of the target during the data association process, and uses the Kalman filter to predict the target in 3D and 2D space respectively. State, and proposed a target motion similarity measurement method that integrates motion features in different spaces; at the same time, this method also designed a network for extracting the apparent features of the detected target, and based on this, proposed a similarity feature Finally, an integrated similarity calculation method integrating motion similarity and apparent similarity is proposed for data association. On the basis of ensuring various tracking indicators as much as possible, the purpose of 3D multitarget tracking is achieved with a small amount of calculation, which can meet the real-time algorithm requirements of service robots and other platforms with low detection accuracy and limited computing power.
Intelligent System Construction Method based on Smart Contract

In recent research, improving the performance and guaranteeing the security of intelligent system is an essential issue for current system. High computation speed and co-construction systems has been developed by researchers to enhance the effectiveness of intelligent system. However, disposing numbers of data by utilizing a safe method is an important challenge for any intelligent system and current researchers focus on changing the system architecture and modeling methods, which ignore the security of developed mechanism and may cause serious system problems. In this article, we propose a novel intelligent mechanism can guarantee the security and high performance of the system. Through disposing the smart contract for data users, owners and processors, the intelligent system central units can utilize the data by a reliable method and other attackers will never access the data without any permission. From our extensively experimental analysis, proposed system can achieve high computation performance with reasonable calculation cost.
Supervised Meta-Reinforcement Learning with Trajectory Optimization for Manipulation Tasks

Learning from small amounts of samples with reinforcement learning (RL) is challenging in many tasks, especially in real-world applications, such as robotics. Meta-Reinforcement Learning (meta-RL) has been proposed as an approach to address this problem by generalizing to new tasks through experience from previous similar tasks. However, these approaches generally perform meta-optimization by focusing direct policy search methods on validation samples from adapted policies, thus requiring large amounts of on-policy samples during meta-training. To this end, we propose a novel algorithm called Supervised Meta-Reinforcement Learning with Trajectory Optimization (SMRL-TO) by integrating Model-Agnostic Meta-Learning (MAML) and iLQR-based trajectory optimization. Our approach is designed to provide online supervision for validation samples through iLQR-based trajectory optimization and embed simple imitation learning into the meta-optimization rather than policy gradient steps. This is actually a bi-level optimization that needs to calculate several gradient updates in each meta-iteration, consisting of off-policy reinforcement learning in the inner loop and online imitation learning in the outer loop. SMRL-TO can achieve significant improvements in sample efficiency without human-provided demonstrations, due to the effective supervision from iLQR-based trajectory optimization. In this paper, we describe how to use iLQR-based trajectory optimization to obtain labeled data and then how leverage them to assist the training of meta-learner. Through a series of robotic manipulation tasks, we further show that compared with the previous methods, the proposed approach can substantially improve sample efficiency and achieve better asymptotic performance.
Bilateral Relation Distillation for Weakly Supervised Temporal Action Localization

Weakly supervised temporal action localization (WSTAL), which aims to locate the time interval of actions in an untrimmed video with only video-level action labels, has attracted increasing research interest in the past few years. However, a model trained with such labels will tend to focus on segments that contributions most to the video-level classification, leading to inaccurate and incomplete localization results. In this paper, we tackle the problem from a novel perspective of relation modeling and propose a method dubbed Bilateral Relation Distillation (BRD). The core of our method involves learning representations by jointly modeling the relation at the category and sequence levels. Specifically, category-wise latent segment representations are first obtained by different embedding networks, one for each category. We then distill knowledge obtained from a pre-trained language model to capture the category-level relations, which is achieved by performing correlation alignment and category-aware contrast in an intra- and inter-video manner. To model the relations among segments at the sequence-level, we elaborate a gradient-based feature augmentation method and encourage the learned latent representation of the augmented feature to be consistent with that of the original one. Extensive experiments illustrate that our approach achieves state-of-the-art results on THUMOS14 and ActivityNet1.3 datasets.
Sub-regional phase error compensation based on a probability distribution function and gamma precoding

In this paper, a phase error compensation method based on a probability distribution function (PDF) is proposed to improve the accuracy of phase extraction, which is helpful for three-dimensional (3D) reconstruction. First, the relationship between the gamma and the gray values is established to segment the projection regions. Then a new method based on a PDF is designed to represent the variation degree of phase error, which fits the precoded gamma value in the minimum range of the phase error. After that, the error compensation method is applied to the self-built system and packaged independently from the 3D reconstruction system to unwrap phases with high precision. The experimental results show that the proposed method can reduce the standard deviation of the phase error by 46.9% compared without phase error compensation, and decrease the standard deviation of the phase error by 30% compared with the whole precoding. Generally, our method can effectively avoid overcompensation or under-compensation caused by single global gamma precoding correction, and better reduce the phase error and improve the 3D reconstruction accuracy in the fringe projection system.
Cybersecurity Challenges and Prevention Strategies in the Construction of Smart Campuses in Higher Education Institutions

With the continuous development of information technology, smart campuses have become an important direction for the informatization construction of higher education institutions.However, the cybersecurity issues in the construction of smart campuses cannot be ignored, and cybersecurity challenges have emerged accordingly.This paper analyzes the current situation and challenges of cybersecurity in smart campuses of higher education institutions, proposes prevention strategies such as improving cybersecurity policies and regulations, strengthening the construction and application of cybersecurity infrastructure and technology, enhancing cybersecurity awareness and capabilities, optimizing cybersecurity management and monitoring, and conducts case analysis and inspiration on the practice of cybersecurity prevention strategies in smart campuses of higher education institutions at home and abroad.The aim of this paper is to provide cybersecurity protection and reference for the construction of smart campuses in higher education institutions.
Developing Interactive Multimedia with The Instructional Games Model for Water Cycle Materials for Fifth Graders

The research purpose was to develop and determine feasibility of interactive multimedia instructional game model. The research method used R&amp;D with 4D stages. The reseach was conducted grade V in SD NEGERI 3 Batudaa Kabupaten Gorontalo Provinsi Gorontalo. The result was average score of 91% from media design experts was very feasible criteria, the material experts obtained an average of 85% was very feasible criteria and the practitioner experts obtained an average of 98% was very feasible criteria. Based on this, the interactive multimedia instructional games model was suitable for use for grade V in water cycle concept.
Research on Big Data Empowerment of Modern Translation Technology in the Context of New Liberal Arts

This article aims to explore the application of big data technology in modern translation, with the background of a new liberal arts course.This article analyzes the challenges and opportunities that big data technology brings to translation, and discusses possible methods and approaches to integrating big data into translation practice.Through comprehensive literature review and case analysis, this article concludes that big data technology has great potential to promote the development of modern translation, and recommends that more attention be paid to and efforts be made to explore and utilize the role of big data in translation research and practice.
A Study on the Factors Influencing the Academic Achievements of Undergraduates in Private Universities

With the continuous development of China's economy and society, if private college students want to keep pace with the times and meet the challenges, they must seize the critical period of social transformation to improve their comprehensive competitiveness.This paper starts with the concept of "academic achievement", analyzes the current situation and influencing factors of students' academic achievements in private undergraduate colleges and universities, and then explores the ways for colleges and universities to improve students' academic achievements from the four dimensions of students, families, schools and society.Through a perfect achievement training mechanism, students can stimulate their potential, improve their academic achievements, and lay a solid foundation for further study and employment in the future.
Optimization of College English Culture Teaching Based on Constructivism Theory

Constructivism theory is a psychological theoretical cognitive model that allows individuals to have new thinking about the world's cognition and understanding through conceptual schema.Under the background of constructivism theory, this paper discusses the optimization thinking strategy of college English culture teaching, which is the main content of the article.College English teachers should combine constructivism theory, change existing teaching methods, and create scenarios to think about English teaching from the perspective of students' psychology, which can effectively guide and help students to learn and understand English culture, and make students easier to accept other foreign cultures.
An Analysis of the Deviation and Return of Open Classes in the New Era

With the continuous development of China's education industry, curriculum reform is gradually deepening.As a new form of curriculum teaching, openness plays a huge role in improving the quality of school teaching, promoting student development, and leading teacher professionalization.However, due to factors such as utilitarianism, teachers' vague teaching theory, incomplete teaching evaluation mechanism, leaders' formalism and so on, the open class has appeared dissimilation tendencies such as faking, showing off, and face project, which completely violates the original intention of its creation, deviates from its original operation track, and makes it unable to play a huge positive energy role.Therefore, reflecting on these issues and proposing reasonable solutions to promote the return of open classes to their true nature has become an urgent task in the field of education in the new era.
The Contribution of Mobile Companies to Sustainable Economic Development in Sub-Saharan Africa

Sub-Saharan African economy envisages thriving its economy through the customer relationship management. The Sub-Saharan African economy has a challenge to take its economy away from the low-income economy to the middle-income economy to 2035. To attain this result, the Sub-Saharan African economy would scrutinize the contribution of mobile companies to sustainable economic development in Sub-Saharan Africa. The objective of this study is to invite the African countries to create an enabling environment spurring training educational technological digital ecosystem permitting them to provide an economic development sustainability. This study should refer to be as a guidance for junior, senior scientists, private and governmental scientific laboratories, as well as companies and professionals that envisage validating their future scientific research. The study in question has used experiments data collection, data collection methods, econometric methods, such as the inference in two variables by putting an accent on the Fisher-test, and that of Schwarz. Table methods, likelihoods and forecasts have been used in order to find out mathematical results. However, the mathematical analysis have been used and validated through excel and Gretel. The results obtained have been based on the estimate of the technological breakthrough, the analyses of the interrelation between the number of subscribers and the revenue of mobile companies. Thus, the contribution of the mobile companies to the Sub-Saharan African economic development has been one of the fundamental results yielded. The assessment of the performance between the 5G and the non-5G has been made.
SDN-Based Task Scheduling to Progress the Energy Efficiency in Cloud Data Center

Increasing the enlargement of data center directs to the huge amount energy expenditure in the cloud data center. The high amount of energy wastage causes the high-level carbon di oxide discharge around the world. In order to diminish the energy usage, this paper gives attention to the Software-Defined Network (SDN)-based task scheduling. In this research paper, the single cloud computers for power management are discussed. In this work, one novel algorithm is proposed that is Energy-Efficient Genetic Algorithm (EEGA). The SDN and EEGA are implemented with fat-tree topological architecture. Fat-tree topological architecture reduces the 50% of the energy consumption. The experiments are clutched with the CloudSim simulator. The results were compared with Round Robin (RR) and Traditional Genetic Algorithm (TGA). In this paper, the differences among TGA and EEGA are explained.
Facial Recognition Approach: As per the Trend of 2022–23 Using Python

The study of the face recognition system existed since 1960, and huge literature exists in this work, the facial recognition system gaining more and more importance from year to year. In these issues, the rapid development of Python programming in the area of Artificial Intelligence and Machine Learning becomes the new trend in the year 2022-23. In these aspects, this article explained the popular facial recognition methods like knowledge-based, Haar feature selection, and Ada boosts algorithms in the literature. This approach can be implemented using Python open-source platform in OpenCV libraries. The methodology described the facial recognition approach and Haar cascade algorithm and the implementation of this approach in crime investigation. The results proved that this is a simple and easy approach to getting recognition faces, and this is simple to use in crime investigations and secure.
Research on Institutional Improvement for Personal Information Collection and Use in the public sector: Use of Video Information collected by Public area CCTV

In recent years, artificial intelligence technology, which enables new technologies to be deployed, has expanded the scope and methods of data collection and utilization of government agencies. Data analysis and AI technology are deployed to enhance the intelligent and accurate use of Closed-circuit television (CCTV) surveillance cameras for crime prevention, security, and public safety purposes. The use of facial recognition technology enables an individual to be identified by processing images captured on CCTV, the suspects to be tracked on the CCTV footage, and crime scenes to be monitored in real time. Amidst this expansion, there is an increasing need for discussions to protect privacy. The need for privacy control is emphasized as data collection, retention, and utilization capabilities resulting in losing control over individual’s own information. The value and importance of personal data have been changed from the previous, and government surveillance issues arises as vast quantities of personal data being stored and easily shared. The need for government data access for various purposes and storage of data on a large scale may lead to the privacy right and the infringement of informational self-determination. In a situation where the use of CCTV information is allowed and gradually expanding, it is difficult to evaluate that legal grounds for controlling the actions of government agencies or guaranteeing the rights of individual subjects are sufficient. This paper aims to examine the current status of data collection by government institutions, especially the institutional basis for the use of CCTV and video information collection by government agencies, and problems with the system regarding personal data protection.
Corrigendum: Introducing a healthcare-assistive robot in primary care: a preliminary questionnaire survey

CORRECTION article Front. Robot. AI, 30 May 2023Sec. Biomedical Robotics Volume 10 - 2023 | https://doi.org/10.3389/frobt.2023.1224492
Green Cloud Computing: Challenges and Opportunities

We discuss the immediate need, challenges, and opportunities to transition into greener cloud computing platforms. Actions must be taken not only by the academy and the industry, but also by computer science practitioners from several areas, such as information systems, scheduling theory, distributed systems, HPC, computer architecture, and approximate computing, to cite a few.
Multi-source power address data fusion based on data mining technology

With the upgrading and transformation of power system intelligence, the number and kinds of power big data have begun to show an explosive growth trend, the development of power grid in our country has already entered into the era of big data. How to quickly collect and sort out useful information in the chaotic data pile is a great challenge for the current power system construction. Multi-source information fusion plays an important role in the research of power big data. It can fuse the data information belonging to the research object from various aspects together to realize the sorting out of the disorganized power grid data and obtain a more detailed and comprehensive analysis of the research object. And relying on the mature development of big data storage and processing technology, it provides a new opportunity for the research of multi-source information fusion of power big data.
Shogun: A Task Scheduling Framework for Graph Mining Accelerators

Graph mining is an emerging application of great importance to big data analytic. Graph mining algorithms are bottle-necked by both computation complexity and memory access, hence necessitating specialized hardware accelerators to improve the processing efficiency. Current accelerators have extensively exploited task-level and fine-grained parallelism in these algorithms. However, their task scheduling still has room for optimization. They use either breadth-first search, depth-first search or a combination of both, leading to either poor intermediate data locality, low parallelism or inter-depth barriers.
Research and Development of Scientific Research Management Information System in Higher Vocational Colleges Based on Distributed Block Technology

This paper is based on the distributed block platform to develop a management information system suitable for the scientific research management needs of higher vocational colleges.This system will become an information platform for the daily work of the scientific research management department, an information platform for the faculty of the college to conduct scientific research work and scientific research achievement management, and an information platform for academic management, professional leaders, and young and middle-aged backbone teachers.Informatization, networking of school teachers' information resource management, and office automation of some businesses in teaching and personnel management.
Unification of machine learning and blockchain technology in healthcare industry

Smart healthcare systems are useful for the prediction of diseases to provide a proper diagnosis, prevention, and cure by analyzing them. Blockchain (BC) is one of the emerging and trending technologies for various smart and intelligent health care systems that are very much required in the current scenario to expand the medical boundaries. The mechanism used in it gives assurance of security of data of patients which is the most important aspect of a healthcare system. But with one issue resolved, cannot stop the other issues to rise like attacks and double spending. To pick up these issues, analytics of data is required on the secured data for better results in the diagnosis and analysis in the healthcare industry. Till now, machine learning algorithms for recognizing the patterns in healthcare, data analyzing, and making some decisions based on the data have also shown their value among different sectors. The basic characteristic of ML is to modify the accuracy of results by involving the rational amount of healthcare data for precise diagnosis or decisions. To make positive decisions, the proper amount of data is required. So, the combination of these two technologies - machine learning and BC technology - can improve the results to a better precision of improvement to the next level which may be very important and useful for medical or healthcare systems. Support vector machines (SVM), deep learning (DL), convolution neural network (CNN), and long - short-term memory (LSTM) are some of the traditional ML techniques which can be useful for the analysis of the issues like the attack on the BC network. In this study, we will discuss about a detailed comparison of smart applications in the healthcare sector that mainly includes applications like smart grid, smart cities, etc. Data security increases by combining both machine learning and block chain.
Fast and Robust Video Deduplication

The popularity of social media networks and mobile devices have skyrocketed in recent years. This has led to the rapid increase in the video content being recorded and uploaded to online platforms like TikTok, YouTube, etc. As a result, a rise in the amount of illegal pirate videos can be observed. These pirate videos have the same content as original videos but with minor editing effects and variations in coding. The task of finding such duplicate videos is known as video deduplication. Also, storing this huge amount of video data is a challenging issue.