Reviewerly/exHarmony huggingface dataset:
Estimation of Human Motion Posture Using Multi-labeling Transfer Learning

Human posture estimation is the basis of many computer vision tasks, such as motion recognition, violence detection and behavior understanding.Therefore, it is of great significance to study the estimation algorithm of human motion posture (HMP).To address the problem of poor estimation effect of traditional HMP estimation algorithm, in this paper, an estimation algorithm for HMP using multi-labeling transfer learning is proposed.First, the original human motion image is labeled by using the multi-label transfer learning, the HMP features are extracted, and the original image classification is completed.Second, a regulator is constructed based on the classification results of the original image, and the regulator is used to adjust the estimation results of HMP based on a convolutional neural networks.Finally, the posture compensation function is used to compensate for the error part to realize the estimation of HMP.In the experiment, the Human3.6mdata set and MPII data set were used as the basis for testing.The results show HIGHLIGHTS• This paper addresses the problem that it is difficult to mine and extract the potential features of human motion posture feature vector.• The posture compensation function is used to compensate the error part to ensure the accuracy of posture estimation results• The posture compensation function is used to correct and compensate the estimation results and improve the reliability of the estimation results.
Semantic-Enriched Code Knowledge Graph to Reveal Unknowns in Smart Contract Code Reuse

Programmers who work with smart contract development often encounter challenges in reusing code from repositories. This is due to the presence of two unknowns that can lead to non-functional and functional failures. These unknowns are implicit collaborations between functions and subtle differences among similar functions. Current code mining methods can extract syntax and semantic knowledge (known knowledge), but they cannot uncover these unknowns due to a significant gap between the known and the unknown. To address this issue, we formulate knowledge acquisition as a knowledge deduction task and propose an analytic flow that uses the function clone as a bridge to gradually deduce the known knowledge into the problem-solving knowledge that can reveal the unknowns. This flow comprises five methods: clone detection, co-occurrence probability calculation, function usage frequency accumulation, description propagation, and control flow graph annotation. This provides a systematic and coherent approach to knowledge deduction. We then structure all of the knowledge into a semantic-enriched code Knowledge Graph (KG) and integrate this KG into two software engineering tasks: code recommendation and crowd-scaled coding practice checking. As a proof of concept, we apply our approach to 5,140 smart contract files available on Etherscan.io and confirm high accuracy of our KG construction steps. In our experiments, our code KG effectively improved code recommendation accuracy by 6% to 45%, increased diversity by 61% to 102%, and enhanced NDCG by 1% to 21%. Furthermore, compared to traditional analysis tools and the debugging-with-the-crowd method, our KG improved time efficiency by 30 to 380 seconds, vulnerability determination accuracy by 20% to 33%, and vulnerability fixing accuracy by 24% to 40% for novice developers who identified and fixed vulnerable smart contract functions.
Simulation of book selection planning based on deep learning and its application

Due to the complexity of network information, it presents an unbalanced feature. For the current book publishers, the publishing theme selection and planning methods of the newsroom can no longer meet the development speed of the Internet background. As the publishing house cannot accurately analyze the needs of users, it is difficult to obtain the specific standards of the book publishing market. Therefore, the demand of consumers for books has decreased, and further generated practical problems such as inventory accumulation and revenue impairment. Based on the demand of extracting and analyzing target information for books, it can be realized by using deep learning methods. Therefore, this study establishes a book selection planning system. If the information of the book itself and the corresponding evaluation information are required, the system first uses Anaconda to crawl the website to obtain the book information data, and then uses the proposed KIEM algorithm to supplement the information of the data crawled by Anaconda. After completing this step, use the first layer of CRF model to separate the evaluation sentence from the opinion sentence, and split the separated target sentence to get the attribute and emotion words. Finally, ARIA algorithm is combined with the improved recommendation algorithm to improve the recommendation performance and achieve the accuracy and personalization of the system. It can be seen from the actual measurement that the MAE value calculated by the application of the system proposed in this paper is relatively small, which shows that the system has certain practicability in prediction accuracy. This paper designs an effective system application by introducing deep learning technology into the field of book topic selection planning.
Security optimization algorithm for public information platform of internet of things based on open architecture

At present, with the rapid growth of electronic information and the continuous improvement of computing power, the Internet of Things (IoT) has become increasingly widely used in practical development. In the field of Public Information (PI), the IoT public Information platform has made good progress and promoted the scientific growth of public information and resource management. However, affected by its sharing and openness, its information and data security also faces large challenges. Maintaining the platform’s information security and improving its reliability have become the primary task in the current public utility construction. In order to solve this dilemma, based on the research of the platform characteristics, information security issues, and the development status of the platform, combined with the open architecture, this paper conducted an in-depth study of the security optimization algorithm of the IoT public Information platform. In order to verify the security optimization algorithm effect of the IoT public Information platform based on the open architecture, this paper took an IoT public Information platform as the test environment and carried out simulation tests on its usability, execution efficiency and protection. The test results showed that, in terms of protection, the average security protection degree of the security optimization algorithm based on open architecture under 80 security risk processing requests was about 86.70%; the average security protection degree of the traditional algorithm under 80 security risk processing requests was about 75.60%. From the test results, the security optimization algorithm of IoT public Information platform based on open architecture has stronger operability.
Research on the Application of Virtual Reality in the Construction of Ageing-Friendly Environment

In order to better meet the needs of the elderly, we provide them with a safe, comfortable and convenient living environment. We conducted a study on the application of virtual reality in the construction of ageing-friendly environment, from the perspective of ageing-friendly design with the concept of "ageing-oriented" design, to study how to adapt the environment to the physiological and psychological needs of the elderly through virtual reality technology and improve the quality of life of the elderly. By studying the impact of virtual reality technology on the aging-friendly environment, we conclude how virtual reality technology can be applied in the construction of aging-friendly environment. Although VR technology has been applied in the medical field, there are few studies that provide services and help for the elderly; the ability to enhance the comfort of the environment for the elderly through virtual reality is a major innovation of this project, which provides a new way of exploration for the field of aging-friendly research.
Enabling Responsible Banking through the Application of Blockchain

Behaving responsibly is a priority for all businesses including, of course, the banking sector. The United Nations Principles for Responsible Banking (PRB), launched in 2019, pursue six internal and external objectives. The aim of this paper is to give an overview of the PRB and to investigate how an innovation such as the blockchain could contribute to achieving the UN objectives. Our research uses a qualitative method, based on the Knowledge Caf&#233; as a data collection tool, enabling us to draw on the experience of ten experts in the banking and/or blockchain domains. Our data analysis identifies a series of nine key concepts that cut across the six PRB. The main findings of our study shed light on the importance of transparency, trust, and the involvement of all stakeholders in the implementation of blockchain solutions within banks. However, there are some barriers to implementation, such as the energy consumption of blockchain, misunderstanding and a need for education on this disruptive technology, and the quality of data. We conclude by presenting the theoretical and managerial implications of our study, including their impact on the funding of SMEs. JEL Codes: G21, M48, N20, O33
Societal Trust, Transparency and Accountability in Fiscal Policy During National Emergencies: A Reflection on the Unfolding Global Corona Virus Pandemic in Africa

Abstract Financing health care is growing in importance as more and more researchers, governments and other stakeholders see that public health goods and services need to be financed. This work is intended to understand what the underlying problems maybe in getting people to understand the magnitude of national and global emergencies in health and how to get them to obey state and global directives which are for their own safety from a fiscal perspective. Here we show that there is a breakdown in communication between states and their societies that is undermining the ability of African states to guide their people to follow health and safety rules. The findings provide a recommendation that an improvement in transparency and accountability will not only result in better management of emergencies but also build better long term fiscal legitimacy and management of the current and future emergencies and enhance societal trust.
FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning

Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are protected within the institution. However, not all clients have the same distribution of datasets, so data imbalance problems occur among clients. The main challenge is to overcome the performance degradation caused by low accuracy and the inability to converge the model. This paper proposes a FedISM method to enhance performance in the case of Non-Independent Identically Distribution (Non-IID). FedISM exploits a shared model trained on a candidate dataset before performing FL among clients. The Candidate Selection Mechanism (CSM) was proposed to effectively select the most suitable candidate among clients for training the shared model. Based on the proposed approaches, FedISM not only trains the shared model without sharing any raw data, but it also provides an optimal solution through the selection of the best shared model. To evaluate performance, the proposed FedISM was applied to classify coronavirus disease (COVID), pneumonia, normal, and viral pneumonia in the experiments. The Dirichlet process was also used to simulate a variety of imbalanced data distributions. Experimental results show that FedISM improves accuracy by up to 25% when privacy concerns regarding patient data are rising among medical institutions.
An architecture refactoring approach to reducing software hierarchy complexity

Summary Software complexity is the very essence of computer programming. As the complexity increases, the potential risks and defects of software systems will increase. This makes the software correctness analysis and the software quality improvement more difficult. In this paper, we present a quantitative metric to describe the complexity of a hierarchical software and a Complexity‐oriented Software Architecture Refactoring (CoSSR) approach to reduce the complexity. The main idea is to identify and then reassemble subcomponents into one hierarchical component, which achieves minimum complexity in terms of the solution algorithm. Moreover, our algorithm can be improved by introducing partition constraint, heuristic search strategy, and spectral clustering. We implement the proposed method as an automated refactoring tool and demonstrate our algorithm through a case study of battery management system (BMS). The results show that our approach is more efficient and effective to reduce the complexity of hierarchical software system.
Online Content Moderation: Does Justice Need a Human Face?

Approaches to content moderation online draw from models used to manage behavior in the offline world—undesirable content and behaviors are identified and sanctioned are issued as a means of deterrence. Recent discussions in both offline and online contexts have emphasized the limits of a sanction-based approach and highlighted the gains that would flow from building self-regulatory models within which users are encouraged to take personal responsibility for following rules. Our concern is whether a procedural justice model—a model which has increasingly been adopted in offline legal settings—can be used for content moderation online, and furthermore, if the benefits of this self-regulatory model persist in an online setting where algorithms play a more central role. We review recent studies which demonstrate that it is possible to promote self-governance by having platforms employ enforcement procedures that users experience as being procedurally just. The challenge of such procedures is that at least some of their features—having voice, receiving an explanation, treatment with respect—appear to be in conflict with a reliance on algorithms used by many online platforms. This review of the literature suggests that there is not necessarily an inherent conflict between the use of algorithms and the user experience of procedural justice. Drawing upon findings from recent empirical work in this space, we argue that the necessary antecedents for procedural justice can be built into algorithmic decision making used in platforms’ content moderation efforts. Doing so, however, requires a nuanced understanding of how algorithms are viewed—both positively and negatively—in building trust during these decision making processes.
Robotic Moment: The Influence Mechanism of Frequency of Socializing on Willingness to Interact with AI Robots

Past research has discovered that the shape design and interaction process design of AI robots, as well as the users’ constant features, are the major factors that affect users’ willingness to interact with AI robots. Currently, AI robots that play a vital part in the daily activities of our society are becoming increasingly prevalent, thus things about AI robots have gone from mythic to prosaic. But when and where people are more likely to adopt AI robots remains an important research topic. With the development of online technology and the long-term impact of COVID-19, there has been a recent trend in the lower frequency of socializing. To investigate whether a state of low socializing frequency is a robotic moment and whether it affects people’s willingness to interact with AI robots, we conducted two-wave questionnaire surveys to collect data from 300 participants from 23 provinces in China. The results showed that the frequency of socializing had a significant negative correlation with the willingness to interact with the AI robots via the mediation role of social compensation. Furthermore, the relationship between social compensation and willingness to interact with the AI robots was demonstrated to be stronger, when participants had a lower anthropomorphic tendency. These findings have theoretical implications for the human–computer interaction literature and managerial implications for the robotics industry.
Combinatorial necessary conditions for regular graphs to induce periodic quantum walks

We derive combinatorial necessary conditions for discrete-time quantum walks defined by regular mixed graphs to be periodic. If the quantum walk is periodic, all the eigenvalues of the time evolution matrices must be algebraic integers. Focusing on this, we explore which ring the coefficients of the characteristic polynomials should belong to. On the other hand, the coefficients of the characteristic polynomials of $\eta$-Hermitian adjacency matrices have combinatorial implications. From these, we can find combinatorial implications in the coefficients of the characteristic polynomials of the time evolution matrices, and thus derive combinatorial necessary conditions for mixed graphs to be periodic. For example, if a $k$-regular mixed graph with $n$ vertices is periodic, then $2n/k$ must be an integer. As an application of this work, we determine periodicity of mixed complete graphs and mixed graphs with a prime number of vertices.
PHY: A performance-driven hybrid communication compression method for distributed training

Distributed training is needed to shorten the training time of deep neural networks. However, the communication overhead often hurts performance efficiency, especially in a distributed computing environment with limited network bandwidth. Hence, gradient compression techniques have been proposed to reduce communication time. But, compression also has the risk of causing lower model accuracy and longer training time due to compression loss and compression time. As a result, compression may not consistently achieve desired results, and there are limited discussions on when and which compression should be used. To address this problem, we propose a performance-driven hybrid compression solution. We make three main contributions. (1) We describe a hybrid compression strategy that chooses the compression method for individual model gradients. (2) We build an offline performance estimator and an online loss monitor to ensure the compression decision can minimize training time without sacrificing mode accuracy. (3) Our implementation can be imported to existing deep learning frameworks and applicable to a wide range of compression methods. Up to 3.6x training performance speedup was observed compared to other state-of-the-art methods.
Optimization Countermeasures of College Canteens based on the Characteristics of College Students' Dietary Consumption Behavior

College canteen work is of great significance to the healthy development of higher education, campus safety, social harmony and stability. In order to achieve student satisfaction, college canteens must master the eating consumption behavior and dietary life patterns of college students, and do a good job according to their characteristics. This paper analyzes the characteristics of the dietary consumption behavior of contemporary college students and its new changes. The deficiencies of university canteens were further analyzed. Countermeasures for the optimization of college canteen work were proposed.
Automatic Obstacle Avoidance Robot based on Artificial Intelligence Detection and Recognition

This paper designs an automatic obstacle avoidance robot based on artificial intelligence detection and recognition, including a shell, and a detection and obstacle avoidance mechanism is set inside the shell; the detection and obstacle avoidance mechanism includes a movable member, a screw, a first electric actuator, a first camera, a gear, a rack and a steering assembly; by setting the detection and obstacle avoidance mechanism, the first camera and the second camera can take pictures of the surrounding environment during the movement of the shell, and when the micro When the computer recognizes the obstacle, it controls the servo motor to run and makes the screw rotate, so that the movable part moves in the direction of no obstacle, and the rack at the bottom of the movable part moves together during the moving process of the movable part, and the gear will be driven to rotate the steering wheel during the moving process of the rack, so that the robot can perform obstacle avoidance operation, and the height of the camera can also be adjusted by adjusting the first electric pusher and the second electric pusher to adjust the By adjusting the first and second electric actuators, the height of the camera can be adjusted to adjust the detection range, making the robot more flexible.
Research on The Copyright Law of Artificial Intelligence Generation

With the continuous development and innovation of science and technology in the current era, at this point, artificial intelligence has been able to generate all kinds of "works" like human beings, which has produced many new intellectual property legal issues in the field of artificial intelligence, which also put forward new legal challenges for its protection. What kind of protection should be given to artificial intelligence products, its copyright, the ownership of the rights of products is the issue of this paper. At present, the author believes that two preconditions should be made clear: first, the "works" of artificial intelligence products are regarded as copyright-protected works from its legal qualitative understanding; Second, the copyright of AI products should be owned by AI investors and users. On this basis, relevant legal protection mechanisms should be established to reduce legal disputes caused by artificial intelligence products.
Distributed Leadership and its Affect to Self-efficacy of University Teachers: Toward a Faculty Leadership Development Program

This paper aims to explore the influence of distributed leadership on university teachers' self-efficacy, and puts forward a teacher leadership development project. By literature review and analysis, this paper analyzes the theory of distributed leadership of university teachers and related research, and puts forward a teacher leadership development project, which includes four steps: explore teachers' leadership potential, establish corresponding distributed leadership training programs for teachers, promote teacher team cooperation and communication and establish a distributed leadership development community for teachers. Through the implementation of this project, university teachers can enhance their leadership and self-efficacy, and improve their teaching performance and student satisfaction.
Fundamentals of Graph for Graph Neural Network

The vertices, which are also known as nodes or points, and the edges, which are responsible for connecting the vertices to one another, are the two primary components that make up a graph. Graph theory is the mathematical study of graphs, which are structures that are used to depict relations between items by making use of a pairwise relationship between them. Graphs can be thought of as a visual representation of a mathematical equation. The principles of graph theory will be covered in this chapter.