Reviewerly/exHarmony huggingface dataset:
Knowledge Management and Blockchain Technology for Organizational Sustainability

Goal: This paper develops a conceptual model representing existing relations between blockchain, knowledge management, and sustainability. Design/Methodology/Approach: A systematic literature review was carried out, and a total portfolio of 43 articles was obtained. These articles were then analyzed using the content analysis technique. Results: Identifying the existing relationships between blockchain and knowledge management and between knowledge management and sustainability was possible. This information allowed the elaboration of the conceptual model, which resulted in the identification of the roles of each theme when related to each other. Limitations of the investigation: The limitations refer to the diversity and complexity of the themes, which made it necessary to limit the search for articles to two databases; the temporality of the research due to the evolution of the blockchain; and the failure to consider the risks arising from the adoption of technology and the negative aspects of the relationships between the themes. Practical Implications: With this article, companies studying blockchain adoption, looking for ways to improve knowledge management, or seeking to adjust their impacts on sustainability, can find information that helps in decision making. Originality/ Value: This article proposes a conceptual model that explains the relationship between three current and increasingly essential topics for organizations. To this end, it seeks to identify in the literature how the application of the first (blockchain) improves the execution of the second (knowledge management) and how this impacts the third (sustainability).
The interplay among human resource information systems, organizational citizenship behavior, and organizational success in Jordanian banks

The objective of this study is to investigate the impact of human resource information systems on organizational success and the moderating role of organizational citizenship behavior in the banking sector of Jordan. The study employed a quantitative-research method, using a stratified random sampling technique, to collect data from 141 employees working in the human resources departments of Jordanian banks. The simple regression results showed a significant positive impact of human resource information systems on organizational success, as indicated by a T-value of 4.195 at a significance level of 0.000, which was less than 0.05, an F-value of 17.569 at a significance level of 0.000. Additionally, the study found that organizational citizenship behavior played a significant moderating role in the relationship between human resource information systems and organizational success, as demonstrated by an increase in the R2 value from the first to the third models. These findings contribute to the existing literature by providing new evidence of the positive impact of human resource information systems on organizational success and the moderation effect of organizational citizenship behavior in the MENA region.
A Defense Method against Backdoor Attacks in Neural Networks Using an Image Repair Technique

With the rapid development of deep learning research and applications, the problem of artificial intelligence security has become increasingly prominent, such as adversarial examples, universal adversarial patch, and data poisoning, especially for the backdoor attack, which is a new type of covert attack, leading to the vulnerability and non-robustness of deep learning models. In a backdoor attack, the attacker will conduct a malicious attack by inserting some poisoned samples into training dataset. Poisoned samples add triggers and modify the labels to the target labels to participate in the training. Infected model has the same accuracy as the clean model in the normal test set, but when confronted with poisoned samples, the triggers will be activated to make the infected model predict the target label. To solve this problem, model parameters adjustment and poisoned data removal methods are widely used. However, they lack real-time performance and accuracy is insufficient. In this paper, we propose a new backdoor attack defense method, in which trigger reverse engineering is used to obtain the right triggers and image repair techniques to make sure that the input model data can be real-time processed without any negative impacts on clean samples.
How to deal with virtual property crime: Judicial dilemma and a theoretical solution from China

In recent years, China has evidenced a large number of criminal precedents on virtual property crime, but how to deal with it remains controversial, both theoretically and practically. Thus, systematic studies based on relevant precedents and doctrines are urgently needed from three perspectives: the conceptual boundary of virtual property, the judicial application of virtual property crime and the determination of the quantitative element of virtual property crime. Accordingly, the current research also seeks to shed light on other countries facing the same issues. In the context of criminal law, virtual property should be screened by three substantive criteria: digitization, value and scarcity. Whether virtual property is identified as a property right or a contractual claim in the field of civil law will not substantially affect the determination of criminal responsibility. Virtual property has both property and data features, and the different legal interests carried by both should be protected by criminal law. For the judicial application of virtual property crime, there are three approaches: property crime, data crime and computer crime. The three do not have a ranking or mutually exclusive relationship, and the principle of ideal concurrence (Idealkonkurrenz) should be applied. When determining the quantitative element of property crime, two different types of users and game operators should be distinguished, and the actual property damage standard should be favoured. When considering data and computer crime to determine the quantitative element, we should not rely excessively on the criteria of illegal income and economic loss, but should make a comprehensive Judgment around the degree of legal interest infringement.
A Study on the Mathematical Beliefs of Elementary School Teachers with a Focus on Mathematical Mindset

Based on the fact that teachers' mathematical mindsets have a great cognitive and emotional impact on students, this study attempted to examine the tendency of mathematical beliefs including mathematical mindsets for elementary school teachers and derive implications for teacher education. To this end, a survey was conducted, and the data of 62 elementary school teachers who responded to this were processed using the SPSS statistics program. As a result of the survey, it was found that elementary school teachers' mathematical beliefs were moderate, and more than half had a neutral mind set. In addition, there are no differences by gender and high school class in mathematical beliefs and mathematical mindsets, but there are differences by teaching period and university grade self-evaluation. Implications for teacher education were proposed through the above contents.
The Use of Whatsapp Social Media Group for Nutrition Counselling among Parents with Children of Congenital Heart Defects

Background: Ignorance about appropriate Infant and Young Child Feeding (IYCF) Practices among parents of children with Congenital Heart Defects (CHD) can exacerbate malnutrition in these children leading to significant morbidity and mortality. Use of novel methods for Intensive Nutrition Counselling such as Whatsapp social media group can lead to considerable improvement in nutritional status of the child.&#x0D; Objectives: To assess the efficacy of Whatsapp group for Intensive Nutritional Counselling to parents of children with CHD. &#x0D; Methods: This is a two-year prospective interventional study, one-group before-after study design. Here, intervention is in the form of specialised focussed nutrition counselling for parents of children with CHD through Whatsapp social media group.&#x0D; Results: Majority of the patients 16 (30.0%) were in the age group of 7-12 months, predominant diagnosis (25 patients i.e. 48.9%) was Ventricular Septal Defect (VSD). Sixty-eight percent patient-families belonged to lower socio-economic class. Before intervention, 71.1% of children (37 of 52) were found to be underweight, 35.5% children were stunted and 65.6% children were wasted. Post intervention there was significant improvement in all the 3 anthropometric indicators. This intervention particularly helped improve IYCF habits for the age group of 7-12 months. Post intervention all parents included the 8 food groups in their childrenâ€™s diet thus providing them wholesome nutrition whereas earlier only 2-3 food groups were given to children on a daily basis.&#x0D; Conclusion: This is the first study to explore use of Whatsapp group to educate parents of children with CHD about specific nutrition counselling, resulting in improvement in child anthropometric indices.
Robust Cough Detection With Out-of-Distribution Detection

Cough is an important defense mechanism of the respiratory system and is also a symptom of lung diseases, such as asthma. Acoustic cough detection collected by portable recording devices is a convenient way totrack potential condition worsening for patients who have asthma. However, the data used in building current cough detection models are often clean, containing a limited set of sound categories, and thus perform poorly when they are exposed to a variety of real-world sounds which could be picked up by portable recording devices. The sounds that are not learned by the model are referred to as Out-of-Distribution (OOD) data. In this work, we propose two robust cough detection methods combined with an OOD detection module, that removes OOD data without sacrificing the cough detection performance of the original system. These methods include adding a learning confidence parameter and maximizing entropy loss. Our experiments show that 1) the OOD system can produce dependable In-Distribution (ID) and OOD results at a sampling rate above 750 Hz; 2) the OOD sample detection tends to perform better for larger audio window sizes; 3) the model's overall accuracy and precision get better as the proportion of OOD samples increase in the acoustic signals; 4) a higher percentage of OOD data is needed to realize performance gains at lower sampling rates. The incorporation of OOD detection techniques improves cough detection performance by a significant margin and provides a valuable solution to real-world acoustic cough detection problems.
Polyester staple fiber cluster matching localization algorithm based on master-slave dual cameras

In the process of detecting the tension of polyester fiber post-spinning drawing cluster, it is necessary to locate the cluster to determine the position of tension detection. A polyester staple fiber cluster matching algorithm based on dual master-slave cameras was proposed to achieve accurate positioning and matching tracking of the cluster. The algorithm uses dual cameras to extract and match features from the cluster’s global and local images to achieve the cluster’s precise positioning. The experimental results show that the algorithm has good detection speed and accuracy in matching polyester fiber cluster and can realize the positioning of the cluster.
A Privacy-Preserving Mechanism Based on Privacy Situation Awareness for Information Sharing in OSNs

Various privacy-preserving mechanisms have been proposed to protect the privacy of individuals in online social networks, which have corresponding advantages in situations where they are primarily concerned. However, most of them applied static policies to all information audiences, resulting in the inability to satisfy the fine-grained requirements of information owners because audiences in the information propagation path are heterogeneous. To this end, we propose a novel privacy-preserving mechanism, in which the impact of each audience on the privacy situation of the information owner is evaluated. Particularly, to meet the subjective requirements of the information owner, the prospect theory is utilized to match the impact of the audience with the requirements of the owner, only the audience who satisfies the matching conditions can be authorized to receive the information. Furthermore, the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is leveraged to implement the proposed mechanism, and the use of CP-ABE can also prevent service providers from obtaining the information of individuals. Therefore, through our mechanism, the information owner could reduce the risk from individuals and service providers, while meeting its personalized requirements. The theoretical analysis and simulation experiment demonstrates that our mechanism has obvious advantages in controlling privacy risk and is compatible with various scenarios.
Virtual Reality-Based Joy Induction: The Role of Interactivity and Prior Mood

This study evaluates the effectiveness of virtual reality (VR) as a novel tool for mood induction, specifically for inducing joy, and examines the role of interactivity and prior mood. We conducted an experiment using a 2 × 2 factorial design with 124 participants who were randomly assigned to either a neutral or negative prior mood condition, and an interactive or noninteractive joy induction condition. Prior mood was experimentally manipulated using a VR scenario that simulated a terror attack at a train station (negative mood condition) compared with a control condition in which no incidents occurred at the train station (neutral mood condition). Subsequently, participants entered a virtual park scenario that either allowed for playful interaction with objects in the park (interactive condition) or not (noninteractive condition). We found that interactive VR experiences lead to lower levels of negative affect compared with a noninteractive experience irrespective of participants' prior mood, but playful interaction in VR only increased joy when participants were in a neutral (not negative) prior mood. We suggest interactivity as a design principle for effective negative mood reduction, but further research is required on how to successfully transform a prior negative mood into joy.
Optimization-based motion primitive automata for autonomous driving

Trajectory planning for autonomous cars can be addressed by primitive-based methods, which encode nonlinear dynamical system behavior into automata. In this paper, we focus on optimal trajectory planning. Since, typically, multiple criteria have to be taken into account, multiobjective optimization problems have to be solved. For the resulting Pareto-optimal motion primitives, we introduce a universal automaton, which can be reduced or reconfigured according to prioritized criteria during planning. We evaluate a corresponding multi-vehicle planning scenario with both simulations and laboratory experiments.
Automatic modulation classification of frequency hopping signals based on decision-level fusion model in multipath channels

In many complex wireless communication scenarios, frequency hopping technology has attracted widespread attention due to its strong anti-fading and anti-interference characteristics. In this paper, we propose an improved support vector machine (SVM) classifier based on the decision-level fusion (DLF) model, referred to as DLF-SVM, for automatic modulation classification (AMC) of frequency hopping signals in multipath channels. To estimate parameters and get the sliced samples, the frequency hopping pattern is first obtained by performing the time-frequency analysis on the received signal. Then, for samples after being preprocessed, we extract the correlation coefficients of the cyclic spectral section as categorical features. Finally, focusing on the idea of the DLF model, an improved SVM classifier based on the prior probability weight matrix is constructed to realize the AMC of frequency hopping signals. Simulation results show that our proposed algorithm can significantly improve the classification accuracy of binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), and minimum shift keying (MSK) signals under low signal-to-noise ratio (SNR) and multipath channels.
The Research of Different Algorithms on Path Planning

The evolution of modern existence is inextricably linked to path planning. Road transportation, urban road planning, and travel plans for people's travel requirements must all be carried out and assured on the basis of effective route planning implementation. However, the path planning algorithms are not ideal. This work brings together three contemporary popular algorithms: classical algorithms, graphics algorithms, and bionics algorithms. The article presents and evaluates their functions, as well as summarizes the benefits and drawbacks of each method. Following that, the experimental data from each algorithm is gathered and compared. The performance difference between different types of methods is analyzed. The collected results are integrated with the prior analysis to form a summary. Finally, the paper summarizes and looks forward to the full text.
An Investigation on The Success and Failure of Charities from The Effective Altruism Perspective

In recent years, charities have encountered major management problems, manifested in the form of a lack of work experience, a one-sided understanding of charity, wealth distribution problems and low public awareness of charities. This study aims to explore why some charities fail while others succeed. With in-depth analysis, the author found that charities, foundations and other causes that rank high on GiveWell tend to have better chances of survival because their philanthropy and foundation behavior is rational and maximizes the effect. This article popularizes the concept of effective altruism, how GiveWell’s final computing concept reflects rationality, and finally focuses on analyzing how Against Malaria Foundation rationally solves management problems and achieves today’s achievements. This paper suggests that it is necessary for philanthropists to use the rational spirit of effective altruism and the rationality of charities. In this way, philanthropy can effectively take into account the issues of efficiency and fairness, promote the common progress of members of society, and have a certain positive significance for social stability, thereby promoting the vigorous development of public welfare undertakings.
Object Removing via Multi-Column Gate Convolutional Neutral Network

Object removal is to remove unwanted objects in a photograph. It can be realized by the inpainting technique with irregular mask. Generative Image Inpainting with Contextual Attention [n] and Partial convolution cannot work well with irregular mask，so we present a Multi-column Gate Convolutional Neutral Network. It can work well with both regular and irregular mask. Our model adopts gate convolution which is more suitable in inpainting task than traditional convolution. Special context loss, semantics loss and spatial reconstruction loss are used for better performance. We train the model with CelebA and Landscape. A comparison is made between our model and some models published on CVPR etc, our model’s results can generate more realistic images than existing ones for object removal.
A Mobile Robot with a Manipulator to Alleviate the Shortage of Health Workers in Hospitals in COVID 19

COVID-19 has brought an unprecedented burden to hospitals. Hospitals are facing many problems, such as understaffing and infected employees. But this has also promoted the development of hospital robots to a certain extent. This paper aimed to develop a robot that can move around the hospital and pick and send stuff to patients. The robot is mainly divided into 2 two parts: the moving part and the grasping part. The moving part includes map construction, localization, and navigation with gmapping, A*, and DWA algorithms that would stimulate on the ROS platform. For the grasping part, the forward and inverse kinematics of the robot arm and PD control would be analyzed using MATLAB with the Robotics System Toolbox, Simulink, and Simscape Multibody to simulate and control the robot arm. Using this approach, the robot can reach the target position and avoid dynamic obstacles. In addition, the robot arm can pick up and drop the ball with the designed trajectory.
Application of Different Artificial Intelligence Methods on Reversi

Using different algorithms in artificial intelligence to perform adversarial agent games. The most popular adversarial game that could be implemented using artificial intelligence algorithms is chess games. The algorithm this research used includes minimax with improvement and deep reinforcement learning. The goal for this research is to compute the popular game Reversi in different artificial intelligence methods successfully. Moreover, the research seeks for improvements in the heuristic part of minimax algorithm and the combination of deep reinforcement learning with Monte Carlo Tree with Neural Network. This paper uses Reversi as an example to analyze different algorithms, including Minimax, Monte Carlo Tree, and Neural Networks. As a result, both algorithms work successfully. The alpha-beta pruning minimax algorithm with improvement in heuristic function and fixed depth cut-off significantly increase the winning probability and time cost of our artificial intelligence agent. The deep reinforcement learning successfully combined MCTS with neural network to train two agents to complete Reversi with great winning probability.
Research of Multi-agent Deep Reinforcement Learning based on Value Factorization

One of the numerous multi-agents’ deep reinforcements learning methods and a hotspot for research in the field is multi-agent deep reinforcement learning based on value factorization. In order to effectively address the issues of environmental instability and the exponential expansion of action space in multi-agent systems, it uses some constraints to break down the joint action value function of the multi-agent system into a specific combination of individual action value functions. Firstly, in this paper, the reason for the factorization of value function is explained. The fundamentals of multi-agent deep reinforcement learning are then introduced. The multi-agent deep reinforcement learning algorithms based on value factorization may then be separated into simple factorization and attention-mechanism based algorithms depending on whether other mechanisms are incorporated and which various mechanisms are introduced. Then several typical algorithms are introduced and their advantages and disadvantages are compared and analyzed. Finally, the content of reinforcement learning elaborated in this paper is summarized.
Study for Performance of Un-Pretrained and Pre-trained Models based on CNN

In recent years, as the accuracy of deep learning algorithms in image classification tasks exceeds that of the human brain, Artificial Intelligence (AI) auxiliary diagnosis systems have attracted more and more attention. In this paper, some commonly used Convolutional Neural Network (CNN) models e.g. MobileNet, VGG and ResNet are trained and compared on the cancer detection dataset, and it is found that the pre-trained models based on the idea of the transfer learning perform better than the newly trained models in terms of training speed and model performance. Thus, it can be seen that the transfer learning method has great potential in the field of cancer diagnosis. This study provides some experimental support and suggestions on how to further improve the property of the transfer learning method in the field of cancer diagnosis. Meantime, the performance of VGG19 can be proved to be better compared to other models (i.e., MobileNet and ResNet).