Reviewerly/exHarmony huggingface dataset:
Analysis of Strategic Evolution Structure of 5-player Werewolf Based on Simulation

Games are widely used as a benchmark in the field of artificial intelligence research due to the clarity of the rules and sufficient complexity. In recent years, the game "werewolf" has received a lot of attention in the field of artificial intelligence. Werewolf is an incomplete information game that is played through dialogue between the players. The artificial intelligence that plays werewolves is called "AIWolf". A platform for AIWolf has been created, and AIWolf competitions have been held to test the performance of the agents running on the platform. In the AIWolf competition, general developers are invited to submit AIWolf agents, which are expected to improve the performance of AIWolf through collective intelligent development. In this study, we first analyzed the logs of previous competitions and found that the agents from previous competitions have evolved to be stronger than the agents created before them. Furthermore, based on the strategy evolution derived from the analysis, we propose a simulation method for 5-player werewolf to investigate whether repeated evolution converges to the dominant strategy. The simulation results show that no dominant strategy is found and that the strategies change continuously and periodically. Our results show that werewolf games are sufficiently complex to be the subject of artificial intelligence research, which we believe will help us to understand the structure of werewolf games and discover appropriate strategies.
Visual Saliency Prediction of Global Attention Based on Relevance Perception

Target detection and tracking is a basic problem in the field of computer vision, and it is also the key and core technology of intelligent video surveillance systems. In order to effectively improve the saliency prediction task, this paper proposes an improved feature extraction method based on the residual neural network model. The architecture forms an encoder-decoder structure, and proposes an associated perception global attention module to capture multi-scale features in parallel. Through this module, global structural information can be captured for better attention learning, high-level visual feature extraction on multiple spatial scales, and global and local associated information for weight calculation, and the result is expressed as the global scene information. Combined to accurately predict visual saliency. The competitive and consistent results of multiple evaluation indicators are achieved on two public saliency benchmarks. Compared with the existing methods, it also has a certain lead compared with other saliency map prediction models.
Digital Link—A

Abstract In a digital link, a transmitter converts bits into signals and a receiver converts the signals it receives into bits. The receiver faces a decision problem that we study in Sect. 7.1. The main tool is Bayes’ Rule. The key notions are maximum a posteriori and maximum likelihood estimates. Transmission systems use codes to reduce the number of bits they need to transmit. Section 7.2 explains the Huffman codes that minimize the expected number of bits needed to transmit symbols; the idea is to use fewer bits for more likely symbols. Section 7.3 explores a commonly used model of a communication channel: the binary symmetric channel. It explains how to calculate the probability of errors. Section 7.4 studies a more complex modulation scheme employed by most smartphones and computers: QAM. Section 7.5 is devoted to a central problem in decision making: how to infer which situation is in force from observations. Does a test reveal the presence of a disease; how to balance the probability of false positive and that of false negative? The main result of that section is the Neyman–Pearson Theorem that the section illustrates with many examples.
Building Social Relationship Skill in Digital Work Design

The aim of this study is to analyze the impact of digital work design on social relationship skill through Digital-Mediated Communication (DMC) for learning methods in the digital era. This study presents how to improve social relationship skill for university lectures. Data were collected by using questionnaire techniques with SmartPLS as the analysis tool. The result found that all hypotheses are confirmed. The Implementation of DMC not only improves social relationship skill but also improve the virtual empathy that rarely appear during digital interaction. The model of social relationship skill improvement through DMC and digital work design can be used by researchers and practitioners to study and achieve the future in various organizations.
RETRACTED ARTICLE: Synchronization Methods of Multiple High Frame Rate Industrial Cameras Using a General-Purpose Computer

Technologies such as identification, detection, and measurement based on multiple industrial cameras have developed vigorously. Most of these technologies require synchronized video signals from multiple cameras, thus a general method of synchronization of multiple industrial cameras has always been an important research direction. To reduce synchronization error and hardware cost of the existing multi-vision sensor synchronization methods, based on multiprocessing or multithreading techniques, this paper designs three synchronous capture schemes for different application scenarios. The proposed schemes are tested for an example system of three industrial cameras with the highest frame rate for 600FPS. For long-time synchronous capture with a low frame rate, a read–write structure is proposed based on multiprocessing, which makes the synchronization error lower than the reciprocal frame rate when the frame rate is lower than 200FPS. For short-time capture with a high frame rate, a read-storage-write structure is used based on multiprocessing, which makes synchronization errors be about 1 ms when the frame rate is lower than 600FPS (theoretically ~ 1000FPS). To relieve the memory usage pressure of the read-storage-write structure, a read-cache-write structure is proposed based on multiprocessing/multithreading, which makes synchronization error be less than 1 ms when the frame rate is lower than 600FPS (theoretically ~ 1000FPS). Experimental results show that the proposed method provides a promising accurate and low-cost solution for general industrial camera synchronization applications.
Development of a Mockup of a Mobile Radio-Controlled Swimming Apparatus for Environmental Monitoring

The purpose of the article is to develop a mock-up of a mobile radio-controlled swimming apparatus for environmental monitoring and collection of samples in hard-to-reach areas of water bodies, which can also be used for catching dead or infected fish, feeding fish, during rescue operations in flood conditions. A review of existing technical solutions for environmental monitoring of water areas is made. The stages of development of a mockup of a mobile unmanned surface vehicle (USV) are considered. The USV system is divided into two parts: information and mechanical. Structural - functional schemes of the system have been developed and described. The software implementation of the USV system is described. USV is tested on small boats in fresh water and then in open water in lakes.
Restoration of occluded face image based on improved CycleGAN model

With the rapid development of VR technology in the past two years, VR has been applied to many fields such as communication, video, game and so on. But for VR wearers, their faces are largely obscured, which hinders access to complete facial information. Aiming at the above problems, the neural network generated by cyclic confrontation is used to realize the restoration of the masked face. The neural network can learn the mapping from the face image with VR glasses to that without VR, GAN is used as the generating model, in which the discriminator can judge whether the image is real enough to ensure that the generated image will not lead to deformity, the improved CycleGAN model can make the map learn the distribution transformation of the image, generate the non-occlusion image from the occluded image, learn the mapping of each other, and guarantee not over-fitting. At the same time, the CycleGAN model is implemented by Pytorch Algorithm, and the trained model is applied to the test data set of 500 different faces of celebA.
Recycling process through 3D videogame technologies for web platforms in Latin-American context

Climate change is a global problem that is gradually beginning to show its relevance for Latin-American context. For this reason, this research has proposed the design of two 3D video games oriented towards recycling. It is worth mentioning that these are the first 2 games made in Ecuador by a university institution that aims to raise awareness of sustainable development among its student population. Under this line, the article seeks to present the technical considerations considered to promote the transversal knowledge and ecological awareness.
Adaptive Genetic Algorithm Renewed by Migration Operator

In the present study, the Genetic Algorithm and the developed Adaptive Genetic Algorithm are used to solve optimization problems faced in modeling of complex systems.While using the Genetic Algorithm and the Adaptive Genetic Algorithm, the most important problem encountered is that these algorithms are prone to getting stuck in local bests.For example, in the complex system data optimization process using the Genetic Algorithm, it has been observed that it is frequently fitted to local bests in a certain period of time.The reasons are that, many mutations cannot be performed in the limited number of iterations, and because the number of individuals is limited, the population is filled with the same set of solutions in a short time.Therefore, the migration operator has been added to the Genetic Algorithm and the Adaptive Genetic algorithm in order to avoid local bests and to provide that these algorithms search in a wider area of the large search space of complex systems.In the present study, we have tested these algorithms using the migration operator in the Lotka Volterra Model.When the results are examined, it is observed that the Adaptive Genetic Algorithm with migration operator outperforms the Genetic Algorithm and the targeted success is achieved in complex system optimization.In the rest of the paper, the algorithms used in the Method Section are explained with outlines.In the Experimental Studies Section, the different algorithms are tested and compared with each other in the Lotka-Voltera model and numerical test functions.In the Conclusions and Discussions Section, brief information is given about general results and future studies.
Recursive Least Squares Policy Control with Echo State Network

The echo state network (ESN) is a special type of recurrent neural networks for processing the time-series dataset. However, limited by the strong correlation among sequential samples of the agent, ESN-based policy control algorithms are difficult to use the recursive least squares (RLS) algorithm to update the ESN's parameters. To solve this problem, we propose two novel policy control algorithms, ESNRLS-Q and ESNRLS-Sarsa. Firstly, to reduce the correlation of training samples, we use the leaky integrator ESN and the mini-batch learning mode. Secondly, to make RLS suitable for training ESN in mini-batch mode, we present a new mean-approximation method for updating the RLS autocorrelation matrix. Thirdly, to prevent ESN from over-fitting, we use the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> regularization technique. Lastly, to prevent the target state-action value from overestimation, we employ the Mellowmax method. Simulation results show that our algorithms have good convergence.
Attitude Towards Adopting Cloud Computing in the Saudi Banking Sector

Cloud computing plays a significant role in business organisations by offering many benefits and opportunities. However, the adoption of cloud computing involves some trepidation. The adoption of cloud computing in developing countries is still in the early phase. The bank sector in Saudi Arabia aims to benefit from opportunities offered by the cloud computing technology; however, some banks continue to hesitate in the implementation of this technology. Therefore, this study aims to investigate factors that influence the attitude of the Saudi Arabian bank sector towards adopting cloud computing. A model that incorporates factors derived from the literature is developed in this study to determine whether specific factors positively or negatively affect the attitude of the Saudi bank sector towards adopting cloud computing. A quantitative method is used and data are collected from decision makers and IT staff members in the Saudi bank sector. Results showed that factors of security and privacy exert a significant negative effect whilst factors of benefits and competition pressure demonstrate a significant positive effect on the Saudi bank sector attitude towards adopting cloud computing. Thus, cloud computing providers must focus more on negative influential factors to reduce the apprehension of the bank sector in adopting the cloud computing technology. In addition, the bank sector must also consider factors that can drive the adoption of cloud computing as well as opportunities and losses if this technology is rejected.
코로나19로 인한 원격 교육에서 학습습관이 대학생들의 학습만족도와 학업 지속의향에 미치는 영향에 관한 연구

In this study, if we look at remote education from an academic perspective, which occurred while remote education was deeply penetrating everyday life, we will look at it by applying Davis' Technology Acceptance Model based on rational behavior. We also wanted to investigate how depression caused by Covid-19 affects learning satisfaction.A summary of this study is as follows. First, it has a positive effect on self-efficacy learning satisfaction. Second, perceived usefulness has a positive effect on learning satisfaction. Third, perceived Ease of Use has not been shown to affect learning satisfaction. As shown in the main study, depression affects the learning satisfaction of college students as the spread of Covid-19 has caused major changes in their daily lives, such as isolation and restraint from going out.
Evaluation of Training Effect of New-Type Professional Farmer Based on Cloud Model

In order to make a comprehensive evaluation of the training effect of the new professional farmers from the training organization and management, training time, training cost and other aspects, and make an accurate judgment on the current situation and development direction of the new professional farmers training in China. Based on the cloud model theory, this paper realizes the transformation of qualitative index and quantitative evaluation, and organically combines fuzziness, randomness and discretization. After the characteristic data of the normal cloud generator is obtained by the reverse cloud generator and the information weight method, the normal cloud image is generated, and the training effect is evaluated according to the cloud image. The evaluation based on cloud model not only takes into account the fuzziness and randomness in the evaluation, but also takes into account the scientific nature of the data and the intuitiveness of the evaluation results. To a certain extent, it overcomes the shortcomings of the traditional evaluation methods, such as unstable evaluation conclusions and complex calculation. The evaluation conclusion is helpful for researchers to make an accurate judgment on the current situation and development direction of the training of new professional farmers in China.
IS IT POSSIBLE TO DEVELOP ENTREPRENEURSHIP AT SCHOOL?

Entrepreneurship will make it possible for young people to adapt to the employment market and participate in not only professional but also social and personal adult life effectively and satisfactorily. This article aimed to emphasize the way of developing entrepreneurship in the educational practice. The base of reflection as Culturally Relevant Pedagogy and Karney’s definition of entrepreneurship. These approaches may open schools as environments for the coordination of all processes hidden in their culture on the way to effectiveness. The category of entrepreneurship understood as a personal and organizational feature, an attitude, an activity mode, and a path of individual and institutional development were considered. When introducing challenges connected with the development of Entrepreneurship, some tools were described which make it possible to evaluate the changes taking place in the didactic process. This means that reflection on the process of development of must appears in various implementation spaces. It would be observable both at the level of individual development (students’ and teachers’ competencies), about activities undertaken within an organization, as well as changes in organizational climate and overall educational environment.
A Light Arabic POS Tagger Using a Hybrid Approach

Part Of Speech (POS) tagging is the ability to computationally determine which POS of a word is activated by its use in a particular context. It is a useful preprocessing tool in many natural languages processing (NLP) applications. In this paper, we expose a new Arabic POS Tagger based on the combination of two main modules: the 1st order Markov and a decision tree models. These two modules allow improving existing POS Taggers with the possibility of tagging unknown words. The tag set used for this POS is an elementary tag set composed of 4 tags {noun, verb, particle, punctuation} that are sufficient for some NLP applications but greatly help increasing the accuracy. The POS tagger has been trained with the NEMLAR corpus. The experiment results demonstrate its efficiency with an overall accuracy of 98% for the full system.
Research and Implementation of Deep Learning Counter Attack Defense Strategy

Abstract In recent years, deep learning technology has made excellent achievements in many fields, such as computer vision, natural language processing, and speech processing. More and more related applications have appeared, which has brought a lot of convenience to people’s lives. However, while deep learning performs well, it also has the flaws in being vulnerable. The attack method can make the deep learning model make wrong judgments by adding some small disturbances to the input samples. This brings huge security issues to deep learn applications At present, there have been many research results from defense against attacks. This article first analyzes a variety of classic adversarial attack methods in detail, and classifies these attack methods according to their attack scope. The differences and common points are abstract from the classification, and it is found that there are sensitive points in the counter disturbance, and the fluctuation of the sensitive points affects the classification of the deep learning model. Inspired by this, we propose a defense strategy that only filters sensitive points in the adversarial sample, avoiding the processing of non-sensitive points, reducing calculations, and improving efficiency.
Improved Optimal Reciprocal Collision Avoidance Algorithm in Racing Games

Autonomous character behavior design is a key to the development of game artificial intelligence. Currently, collision avoidance technology, which is widely used in the field of robotics, cannot be directly applied to the autonomous character behavior design in racing games without specific adjustment. This paper makes some improvements to the classical Optimal Reciprocal Collision Avoidance (ORCA) algorithm to propose a novel collision avoidance algorithm (IORCA) suitable for racing games. Relevant concepts unique to racing games are put forward. The collision handling principles followed by racing AI on the straight lines and curves are further presented. By implementing the two systems equipped with ORCA and IORCA, the collision avoidance behavior of the racing AI in multiple scenarios are compared. It could be found that the collision avoidance behavior under IORCA is more suitable for racing games, and is able to give players a better gaming experience.
A Significant Fallacy of Built-in Correlation Functions in Recommender Systems

Especially in shopping and entertainment applications, having personalized recommendation modules is essential for customer comfort. In the frameworks of applications, the user or item similarity analysis is expected to be performed throughout the algorithm, which directs the user to the right items, thereby making suitable suggestions. Developers tend to choose readily available functions by taking advantage of the development environment; however, in some cases, internal functions of the programming platforms may be missing the requirements of the applied scientific field. In this study, the fallacy originating from the built-in Pearson correlation function utilized in user-based collaborative filtering is analyzed. The way of processing the vectors related to some arguments in the formula, such as handling the user rating history, may differentiate from the expected processing. This study shows how much the utilization of the inline function alters the outcomes. With the aid of three different datasets and various performance metrics, a large amount of experimental monitoring underlines the importance of handcrafted correlation functions special to the applied field but not the built-in methods. The results confirm that this simple fallacy has considerable implications.
Improving Domain-Independent Heuristic State-Space Planning via plan cost predictions

Automated planning is a prominent Artificial Intelligence (AI) challenge that has been extensively studied for decades, which has led to the development of powerful domain-independent planning systems. The performance of domain-independent planning systems are strongly affected by the structure of the search space, that is dependent on the application domain and on its encoding.This paper proposes and investigates a novel way of combining machine learning and heuristic search to improve domain-independent planning. On the learning side, we use learning to predict the plan cost of a good solution for a given instance. On the planning side, we propose a bound-sensitive heuristic function that exploits such a prediction in a state-space planner. Our function combines the input prediction (derived inductively) with some pieces of information gathered during search (derived deductively). As the prediction can sometimes be grossly inaccurate, the function also provides means to recognise when the provided information is actually misguiding the search. Our experimental analysis demonstrates the usefulness of the proposed approach in a standard heuristic best-first search schema.
An Interpretable Regularization Method Based on Minimizing Mutual Information

Deep learning model is popular and widely used. Its generalization performance is superior to traditional machine learning methods. Traditional statistical learning theory cannot explain its success. Most of network architectures are designed artificially and empirically. There are few methods to design new structures and mathematical tools to evaluate feature representation capabilities of networks. To address this issue, based on information theory, we propose an interpretable regularization method named Minimize Mutual Information Method (MMIM), which reduce the generalization error by minimizing the mutual information between hidden layer neurons. We derive a tighter generalization error boundary and propose a two-step method to reduce the neural network generalization error boundary. The experimental results verify the effectiveness of our proposed MMIM on Fashion-MNIST dataset and CIFAR datasets. Inspired by ensemble learning, we discuss why our method is effective and suggest how to design new networks.