Reviewerly/exHarmony huggingface dataset:
DQN for Coordinating Multi-agent Cooking

Reinforcement learning (RL) is a very widely used field. The difference between RL and other branches of machine learning (such as supervised learning and unsupervised learning) is that RL centers on interactive learning. The RL model (also known as the agent) learns in interaction with the environment to maximize the reward function. In the paper "too many cooks," the authors developed a method called Bayesian Delegation to enable human-like coordination by inferring the sub-tasks of others quickly. However, limitations still exist in the partial order of sub-tasks. First, implementing the sub-task in terms of efficient actions or which agent(s) should work on it is not specified. Second, the sub-tasks may be finished in many different orders since the ordering of sub-tasks is partial. The project proposes solutions to these challenges using Deep Q-Learning (DQN) and Bayesian Inference. In the DQN experiment, value approximation performs well in the simple multi-agent environment.
A CNN-Based Facial Expression Recognition System

Facial expression recognition has long attracted researchers from a variety of domains, such as human-computer interaction, emotion analysis, intelligent medical care, and so on. Various human-designed features have been used in FER to extract image appearance features, but human-designed methods are difficult to be used to recognize new face images, which poses challenges for FER in uncontrolled environments. Recently, CNNs and FNNs have been used for facial expression recognition. The new CNN-based recognition approach yields excellent FER results. The numerous convolution and pooling layers that CNN possesses allow it to extract higher and multi-level features of the entire face or local regions. Additionally, CNN has excellent classification performance when it comes to the feature extraction of facial expression pictures. However, the mechanism through which FNN remains largely controversial. In this work, the experiments are carried out on the comparison of three facial expression recognition algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), and the experimental results show that, using CNN can get more stable recommendation results of better quality.
A Survey on Improved GAN based Image Inpainting for Different Aims

Computer Vision (CV) is an important field of Artificial Intelligence (AI) that focuses on processing and analyzing the visual data input like images or videos. And image inpainting technique is a task of reconstructing image with missing parts by using the information of the existing part, which becomes a crucial part in CV. It has various image processing applications such as digital cultural heritage preservation, old photograph restoration and object removal. The researchers found that using generative adversarial network can extract the feature from image without huge amount of computation which are two main challenges of image inpainting, therefore GAN-based inpainting method has recently been a hub for research. In this paper, we review most of the latest improved image inpainting methods and divide them into three categories according to their different aims: for semantic image inpainting, for generating higher diversity and for irregular or free-form image input. Firstly, we review the methods from structure and algorithm. Then we analyze the advantages and disadvantages of each method. Finally, we conclude the challenges or limitations raised from our findings and some potential development trend.
Gold and Bitcoin Price Prediction based on KNN, XGBoost and LightGBM Model

In the past few decades, there has been an increasing demand for assets trading with help of machine learning. Contemporarily, the cryptocurrency and gold market has become prosperous with extremely dramatical fluctuations. This paper aims to study the trading price laws based on machine learning scenarios of Bitcoin and Gold to predict the price of the two currencies. To be specific, this study gives an inside view of the application of a method combined three algorithms (i.e., KNN, XGBoost and LightGBM) to predict the future Gold and Bitcoin price browser based on past data from 2017 to 2022. According to the analysis, the study shows the difference of three models, the accuracy of the combined algorithms and proves the related metrics to predict the price of the Gold and Bitcoin. Overall, these results give a guideline for the investor to make sensible decisions about Bitcoin and Gold price and shed light on guiding further exploration of price forecasting in terms of machine learning approaches.
Study on the Technical Model Design of a Tourist Attraction from the Metaverse Perspective

With the development of digital technology and the epidemic's impact in recent years, the public's demand for "cloud tourism" using various virtual technologies has gradually increased. The concept of building a tourist attraction in a metaverse is studied based on the metaverse perspective and the current development of metaverse technology. The rationality and feasibility of building a metaverse tourist attraction are analyzed. The application of high technology in various fields in recent years in constructing metaverse tourism scenery is studied and analyzed. A feasible technical model of metaverse tourism scenery is proposed based on this research.
Machine Learning Algorithms for Speech Emotion Classification

Artificial intelligence products work by imitating the way humans think. ‘Communication’ is one of the most important links in the sustainable development of human society. Meanwhile, allowing artificial intelligence to communicate with humans is also one of the main aspects of scientific research. In recent years, the level of development of artificial intelligence has already made some smart products that can chat with humans when they are bored. It can be predicted that in the future, we can have robot pets to accompany us. In order to make companion-type products to be more similar to humans, it is necessary to implant algorithms that could recognize human’s expressions of emotion. this paper is introduced two methods that can help AI to recognize emotion in human speech. One is to recognize words that express emotion, and the other is to recognize the tone of speech to judge emotion. Finally, it discusses the feasibility of this algorithm in reality.
Bitcoin: A Blessing or a Curse

Bitcoin is considered the first and largest crypto currency with a blockchain system of recording transactions on thousands of machines at once enabling peer to peer transfer. There is no doubt that Bitcoin has received strong attention from the real world. Its innovative concept, reliable technology and advantages in application attract many people's attention. But Bitcoin has also brought many negative effects. To provide an organized response to the question around the potential benefits and harms of using Bitcoin, we designed an Impact Index comparing the impact from both sides of this topic. We found that the impact of the curses (negatives) is significantly bigger than the impact for benefits. However, more advanced technology, more regulated approach and changes in payment and financing methods may become the savior of Bitcoin.
The Advance of Digital Signature with Quantum Computing

A digital transaction's sender or receiver's identity is verified using digital signature technology, a type of encryption. It allows users to prove their identity and the integrity of their message without relying on a third-party verification. Digital signatures are becoming increasingly important in the blockchain technology world. They enable users to verify the authenticity of digital assets, such as cryptocurrencies, and ensure that transactions remain secure. They also enable users to securely store their digital assets and prevent the unauthorized transfer of funds. Smart contracts, which are self-executing agreements that take effect when specific criteria are satisfied, also employ digital signatures. In addition, digital signatures are used to create digital signatures for digital documents, such as contracts, which allows users to securely sign documents without the need for a third-party verifier. This article presents the three primary digital signature algorithms presently utilized in blockchain technology: RSA, DSA, and ECDSA. It also discusses how quantum computers, which are currently being researched by some of the world's leading research companies, will affect traditional digital signature technology and what plans companies and countries have for this technology.
Co-training by Experience Replay for Reinforcement Learning

In this paper, to improve the efficiency of the reinforcement learning model to explore the environment and get better results, a new method which involves the co-training process in reinforcement learning by sharing the experience pool of each agent in the training process has been developed. In this method, agents can gain a better understanding of the environment since agents use different policies to make action and explore the environment. At the same time, this paper designed an agent called Hard Memory Collector by modifying the value function and combining this agent and a normal agent for co-training. As an experimental result on the ViZDoom platform, the model achieved better results than the original Duel DQN network in terms of score, steps used per game and loss value.
Studies Advanced in Image Style Transfer based on Deep Learning

A popular project in computer vision in recent years is called "image transfer", which attempts to create new images containing features from abstract properties of the content and style of a given image. Convolutional neural networks have generated the basic components of image style transfer, significantly improving transfer accuracy and visualization effect, thanks to deep learning's quick progress. The purpose of this study is to highlight the most recent developments in the area of deep learning-based picture style transfer. We first provide a detailed introduction to the traditional style transfer algorithms from the frameworks of generative adversarial networks and convolutional neural networks., including the design ideas research issues, key steps, advantages and disadvantages. On a few application tasks, we qualitatively compare the visualization outcomes of various approaches, and we talk about the subject's current issues and potential future developments.
Making Decisions Like Human, the Application of Deep Learning Algorithms in Game Testing

Artificial intelligence (A.I.) is currently used widely in testing of video games. A.I. had surprisingly performance in board games such as chess and go. How does it perform in modern digital games? Currently, A.I. is at trials of checking the operation of the game and figured out bugs by simulations. However, the algorithms that were applied to the first generation of A.I cannot perform well in current digital games due to completely different constraints. Hence, Deep Learning algorithms are implemented on artificial intelligences or agents that are created for the digital game testing. Deep learning algorithms not only let A.I. can simulate how a human player is thinking when they are playing a digital game, but they also let A.I. can gain experience from billions of simulations based on the extreme speed of calculating. This study would give analysis on performance of A.I. or agents with deep learning algorithms, and show the limitations and barriers of automation of video game testing. Eventually, we explained why automated video game testing is still far away from us.
E-Commerce Recommendation Algorithm based on Graph Neural Network

While recommender system is becoming an increasingly essential component in e-commerce websites, although previous models, which directly calculate the similarity of user/item history record, has obtained evidence of effectiveness, recommendations based solely on users' current sequence of actions, when user identity and history preference are not present, has been a popular area due to the growing privacy concerns. This paper demonstrates a model using a graph neural network, which takes the user's sequence of purchasing events as input and constructs a graph derived from it, to make the prediction of the most likely subsequent product that the customer may purchase and make personalized recommendations by the combination of session preference and user’s current interest. Experiments on a real-world e-commerce purchasing event dataset and analysis are carried out to test the model’s performance, as well as how the length of sequence may affect the model preference. The result shows that the model performance has attained a local peak on the dataset used.
Image Denoising based on Deep Learning

Image denoising has always been one of the research hotspots in the field of image processing, which aims to remove the noise from the imaging device or external noise environment and other interfering factors in the image to restore the noisy image to the original clean and noise-free image. Mature algorithms and machine learning techniques have been developed previously for different application situations and specific computer vision works. Image denoising based on deep learning can adaptively learn image content and is suitable for image denoising tasks in high-noise environments. This paper builds a model based on the representative image denoising algorithm DnCNN, and discusses the performance difference with other denoising algorithms. All the results show the new methods are usually more efficient than traditional ones which can process pictures that are under more complex conditions.
Component‐based specification, design and verification of adaptive systems

Abstract Control systems are typically tightly embedded into their environment to enable adaptation to environmental effects. As the complexity of such adaptive systems is rapidly increasing, there is a strong need for coherent tool‐centric approaches to aid their systematic development. This paper proposes an end‐to‐end component‐based specification, design and verification approach for adaptive systems based on the integration of a high‐level scenario language (sequence chart variant) and an adaptation definition language (statechart extension) in the open source Gamma tool. The scenario language supports high‐level constructs for specifying contracts and the adaptation definition language supports the flexible activation and deactivation of static contracts and managed elements (state‐based components) based on internal changes (e.g., faults), environmental changes (e.g., varying context) or interactions. The approach supports linking managed elements to static contracts to formally verify their adherence to the specified behavior at design time using integrated model checkers. Implementation can be derived from the adaptation model automatically, which can be tested using automated test generation and verified at runtime by contract‐based monitors.
CANet: Context-aware Aggregation Network for Salient Object Detection of Surface Defects

Surface defect detection has become more and more important in the industrial manufacture and engineering application in recent years. However, due to the lack of overall perception and interaction among features layers, lots of computer vision-based detection methods cannot grab the complete details of defects when dealing with complex scenes, such as low contrast and irregular shape. Therefore, in this paper, we propose a Context-aware Aggregation Network (CANet) to accurately pop-out the defects, where we focus on the mining of context cues and the fusion of multiple context features. To be specific, embarking on the multi-level deep features extracted by encoder, we first deploy a sufficient exploration to dig the context information by deploying the weighted convolution pyramid (WCP) module, which extracts multi-scale context features, transfers the information flow between different resolution features, and fuses the features with same resolution. By this way, we can obtain the effective context pyramid features. Then, the decoder deploys the weighted context attention (WCA) module to filter the irrelevant information from context features and employs the cascaded fusion structure (CFS) to aggregate the multiple context cues in a hierarchical way. Following this way, the generated high-quality saliency maps can highlight the defects accurately and completely. Extensive experiments are performed on four public datasets, and the results firmly prove the effectiveness and superiority of the proposed CANet under different evaluation metrics.
Achieving descriptive accuracy in explanations via argumentation: The case of probabilistic classifiers

The pursuit of trust in and fairness of AI systems in order to enable human-centric goals has been gathering pace of late, often supported by the use of explanations for the outputs of these systems. Several properties of explanations have been highlighted as critical for achieving trustworthy and fair AI systems, but one that has thus far been overlooked is that of descriptive accuracy (DA), i.e., that the explanation contents are in correspondence with the internal working of the explained system. Indeed, the violation of this core property would lead to the paradoxical situation of systems producing explanations which are not suitably related to how the system actually works: clearly this may hinder user trust. Further, if explanations violate DA then they can be deceitful, resulting in an unfair behavior toward the users. Crucial as the DA property appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions of formalizing DA and of analyzing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialectical DA, using the family of probabilistic classifiers as the context for our analysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attribution methods from the literature, variants thereof and a novel form of explanation that we propose. We conduct experiments with a varied selection of concrete probabilistic classifiers and highlight the importance, with a user study, of our most demanding notion of dialectical DA, which our novel method satisfies by design and others may violate. We thus demonstrate how DA could be a critical component in achieving trustworthy and fair systems, in line with the principles of human-centric AI.
Educational Donors’ Expectations and Their Outcomes in the COVID-19 Era: The Moderating Role of Motivation during Sequential Evaluation Phases

Donors’ prior expectations of reinforcing satisfaction and favorable attitudes are predicted using Oliver’s satisfaction cycle model. This study aims to investigate how prior expectations drive sustainable donation behavior by demonstrating the moderating role of educational donors’ motivation to predict further participation behavior. Using three time-lag intervals, our findings show that the relationship between prior expectations (T1) and attitudes toward educational donation (T3) is positive on a longitudinal basis. While this relationship is negatively moderated by the role of donor motivation, the relationship between the expectation of satisfaction and attitudes toward educational donations is not significantly moderated by motivation. In particular, favorable attitudes toward educational donation increase rapidly when prior expectations are high, and motivation is low. This study contributes to the literature by providing evidence for the theoretical mechanisms of the satisfaction cycle model and practical insights for managers during educational donations events.
Modeling needs user modeling

Modeling has actively tried to take the human out of the loop, originally for objectivity and recently also for automation. We argue that an unnecessary side effect has been that modeling workflows and machine learning pipelines have become restricted to only well-specified problems. Putting the humans back into the models would enable modeling a broader set of problems, through iterative modeling processes in which AI can offer collaborative assistance. However, this requires advances in how we scope our modeling problems, and in the user models. In this perspective article, we characterize the required user models and the challenges ahead for realizing this vision, which would enable new interactive modeling workflows, and human-centric or human-compatible machine learning pipelines.
Effect of Audiovisual Media on the Level of Knowledge of Boarding Students of the West Sumatra Student Coaching and Sports Training Center about the Occurrence of Avulsion

Abstract: Dental trauma inter alia tooth avulsion is often found among athletes especially of contact sports. This study aimed to evaluate the effect of audiovisual media on the level of knowledge of boarding students of the West Sumatra Student Coaching and Sports Training Center about the occurrence of avulsion. This was a pre-experiment quantitative study with one group pretest-posttest design. The population were all athletes of the Center for Coaching and Sports Training of Students West Sumatera in soccer, athletics, sepak takraw, gymnastics, archery, bicycle, judo, karate, wrestling, boxing, taekwondo and pencak silat. There were 116 respondents obtained by using purposive sampling techniques. We used questionnaires as the research instrument. Data were analyzed with the Wilcoxon test. The results showed that the average knowledge before the the audio-visual media intervention was 10.39 and after the intervention was 12.43. The non-parametric Wilcoxon test showed a significancy of 0.000 (&lt;0.05) which meant that there was an effect of audio-visual media intervention on the knowledge level of respondents. In conclusion, there is a significant effect of the audio-visual media intervention on the level of knowledge of the students of the West Sumatra Coaching and Sports Training Center about the occurrence of avulsion. Keywords: knowledge about dental trauma; tooth avulsion; audiovisual media; students of Coaching and Sports Training Center
Revealing concealed spontaneous facial micro-expression: Are we a step closer to unveil real-life behavioral expressions?

Facial micro-expression (ME) expression analysis has aroused interdisciplinary attention from the realms of computer vision and psychology due to its advantages of divulging nonverbal emotion via subtle involuntary facial muscle changes. However, developing and commercializing ME recognition relevant products has been hindered in current progress due to the scarcity of databases depicting real-world conditions. This paper aims to give a strong impetus to facilitating the advancement of ME recognition systems, particularly in in-the-wild applications. Succinctly, an efficient recognition system is introduced herein, which incorporates the core processes like 3D facial reconstruction, apex spotting, and emotion recognition tasks. Concretely, all faces are first rendered into 3D point cloud form for the brevity of face alignment along the video. Then, the optical flow-guided components are employed as the primary features to represent the motion details. Subsequently, a 3-Stream channel based on 3-Dimensional faces Network (3T3D-Net) with skip connection via multiplication is tailored to cope with the small-size input image. As a result, pleasing recognition performance is yielded via the suite of novel techniques devised, by producing a UAR of 69.44% and UF1 of 70.71%, when evaluated on the in-the-wild ME dataset, viz, MEVIEW. In addition, extensive studies and analyses are presented to verify the contribution of each component of the proposed pipeline. It is envisioned that the remarkable result attained by casting an invigorating perspective will shed light and pave a new prospect for analysis in ME applications.