Reviewerly/exHarmony huggingface dataset:
Sim–Real Mapping of an Image-Based Robot Arm Controller Using Deep Reinforcement Learning

Models trained with Deep Reinforcement learning (DRL) have been deployed in various areas of robotics with varying degree of success. To overcome the limitations of data gathering in the real world, DRL training utilizes simulated environments and transfers the learned policy to real-world scenarios, i.e., sim–real transfer. Simulators fail to accurately capture the entire dynamics of the real world, so simulation-trained policies often fail when applied to reality, termed a reality gap (RG). In this paper, we propose a search (mapping) algorithm that takes in real-world observation (images) and maps them to the policy-equivalent images in the simulated environment using a convolution neural network (CNN) model. The two-step training process, DRL policy and a mapping model, overcomes the RG problem with simulated data only. We evaluated the proposed system with a gripping task of a custom-made robot arm in the real world and compared the performance against a conventional DRL sim–real transfer system. The conventional system achieved a 15–57% success rate in gripping operation depending on the position of the target object while the mapping-based sim–real system achieved 100%. The experimental results demonstrated that the proposed DRL with mapping method appropriately corresponded the real world to the simulated environment, confirming that the scheme can achieve high sim–real generalization at significantly low training costs.
The Influence of Seasonal Cloud Cover, Ambient Temperature and Seasonal Variations in Daylight Hours on the Optimal PV Panel Tilt Angle in the United States

A variety of variables influence the optimal tilt angle of a PV panel, including the characteristics of the panel, the local seasonal weather variations, the number of daylight hours the panel is exposed to and the ambient temperature of the surroundings. In this study, the optimal PV tilt angle and maximum energy output of PV arrays was calculated for every county in the United States and compared against the practice of setting the PV tilt angle to be equivalent to the latitude angle of the PV geographic location. A PVWatts API, implemented through Python, was used in conjunction with the SciPy optimization package to find the optimal tilt angle for each county using a direct line search algorithm. Most counties (95.8%) showed a difference between the location latitude and the optimal tilt of more than one degree. Many counties showed a deviation of 2–6° lower than the location latitude. The variation of daylight hours had the largest influence on tilt angle and seasonal cloud cover and ambient temperature had varying levels of influence. Generally, winter cloud cover decreased the optimal tilt angle whereas high summer temperatures increased the tilt angle.
Towards Predicting Architectural Design Patterns: A Machine Learning Approach

Software architecture plays an important role in software development, especially in software quality and maintenance. Understanding the impact of certain architectural patterns on software quality and verification of software requirements has become increasingly difficult with the increasing complexity of codebases in recent years. Researchers over the years have proposed automated approaches based on machine learning. However, there is a lack of benchmark datasets and more accurate machine learning (ML) approaches. This paper presents an ML-based approach for software architecture detection, namely, MVP (Model–View–Presenter) and MVVM (Model–View–ViewModel). Firstly, we present a labeled dataset that consists of 5973 data points retrieved from GitHub. Nine ML methods are applied for detection of software architecture from source code metrics. Using precision, recall, accuracy, and F1 score, the outstanding ML model performance is 83%, 83%, 83%, and 83%, respectively. The ML model’s performance is validated using k-fold validation (k = 5). Our approach outperforms when compared with the state-of-the-art.
Mode I interlaminar fracture behavior of none-felt needled composites

None-felt needling technology is a novel 3D preform preparation technology, which improved the mechanical properties of needled preforms effectively. However, the mode I interlaminar fracture behavior of none-felt needled composite has not yet been clarified. In this paper, none-felt needled fabrics and their composites were prepared, the structure of the fabrics was characterized, and mode I interlaminar fracture performance of the composites were carried out through experiments and simulation method. It was found that none-felt needled composites showed effect on improving mode I interlaminar fracture property compared with the traditional needled composites, the maximum failure load and interlaminar fracture toughness (GIC) increased by up to 107% and 129% respectively. The double cantilever beam (DCB) experiment revealed that the failure mode of the none-felt needled composites were manifested as cracking of the matrix and brittle fracture of the needled fiber bundles. More content and thicker needled fiber bundles of none-felt needled fabric are important factors to improve the mode I interlaminar performance. Moreover, based on the failure mechanism and the structural features, mode I simulation model of none-felt needled composites was developed. The load–displacement curves obtained from the simulations were well closed to the experiment, and the error range was between 2.0% and 7.5%. None-felt needled composite structure with high interlaminar properties is hopeful to be applied in composite thin-walled components such as radomes and cabin sections of high-speed aircraft.
Nexus between Governance and Economic Growth: Learning from Saudi Arabia

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Instrumenting a virtual reality headset for at-home gamer experience monitoring and behavioural assessment

Measuring a gamer’s behaviour and perceived gaming experience in real-time can be crucial not only to assess game usability, but to also adjust the game play and content in real-time to maximize the experience per user. For this purpose, affective and physiological monitoring tools (e.g., wearables) have been used to monitor human influential factors (HIFs) related to quality of experience (QoE). Representative factors may include the gamer’s level of engagement, stress, as well as sense of presence and immersion, to name a few. However, one of the major challenges the community faces today is being able to accurately transfer the results obtained in controlled laboratory settings to uncontrolled everyday settings, such as the gamer’s home. In this paper, we describe an instrumented virtual reality (VR) headset, which directly embeds a number of dry ExG sensors (electroencephalography, EEG; electrocardiography, ECG; and electrooculography, EOG) to allow for gamer behaviour assessment in real-time. A protocol was developed to deliver kits (including the instrumented headset and controllers, laptop with the VR game Half-life Alyx, and a second laptop for data acquisition) to participants’ homes during the COVID-19 lockdown. A brief videoconference session was made to provide the participants with instructions, but otherwise the experiment proceeded with minimal experimenter intervention. Eight participants consented to participate and each played the game for roughly 1.5 h. After each gaming session, participants reported their overall experience with an online questionnaire covering aspects of emotions, engagement, immersion, sense of presence, motion sickness, flow, skill, technology adoption, judgement and usability. Here, we describe our obtained findings, as well as report correlations between the subjective ratings and several QoE-related HIFs measured directly from the instrumented headset. Promising results are reported.
Value function factorization with dynamic weighting for deep multi-agent reinforcement learning

In many real-world scenarios, multiple agents necessitate coordination with each other because of their limited observation and communication capability. Deep multi-agent reinforcement learning has demonstrated significant success in such challenging settings making use of value decomposition. One of the representative methods is QMIX, which factorizes the multi-agent global Q-value into individual Q-values and limits the joint action Q-value to a monotonic assumption leveraging an implicit mixing method. However, this assumption restricts it to representing certain value functions in which the ordering of an agent’s actions is based on the actions of others. WQMIX presents two weighting schemes to tackle this restriction but the weighting function is simple that limits the performance of methods, more appropriate weighting scheme is required to be considered. To tackle this issue, we present a more complex and accurate weighting scheme, which we call Dynamic Weighting (DW), as opposed to the fixed weighting in WQMIX. Our proposed method DW-QMIX guarantees a more general decomposition than QMIX or WQMIX and places accurate importance on the better joint actions thus leading to obtaining the optimal policy. Extensive experiments on the simulation environments and real-life systems demonstrate that our proposed method outperforms the existing multi-agent reinforcement learning methods.
Large-factor Micro-CT super-resolution of bone microstructure

Background: Bone microstructure is important for evaluating bone strength and requires the support of high-resolution (HR) imaging equipment. Computed tomography (CT) is widely used for medical imaging, but the spatial resolution is not sufficient for bone microstructure. Micro-CT scan data is the gold standard for human bone microstructure or animal experiment. However, Micro-CT has more ionizing radiation and longer scanning time while providing high-quality imaging. It makes sense to reconstruct HR images with less radiation. Image super-resolution (SR) is adapted to the above-mentioned research. The specific objective of this study is to reconstruct HR images of bone microstructure based on low-resolution (LR) images under large-factor condition. Methods: We propose a generative adversarial network (GAN) based on Res2Net and residual channel attention network which is named R2-RCANGAN. We use real high-resolution and low-resolution training data to make the model learn the image corruption of Micro-CT, and we train six super-resolution models such as super-resolution convolutional neural network to evaluate our method performance. Results: In terms of peak signal-to-noise ratio (PSNR), our proposed generator network R2-RCAN sets a new state of the art. Such PSNR-oriented methods have high reconstruction accuracy, but the perceptual index to evaluate perceptual quality is very poor. Thus, we combine the generator network R2-RCAN with the U-Net discriminator and loss function with adjusted weights, and the proposed R2-RCANGAN shows the pleasing results in reconstruction accuracy and perceptual quality as compared to the other methods. Conclusion: The proposed R2-RCANGAN is the first to apply large-factor SR to improve Micro-CT images of bone microstructure. The next steps of the study are to investigate the role of SR in image enhancement during fracture rehabilitation period, which would be of great value in reducing ionizing radiation and promoting recovery.
Evaluation of Multimedia Physical Education Teaching Quality considering Data Analysis Model

The 21st century is in the era of information explosion, the level of information technology is also increasing day by day, and the fields of its application are also wider and wider. As a new classroom teaching method that relies on information technology, multimedia teaching has become an important knowledge transfer method in colleges and universities. As a practical major, physical education (PE) often uses multimedia teaching in teaching. However, in many schools, the advantages of multimedia PE have not been fully exerted, which also makes the quality of multimedia PE not high. For the purpose of providing solutions to the problems existing in the current multimedia PE teaching, based on the characteristics and requirements of multimedia PE classroom teaching, this paper selected teachers and students from a college of PE to conduct interviews and questionnaires. Through the theoretical analysis of quality evaluation and the introduction of research methods, the evaluation indicators and weights of multimedia physical education teaching quality were determined, and the quality evaluation model of multimedia PE teaching in this university was established, and the practical application analysis was carried out. After determining the quality evaluation indicators and weights of multimedia PE, the actual application analysis included the evaluation of teachers with different titles and genders, as well as the evaluation of teachers by students of different grades. According to the assessment results, students’ evaluation of teaching quality was not obvious in terms of teachers’ gender, but there were differences in professional titles. Among them, associate professors had the highest evaluation score of 82.14, and students of different grades had different evaluations of teachers. The empirical analysis proves that the evaluation index and data analysis model of this paper have research significance.
Improving Streaming Video with Deep Learning-Based Network Throughput Prediction

Video streaming represents a significant part of Internet traffic. During the playback, a video player monitors network throughput and dynamically selects the best video quality in given network conditions. Therefore, the video quality depends heavily on the player’s estimation of network throughput, which is challenging in the volatile environment of mobile networks. In this work, we improved the throughput estimation using prediction produced by LSTM artificial neural networks (ANNs). Hence, we acquired data traces from 4G and 5G mobile networks and supplied them to two deep LSTM ANNs, obtaining a throughput prediction for the next four seconds. Our analysis showed that the ANNs achieved better prediction accuracy compared to a naive predictor based on a moving average. Next, we replaced the video player’s default throughput estimation based on the naive predictor with the LSTM output. The experiment revealed that the traffic prediction improved video quality between 5% and 25% compared to the default estimation.
Are Illicit Drugs a Driving Force for Cryptomarket Leadership?

Cryptomarkets, i.e., illicit online marketplaces, have gained considerable attention from the media, law enforcement agencies, and researchers. An increasing number of studies have revealed various aspects of these cryptomarkets; however, whether drugs play a major role for competing cryptomarkets to be the market leader, has not been addressed. Weekly sales and the number of listings for the major products on three leading cryptomarkets (Silk Road 2, Agora, and Evolution) were examined using Granger causality tests and interrupted time series analysis. Not only drugs trading on cryptomarkets played a pivotal role in the growth of each cryptomarket, but also a higher increase in drug supply than in competing marketplaces is crucial to become market leaders. The relative supply of drugs plays a larger role when leading marketplaces disappear. Law enforcement agencies should focus on monitoring marketplaces with a larger increase in drug supplies than on competing marketplaces.
Imbalance example-dependent cost classification: A Bayesian based method

Example-dependent cost classification is a special case of pattern classification where the costs are specific for each individual pattern. Most of the practical applications related to this kind of classification problem exhibit class imbalance in the available data, thus including an additional difficulty to the classification task. This problem has high practical importance because it appears intrinsically in relevant application fields, such as Finance or Health. We propose to use a 2-step Bayesian methodology to solve this problem because its formulation allows the inclusion of the individual example costs in the classification and takes into account the class probabilities. In particular, the main contribution is to apply principled rebalancing classification algorithms in the first step: We propose 3 Neural Network based learning machines, WR-MLP, WSR-MLPE and WSR-DNN, to provide the estimates of the required conditional probabilities for the Bayesian test. Unlike some similar approaches in the literature that use heuristic methods in the first step, which in most cases require calibration mechanisms to compensate for the estimation biases, the consistency of the proposed estimates is theoretically supported, thus providing a clear potential advantage. Experiments with seven real-world datasets show that the proposed methods are competitive against eleven state-of-the-art benchmarks, and provide an advantage in the less favourable situations: cases with a strong imbalance and highly nonlinear classification borders.
A Survey of Reinforcement Learning Toolkits for Gaming: Applications, Challenges and Trends

The gaming industry has become one of the most exciting and creative industries. The annual revenue has crossed $200 billion in recent years and has created a lot of jobs globally. Many games are using Artificial Intelligence (AI) and techniques like Machine Learning (ML), Reinforcement Learning (RL) gained popularity among researchers and game development community to enable smart games involving AI-based agents at a faster rate. Although, many toolkits are available for use, a framework to evaluate, compare and advise on these toolkits is still missing. In this paper, we present a comprehensive overview of ML/RL toolkits for games with an emphasis on their applications, challenges, and trends. We propose a qualitative evaluation methodology, discuss the obtained analysis results, and conclude with future work and perspectives.
Modern Information Technologies for Teaching and Control of Knowledge Acquisition of Older Preschoolers and Primary School Students

The information technologies of forming a successful personality of older preschoolers and primary school students have been investigated in the article. The praxeological direction of modern information education has been considered: the formation of media literacy to achieve success in life. A new program product-a praxeological tale - has been proposed as a means of modern education technologies. A praxeological tale - a fairy tale, the plot of which embodies the main factors, rules and ways to achieve success. It has been proved that praxeological tales as a product are insufficiently used to form the success of a preschool and primary school child in general and knowledge about achieving success in particular. The model of formation of knowledge about achieving success by means of information technologies has been developed. The results of the confirmatory and forming experiment have been given.
Software Architecture for Mathematical Modelling Based on Interval and Ontology Approach

The paper substantiates the use of the ontological approach as an effective tool for managing the processes of building mathematical models based on interval data and using these models for applied problems. The paper also presents the features of building a software architecture for mathematical modeling based on interval analysis and an ontological approach. The technology for creating software based on the developed ontological add-on for mathematical modeling using interval data for various objects, as well as various forms of user interface implementation, is described.
Towards Data Normalization Task for the Efficient Mining of Medical Data

The paper investigates the problem of data normalization in solving medical diagnostics tasks by machine learning algorithms. The authors describe five different data normalization methods’ operations, advantages, and disadvantages. The effectiveness of their work was evaluated using two data sets with different Imbalanced Ratio, which is typical for medical tasks. The modeling was performed by solving a binary classification task using three different machine learning methods based on decision trees. It is experimentally established that the method of normalization ScalerOnCircle, unlike others, increases the efficiency of analyzing medical data based on researched machine learning methods. There was a significant increase in the F1-score value when using this normalization method. It is because ScalerOnCircle, in addition to normalization by columns, provides the possibility of considering relationships between the attributes of each vector of a given dataset. This problem is very acute in the medical field, where data sets designed for intellectual analysis are characterized by many attributes and complex nonlinear relationships between them. This fact must be taken into account when mining such datasets. ScalerOnCircle opens up several benefits for the efficient mining of medical data.
A Secure and Efficient Data Deduplication Scheme with Dynamic Ownership Management in Cloud Computing

Encrypted data deduplication is an important technique for saving storage space and network bandwidth, which has been widely used in cloud storage. Recently, a number of schemes that solve the problem of data deduplication with dynamic ownership management have been proposed. However, these schemes suffer from low efficiency when the dynamic ownership changes a lot. To this end, in this paper, we propose a novel server-side deduplication scheme for encrypted data in a hybrid cloud architecture, where a public cloud (Pub-CSP) manages the storage and a private cloud (Pri-CSP) plays a role as the data owner to perform deduplication and dynamic ownership management. Further, to reduce the communication overhead we use an initial uploader check mechanism to ensure only the first uploader needs to perform encryption, and adopt an access control technique that verifies the validity of the data users before they download data. Our security analysis and performance evaluation demonstrate that our proposed server-side deduplication scheme has better performance in terms of security, effectiveness, and practicability compared with previous schemes. Meanwhile, our method can efficiently resist collusion attacks and duplicate faking attacks.
Phishing Detection Based on Multi-Feature Neural Network

Phishing detection methods are used to protect Internet users from leaking private information to phishing websites. However, the passive phishing detection method, which is widely used and based on blacklists, has limitations on timeliness and defense against zero-day phishing attacks and active phishing detection models with single-feature can be easily targeted by attackers. It is necessary to design and establish an active phishing detection model with high timeliness and strong adaptability. We propose a phishing detection method based on multi-feature extraction and deep learning technology. The model is constructed of a multilayer perceptron (MLP) for self-defined feature, a convolutional neural network (CNN) for image feature, a recurrent neural network (RNN) for text feature to extract feature vectors, and a classification network to fuse features and make the judgement. Our model’s accuracy achieves 0.9775 and recall reaches up to 0.9901. Experiment results of our model prove superior performance to those of other classification algorithms, demonstrating our model’s ability to deal with complex and changeable phishing detection tasks at this stage.