Reviewerly/exHarmony huggingface dataset:
The Sustainable Development of Psychological Education in Students’ Learning Concept in Physical Education Based on Machine Learning and the Internet of Things

Aim: This paper aims to enhance the emphasis of college physical education (P.E.) in the psychological education of P.E. students and provide a reference for the innovation of P.E. teaching methods. Methodology and procedures: On the basis of the Internet of Things (IoT) and a deep-learning algorithm, combined with psychological education, the teaching effect and the influence on learning philosophy are comprehensively evaluated through the construction of teaching evaluation index system for college P.E. students. Results: The theoretical courses of P.E. students in colleges and universities lack the integration of psychological-education concepts. It is found that the new teaching mode not only has a significant effect on improvement of training courses, but also promotes learning enthusiasm and theoretical courses. In the aspect of psychological quality evaluation, emotional-control ability significantly improved, the average score increased from below 60 to above 79, and self-challenge ability and adaptability to adversity also effectively improved. In the evaluation of deep-learning ability, students’ critical thinking ability improved most obviously, and their complex problem-solving ability also improved to some extent. Conclusions: Based on the IoT and machine learning, college P.E. teaching mode can effectively improve students’ psychological quality and ability, effectively improve students’ training and theoretical achievements, and significantly improve their academic achievements. It can also improve students’ self-learning ability. Practical applications: This paper reforms the traditional P.E. teaching mode, effectively demonstrates the hypothesis through practical teaching, designs the teaching evaluation index system of college P.E. students, and improves their learning ability and comprehensive achievements.
Crowdsensing Intelligence by Decentralized Autonomous Vehicles Organizations and Operations

With the rapid growth of connected and autonomous vehicles (CAVs), vehicular crowdsensing (VCS) has emerged as an effective way in a wide range of applications, especially in intelligent transportation systems. However, centralized VCS frameworks have confronted many problems, such as privacy, security, utility, and dependability. To remedy these challenges, blockchain technology can be applied in VCS systems for effectively forming decentralized autonomous vehicles organizations and operations. This article briefly introduces blockchain-based VCS solutions addressing the current problems and presents potential directions for future research.
Bat algorithm based control to decrease the control energy consumption and modified bat algorithm based control to increase the trajectory tracking accuracy in robots

From the control theory, the best control gain produces a balance between the trajectory tracking accuracy and control energy consumption. The random search of the bat algorithm is one alternative to find the best control gain. In this paper, (1) a bat algorithm based control is proposed to decrease the control energy consumption in robots, where a bat algorithm is used to find the best control gain; and (2) a modified bat algorithm based control is proposed to increase the trajectory tracking accuracy in robots, where a modified bat algorithm is used to find the best control gain. The comparison between the two proposed controls and the simplex based control is illustrated for the trajectory tracking accuracy and control energy consumption in two robots.
Human Action Recognition: A Taxonomy-Based Survey, Updates, and Opportunities

Human action recognition systems use data collected from a wide range of sensors to accurately identify and interpret human actions. One of the most challenging issues for computer vision is the automatic and precise identification of human activities. A significant increase in feature learning-based representations for action recognition has emerged in recent years, due to the widespread use of deep learning-based features. This study presents an in-depth analysis of human activity recognition that investigates recent developments in computer vision. Augmented reality, human–computer interaction, cybersecurity, home monitoring, and surveillance cameras are all examples of computer vision applications that often go in conjunction with human action detection. We give a taxonomy-based, rigorous study of human activity recognition techniques, discussing the best ways to acquire human action features, derived using RGB and depth data, as well as the latest research on deep learning and hand-crafted techniques. We also explain a generic architecture to recognize human actions in the real world and its current prominent research topic. At long last, we are able to offer some study analysis concepts and proposals for academics. In-depth researchers of human action recognition will find this review an effective tool.
Noisy intermediate-scale quantum computers

Quantum computers have made extraordinary progress over the past decade, and significant milestones have been achieved along the path of pursuing universal fault-tolerant quantum computers. Quantum advantage, the tipping point heralding the quantum era, has been accomplished along with several waves of breakthroughs. Quantum hardware has become more integrated and architectural compared to its toddler days. The controlling precision of various physical systems is pushed beyond the fault-tolerant threshold. Meanwhile, quantum computation research has established a new norm by embracing industrialization and commercialization. The joint power of governments, private investors, and tech companies has significantly shaped a new vibrant environment that accelerates the development of this field, now at the beginning of the noisy intermediate-scale quantum era. Here, we first discuss the progress achieved in the field of quantum computation by reviewing the most important algorithms and advances in the most promising technical routes, and then summarizing the next-stage challenges. Furthermore, we illustrate our confidence that solid foundations have been built for the fault-tolerant quantum computer and our optimism that the emergence of quantum killer applications essential for human society shall happen in the future.
Heterogeneous cognitive learning particle swarm optimization for large-scale optimization problems

Large-scale optimization problems (LSOPs) become increasingly ubiquitous but complicated in real-world scenarios. Confronted with such sophisticated optimization problems, most existing optimizers dramatically lose their effectiveness. To tackle this type of problems effectively, we propose a heterogeneous cognitive learning particle swarm optimizer (HCLPSO). Unlike most existing particle swarm optimizers (PSOs), HCLPSO partitions particles in the current swarm into two categories, namely superior particles (SP) and inferior particles (IP), based on their fitness, and then treats the two categories of particles differently. For inferior particles, this paper devises a random elite cognitive learning (RECL) strategy to update each one with a random superior particle chosen from SP. For superior particles, this paper designs a stochastic dominant cognitive learning (SDCL) strategy to evolve each one by randomly selecting one guiding exemplar from SP and then updating it only when the selected exemplar is better. With the collaboration between these two learning mechanisms, HCLPSO expectedly evolves particles effectively to explore the search space and exploit the found optimal zones appropriately to find optimal solutions to LSOPs. Furthermore, to help HCLPSO traverse the vast search space with promising compromise between intensification and diversification, this paper devises a dynamic swarm partition scheme to dynamically separate particles into the two categories. With this dynamic strategy, HCLPSO gradually switches from exploring the search space to exploiting the found optimal zones intensively. Experiments are executed on the publicly acknowledged CEC2010 and CEC2013 LSOP benchmark suites to compare HCLPSO with several state-of-the-art approaches. Experimental results reveal that HCLPSO is effective to tackle LSOPs, and attains considerably competitive or even far better optimization performance than the compared state-of-the-art large-scale methods. Furthermore, the effectiveness of each component in HCLPSO and the good scalability of HCLPSO are also experimentally verified.
UsCoTc: Improved Collaborative Filtering (CFL) recommendation methodology using user confidence, time context with impact factors for performance enhancement

In today’s society, time is considered more valuable than money, and researchers often have limited time to find relevant papers for their research. Identifying and accessing essential information can be a challenge in this situation. To address this, the personalized suggestion system has been developed, which uses a user’s behavior data to suggest relevant items. The collaborative filtering strategy has been used to provide a user with the top research articles based on their queries and similarities with other users’ questions, thus saving time by avoiding time-consuming searches. However, when rating data is abundant but sparse, the usual method of determining user similarity is relatively straightforward. Furthermore, it fails to account for changes in users’ interests over time resulting in poor performance. This research proposes a new similarity measure approach that takes both user confidence and time context into account to increase user similarity computation. The experimental results show that the proposed technique works well with sparse data, and improves accuracy by 16.2% compared to existing models, especially during prediction. Furthermore, it enhances the quality of recommendations.
YOLOv5s-CA: A Modified YOLOv5s Network with Coordinate Attention for Underwater Target Detection

Underwater target detection techniques have been extensively applied to underwater vehicles for marine surveillance, aquaculture, and rescue applications. However, due to complex underwater environments and insufficient training samples, the existing underwater target recognition algorithm accuracy is still unsatisfactory. A long-term effort is essential to improving underwater target detection accuracy. To achieve this goal, in this work, we propose a modified YOLOv5s network, called YOLOv5s-CA network, by embedding a Coordinate Attention (CA) module and a Squeeze-and-Excitation (SE) module, aiming to concentrate more computing power on the target to improve detection accuracy. Based on the existing YOLOv5s network, the number of bottlenecks in the first C3 module was increased from one to three to improve the performance of shallow feature extraction. The CA module was embedded into the C3 modules to improve the attention power focused on the target. The SE layer was added to the output of the C3 modules to strengthen model attention. Experiments on the data of the 2019 China Underwater Robot Competition were conducted, and the results demonstrate that the mean Average Precision (mAP) of the modified YOLOv5s network was increased by 2.4%.
VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography

Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.
Linear Discriminant Analysis

Linear discriminant analysis (LDA) is widely studied in statistics, machine learning, and pattern recognition, which can be considered as a generalization of Fisher’s linear discriminant (FLD). LDA is designed to find an optimal transformation to extract discriminant features that characterize two or more classes of objects. In this chapter, we give a brief introduction to FLD and mainly focus on LDA. Besides the derivation of FLD, we also discuss the relationship between FLD and logistic regression and the relationship between FLD and linear regression. Then we introduce the Fisher discriminant criterion and use this criterion to obtain the transformation matrix of LDA. We also derive the Foley-Sammon optimal discriminant vectors, which can be obtained by maximizing the Fisher discriminant criterion with the orthogonal constraints of the discriminant vectors. Finally, through experiments, we show how LDA can be used in face recognition.
Quality and Readability of English-Language Internet Information for Voice Disorders

<h2>Summary</h2><h3>Purpose</h3> The purpose of this study is to evaluate the readability and quality of English-language Internet information related to vocal hygiene, vocal health, and prevention of voice disorders. This study extends recent work because it evaluates readability, content quality, and website origin across broader search criteria than previous studies evaluating online voice material. <h3>Method</h3> Eighty-five websites were aggregated using five different country-specific search engines. Websites were then analyzed using quality and readability assessments. The entire web page was evaluated; however, no information or links beyond the first page was reviewed. Statistical calculations were employed to examine website ratings, differences between website origin and quality and readability scores, and correlations between readability instruments. <h3>Result</h3> Websites exhibited acceptable quality as measured by the DISCERN. However, only one website obtained the Health On the Net certification. Significant differences in quality were found among website origin, with government websites receiving higher quality ratings. Approximate educational levels required to comprehend information on the websites ranged from 8 to 9 years of education. Significant differences were found between website origin and readability measures with higher levels of education required to understand information on websites of nonprofit organizations. <h3>Conclusion</h3> Current vocal hygiene, vocal health, and prevention of voice disorders websites were found to exhibit acceptable levels of quality and readability. However, highly rated Internet information related to voice care should be made more accessible to voice clients through Health On the Net certification.
Interest-based recommendations for business intelligence users

It is quite common these days for experts, casual analysts, executives and data enthusiasts, to analyze large datasets through user-friendly interfaces on top of Business Intelligence (BI) systems. However, current BI systems do not adequately detect and characterize user interests, which may lead to tedious and unproductive interactions. In this paper, we propose a collaborative recommender system for BI interactions, specifically designed to take advantage of identified user interests. Such user interests are discovered by characterizing the intent of the interaction with the BI system. Building on user modeling for proactive search systems, we identify a set of features for an adequate description of intents, and a similarity measure for grouping intents into coherent clusters. On top of these automatically identified interests, we build a collaborative recommender system based on a Markov model that represents the probability for a user to switch from one interest to another. We validate our approach experimentally with an in-depth user study, where we analyze traces of BI navigation. Our results are two-fold. First, we show that our similarity measure outperforms a state-of-the-art query similarity measure and yields a very good precision with respect to expressed user interests. Second, we compare our recommender system to two state-of-the-art systems to demonstrate the benefit of relying on user interests.
Linear Kalman Filtering Algorithm With Noisy Control Input Variable

This brief focuses on the development of a linear Kalman filtering algorithm when the control input variable is corrupted by noises. The noisy input is considered in the derivation process of the Kalman filter, and an extra term is included in the covariance matrix of the one step error. A bias estimation is naturally generated by the input noise. To reduce the bias, a new cost function of the state estimation error with a regularization term is proposed to obtain the Kalman gain matrix. Simulation results in the context of discrete time state estimation demonstrate that the proposed algorithm can achieve excellent estimation performance in terms of the steady-state misalignment under noisy input environments.
Tuning self-adaptation in cyber-physical systems through architectural homeostasis

Self-adaptive software-intensive cyber-physical systems (sasiCPS) encounter a high level of run-time uncertainty. State-of-the-art architecture-based self-adaptation approaches assume designing against a fixed set of situations that warrant self-adaptation. As a result, failures may appear when sasiCPS operate in environment conditions they are not specifically designed for. In response, we propose to increase the homeostasis of sasiCPS, i.e., the capacity to maintain an operational state despite run-time uncertainty, by introducing run-time changes to the architecture-based self-adaptation strategies according to environment stimuli. In addition to articulating the main idea of architectural homeostasis, we introduce four mechanisms that reify the idea: (i) collaborative sensing, (ii) faulty component isolation from adaptation, (iii) enhancing mode switching, and (iv) adjusting guards in mode switching. Moreover, our experimental evaluation of the four mechanisms in two different case studies confirms that allowing a complex system to change its self-adaptation strategies helps the system recover from run-time errors and abnormalities and keep it in an operational state.
A Deep Reinforcement Learning Based Multi-Step Coarse to Fine Question Answering (MSCQA) System

In this paper, we present a multi-step coarse to fine question answering (MSCQA) system which can efficiently processes documents with different lengths by choosing appropriate actions. The system is designed using an actor-critic based deep reinforcement learning model to achieve multistep question answering. Compared to previous QA models targeting on datasets mainly containing either short or long documents, our multi-step coarse to fine model takes the merits from multiple system modules, which can handle both short and long documents. The system hence obtains a much better accuracy and faster trainings speed compared to the current state-of-the-art models. We test our model on four QA datasets, WIKEREADING, WIKIREADING LONG, CNN and SQuAD, and demonstrate 1.3%-1.7% accuracy improvements with 1.5x-3.4x training speed-ups in comparison to the baselines using state-of-the-art models.
Control of a 9-DoF Wheelchair-Mounted Robotic Arm System

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type.The control of this 9-DoF system expands on the conventional control methods and combines the 7-DoF robotic arm control with the 2-DoF power wheelchair control.The 3-degrees of redundancy are optimized to effectively perform activities of daily living (ADLs) and overcome singularities, joint limits and some workspace limitations.The control system is designed for teleoperated or autonomous coordinated Cartesian control, and it offers expandability for future research, such as voice or sip and puff control operations and sensor assist functions.
E-Reading in Organizations: Users’ Satisfaction and Preference

This paper defines electronic reading (e-reading) and then continues to discuss the diverse definitions of the main resource of e-reading, which are, electronic books (e-books). It then proceeds to describe the formats and standards of existing e-book initiatives, which are gaining wider interest since the introduction of portable electronic reading devices and software-based readers that provide users with a more realistic book reading experience. Advantages, disadvantages, and problems with paper-based reading are also explained. In addition, a study concerning an evaluation of e-reading satisfaction is described. Three different e-book formats (LIT, PDF, and HTML) were utilised to accomplish the primary aims of the study, which were to identify the most preferred format, associated software-based reader (Microsoft Reader, Adobe Acrobat Reader, Internet Explorer), and the potential of e-reading in the workplace. Based on a proposed preferred index, the results seem to suggest that Microsoft Reader is more preferable when compared to the other two. However, most participants preferred reading on paper rather than on screen. Although this was the case, some participants would e-read depending on resources and situations.
Probably Approximately Correct Heuristic Search

A* is a best-first search algorithm that returns an optimal solution. w-admissible algorithms guarantee that the returned solution is no larger than w times the optimal solution. In this paper we introduce a generalization of the w-admissibility concept that we call PAC search, which is inspired by the PAC learning framework in Machine Learning. The task of a PAC search algorithm is to find a solution that is w-admissible with high probability. In this paper we formally define PAC search, and present a framework for PAC search algorithms that can work on top of any search algorithm that produces a sequence of solutions. Experimental results on the 15-puzzle demonstrate that our framework activated on top of Anytime Weighted A* (AWA*) expands significantly less nodes than regular AWA* while returning solutions that have almost the same quality.
A Model of Collaboration-based Reputation for the Social Web

In this paper we describe a generic approach to modeling user reputation in online social platforms based on an underlying model of collaboration. This distinguishes our approach from more conventional reputation models which are often based around ad-hoc activity metrics. We evaluate our model with respect to a conventional reputation model used by three social Q&amp;A websites, each based on a different topical domain.