Reviewerly/exHarmony huggingface dataset:
EFBH: Collaborative Filtering Model Based on Multi-Hypergraph Encoder

Recommendation systems are necessary to enhance the consumer shopping experience. As one of the classical algorithms, collaborative filtering (CF) is the main recommendation model. Currently, the graph neural network realize collaborative filtering through graph convolution and model the interaction between users and items. However, the ability of ordinary graph to express the higher-order relationship between vertices and to process sparse data is limited. It cannot fully capture the potential interaction between users and items. So the hypergraph is used to model the interaction, and the recommendation question is transformed into the link prediction problem on the hypergraph. An Encoder Framework based on Bipartite Hypergraph (EFBH) is proposed. First, the hypergraph convolution module is constructed, and the user-user hypergraph and the item-item hypergraph are constructed to solve the data sparsity problem; then the multi-hypergraph encoder module is constructed to encode the cooperation signals hidden in the user-item interaction to obtain high-quality vertex embedding vectors and better recommendation results. Experiments on three common datasets show that our model improves the AUC on average by 5.35%, 2.91%, and 3.26% respectively. The RMSE also decreased by an average of 1.43%, 15.33%, and 16.06% respectively, which is the best one compared to the existing models.
CNN-Based Anomaly Detection in Complex Systems

Anomaly detection is an approach used in the field of Machine learning which aims to identify the abnormal patterns. This is the vast developing area with respect to various domains such as cyber security, healthcare, fraud detection, etc. This research paper provides a comprehensive study of CNN based architecture for anomaly detection used in the complex systems. This examines and explores the machine learning approaches of CNN in reference to anomaly detection. The paper also highlights the real time applications and the challenging issues of anomaly detection. The paper further focuses on the understanding of CNN architecture and the future advancements of the vital field.
Predicting product sales based on sentiment analysis using reviews: The case of Amazon's kindle

This paper proposed a novel approach to predict sales by analyzing the sentiment of online product reviews. The contributions of this paper are: (1) examine sentiment analysis to extract emotions such as anger, fear, or participation from the product reviews; (2) identified the time lag of the interaction between reviews and sale fluctuation; (3) applied three multiclass classification methods to forecast sales and combined them using a majority vote classifier for better performance and accuracy. Online sellers can utilize the proposed models to analyze the potential influence of reviews as an addition to traditional sale forecasting methods.
A new measure of preferred direction for circular data using angular wrapping

The statistical techniques which are developed for the analysis of data in the linear number system cannot be applied to directional data directly. Circular data may be discontinuous in some principal interval. These discontinuities cause failure results in the circular statistics. Because of that the proposed wrapping operator must be used for data, which are defined in the discontinuous range. However, in both continuity and discontinuity, the wrapping operator works correctly. The most common preferred directions for circular data are circular mean and variance summarizing and comparing them. Although circular data has a very important role in statistics, the literature is weak in terms of statistical analysis of circular data. It creates a gap in this field. This study examines the preferred direction of circular data to fill this gap and presents a new measure of preferred direction for circular data using angular wrapping. Four different artificial and three real datasets are employed to evaluate the performance of the proposed methods. The results demonstrate the superiority of the proposed methods in terms of the absolute error and absolute percentage error. Consequently, it has been seen that the proposed methods giv e more consistent and more accurate results than thevectorial methods.
Model Compression Method Based on Knowledge Distilling and Adversarial Learning

For airborne computing platform, fault diagnosis model is a very important part of Prognostics and Health Management System. Because of the lack of labels, unsupervised learning is commonly used in fault diagnosis model. However, unsupervised learning method requires too much computing power and storage, so it’s hard to be deployed to airborne computing platform with limited computing power and storage. In this paper, we proposed a model compression method named Optimized Adversarial Distilling Model Compression (OADMC) that combines Knowledge Distilling and Adversarial Learning to solve this problem. In our method, we trained an Artificial Neural Network (ANN) model to imitate the unsupervised model, and use Simulated Annealing (SA) method to find the difference between them. OADMC can made required computing power reduced to less than 25% and required storage reduced to less than 5% and the accuracy is higher than 95%, whose performance is better than traditional model compression such as pruning algorithm.
Mitigating Accidental Code Plagiarism in a Programming Course Through Code Referencing

Code plagiarism – taking code from external sources and using it without reference in one’s own programs – can be a serious issue for programming students, depending on the policies being applied by their instructors. However, plagiarism can be inadvertent, due to a lack of knowledge among students. Our research shows varied understandings of correct code reuse, suggesting that students are not provided with appropriate guidelines. Our goal is to introduce good code referencing practice to students, to help raise students’ awareness of academic integrity and reduce the possibility of accidental plagiarism. We present Corona, a code referencing system that can assist students in creating references for their code while simultaneously educating them about ethical code reuse. Technical evaluation of the system shows that Corona can successfully generate references for code taken from 20 of 24 distinct programming assistance websites, and that it can find matches between students’ code and instructors’ example code and generate appropriate references. Our research in a small-scale environment suggests that the use of Corona as a demonstration tool in a lecture about code referencing increases student awareness of correct referencing practice. To improve our intervention, we also show steps that lecturers can take to further elevate students’ engagement in code referencing.
Single image dehazing using frequency-guided filtering and progressive physics learning

Current learning-based dehazing methods simply rely on the paired synthetic datasets and physical models, which can hardly describe the complicated degradation for practical applications. These methods still struggle to achieve haze removal, color distortion, and detail restoration instantly, and they ignore the frequency characteristic differences and prior knowledge importance. To address these problems, we propose an unpaired stage-wise framework integrating frequency-guided filtering and progressive physics learning in an adversarial dehazing network, called FPD-Net. To be specific, a guided filter based on frequency information is employed to decompose the high and low frequency components for better feature extraction. We further merge the prior and physical knowledge to form progressive physics learning, which produces pleasing haze-free outputs with high visibility and reality. For better atmospheric light estimation, the variational auto-encoder and Kullback–Leibler loss are included to represent the illumination message. Extensive experiments on both synthetic and real datasets prove that our designed FPD-Net achieves better performance visually and quantitatively than the comparison dehazing models.
Fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement

Abstract The fuzzy C‐means clustering (FCM) algorithm is widely used in greyscale and colour image segmentation, especially in real colour images. However, in the process of interested regions extraction, it performs barely satisfactory due to the use of single distance metric in traditional FCM. In order to address the issue, a fuzzy C‐means clustering algorithm based on superpixel merging and multi‐feature adaptive fusion measurement (FCM‐SM) are proposed. First of all, since superpixel image adapts well to irregular image boundaries, the FCM algorithm is elevated to the superpixel level. Moreover, the superpixel merging module based on colour similarity is introduced to improve the traditional watershed transformation by incorporating more gradient and intensity information. Finally, the multi‐feature adaptive fusion measurement is designed in the clustering process to incorporate multiple sources of spatial information, which comprehensively measures the information from different fields to enhance the segmentation capability further. Experiments performed on Berkeley Segmentation Dataset and Brain Tumor Segmentation Dataset demonstrate that the proposed algorithm provides better segmentation results than benchmarks during the object extraction.
Automated Reporting of Code Quality Issues in Student Submissions

Despite its importance in industry, code quality is often overlooked in academia. A number of automated tools to report code quality have been developed but many of them are impractical to use. They either are developed as a standalone tool, require the use of a particular IDE, and/or need historical data. This paper presents code quality issues reporter (CQIS), a tool that can be embedded in an assessment submission system; it identifies code quality issues for each student submission via static analysis, and reports those in an HTML page whose link is sent via email. The tool covers 52 code quality issues specifically curated for academia, 32 for Java and 20 for Python. According to four quasi-experiments with a total of 274 students, students with CQIS are likely to have fewer code quality issues so long as the expected solutions are long and complex and code quality is considered as part of the marking. These students are also more aware of code quality, and readability in particular.
CECR: Collaborative Semantic Reasoning on the Cloud and Edge

Semantic reasoning could exploit the implicit information hidden in the graph and enrich the incomplete knowledge graph. Most existing research in semantic reasoning mainly focuses on the completeness of reasoning results and the efficiency of the reasoning algorithm, which neglects the practicality and scalability of reasoning systems. Especially in the Internet era of data explosion, traditional reasoning systems are gradually struggling to meet the demands of large-scale data reasoning. Therefore, scalability has become a focus for reasoning systems. In this paper, we combine cloud and edge computing for scalability with distributed storage based on data correlation and task scheduling. We specifically propose the query-driven backward reasoning optimization algorithm to improve efficiency and overcome the resource limitation of edge nodes. A reasoning system named CECR (Cloud and Edge Collaborative Reasoning System) is implemented to validate our approach. Experiments on three benchmarks demonstrate the soundness and completeness of reasoning results and scalability of CECR.
Non-Invasive Video Analysis Technique for Detecting Sleep Apnea

Sleep apnea is a type of sleep disorder in which the breathing of a person starts and stops repeatedly during sleep. If this condition is ignored for a long period, it can lead to serious health issues. This paper proposes a robust method for detecting sleep apnea and keeping track of the region of interest automatically. The methodology involves converting the captured video into grayscale images and then applying Gaussian blur and Canny edge detection to detect noise-free edges of the body. Further, the current frame is compared to the previous frame to detect body movements accurately. The proposed method is experimented with in different testing conditions to demonstrate its efficiency and accuracy in identifying sleep apnea. This paper also introduces a novel algorithm for automatically adjusting the region of interest within the frame. Furthermore, the advantages and limitations of the proposed algorithm are also being discussed.
Event segmentation structures temporal experience: Simultaneous dilation and contraction in rhythmic reproductions.

We experience the world in terms of both (continuous) time and (discrete) events, but time seems especially primitive-since we cannot perceive events without an underlying temporal medium. It is all the more intriguing, then, to discover that event segmentation can itself influence how we perceive the passage of time. We demonstrated this using a novel "rhythmic reproduction" task, in which people listened to irregular sequences of musical tones, and then immediately reproduced those rhythmic patterns from memory. Each sequence contained a single salient (and entirely task-irrelevant) perceptual event boundary, but the temporal placement of that boundary varied across multiple trials in which people reproduced the same underlying rhythmic pattern. Reproductions were systematically influenced by event boundaries in two complementary ways: tones immediately following event boundaries were delayed (being effectively played "too late" in the reproductions), while tones immediately preceding event boundaries were sped up (being effectively played "too early"). This demonstrates how event segmentation influences time perception in subtle and nonuniform ways that go beyond global temporal distortions-with dilation across events, but contraction within events. Events structure temporal experience, facilitating a give-and-take between the subjective expansion and contraction of time. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
The Impact of E-satisfaction, brand reputation and E-trust on Eloyalty: An Empirical Study of apparel industry of Rawalpindi and Islamabad

The objective of this paper is to examine online brand relationships, and the connection between satisfaction, trust, and loyalty on the online level. The website is considered to be an extension of the parent brand. It also seeks to explore the effects of offline experiences on online level experiences. Methodology employed included a survey through distributing close-ended questionnaires individually to people of Rawalpindi. The sample size for this study was 200. Responses of questionnaires were analyzed through SPSS statistics by performing Regression and Correlation analysis. The results confirm the results of previous studies, indicating that satisfaction and trust at the site level is a determining factor in the retention of online businesses. However, they also show that brand-level experiences can vary depending on online satisfaction, trust and loyalty, depending on the relationship between the consumer and the brand. Interestingly, in this case, the duration of the user’s history and the registration of the website have a negative impact on online trust. This article combines the literature on the satisfaction and reputation of online brand loyalty. The findings of the study are based on large-scale surveys that provide researchers and practitioners with valuable insights into how the brand experience is related to clients’ attitudes and commitments to health. ’Internet. The context of the study, the magazine publishing industry, has drawn less attention from researchers, although several publishers have expanded their brands online.
A Generic Intelligent Agent Design Approach Based on Artificial Neural Networks

Artificial intelligence in general and software agents in particular are recognized as computer science disciplines that aim to model or simulate so-called intelligent human behaviors such as perception, decision-making, understanding, learning, etc. This work presents an approach to designing a generic Intelligent Agent that can be used in a multi-agent system to solve a complex problem. The generic agent that is proposed can be instantiated as a concrete agent, which is enabled with learning and autonomy capabilities by using Artificial Neural Networks. To highlight the generic aspect, the proposition is instantiated to be used in agriculture, health and education. The instantiated software agent applied in agriculture can process images in real time and detect defect on plants’ leaf. In the health field, the agent process image to diagnose breast cancer. When applied in Education, the agent can load an image of a student’s script and grade it. The performance of the designed agent system has the same accuracy as that of the respective neural networks used to instantiate them. In the educational field, the software agent has an accuracy of 98.9% and in the health field, it has an accuracy of 99.56% while in the agricultural field, it has an accuracy of 97.2%.
Tell me your position: Distantly supervised biomedical entity relation extraction using entity position marker

A significant amount of textual data has been produced in the biomedical area recently as a result of the advancement of biomedical technologies. Large-scale biomedical data can be automatically obtained with the help of distant supervision. However, the noisy data brought by distant supervision methods makes relation extraction tasks more difficult. Previous work has focused more on how to restore mislabeled relationships, but little attention has been paid to the importance of labeled entity locations for relationship extraction tasks. In this paper, we present a “four-stage” model based on BioBERT and Multi-Instance Learning by using entity position markers. Firstly, the sentence is marked with position. Secondly, BioBERT, a biomedical pre-trained language model, is used in the final sentence feature vector representation not only with the global position marker but also with the start and end marker of both the head and tail entity. Thirdly, the aggregation of sentence vectors in the bag is used as the vector feature of the bag by three aggregation methods, and the performance of different sentence feature vectors combined with different bag encoding methods is discussed. At last, relation classification is performed at the bag level. According to experimental results, the presented model significantly outperforms all baseline models and contributes to noise reduction. In addition, different bag encoding methods need to match corresponding sentence encoding representation to achieve the best performance.
Complete Coverage Path Planning Using Adaptive GBNN for Omnidirectional Sweeping Robot

Outdoor cleaning while maximizing the area coverage is a complex task because of confined spaces, precision motion, and planning challenges. This paper proposes a novel design of an omnidirectional self-reconfigurable robot named PantheraV3. The novelty here is in terms of the ability of the sweeping brushes to rotate and brush along its length and width, resulting mainly in two states of the robot during the cleaning. This paper proposes the Complete Coverage Path Planning (CCPP) method by adapting Glasius Bio-inspired Neural Network (GBNN) to the robot form factor for more efficient area coverage. The algorithm is made generic which can support any form factor or footprint of robots. Real-world experiments were conducted with PantheraV3. Using the proposed aGBNN and PantheraV3 was able to complete area coverage with lesser path length, i.e., 40% reduction in distance traveled than with GBNN in a selected environment.
Time-Spatial Feature Fusion for Contact Detection in Sports Using Multi-Sensor Data

The detection of athlete contact is of great significance for the safety of athletes and the fairness of sports events. The commonly used method is to have athletes wear sensors and set up multi-camera positions on the field, and then perform contact detection by analyzing the data from the sensors and cameras. This paper focuses on the scenario of dual video streams and multiple sensor data, where the images are sampled over time to obtain information features related to the temporal dimension. Multi-layer perceptron and XGBoost are used to fit the sensor data, and the EfficientNetB4 model is selected to extract features from multi-angle images stacked along the time dimension, thus achieving feature fusion based on both the time and spatial dimensions. The experimental results demonstrate the effectiveness of the proposed method in contact detection using multi-sensor data.
Perch a Quadrotor on Planes by the Ceiling Effect

Perching is a promising solution for a small unmanned aerial vehicle (UAV) to save energy and extend operation time. This paper proposes a quadrotor that can perch on planar structures using the ceiling effect. Compared with the existing work, this perching method does not require any claws, hooks, or adhesive pads, leading to a simpler system design. This method does not limit the perching by surface angle or material either. The design of the quadrotor that only uses its propeller guards for surface contact is presented in this paper. We also discussed the automatic perching strategy including trajectory generation and power management. Experiments are conducted to verify that the approach is practical and the UAV can perch on planes with different angles. Energy consumption in the perching state is assessed, showing that more than 30% of power can be saved. Meanwhile, the quadrotor exhibits improved stability while perching compared to when it is hovering.
High-performance Work System and Employee Retention: A mediated Mediation Model

High performance work systems HPWS are integrated human resource approaches designed to enhance employees' skills and efforts, resulting in higher levels of performance and productivity (Zhu, et al., 2018). The major components of HPWS include selective hiring procedures, employment security, decentralization of decision-making, extensive training, information exchange, and fair paying, all of which are interconnected and work in concert (Zhang et al., 2019). Performance is increased when HPWS is employee service-oriented, while morale and employee retention ER are increased when HPWS is creativity-oriented (Jo et al., 2023). HPWS is more efficient than traditional organizational systems, nevertheless, it is yet unclear how this happens. Multiple studies have produced conflicting findings, and some even contend that HPWS may increase employee effort and stress (Jensen et al., 2022; Cooke et al., 2019). There is a gap on previous researches explaining how HPWS can help translate EPB, JE, and EP into better ER outcomes and whether this relationship differs depending on the organizational setting or the industries, these connections need to be explored further in order to find practical methods improve organizational capacity for employee retention. In accordance with Vision 2030 and the (National Transformation Program) the Saudi government is increasing the private sector's contribution to the nation's healthcare system (Rahman &amp; Al-Borie, 2021).Even though a lot of work has been put into HPWS, there are still large research gaps in Saudi Arabia.So this study is enhancing the current knowledge by examining possible mediators (i.e., EPB, JE, and EP) between HPWS and ER helps further knowledge of the function of HPWS in the Saudi Arabian healthcare system in light of Vision 2030. Keywords: High performance work system, Employee Proactive Behaviour, Job engagement, Employee performance, Employee retention