Reviewerly/exHarmony huggingface dataset:
Hybrid Teaching Experiment Design of Automatic Control Principles Based on Simulink

Hybrid experimental experiment is experiment method that combines traditional experimental teaching with modern online learning concepts, fully utilizes various resources, and achieves complementary advantages of “online+offline”. Online refers to virtual simulation experiments using various internet resources, while offline refers to physical experiments. In view of the shortcomings of the traditional experimental methods of automatic control principles, the experiment is designed and developed again based on the original physical connection by using Simulink, and adds comprehensive design experiments. A total of four required experiments design examples are given. Taking the comprehensive design of resistance furnace as an example, a specific implementation process is given. The hybrid experimental teaching can transform students from passive and step-by-step experiments to design experiments and solve problems by themselves. And it exercises students’ hands-on abilities by physical objects. Because it is not limited by time, location, or equipment by Simulink, students can do the experiments anytime and anywhere. Through the mixed use of various teaching methods, students can choose the most appropriate learning method based on their own actual situation, and can also communicate and interact with teachers and other classmates through multiple forms of learning. Ultimately, under the guidance and assistance of teachers, students can truly become a learner with autonomous learning ability, stimulating their interest in experiments.
IGCN: Item Influence Enhanced Graph Convolution Networks for Recommendation of Cold-Start Items

Recommender systems supported by collaborative filtering (CF) have recently been extensively deployed in various e-commerce platforms. However, CF models suffer from the cold-start issue, i.e., limited preference history of cold-start users/items causes inaccurate similarity measurements. Addressing the cold-start issue has attracted considerable attention. However, existing work mainly focuses on the user cold-start issue and pays less attention to the item cold-start issue, and their performance is still unsatisfactory. To fill this gap, in this paper, we aim to address the item cold-start issue and propose a novel recommendation framework called Item Influence Enhanced Graph Convolution Networks (IGCN). We handle the item cold-start issue via influence-aware item representation learning. In particular, we mine the item influence signal to perform item influence graph modeling. Then we propose an influence-aware neural item aggregation module to capture high-order item-item influence signal for item representation via iteratively aggregating the influence-related item context. With the help of influence-aware item representation learning, both local and global item influence signals are captured to enrich the item representation, even for the cold-start item. Furthermore, we propose a residual-connected factorization machine to join the user embedding and item embedding for scoring and prediction incorporating second-order interactions. Empirical results on three benchmark recommendation datasets demonstrate significant performance-boosting compared to existing state-of-the-art methods in not only cold-start setting (up to 14% gain) but also regular setting (up to 17% gain). The further efficient study also shows that the proposed method has lower complexities in time (up to 44% drop in epoch number and up to 27% drop in training time) and space (up to 14% drop in graph size).
GUI Component Detection Using YOLO and Faster-RCNN

The graphical user interface (GUI) is crucial for communicating with software users. The detection of GUI elements holds significant importance for various software test automation tasks. In this study, two different object detection models such as YOLOv8 and Faster R-CNN are used to address challenging GUI component detection problems in mobile applications. Two different datasets were used. The first dataset is the RICO data, which consists of 5 classes. The second dataset consists of 600 mobile application screenshots collected from the open-source. This dataset consists of 7 classes and has been labeled and trained on Roboflow. In addition, an automation system was developed to test the object detection models for errors after the version change in the mobile application. With this test automation, the locations of the errors in the mobile application in real-world scenarios were determined and reported. In this experiment, 2 different data were trained in 2 different models, and the data we labeled gave the best result with a mAP value of 0.81 in the YOLOv8 model.
Detection Algorithm for Safety Helmet Wearing of Chemical Plant Personnel Based on Improved YOLOv5m

Wearing a safety helmet at the work site of a chemical plant can effectively prevent safety accidents caused by head injuries, so it is very important to detect whether employees wear safety helmets. In order to solve problems such as serious information loss, large number of parameters, and weak detection ability of small targets in complex scenes in the sampling process of helmet detection algorithm. In this paper, an improved lightweight safety helmet detection algorithm LP- YOLOv5m (Lightweight And High Precision YOLOv5m) based on YOLOv5m is designed. LP-YOLOv5m incorporates the Wise-IoU (Wiou) loss function to enhance model generalization. Additionally, we add a small object detection layer to improve the feature extraction ability for small objects. Furthermore, upsampling was performed using CARAFE. Finally, a lightweight module named C3-F was proposed, which replaced the Bottleneck in the C3 module with FasterNet Block, which could effectively reduce the network model size. Comparative laboratory findings demonstrate that our improved model achieves a mAP50 of 92.1%, surpassing YOLOv5m by 3 percentage points while reducing the model size by 23.3%. The LP-YOLOv5m model can be better applied to practical scene requirements, and can effectively solve practical problems.
Big OLAP Data Cube Compression Algorithms in Column-Oriented Cloud/Edge Data Infrastructures

This paper provides a brief overview on the innovative problem of devising and implementing big OLAP data cube compression algorithms in column-oriented Cloud/Edge data infrastructures, an emerging need for next-generation big data analytics systems.
A Survey on Driver Behavior Detection-Based Internet of Vehicle: Issues, Challenges, Motivation and Recommendations

Numerous academic studies and government publications concur that careless driving behaviors on the road are the primary contributor to accidents. Therefore, studying driver behavior is seen as being of great use in the design and development of Intelligent Transportation Systems (ITS). The rising number of traffic accidents is ascribed to irresponsible driving, inadequate preventative measures, as well as dependence on sluggish and ineffective support systems. There are multiple smartphone applications available to help drivers become better drivers. Recently, vehicles have gained access to the computational capacity of cloud servers to run machine learning (ML) algorithms owing to mobile cloud computing. In order to address issues that have not yet been addressed by previous studies, this study intends to analyze and carefully examine the literature in the driver behavior-based Internet of Vehicles (IoV) domain. Thus, the development of intelligent transportation systems' crucial behavior analysis is covered. Sensors utilized in investigations, the impact of thresholds on labelling procedures, data balance and classification accuracy, and the thresholds in recognizing driving styles are among the topics covered in the examined studies.
Action Recognition in Videos using 3D ConvNets with Multi-head Attention

Deep learning continues to reshape the potential of video action recognition, offering a robust framework to capture the basics of dynamic human movements. Despite the progress, efficient recognition of actions in videos remains a challenge. In our study, we introduce a comprehensive deep-learning model that effectively addresses this challenge. Specifically, our model is based on the spatiotemporal 3D convolutional neural network architecture with a multi-head attention mechanism to distinguish complex actions across video frames. The multi-layered architecture, including multiple linear layers and a multi-head attention mechanism, ensures both depth and precision in action classification. This paper elaborates throughout our study from raw video inputs to recognized actions, emphasizing the transformative potential of deep learning in enhancing action recognition capabilities. Our results on two benchmark datasets, namely UCF and HMDB, demonstrate that our proposed architecture improves the 3D ConvNet performance.
The Transfer Learning Models for Face Recognition: A Survey

Facial recognition has many uses in marketing, biometric security, and healthcare. Facial recognition has emerged as the most attractive field of study among other biometrics, such as fingerprints, iris, and voice. Increasing safety and security, avoiding crime, minimizing human interaction, and even supporting medical efforts are just a few of the advantages that facial recognition technology provides for society. Several face recognition methods have been introduced, including Fisher faces, eigenvectors, and the local binary pattern graph (LBPH). Because of the low accuracy of these methods, a convolutional neural network is used because it has been proven through many experiments that it is the best among other algorithms in terms of accuracy and time. Computer-based face recognition is an established and trustworthy technology widely used in many access control settings. Transfer learning is critical for addressing the underlying problem of insufficient training data in machine learning. It moves knowledge from the source domain to the destination domain. This paper compares previous work using learning models with deep learning algorithms. This paper aims to explain why transfer learning is used to identify faces and to describe the characteristics and advantages of these transfer learning models, in addition to comparing the previous studies on face recognition and the algorithms used in devising the best methods to obtain high-accuracy results when recognizing the face.
Text Steganography in Images Using Turbo Code-Walsh Code Transforms

Numerous methods, such as integration between steganography and cryptography, have been suggested to protect the privacy of information exchanged. Steganography systems have come to be recognized as a crucial method for enhancing information security by using multimedia to cover up sensitive information which includes information that is invisible to the human eye. In this paper, text was concealed within a digital image using Turbo code and Walsh-code algorithm, in order to result an electronic lung x-ray containing the diagnosis of the condition embedded inside the image. The algorithm was implemented using the MATLAB programming language. The new way is exposed to multi-image noise like Gaussian, slot and paper noise, and it is found that the proposed algorithm has PSNR, BER, RMS, MSE results for Gaussian equal to 84.871, 0.0003, 0.0525, and 0.0027 respectively. These results are considered very suitable as compared to no attack.
An Approach for Data Publishing with Sensitive Attribute Synthesis

Data privacy preservation has been an important problem worthy of study. With the deepening of research, more targeted solutions for different requirements are proposed. Considering that in the most cases, only some of the data are sensitive, and there is no need to provide privacy preservation for non-sensitive data. This paper mainly addresses the problem of privacy preservation only for the sensitive attributes, and the non-sensitive data can be shared with the original values. A privacy preservation approach is presented in this paper, which synthesizes the values of sensitive data attributes and keeps the values of non-sensitive data attributes untouched during data publishing. The experimental results show that this approach can reduce the loss of information and thus improve the utility of published data.
Clustering with Entropy-based Recombination for Training GCNs on Large Graphs

With the development of deep learning methods on non-grid graph data, Graph Convolutional Networks (GCNs) are playing important roles in a wide range of scenarios. When dealing with graphs with ever-growing sizes, even if the compute capability of one GPU card is sufficient, its limited memory capacity would make the training on large graphs infeasible. Sampling methods on three levels (i.e., node-level, layer-level, and subgraph-level) have been proposed to improve the scalability of GCNs. However, there still exist drawbacks in sampling-based approaches including time-consuming sampling processes and biased node representations. To tackle these issues, we propose a novel subgraph-based sampling method considering the generalized distance of label distribution between each subgraph and the whole graph. Specifically, our method introduces two pre-steps before training: (1) partitioning all the nodes in the original graph into different clusters through an efficient clustering algorithm; (2) combining the clusters obtained in the first step into a set of bigger groups (subgraphs) based on the information entropy theory. Experiments show that our work could reserve similar label distribution to that on the whole graph and outperform SOTA models in terms of classification accuracy on different datasets. Besides, the time cost of our pre-processing procedure is acceptable compared with the time spent in training.
Historical Review of Mobile Robots with Moving Arm

The robotics industry has developed significantly in the last three decades due to the tremendous industrial development witnessed by factories and laboratories in various medical, military, service, and civil fields and device industries. This article reviews the most important and recent scientific studies, references, and literature that deal with controlling mobile robots with a moving arm. Several studies have been presented, especially those dealing with the mechanisms of control and controlling robots with a mobile arm, and the classification of controlling methods by mobile or self-programming. Various mobile robotics driving techniques, such as Wi-Fi, IoT, and microcontrollers, have been compared with their different advantages and applications.
Control of an Electrical System by Recurrent Neural Network RSOM map

This paper discusses an intelligent strategy using the Recurrent Self Organizing Map RSOM for anomaly detection over a motor pump. The proposed idea involves the most significant features of RSOM to locate and appreciate the fault origin. It concerns the topological structure of the map, the unsupervised deep learning algorithm, as well as the Unified Modeling Language (UML) activity diagram. The measurement and treatment of the coming signal from an appropriate sensor fixed on the experimental bench, was doneat a precise moment in the Automatism and Industrial Computing Laboratory. A comparative analysis of the flaw detection performance was carried out using the RSOM neural map and the spectral analysis method.
Formation Analysis Method for Team Sports Using Deep Graph Convolutional Neural Network with Geometric Formation Features as Input

In the last decade, player and ball tracking data have increasingly been gathered and employed in various team sports, in particular invasion team sports, such as soccer, basketball, rugby, and American football. Additionally, the effectiveness of geometric analysis of formations using such tracking data has been demonstrated. Moreover, deep neural network-based methods for analyzing such tracking data have also been proposed. However, to the best of our knowledge, no research has been found that combines both, even though the benefits are seemingly apparent. In this study, against this background, we propose a formation analysis method for team sports using a deep graph neural network with geometric formation features. This method uses geometric formation features derived from a geometric formation analysis for tracking data. For initial validation, we also conduct an experiment applying this method to a soccer shooting scene classification problem with real soccer tracking data in Japan. Based on the results, we will discuss the method’s effectiveness.
Enhancing Classification in UTD-MHAD Dataset: Utilizing Recurrent Neural Networks in Ensemble-Based Approach for Human Action Recognition

This study demonstrates the ensemble approach to perform Human Action Recognition on the UT Dallas’ Multimodal Human Action Data, where number of actions by humans is 27. Our ensemble approach gained an accuracy of 0.821 on the validation data, a remarkable uplevelling as compared to the accuracy of baseline paper which is 0.672. The paper also shows the train-val performances of other models we experimented using only the Inertial and Skeleton dataset. The link to Github repository which holds to code can be found here, and the link to get the dataset can be found here.
Match and Retrieval: Legal Similar Case Retrieval via Graph Matching Network

Legal document retrieval is a crucial application in the field of legal Artificial Intelligence (AI). It involves retrieving the most relevant legal cases from a legal document database when inputting a query. Mainstream solutions typically employ pre-trained language models or large language models for retrieval. However, both of these methods rely on black-box models trained on unlabeled data, resulting in a lack of interpretability in the model's retrieval outcomes. Graph Neural Networks (GNNs) possess strong representational and interpretable capabilities. Therefore, representing legal cases using graph-structured data and performing the Legal Similar Case Retrieval (LSCR) task through GNNs is a promising approach. Nevertheless, typical GNNs primarily focus on node information within graph-structured data and overlook edge and inter-graph interaction information, leading to lower retrieval accuracy. To address these issues, in this paper, we propose a Legal case retrieval model utilizing a Graph Matching Network, called Legal-GMN. We evaluate the model on a dataset composed of real legal judgment cases. Experimental results demonstrate that Legal-GMN effectively enhances the retrieval accuracy for the LSCR task. Compared with baseline methods, Legal-GMN improves retrieval precision by approximately 15% and enhances retrieval efficiency by approximately 90%. Remarkably, while maintaining State-Of-The-Art (SOTA) performance, Legal-GMN can also generate visualized graphs of the retrieval process, significantly enhancing its interpretability.
Improvement of Retrieval Process for Similar Outfit Portraits in Prototype Fashion Chatbot

We have proposed a prototype fashion chatbot designed to retrieve similar outfit portraits and recommend reference users based on user-submitted outfit portraits. This chatbot uses a body part segmentation method to extract regions of body parts and calculates color feature values for the extracted body parts. This chatbot also employs an item segmentation method to extract fashion items present in the outfit portrait. Despite its capabilities, this chatbot showed limitations, notably retrieving portraits featuring incorrect types of outerwear. Furthermore, its retrieval process did not fully utilize the extracted results of the fashion item segmentation method. Thus, in this study, we aimed to address these issues by incorporating specific enhancements. We performed an initial validation experiment for the enhancements using outfit portraits posted on a Japanese fashion website.
Solar Irradiance Forecasting using Gaussian Process Regression Model

Photovoltaic power generation presents a significant problem for power system security due to their stochastic nature. Thus, photovoltaic energy forecasting plays a key role in addressing the challenges of grid stability. Then, accurate PV energy forecasting improve the output power and therefore reduce the need for reserve energy to balance energy. Also, it improves dispatch and planning power. In this paper, we study a short term forecasting of PV power using a Gaussian Process Regression (GPR) model. The meteorological conditions such as temperature is used as input, while solar irradiance, is used as output for GPR model.The data set are illustrated from data NOAA'S Surface Radiation (SURFRAD) Network at Bondville station, located in the USA. A comparative study with other machine learning techniques is developed in terms of calculation time (CT), RMSE, MAE, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Towards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems

Data annotated by humans is a source of knowledge by describing the peculiarities of the problem and therefore fueling the decision process of the trained model. Unfortunately, the annotation process for subjective natural language processing (NLP) problems like offensiveness or emotion detection is often very expensive and time-consuming. One of the inevitable risks is to spend some of the funds and annotator effort on annotations that do not provide any additional knowledge about the specific task. To minimize these costs, we propose a new model-based approach that allows the selection of tasks annotated individually for each text in a multi-task scenario. The experiments carried out on three datasets, dozens of NLP tasks, and thousands of annotations show that our method allows up to 40% reduction in the number of annotations with negligible loss of knowledge. The results also emphasize the need to collect a diverse amount of data required to efficiently train a model, depending on the subjectivity of the annotation task. We also focused on measuring the relation between subjective tasks by evaluating the model in single-task and multi-task scenarios. Moreover, for some datasets, training only on the labels predicted by our model improved the efficiency of task selection as a self-supervised learning regularization technique.
Enhanced U-Net Approach: Semantic Segmentation for Self-Driving Cars Applications

Semantic segmentation is a crucial task for the development of autonomous vehicles. Autonomous Driving (AD) stack is mainly classified into three subsystems, which are respectively control, planning, and perception. This work deals with the perception subsystem of AD systems. Indeed, on-road semantic segmentation for the AD systems is addressed using deep-learning algorithms. This paper proposes a deep learning approach based on an enhanced U-Net model that exploits squeeze and excitation (SE) networks. In order to demonstrate the efficiency of the added SE blocks, we opted to use U-Net as our baseline. In these experiments, the two models are trained from scratch on the CamVid dataset. Compared with the U-Net baseline, the mean class accuracy (mCA) and the mean intersection over union (mIoU) of the proposed model are increased by 5.54% and 5.87%, respectively. It achieves a mIoU of 59.88 % and a mCA of 84.15% on the CamVid dataset. The achieved results reveal that the improved U-Net reaches a better compromise between accuracy and computational complexity than previous studies.