Reviewerly/exHarmony huggingface dataset:
Practical study for comparing edge detection filters in digital image processing

Edge detection is a fundamental and important tool in image processing and computer vision. The research topic discusses edge extraction of a digital image using various digital image processing techniques. Digital image edge identification using variety of digital image processing methods. The most popular technique for identifying discontinuities in intensity levels is edge detection. The actual image contains noise that could affect the digital image's quality. Some edge detection filters were used such as Canny, Sobel, and Prewitt. Laplace of Gaussian (LOG) edge recognition, Robert boundary detection, zero edge recognition and analysis. In light of the results of the comparison of edge filters the best edge filter had been obtained. Also, a hybrid method that merges between Canny filter and morphology operations were produced for edge detection. The research aims to reach an appropriate image purification by using different types of filters and to reduce distortions at the edges of the image and to determine which type of filter is the best. As well as, a comparison operation is applied between the traditional method and the hybrid method. Following the results explained by the filters used and displaying the resulting images. The results evaluated from hybrid filter show that this method is optimal than the traditional filters based on the Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) with a value equals 43.43. A smaller score indicates better perceptual quality. Also, the edged images quality consequences from the traditional filters were measured by this metric. The results produced from the traditional filters prove that the best filter for edge image detection is Canny filter based on Blind/Reference less Image Spatial Quality Evaluator (BRISQUE) that reaches 46.01. In addition, the correlation coefficient was estimated to find which of the resulting images are better and closer to the original image. While for correlation coefficient the better filter was Prewitt filter with 0.3133 value. This study program was applied under the MATLAB R2020b system.
ASRSR: Adaptive Sending Resolution and Super-resolution for Real-time Video Streaming

Real-time video streaming application has been adopted for a wide range of services in recent years. A major challenge for real-time video streaming is the low resolution and high latency caused by limited and unstable network bandwidth. A straightforward solution is to invest directly in network infrastructure, but it is cost inefficient and still limited to the simple transmitter of the video sender. To address these challenges, we are motivated to develop an alternative solution by leveraging video super-resolution (VSR). We propose a new adaptive sending resolution and super-resolution (ASRSR) scheme for real-time video streaming. ASRSR jointly decides the sending resolution for the sender and the super-resolved resolution for the VSR model at the receiver, according to changing network conditions to simultaneously optimize bandwidth demand and video quality. We evaluate the ASRSR system in a trace-driven simulation environment, demonstrating ASRSR system outperforms all benchmarks, and both the VSR and resolution adaptation algorithm contributes to significant performance gain.
Parameter Identification for Fictitious Play Algorithm in Repeated Games

In the previous works [1] and [2], we solved the optimal strategy of the human player against a machine player who makes decisions based on Fictitious Play in infinitely repeated $$2 \times 2$$ games, in which the information is assumed to be complete and perfect. In this paper, we consider the problem of identification when the human player does not know the initial assessment of the machine player. In this scenario, we propose an identification algorithm for the human player and prove that the process of identification will end successfully in a finite time if the machine’s payoff parameter is rational. When the machine’s payoff parameter is irrational, the identification process will not end, which implies some advantage for the algorithm with irrational parameters.
An Attention Detection System Based on Gaze Estimation Using Self-supervised Learning

With the rapid development of online education around world, it becomes difficult for teachers to evaluate the effect of learning during the online class. To solve this problem, an attention detection system based on gaze estimation using self-supervised learning is proposed. Self-supervised learning is used to train the gaze estimation network and it achieved an average gaze angle error of $$6.5^{\circ }$$ on the MPIIFaceGaze dataset. Cross dataset testing was performed by 5 test subjects in the RT-GENE dataset and all samples in the Columbia dataset, in which achieved average gaze angle error $$13.8^{\circ }$$ and $$5.5^{\circ }$$ . It is found that the network has good cross dataset generalization performance, but it is susceptible to factors such as feature occlusion and low image clarity. Then, we developed an attention detection system which has functions of unmanned detection, eye state detection, and gaze detection. Online classroom experiments were conducted to verify the effectiveness of the proposed method, in which the average accuracy of the detection of distraction is around 60%.
Variable Photo-Model Stereo Vision Pose and Size Detection for Home Service Robot

This paper proposes a method for estimating the pose and size of an object using binocular stereo vision. It utilizes a variable photo-model approach which only requires one shooting condition unknown photograph of the target object. The method constructs a 2D pixel model of the object’s bounding box using the pre-trained YOLOv4 weight from the MS COCO dataset, and converts it into 3D flat photo-models of varying sizes. The estimation of the object’s pose and size is achieved through model-based stereo-vision matching and the use of Genetic Algorithm (GA). This approach eliminates the need for extensive data and pre-training time, making it cost-effective and efficient. Additionally, it extends the application range of the traditional photo-model algorithm. Experimental results conducted in indoor scenes demonstrate the effectiveness of the variable photo-model method in estimating pose and size using a photograph of the same class.
A Constant Air Flow Controller Based on Interval Type-2 Fuzzy PID Controller

In order to improve the sampling accuracy and the adaptability to different loads, a constant air flow controller based on closed-loop control is designed and realized. The mathematical model of this constant air flow controller is established, and an interval type-2 fuzzy PID controller is designed. The stability of the closed-loop constant flow controller is analyzed theoretically. Constant flow control simulations and experiments using PID controller and the interval type-2 fuzzy PID controller are done. The simulation and experiment results show that the constant air flow controller based on interval type-2 fuzzy PID controller has satisfied performance and adaptability to different loads.
Helmet Detection Algorithm of Electric Bicycle Riders Based on YOLOv5 with CBAM Attention Mechanism Integration

Object detection algorithms can assist in detecting the helmet-wearing status of electric bicycle riders, thereby saving regulatory manpower costs. However, there is currently a lack of standardized and publicly available datasets. Additionally, the basic YOLOv5s object detection algorithm, due to its limited feature extraction capabilities, may lead to numerous instances of both false negative and false positive. To enhance the model’s focus on critical information within the feature maps, this paper introduces the CBAM attention mechanism module into the Backbone section of YOLOv5s. This module sequentially infers attention maps from the input feature maps along both channel and spatial dimensions independently, and then multiplies these attention maps with the input feature maps to achieve adaptive feature optimization. This paper have established self-built dataset for experimental research, and the results indicate that compared to the original YOLOv5s model, the proposed method has improved the model’s overall mAP score by 1.89%.
Design and Implementation of ANFIS on FPGA and Verification with Class Classification Problem

In this research, we implemented ANFIS (Adaptive-Network-Based Fuzzy Inference System) on an FPGA(Field Programmable Gate Array) as a System-on-Chip (SoC) system and evaluated its performance using iris flower classification. Additionally, we developed and tested an IEEE 754 16-bit floating-point format tailored for ANFIS operations. The evaluation focused on computational accuracy and speed. The results demonstrate that this FPGA-based ANFIS implementation, coupled with the 16-bit floating-point format, yields high efficiency and superior computational performance.
Research on Algorithms of Lateral Face Recognition Based on Data Generation

With the rapid progress of artificial intelligence, the methods of face recognition have also achieved considerable progress. However, when multiple head poses are present, the accuracy of face recognition decreases due to angular deviation. Therefore, how to realize the enhancement of accurate precision in two-dimensional multiple head poses is still a worthy research topic. In this paper, based on the ResNet50 residual network structure, data enhancement and attention mechanism are adopted, and the Public Figures Face Database public face database is used to partition the training and test sets. This hybrid approach effectively improved the accuracy of face recognition in lateral. However, the hybrid method has low accuracy for large angle deflection face recognition. Therefore, based on the hybrid method, this article proposes a side face recognition algorithm based on data generation, incorporating a data generation algorithm. The recognition accuracy of the front face of this algorithm is as high as 96.3%. This article presents a data-generated lateral face recognition algorithm that further improves the accuracy of large angle deflection face recognition.
Exploring Whether CNN-Based Segmentation Models Should Extract Features in Earlier or Later Stages for MRI Images

Segmentation of major brain tissues from 3D medical images can contribute to improving diagnostic quality and reducing workload. This study aims to explore the proper structure to segment brain regions from MRI volumes. The dataset used was the preprocessed IXI dataset, and segmentation was performed on 46 regions from the head MRI images. Experimental results show that it is important to extract features in the first stage, when the resolution is large.
Pipe Alignment with the Image Based Visual Servo Control

Tube alignment is an important task characterized by the complexity of processing and aligning large-diameter tube segments. Traditional optoelectronic measurement systems consist of a large number of components, and a significant amount of time is spent on preliminary system calibration. In this article, a method of visual servo control using a FishEye camera and structured light is proposed to optimize the tube center position. The proposed technical vision system contains fewer elements, simplifying the system configuration and setup. Firstly, the optical axis of the technical vision system is aligned with the inner surface axis of the tube through calibration. Then, laser points belonging to the inner surface of the tube are extracted. Next, a visual servo control law is applied to control the deviation between the desired and current positions of the tube center. Finally, experiments are conducted in a developed virtual environment that replicates the real technological process to demonstrate the effectiveness and reliability of the proposed method. Real-world experiments involve measurement uncertainties, making it challenging to compare different methods. Additionally, it is difficult or impossible to estimate certain parameters in real conditions. The virtual environment helps overcome these issues. The simulation environment and testing code are available online: https://github.com/kholodilinivan/Pipe-Alignment-IBVS .
Zero-Shot Action Recognition with ChatGPT-Based Instruction

As deep learning continues to evolve, datasets are becoming increasingly larger, leading to higher costs for manual labeling. Zero-shot learning eliminates the need for substantial labor costs to label training datasets. It even allows the model to predict new classes with slight modifications. However, the accuracy of zero-shot learning still remains relatively low and makes practical applications challenging. Some recent research has tried to improve accuracy by manually annotating features, but this approach again requires labor-intensive input. To reduce these labor costs, we employ ChatGPT, which can generate features automatically without any manual involvement. Importantly, this approach maintains high accuracy levels, surpassing other automated methods.
A System for Estimating the Importance of Speech Based on Acoustic Features

With the development of AI technology, the accuracy of speech recognition and the range of its use are advancing. With AI-based speech processing, many services such as automatic speech transcription, speech recognition, and speech summarization are now available. In this paper, a method is proposed for determining whether each utterance is important or not based on acoustic information of the utterances. As acoustic features, statistical measures of various acoustic features for each utterance are used. To determine the importance of the utterance, the importance of the transcribed text is labeled using the LLM chat system and trained as a supervised input. In this experiment, TED video speeches on YouTube are used as speech materials. A machine learning model with a random forest classifier is used to determine the importance. As a result, a model that can classify the importance for the training data is obtained. It is found that statistical measures of acoustic features related to the fundamental frequency (F0) of the utterance are frequently used as important features for classification. However, when evaluated on test data, it is found that sufficient accuracy is not achieved, and further examination is necessary.
Critical Success Factors for Implementing Cloud ERP in SMEs: A Systematic Review

Digital transformation has transformed the business processes and operations of firms worldwide. Companies need to adopt new technologies capable of automating and boosting the business to maintain competitiveness in a global market. This study aims to identify the critical success factors (CSFs) that impact Cloud ERP implementation in small and medium enterprises (SMEs). A systematic literature review was conducted with the support of the PRISMA-P. The identified factors were classified following the technology–organization–environment (TOE) framework. The CSFs that stood out most were system compatibility with the organization’s needs, data security, and senior management commitment support. This study contributes to the literature by identifying and grouping the CSFs that most impact the implementation of Cloud enterprise resource planning (ERP) in SMEs and to the practice by serving as a guide for managers and decision-makers of SMEs.
Simulation for Development of Microcomputer Car with White Line Following Controller

The objective of this research is to develop a controller that accurately tracks white lines and propose a control method for white line tracking vehicles using a camera. In this control method, a Convolutional Neural Network (CNN) is applied to the line image, and the control amount is determined based on the class probabilities obtained. To optimize the parameters, Differential Evolution is utilized. When creating the model, the same image is included in multiple classes to introduce ambiguity in the output probabilities and ensure smoother transitions between courses. Furthermore, the generated model is compared with the driving data acquired at the time of image capture to confirm its tracking performance.
Energy Content Analysis of the Large 2019 Ridgecrest, California Seismic Sequence

The energy content of ground motion records obtained during the 2019 M6.4, M7.1 Ridgecrest, California earthquakes which occurred closely in space is analyzed in this paper. Wavelet transform as a powerful tool, that is used to evaluate the energy content characteristics including cumulative energy profiles and the total energy for different components of records, was obtained at three stations named China Lake Christmas Canyon, China Lake and Tower 2. Due to the fact that the energy-based parameters could be well correlated with damage distribution, this path has been taken into consideration in this research work with the light of the powerful wavelet tool. For this purpose, the discrete wavelet transform is efficiently used for multi-resolution decomposition of acceleration time history of ground motions and a new framework is presented to estimate the energy level of earthquake sequences. Finally, the pseudo-period corresponding to each corresponding level is introduced and the distribution of energy vs. the pseudo-period is discussed. The results of this paper provide a platform to compare the energy levels of different earthquake sequences in different periods. Analysis showed that wide-band energy distribution could be identified at China Lake Christmas Canyon and Tower 2 stations.
IHGWO-Based Optimization of IoT Wireless Sensor Networks

The Internet of Things (IoT) is a rapidly expanding field, with billions of devices now online. This expansion has increased demand for efficient and dependable wireless sensor connectivity. A new method for reducing wireless sensor connectivity time in IoT systems using the Improved Hybrid Grey Wolf Optimization (IHGWO) strategy is proposed. Grey Wolf Optimization (GWO) is a population-based metaheuristic algorithm based on wolf hunting behaviour. It has been demonstrated to be effective in solving a wide range of optimization problems, including wireless sensor network issues. We have used IHGWO in this paper to optimize the placement and connectivity time of wireless sensors in an IoT system. Our method is evaluated in three scenarios: (1) without optimization, (2) with IHGWO and 5G critical and hybrid additions, and (3) with IHGWO and optimized sensor positions determined by the defined algorithm. These results demonstrate the potential of IHGWO as a method for reducing the amount of time wireless sensors require to connect in Internet of Things (IoT) systems. Using IHGWO, we were able to reduce wireless sensor connectivity time significantly.
Keyword-based Research Field Discovery with External Knowledge Aware Hierarchical Co-clustering

Helping researchers in understanding the current position and significance of their research field benefits both the individual and the development of science and technology. Clustering techniques are traditional approaches based on text mining of papers and their metadata, but suffer from inconsistent representations of words in metadata uniquely assigned by authors, resulting in low accuracy. To address this issue, we propose the application of HICCAM, a hierarchical co-clustering method that exploits auxiliary knowledge of clustered objects to discover research fields. Our first step involved constructing augmented matrices representing paper abstracts and research keywords from external domains. We then cooperatively and accurately clustered a relational matrix of these objects using these augmented matrices. To validate the effectiveness of our framework, we conducted a case study using conference papers published in the field of computer science and various auxiliary knowledge. The comparative analysis identified the domain of auxiliary knowledge that contributes to the research field detection, and the visualization results showed it effective for field discovery in terms of easy interpretation and scalability brought by the hierarchical algorithm.
Algorithm for Human Abnormal Behavior Recognition Based on Improved Spatial Temporal Graph Convolutional Networks

With the increasing demand for public safety, the field of abnormal human behavior recognition has undergone significant development. In addressing the low accuracy issue of existing abnormal behavior recognition algorithms due to factors such as environmental influences, changes in viewpoint, and scale variations, this study proposed an improved Spatial temporal graph convolutional network. By incorporating spatial attention and channel attention mechanisms at relevant positions in the network, a dynamic optimization of the skeletal structure graph of the human body was achieved. This ensured that key nodes expressing motion information in the skeletal graph received greater weight values, ultimately improving the accuracy of abnormal behavior classification. To this end, an abnormal behavior dataset was constructed and transformed into skeletal information recognizable by the proposed algorithm using OpenPose. Extensive experiments were conducted on this dataset as well as the large-scale NTU RGB + D dataset using the improved algorithm. The results demonstrate that the algorithm has achieved an increase of approximately 5% in recognition accuracy compared to its pre-improvement state, placing it among the top-performing algorithms in various comparative evaluations.
Design of a Rotating Inverted Pendulum Control System Based on Qube-Servo2

In this paper, based on the Qube-Serco2 rotating inverted pendulum system, the mathematical model and state space expression are established, and a stable pendulum control algorithm based on BP neural network is proposed. On this basis, the consistency algorithm is used to control the three inverted pendulums, and the experimental results verify the effectiveness of the algorithm.
An End-to-End Intent Recognition Method for Combat Drone Swarm

In the field of intent recognition of combat drone swarm, traditional methods are based on the data characteristics which are only from a single target and at a single moment. It is difficult to capture the feature information of the entire swarm on time series. This paper proposes an end-to-end UAV swarm intent recognition method. Firstly, the distance threat coefficient and angle threat coefficient between UAVs are used to model the graph structure data of UAV swarm. Secondly, a novel deep learning method based on graph attention network, graph pooling method and gated recurrent unit (GAT-AP-GRU) is designed. This network can process the graph structure data obtained by modeling and identify the intention of the swarm. Experiments comparing with other methods and ablation experiments demonstrate that GAT-AP-GRU outperforms state-of-the-art methods in terms of accuracy of intent recognition.