Reviewerly/exHarmony huggingface dataset:
The Implementation of Pedestrian Detection Based on YOLOv5s

Pedestrian detection, as a very important function in intelligent driving systems in the driver-less field, has been receiving high attention in recent years. With the development of artificial intelligence (AI) identification, pedestrian detection using AI identification technology different from traditional identification methods has become a popular research topic. In this study, a pedestrian detection system using YOLOv5s is implemented. This practical operation can be divided into three steps. First, Backbone with YOLOv5s is used to calculate the feature map. The Backbone is composed of multiple CSPI blocks, and CSPI is composed of multiple CBSs. A CBS is divided into three parts, namely Convolution, BatchNormalization and SiLu. Then, Neck is used to capture the feature maps of different sizes from Backbone computation process for subsequent processing. The Neck is composed of multiple CSP2 blocks, and CSP2 is also composed of multiple CBSs. Those feature maps with different sizes are obtained through UpSample, and used to perform convolution operations separately, and finally three sets of feature maps with different sizes are output. Lastly, the matrix of all the recognition frames is calculated by Head, which is composed of three convolutional layers of different size. Respectively for the three sizes of the Neck outputs, Head outputs three matrices of 19200$\times$6,4800$\times$6, and 1200$\times$6. They are then combined to a matri$\times$ of 25200$\times$6, which contains 25200 numbers of recognition frames and 6 metrics: X Coordinate, Y coordinate, width, height, confidence and class. To consider different pedestrian poses, the training pictures of the model contain a variety of human poses, so the trained model can also be used for a variety of poses. The experimental results show good recognition rates.
Technology Organization Environment in Predicting Auditor Intention to Study Metaverse Audit

Currently, in the era of industry 4.0, the business world is experiencing rapid changes. This change is marked by various developments in the world of technology, including the phenomenon of the metaverse world. Many companies already have digital assets in the metaverse world, such as: land, property and also have deposits in cryptocurrency. This creates new challenges for accounting and auditing. It is urgent to be prepared by accountants and auditors regarding this metaverse world, which triggers us to conduct research. Our research is a quantitative study using a descriptive statistical approach. We examine the auditor's willingness to study the audit metaverse using the Technology Organization Environment Theory. Our research object are auditors who works in a public accounting firm both big four and non-big four audit. We use statistical data processing using SMART PLS software. The results of our study state that organization context and environment context have significant influence to auditors' intention to study metaverse audit. While technology context must be supported by moderating variable of firm size in order to significantly affect intention to study metaverse audit.
Using Stereo Depth Estimation Network and LiDAR-Assisted Camera for Dehazing

Dehazing research is crucial to ensuring the safety of autonomous driving. To estimate the scattering coefficient of the scene, we use the point cloud produced by LiDAR. To acquire a more precise scene depth, we employ a stereo depth network. Finally, we dehaze the image using the transmission map of the atmospheric scattering model and the atmospheric light value. Experimental results show that the proposed dehazing method works better in object detection than previous dehazing methods.
Using AI Platforms in AI Liberal Art Class

With the advent of deep learning technology, the interest in artificial intelligence (AI) has been fast growing and many countries and institutions have devoted their energies into AI education with different levels from novice to expert. Especially, many Korean universities offer mandatory AI liberal art class to non-engineering major students since AI literacy, the ability to know, use, and evaluate AI fundamentals and applications, becomes an important skill. However, these students tend to be not motivated or interested in AI education because the contents of the AI liberal art class have no direct relation to their majors. To solve this problem, the curriculum of using various no-coding AI platforms was designed and implemented, such as Deep Dream Generator, Text Summarization Tool, Google Teachable Machine, and Orange3. The survey was performed after the three-month course to prove the effect of using AI platforms in AI literacy education.
Computation Sourcing in Smart Surveillance Systems for Maximum Information Retrieval

With the booming of artificial intelligence and the advance of embedded systems, smart surveillance systems can now deploy video analysis techniques, such as human detection and object classification, on surveillance cameras directly. Such an approach decentralizes the computation and allows elastic camera deployment without upgrading the centralized server. Nevertheless, taking human detection as an example, cameras at different locations could have very different numbers of people walking by at different times. This condition could make specific cameras run their detection tasks at the highest frame per second (FPS) but still lose information if FPS is lower than a certain value, while a small group of cameras has almost zero tasks, thus wasting valuable computation power. In addition, as the smart surveillance system grows, cameras deployed at different periods could have different specifications and computation capabilities; therefore, properly utilizing all the computation resources has become essential for smart surveillance systems. The aforementioned observations motivate this study to propose a novel computation souring method by following the concept of crowdsourcing to enable efficient resource sharing within smart surveillance systems and maximize the amount of retrieved information. Promising results have been demonstrated through a series of experiments with different smart cameras of different specifications.
BAMTGAN: A Balanced Augmentation Technique for Tabular Data

This paper presents BAMTGAN, a novel data augmentation technique that addresses the class imbalance problem and prevents mode collapse by utilizing a modified DCGAN model and a new similarity loss to generate diverse and realistic tabular data. BAMTGAN encodes each column to produce a feature map for each record, which is then converted back to its original tabular form an intermediate image format. Experimental results demonstrate that BAMTGAN provides a more substantial improvement in developing high-quality predictive models than existing augmentation methods. Github: https://github.com/uos-dmlab/Structured-Data-Augmentation.git
Fourier-Legendre series neural networks for regression

In this study, an effective artificial neural network (ANN) using Fourier-Legendre series expansions for hidden neurons are successfully proposed, named FLSNN. The performance of the FLSNN was examined for regulation problems in machine learning applications. The results of two numerical examples of Boston price regulation problems using FLSNN are compared with classical well-known two ANNs, i.e. the backward propagation neural networks (BPANN) and radial basis function neural networks (RBFNN). It is shown that FLS offers high R-square in regression analyses.
Automatic Pipe Routing in Buildings

Concealed pipes are widely used to transport water, air, natural gas, and sewage in buildings. In this paper, we propose an algorithm for designing concealed pipes in buildings such that the productivity of construction industry is enhanced. When routing concealed pipes in a building, the walls, floors, and ceilings form the workspace of the piping computation. We treat these slabs as vertices and generate edges between them, based on their connectivity. Consequently, the frame of the building is encoded in a graph, which constitutes the top-level representation of the workspace. Then all the slabs are divided into voxels by using regular grids, and the bottom-level representation of the workspace is created. The pathfinding of a pipe is carried out in two stages. First, the routing process generates a shortest path in the top-level graph to identify the slabs penetrated by the pipe. Then, the routing process creates the detail pipe path by using the voxels of these slabs in the bottom-level representation. Once this pipe has been constructed, the voxels occupied by it are deleted from the workspace and the routing computation is repeated to arrange other pipes.
The Efficient Traffic Sign Detection and Recognition for Taiwan Road Using YOLO Model with Hybrid Dataset

According to the trend of worldwide car sales have grown up, this cause may increase accidents on the road due to human error. The self-driverless car has been developed to solve this problem. One task of the self-driverless car is traffic sign detection and recognition (TSDR), which will help drivers notify the traffic sign installed on the road in advance. Taiwan roads have specific traffic signs, and no Taiwan traffic sign public dataset is available. In this paper, our proposed object detection method was experimentally performed using YOLOv5s6 and YOLOv8s models on three different datasets, as Tsinghua-Tencent 100K (TT100k), the self-created Taiwan traffic sign (TWTS), and the hybrid dataset, which combine the traffic scenes between TT100k and TWTS dataset. The output results from each dataset and each model, which is trained on the same parameter, will be compared to validate the proposed method. The experiment results’ comparison of the hybrid dataset between YOLOv5s6 and YOLOv8s models display the results of the mAP@.5 is about 65% and 76.2%, respectively, which means the performance of the YOLOv8s is higher than the YOLOv5s6 when using hybrid dataset.
An URL Protection Approach for Apps on Smartphone by Built-in VPN Mechanism

This paper presents a security system that uses the built-in Virtual Private Networks (VPN) mechanism on Android to monitor the Uniform Resource Locators (URLs) linked to when apps are executed. Then, the proposed system sends the URLs to VirusTotal, a third-party security website for evaluation. Finally, the security score of each URL is presented to the smartphone user, providing them the choice to decide whether to remove an app with security concerns.
Frames Extraction from Table Tennis Competition Videos for Action Classification Using Optical Flow and Fuzzy Rules

To recognize actions using a neural network model, it is necessary to extract the correct frames from the video for the input of model. Extraction of frames is an important issue that could be poor recognition results or costs computation time. This study proposes a new extraction method that combines optical flow and fuzzy rules. First, optical flow is used to calculate the values of the x and y vectors of the motion in consecutive frames. After expert discussion, rules are formulated to define the optical flow values for each action as fuzzy semantic words and stored as a fuzzy rule base. For the experiment, serving action is further subdivided into tossing, hitting and receiving parts in table tennis video. Using fuzzy rules based on the x and y optical flow values of different actions, the current action type can be determined, and action frames can be extracted more accurately, improving the accuracy of table tennis action recognition, the final result of table tennis action recognition reached up to 69.8% accuracy.
Design and Development of an Interactive Chess Board for Quarto

This research designed and developed an interactive chess board capable of playing Quarto game against humans. The designed interactive chess board utilizes CoreXY mechanism to manipulate the electromagnet to achieve two-dimensional movement under the chess board. Furthermore, the designed interactive chess board employs a depth camera with the YOLOv5 algorithm to identify the features and position of each chess piece. Finally, the game strategy mechanics of the Quarto game are combined to the system for the chess board.
Deploying Pre-Quantized Deep Learning Models on Heterogeneous Platforms with Operator Flow Recognition and Quantization Parameter Optimization

Quantized deep learning models are suitable for the embedded devices with limited computation resource. For computation-intensive neural network operators such as convolution, heterogeneous platforms with a set of processing units of different types become common in the embedded devices. These embedded devices usually operate on fixed-point calculations; moreover, they rely on customized kernel functions to deploy deep learning models. In this paper, a flow of deploying pre-quantized deep learning models on heterogeneous platforms using TVM is presented. We propose an optimization to convert quantization parameters. To leverage customized kernel functions, we propose the operator flow recognition. To demonstrate our flow, we utilize embARC Machine Learning Inference (embARC MLI), an open-source software library targeted for low-power applications. A set of pre-quantized deep learning models are deployed on a heterogeneous platform comprising x86 and embARC MLI. Experimental results show that for each model, the accuracy obtained from the heterogeneous platform is much the same as the one obtained from an x86 platform.
A High Efficiency Hardware Accelerator for Convolution Neural Network

A convolutional neural network accelerator that can efficiently process AlexNet’s convolutional neural network architecture is developed. The processing element (PE) array can perform operations on each layer of convolution by simply setting the input feature map (ifmap) size, filter size, and other convolutional model settings before inputting data. The PE array then selects the optimal segmentation method based on these settings for each operation. The calculation values are transmitted through a data bus from the global buffer, which stores input feature maps, filters, and other relevant data. The partial sums obtained by the PE operation are also transmitted back to the global buffer through the data bus. After the complete operation, the output feature map is passed through the ReLU function and data compression encoder before being transmitted back to the off-chip memory through another data bus. Both numbers and times of ifmap passed will be greater than those of filters. To accommodate the high throughput of ifmap, the width of the scratch pad (spad) and buses for ifmap are designed to be larger.
Real-time Retweet Count Prediction using GNN Model

Twitter users usually care about the popularity of their tweets, and retweet count is always a good measure. Along with Neural Networks have achieved outstanding accomplishments, Graph Neural Network (GNN) becomes a new research field. In this article, we select a group of active users on a Twitter page. After observing their recent retweet behaviors, different GNN models are constructed and labeled. These GNN models are used to predict the user retweet behavior and estimate the retweet count in the early stage.
Reinforcement Learning for Stock Price Trading with Keywords in Google Trends

In this paper, we apply the Proximal Policy optimization (PPO) algorithm to train an agent for automated stock trading. In additional to the conventional trading indicators, we also add the strength of keywords obtained from the Google Trends for training the agent. We conduct two experiments to test the effectiveness of adding keywords. The first experiment uses general keywords, such as inflation. The second experiment uses stock-specific keywords, such as AAPL for trading apple stock. The experimental results confirm that the proposed approach can improve trading performance.
A Research Structure of Big Data Analysis and Application for Table Tennis Match Tactics Based on Computer Vision

In recent years, many deep learning techniques have been widely applied in sports events. Therefore, the research based on the collection of big data and applied to the analysis of the overall playing tactics of table tennis is competitive. This study proposes a structure to support this idea, which includes the match video collection raw database, video processing, action classification machine learning model, knowledge database and big data analysis website. Under the above structure, this research focuses on using machine learning model to automatically classify the types of serve motions. The table tennis motion dataset is created by professional players. They cut and label the competition video to complete the database. Then, use these data to train a 3-dimension convolutional neural network (3D-CNN). This experiment selected three common types of serve motions to classify. After training the model, with the validation dataset, the accuracy can reach 89.5%. This result shows that machine learning models have sufficient accuracy to recognize motion categories in table tennis serve motions. Therefore, the proposed method will also be extended to all kinds of motion classification to accomplish efficient and accurate table tennis player competition record. Finally, hoping this model structure can be applied to the variety of sports.
The Deep Learning-Based Image Enhancement Method for High-Contrast Low-Light Images

This paper presents an effective deep learning network to achieve higher enhancement effect for the high-contrast low-light text & digit images. The originality of our work is to use the channel attention layer for finding more effective features to improve the enhancement effect and exploit the weighing feature map to substantially enhance the details of text & digit images. Experimental result shows that our work has a higher natural visual-comfortable effect and high-quality detail enhancement than other methods.
Using Block Filter Device for Acceleration of Storage Synchronization on Fault-tolerant Virtual Machines

A fault-tolerant virtual machine (VM) system has the ability to continue its execution even when it encounters a failure. Such a system typically consists of a pair of VMs, the primary VM and the backup VM. The primary VM hosts user applications and continuously transfers its internal state to the backup VM, while the backup VM needs to perform failover upon a failure on primary VM. The synchronization efficiency of a fault-tolerant VM system is a major concern since the primary VM needs to stop execution of user applications to perform synchronization. This study focuses on the issue of disk synchronization efficiency for the fault-tolerant VM system, and proposes a new approach for efficient disk synchronization under the active-passive VM fault tolerance model. Our experimental results show that, the proposed approach has nearly the same performance as the shared-storage approach (the no-disk-synchronization approach).
GNN-based Approach for User Retweet Behavior Prediction

As Neural Networks have achieved excellent results in many fields, researchers attempt to use Neural Networks to solve graph processing tasks such as node classification, link prediction, and graph classification. Graph Neural Network (GNN) is a machine-learning model that takes graphs as input. In this article, we select active users on a Twitter page and predict their retweet behaviors using Graph Attention Network (GAT). Negative sampling is also applied due to the imbalanced dataset. Experimental results show that GAT well predicts the retweet behavior and tweet count.
Application of VR Technology in Middle School Students’ English Speaking

With the continuous deepening of education informatization reform, digital learning has gradually become an important part of education. Among them, the virtual learning environment is created through Virtual Reality (VR) technology, and the abstract learning content is embodied in the virtual environment in combination with the game teaching method of educational games. This not only reduces the requirements for learners’ transfer ability but also can be better applied to English teaching. Based on the characteristics of VR, the author designed a VR education application for middle school students. There are five parts to this article; the first part introduces the background information about VR and the problems that Chinese middle school students are facing. The second part analyzes VR’s characteristics and demonstrates the feasibility of VR in oral English learning based on VR characteristics. The third part describes the application’s activities in detail and the fourth part is to analyze the advantages and disadvantages of the application. The last part summarizes the outcomes and limitations of the application and predicts this application’s future development.
Improving The Middle School STEM Education in Rural China Through Virtual Reality

Left-behind children in rural China are troubled by educational inequalities and a lack of effective education; STEM education implemented by VR might be an answer to the problems they face. This article discusses the application of VR specifically targeting middle school STEM education in rural China. VR is characterized by its immersive, interactive, multimedia, multisensorial, engaging, authentic, spatial, and social affordances, possessing abilities to offer enhanced learning experiences different than other educational technologies. Specifically, case studies evaluated in this article show how VR can help better deliver STEM content by providing opportunities for practical, collaborative, and problem-based learning essential in effectively conveying the abstract and complicated theoretical knowledge of STEM. Learners are more motivated and achieved better learning outcomes, proving the effectiveness of VR in STEM education. Applying the insights in rural China, the creative and hands-on aspects of STEM education can bring out the left-behind children’s motivations and incentives for learning. Among all available technologies, VR is a suitable media choice to implement STEM, given its additional utilities of providing help for the personal and emotional challenges faced by the left-behind children, including their lack of incentives, positive attitudes, and company from parenting figures.
Experimental research on virtual simulation teaching of exhibition design based on immersive experience

Presently, in the design practice teaching courses at Chinese universities, students generally lack construction experience. Although some universities have training rooms, it is challenging to keep pace with the development of the industry due to high construction costs, long procurement cycles, and other reasons. There are potential safety hazards in some teaching practices. "Metauniverse" provides an immersive experience, and teachers and students can independently practice the design and construction without limitation of time and space. The "Metauniverse" exhibition designs a virtual simulation teaching laboratory through research, excavates students' needs, combines AR, VR, and other virtual reality technologies with somatosensory equipment, and provides channels to understand materials, experience construction, observe and learn, and correct errors in time, to improve the level of exhibition design education.