Reviewerly/exHarmony huggingface dataset:
Design And Implementation of Laser Radar-Based Railway Foreign Object Intrusion Detection System

This study presents the design and implementation of a laser radar-based system for detecting foreign object intrusion on railways. The system uses laser radar to scan the railway tracks and automatically detects foreign object intrusion through data processing and analysis, promptly triggering alarms. It consists of a laser radar acquisition module, data processing module, and foreign object intrusion detection algorithm. Experimental results demonstrate that the system can rapidly and accurately detect foreign object intrusion events, effectively addressing issues such as high manpower costs and low efficiency associated with traditional manual inspection methods. This research provides a novel approach and technological means for ensuring railway safety.
Lightweight Gesture Tracking Algorithm for Mobile Smart Devices

Image-based gesture keypoints tracking has emerged as a prominent research topic in the field of artificial intelligence. The advent of deep learning has propelled the widespread adoption of neural networks as the prevailing approach to estimate key points of hand gestures in image data. However, existing neural network methods impose substantial computational demands, rendering them unsuitable for deployment on mobile terminals and similar devices. Therefore, this paper presents a novel gesture tracking method characterized by high speed and model accuracy to facilitate rapid inference on mobile terminals. To mitigate the computational overhead associated with neural networks, our method employs a lightweight neural network architecture for efficient image feature extraction. Furthermore, to minimize gesture tracking errors, we incorporate a training strategy based on random erasing, effectively augmenting the available data. To assess the effectiveness of the proposed algorithm, we collected a dataset comprising 79,082 gesture images and conducted comprehensive comparisons with alternative techniques. The experimental results demonstrate that our algorithm significantly reduces model parameters and computation, resulting in reduced execution time and improved accuracy. Consequently, it enables precise and swift recognition and tracking of the target gesture.
A Lossy Image Compression Algorithm Based On Fusion Of Multi-scale Cascaded Neural Networks And Discrete Cosine Transform

This paper introduces an efficient lossy image compression algorithm which combines multi-scale cascaded neural networks with Discrete Cosine Transform (DCT), in response to MPEG VCM Call for Evidence. The multi-scale cascaded neural networks are firstly used to compress the features and obtain a low-dimensional compact representation. Although this low-dimensional compact representation loses feature details, it capturing several key important cues within the feature at an extremely low bit-rate cost. Then, DCT is used to encode and decode the condensed features. At last, the decoded features produced by DCT are restored by the multi-scale cascaded neural networks. The proposed image compression algorithm is evaluated on multi-object tracking task and the experimental results show that this method just needs much fewer time to compress image at the cost of slightly sacrificing image quality in comparison to the standard methods given by MEPG.
Optimizing the performance of WebXR applications using Basis textures

With the replacement of the WebVR API by the WebXR API, users can now access virtual reality scenes through web browsers without the need to download separate applications for augmented reality (AR) or virtual reality (VR) functionalities. However, as the API capabilities improve and user demands increase, developers have to create more complex and engaging virtual reality scenes to meet user needs. Nevertheless, as web-based XR scenes become more intricate and feature-rich, users are limited by network bandwidth and page rendering effects when accessing virtual reality websites. Even a few milliseconds of delay can have a significant impact on user experience. One method to optimize the performance of WebXR applications is to utilize Basis textures. Basis textures are highly efficient image compression formats that can significantly reduce the size of texture files and improve loading and rendering speeds. By employing Basis textures, network transmission and loading times can be reduced while enhancing the framerate and responsiveness of the application, ultimately improving user experience.
A Lightweight Image Dehazing Algorithm Based on Deep Learning

The dehazing algorithm based on deep learning has good performance. However, this way has some problems such as high computational complexity and long latency time still exist, making it difficult to apply in real-time systems. To address this issue, this paper proposes a lightweight network called zUNet based on UNet. Firstly, a lightweight convolution module is designed to obtain redundant feature maps through low-cost computation. Then, the channel attention mechanism is used to extract global features. Replace The convolution modules in UNet are replaced with gate mechanism residual blocks to dynamically fuse feature maps. Finally, the proposed zUNet algorithm is compared with mainstream algorithms such as FFA-Net on the SOTS-IN and SOTS-OUT datasets. The results show that the peak signal-to-noise ratio (PSNR) of zUNet reaches an average of 37.38 dB, which is the best among all the evaluated algorithms. The number of parameters (Param) are 0.552M, the multiply– accumulate operations (MACs) are 1.933G, and the latency time is 6.031ms, at least 42%, 90%, and 39% lower, respectively.
Novel Regularization Method Exploiting Mutual Information for Deep Neural Networks

Dropout is a powerful way for preventing model overfitting. However, it is inefficient due to it randomly ignoring some neurons. Although there are many ways on Dropout, they are still either inefficient on improving generalization ability or not effective enough. In this paper, we propose Mutual Information Dropout, which is an efficient Dropout based on dropping neurons with low mutual information. In Mutual Information Dropout, instead of randomly ignoring some neurons, we first evaluated the mutual information of neurons to dropout with mutual information below a certain threshold. In this way, Mutual Information Dropout can achieve effective improving generalization ability with evaluate neurons. Extensive experiments on Three datasets show that Mutual Information Dropout is much more efficient than many existing Dropout and can meanwhile achieve comparable or even better generalization ability.
Integral Attack on 5 Rounds SLIM Algorithm

The resilience of the SLIM algorithm against integral attacks can be tested by activating one to four nibbles at positions 5, 6, 7, and 8 for all possible positions. Attacks can be carried out to recover the keys of the third, fourth, and fifth rounds using an integral distinguisher with 16 unbalanced nibbles. Key recovery is performed by searching for slices of the key candidates from three delta sets for each integral distinguisher. To find the seed key, it’s done by looking for the round key from the first round to the fifth round. The third round key is obtained from research on the delta set that have a three round integral distinguisher, while the fourth round key has a four rounds integral distinguisher. Applying an integral attack on the SLIM algorithm by activating one to four nibbles with all position variations is ineffective after five rounds, because all the nibbles on the SLIM algorithm are unbalanced, so that the full rounds of the SLIM algorithm is safe against integral attack.
Classification Research Based on Quantitative Expansion of Short Text Feature Correlation

Short text categorization is one of the important problems in Natural language processing. Due to the sparsity, eal-time, and non standardization of short text features, traditional text classification algorithms cannot effectively classify short texts. Feature vectors are commonly used forms of representation, but due to the limited length of short text, feature vectors often cannot accurately express short text information. Therefore, in order to improve the accuracy and reliability of classification, we propose a short text feature correlation quantification extended classification algorithm called IMIR. The IMIR algorithm is based on the idea of distinguishing features by class. By calculating the correlation between features, we can expand the feature vector and obtain more relevant information. After experimental verification, this method can significantly improve accuracy in short text classification. By introducing the IMIR algorithm, we can fully utilize the correlation between short texts and enhance the expression ability of feature vectors.
A Dual-arm Handling Robot for Nuclear Emergency Tasks

Nuclear emergency response requires a function robot with strong mobile ability, large load capacity, high reliability and flexible operation. For these requirements, this paper describes a dual-arm handling robot: the mobile base is designed as a track structure to adapt to complex emergency terrain; the drive part consists of the hydraulic and electricity units to support the base and two arms; the main controller and the irradiation tolerant electrohydraulic controller are combined to ensure the reliable control operation; the main and secondary work units are associated with two arms to meet the dexterous operations in complex workspaces. Lastly, a complete robot system is assembled, and the physical test shows that it can adapt to the nuclear radiation environment and meet the emergency operation needs.
A mobile industrial robot design

In this thesis, we focus on the chassis design and barrier traversal structure of a logistics distribution robot with four independent driving shop floor wheels, and establish a three-dimensional model. The chassis design with four independent driving wheels and an independent steering structure is primarily performed to realize the omnidirectional motion of the workshop logistics robot; the design of the barrier crossing mechanism allows the robot to have a barrier crossing function and be able to work in more complex road conditions, and it is verified that the robot meets the design requirements via Adams simulation.
Research on Seismic Wave First Arrival Picking Based on Improved U-Net3+

Seismic P-wave first arrival picking is a fundamental and vital component of seismic monitoring, with its accuracy playing a critical role in subsequent data processing. In order to address the problems of low signal-to-noise ratio data pickup accuracy and poor robustness of the traditional seismic wave first arrivals, this paper proposes an improved U-Net3+ seismic P-wave first arrivals picking method. Compared with the traditional method, the model does not require manual setting of thresholds and pre-processing operations such as filtering of data, and only requires input of normalized three-component seismic waveform data to intelligently identify seismic P-wave first arrivals. To minimize information loss during seismic waveform extraction and enhance the utilization of effective feature information at the P-wave first arrival time, the residual unit is incorporated solely in the encoder and decoder part of U-Net3+. The model exhibits a lightweight structure and excels in feature extraction, enabling effective identification of low signal-to-noise ratio data. The results demonstrate hit rates of 81.4%, 86.5%, and 87.5% for data with signal-to-noise ratios below 5dB, 10dB, and 20dB, respectively. The average pickup error is 0.054s, outperforming mainstream deep learning methods like U-Net and U-Net3+. These findings present a novel approach for automated P-wave first arrival picking.
A multi-sensor schedule and radiation control method based on RWSO algorithms

To address the radiation risk problem in the sensor system during target tracking, we present a long-term scheduling method of radiation cooperative positioning under multi-sensor platform. First, quantify the tracking accuracy and radiation risk, which is associated with the distance between the sensor and the target, the transmitted power. Second, design the optimization variables including discrete variables and continuous variables, and the target optimization function balanced tracking accuracy with radiation risk. Finally, considering the effect of local decisions, establish the sensor forward scheduling model and solve the scheduling scheme by the war strategy algorithm based on the inverse learning mechanism. Simulation results show that the proposed scheduling method can effectively balance tracking precision and radiation risk, and realize radiation risk control in target tracking process.
Vision-based head posture control wheelchair system research

In terms of interactive equipment with traditional man-machines, now it can directly realize human-computer interaction in the field of computer vision. In order to solve the problems of daily operations of disabled people, build a wheelchair control system combined by soft hardware. Users only need to use simple devices to complete the goals of daily operations. The control system mainly uses the real-time facial key point detection method of MediaPipe. Use a USB camera to achieve precise head posture estimation. For the error ratio of the algorithm, the average error ratio of the partial yaw angle obtained in the end is (0.0579), the average error ratio of the pitch angle is (0.0518), and the average error ratio of the roll angle is (0.0294). The maximum error is the yaw angle measurement angle (0.065), The overall error is within 1 degree to meet the requirements of experimental accuracy. The real-time motion control task of the wheelchair is completed through the non-contact human-computer interaction mode of camera head attitude estimation, and the wheelchair drive control is realized in the ROS system. Through experimental verification, the user successfully reaches the destination in a complex environment through head attitude control in a visual way, providing a solution to the human-computer interaction problem of the wheelchair; This can assist those with special disabilities, making it more user-friendly for real-time control of wheelchairs.
Intelligent Unmanned Aerial Vehicle Flight Control and Task Execution System

To tackle a host of challenges, such as the lack of intelligence and autonomy in the existing Unmanned Aerial Vehicle (UAV) system as well as the need for frequent manual intervention in task execution, this paper explores the intelligent operation in the execution of UAV tasks. Additionally, this paper deeply investigates the subsystems with high intelligent demands in UAV systems, analyzing their realization based on the existing technology. Finally, the prospect concerning the intelligent control of UAVs in combination with the actual situation is discussed.
Feature Expansion Using GloVe for Hate Speech Detection using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) Method in Twitter

In this day and age, people have easy access to social media, allowing them the freedom to express their opinions. Consequently, hate speech against individuals or groups can be easily found on social media platforms. This paper proposes a system to classify hate speech in Indonesian language tweets. Three distinct deep learning techniques were employed: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Hybrid RNN-CNN. The dataset used for training and testing was collected from Twitter. The collected tweets were categorized into hate speech and non-hate speech. Feature extraction and expansion were performed using Term Frequency-Inverse Document Frequency (TFIDF) and Global Vectors (GloVe) methods. Several scenarios were considered to compare the effectiveness of various approaches and determine the model with the highest accuracy. Among these approaches, the RNN method with feature expansion on the top 5-word similarity achieved the highest accuracy rate of 91.34%. Following closely, the Hybrid RNN-CNN method with feature expansion on the top 5-word similarity obtained an accuracy of 90.69%, while the CNN method with feature expansion on the top 5-word similarity yielded an accuracy of 90.64%. Notably, all three highest models utilized a corpus constructed by combining the tweet and news datasets.
Detection of Kids Handwriting for Numbers With Convolutional Neural Network Algorithm

In this age of globalization, where technology has impacted many demographics, including preschoolers, there are numerous detrimental effects on children from technology, such as stunted motor skill development. Applications for practicing writing and counting are one way to help preschoolers develop their cognitive skills. A system to identify children’s handwriting in the form of an image classifier employing a convolutional neural network (CNN) is needed for the creation of an application like this. So, in this study, researchers built the system using CNN. This study seeks to learn how to construct the CNN algorithm with the Softmax activation for image classification, how to evaluate the CNN method’s performance for numeric image classification, and create Drawing Apps for Kids utilizing Stream-lit. The dataset utilized consists of two-dimensional images of the numbers 0 to 9 that have been manually drawn using the paint program. Each data class’s 200 images have a varied resolution, making up a total of 2000 images in the collected data. Later, the data will be split into training data (70%) and validation data (20%), and test data (10%). Five convolutional layers with the ReLU activation function, one layer with a filter size of 64, and two layers with a filter size of 32 make up the CNN model. The researcher additionally used five pooling layers, each with a $2 \times 2$ matrix size. With training data that has the same resolution as $350 \times 350$ pixels, the CNN model is trained.
A Significant Wave Height and Peak Wave Period Prediction with Transformer and LSTM Approach in Cilacap, Indonesia

Wave phenomena in the ocean can fluctuate like other weather parameters, making forecasting challenging for those who carrying out the activities at sea. Wave forecasting is very necessary to support daily marine activities such as marine transportation scheduling and daily operation offshore or in the harbor, so that these activities can be carried out with careful planning and can minimize the risk of accidents. Significant wave height (SWH) and peak wave period (Tp) predictions are essential to wave forecasting. In this research, we perform a time series wave forecasting for SWH and Tp using a relatively recent deep learning model, i.e., Transformer. As a case study, we choose a location in the southern part of Java island, Indonesia, i.e., on the Cilacap coast. We also compare the Transformer results with the well-known LSTM model. Transformer is used because it has an architecture based on encoder and decoder methods which allows this model to learn data better and also train the model faster in terms of performance. Meanwhile, LSTM is used because it’s ability to remember long backward historical data. The result shows that the Transformer model performs better in terms of correlation coefficient and root mean squared error than the LSTM model for Hs. At the same time, LSTM came as a better model for Tp than the Transformer.
Mean Square Error Analysis of Temperature and Humidity in Telkom University Landmark Tower (TULT) Using Multiple Linear Regression (MLR) and Internet of Things (IoT)

Differences in temperature and humidity are observed across various regions and altitudes, attributed to factors such as altitude’s inherent effects like solar radiation and the influence of surrounding winds. Daytime hours typically exhibit higher air humidity levels, a phenomenon similarly observed in building heights. Take, for instance, the Telkom University Landmark Tower (TULT) building. Varied heights within the structure influence temperature and humidity levels. In the academic year 2022/2023, Telkom University in Bandung, West Java, admitted 8,036 new students, a fraction of whom engage in lectures within the TULT building. It is plausible that the influx of students through different entrances to TULT could impact temperature at various altitudes. To address this, the application of Multiple Linear Regression (MLR) and Internet of Things (IoT) methodologies becomes pertinent for predicting temperature and humidity at distinct TULT heights. This involves utilizing the Blynk application for data collection and analysis. Subsequently, the MLR and IoT algorithms are employed to estimate temperature and humidity values. The experimental results are then scrutinized utilizing the Mean Squared Error (MSE) to anticipate error values for each trial. Additionally, the coefficient of determination, R2 or R Square, is utilized for analysis. The MLR model, utilizing training data, exhibits superior performance in predicting temperature and humidity on the 6th floor of the TULT building compared to using testing data. The MLR model effectively elucidates data variability in temperature and humidity when trained data is employed.
Text Readability Analysis for Indonesian School Textbook

In Indonesia, the Merdeka Curriculum’s learning process mandates using books as essential teaching tools. The Ministry of Education, Culture, Research and Technology of the Republic of Indonesia regulates book quality standards to ensure book quality. To meet the book quality standards outlined in this regulation, it is necessary to have a tool that can analyze the text readability in the book. Assessing text readability can be accomplished by employing a readability formula and constructing a machine-learning model. In this study, we focus on analyzing the readability of Indonesian texts. We build readability models by applying readability formula and machine learning approach as a two-step process. The first step is classifying the text into two categories: complex and simple, and the next step is identifying the text difficulty level according to readability formula. The readability formula measures the grade level and reader age. The experimental results show that the machine learning model successfully classified the text with 83.1% accuracy. On the other hand, the result of the readability formula is that the readability age is higher than the reader’s age. The result of the readability formula is the predicted readability age is higher than the intended reader age. We analyzed the readability result and found that Indonesian textbook has more complex linguistic feature than English textbook, therefore the predicted reader age is higher.