Reviewerly/exHarmony huggingface dataset:
Enhanced Security with Encrypted Vision Transformer in Federated Learning

Federated learning is a learning method for training models over multiple participants without directly sharing their raw data, and it has been expected to be a privacy protection method for training data. In contrast, attack methods have been studied to restore learning data from model information shared with clients, so enhanced security against attacks has become an urgent problem. Accordingly, in this article, we propose a novel framework of federated learning on the bases of the embedded structure of the vision transformer by using the model information encrypted with a random sequence. In image classification experiments, we verify the effectiveness of the proposed method on the CIFAR-10 dataset in terms of classification accuracy and robustness against attacks.
Generative Adversarial Network Based NDVI Super-resolution Reconstruction

Low resolution satellite remote sensing images remains a challenge for obtaining high-precision Normalized Difference Vegetation Index (NDVI), and contain some important vegetation cover information for supporting environmental analyses of the surface in large-scale regions, since high-resolution satellite remote sensing observations are still incomplete nowadays. In this paper, we consider the spatial resolution and time series characteristics of remote sensing images, and propose a super-resolution reconstruction algorithm for NDVI fused with Time features by optimizing the Super-Resolution Generative Adversarial Network (TSRGAN). Empirical research in the Salween River estuary area shows that the proposed approach has performance significant advantages in terms of PSNR and SSIM.
A development of a sensible dialogue agent that acquires personas from user utterances

With the recent development of natural language processing, the demand for personalized dialogue agents has increased. One way to personalize an agent is training a language model using a persona chat dataset incorporating the user’s information as personas. However, this method is inconsistent for dialogues unrelated to the persona, and the pre-defined persona does not change. This research aims to make an agent friendlier to users by adding personas extracted from the user’s utterances as well as the agent’s utterances. We conducted two user experiments and reconfirmed the improvement in performance by adding personas. We discuss the personality and friendliness acquired by the proposed method.
A Manipulating Simulator for Electric-Powered Wheelchair Utilizing Virtual Reality Technology

Electric-powered wheelchairs have provided individuals with mobility challenges significant opportunities to explore the outdoors, engage in work, and participate in activities. However, operating them can be demanding and sometimes result in serious accidents and injuries. In response, we have developed a driving simulator for electric powered wheelchairs utilizing virtual reality technology. It is worth noting that virtual reality experiences frequently induce motion sickness. In this study, we introduced this simulator and conducted an evaluation to assess the occurrence of motion sickness.
Performance Improvement of a Calibration-Free Visual Feedback Controller Using Lens Distortion Parameters

This study addresses performance improvement of a calibration-free visual feedback controller proposed in our previous work. In our previous work, lens distortion is compensated by robust controllers designed using the nominal model of the generalized plant model containing lens distortion parameter. In contrast, we examine the effect of the supposed nominal model which is used in robust controller design. That is, we design visual feedback controllers using artificially set nominal models from the supposed parametric models. The results of numerical simulations reveal the performance improvement of the controllers designed by our new method compared to our previous result.
Anomaly Detection Method for Online Monitoring Data of Dissolved Gas in Transformer Using Stacking Ensemble Learning

The concentration of dissolved gases in transformer oil can be utilized to diagnose faults in transformers. However, substandard online monitoring data may lead to inaccurate fault diagnosis outcomes, resulting in severe repercussions. Hence, this study presents a novel approach for anomaly detection in dissolved gas online monitoring data in transformer oil using stacking ensemble learning. Firstly, a sliding time window is employed to preprocess the monitoring data and generate a dataset consisting of time series monitoring data. Subsequently, evaluation metrics and diversity measures are applied to select distinct base learners and a meta-learner for the stacking model. This approach amalgamates the strengths and disparities of various learners. Lastly, comparative analysis of case studies demonstrates the effectiveness of the proposed method in distinguishing different types of anomalies in dissolved gas online monitoring data, exhibiting superior performance in terms of accuracy, F1 score, and area under curve(AUC).
Investigation of Usefulness of "PMusic": A Live Coding Language Using Probability Music

Live Coding is a type of performance in which a performer creates music by improvising and writing code. There are many platforms for Live Coding such as Sonic Pi, Tidal Cycles, and SuperCollider, but Live Coding has not penetrated the world that much. I think this is due to the fact that people feel the act of music production is a high psychological hurdle.The main purpose of this study is to create a new Live Coding language named "PMusic " and its interpreter with Go language, which mainly focuses on probability music beyond the framework of contemporary music, so that even people with no musical knowledge can easily perform it. As a result of this study, it is expected that the number of Live Coding performers will increase, that awareness of Live Coding will rise, and that expression using Live Coding will become richer and freer.The result of this study is to lower the psychological hurdle of music creation for people with no musical experience by providing three simple built-in functions for generating probability music. In addition, WebAudioAPI also allows Live Coding to be used on various browsers, including Google Chrome.
Reinforcement Learning Speed Control Algorithm Based on Giants Teaching Mechanism for JT9D Low Pressure Shaft

As a benchmark of the aviation industry, JT9D aero engine is a general object to study the new algorithms. Controlling the low pressure shaft speed is of great significance to the output power of JT9D. But the traditional PID controller often has overshoot. This kind of “vibration” is a challenge to the various parts of the sophisticated aero engine. Solving this control problem with the help of reinforcement learning algorithms has become a new research direction. The basic framework of reinforcement learning is to explore a strategy that maximizes the expected reward through the continuous interaction between Agent and Environment. But in the interacting process, the behavior of Agent may be dangerous, especially when the interactive Environment is a complex or sophisticated system, such as aero engine. In order to solve the above problem, inspired by the inheritance and development of knowledge in human society, this paper proposes a reinforcement learning algorithm with giants teaching mechanism. To examine the effectiveness, this paper takes the JT9D aero engine as the Environment to verify the effect on the speed control of the low pressure shaft. And the experiments of Proportion Integral Differential(PID) and Soft Actor-Critic(SAC) algorithms are also studied as a comparison. Experimental results show that our algorithm is more effective in limiting low pressure shaft speed overshoot and avoiding algorithm runaway.
A Residual Attention-based EfficientNet Homography Estimation Model for Sports Field Registration

Accurate sports field registration plays a crucial role in computer vision for effective team sports analysis, as it provides the ability to track and recognize the positions, movements, and interactions of players from image space to real-world field space. In this paper, we propose a two-stage deep learning framework for sports field registration. In the first-stage network, we utilize a Residual EfficientNet-Attention U-Net architecture to estimate the initial homography matrix using predefined keypoints to register sports fields. Subsequently, our second-stage network refines the initial homography for improved accuracy. We evaluate our proposed method on the public sports datasets to show its outperform results against the state-of-the-art methods in some evaluation metrics.
A Hybrid Unsupervised Learning Strategy for Monitoring Complex Industrial Manufacturing Processes

Industrial production processes are complex systems that require continuous monitoring to ensure efficiency, product quality, and safety. Anomaly detection is a crucial component of these monitoring systems, with machine learning (ML) methods offering significant advantages over traditional statistical techniques. Clustering-based unsupervised anomaly detection algorithms, such as Self-Organizing Maps (SOMS), are particularly valuable in process monitoring, as they can detect anomalies and identify process phases without requiring labeled training data. However, unsupervised learning methods can be sensitive to the inherent properties of the training data, particularly in cases of strong correlation between features or unbalanced datasets. This paper presents a hybrid unsupervised learning strategy (HULS) for monitoring complex industrial production processes. The proposed method combines existing techniques to address the challenges posed by correlated and unbalanced data. Experimental results from a synthetic dataset demonstrate the effectiveness of the developed hybrid approach.
Visualization of Game Strategies from Imitation Learning Agents

It is possible to use the agent as an agent that behaves like a human by machine learning how a human learner behaves in a certain situation. Such an imitation learning agent can be used for various educational purposes. It can be used to learn its own strategies and those of others by using XAI technology to visualize how the imitation learning agent evaluates the board.
Underwater Image Enhancement of Nuclear Power Plant Based on U-Net Model

High quality underwater image plays a key role in the underwater equipment inspection of nuclear power plant (NPP). However, when the light propagates in the water, attenuation and scattering occur, that lead to some problems such as low contrast, color distortion and artifact of obtained images owing the particular underwater environment of NPP. In this paper, a U-Net based end-to-end underwater image enhancement network with an inter-connection path and context-aware pyramid mechanism, called APU-NET, is proposed. Owing to poor image pair set for the APU-Net training, underwater imaging model is modified according to the particular environment of NPP, and a GAN based network is constructed to generate realistic underwater images to compose image pairs. Experiments show that the proposed APU-Net model, training with our synthetic underwater image pairs, can enhance underwater images of nuclear power plant effectively. Comparing with those of competitive algorithms, images enhanced with the proposed APU-Net are clearer with fine detail, fewer of color cast.
Rural medical search algorithm on heterogeneous information network with attribute equity

In recent years, rural medical search on heterogeneous information network has attracted more and more attention, and has been widely used in graph analysis. However, the existing rural search problems on heterogeneous information networks do not consider the fairness of attributes on subgraphs. By combining attribute fairness with kPcore mining on heterogeneous information networks, a maximum core mining problem on heterogeneous information networks based on attribute fairness is proposed. To solve this problem, a traversal methodwith vertex sign method is proposed to improve the efficiency of obtaining P_neighbor with high points. A large number of experiments on heterogeneous information network data sets prove the effectiveness and efficiency of the proposed method.
On the Phase Estimation Amplitudes in the Quantum Matrix Equation Solver

The phase estimation amplitude in the quantum matrix equation solver, namely the Harrow/Hassidim/Lloyd (HHL) algorithm is studied in this work. It is the periodic behavior of the amplitude with respect to the eigenvalue estimation error indicates the choice of the simulation time. The impact of the initial states on the quantum phase estimation (QPE) is also provided. This work is expected to be instructive for the formulation of the error analysis in HHL variants.
Cluster Dominance Analysis of Strength Training Motion Characteristics

This paper presents an approach to analyze clusters as a means to determine the characteristics of strength training motion patterns. The proposed method emphasizes the observation of dominance sequences within clusters and is reinforced by the formation of specific characteristics within each cluster. Data collection is performed using video-guided strength training exercises equipped with 1 kg dumbbells and recorded by a sensor embedded in smartwatches. The analysis method involves applying the concept of density affinity, which calculates the density ratio of clusters to the recognized motions. Subsequently, the dominance sequence is observed to identify which clusters exhibit distinct characteristics, ultimately determining the intended motions. The research findings demonstrate the potential for further investigation into a more comprehensive understanding of motion patterns, leading to the development of models that can be integrated into mobile devices or smartwatches.
Multistage Channel–Spatial Attention Network for Offline Text-Independent Writer Identification

Offline writer identification is used in security fields to identify a writer from a piece of handwriting. It has traditionally concentrated on page-level document images containing multiple paragraphs and sentences, relying on global and local features extracted from entire handwritten documents. In the recent digital age, there has been an increasing demand for word-level writer identification from a minute amount of handwritten text. However, the performance obtained when using the traditional handcrafted features is poor because large amounts of text data are required to obtain sufficient statistical information for writer identification. To alleviate the difficulty and improve performance in offline text-independent writer identification, we proposed a novel multistage channel–spatial attention network. The experimental results using the public CERUG-EN dataset confirm the effectiveness of our proposed method.
Python Based Electrical Engineering Training for Computer Engineers

The manuscript presents the capabilities of Python for solving tasks from the electrical engineering course. In this way, it becomes possible for computer science students to use programming abstractions and models in their electrical engineering education. This is useful in view of the increasing problems related to the acquisition of the basic material in electrical engineering related to conducting DC, AC analysis of various electric circuits. The proposed learning approach is illustrated with several examples related to the study of transient and established modes in various electric circuits, which are studied in electrical engineering by the students at the Technical University of Sofia, Bulgaria.
Evaluation of the Readability of QR Codes on High Reflective Stainless Steel

In recent years, the manufacturing industry has made many efforts to more reliably track the life cycle of products making use of special markings added during production. This marking must be long lasting and chemically neutral. In this study, marking with QR codes of different sizes and modules printed on stainless steel is analyzed using additive laser marking technology. The subject of the presented research experiments are the challenges in decoding QR codes due to the high reflectivity of the material surface.
Research of English Phonetic Training System on Cloud Platform

With the trend of globalization, modern information technology breathes new life into all walks of life. Concerning the complexity of English phonetic training, it’s urgent to explore a practical system related to information technology. Cloud service system provides a possible new educational technique, which would help English phonetic training achieve man-machine interaction, computing, Cloud storing, and integrating conveniently and efficiently. This paper introduces the background of English phonetic training under the circumstances of Cloud Platform. Then, current information technologies related to English phonetic training are exhibited. It proposes the feasible ways to construct English phonetic training system on Cloud Platform. By experiment, it makes quantitative and qualitative analysis, and further testifies the validity of this construction by analysing and discussing the results of the experiment due to statistical data. It draws the conclusion that to construct English phonetic training system on Cloud Platform is feasible and effective. It helps arouse more scholars to more concern about application of English phonetic training system on Cloud Platform.
Eating and Drinking Behavior Recognition Using Multimodal Fusion

The automatic recognition of eating and drinking behavior is very important for human health tracking. However, these behaviors are quite challenging to recognize automatically due to their sporadic nature. Previous studies have focused on the recognition of a few behaviors, whereas our work focuses on the recognition of the nine most common eating and drinking behaviors to provide more in-depth information. We used three cameras and three inertial measurement unit (IMU) sensors to collect data, and we used different fusion techniques to automatically recognize these consumption behaviors. Using a novel approach with a multimodal Multi-Stage Temporal Convolutional Network, we were able to achieve an accuracy of 83.03% with a reasonable F1 score for individual classes. Additionally, we compared the performance of our approach with conventional approaches for insights. Our work may be helpful to future research on dietary monitoring and activity tracking.
Caption Unification for Multiple Viewpoint Lifelogging Images and Its Verification

With the development of organization and retrieval systems for visual lifelogging data, many studies have attracted more attention in adjusting the raw visual lifelogging data to facilitate the systems. Although caption generation is one of the effective methods to adjust visual lifelogging data such as images and videos, an existing image captioning model generates different captions for lifelogging images of the same scene from multiple viewpoints. This prevents efficient organization and retrieval for shared lifelogging images, and the caption unification is expected to be the solution. In this paper, we assume that the above problem is caused by the diversity of objects in each lifelogging image and propose a new caption unification method. We remove selected common objects from lifelogging images of the same scene from multiple viewpoints by an inpainting approach and also verify the performance of the caption unification in the latent representations before and after the inpainting. The experimental results show the validity of our assumption.
SAR Data Expansiontechnology Based on Improved DCGAN

Due to the difficulty of obtaining SAR images for non-cooperative targets, the application and development of SAR target recognition technology is greatly limited. This paper proposes a method based on improved DCGAN for SAR image data augmentation. By making appropriate improvements to the DCGAN network, the quality of the SAR images generated by the network under small sample conditions is effectively improved. Finally, the experimental results of the sample data set expansion effect are verified by YOLO v5. The experiments show that compared with two typical GAN models, DCGAN and WGAN, the SAR sample data set expanded by the improved DCGAN not only has high image quality, but also can effectively improve the recognition effect of the recognition network.
Facial Expression Recognition Based on Time Series Self-Attention Feature Fusion

At present, most of the image-based video facial expression recognition methods only focus on the facial expression features of each frame of the image, and a small part of the work takes the time dimension information into account. It is noteworthy that the facial expression intensity of each frame in the video varies greatly from different angles. In order to make full use of the spatiotemporal dimension information in the video sequence and the force difference characteristics of different frame images on video expression recognition, a time series self-attention LSTM-Transformer network model facial expression recognition method is proposed. This method can effectively integrate the feature sub-modules of two different dimensions in time domain and space domain to ultimately improve the recognition performance of facial expressions. Finally, using CK+as the data source, the expression is recognized by the above model. The results show that the recognition effect of LSTM Transformer feature fusion is still higher than that of any single feature model.
Local Attention base on Time Masks for Weakly Supervised Video Anomaly Detection

Video anomaly detection (VAD) aims to use computer technology to automatically detect and recognize abnormal patterns in videos. Based on multi-instance learning, a weakly supervised anomaly detection method treats training videos as bags and assigns video labels by selecting key video segments from the bags to achieve fine-grained classification of video frames. Videos contain rich temporal information, and previous works model the global temporal information of videos to detect anomalies. However, monitoring videos usually have long duration and contain a lot of redundant information. Therefore, this paper proposes a local attention module that integrates video segments of different lengths through four different masking mechanisms to guide the network to model short-term temporal relationships in videos, abandoning the global modeling approach. Additionally, a joint distance metric ranking loss is proposed to guide the network to generate more discriminative feature representations for better anomaly detection performance. The proposed method is extensively tested on two publicly available anomaly detection datasets and its effectiveness is validated.
Research on automatic obstacle avoidance algorithm for intelligent networked vehicles

This paper proposes an optimised artificial potential field obstacle avoidance algorithm, which constructs an on-board model for automatic obstacle avoidance based on biological intelligence and physical phenomena, and uses multiple parametric estimates of car orientation information and car-obstacle distance parameters to perform obstacle avoidance control in three steps: detecting the obstacle, developing an adjustment plan, and returning to the normal route. The experimental data show that the intelligent optimisation algorithm outperforms the conventional algorithm in terms of distance to obstacles, algorithm computation time, and path planning time.