Reviewerly/exHarmony huggingface dataset:
A Efficient Prototype-Assisted Clustered Federated Learning Framework for Industrial Internet of Things

The Industrial Internet of Things (IIoT) is a critical enabler of the industrial future and offers a novel paradigm for Industry 4.0 concepts. Federated learning (FL) technology is applied to the IIoT as a distributed machine learning method. It can protect user privacy by harmonising multiple IIoT equipment for AI model training at the edge of the network, while ensuring that no locally sensitive data is exchanged. However, FL is still challenged by non-independent identical distribution (non-IID). Due to the uncertainty of information distribution, model fusion may have parameter mismatches, resulting in model structure misalignment and many other problems. To address the above issues, we propose an Efficient Prototype-assisted Clustered Federated Learning Framework (PClusterFL) for IIoT in the field of cluster federation, which adopts the cosine distance of the prototype as a similarity measure instead of the distance between model parameters, thereby reducing the communication burden. In addition, we design a prototype-assisted aggregation strategy to improve aggregation accuracy. Numerous experiments are performed between PClusterFL and the latest baseline method. Experimental results indicate that PClusterFL is better than several state-of-the-art FL methods on several datasets.
Behavioral Threat Assessment of Power Monitoring Systems Based on Machine Learning

An evaluation test method based on machine learning has been created to address the power monitoring system’s lack of internal operation behavior detection assessment.By employing this technique, problems with the power monitoring system’s mixed operation behavior, the operation role’s ambiguity, and inaccurate evaluation of operation behavior have been resolved. To pre -classify operational behavior, deepen the potential correlations between different types of information, extract behavioral traits from different operational roles to build a person image, and then train LOF, iForest and K-means anomaly directive detection models using the idea of bagging in integrated learning to evaluate operational behavior are all steps in the method. The method, which has been experimentally verified, works well for assessing operational behavior in a power monitoring system.
Why Always Distance: Dense-Based Clustering in Side-Channel Analysis

Side-Channel analysis poses a significant threat to the security of cryptographic devices. Distance-based clustering methods include K-means and its variants are popularly applied on side-channel analysis in public key cryptography. However, distance-based clustering perform poorly in irregular data distributions. In this paper, we combine density-based clustering methods, such as DBSCAN and OPTICS, with side-channel analysis. In comparison to distance-based clustering methods, density-based methods are better suited for handling irregular data distributions and demonstrate greater robustness against noise. With density-based clustering methods, We successfully recover the operations on power traces with accuracy of 100% on both ECC-Card and ECC-FPGA, while the $\displaystyle \max$ accuracy of distance-based clustering methods is only 68.66% on the two datasets.
A Region of Interest Extraction Algorithm for Underwater Surveillance Video

Underwater Surveillance Video (USV) plays important role in the research and development of marine resources. The harsh underwater environment means that USV can only be transmitted over hydroacoustic channel with narrow bandwidth. Region of Interest (ROI)-based rate control algorithm, which automatically adjusts the code rate to accommodate bandwidth changes based on user’s interest. So the ROI Extraction Algorithm (ROI-EA) is a key process. But the existing ROI-EA is mainly aimed at conventional video. It does not take into account the characteristics of USV. Thus, there is still room for improvement in its performance. Therefore, an ROI-EA for USV is proposed in this paper. The characteristics of USV was explored first. Then a underwater image enhancement algorithm based on improved gamma-corrected was proposed. Finally, the proposed ROI-EA, according to the characteristics of USV, was demonstrated. We also compared the performance of our method with existing ROI-EAs. The results show the superiority of our algorithm.
Key Technologies on High-capacity Satellite Imagery Compression

With the continuous progress of science and technology, the accuracy of satellite remote sensing, detection and reconnaissance technologies is getting higher and higher, and the amount of data generated by them is also getting larger and larger. The amount of image data generated by high-resolution satellite imaging systems often reaches hundreds of Gb/s, which, coupled with the limited transmission bandwidth and storage space constraints, puts great pressure on data caching and transmission. The upper limit of transmission rate provided by traditional satellite data transmission methods has been difficult to meet the demand of inter-satellite and satellite-ground high-speed communication. To address this, a high-capacity satellite image data compression algorithm is proposed. Firstly, for the problem of redundancy of handover satellite image data, image fusion or stitching technology is used to extract valuable information from different images and synthesize high-quality images to reduce data redundancy. Secondly, to address the problem of repeated transmission of re-entry satellite data, multi-temporal data is analyzed to determine the change process of re-entry satellite image features and segment the change region in the image. In the re-entry process, only the information of the change region needs to be transmitted, avoiding multiple transmissions of duplicate information. Finally, to solve the problem of low compression efficiency of existing on-satellite compression algorithms, a new “image-information-image” data compression strategy is explored to achieve high compression ratio and high fidelity of satellite image compression. In order to achieve high capacity compression and efficient transmission of satellite images with limited bandwidth and storage space, and to lay the foundation for fast and efficient intelligent services for all-day, all-weather observation of the Earth.
GFSPP-YOLO: A Light YOLO Model Based on Group Fast Spatial Pyramid Pooling

The YOLO object detection model for PC environments is widely used in computer vision due to its high accuracy and good real-time performance. However, when faced with the embedded environment of mobile devices, the use of YOLO models in mobile devices is still challenging due to the large computational requirements and memory consumption. To address these issues, this paper proposes a lightweight YOLO model based on grouped fast spatial pyramidal pooling. Different from the existing YOLOv5 model, firstly, at the end of the backbone network, the receptive field is expanded using the ideas of CSPNet and group convolution to build a group fast spatial pyramidal pooling structure GFSPP to avoid false and missed detections caused by image distortion; and a CBAM attention mechanism is introduced in the backbone network to improve the characterization of network features. Secondly, the slim neck paradigm combined with the lightweight convolutional module GhostConv is used in the neck network to compress the network structure. Finally, migration learning techniques are used to further improve the detection performance of the model. Experimental results show that the GFSPP-YOLO model proposed in this paper reduces the complexity and parameter costs by 10% and 3.5% respectively compared to the traditional YOLOv5s model on the PASCAL VOC2007+12 dataset, while the mAP0.5 is improved by 2%, making the model in this paper more suitable for applications in embedded environments of mobile terminals.
Side-Channel Analysis and Countermeasures of Domestic Block Cipher uBlock

Nowadays, IoT devices are ubiquitous. Addressing data security issues in resource-limited devices is a key focus of IoT security. Therefore, lightweight cryptographic algorithms and their security research have received increasing attention. The uBlock is a lightweight block cipher proposed in 2019, which has advantages in software performance and hardware implementation, such as efficiency and low energy consumption. Research shows that the uBlock can effectively resist traditional analysis methods such as integral analysis and differential analysis. However, there is currently a lack of related research showing that the uBlock can effectively resist side-channel analysis. In this paper, we successfully complete the correlation power analysis on the uBlock and give specific experimental results. Subsequently, we propose a masking countermeasure for the uBlock software implementation and prove its security against correlation power analysis. Its implementation shows that the protected uBlock has increased code size, time, and RAM consumption by 37.73%, 38.31%, and 20.80%, respectively, compared to the unprotected one.
IoT-Based Edge Computing and Image Processing for Occupancy Detection

A primary strategy for the energy-efficient operation of commercial office buildings is to offer dynamic building services, including lighting, heating, ventilating, and air conditioning (HVAC). Therefore, it is necessary to propose an effective and high-accuracy guaranteed real-time video occupancy detection optimisation algorithm according to a model predictive control (MPC) system. In this paper, we propose a YOLOv5 occupation detection algorithm to enhance the accuracy of making informed decisions and control actions on video occupancy detection systems, where the MPC system uses multiple cameras to capture and average the number of people, and a DHTII sensor will be used to measure temperature and humidity and sent to the MPC program through a Raspberry Pi-based script. Simulation results demonstrate the effectiveness of deep learning-based methods in enhancing the accuracy and real-time performance of video occupancy detection systems, paving the way for more efficient and intelligent building management solutions.
Time-series Data Modeling Guided by Visual Information

Time-series data typically contain issues such as missing data and noise, which can impact the model's precision and stability. This paper proposes a Transformer structure-based visual information-guided temporal data modeling algorithm to address the issues as mentioned above. The algorithm effectively captures the time-series structure of the time-series data, thereby enhancing the model's precision and stability. To evaluate the performance of the proposed algorithm, a dataset containing visual information aligned with time-series data is compiled, and a comprehensive quantitative and qualitative analysis is performed. Conduct a comprehensive quantitative and qualitative analysis. The results indicate that visual information can assist time-series data in capturing the intricate dynamics of the time-series data, thereby enhancing the performance of the proposed algorithm and facilitating its comprehension. The results indicate that visual information can assist time-series data in capturing the complex dynamics of time-series data, and thus in comprehending and predicting their behavior and trends. The application of this algorithm will advance research in the field of modeling and predicting time series data. Applying this algorithm will advance research and practice in modeling and forecasting time series data.
Research on Lightweight Ship Target Detection Algorithm Based on Improved YOLOv5

Intelligent ship detection is a fundamental problem in ship traffic services, port management, maritime security, and automated fishery management. However, ship monitoring devices are often deployed on embedded devices, which generally have lower performance than large computers, and the effect of neural network models is usually limited. To reduce the algorithm's parameter volume and FLOPs and improve ship detection accuracy, this paper proposes a DCS-YOLO model based on YOLOv5. In the backbone of the model, an improved ShuffleNetv2 network with an attention mechanism is designed. In the head, the feature map with 32-fold down-sampling is removed, and Ghost convolution is used instead of the convolution structure. Experimental results show that compared with the original YOLOv5 algorithm, the improved algorithm increases the 0. 5:0.95m AP by 2.4% and reduces the model size by 70.3%.
Network Topology Inference with Sparsity and Laplacian Constraints

We tackle the network topology inference problem by utilizing Laplacian constrained Gaussian graphical models, which recast the task as estimating a precision matrix in the form of a graph Laplacian. Recent research [1] has uncovered the limitations of the widely used ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm in learning sparse graphs under this model: empirically, the number of nonzero entries in the solution grows with the regularization parameter of the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm; theoretically, a large regularization parameter leads to a fully connected (densest) graph. To overcome these challenges, we propose a graph Laplacian estimation method incorporating the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> -norm constraint. An efficient gradient projection algorithm is developed to solve the resulting optimization problem, characterized by sparsity and Laplacian constraints. Through numerical experiments with synthetic and financial time-series datasets, we demonstrate the effectiveness of the proposed method in network topology inference.
CLAST: Efficient Certificateless Sequential Aggregate Signature Scheme for Industrial Internet of Things Environments

The Internet of Things (IoT) has experienced rapid growth in recent years, drawing significant attention from both academics and industry. As IoT gradually gains popularity in the industrial market, industrial internet of things(IIoT) has emerged as a necessary solution to meet the demands of the times. The limitations posed by the small storage space of certain devices, restricted network throughput coupled with timing and reliability requirements, and the need for safety assurance in the IIoT, all present significant challenges. In particular, security threats are constantly increasing, posing a significant risk to the rapid development of IIoT. Therefore, CLAST scheme was proposed to address these issues, which based on elliptic curve cryptography and bilinear mapping to ensure the security of information. The security analysis of the scheme in the random oracle model was proved, affirming that it satisfies the security requirements for signature verification. Furthermore, the scheme reduces the complexity of the cryptographic operation process and reduces the computational overhead of the signature verification algorithm. These benefits have been substantiated through security analysis performance analysis.
Generating Out-of-Distribution Examples via Feature Crossover against Overconfidence Issue

The problem of overconfident predictions on out-of-distribution (OOD) samples poses a significant challenge to the reliability and robustness of deep neural networks (DNNs). The reason for OOD overconfidence issue is that DNNs only learn the features of in-distribution (ID) samples in the training stage, while lacking the supervisory signal of OOD samples. Therefore, we can alleviate this problem by constructing an auxiliary OOD dataset. If the auxiliary OOD dataset belongs to OOD and is close to ID, it can teach the model more OOD knowledge, and further distinguish OOD samples from ID, which is manifested by the model outputting more evenly distributed confidence score for OOD samples. The key to solving this problem lies in how to construct such a high-quality auxiliary OOD dataset. In this paper, we have made preliminary explorations into the fusion of high-level features between samples, and proposed a simple but efficient method of generating OOD samples through feature crossover in the feature space. This method only requires ID data to generate a large number of OOD samples. Experimental results have demonstrated that using our method to construct OOD samples can effectively alleviate the problem of DNNs being overly confident on OOD samples.
Network Security Situation Prediction Based on Sequential Extreme Learning Machine

To improve the prediction accuracy of Extreme Learning Machine (ELM), this paper establishes a sequential ELM algorithm with a forgetting factor by applying exponential weighting to new and old sample data, and obtains the optimal forgetting factor through simulation analysis. First, considering the different contribution rates of new and old data to the accuracy of the prediction model, this paper presents an ELM external weight online identification algorithm with a forgetting factor for downgrading old information. Then, based on the actual situation of network security attacks in a certain enterprise, the optimal forgetting factor for ELM external weight online learning is obtained through simulation analysis. Finally, the network security situation prediction accuracy of three ELM prediction models is compared. The simulation results show that ELM with forgetting factor has higher accuracy than traditional ELM and online sequential ELM.
Research on Multi-Labels Image Classification Based on Self-Supervised Model

At present, image classification model has become an essential component for detection system. However, existing identification models are primarily concentrated on the convolutional neural network or utilize transformer model to capture the features of training images, which can obtain acceptable accuracy for single objectives and is hard to dispose multiple objectives with unsatisfied optimization results. In this work, we utilize the self-supervised mechanism to dispose the optimization process for traditional convolutional neural network. Initially, we construct the contrastive learning module to generate the amount of objectives label for the training data, which is utilized to reduce the extraction loss for multiple tasks. Subsequently, the convolutional neural network is established to classify the objectives in the images. From our extensive experimental results and compared analysis with existing classification models, we can directly observe that our proposed model can achieve the multiple images classification with more than 85% identification accuracy with reasonable responding costs.
SE-PifPaf: A Multi-Person Human Pose Estimation Methond for Complex Scenes

Images in complex scenes are prone to cross-obscuration of people, which brings great challenges to multi-person human pose estimation. Previous multi-person human pose estimation algorithms suffer from limited accuracy and human skeleton loss in complex scenes. To solve the above problems, a novel pose estimation optimization algorithm SE-PifPaf is proposed. Firstly, the grouped convolution network is employed to mine semantic features relevant to the human pose estimation task. Secondly, the attention mechanism is applied to realize the interaction among channels, obtaining the weight of each channel on the pose estimation task in complex scenes. Experimental results show that SE-PifPaf achieves an accuracy of 72.3% on COCO 2017 dataset. More importantly, it improves the accuracy of pose estimation by 1.1% in complex scenes on CrowdPose dataset.
Helmet Detection Recognition Algorithm Based on Improved Yolov5

The existing helmet detection algorithm is mainly based on a single-stage object detection algorithm, which has high detection speed and can achieve the requirement of real-time detection. Still, the accuracy of detecting small objects and objects with obstacles is no high. Based on this, this paper proposes a helmet detection recognition algorithm based on improved YOLOv5. The SE attention mechanism has the advantages of low complexity, few parameters and low computational effort. Firstly, the SE attention module is added to the Yolov5 backbone network to weigh the features of different channels and improve the model's attention to important features. Secondly, using softnms to replace nms in the original network can retain more small target prediction frames, improve the detection accuracy, and improve the model's ability to detect small targets. The experiments show that the average accuracy, precision, and recall of the proposed method reach 96.51%, 95.20%, and 90.08%, respectively, and the real-time performance meets the need for helmet detection in practical work.
皮膚電気刺激が捕球感覚提示に与える影響

Most sports training in Virtual Reality uses only eyesight and hearing, not touch. So we considered that we gave haptics to VR sports. Among them, we focused on the catching movement, which is the basis of skills such as baseball and softball. To improve the reality of catching a ball, we proposed to present the sensation of catching a ball to the palm using traction force and electrical skin stimulation. As a result, it was partially confirmed that the sensation in catching was improved by the electrical skin stimulation compared with the traction force alone, but the difference from real catch was remained.
Development of In-hand Operable Robot Gripper for Food Grasping

A robot gripper for correction work has been developed to automate rework of frozen foods and other food products. It is not known in advance which part of a food product needs to be reworked. Therefore, we developed a gripper that can rotate the gripped food in two directions in the hand so that the surface to be reworked can be found by rotating the gripped food in various directions.
VRトレーニング環境下における時間歪曲が技能向上に与える影響

In recent years, sports training systems have been proposed that use Virtual Reality (VR), which can create an environment different from reality by modifying physical properties, to promote skill development and acquisition. We focused on the speed of the flow of time among physical properties and developed a VR training system focusing on smashes in tennis in order to verify the effect of time manipulation in VR training. In this system, smashes were practiced repeatedly in a VR space that reproduces the actual tennis environment. The speed of the time flow in the VR space was gradually reduced to the same speed as in reality, making it easier for the player to grasp the timing of smashing the ball. Using this system, we conducted a user study to verify the effect of time manipulation and to examine the difference from the previous study of VR tennis training using gravity manipulation.
Fundamental Research of Induction of Shoulder abduction by Utilizing Clothes Transformation with Thin Artificial Muscles

Visual instruction with video and instructor-led instruction are the most common methods when we teach someone how to move. However, visual instruction is difficult to convey a sense of movement, and instructor-led instruction requires the skill of the instructor. To solve this problem, assist suits are attracting attention, but conventional assist suits are not suitable for multi-joint parts like shoulder and trunk because of their hard structure. Therefore, we focus on ”Fabric Actuator” which can be transformed into various shapes, and aim to achieve instruction of complex motion by transforming with movements. In this paper, we examine the placement of artificial muscles, and evaluate performance about the induction of shoulder abduction as an initial study.