Reviewerly/exHarmony huggingface dataset:
Steganalysis by Codeword Error Bits Run-length against Coding Channel Steganography

Coding channel steganography is a kind of approach that utilizes the redundancy brought by channel coding to perform reversible information hiding. However, steganalysis against coding layer steganography is still a pressing problem. This paper proposes a coding channel steganalysis scheme based on the run-length of error bit number in codewords. By counting the number of error bits in each codeword, the distribution of different lengths of runs can be extracted as features. Support Vector Machine is used as the classifier. Experimental works, which are divided into two parts, are presented to prove that the proposed scheme significantly outperforms prior to traditional image-based steganalysis methods which fail to perform well in detecting hidden information in the coding layer. Particularly, we discuss the attack effect of the algorithm against three steganographic algorithms in two cases and analyze the experimental results from different perspectives.
Detection of Electrical Diagram based on QueryDet

A significant challenge in the field of grid power system is the need for scheduling and maintenance personnel to manually draw and import CAD design drawings, which can be a labor-intensive and error-prone process. To address this issue, we proposal a electrical diagram detection method based on Querydet, which significantly improves the efficiency and accuracy of detection. By utilizing the Cascade Sparse Query (CSQ) mechanism and sparse convolution methods, this method can rapidly identify electrical components on high-resolution feature maps, overcoming the difficulty of detection components in high-resolution diagrams. Next, contour tracking algorithms are used to extract the contours of connected regions of the diagram. Finally, the topological relationships of the electrical diagram are constructed based on the contours and the positions of the electrical components. Testing was conducted on a dataset of actual power grid electrical diagrams exported from the scheduling system, and the results demonstrate that the proposal method can effectively detect various electrical components and their topological relationships in electrical diagrams, and is robust to complex interference factors.
Research on Sonar Image Denoising Based on Optimized Spatial Pixel Ranking Algorithm

Spatial separation-based methods are applied in the field of sonar image denoising due to their sufficient performance. We analyzed the types and causes of noise in sonar images. Building upon the principles of classical spatial image denoising theory, we propose an algorithm suitable for processing salt and pepper noise. It is called the optimized Spatial Pixel Ranking (OSPR) Algorithm. It decomposes and clusters the pixels of the image from the spatial, and then sorts the image according to the pixel value. The same values will be screened and fused. Finally, the sorted and filtered values are used for image reconstruction. To evaluate its denoising performance, we compare the OSPR algorithm with six classic image denoising algorithms. Through simulation tests on images with varying noise ratios, the OSPR algorithm demonstrates an average improvement range of 1.36-6.32dB in terms of peak signal-to-noise ratio (PSNR). Simultaneously, the average structural similarity index (SSIM) improvement ranges from 0.061-0.224. These results indicate that the algorithm effectively removes noise from sonar images while preserving important edge information. Furthermore, the algorithm will be more applied in target recognition and marine data map construction.
Training Method of Causal Extensional Model of Boltzmann Machines

Boltzmann machine is a kind of probability graphical model, which includes visible layer and hidden layer, and the nodes are fully connected. But this fully connected structure may not be perfect. It is not necessarily to connect or not connect the visible layer as well as the hidden layer. There should be a trade-off between the two, that is, the nodes in the visible layer and the hidden layer are partially connected. According to the restricted Boltzmann machine causality extensional model, the fully connected Boltzmann machine model is introduced. Starting from the training of Boltzmann machine model, this paper introduces two training methods which shows the application solution of the new model.
Design and Implementation of Fitness Dance Scoring System Based on Human Posture Recognition

In response to the frequent occurrence of disability incidents resulting from improper home fitness practices, a fitness dance scoring system based on human pose recognition has been developed. The system comprises several modules, including the standard video preprocessing module, action information storage module, user action processing module, action scoring module, follow-along guidance module, and UI interaction module. Keyframe sequences are extracted from fitness dance videos, and coordinate information of skeletal points is extracted using OpenPose. It employs various similarity algorithms such as dynamic time warping and frame difference to calculate the similarity between standard dance motions and user motions. Real-time scoring is provided along with guidance to help users improve their movements. The system has been tested and has demonstrated high real-time performance and accuracy on personal computers. It enables comprehensive and detailed evaluation of user performance, providing ordinary users with a scientific and safe home fitness solution, and effectively preventing disability incidents caused by improper exercise.
A Novel Attention-Based LSTM Model for Non-Profiled Side-Channel Attacks

Non-profiled side-channel attacks represent a powerful class of attacks for no excessive information from the target device is required. Recently, the application of deep learning techniques on non-profiled side-channel attacks has yielded promising results. However, some existing studies have not fully exploited the properties of power traces as a time series. In this paper, an attention-based Long Short-Term Memory (LSTM) model for non-profiled attacks is proposed. This novel architecture combines LSTM and an attention mechanism in order to precisely extract the features of time samples in long-term traces. Through experiments on a public dataset, this architecture outperforms previous methods by producing more distinguishable results in identifying the correct key. Moreover, this architecture demonstrates reliable results in attacking power traces with additional Gaussian noise.
FPGA-Based Implementation of the Quantized Neural Network for Image Super-Resolution

The convolutional neural network(CNN)-based method for image super-resolution(SR) reconstruction has been becoming an important part in the fields of image processing such as security monitoring, object tracking. However, the huge computational complexity of neural networks make it shackled to satisfy the requirements for power consumption and complexity of reconstruction systems in specific application circumstances. In this paper, we propose a structure-unsophisticated and parameter-slight CNN model, and design a quantization scheme that allows inference to be performed by using integer-only arithmetic to alleviate parameter storage and communication pressure in hardware. Furthermore, convolution loop unroll and parallel acceleration analysis are performed for the model under the premise of board resources to optimize hardware inference. The results show that when the quantization precision is 8 bits, memory requirement of parameters is reduced by 75%, and the inference peak signal-to-noise ratio(PSNR) value loss is less than 1%. Quantization model was performed on the Xilinx Zynq7035 platform with inference losses not exceeding 2%, while hardware resources consumption is greatly reduced.
A English Pronunciation Error Detection and Quality Evaluation Model Based on English Text

In English learning, speaking practice is crucial, but traditional classroom teaching can hardly meet the needs of most learners. In this paper, we investigate and improve the two essential techniques of pronunciation error detection and pronunciation quality evaluation in the Computer Assisted Language Learning (CALL) system. In terms of pronunciation error detection, We extracted phoneme pairs’ discriminative acoustic and phonological features. We used Support Vector Machine (SVM) for pronunciation error detection, and the accuracy was improved to 81.26%. For pronunciation quality evaluation, we added two new dimensions of pronunciation fluency and intonation, innovatively designed the word duration ratio, and introduced the Support Vector Regression (SVR) algorithm to achieve the effective integration of different dimensional features. The experiments show that the improved pronunciation quality evaluation model has higher performance and practical value.
A Self-Adaptive Image Segmentation-Based Dual Watermarking Technique for Medical Images

The smart healthcare system plays a more critical role than ever before. It is designed to share electronic patient records (EPR) to improve medical care and facilitate research. However, protecting the security of EPR data is still a significant challenge. This paper proposes a new method for safeguarding EPR data in such systems. The method is a self-adaptive image segmentation-based dual watermarking technique that embeds both EPR data and authentication information in the wavelet domain of selected region of non-interest (RONI) for enhanced security, imperceptibility, and robustness. The experiments are based on a large dataset of medical data, The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD). The results found that the method is effective for the smart healthcare system. Compared with existing techniques, the proposed approach is better regarding security, robustness, and capacity.
An Optimized Non-profiled Deep Learning-Based Power Analysis with Self-supervised Autoencoders

With the rapid development of deep learning, it has been adopted as a primary method of analysis in non-profiled side-channel attacks. However, due to the noises in the collected power traces and the significant amount of data required to train a deep learning neural network, the non-profiled deep learning analysis method faces challenges in practical application. In this paper, a novel non-profiled differential deep learning analysis architecture that incorporates a self-supervised autoencoder is proposed. The autoencoder is designed to reduce the noise and strengthen the features of power traces before they are used as training data for the neural network. The experiment results indicate that not only the architecture outperforms the traditional differential deep learning network with more distinction, but it also distinguishes the correct key with a lower computational cost. The architecture is also examined with small datasets and is proved to be able to maintain the capability of recovering the correct key when the traditional architecture has failed.
SmartPhone-oriented Trust Evaluation Model Based on Cloud theory

Access control relies heavily on trust evaluation, and because trust is ambiguous and uncertain, converting trust concepts into concrete values is difficult. To solve this problem, this paper proposes a model for evaluating smartphone trust in an online office scenario. By applying forward and inverse cloud generators in cloud theory, the model completes the two-way conversion of trust concepts and concrete values. To clarify the degree of smartphone deviation from the baseline, a indicator system and corresponding normalization algorithm are also created in order to measure the degree to which the smartphone deviates from the baseline. The simulation’s results showed that the model can identify malicious smartphones with greater efficiency than another evaluation model based on cloud theory.
VCE-Net: A New Type of Real-time Polyp Segmentation Network

Colonic polyps are a frequent gastrointestinal disease, and segmenting colonic polyp images is important for colorectal cancer prevention, detection, and therapy. Colonic polyps have issues with varying sizes, forms, and unclear boundaries; therefore, this article suggests combining colonic polyp features to create VCE-Net, which has a greater segmentation accuracy. Firstly, to improve the network’s sensitivity with colon polyp size, through VAN-Base network improves the backbone feature extraction network of CE-Net to obtain richer semantic information of colonic polyps. Secondly, we fully integrate spatial and channel information to embed a mix attention mechanism, so as to reduce the interference information in the extracted feature values, and make it better cope with the problem of blurred boundaries of colonic polyps. Finally, the network’s partial skip connections are altered to a Res Path to decrease the semantic gaps between the encoder module and decoder module, improve the ability to address the issue of varied colonic polyp shape. On the public Kvasir-SEG dataset, VCE-Net was put to the test, Dice and MIoU of 89.4% and 88.8%, respectively, and its FLOPs of 44.97GMac. The experimental results show that its segmentation capability and real-time performance are better than the state-of-the-art medical segmentation networks.
Design of ESS-based Adaptive Particle Filter for Real-time Tracking

Particle filters (PFs) are a set of Bayesian approaches to estimate the posterior densities of the state of the systems from given observations using a set of weighted samples. Due to their superiority in dealing with non-linear and non-Gaussian systems, PFs are widely used in real-time applications such as localization and tracking. However, they are computationally demanding for real-time applications since large number of particles are used. Besides, the filtering performance doesn’t always increase as the number of particles increases, and redundant particles increase the computational complexity. Towards this problem, this paper proposes an adaptive particle filter method which adapts the size of particle set dynamically based on its effective sample size (ESS) under current observations. This method enables effective number of particles used for real-time target tracking while maintaining the tracking quality. Experimental results demonstrate that the proposed method achieves significant particle reduction, which is up to 33% under the same tracking quality compared to the standard PFs, confirming the effectiveness of our proposed method in terms of both tracking performance and speed.
Video Anomaly Detection with Video Vision Transformer

Video anomaly detection refers to the identification of unusual events or activities and represents irregular behavior. However, it is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams. Most of the existing methods rely on autoencoder, which fail to capture the global context information of the video. In this paper, we propose a new video anomaly detection method called VadVVT using Video Vision Transformer (ViViT). This model uses Transformer to automatically learn video representation and global contexts. It utilizes the transform encoder to extract spatio-temporal features from the video sequence and employs a decoder to predict the next frame. Our method models normality in the training phase and identifies an event with unpredictable as anomalies during the test phase. The experiments results on three public datasets demonstrate that our method outperforms existing methods.
Vehicle Tracking Algorithm Based on Full Attention Mechanism

Vehicle tracking performance is significantly reduced due to problems such as blurring, target and background similarity, local occlusion, background blurring, dim light and so on. To overcome these problems, we propose a vehicle tracking algorithm based on full attention mechanism. Firstly, Swin Transformer based on attention mechanism is introduced to sufficiently excavate feature representation of target region and search region, and then use the Transformer encoder to fuse the above features. Finally, a simple stacked full convolution network is utilized to predict the target location. The experimental results show that the proposed algorithm has good robustness in blur scene, complex background, similar objects, dim light and scale transformation. It has better tracking performance than other popular trackers on the precision plots and success plots. Compared with the baseline STARK-S50, success score of our tracker is increased by 2.1% on the OTB2015 dataset, and the tracking speed can reach 47 fps.
Cross-Reality Gaming: Comparing Competition and Collaboration in an Asymmetric Gaming Experience

Due to the level of immersion and differences in the user interface, there can be a very large discrepancy in the user experience between users in immersive systems and non-immersive systems when playing games together. To investigate the impact of the cross-reality experience, which refers to the asymmetric use of eXtended Reality, we aim to understand the different affordances and experiences in an asymmetric setup, where one participant uses a desktop setup with a mouse and keyboard, and one uses a virtual reality (VR) headset and controller in two different task modes, Competition or Collaboration. In our research, a pair of participants played a game in real-time, using either the VR setup or the desktop setup. In Competition mode, the two participants were asked to defeat each other. In Collaboration mode, the pair of participants played as a team and were asked to defeat a pair of AI enemies. Our results show the VR group reported a better gaming experience and perceptual responses compared to the desktop group regardless of game mode, but that the desktop group showed superior gaming performance compared to the VR group in Competition mode.
A 1000Base-T Physical Layer Fingerprint Extraction and Identification System

In 1000Base-T Ethernet, terminal access problems are often ignored. This paper proposes a novel method of extracting 1000Base-T physical layer fingerprint and builds a fingerprint identification system. It can prevent the network from being attacked by media access control (MAC) spoofing. The fingerprint is extracted from the signal characteristics of network devices so it is hard to counterfeit. The single-end signal is calculated by the pretrained convolutional neural network so we can extract the spectrum of the single-end signal. The fingerprint is extracted from the spectrum of the single-end signal and is classified after the feature extraction. In the classification and identification experiments on 8 devices, we achieve an accuracy of 85.9% on multi-classification and a high accuracy on binary-classification. This method can be used to enhance the security of wired networks, especially wired Internet of Things networks.
Exploring the Stability of Behavioral Biometrics in Virtual Reality in a Remote Field Study: Towards Implicit and Continuous User Identification through Body Movements

Behavioral biometrics has recently become a viable alternative method for user identification in Virtual Reality (VR). Its ability to identify users based solely on their implicit interaction allows for high usability and removes the burden commonly associated with security mechanisms. However, little is known about the temporal stability of behavior (i.e., how behavior changes over time), as most previous works were evaluated in highly controlled lab environments over short periods. In this work, we present findings obtained from a remote field study (N = 15) that elicited data over a period of eight weeks from a popular VR game. We found that there are changes in people’s behavior over time, but that two-session identification still is possible with a mean F1-score of up to 71%, while an initial training yields 86%. However, we also see that performance can drop by up to over 50 percentage points when testing with later sessions, compared to the first session, particularly for smaller groups. Thus, our findings indicate that the use of behavioral biometrics in VR is convenient for the user and practical with regard to changing behavior and also reliable regarding behavioral variation.
A High-Resolution Approach for Dynamic Traffic Police Gestures Recognition Based on Spatial Context and Temporal Features Fusion

According to the requirement of recognizing traffic police gestures for driver assistance systems and intelligent vehicles, a universal model for dynamic traffic police gesture recognition is firstly introduced, of which can accurately present the spatial context (such as the relative lengths of skeletons, the angles between each skeleton w. r. t. gravity, and part features) of the traffic police gestures. Secondly, an architecture which can respectively extract spatial context and temporal features of dynamic traffic police gesture is proposed. Meanwhile, deep neural network and LSTM are introduced to build a high-resolution traffic police gestures recognizer (namely HRTPGR). At last, the open Police Gesture Dataset is used to train and test TPGR, and the experimental results show that the TPGR achieves a state-of-the-art accuracy with 98.7% for dynamic traffic police gestures recognition, and has strong anti-interference ability to light, background and gesture shape changes.
Vicarious: Context-aware Viewpoints Selection for Mixed Reality Collaboration

Mixed-perspective, combining egocentric (first-person) and exocentric (third-person) viewpoints, have been shown to improve the collaborative experience in remote settings. Such experiences allow remote users to switch between different viewpoints to gain alternative perspectives of the remote space. However, existing systems lack seamless selection and transition between multiple perspectives that better fit the task at hand. To address this, we present a new approach called Vicarious, which simplifies and automates the selection between egocentric and exocentric viewpoints. Vicarious employs a context-aware method for dynamically switching or highlighting the optimal viewpoint based on user actions and the current context. To evaluate the effectiveness of the viewpoint selection method, we conducted a user study (n = 27) using an asymmetric AR-VR setup where users performed remote collaboration tasks under four distinct conditions: No-view, Manual, Guided, and Automatic selection. The results showed that Guided and Automatic viewpoint selection improved users’ understanding of the task space and task performance, and reduced cognitive load compared to Manual or No-view selection. The results also suggest that the asymmetric setup had minimal impact on spatial and social presence, except for differences in task load and preference. Based on these findings, we provide design implications for future research in mixed reality collaboration.
Automatic Patching of Smart Contract Vulnerabilities Based on Comprehensive Bytecode Rewriting

Smart contracts are the latest extension of decentralized cryptocurrencies, how to patch vulnerable smart contracts in an automated way to combat the intensifying attacks has become an extremely critical task in recent years. However, the optimization of code size and gas overhead by existing patching tools is not enough, and vulnerability types supported by these tools are relatively single. This paper presents EtherEditor, a framework that can directly rewrite the bytecode without the need for high-level language source code. The main goal is to minimize the code size and gas overhead. Meanwhile, EtherEditor can detect and patch 7 vulnerability types in an automated way. To evaluate the effectiveness of EtherEditor, this paper applies EtherEditor to patch contracts in a CVE dataset and a large-scale dataset containing 14107 contracts. Experiment results demonstrate that EtherEditor can effectively fix insecure bytecode. Furthermore, this paper compares EtherEditor with other automatic patching tools, and results show that EtherEditor has better performance, compared with existing tools.