Reviewerly/exHarmony huggingface dataset:
Interpretable Image/Video Compression by Extracting the Least Context Map

Current deep neural networks based image compression methods lack interpretability. Most of them follow a standard encoder-decoder framework and cannot be directly applied to video compression. We present a novel and interpretable finder-generator framework for image/video compression. The finder analyses the input image and selects important points on a one-channel binary map of the original width and height rather than compresses images into a multi-channel bitstream in a downsampled bottleneck layer. The binary one-channel map output by the finder retains the original width and height to keep the spatial information. We name it the least context map (LCM). The generator analyses the LCM to restore the original image based on its trained parameters. We put forward two different selection strategies for guiding the finder to extract the LCM. By extracting LCMs from images, our framework can reduce the size of real-world traffic surveillance videos by 96% compared to most common video codecs and by 85% compared to the next generation video compression codec VP9. This size reduction results from that adjacent frames always share similar LCMs and thus LCMs can be significantly compressed along the time axis. In addition, extensive experiments on Kodak dataset demonstrate our model surpasses the state-of-the-art image compression methods at low bit-rates. We only require an average compressed size of 2.01 kilobytes to achieve a high average MS-SSIM score of 0.9. This size is 50% smaller than JPEG, 43% smaller than FRRNN, and 11% smaller than WebP. Further comparative experiments on image generation demonstrate the LCM is superior to the semantic map and the edge map in higher information capacity and less required storage.
Information extraction method of topic webpage based on multi-angle feature learning

It's difficult to find topic information in the web page because it is slow to find specific information by labor in the process and the result of commonly used methods is inaccurate. This paper proposes a multi-angle feature analysis method for web information identifying. With this method, it mines the characteristics of web page information content in a comprehensive way. Focusing on the characteristics of the web page, the text is segmented, and features are extracted and quantified from multiple perspectives. The fully connected neural network deep learning model is used for training. Besides, use linear classifiers to classify web page. The final experiment shows that this method improves the F value by more than 4% compared with the keyword method and the SVM (Support Vector Machine) method.
Convolutional neural network-based breast image diagnosis and analysis system

In order to achieve accurate screening and effective diagnosis of breast at early stage and thus improve the treatment rate of breast cancer patients, this paper proposes a convolutional neural network-based breast image diagnosis and analysis system, which aims to achieve rapid and accurate detection and diagnosis of early breast cancer through digital mammogram impact and breast pattern processing and digital feature recognition. The system uses convolutional neural network algorithm for classification, segmentation and diagnosis of breast images, which has the advantages of high accuracy, high efficiency and automation. In this paper, the algorithm principle, experimental method and experimental results of the system are introduced and analyzed, and compared with other breast image diagnosis and analysis systems. Finally the system provides real-time display and image recognition display of the results of diagnostic analysis, which is easy for personnel to view and modify. The experimental results show that the system has an accuracy rate of over 90% for the specified types of breast abnormalities affected and can be used as an important tool for diagnostic analysis of breast images.
ConTra: A Covert Timing Channel Detection Approach for Little Covert Information in a Network

A covert timing channel is a technical means that enables information hiding and covert communication. Due to the fact that covert timing channels can elude detection by security defence measures, they offer a substantial security risk to the information of Internet users when they are exploited for illicit reasons. The attacker sends at a low rate to ensure the stealthiness of the covert timing channel communication process, making the number of inter-arrival time encoded as covert information much less than the normal number of inter-arrival time, resulting in the low detection accuracy of existing detection methods. In this paper, we propose a covert timing channel detection method based on one-dimensional convolution and self-attention mechanism. The method begins with local feature extraction of the input inter-arrival time sequence by a one-dimensional convolutional layer, then characterizes the correlation between each inter-arrival time by a self-attention mechanism in the encoder, and finally through the full connection layer to produce the type output. In this study, experimental results on a public dataset and 16 self-built datasets demonstrate that the detection method delivers optimal detection results and efficient detection of covert information in low-volume communications.
Application for Integration of Haar Cascade and Local Binary Pattern Histogram

The use of machine learning in AI solutions can speed up the delivery of results, allowing these tools to achieve even more impressive feats. The present study presented a developed system that can be applied and used in many fields, such as a smart attendance system in a classroom setting and an intruder detection system. The two fused algorithms—Haar Cascade and Local Binary Pattern Histogram are used to train and utilize a system to recognize a registered face. Amid health crises, multiple face detection and face mask detection systems were also taken into consideration in the developed system.
A joint improved DETR network face recognition algorithm based on TSM module and DANN.

As one of the most mature application fields of artificial intelligence, face recognition system has been widely used in production and life. But at the same time as large-scale commercialization, face recognition technology also faces more challenges. How to further improve the accuracy of face recognition function and improve the defense ability of the recognition system against face against samples is an important research direction of algorithms. At present, the algorithm mostly focuses on the picture features of a single portrait, ignores the details difference in the time domain of the fake video, and has the problems of weak generalization ability and overfitting of the model. In this paper, an improved DETR network is proposed, which uses the TSM module to perform time domain displacement on the extracted video features and averages the time features of pooled learning samples to better distinguish dynamic adversarial sample instances. At the same time, DANN is introduced as a systematic classification and discrimination network, which uses the domain classifier to domain discrimination of the feature space and uses the adversarial loss function to update the feature extractor and domain classifier parameters. Actual tests show that the recognition accuracy of the network on the FaceForensics dataset is improved by an average of 4%-7% compared with the improvement period, the error rate is less than 7%, and the model recognition speed index is not higher 400ms, which proves that the model has high accuracy rate and good real-time solving ability.
Transformer-Based Image Inpainting Detection via Label Decoupling and Constrained Adversarial Training

Image inpainting based on generative adversarial networks (GANs) has achieved great success in producing visually plausible images and plays an important role in many real tasks. However, the techniques of image inpainting might also be maliciously used, e.g., altering or removing interesting objects to report fake news. Despite the promising performance of recently developed inpainting detection algorithms, they are built on convolutional neural networks (CNNs) with limited receptive fields. Consequently, they fail to fully capture the disparity between the inpainted regions and untouched regions and thus are ineffective in obtaining fine-grained detection results. In this work, we develop a new image inpainting detection approach. First, we propose a locally enhanced transformer architecture tailored for image inpainting detection. Unlike previous CNN-based methods, our approach leverages both the short-range and long-range dependencies of pixels, enabling the learning of diverse statistical behaviors of inpainted and untouched regions. Second, to mitigate the distraction caused by near-edge pixels with a mixed nature during training, we propose decoupling the label into a body map and a soft-edge map, and then a cross-modality attention module is designed to propagate their information interactively. It demonstrates that our decoupling strategy outperforms the conventional edge supervision in enhancing detection accuracy. Finally, we devise a constrained adversarial training methodology in consideration of the confrontational generation procedure of deep image inpainting methods. It shows that our constrained adversarial training further enhances the detection performance by adaptively introducing interference noise in the inpainted regions. Extensive experiments validate the superiority of our scheme compared to existing CNN-based methods, showcasing its desirable detection generalizability for both deep inpainting and traditional inpainting algorithms.
Cross-Platform Simulator for Railway University Students

A review of modern simulation systems (technologies and programs) for rolling stock management, designed to train students of transport universities and employees of Russian Railways, was carried out. A cross-platform simulator has been developed based on the use of an application creation platform and interactive content. The 2D RailWay Simulator cross-platform simulator is designed to optimize the educational process, which allows you to assess the level of training of students both in preparation for the exam and as the final certification of students by profession as an assistant driver of an electric locomotive. The simulator can be adapted when studying the controls of the electric locomotive in the discipline “Rolling stock of railways” for the specialty 23.05.03 Rolling stock of railways of all forms of training. The simulator creates the effect of presence in a real locomotive by displaying the track situation of locomotive cabins and control panels, as well as safety devices and auto-driving systems based on 3D graphics. This complex is developed on the basis of updated design documentation and original algorithms for rolling stock management. The functionality of the cross-platform application is based on a special modeling environment and a system of mathematical models of devices used on locomotives and motor car rolling stock. A methodology for training students on a cross-platform simulator has been developed.
Privacy-Preserving Digital Twin for Vehicular Edge Computing Networks

As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.
When Blockchain Meets Urban Rail Transit: Current Prospects, Case Studies, and Future Challenges

Thanks to the vigorous development of artificial intelligence, urban rail transit (URT) is undergoing a new round of intelligent upgrades. While its intelligence level is improving, URT suffers from a weak trust foundation, high data sharing costs, and low collaboration efficiency. Driven by outstanding features of decentralization, resilience against tampering, and traceability, blockchain can provide a safe and efficient value-trust exchange infrastructure for URT. This article focuses on the current prospects, case studies, and future challenges of blockchain-empowered URT. We first introduce blockchain fundamentals and mainstream blockchain platforms, comparing the technology’s advantages and highlighting the motivation of using it in URT. The prospects of using blockchain in the lifecycle of URT, which includes planning and construction, operation and management, control and security, and upgrading and transformation, are explored. Furthermore, a concrete case study of using blockchain in a distributed authentication scheme for URT is described. Extensive testing results show that the proposed blockchain-based distributed authentication scheme can enhance the security of the train control system without sacrificing communication performance. Finally, we summarize the challenges and problems when using blockchain in future URT systems.
Multimodal Hateful Memes Detection via Image Caption Supervision

A large amount of hateful speech exist on the Internet in the form of text and images uploaded by social media users. Recently, multimodal hateful speech detection task has attracted more and more researchers to invest, producing some representative work for perceiving the negative samples. For this special multimodal task, the ability of multimodal semantic information understanding is particularly crucial. However, the existing models have insufficient understanding ability of image modality semantic compared with the text modality, due to the appearance complexity of each image. Therefore, this paper utilizes the text modality which is well understood by the model to improve understanding ability of image modality semantic. Specifically, this paper proposes an image caption supervision (ICS) auxiliary method for multimodal hateful speech detection, where the image caption is designed to supervise the feature learning of images for further understanding the semantic information. On the Facebook Hateful Memes dataset, the proposed ICS method outperforms some state-of-the-art unimodal and multimodal baselines, demonstrating the effectiveness of ICS.
Mixed Spearman’s Correlation Suitability for Sine-Shaped Independent Variables in Natural Sciences

Pearson’s and Spearman’s methods are most often used in popular software packages to calculate the correlation coefficient between two variables in natural sciences. The (1) difference between them is that the Pearson’s method takes actual numerical values into account whereas the Spearman’s operates with their ranks. Ranking is often efficient in dealing with nonlinearities and outliers in the data. However, not both independent (x) and outcome (y) variable have to be suitable for this. We propose not to rank the independent variable in case it comes sine-shaped. Most notable example of such a variable in nature is the temperature. It is sine-shaped due to day-night and summer-winter transitions. It does not contain many outliers, not only due to the shape, but also because it is often sampled at multiple points and averaged (to be linked with a single outcome variable observation). The proposed method is evaluated in simulations and with experimental data obtained in South Adriatic zooplankton research. Practical advices on how to apply it using popular software tools are given.
Atomyze - The Scalable Hyperledger Fabric-Powered Blockchain Platform Revolutionizing Asset-Backed Tokenization

In recent years tokenization of different assets has become widespread in the crypto industry. There are many examples of tokenization platforms that are increasingly attracting new participants. However, the architecture of these platforms is often concealed from a wide range of possible users, which does little to contribute to the idea of transparency and discoverability. The Atomyze platform offers a working solution for tangible assets tokenization based on the Hyperledger Fabric permissioned blockchain technology with the ability to connect validators. Atomyze provides a flexible architecture and easily scalable multichannel approach in token development as well as solves complicated problems of improving the quality and speed of transaction processing using batching and randomizer to deal with the endorsement selection by adding randomness to endorsement rules as well as the BFT consensus for high throughput, low transaction costs, and more scalability.
VrT: A CWE-Based Vulnerability Report Tagger : Machine Learning Driven Cybersecurity Tool for Vulnerability Classification

vulnerability reports play an important role in the software maintenance domain. Disclosing vulnerabilities that attackers can exploit depends on the time of mitigating that software vulnerability. The information on vulnerability reports reported by several security scanning software tools facilitates vulnerability management, trends, and secure software development automation. Tagging of vulnerability reports with vulnerability type has thus far been performed manually. Therefore, human-induced errors and scalability issues suffered due to the shortage of security experts. This paper introduces a tool called Vulnerability Report Tagger (VrT), which leverages machine-learning strategies on vulnerability descriptions for automatically labeling NVD vulnerability reports. VrT automatically predicts the cybersecurity labels to assign to vulnerability text to encourage the use of labeling mechanisms in vulnerability reporting systems to facilitate the vulnerability management and prioritization process. Along with the presentation of the tool architecture and usage, we also evaluate our tool effectiveness in performing the vulnerabilities classification (i.e., tagging) process. Link to the tool: https://rb.gy/cz7hwa
Weakly Supervised Multi-Modal 3D Human Body Pose Estimation for Autonomous Driving

Accurate 3D human pose estimation (3D HPE) is crucial for enabling autonomous vehicles (AVs) to make informed decisions and respond proactively in critical road scenarios. Promising results of 3D HPE have been gained in several domains such as human-computer interaction, robotics, sports and medical analytics, often based on data collected in well-controlled laboratory environments. Nevertheless, the transfer of 3D HPE methods to AVs has received limited research attention, due to the challenges posed by obtaining accurate 3D pose annotations and the limited suitability of data from other domains.We present a simple yet efficient weakly supervised approach for 3D HPE in the AV context by employing a high-level sensor fusion between camera and LiDAR data. The weakly supervised setting enables training on the target datasets without any 2D / 3D keypoint labels by using an off-the-shelf 2D joint extractor and pseudo labels generated from LiDAR to image projections. Our approach outperforms state-of-the-art results by up to ~ 13% on the Waymo Open Dataset in the weakly supervised setting and achieves state-of-the-art results in the supervised setting.
Mercury: A High-Performance Streaming Graph Method for Broad and Deep Flow Inspection

Deep flow inspection (DFI) has been increasingly important for network management and security protection. Network flows have rich correlations due to multiparty communications such as video conferences and DDoS attacks, and furthermore, rich features are vital for artificial intelligence-driven DFI models and algorithms. However, existing approaches do not correlate network flows by just providing simple counters for key-valued network flow records due to the limitations of the measurement models.We present Mercury, a broad and deep flow inspection system, which continuously collects a wide range of features for large scales of correlated network flows and supports expressive query interfaces for diverse network measurement tasks. Mercury organizes rich and dynamic features of the network traffic based on a streaming graph model, which defines rich states for each network flow to the edge of the graph and incrementally updates these states in high-performance pipelines. Mercury raises significant challenges for storing and updating states in the streaming graph model due to the fast arrival rate and the in-memory storage limits. We present a nested two-dimensional data store, called cuckoo matrix, to provide a two-dimensional index for the streaming graph model. The cuckoo matrix optimizes the state-update throughput and reduces the memory cost through the multiple dimensions of the cuckoo tables. We speed up the ingestion process based on a threaded lock-free butter and update the feature statistics based on a unified streaming framework.Extensive evaluation with real-world data sets demonstrates that Mercury achieves a significantly better trade-off between throughput and memory consumption than state-of-the-art methods. Mercury supports both traditional machine learning methods and accurate graph neural networks for traffic classification through extensive and efficient graph query APIs.
EPCA-ENet facilitates robotic hand recognition accuracy with flexible pressure sensor

Convolutional neural networks (CNNs) are now widely used in the fields of image classification and object detection, which promote the development of the above two fields. Recently, the performance of deep CNNs have been improved significantly by the study of channel attention and spatial attention. However, with the use of more sophisticated attention mechanisms, the complexity of the model has increased. To improve the recognition performance of our flexible pressure sensor array images dataset, we propose a low model complexity and plug-and-play Split and Concat with ECA(SCE) module. And the features of the ResNet structure are combined with the embedded SCE module to form a new Pyramid Channel with ECA(PCE) module, which can enhance the ability of the model to obtain contextual feature information and the cross-channel interaction. Finally, channel and spatial attention characteristics are combined to form our proposed EPCA-ENet. We validate our EPCA-ENet through extensive experiments on CIFAR-10, CIFAR-100 and ImageNet datasets. As compared to the ResNet50, the accuracy is improved by 3.22% on CIFAR-10 dataset, the accuracy is improved by 6.25% on CIFAR-100 dataset and the accuracy is improved by 2.96% on ImageNet dataset. By using our model, the experiments are conducted on the flexible pressure sensor array images dataset obtained by the robotic hand. Our model achieves 79.04% recognition rate in the described dataset, which reflects a high level of interactivity. The experimental results show our model has satisfactory performance in the field of image classification. Our source code is available at: https://github.com/zbeibei/EPCA.