Reviewerly/exHarmony huggingface dataset:
Resampling Estimation Based RPC Metadata Verification in Satellite Imagery

Recent advances in machine learning and computer vision have made it simple to manipulate a variety of media, including satellite images. Most of the commercially available satellite images go through the process of orthorectification to remove potential distortions due to terrain variations. This orthorectification process typically involves the use of rational polynomial coefficients (RPC) that geometrically remap the pixels in the original image to the rectified image. This paper proposes the first method to verify the authenticity of RPC metadata in an orthorectified satellite image. The steps include calculating the Residual Discrete Fourier Transform (DFT) pattern from the image using a linear predictor based residual spectral analysis and comparing with Expected Residual DFT pattern that is obtained using the RPC metadata associated with the image. If the metadata associated with orthorectified image is correct, then the Residual-DFT pattern (which represents image data) and the Expected-Residual-DFT pattern (which represents metadata) should be similar. We use SSIM (Structural Similarity Index Metric) to quantify the similarity and thereby verify if the data has been tampered or not. Detailed experimental results demonstrate that our method achieves over 97% accuracy in the majority of binary tampering detection tests.
MAFIA: Protecting the Microarchitecture of Embedded Systems Against Fault Injection Attacks

Fault injection attacks represent an effective threat to embedded systems. Recently, Laurent et al. have reported that fault injection attacks can leverage faults inside the microarchitecture. However, state-of-the-art counter-measures, hardwareonly or with hardware support, do not consider the integrity of microarchitecture control signals that are the target of these faults. We present MAFIA, a microarchitecture protection against fault injection attacks. MAFIA ensures integrity of pipeline control signals through a signature-based mechanism, and ensures fine-grained control-flow integrity with a complete indirect branch support and code authenticity. We analyse the security properties of two different implementations with different security/overhead trade-offs: one with a CBC-MAC/Prince signature function, and another one with a CRC32. We present our implementation of MAFIA in a RISC-V processor, supported by a dedicated compiler toolchain based on LLVM/Clang. We report a hardware area overhead of 23.8 % and 6.5 % for the CBC-MAC/Prince and CRC32 respectively. The average code size and execution time overheads are 29.4 % and 18.4 % respectively for the CRC32 implementation and are 50 % and 39 % for the CBC-MAC/Prince.
A Novel Covert Timing Channel Based on Bitcoin Messages

Covert channels serve the construction of cyberspace security. By realizing the secure transmission of data, it is widely used in political and financial fields. Blockchain covert channels have higher reliability and concealment compared to traditional network-based covert channels. However, existing blockchain covert storage channels need to create a large number of transactions to transmit covert information. Creating transactions requires a transation fee, which means that the implementation of blockchain covert storage channels requires a high cost. Besides, created transactions remain on-chain permanently, leading to the threat of covert information being detected. To overcome these limitations, we propose a blockchain covert timing channel framework. Specifically, we utilize inv and getdata messages in the Bitcoin transaction broadcast as carriers and propose three modulation modes to achieve covert channels without cost and leaving no trace. We evaluate the concealment of our modes by K-S, KLD tests, and machine learning approaches. Experimental results show the indistinguishability between traffic carrying covert information and normal traffic. Our channels promise a capacity of 2.4 bit/s.
A Computationally Efficient Robust Cubature Kalman Filter With Multivariate Laplace Distribution

This paper investigates the nonlinear state estimation under heavy-tailed process and measurement noises (PAMNs), the noises may be induced by sensor failures, measurement loss, modeling errors, environmental changes, or malicious cyberattacks. Considering that the multivariate Laplacian (ML) distribution has obvious heavy-tailed characteristic, we employ the distribution to describe heavy-tailed PAMNs. Furthermore, to improve the computational efficiency of the existing variational Bayesian (VB) iteration process, we design an improved VB iteration method, which can separately calculate the posterior distributions of the state vector and unknown noise parameters. Employing the ML distribution and improved VB inference process, a computationally efficient robust cubature Kalman filter (CEMLRCKF) is derived. Simulation and vehicle experiment results illustrate the superiority of the proposed filter.
MhaGNN: A Novel Framework for Wearable Sensor-Based Human Activity Recognition Combining Multi-Head Attention and Graph Neural Networks

Obtaining robust feature representations from multi-position wearable sensory data is challenging in human activity recognition (HAR) since data from different positions can have unordered implicit correlations. Graph neural networks (GNNs) represent data as structured graphs by mining complex relationships and interdependency via message passing between the nodes of graphs. This paper proposes a novel framework (MhaGNN) that combines GNNs and the multi-head attention mechanism, aiming to learn more informative representations for multi-position HAR tasks. The MhaGNN framework takes the sensor channels from multiple wearing positions as nodes to construct graph-structured data from the spatial dimension. Besides, the multi-head attention mechanism is introduced to complete the message passing and aggregation of the graphs for spatial-temporal feature extraction. The MhaGNN learns correlations among sensor channels that can be used as compensatory features together with the captured features from each single sensor channel to enhance HAR. Experimental evaluations on three publicly available HAR datasets and a ground-truth dataset demonstrate that our proposed MhaGNN achieves state-of-the-art recognition performance with the captured rich features, including PAMAP2, OPPORTUNITY, MHAEATH and MPWHAR.
Crisscross-Global Vision Transformers Model for Very High Resolution Aerial Image Semantic Segmentation

Semantic segmentation is a key means for understanding very-high resolution (VHR) aerial imagery. With the explosive development of deep learning, deep learning methods are being applied to the segmentation of VHR images, with convolutional neural networks (CNNs) as the basic framework. However, owing to the highly complex details present in VHR images and the high spatial dependence of geographical objects, CNN-based methods are inadequate. This is because the inherent locality of CNNs limits the size of the receptive field, thus limiting the ability to obtain long-range context information. To solve this problem, in this paper, we propose a transformer-based novel deep learning model called crisscross-global vision transformers (CGVT). CGVT exploits the transformer’s inherent ability to obtain long-range context information to solve the restricted receptive field problem. Specifically, we redesign the self-attention mechanism in the transformer and call it crisscross-global attention. It consists of two parts: crisscross transformer encoder block (CC-TEB) and global squeeze transformer encoder block (GS-TEB). CC-TEB overcomes the limitation of the traditional self-attention design (specifically, difficulty applying it to VHR aerial image segmentation) and further increases the local feature representation ability of the model. GS-TEB increases the global feature representation ability of the model. The results of experiments conducted on the popular ISPRS Vaihingen, IEEE GRSS Data Fusion Contest Zeebrugge, and LoveDA Semantic Segmentation Challenge datasets verify the effectiveness and superiority of our proposed method. Specifically, it achieved state-of-the-art performance on both Zeebrugge and LoveDA datasets, and is currently ranked second in Vaihingen dataset.
RISTrack: Robust Infrared Ship Tracking With Modified Appearance Feature Extraction and Matching Strategy

Infrared (IR) ship tracking is becoming increasingly important in various applications. However, it remains a challenging task as the information that can be obtained from infrared images is limited. Aiming at enhancing IR ship tracking accuracy, we propose an innovative approach by presenting feature integration module (FIM) and backup matching module (BMM). FIM takes appearance feature, complete intersection over union (CIoU), and motion direction metrics into account. Regarding appearance feature extraction, an end-to-end characteristic learning strategy with a cross-guided multi-granularity fusion network is proposed to obtain more integral appearance features and enhance re-identification accuracy, which helps to distinguish individual IR ship targets better. Besides, a backup matching strategy is then used to match the unmatched tracks and detections after cascaded matching. Virtual trajectories are generated for the matched tracks to optimize parameters by parameter optimization module (POM). The accumulation of errors caused by the lack of observations in the Kalman filter is reduced. Thus, the position of IR ships can be estimated more accurately, and more robust IR ship tracking can be achieved. In addition, we present a sequential frame IR ship tracking dataset, providing the first public benchmark for testing IR ship tracking performance. Experimental results indicate that the MOTA, MOTP and IDs of the proposed method are 73.441, 80.826, and 32, respectively, outperforming other state-of-the-art methods. This demonstrates the superior robustness of the proposed method, particularly when the IR ships are occluded or the target texture information is lacking. Our dataset is available at https://github.com/echo-sky/SFIST.
Bird's-Eye-View Semantic Segmentation With Two-Stream Compact Depth Transformation and Feature Rectification

Bird's-eye-view (BEV) perception has gained popularity since it provides a 3D world representation with scale consistency. Although existing camera-based solutions achieve excellent performance, the BEV positions related to features are still less accurate. In this article, a BEV semantic segmentation framework with two-stream compact depth transformation and feature rectification is proposed. To balance the conflict that the feature maps ensemble tends to use two temporal frames with long interval, while shorter temporal frames are more beneficial to depth prediction, a two-stream compact depth transformation is designed. Between original temporal frames, we introduce an intermediate frame to decouple the joint depth estimation of original frames. The local representations of the intermediate frame are respectively matched with each original temporal frame to achieve stereo depth predictions, where compact cost volumes are built to significantly reduce memory usage with high discriminability in depth-dimension. Further, virtual camera intrinsic parameters are derived to realize adaption of compact cost volume to various 2D data augmentation and improve generalization. On this basis, BEV feature maps are obtained via feature transformation. With the influence of depth distribution errors to BEV feature map, a feature rectified segmentation network is proposed to dynamically adjust the position offsets of input features via deformable convolution and semantic information-guided feature learning. As a result, a dense and accurate BEV semantic map is obtained. In addition, a self-supervised depth estimation teacher is adopted to provide extra supervision for depth prediction of our segmentation framework. The effectiveness of the proposed method is verified on public datasets.
Target-Aware Transformer Tracking

Object tracking is aimed at locating a specific object in the image sequence, such as pedestrians, vehicles, and so on. The existing algorithms based on siamese neural network predict the target through similarity matching. Although these algorithms have achieved satisfactory performance, in the process of similarity calculation between template image and search image, only local information is often concerned, which makes the algorithms difficult to obtain the optimal solution. To deal with the abovementioned problems, we propose a model based on Transformer, named TaTrack. Specifically, we first use the encoders to enhance the features. Then, the dependency between template features and search features is established through the target-aware module. Finally, we utilize the classification regression network to locate the target, and use the classification score to adapt to update the template image. Experiments show that our model can achieve great performance on GOT-10k, LaSOT, and TrackingNet datasets.
Research on Multi-dimensional Bilingual Teaching Model of Computer Courses Supported by Artificial Intelligence

Aiming at the problems of the integration of computer course teaching with international teaching and engineering certification, this study designs a multi-dimensional teaching mode of computer courses through the support of Artificial Intelligence. It analyzes the teaching resources, learning services, teaching process and teaching means involved in computer course teaching. Taking the course of “Computer Introduction” as an example, this study explores the multi-dimensional bilingual teaching mode supported by Artificial Intelligence, trains computer talents who meet the ability requirements of the era of Artificial Intelligence, and provides ideas for the teaching innovation of other courses.
Steganography in Colour Images with Proposed Arnold Chaotic Map and Optimized Curvelet Transform

Steganography seems to be the process of concealing secret information within a non-secret file or communication to prevent discovery; this hidden information is then retrieved at its destination. Steganography could be used in conjunction with encryption to conceal or safeguard data. As a result, numerous researchers have concentrated their efforts in this field in order to offer safe data transfer. In addition, we proposed a unique strategy called steganography in colour pictures with proposed Arnold chaotic map and optimized curvelet transform which has two working stages: image embedding and image extraction. In the first stage, the 3-level DWT with curvelet transform is being used to embed the original picture with a watermarking image which uses self-improved Aquila optimization (SIAO) to get the better performance by optimizing the scaling factors. During the embedding phase, in the scrambling process, proposed Arnold chaotic map is used to scramble the image. In the second stage, the water marks get extracted from this water marking images by inverse 3-level DWT. Consequently, secret image was generated by applying inverse Arnold chaotic map transforms on the cover image. The performance of proposed work is compared over the other conventional models like existing Aquila optimizer, cat swarm optimization, spider monkey optimization, poor rich optimization, etc., which shows that our proposed steganography technique has greater performance.
A Study on Behavioral Engagement of College English Learners in Blended Teaching Model

The issue of student behavioral engagement in the blended teaching mode has become increasingly significant to ensure students’ learning outcome. This study focuses on the features of college English learners’ online behavioral engagement in a blended teaching context: gender difference, major difference, and its correlation with academic achievement. A sample of 106 first-year college non-English students were involved in this study. All the collected valid data were analyzed by software SPSS 22.0, including independent-sample t-tests, Pearson correlation analysis and regression analysis. This study found that (1) there are significant differences in behavioral engagement and academic achievement between male students and female students, and the behavioral engagement and academic achievement of females are significantly higher than those of males. (2) international business students’ behavioral engagement and academic achievement are significantly higher than science & engineering students. (3) students’ overall behavioral engagement was significantly related to midterm, final and CET-4 scores, but not significantly related to CEE scores. (4) student’s effort value for online listening practice, midterm and final scores can predict 66.6% of the variance of the CET-4 score.
Segmentation of Gujarati Handwritten Characters and Numerals with and Without Modifiers from the Scanned Document

Gujarati is more challenging to understand than other languages because of its curves, encompassing of modifiers, and usage of joint characters. So, to make it convenient in reading the Gujarati language, systems like OCR are used. Optical character recognition (OCR) also referred to as text-recognition. Day by day, the use of OCR technology is increasing worldwide to convert either handwritten text or printed text from a required image virtually to machine-readable text. So great effort has been laid into the literature for Gujarati OCR. The accuracy of the OCR devices for isolated handwritten Gujarati characters depends upon the genuine segmentation of characters. This paper focuses on box segmentation and character image extraction using Python language and experimental analysis using convolutional neural network (CNN) has been carried out on some of the well-segmented newly generated handwritten Gujarati characters and numerals dataset which includes 10 consonants, 5 vowels, and 10 handwritten numerals. As a result, 350 images for each 490-characters including consonants, vowels, numerals, and modifiers are extracted.
Face Recognition-Based Automated Attendance System for Educational Institutions Utilizing Machine Learning

Face recognition technology, defined as the process of detecting and verifying individuals’ identities via image processing techniques in computer vision, has become increasingly vital in today’s technologically and digitally evolved world. Automating the attendance system with face recognition software eliminates inefficiencies like tampered records, proxies, and security risks. This paper proposes incorporating facial recognition technology, Haar Cascade classifiers (Li et al. in ICEMI 2017-Proceedings IEEE 13th International conference on electronic measurement and instruments, vol 2018-Janua, pp 483–487), and the Local Binary Pattern Histogram (LBPH) (Ahmed et al. in 2018 International conference artificial intelligent big data, (ICAIBD 2018), pp 144–147) machine learning approach can be used for identifying faces to create a secure and reliable solution for automating attendance systems. The proposed method produces more accurate results than any other algorithms used earlier. Compared to existing algorithms, the suggested system showed better results, reliability, and robustness in accurately recognizing faces. Its facial recognition accuracy is 87%, and the false-positive rate is only 7%. The system can also reliably identify a person who has undergone unintentional changes in their appearance, such as growing a beard or putting on spectacles.
Design and Implementation of a Cloud-Native Platform for Financial Big Data Processing Course

With the advent of big data, financial technology steps into a more intelligent era. The demand for big data-related financial application development and operation continues to expand. Hadoop and Spark are popular parallel computing frameworks that are adopted as big data processing platforms, but the time cost of their environment deployment is a significant expense for beginners. BDKit, a big data application development and operation platform based on container cloud, is proposed in this paper, aiming to provide customized and scalable cloud service solutions for teaching practice. It is based on Kubernetes ecosystem and relies on the powerful self-repair and load balancing capability provided by Kubernetes to ensure the stable operation of users’ virtual clusters. At the same time, BDKit encapsulates a variety of Docker images to achieve the construction of big data application development and operation environment according to the components required by different users. The services provided by BDKit can greatly reduce the pressure of preparing big data processing environment and help to achieve the purpose of teaching students according to their aptitude.
A Study of Breast Cancer Identification with Deep Learning Techniques

Breast cancer is currently the utmost often analysed malice in females, and number of cases is steadily rising. If breast cancer is detected and treated early enough, there is a high possibility of a positive outcome. As a result, some researchers have developed deep automated algorithms for forecasting the development of cancer cells using medical imaging modalities for their efficiency and accuracy. There are currently just an insufficient review papers on breast cancer identification that synthesise about the previous trainings. These investigations, however, were impotent to report new structures and modalities in the discovery of breast cancer. The changing structural design for deep learning-based breast cancer identification is the subject of this review. This assessment explores the merits and drawbacks of existing deep learning structures, investigates the datasets employed, and assesses picture pre-processing approaches in the sections that follow. A detailed overview of several modalities of medical images, performance measures and findings, obstacles, and investigation prospects for forthcoming researchers is also provided.
An Application for Privacy-Preserving Contact Tracing and Public Risk Assessment Using Blockchain for Covid-19 Pandemic

Many nations are debating on the ways to revert to the new regular state of living as the count of reported infections and victims of the novel COVID-19 outbreak drops with each day gradually. One option examined is the use of contact tracking to bring the spreading of the illness reducing and preventing another round of transmission. Contact tracing still has security and privacy difficulties, regardless of the fact that there are multiple solutions, which makes widespread usage challenging. On the basis of that, a unique approach for contact traceability is introduced, utilizing Blockchain like its foundation. User confidentiality may be protected as well as a comprehensive view of the public for all verified instances can be made available to individuals and governmental entities through connecting distributed ledger alongside contacts tracking apps. By calculating the danger of COVID-19 exposure to the wider populace and disclosing it in a ledger, it also explores how open places may assist in contact tracking. With the use of an Android app, the system can detect the position, and this position is kept on the ledger. These places can efficiently notify possible contamination concerns whilst still ensuring the data's confidentiality and reliability. Finally, findings were made when numeric figures are presented in various settings.