Reviewerly/exHarmony huggingface dataset:
Practical Public Template Attacks on CRYSTALS-Dilithium With Randomness Leakages

Side-channel security has become a significant concern in the NIST post-quantum cryptography standardization process. The lattice-based CRYSTALS-Dilithium (abbr. Dilithium) becomes the primary signature standard algorithm recommended by NIST for most use cases in July 2022 due to its excellent performance in security and efficiency. Compared to Dilithium’s rich theoretical security analysis results, the side-channel security of its physical implementations needs to be further explored. In 2021, Liu et al. proposed a two-stage randomness leakage attack against Dilithium, in which only one randomness bit with a probability <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt; 0.5$ </tex-math></inline-formula> per signature is enough to recover the private key. However, they only carried out proof-of-concept experiments on “research-oriented” reference implementation of polynomial addition. Whether this method applies to complete real-world implementations of Dilithium is unknown. In this paper, we put this randomness leakage attack into real-world and recover the private key of unprotected and masked Dilithium on Arm Cortex-M4 processor using non-profiled power analysis attacks. Since randomness is introduced in the signing process, it is challenging to recover the randomness bit of Dilithium with high success rate in only one trace. Inspired by Liu et al., we propose a new non-profiled attack called Public Template Attack (PTA), a template-attack-like method that builds templates using public information. With PTA, we recover the randomness bit of unprotected and masked Dilithium with a success rate of 95% and 62% in one power trace, respectively. To demonstrate practicality, we perform practical power analysis attacks against different security levels of round 3 unprotected and masked Dilithium on STM32F405 microprocessor. Using 10,000 traces, the private key of unprotected Dilithium2 is recovered in 0.5 hours with an ordinary PC desktop. Our attack is 240 times faster than the state-of-the-art non-profiled attack. Moreover, the private key of masked Dilithium2 is recovered using 680,000 traces in 38 hours. To the best of our knowledge, we are the first to successfully attack masked Dilithium using non-profiled attacks.
The use of texture analysis and band transformation on multispectral imagery to map open-pit mines using machine learning

Abstract Indonesia has the potential for abundant natural mining resources. The Indonesian government needs to monitor mining activity to maintain environmental sustainability and the availability of mining materials in Indonesia. This study aims to map open mining areas based on remote sensing data. This mapping is one of the actions to support sustainable development goals for ensuring sustainable management and efficient use of natural resources. This study was conducted in Central Bangka Regency, Bangka Belitung Island Province, Indonesia, using the multitemporal Sentinel-2 year of 2020-2021. Gray Level Co-Occurrence Matrix and Principal Component Analysis were applied to improve the input band capability in mapping the distribution of open-pit mining locations. A pixel-based machine learning algorithm, Random Forest, was applied to classify mining and non-mining. Classification using texture analysis and spectral transformation mapped an open mining area of 30.67 km 2 . Classification using only image bands resulted from a mining area of 18.38 km 2 . The assessment showed that texture analysis and spectral transformation provided an accuracy of 1.22 % higher than the classification using a direct image input band. Overall, the accuracy obtained by both methods was 96.93 % and 95.71 %. Further research on validation with high-resolution data is still needed.
Anomaly Detection of Distribution Network Based on Adversarial Dual Autoencoder

Abstract Anomaly detection on attributed graphs aims to identify individuals or groups whose patterns deviate significantly from most samples, which is very important for internet security, power system, and other applications. Especially in the power system, the energy flow pattern of the distribution network is very similar to the graph topological constraints. Taking each equipment and bus as nodes and the relevant electrical measurement data as attributes, the distribution network fault location problem is mapped to the anomaly detection task on the attributed graph. The traditional GNN-based anomaly detection algorithm for distribution networks has some problems, such as over-smoothing and inefficient embedding learning. To solve the problem of anomaly detection in the distribution network, an anomaly detection algorithm based on adversarial dual autoencoder (DGAE_GAN) is proposed, which is composed of a structural encoder and attribute encoder based on residual connection GAT. Through joint learning, the attributed graph is mapped to the low-dimensional space, and the decoders reconstruct the attributed graph. Then, a discriminator is invited to learn the sample similarity, and the game strategy can effectively alleviate the over-smoothing problem. Finally, the anomaly node detection task is realized by calculating the reconstruction errors from both the structure and attribute levels. We conducted multiple experiments on three real-world datasets, and the IEEE standard example shows that our proposed model is superior to the existing methods.
MADPL-net: Multi-layer attention dictionary pair learning network for image classification

With the great success of deep neural networks, combining deep learning with traditional dictionary learning has become a hot issue. However, the performance of these methods is still limited for several reasons. First, some existing methods update dictionary learning and classifier as two independent modules, which limits the classification performance. Second, the non-attention dictionary is learned to represent all images, reducing the model representation flexibility. In this paper, we design a novel end-to-end model named Multi-layer Attention Dictionary Pair Learning Network (MADPL-net), which integrates the learning schemes of the convolutional neural network, deep encoder learning, and attention dictionary pair learning (ADicL) into a unified framework. The encoder layer contains the ADicL block, which selects more image-attentive atoms in the dictionary pair block via the softmax function to ensure MADPL-net classification capability. In addition, ADicL schema can yield discriminative dictionary atoms and feature maps with high inter-class separation and high intra-class compactness. To improve the sparse representation learning performance, MADPL-net adds l1−norm constraint of the analysis dictionary to the cross-entropy loss function. Extensive experiments show that MADPL-net can achieve excellent performance over other state-of-the-arts.
Comprehensive Framework of Major Power Project Management Based on System Thinking

Major power projects are normally critical nodes of the national power producing and transmission networks. However, the management of major power projects faces a variety of difficulties such as large scale, technical complexity, high quality standard, and frequent coordination. To improve the scientificity and effectiveness of the construction management of major power projects, a comprehensive framework of major power projects is established, which can be further decomposed into 8 subsystems and 28 indicators through system thinking. Then, by combining the G1 method with the cloud model, a new way of management performance evaluation of power projects is provided. Finally, taking the Kunliulong DC demonstration project as a case, the total performance evaluation result using the cloud model is between “excellent” and “great,” and more inclined to “excellent,” the performance of Schedule Control is close to the “outstanding” level, while the performance of Project Breakdown Structure is reaching the “good” level. The result shows that the established management evaluation system of major power engineering is relatively comprehensive, which can provide a reference for the realization of multiobjective and full-cycle overall management. Meanwhile, the exposed weak links in management, such as structural decomposition and environmental protection, should be highly valued and strictly controlled.
Traffic Sign Detection: Appropriate Data Augmentation Method from the Perspective of Frequency Domain

This study introduces a challenge faced by CNN in the task of traffic sign detection: how to achieve robustness to distributional shift. At present, all kinds of CNN models rely on strong data augmentation methods to enrich samples and achieve robustness, such as Mosaic and Mixup. In this study, we note that these methods do not have similar effects on combating noise. We explore the performance of augmentation strategies against disturbance in different frequency bands and provide understanding from the Fourier analysis perspective. This understanding can provide a guidance for selecting data augmentation strategies for different detection tasks and benchmark datasets.
Sentiment Analysis of Twitter Data for COVID-19 Posts

COVID-19 (Coronavirus Disease of 2019) is now a genuine danger to the health of the world's population. Vaccines capable of halting this pandemic have received much scientific and financial attention. These days expressing emotions over any social media platform is becoming a trend. This paper aims to study a technique known as sentiment analysis to learn about the sentiments of an individual. It takes a keyword as an input and will give you the polarity (positive or negative) as an output. Tweets can be positive for some and harmful for others. In order to get insight into how people perceive a particular tweet, the necessary steps are performed over a set of data. There is a tremendous amount of data available on the web for internet users, and much more data is being created as online technology continues to progress and flourish. The Internet has evolved into a medium for online education, idea exchange, and the expression of personal views. Twitter, Facebook, and Google + are among the most popular social networking sites because they allow users to share and express their opinions on various issues, engage with individuals from all backgrounds, and send messages worldwide. Sentiment analysis of Twitter data has gotten much attention. As a result, this survey focuses primarily on sentiment analysis of Twitter data, which helps analyze information in highly unstructured, varied, and neutral tweets. Sentiment analysis on Twitter has been explored as a whole and specific issues and uses. Some people favor getting vaccinations, while others are against them. This has led to much controversy among individuals. This study aims to develop a framework model for categorizing the sentiment and opinions expressed in tweets about the COVID-19 vaccinations. In order to better understand the people's views on vaccines and focus their efforts on them, public health organizations need to identify such viewpoints. SVM and NB are two machine learning classification models employed here. Pre-processing approaches for unstructured tweets were also utilized.
A Survey on Hybrid PSO and SVM Algorithm for Information Retrieval

This research study investigates different methods for rating an archive by using AI methodologies for determining Information Retrieval [IR] ranking position. SVM (Support Vector Machine) and PSO (Particle Swarm Optimization) are considered as the significant IR positioning methods. Selecting matching bounds in SVM is a complex process, yet it provides possible alternatives for positioning. One of the streamlining solutions, Particle Swarm Optimization (PSO), is simple to use and has a global inquiry capacity. As a result, a hybrid SVM-PSO model is presented to search for the fitness capability in order to advance the positioning of report recovery. This study has developed a novel PSO-SVM model, a combination of PSO and SVM to improve the order exactness with a limited and suitable element subset. This streamlining tool combines the discrete PSO with the continuous PSO to advance the information included in subset determination and the SVM bit boundary establishment. To decrease computational time, the hybrid PSO-SVM information mining framework was executed by using a distributed design accompanied with web administration innovation.
STFF-SM: Steganalysis Model Based on Spatial and Temporal Feature Fusion for Speech Streams

The real-time detection of speech steganography in Voice-over-Internet-Protocol (VoIP) scenarios remains an open problem, as it requires steganalysis methods to perform for low-intensity embeddings and short-sample inputs, as well as provide rapid detection results. To address these challenges, this paper presents a novel steganalysis model based on spatial and temporal feature fusion (STFF-SM). Differing from the existing methods, we take both the integer and fractional pitch delays as input, and design subframe-stitch module to organically integrate subframe-wise integer delays and frame-wise fractional pitch delays. Further, we design a spatial fusion module based on pre-activation residual convolution to extract the pitch spatial features and gradually increase their dimensions to discover finer steganographic distortions to enhance the detection effect, where a Group-Squeeze-Weighting block is introduced to alleviate the information loss in the process of increasing the feature dimension. In addition, we design a temporal fusion module to extract pitch temporal features using the stacked LSTM, where a Gated Feed-Forward Network is introduced to learn the interaction between different feature maps while suppressing the features that are not useful for detection. We evaluated the performance of STFF-SM through comprehensive experiments and comparisons with the state-of-the-art solutions. The experimental results demonstrate that STFF-SM can well meet the needs of real-time detection of speech steganography in VoIP streams, and outperforms the existing methods in detection performance, especially with low embedding strengths and short window sizes.
Holistic Feature Reconstruction-Based 3-D Attention Mechanism for Cross-Spectral Periocular Recognition

Recognition of individuals in cross-spectral environments involves situations where probe and gallery images are captured in distinct wavelength ranges and hence differ significantly in terms of appearance and illumination. In case of heterogeneous periocular images, alleviating this wide appearance gap and learning to extract illumination-invariant features from such local regions become cumbersome, giving rise to a challenging research problem. In this work, we design a novel holistic feature reconstruction-based attention module (H-FRAM) to refine and generate discriminative convolutional features. In contrast to existing spatial and channel attention mechanisms that compute 2-D or 1-D attention weights respectively, H-FRAM calculates the importance of each feature location by performing multi-linear principal component analysis on full 3-D tensor space. In H-FRAM, we claim that the feature reconstruction error, computed in a holistic manner, plays a crucial role in determining the relevance of feature locations. This reconstruction error is found by projecting the input feature map onto a multi-dimensional eigenspace that captures most of the important variations. To our knowledge, this is the first work which explores subspace learning approach in the context of 3-D attention mechanism to capture discriminative feature information. We have created an in-house cross-spectral periocular dataset containing visible and near-infrared images from 200 classes. The images are captured in unconstrained acquisition setup involving unsupervised eye and head movements as well as accessory variations (face masks and eyeglasses). Extensive experiments and ablation studies show that the proposed network achieves state-of-the-art recognition performances on the existing and in-house datasets for heterogeneous and homogeneous periocular recognition as well as heterogeneous face recognition.
A Robust Tuned K-Nearest Neighbours Classifier for Software Defect Prediction

If the software fails to perform its function, serious consequences may result. Software defect prediction is one of the most useful tasks in the Software Development Life Cycle (SDLC) process where it can determine which modules of the software are prone to defects and need to be tested. Owing to its efficiency, machine learning techniques are growing rapidly in software defect prediction. K-Nearest Neighbors (KNN) classifier, a supervised classification technique, has been widely used for this problem. The number of neighbors, which measure by calculating the distance between the new data and its neighbors, has a significant impact on KNN performance. Therefore, the KNN’s classifier will perform better if the k hyperparameters are properly tuned and the independent inputs are rescaled. In order to improve the performance of KNN, this paper aims to presents a robust tuned machine learning approach based on K-Nearest Neighbors classifier for software defect prediction, called Robust-Tuned-KNN(RT-KNN). The RT-KNN aims to address the two abovementioned problems by (1) tuning KNN and finding the optimal value for k in both the training and testing phases that can lead to good prediction results, and (2) using the Robust scaler to rescale the different independent inputs. The experiment results demonstrate that RT-KNN is able to give sufficiently competitive results compared with original KNN and other existing works.
Myoelectric human computer interaction using CNN-LSTM neural network for dynamic hand gesture recognition

Human-computer interaction(HCI) has broad range of applications. One particular application domain is rehabilitation devices. Several bioelectric signals can potentially be used in HCI systems in general and rehabilitation devices in particular. Surface ElectroMyoGraphic(sEMG) signal is one of the more important bioelectric signals in this context. The sEMG signal is formed by muscle activation although the details are rather complex. Applications of sEMG are referred is commonly referred to as myoelectric control since the dominant use of this signal is to activate a device even if (as the term control may imply) feedback is not always used in the process. With the development of deep neural networks, various deep learning architectures are used for sEMG-based gesture recognition with many researchers having reported good performance. Nevertheless, challenges remain in accurately recognizing sEMG patterns generated by gestures produced by hand or the upper arm. For instance one of the difficulties in hand gesture recognition is the influence of limb positions. Several papers have shown that the accuracy of gesture classification decreases when the limb position changes even if the gesture remains the same. Prior work by our team has shown that dynamic gesture recognition is in principle more reliable in detecting human intent, which is often the underlying idea of gesture recognition. In this paper, a Convolutional Neural Network (CNN) with Long Short-Term Memory or LSTM (CNN-LSTM) is proposed to classify five common dynamic gestures. Each dynamic gesture would be performed in five different limb positions as well. The trained neural network model is then used to enable a human subject to control a 6 DoF (Degree of Freedom) robotic arm with 1 DoF gripper. The results show a high level of accurate performance achieved with the proposed approach. In particular, the overall accuracy of the dynamic gesture recognition is 84.2%. The accuracies vary across subjects but remain at approximately 90%for some subjects.
Generative face inpainting hashing for occluded face retrieval

COVID-19 has resulted in a significant impact on individual lives, bringing a unique challenge for face retrieval under occlusion. In this paper, an occluded face retrieval method which consists of generator, discriminator, and deep hashing retrieval network is proposed for face retrieval in a large-scale face image dataset under variety of occlusion situations. In the proposed method, occluded face images are firstly reconstructed using a face inpainting model, in which the adversarial loss, reconstruction loss and hash bits loss are combined for training. With the trained model, hash codes of real face images and corresponding reconstructed face images are aimed to be as similar as possible. Then, a deep hashing retrieval network is used to generate compact similarity-preserving hashing codes using reconstructed face images for a better retrieval performance. Experimental results show that the proposed method can successfully generate the reconstructed face images under occlusion. Meanwhile, the proposed deep hashing retrieval network achieves better retrieval performance for occluded face retrieval than existing state-of-the-art deep hashing retrieval methods.
OCR for Devanagari Script Using a Deep Hybrid CNN-RNN Network

Optical character recognition (OCR) involves the electronic transcription of handwritten, printed text from either scanned documents or any sort of images. This multiclass classification problem comprising of recognizing the various characters in a language and correctly classifying them has found its way in plenty of computer vision applications. Although much work has been done for the English language, there have been only a few explorations pertaining to OCR for the Devanagari script. This paper proposes a hybrid CNN-RNN model to classify characters using the Devanagari handwritten character dataset. The main objective is to design a model with higher accuracy than the CNN model reported in literature for the same purpose. The models are trained and evaluated using the same procedures. On evaluating the models, the hybrid CNN-RNN model has a testing accuracy of 98.71%, which is higher than the CNN model, having 97.71% testing accuracy. It also fares better than the standard neural network architectures-VGG16 and AlexNet which when taken without the pre-trained weights result in 97.62 and 98.20% testing accuracy respectively. Hence, this successfully demonstrates the attributes of RNN in improved feature extraction when used along with convolutional layers.