Reviewerly/exHarmony huggingface dataset:
A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques

Digital signature schemes are practical mechanisms for achieving message integrity, authenticity, and non-repudiation. Several asymmetric encryption techniques have been proposed in the literature, each with its proper limitations. RSA and El Gamal prove their robustness, but are unsuitable in several domains due to their computational complexity. Other asymmetric encryption schemes have been proposed to provide a cloud homomorphic encryption service, where the researchers focused only on how to ensure the homomorphic property. This paper proposes a new digital signature scheme dedicated to a family of encryption techniques. The proposal consists of two parts: the first focused on the secret key, and the second focused on the public key. Signature validity checking was performed by multiplying these two parts to reform again the sender’s public key, then comparing the result with the decrypted message. The validation of the decrypted message guarantees data integrity, where the signer public key is used to ensure authenticity. The proposed scheme takes a shorter execution time for the entire signature operation, including signing and verification, compared to other modern techniques. The analysis showed its robustness against private key recovery and forgery attacks. The implementation results of the proposed scheme showed promising performance in terms of complexity and robustness. The results confirmed that the proposed scheme is efficient and effective for signature generation and verification.
Continuous word level sign language recognition using an expert system based on machine learning

The study of sign language recognition systems has been extensively explored using many image processing and artificial intelligence techniques for many years, but the main challenge is to bridge the communication gap between specially-abled people and the general public. This paper proposes a python-based system that classifies 80 words from sign language. Two different models have been proposed in this work: You Only Look Once version 4 (YOLOv4) and Support Vector Machine (SVM) with media-pipe. SVM utilizes the linear, polynomial and Radial Basis Function (RBF) kernels. The system does not need any additional pre-processing and image enhancement operations. The image dataset used in this work is self-created and consists of 80 static signs with a total of 676 images. The accuracy of SVM with media-pipe is 98.62% and the accuracy of YOLOv4 obtained is 98.8% which is higher than the existing state-of-the-art methods. An expert system is also proposed which utilizes both the above models to predict the hand gesture more accurately in real-time.
Vision-knowledge fusion model for multi-domain medical report generation

Medical report generation with knowledge graph is an essential task in the medical field. Although the existing knowledge graphs have many entities, their semantics are not sufficient due to the challenge of uniformly extracting and fusing the expert knowledge from different diseases. Therefore, it is necessary to automatically construct specific knowledge graph. In this paper, we propose a vision-knowledge fusion model based on medical images and knowledge graphs to fully utilize high-quality data from different diseases and languages. Firstly, we give a general method to automatically construct every domain knowledge graph based on medical standards. Secondly, we design a knowledge-based attention mechanism to effectively fuse image and knowledge. Then, we build a triples restoration module to obtain fine-grained knowledge, and the knowledge-based evaluation metrics are first proposed which are more reasonable and measurable from different dimensions. Finally, we conduct experiments to verify the effectiveness of our model on two different diseases datasets: the IU-Xray chest radiograph public dataset and the NCRC-DS dataset of Chinese dermoscopy reports we compiled. Our model outperforms previous benchmark methods and achieves excellent evaluation scores on both datasets. Additionally, interpretability and clinical usefulness of the model are validated and our method can be generalized to multiple domains and different diseases.
A data analytics framework for anomaly detection in flight operations

In the air transport system, there has been a continuous effort to develop policies, tools, and methodologies that increase and standardize safety levels across the entire commercial aviation market, while also enhancing operational efficiency. Furthermore, there is a current focus on proactive approaches for aviation performance management. Within this context, data mining initiatives such as anomaly detection have become more prominent. Dealing with anomalies is a natural step for achieving the goals regarding operational safety and efficiency, as anomalies are often related to hazardous and inefficient operations. In this work, we propose a systematic flight data analytics framework for anomaly detection in flight operations in order to provide a comprehensive and reusable pipeline for model building, application, and explanation. The solution is designed to be applicable to both online and offline regimes and at multiple scales, while also building on domain-expert analysis. We demonstrate the framework applicability in two scenarios of routine flight operations monitoring considering both airline and air traffic management perspectives. In the first one, the framework is applied to aircraft performance data within an unsupervised learning setting with a density-based clustering approach for anomaly detection in landing operations at Minneapolis–Saint Paul International Airport (KMSP). The results are compared with those obtained with exceedance-based methods used in the current practice, revealing the detection of operationally significant anomalies beyond the benchmark. In the second case study, we apply the framework on flight tracking data within a supervised learning setting with the development of an autoencoder classifier for offline anomaly detection in terminal airspace arrival operations at Sao Paulo/Guarulhos International Airport (SBGR). Additionally, supervised learning models are developed for anomaly explanation. The autoencoder classifier was able to detect operationally significant anomalies, while the explanatory models provided novel insights about contributing factors to the anomalies identified. For instance, we learned that anomalous arrival trajectories are more likely to be associated with landing operations on runway 27 under wind scenarios, with an increase in the odds ratio of 62% and 58% for tailwinds and headwinds, respectively. In addition, we also observed a positive association between anomalies and wind gusts situations.
Super-resolution of X-ray CT images of rock samples by sparse representation: applications to the complex texture of serpentinite

X-ray computed tomography (X-ray CT) has been widely used in the earth sciences, as it is non-destructive method for providing us the three-dimensional structures of rocks and sediments. Rock samples essentially possess various-scale structures, including millimeters to centimeter scales of layering and veins to micron-meter-scale mineral grains and porosities. As the limitations of the X-ray CT scanner, sample size and scanning time, it is not easy to extract information on multi-scale structures, even when hundreds meter scale core samples were obtained during drilling projects. As the first step to overcome such barriers on scale-resolution problems, we applied the super-resolution technique by sparse representation and dictionary-learning to X-ray CT images of rock core sample. By applications to serpentinized peridotite, which records the multi-stage water-rock interactions, we reveal that both grain-shapes, veins and background heterogeneities of high-resolution images can be reconstructed through super-resolution. We also show that the potential effectiveness of sparse super-resolution for feature extraction of complicated rock textures.
Effects of wind speed and tropospheric height on solar power generation: Energy exploration above ground level

Terrestrial and extraterrestrial factors hinder the exploitation of solar power using a ground platform. This paper is concerned with the generation of solar power above ground level. This paper employs modeling and simulations coupled with experimentation to establish a functional relationship between the percentage of solar power gain, tropospheric height, and wind speed. The finite difference method based on the Cranks–Nicolson scheme was used to realize the model solution. The wind flow equation established a linear relationship between tropospheric height and wind speed, between tropospheric height and the percentage of solar power gain. It is evident that an infinitesimal percentage of solar power gain (≈2.5%) corresponded to 1000 m above ground level; the tropospheric height of 8100 m recorded 23% of solar power gain. Furthermore, wind speeds of 0 to 33 ms −1/distance (1000 m) correspond to ground level and tropospheric height of about 8100 m respectively. However, there is a prospect of achieving more percentage of solar power gain by applying high-altitude platforms. The results obtained apply to other study areas having elevations close to 1100 m, it serves as a guide in the estimation of the percentage of solar power gain by the virtue of tropospheric height and wind speed.
Secure authentication and privacy-preserving blockchain for industrial internet of things

Blockchain (BC) technology has overtaken Industrial Internet of Things (IIoT) platforms. It is necessary to explore efficient implementation. Fault tolerance, decentralised control, authentication, cryptographic security, immutability, data integrity, and BC smart contracts are recommended IIoT features. If entities are authenticated and trusted, the internet can be used for industrial activities. Despite several methods, communication is insecure due to scalability, dependability, latency, insufficient transmission security, and uneven data loads. The paper created safe User authentication and optimal BC node selection using AFHENN (Fully Homomorphic encryption neural network) for IIoT to solve the problem. Mutual authentication, secrecy, and integrity protect user data. A registration process secures new User authentication. To protect registered data, it uses cryptographic methods like Transient key congruential generator based Elliptic curve cryptography (TKCG-ECC) and Dual keyed Cipolla's Extended Euclidean Algorithm based lattice cryptosystem (DKCEED-LC). To access BCN, the gateway verifies registered users utilising keyed-based Zero Knowledge of Proof (k-ZKP) and Approximation Fully Homomorphic encryption neural network-based Blockchain. Finally, Approximation Fully Homomorphic encryption neural network-based Blockchain networking authenticates data (AFHENN-BCN). The BCN avoids legal selection of miner nodes and harmful activities. Compared to top techniques, the proposed work achieves improved throughput and PDR (Packet Delivery Ratio) values with minimal computing time and strong security.
Detecting abusive comments at a fine-grained level in a low-resource language

YouTube is a video-sharing and social media platform where users create profiles and share videos for their followers to view, like, and comment on. Abusive comments on videos or replies to other comments may be offensive and detrimental for the mental health of users on the platform. It is observed that often the language used in these comments is informal and does not necessarily adhere to the formal syntactic and lexical structure of the language. Therefore, creating a rule-based system for filtering out abusive comments is challenging. This article introduces four datasets of abusive comments in Tamil and code-mixed Tamil–English extracted from YouTube. Comment-level annotation has been carried out for each dataset by assigning polarities to the comments. We hope these datasets can be used to train effective machine learning-based comment filters for these languages by mitigating the challenges associated with rule-based systems. In order to establish baselines on these datasets, we have carried out experiments with various machine learning classifiers and reported the results using F1-score, precision, and recall. Furthermore, we have employed a t-test to analyze the statistical significance of the results generated by the machine learning classifiers. Furthermore, we have employed SHAP values to analyze and explain the results generated by the machine learning classifiers. The primary contribution of this paper is the construction of a publicly accessible dataset of social media messages annotated with a fine-grained abusive speech in the low-resource Tamil language. Overall, we discovered that MURIL performed well on the binary abusive comment detection task, showing the applicability of multilingual transformers for this work. Nonetheless, a fine-grained annotation for Fine-grained abusive comment detection resulted in a significantly lower number of samples per class, and classical machine learning models outperformed deep learning models, which require extensive training datasets, on this challenge. According to our knowledge, this was the first Tamil-language study on FGACD focused on diverse ethnicities. The methodology for detecting abusive messages described in this work may aid in the creation of comment filters for other under-resourced languages on social media.
Deep pairwise learning for user preferences via dual graph attention model in location-based social networks

The rapid growth of location-based social networks (LBSN) has generated massive multi-mode entities such as users, locations and social topics, as well as multi-relationships among them. Learning deep user preferences over point of interests (POIs) and social topics in LBSN has attracted more and more attentions. However, most of the previous studies have failed to incorporate the severely sparse and multi-relational data of heterogeneous LBSN, which plays a vital role to enhance the prediction performance. Toward this challenge, in this paper, we propose a unified pairwise relationship prediction framework based on graph neural networks combined with dual attention mechanism model, dubbed GE2AT, which has fully considered to incorporate three types of user behavioral relations to better capture user preference over different entities. Specifically, we first model LBSN as one user social graph only including social relations, and other two bipartite graphs involved user-location check-in relations and user-social topics following relations respectively. Then, we propose dual graph attention networks to jointly learn deep latent representations of different entities by aggregating features from neighbors with different importance weights. After that, we adopt two multi-layer perceptron (MLP) to output the pairwise preferences of users over POIs and social topics. In addition, to alleviate data sparsity problem, on the basis of MLPs output results, we further leverage social relationships as social-aware influence for similar user preferences so as to enhance the pairwise prediction performance. Extensive experiments on two real-world datasets have demonstrated that our proposed framework GE2AT significantly outperform the state-of-the-art baselines on both prediction tasks for user preferences on POIs and social topics.
Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams

In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results.
Decentralized Real-Time Anomaly Detection in Cyber-Physical Production Systems under Industry Constraints

Anomaly detection is essential for realizing modern and secure cyber-physical production systems. By detecting anomalies, there is the possibility to recognize, react early, and in the best case, fix the anomaly to prevent the rise or the carryover of a failure throughout the entire manufacture. While current centralized methods demonstrate good detection abilities, they do not consider the limitations of industrial setups. To address all these constraints, in this study, we introduce an unsupervised, decentralized, and real-time process anomaly detection concept for cyber-physical production systems. We employ several 1D convolutional autoencoders in a sliding window approach to achieve adequate prediction performance and fulfill real-time requirements. To increase the flexibility and meet communication interface and processing constraints in typical cyber-physical production systems, we decentralize the execution of the anomaly detection into each separate cyber-physical system. The installation is fully automated, and no expert knowledge is needed to tackle data-driven limitations. The concept is evaluated in a real industrial cyber-physical production system. The test result confirms that the presented concept can be successfully applied to detect anomalies in all separate processes of each cyber-physical system. Therefore, the concept is promising for decentralized anomaly detection in cyber-physical production systems.
A Multi-Criteria Decision-Making Process for the Selection of an Efficient and Reliable IoT Application

Saudi Arabia initiated its much-anticipated Vision 2030 campaign, a long-term economic roadmap aimed at reducing the country’s reliance on oil. The vision, which is anticipated to be accomplished in the future, underlines compliance, fiscal, and strategy adjustments that will significantly affect all the important features of Saudi economic growth. Technology will be a critical facilitator, as well as controller, of the initiative’s significant transformation. Cloud computing, with the Internet of things (IoT), could make significant contributions to Saudi Vision 2030’s efficient governance strategy. There are multiple IoT applications that cover every part of everyday life, as well as enabling users to use a variety of IoT applications. Choosing the best IoT applications for specific customers is a difficult task. This paper concentrates on the Kingdom’s advancement towards a fresh, as well as enhanced, method of advancing the development phases pertaining to digital transformation, through implementing and adopting modern communications infrastructure and ICT technology. In addition, this study proposes a recommendation system that relies on a multi-criteria decision-making investigation focusing on the fuzzy TOPSIS method for selecting highly efficient IoT applications. The prototype, as well as the hierarchy, was created to assess and correlate critical criteria based on specialist preferences and recommendations. The T5 IoT application alternative was shown to be the most highly effective and reliable choice according to the findings of both fuzzy TOPSIS and TOPSIS.
A novel attention-based enhancement framework for face mask detection in complicated scenarios

In the context of COVID-19 pandemic prevention and control, it is of vital significance to realize accurate face mask detection via computer vision technique. In this paper, a novel attention improved Yolo (AI-Yolo) model is proposed, which can handle existing challenges in the complicated real-world scenarios with dense distribution, small-size object detection and interference of similar occlusions. In particular, a selective kernel (SK) module is set to achieve convolution domain soft attention mechanism with split, fusion and selection operations; a spatial pyramid pooling (SPP) module is applied to enhance the expression of local and global features, which enriches the receptive field information; and a feature fusion (FF) module is utilized to promote sufficient fusions of multi-scale features from each resolution branch, which adopts basic convolution operators without excessive computational complexity. In addition, the complete intersection over union (CIoU) loss function is adopted in the training stage for accurate positioning. Experiments are carried out on two challenging public face mask detection datasets, and the results demonstrate the superiority of the proposed AI-Yolo against other seven state-of-the-art object detection algorithms, which achieves the best results in terms of mean average precision and F1 score on both datasets. Furthermore, effectiveness of the meticulously designed modules in AI-Yolo is validated through extensive ablation studies. In a word, the proposed AI-Yolo is competent to accomplish face mask detection tasks under extremely complex situations with precise localization and accurate classification.
BDNet: A BERT-based dual-path network for text-to-image cross-modal person re-identification

Text-to-image person re-identification (TI-ReID) aims to provide a descriptive sentence to find a specific person in the gallery. The task is very challenging due to the huge feature differences between both image and text descriptions. Currently, most approaches use the idea of combining global and local features to get more fine-grained features. However, these methods usually acquire local features with the help of human pose or segmentation models, which makes it difficult to use in realistic scenarios due to the introduction of additional models or complex training evaluation strategies. To facilitate practical applications, we propose a BERT-based framework for dual-path TI-ReID. Without the help of additional models, our approach directly employs visual attention in the global feature extraction network to allow the network to adaptively learn to focus on salient local features in image and text descriptions, which enhances the network’s attention to local information through a visual attention mechanism, thus strengthening the global feature representation and effectively improving the global feature representation. In addition, to learn text and image modality invariant feature representations, we propose a convolutional shared network (CSN) to learn image and text features together. To optimize cross-modal feature distances more effectively, we propose a global hybrid modal triplet global metric loss. In addition to combining local metric learning and global metric learning, we also introduce the CMPM loss and CMPC loss to jointly optimize the proposed model. Extensive experiments on the CUHK-PEDES dataset show that the proposed method performs significantly better than the current research results, achieving a Rank-1/mAP accuracy of 66.27%/ 57.04%.