Reviewerly/exHarmony huggingface dataset:
An Implementation of Real-Time Traffic Signs and Road Objects Detection Based on Mobile GPU Platforms

Thanks to the rapid development of computer vision and deep learning technologies, advanced driver assistance systems (ADAS) have recently become widespread. These systems aim to increase driving safety and reduce the number of traffic accidents. Modern cars usually have ADAS systems integrated into their electronics, but other vehicles do not have such an integrated system. This paper presents a portable and image-based ADAS system for real-time detection of traffic signs, vehicles, and pedestrians. To realize real-time detection, the developed system uses the YOLO v5 algorithm. This single-stage detector is very popular as it has high detection speed and accuracy. The model was trained on the Tesla P100 graphics processing unit (GPU) with nearly 2500 images and 8 hours using GTSRB and study-specific dataset to analyze the developed system. Then, the implementation metrics (F1 score, P, R, PR curves) were calculated to evaluate the training and testing performances of the model. In addition, the model was compared in low-power, high-performance embedded platforms and in a computer to measure the real-time performance. The comparison results for (Jetson Xavier AGX, Jetson Xavier Nx, Jetson Nano and Test PC) achieved a speed of (43.59, 23.17, 6.4 and 3.41) frames per second for real-time detection, respectively. Considering the excellent accuracy and high speed, this study will guide researchers in demonstrating the efficiency and suitability of real-time road object detection with YOLO v5 on mobile platforms.
A survey of outlier detection in high dimensional data streams

The rapid evolution of technology has led to the generation of high dimensional data streams in a wide range of fields, such as genomics, signal processing, and finance. The combination of the streaming scenario and high dimensionality is particularly challenging especially for the outlier detection task. This is due to the special characteristics of the data stream such as the concept drift, the limited time and space requirements, in addition to the impact of the well-known curse of dimensionality in high dimensional space. To the best of our knowledge, few studies have addressed these challenges simultaneously, and therefore detecting anomalies in this context requires a great deal of attention. The main objective of this work is to study the main approaches existing in the literature, to identify a set of comparison criteria, such as the computational cost and the interpretation of outliers, which will help us to reveal the different challenges and additional research directions associated with this problem. At the end of this study, we will draw up a summary report which summarizes the main limits identified and we will detail the different directions of research related to this issue in order to promote research for this community.
Comprehensive Multi-view Representation Learning

Recently, Multi-view Representation Learning (MRL) has drawn immense attentions in the analysis of multi-source data and ubiquitously employed across different research fields. This important issue is designed to learn a feature representation with sufficient information from multiple views. In this paper, we propose a novel Comprehensive Multi-view Representation Learning (CMRL), which can fully explore available information contained in both the feature representations and subspace representations of multiple views. The desired feature representation learned in CMRL profits from the consistency and complementarity of multi-view data. Specifically, the complementary information is mined by applying the degeneration mapping model on multiple feature representations, the consensus information is explored by imposing a low-rank tensor constraint on multiple subspace representations. Further, the objective function of CMRL is optimized by an Augmented Lagrangian Multiplier (ALM) based algorithm. Finally, our CMRL is evaluated on seven benchmark multi-view datasets and compared with several state-of-the-art methods, experimental results illustrate the superiority and effectiveness of the proposed method. What is more, we find that the proposed method can also be successfully applied to multi-view subspace clustering and achieves promising clustering results.
Learning Appearance-Motion Normality for Video Anomaly Detection

Video anomaly detection is a challenging task in the Computer vision community. Most single task-based methods do not consider the independence of unique spatial and temporal patterns, while two-stream structures lack the exploration of the correlations. In this paper, we propose spatial-temporal memories augmented two-stream auto-encoder framework, which learns the appearance normality and motion normal-ity independently and explores the correlations via adversar-ial learning. Specifically, we first design two proxy tasks to train the two-stream structure to extract appearance and motion features in isolation. Then, the prototypical features are recorded in the corresponding spatial and temporal memory pools. Finally, the encoding-decoding network performs ad-versariallearning with the discriminator to explore the corre-lations between spatial and temporal patterns. Experimental results show that our framework outperforms the state-of-the-art methods, achieving AUCs of 98.1% and 89.8% on UCSD Ped2 and CUHK Avenue datasets.
CATCHM: A novel network-based credit card fraud detection method using node representation learning

Advanced fraud detection systems leverage the digital traces from (credit-card) transactions to detect fraudulent activity in future transactions. Recent research in fraud detection has focused primarily on data analytics combined with manual feature engineering, which is tedious, expensive and requires considerable domain expertise. Furthermore, transactions are often examined in isolation, disregarding the interconnection that exists between them. In this paper, we propose CATCHM, a novel network-based credit card fraud detection method based on representation learning (RL). Through innovative network design, an efficient inductive pooling operator, and careful downstream classifier configuration, we show how network RL can benefit fraud detection by avoiding manual feature engineering and explicitly considering the relational structure of transactions. Extensive empirical evaluation on a real-life credit card dataset shows that CATCHM outperforms state-of-the-art methods, thereby illustrating the practical relevance of this approach for industry.
An empirical study for blockchain-based information sharing systems in electronic health records: A mediation perspective

Electronic health record (EHR) systems have a considerable impact on reducing healthcare costs by offering better disease management and fewer medical errors, while current systems fail to encourage the majority of patients to share their health records. Blockchain is a reliable alternative among the options to improve current systems. Little is known about whether blockchain-based health information systems contribute to healthcare networks regarding patients' intention to share their health records. In particular, there is no empirical research documenting patients' behavior in blockchain-based EHR systems. This study involved an experimental designed to contribute to prior research by examining how blockchain can be related to patients’ behavior with EHR systems. Confirmatory factor analysis is used for the validity and reliability tests of the model. Moreover, this research addressed the endogeneity problems using two-stage least squares regression analysis. Results were based on regression analysis and structural equation modeling. The results show that blockchain-based information systems can empower patients by providing the perception of control over their health records. The usage of blockchain technology motivates patients to share information with healthcare provider systems and has the advantage of reducing healthcare costs and improving diagnosis management.
TransUNet＋: Redesigning the skip connection to enhance features in medical image segmentation

The new architecture TransUNet, which combines convolutional neural networks (CNNs) and transformers, has displayed competitive performance in medical image segmentation. In this paper, an efficient model named TransUNet＋, which can achieve promising results with a redesigned skip connection, is proposed for medical image segmentation. The redesigned skip connection contains an enhancement module, which can effectively enhance the skip features to improve global attention by using the score matrix of the transformer block. As the column vectors of the score matrix represent the relationship between the patches and the whole image, they can be used for feature enhancement. To validate the proposed TransUNet＋, series of experiments are performed based on three different medical image segmentation datasets covering multiple imaging modalities. Experimental results show that the proposed TransUNet＋ outperforms other state-of-the-art methods based on three datasets, and the proposed TransUNet＋ displays better performance in small organ segmentation.
Securing federated learning with blockchain: a systematic literature review

Federated learning (FL) is a promising framework for distributed machine learning that trains models without sharing local data while protecting privacy. FL exploits the concept of collaborative learning and builds privacy-preserving models. Nevertheless, the integral features of FL are fraught with problems, such as the disclosure of private information, the unreliability of uploading model parameters to the server, the communication cost, etc. Blockchain, as a decentralized technology, is able to improve the performance of FL without requiring a centralized server and also solves the above problems. In this paper, a systematic literature review on the integration of Blockchain in federated learning was considered with the analysis of the existing FL problems that can be compensated. Through carefully screening, most relevant studies are included and research questions cover the potential security and privacy attacks in traditional federated learning that can be solved by blockchain as well as the characteristics of Blockchain-based FL. In addition, the latest Blockchain-based approaches to federated learning have been studied in-depth in terms of security and privacy, records and rewards, and verification and accountability. Furthermore, open issues related to the combination of Blockchain and FL are discussed. Finally, future research directions for the robust development of Blockchain-based FL systems are proposed.
A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms-XGboost and random forest (RF)-used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.
Privacy and Security in Federated Learning: A Survey

In recent years, privacy concerns have become a serious issue for companies wishing to protect economic models and comply with end-user expectations. In the same vein, some countries now impose, by law, constraints on data use and protection. Such context thus encourages machine learning to evolve from a centralized data and computation approach to decentralized approaches. Specifically, Federated Learning (FL) has been recently developed as a solution to improve privacy, relying on local data to train local models, which collaborate to update a global model that improves generalization behaviors. However, by definition, no computer system is entirely safe. Security issues, such as data poisoning and adversarial attack, can introduce bias in the model predictions. In addition, it has recently been shown that the reconstruction of private raw data is still possible. This paper presents a comprehensive study concerning various privacy and security issues related to federated learning. Then, we identify the state-of-the-art approaches that aim to counteract these problems. Findings from our study confirm that the current major security threats are poisoning, backdoor, and Generative Adversarial Network (GAN)-based attacks, while inference-based attacks are the most critical to the privacy of FL. Finally, we identify ongoing research directions on the topic. This paper could be used as a reference to promote cybersecurity-related research on designing FL-based solutions for alleviating future challenges.
PyramidFL

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with enhanced privacy, aiming to achieve a "good" ML model for as many as participants while consuming as little as wall clock time. By executing across thousands or even millions of clients, FL demonstrates heterogeneous statistical characteristics and system divergence widely across participants, making its training suffer when adopting the traditional ML paradigm. The root cause of the training efficiency degradation is the random client selection criteria. Although existing FL paradigms propose several optimization schemes for client selection, they are still coarse-grained due to their under-exploitation on the clients' data and system heterogeneity, yielding sub-optimal performance for a variety of FL applications. In this paper, we propose PyramidFL1 to speed up the FL training while achieving a higher final model performance (i.e., time-to-accuracy). The core of PyramidFL is a fine-grained client selection, in which PyramidFL does not only focus on the divergence of those selected participants and non-selected ones for client selection but also fully exploits the data and system heterogeneity within selected clients to profile their utility more efficiently. Specifically, PyramidFL first determines the utility-based client selection from the global (i.e., server) view and then optimizes its utility profiling locally (i.e., client) for further client selection. In this way, we can prioritize the use of those clients with higher statistical and system utility consistently. In comparison with the state-of-the-art (i.e., Oort), our evaluation on the open-source FL benchmark shows that PyramidFL improves the final model accuracy by 3.68% -- 7.33%, with a speedup of 2.71 x -- 13.66X on the wall clock time consumption.
Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification

Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.
Effects of Explainable Artificial Intelligence on trust and human behavior in a high-risk decision task

Understanding the recommendations of an artificial intelligence (AI) based assistant for decision-making is especially important in high-risk tasks, such as deciding whether a mushroom is edible or poisonous. To foster user understanding and appropriate trust in such systems, we assessed the effects of explainable artificial intelligence (XAI) methods and an educational intervention on AI-assisted decision-making behavior in a 2 × 2 between subjects online experiment with N=410 participants. We developed a novel use case in which users go on a virtual mushroom hunt and are tasked with picking edible and leaving poisonous mushrooms. Users were provided with an AI-based app that showed classification results of mushroom images. To manipulate explainability, one subgroup additionally received attribution-based and example-based explanations of the AI’s predictions; for the educational intervention one subgroup received additional information on how the AI worked. We found that the group that received explanations outperformed that which did not and showed better calibrated trust levels. Contrary to our expectations, we found that the educational intervention, domain-specific (i.e., mushroom) knowledge, and AI knowledge had no effect on performance. We discuss practical implications and introduce the mushroom-picking task as a promising use case for XAI research.
Knowledge Discovery: Methods from data mining and machine learning

The interdisciplinary field of knowledge discovery and data mining emerged from a necessity of big data requiring new analytical methods beyond the traditional statistical approaches to discover new knowledge from the data mine. This emergent approach is a dialectic research process that is both deductive and inductive. The data mining approach automatically or semi-automatically considers a larger number of joint, interactive, and independent predictors to address causal heterogeneity and improve prediction. Instead of challenging the conventional model-building approach, it plays an important complementary role in improving model goodness of fit, revealing valid and significant hidden patterns in data, identifying nonlinear and non-additive effects, providing insights into data developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to incorporate this new paradigm of predictive modeling with the classical approach of parameter estimation regressions to produce improved models that combine explanation and prediction.
UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased

In recent decades, RRT* algorithm has attracted much attention because of its asymptotic optimization. However, the RRT* algorithm still suffers from slow convergence rate and large randomness of search range. To overcome the shortcomings of this algorithm, this paper proposes UAV trajectory planning based on bi-directional APF-RRT* algorithm with goal-biased. Firstly, goal-biased strategy is used to guide the generation of random sampling points, and two mutually alternating random search trees are established by the bi-directional RRT* algorithm to perform the search, thus increasing the convergence rate of the algorithm. Secondly, the number of iterations is greatly reduced by incorporating an modified artificial potential field method into the bi-directional growth tree. In the process of smoothing the paths, a cubic spline interpolation algorithm is applied to optimize the paths to obtain the best trajectory. The combination of the two algorithms improves the direction of new node generation and reduces the path cost. Finally, the algorithm of this paper is compared with Informed-RRT*, Bi-RRT* and improved P-RRT* algorithms, and it enhances the search performance of the growing tree.
Towards SDN-based smart contract solution for IoT access control

Access control is essential for the IoT environment to ensure that only approved and trusted parties are able to configure devices, access sensor information, and command actuators to execute activities. The IoT ecosystem is subject to various access control complications due to the limited latency between IoT devices and the Internet, low energy requirements of IoT devices, the distributed framework, ad-hoc networks, and an exceptionally large number of heterogeneous IoT devices that need to be managed. The motivation for this proposed work is to resolve the incurring challenges of IoT associated with management and access control security. Each IoT domain implementation has particular features and needs separate access control policies to be considered in order to design a secure solution. This research work aims to resolve the intricacy of policies management, forged policies, dissemination, tracking of access control policies, automation, and central management of IoT nodes and provides a trackable and auditable access control policy management system that prevents forged policy dissemination by applying Software Defined Network (SDN) and blockchain technology in an IoT environment. Integration of SDN and blockchain provides a robust solution for IoT environment security. Recently, smart contracts have become one of blockchain technology's most promising applications. The integration of smart contracts with blockchain technology provides the capability of designing tamper-proof and independently verifiable policies. In this paper, we propose a novel, scalable solution for implementing immutable, verifiable, adaptive, and automated access control policies for IoT devices together with a successful proof of concept that demonstrates the scalability of the proposed solution. The performance of the proposed solution is evaluated in terms of throughput and resource access delay between the blockchain component and the controller as well as from node to node. The number of nodes in the IoT network and the number of resource access requests were independently and systematically increased during the evaluations. The results illustrate that the resource access delay and throughput were affected neither linearly nor exponentially; hence, the proposed solution shows no significant degradation in performance with an increase in the number of nodes and/or requests.