Reviewerly/exHarmony huggingface dataset:
Causal Feature Selection with Missing Data

Causal feature selection aims at learning the Markov blanket (MB) of a class variable for feature selection. The MB of a class variable implies the local causal structure among the class variable and its MB and all other features are probabilistically independent of the class variable conditioning on its MB, this enables causal feature selection to identify potential causal features for feature selection for building robust and physically meaningful prediction models. Missing data, ubiquitous in many real-world applications, remain an open research problem in causal feature selection due to its technical complexity. In this article, we discuss a novel multiple imputation MB (MimMB) framework for causal feature selection with missing data. MimMB integrates Data Imputation with MB Learning in a unified framework to enable the two key components to engage with each other. MB Learning enables Data Imputation in a potentially causal feature space for achieving accurate data imputation, while accurate Data Imputation helps MB Learning identify a reliable MB of the class variable in turn. Then, we further design an enhanced kNN estimator for imputing missing values and instantiate the MimMB. In our comprehensively experimental evaluation, our new approach can effectively learn the MB of a given variable in a Bayesian network and outperforms other rival algorithms using synthetic and real-world datasets.
Cross-Part Learning for Fine-Grained Image Classification

Recent techniques have achieved remarkable improvements depended on mining subtle yet distinctive features for fine-grained visual classification (FGVC). While prior works directly combine discriminative features extracted from different parts, we argue that the potential interactions between different parts and their abilities to category predictions should be taken into consideration, which enables significant parts to contribute more to the decision of the sub-category. To this end, we present a Cross-Part Convolutional Neural Network (CP-CNN) in a weakly supervised manner to explore cross-learning among multi-regional features. Specifically, the context transformer is implemented to encourage joint feature learning across different parts under the guidance of a navigator. The part with the highest confidence is regarded as a navigator to deliver distinguishing characteristics to the others with lower confidence while the complementary information is retained. To locate discriminative but subtle parts precisely, a part proposal generator (PPG) is designed with the feature enhancement blocks, through which complex scale variations caused by the viewpoint diversity can be effectively alleviated. Extensive experiments on three benchmark datasets demonstrate that our proposed method consistently outperforms existing state-of-the-art methods.
A deep neural network model for multi-view human activity recognition

Multiple cameras are used to resolve occlusion problem that often occur in single-view human activity recognition. Based on the success of learning representation with deep neural networks (DNNs), recent works have proposed DNNs models to estimate human activity from multi-view inputs. However, currently available datasets are inadequate in training DNNs model to obtain high accuracy rate. Against such an issue, this study presents a DNNs model, trained by employing transfer learning and shared-weight techniques, to classify human activity from multiple cameras. The model comprised pre-trained convolutional neural networks (CNNs), attention layers, long short-term memory networks with residual learning (LSTMRes), and Softmax layers. The experimental results suggested that the proposed model could achieve a promising performance on challenging MVHAR datasets: IXMAS (97.27%) and i3DPost (96.87%). A competitive recognition rate was also observed in online classification.
A Geometric Model-Based Approach to Hand Gesture Recognition

Arm-and-hand tracking by technological means allows gathering data that can be elaborated for determining gesture meaning. To this aim, machine learning (ML) algorithms have been mostly investigated looking for a balance between the highest recognition rate and the lowest recognition time. However, this balance comes mainly from statistical models, which are challenging to interpret. In contrast, we present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu C^{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu C^{2}$ </tex-math></inline-formula> , two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu C^{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu C^{2}$ </tex-math></inline-formula> with respect to two classical ML algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), and two state-of-the-art (SotA) deep learning (DL) models, bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU), on an experimental dataset of ten gesture classes from the Italian Sign Language (LIS), each repeated 100 times by five inexperienced non-native signers, and gathered with wearable technology (a sensory glove and inertial measurement units). As a result, we achieve a compromise between high recognition rates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;90\%$ </tex-math></inline-formula> ) and low recognition times ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$ &lt; 0.1 {\mathrm{ s}}$ </tex-math></inline-formula> ) that is adequate for human–computer interaction. Moreover, we elaborate on the algorithms’ geometric interpretation based on geometric algebra, which supports some understanding of the recognition process.
TE-YOLOF: Tiny and efficient YOLOF for blood cell detection

Blood cell detection in microscopic images is an essential branch of medical image processing research. The research and application of computer vision algorithms in this field are more concerned about the trade-off between accuracy and model complexity. The FED detector modified by YOLOv3 is a representative light-weight model to detect blood cell objects such as red blood cells, white blood cells and platelets. But the detection precision of red blood cells in the FED model is relatively low compared with platelets and white blood cells due to the imbalance distribution of different types of cells. In this research, we propose a light-weight model based on YOLOF to address the relatively low precision of red blood cell detection problem, in order to achieve the overall improvement of detection precision. This object detector is called TE-YOLOF, Tiny and Efficient YOLOF. Model light-weighting is accomplished with the excellent feature extraction capabilities of EfficientNet as backbone and the ability of the Depthwise Separable Convolution to reduce the number of parameters while maintaining precision. Furthermore, the Mish activation function is employed to increase the precision. Extensive experiments on the BCCD dataset prove the effectiveness of the proposed model, which can achieve higher precision with less parameters than FED. TE-YOLOF is also effective on other cross-domain blood cell detection experiments.
An efficient harmonic estimator design based on Augmented Crow Search Algorithm in noisy environment

In this paper, an intelligent harmonic estimator based on an improved version of crow search algorithm is proposed for identification of inter, sub and power harmonics. The algorithm is named as Augmented Crow Search Algorithm (ACSA). Harmonic estimation design problem is considered as an estimation problem of phase and amplitude component of harmonic signals. It is a known fact that certain modifications are essential to make an algorithm compatible for real applications. Keeping this fact in consideration, an acceleration factor driven crow search algorithm is proposed that also incorporates opposition-based learning in initialization phase. Further, an error function is derived from the mean square values of difference of real and estimated values of the amplitude and phase components. An iterative optimization process is followed for identification of these components. A fair comparison is carried out on the set of four test benches of diverse properties and shapes. Comparison of different contemporary algorithms and a few of crow search algorithm variants is carried out to showcase efficacy of the proposed version of the crow search. It is observed that proposed version is competitive.
Understanding Users’ Social TV Content using Artificial Intelligence Approach: A Survey

Social networking sits with the rapid technological advancement have changed our concept of TV landscape, as a result of the increased use of artificial intelligence in choosing online content for users’ interaction such as Social TV content in YouTube. With the conceptual of sociability, this paper analyzes the impact of artificial intelligence in choosing Social TV content for Jordanian YouTubers. It aims to understand how respondents interact and attitudes towards Social TV content and its sociability based on artificial intelligence tool. Methodologically, a cross-sectional design survey was used with a randomly selected of 300 Jordanian participants currently having YouTube accounts and prefer watching Social TV on YouTube. Data were analyzed using SmartPls approach. Results indicated a strong, positive relationship between interaction, elements, and digital content and artificial intelligence in Social TV content selection. However, the relationship between interaction and Social TV content selection, and artificial intelligence and Social TV content selection is strongly mediated by the YouTubers. Thus, respondents’ customization of settings enabled artificial intelligence to choose the Social TV content for them.
Blockchain-Based Management of Blood Donation

Today's a large number of blood donation management systems fall short in providing traceability, immutability, transparency, audit, privacy, and security features.Also, they are vulnerable to the single point of failure problem due to centralization.In this paper, we propose a private Ethereum blockchain-based solution to automate blood donation management in a manner that is decentralized, transparent, traceable, auditable, private, secure, and trustworthy.The proposed solution stores non-critical and large data off-chain using the decentralized storage of the InterPlanetary File System (IPFS).We present the system architecture, sequence diagrams, entity-relationship diagram, and algorithms to briefly explain the working principles of our blood donation management solution.We evaluate the performance of our solution in terms of efficiency and effectiveness through performing security analysis.We make our smart contract code publicly available on Github 1 .
A Hybrid Physics-Based Data-Driven Framework for Anomaly Detection in Industrial Control Systems

A method referred to as PbNN is proposed to detect cyber-physical attacks through the identification of resulting anomalies in the process dynamics of the underlying ICS. Unlike existing anomaly detectors based on an abstract knowledge acquired from operational data, PbNN utilizes the design knowledge of ICS to learn the complex relationships among the correlated components. Such relationships are accurately modeled using operational data through the application of the deep convolution neural network. The proposed detector was implemented and evaluated in an operational secure water treatment plant by launching several real-time stealthy and coordinated attacks. The results indicate that PbNN outperforms the existing state-of-the-art machine learning anomaly detectors when compared using detection accuracy and the rate of false alarms.
Training time minimization for federated edge learning with optimized gradient quantization and bandwidth allocation

Training a machine learning model with federated edge learning (FEEL) is typically time consuming due to the constrained computation power of edge devices and the limited wireless resources in edge networks. In this study, the training time minimization problem is investigated in a quantized FEEL system, where heterogeneous edge devices send quantized gradients to the edge server via orthogonal channels. In particular, a stochastic quantization scheme is adopted for compression of uploaded gradients, which can reduce the burden of per-round communication but may come at the cost of increasing the number of communication rounds. The training time is modeled by taking into account the communication time, computation time, and the number of communication rounds. Based on the proposed training time model, the intrinsic trade-off between the number of communication rounds and per-round latency is characterized. Specifically, we analyze the convergence behavior of the quantized FEEL in terms of the optimality gap. Furthermore, a joint data-and-model-driven fitting method is proposed to obtain the exact optimality gap, based on which the closed-form expressions for the number of communication rounds and the total training time are obtained. Constrained by the total bandwidth, the training time minimization problem is formulated as a joint quantization level and bandwidth allocation optimization problem. To this end, an algorithm based on alternating optimization is proposed, which alternatively solves the subproblem of quantization optimization through successive convex approximation and the subproblem of bandwidth allocation by bisection search. With different learning tasks and models, the validation of our analysis and the near-optimal performance of the proposed optimization algorithm are demonstrated by the simulation results.
A Scalable CMOS Ising Computer Featuring Sparse and Reconfigurable Spin Interconnects for Solving Combinatorial Optimization Problems

No existing algorithms can find exact solutions to the combinatorial optimization problems (COPs) classified as non-deterministic polynomial-time (NP) hard problems. Alternatively, Ising computer based on the Ising model and annealing process has recently drawn significant attention. The Ising computers can find approximate solutions to the NP-hard COPs by observing the convergence of dynamic spin states. However, they have encountered challenges in mapping the optimization problems to the inflexible Ising computers with fixed spin interconnects. In this article, we propose a scalable CMOS Ising computer with sparse and reconfigurable spin interconnects for arbitrary mapping of spin networks with minimal overhead. Without a mapping algorithm, the proposed Ising computer provides a method for directly mapping COPs to the reconfigurable hardware. A 65-nm CMOS Ising test chip with 252 spins is fabricated and used for solving COPs, including max-cut problems.
Chaotic-Nondominated-Sorting Owl Search Algorithm for Energy-Aware Multi-Workflow Scheduling in Hybrid Clouds

Since a single private cloud cannot satisfy the increasing computational requirements for executing multiple workflows simultaneously, hybrid clouds are often adopted to perform such execution. Multi-workflow scheduling is challenging as users may request various applications with different QoS requirements. This work proposes a Chaotic-nondominated-sorting Owl Search Algorithm (COSA) by combining an Owl Search Algorithm (OSA) with a Nondominated Sorting Genetic Algorithm II (NSGA-II) to schedule resource-constrained multiple workflows in hybrid clouds with makespan, cost and energy consumption minimized under the given deadline and budget constraints. First, a hierarchical evolving mechanism is designed to update the better half and worse half of population by NSGA-II and OSA, respectively to guarantee a good trade-off between exploration and exploitation. Second, a chaotic sequence is introduced to adaptively adjust OSA's step size during population evolution for better exploration. Third, we adopt a chaotic operator for searching around the resulting Non-Dominated Solutions (NDS) to improve COSA's local search ability. Experiments are conducted to compare COSA with four peers and the results show its superiority in the number of obtained NDS, diversity preservation and convergence towards the near optimal Pareto set. In particular, it can find at least 19% more NDS than its peers.
GNES: Learning to Explain Graph Neural Networks

In recent years, graph neural networks (GNNs) and the research on their explainability are experiencing rapid developments and achieving significant progress. Many methods are proposed to explain the predictions of GNNs, focusing on “how to generate explanations” However, research questions like “whether the GNN explanations are inaccurate”, “what if the explanations are inaccurate”, and “how to adjust the model to generate more accurate explanations” have not been well explored. To address the above questions, this paper proposes a GNN Explanation Supervision (GNES) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> framework to adaptively learn how to explain GNNs more correctly. Specifically, our framework jointly optimizes both model prediction and model explanation by enforcing both whole graph regularization and weak supervision on model explanations. For the graph regularization, we propose a unified explanation formulation for both node-level and edge-level explanations by enforcing the consistency between them. The node- and edge-level explanation techniques we propose are also generic and rigorously demonstrated to cover several existing major explainers as special cases. Extensive experiments on five real-world datasets across two application domains demonstrate the effectiveness of the proposed model on improving the reasonability of the explanation while still keep or even improve the backbone GNNs model performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Code available at: https://github.com/YuyangGao/GNES.
Evaluating Robustness of Counterfactual Explanations

Transparency is a fundamental requirement for decision making systems when these should be deployed in the real world. It is usually achieved by providing explanations of the system's behavior. A prominent and intuitive type of explanations are counterfactual explanations. Counterfactual explanations explain a behavior to the user by proposing actions-as changes to the input-that would cause a different (specified) behavior of the system. However, such explanation methods can be unstable with respect to small changes to the input-i.e. even a small change in the input can lead to huge or arbitrary changes in the output and of the explanation. This could be problematic for counterfactual explanations, as two similar individuals might get very different explanations. Even worse, if the recommended actions differ considerably in their complexity, one would consider such unstable (counterfactual) explanations as individually unfair. In this work, we formally and empirically study the robustness of counterfactual explanations in general, as well as under different models and different kinds of perturbations. Furthermore, we propose that plausible counterfactual explanations can be used instead of closest counterfactual explanations to improve the robustness and consequently the individual fairness of counterfactual explanations.
Exploring the Influence of Direct and Indirect Factors on Information Security Policy Compliance: A Systematic Literature Review

Information systems security is considered one of the key issues concerning organizations’ management. Despite the massive investment that organizations make to safeguard their systems, there are still many internal security breaches. The increase in insider threats to information systems can be related to the employees’ compliance toward information security policy. Several review papers were conducted to explore information security policy compliance behavior research. However, the literature lacks insight into the positive and negative (direct or indirect) influence of human and organizational theories and their factors influencing information security policy compliance behavior. Therefore, this paper provides a systematic literature review synthesizing the psychological theories, organizational theories, and other internal and external factors on information security policy compliance researches. The results analysis of 87 studies showed that the general deterrence theory, theory of planned behavior, and protection motivation theory are the most frequently used. The influencing factors of theories are mostly similar in the results. Furthermore, information security education, training and awareness, trust, and leadership, among many other internal and external factors, are highly used. This study is one of the first researches that explores the relationship types among the influencing factors; emphasizing the direct and indirect effect, and information security policy compliance behavior. This paper also identifies some gaps in information security policy compliance behavior research and proposes future works. In addition, it provides a theoretical contribution and practical insight in the context of information security policy compliance.
A machine learning approach in analysing the effect of hyperboles using negative sentiment tweets for sarcasm detection

This paper investigates negative sentiment tweets with the presence of hyperboles for sarcasm detection. Six thousand and six hundred pre-processed negative sentiment tweets comprising #Chinesevirus, #Kungflu, #COVID19, #Hantavirus and #Coronavirus were gathered for sarcasm detection. Five hyperbole features, namely interjection, intensifier, capital letter, punctuation mark and elongated word were analysed using three renowned machine learning algorithms, that is, Support Vector Machine, Random Forest, and Random Forest with Bagging. With the presence of hyperbolic words in the tweets in an unbiased dataset, the proposed model with elongated word achieved an accuracy and F-score of 78.74% and 71%, respectively. Intensifier was found to be the most significant hyperbole (p < .0001). Experiments and analysis conducted in this study concluded that hyperboles exist in an unbiased dataset which helps enhance the sarcasm detection as well.