Reviewerly/exHarmony huggingface dataset:
TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security

Despite artificial intelligence (AI)’s significant growth, its “black box” nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in IoT high-risk applications, such as critical industrial infrastructures, medical systems, financial applications, etc. Explainable AI (XAI) has emerged to help with this problem. However, designing appropriately fast and accurate XAI is still challenging, especially in numerical applications. Here, we propose a universal XAI model, named the transparency relying upon statistical theory (TRUST), which is model-agnostic, high performing, and suitable for numerical applications. Simply put, TRUST XAI models the statistical behavior of the AI’s outputs in an AI-based system. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information (MI) to rank these variables and pick only the most influential ones on the AI’s outputs and call them “representatives” of the classes. Then, we use multimodal Gaussian (MMG) distributions to determine the likelihood of any new sample belonging to each class. We demonstrate the effectiveness of TRUST in a case study on cybersecurity of the Industrial Internet of Things (IIoT) using three different cybersecurity data sets. As IIoT is a prominent application that deals with numerical data. The results show that TRUST XAI provides explanations for new random samples with an average success rate of 98%. Compared with local interpretable model-agnostic explanations (LIME), a popular XAI model, TRUST is shown to be superior in the context of performance, speed, and the method of explainability. In the end, we also show how TRUST is explained to the user.
Transferability of optimal QAOA parameters between random graphs

The Quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. In a typical QAOA setup, a set of quantum circuit parameters is optimized to prepare a quantum state used to find the optimal solution of a combinatorial optimization problem. Several empirical observations about optimal parameter concentration effects for special QAOA MaxCut problem instances have been made in recent literature, however, a rigorous study of the subject is still lacking. We show that convergence of the optimal QAOA parameters around specific values and, consequently, successful transferability of parameters between different QAOA instances can be explained and predicted based on the local properties of the graphs, specifically the types of subgraphs (lightcones) from which the graphs are composed. We apply this approach to random regular and general random graphs. For example, we demonstrate how optimized parameters calculated for a 6-node random graph can be successfully used without modification as nearly optimal parameters for a 64-node random graph, with less than 1% reduction in approximation ratio as a result. This work presents a pathway to identifying classes of combinatorial optimization instances for which such variational quantum algorithms as QAOA can be substantially accelerated.
Online Continual Learning For Visual Food Classification

Food image classification is challenging for real-world applications since existing methods require static datasets for training and are not capable of learning from sequentially available new food images. Online continual learning aims to learn new classes from data stream by using each new data only once without forgetting the previously learned knowledge. However, none of the existing works target food image analysis, which is more difficult to learn incrementally due to its high intra-class variation with the unbalanced and unpredictable characteristics of future food-class distribution. In this paper, we address these issues by introducing (1) a novel clustering based exemplar selection algorithm to store the most representative data belonging to each learned food for knowledge replay, and (2) an effective online learning regime using balanced training batch along with the knowledge distillation on augmented exemplars to maintain the model performance on all learned classes. Our method is evaluated on a challenging large scale food image database, Food-1K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , by varying the number of newly added food classes. Our results show significant improvements compared with existing state-of-the-art online continual learning methods, showing great potential to achieve lifelong learning for food image classification in real world.
Adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization

This paper proposes an adaptive memetic differential evolution with multi-niche sampling and neighborhood crossover strategies for global optimization. In the proposed algorithm, a multi-niche sampling strategy is designed to sample a subpopulation for evolution at each generation. In this strategy, the entire population is firstly divided into multiple niches by employing a certain niching strategy at each generation. A subpopulation is then dynamically sampled from the resulting niches such that supporting a diverse search at the early stage of evolution while an intensive search towards the end of evolution. The above strategy will be further coupled with a neighborhood crossover, which is devised to encourage high potential solutions for exploitation while low potential solutions for exploration, thus appropriately searching the solution space. Additionally, an adaptive local search (ALS) scheme along with an adaptive elimination operation (AEO) have been designed. The ALS aims to appropriately fine-tune promising solutions in the sampled subpopulation while the AEO tends to adaptively eliminate unpromising individuals in the population during evolution. The performance of the proposed algorithm has been evaluated on CEC'2015 benchmark functions and compared with related methods. Experimental results show that our algorithm can achieve a superior performance and outperform related methods. The results also confirm the significance of devised strategies in the proposed algorithm.
iContractML 2.0: A domain-specific language for modeling and deploying smart contracts onto multiple blockchain platforms

Smart contracts play a vital role in many fields. Despite being called smart, the development of smart contracts is a tedious task beyond defining a set of contractual rules. In addition to business knowledge, coding a smart contract requires strong technical knowledge in a multiplex of new and rapidly changing domain-specific languages and blockchain platforms. The goal of this paper is to assist developers in building smart contracts independently from the language or the target blockchain platform. In which, we present our second-generation smart contract language iContractML 2.0. We follow a feature-oriented approach to analyze three different blockchain platforms and propose an enhanced reference model and a modeling framework for smart contracts (iContractML 2.0). Then, we evaluate the coverage and extensibility of iContractML 2.0, first through mapping the concepts of the reference models to the constructs within each of the platforms used in devising the reference model, and second through mapping its concepts to a new smart contract language not previously considered. Finally, we demonstrate the capabilities of iContractML 2.0 using five case studies from different business domains. iContractML 2.0 extends our first generation language to support DAML, which is another standardized language for smart contracts. This makes iContractML 2.0 supports the platforms that DAML support by extension. Moreover, iContractML 2.0 supports generating the structural and deployment artifacts in addition to the smart contract behavior by implementing templates for some of the common functions. The results of evaluating the generality of the iContractML 2.0 reference model show that it is 91.7% lucid and 72.2% laconic. Moreover, the reference model is able to capture all the elements of the new language with 83.3% of the components which have a direct one-to-one mapping. iContractML 2.0 is an extensible framework that empowers developers to model and generate functional smart contract code that can be deployed onto multiple blockchain platforms.
Institutional quality and human capital development in Africa

This study investigates the role of institutional quality in human capital development using a panel of 49 African countries over the period 1996–2018. The study employs a dynamic model based on a two-step system generalized method of moments. The results show that improving institutional quality promotes access to primary, secondary, and tertiary education overall and for females. In particular, government effectiveness, control of corruption and political stability, and the absence of violence, including terrorism, are the most important dimensions that foster human capital development. The results suggest that fostering these particular institutional quality dimensions is critical to improving human capital development in Africa.
METoNR: A meta explanation triplet oriented news recommendation model

Personalized news recommendation is an important task for online news platforms to target user interests and alleviate information overload. Most existing methods leverage news contents to make recommendation due to the importance of contents to distinguish pieces of news. Although Heterogeneous Graph (HG) shows great potential in organizing and exploiting varieties of side information to boost recommendation performance in general recommender systems, and at the same time there exist rich side information in real news recommendation scenario, existing methods lack attention to utilizing HG to exploit multiple kinds of side information to enhance news recommendation accuracy. In addition, they pay no attention to providing understandable explanations to improve user satisfaction. To this end, we propose a meta explanation triplet oriented news recommendation model. Specifically, we first construct an HG to extract high-order relatedness knowledge between users and news from various side information. Then, a dedicated neural network is designed to leverage rich side information and contents in a joint way to make news recommendation. Finally, we provide user-centered and news-centered recommendation explanations for users based on meta explanation triplets. Extensive experiments on two benchmark real-world datasets show that our model could improve news recommendation performance compared with state-of-the-art methods and provide effective explanations.
An efficient adversarial example generation algorithm based on an accelerated gradient iterative fast gradient

Most existing deep neural networks are susceptible to the influence of adversarial examples, which may cause them to output incorrect prediction results. An adversarial example is the addition of small noise disturbances to the input data samples, which will deceive the classification. Generally, these modifications are very small noise disturbances, which are not easily noticed by the human eye. However, most existing adversarial attacks achieve only low success rates in typical black-box settings, where the attackers have no prior knowledge about the model structures and/or model parameters. To tackle this problem, we propose an iterative algorithm based on an acceleration gradient to enhance the adversary attack. Our method accumulates the gradient of the loss function in each iteration and uses the next gradient information to influence the future gradient. We also introduce the scaling invariance principle of a deep neural network to optimize the input images for black-box attacks. In addition, to handle the drawbacks of a traditional iterative fast gradient sign method, we further present two gradient optimization methods. Experimental results on the ImageNet dataset show that our attack methods can achieve good transferability. In addition, those models obtained by integrated adversarial training with strong defense capability are also quite vulnerable to our black-box attack.
Securing the future internet of things with post‐quantum cryptography

Traditional and lightweight cryptography primitives and protocols are insecure against quantum attacks. Thus, a real-time application using traditional or lightweight cryptography primitives and protocols does not ensure full-proof security. Post-quantum Cryptography is important for the Internet of Things (IoT) due to its security against Quantum attacks. This paper offers a broad literature analysis of post-quantum cryptography for IoT networks, including the challenges and research directions to adopt in real-time applications. The work draws focus towards post-quantum cryptosystems that are useful for resource-constraint devices. Further, those quantum attacks are surveyed, which may occur over traditional and lightweight cryptographic primitives.
DeepTSQP: Temporal-aware service QoS prediction via deep neural network and feature integration

Quality of service (QoS) has been mostly applied to represent non-functional properties of web services and differentiate those with the same functionality. How to accurately predict unknown service QoS has become a hot research issue. Although existing researches have been investigated on temporal-aware service QoS prediction, conventional approaches are restricted to a couple of limitations. (1) most of them cannot well mine the time-series relationships and the interaction invocation information among users and services. (2) even although some sophisticated approaches make use of recurrent neural networks for temporal service QoS prediction, they mainly focus on the learning of user-service temporal relationship and have paid less attention to more effectively represent implicit features, resulting in low accuracy on service QoS prediction. To deal with the challenges, we propose a novel deep learning based approach called DeepTSQP to perform the task of temporal-aware service QoS prediction by feature integration. In DeepTSQP, we first present an improved temporal feature representation of users and services by integrating binarization feature and similarity feature. Then, we propose a deep neural network with gated recurrent units (GRU), learning and mining temporal features among users and services. Finally, DeepTSQP model can be trained by parameter optimization and applied to predict unknown service QoS. Extensive experiments are conducted on a large-scale real-world temporal QoS dataset WS-Dream with 27,392,643 historical QoS invocation records. The results demonstrate that DeepTSQP significantly outperforms state-of-the-art approaches for temporal-aware service QoS prediction in terms of multiple evaluation metrics.
Intuitionistic fuzzy twin support vector machines with the insensitive pinball loss

Due to the use of membership and nonmembership functions of samples from intuitionistic fuzzy sets(IFSs), intuitionistic fuzzy twin support vector machines (IFTSVMs) can effectively suppress noise in the data. However, the objective function of IFTSVMs partially considers score values of samples and employs the hinge loss function which leads to the sensitivity to feature noise and instability to re-sampling. To enhance the performance of IFTSVMs, we propose novel IFTSVMs with the insensitive pinball loss function. In the proposed convex optimization models, a simple strategy is devised to achieve the score value of each training sample and score values of samples in both classes are defined by using IFSs. Unlike previous methods, we introduce two groups of slack variables to derive the dual formulations of convex models which make them have compact representations. Some properties of the proposed models including geometric properties and noise insensitivity are theoretically analyzed. We also explain the proposed models in terms of the idea of the weighted scatter minimization, which provides theoretical foundations for the proposed models. Experiments on a series of data sets are performed and experimental results demonstrate that the proposed convex models are superior to some existing learning models in the presence of feature noise or label noise.
Learnable Leaky ReLU (LeLeLU): An Alternative Accuracy-Optimized Activation Function

In neural networks, a vital component in the learning and inference process is the activation function. There are many different approaches, but only nonlinear activation functions allow such networks to compute non-trivial problems by using only a small number of nodes, and such activation functions are called nonlinearities. With the emergence of deep learning, the need for competent activation functions that can enable or expedite learning in deeper layers has emerged. In this paper, we propose a novel activation function, combining many features of successful activation functions, achieving 2.53% higher accuracy than the industry standard ReLU in a variety of test cases.
A new approach based on Bezier curves to solve path planning problems for mobile robots

Path planning algorithms are used in known environments to find the shortest, smooth and optimal way without collision from the starting point to the target point. However, excessive nodes and pointed spiking points that occur during this path planning process pose problems. Bezier curves offer highly effective possibilities for path forming problems. In this article, a new approach based on Bezier curves is proposed for solving such problems. First, grid maps are used to model the environment Second, a path is found between the start and endpoints using traditional algorithms. Third, the excess knots are discarded by pruning based on Bezier curves. Finally, the spikes are smoothed using Bezier curves to ensure smoothness and continuity. Looking at the results from the proposed approach, it has proven that its effectiveness in obtaining an optimum path between the starting and target points in known environments.
Deep multi-graph neural networks with attention fusion for recommendation

Graph neural networks (GNNs), with their promising potential to learn effective graph representation, have been widely used for recommender systems, in which the given graph data contains abundant users, items, and their historical interaction information. How to obtain preferable latent representations for both users and items is one of the key issues for GNN-based recommendation. This paper develops a novel deep GNN model with multi-graph attention fusion, MAF-GNN. This framework constructs two feature graph attention modules and a multi-scale latent features module, to generate better user and item latent features from input information. Specifically, the dual-branch residual graph attention (DBRGA) module is presented to extract neighbors’ similar features from user and item graphs effectively and easily. Then multi-scale latent matrices are captured by applying non-linear transformations which are embedded to reduce the cost of dimension selection. Furthermore, a hybrid fusion graph attention (HFGA) module is designed to obtain valuable collaborative information from the user–item interaction graph, aiming to further refine the latent embedding of users and items. Finally, the whole MAF-GNN framework is optimized by a geometric factorized regularization loss. Extensive experiment results on both synthetic and real-world datasets illustrate that MAF-GNN can achieve better recommendation performance with a certain level of interpretability than some existing approaches.
Alternate search pattern-based brain storm optimization

Brain storm optimization (BSO) groups population into several clusters and generates new individuals by using the information of these clusters. However, this mechanism limits the ability of exploration because it prevents new individuals from searching regions far away from current clusters. In this paper, we innovatively propose a grid-based search operator (GBS) to improve the exploration by dividing the given search space into smaller ones. Then, we modify the cluster, replacement, and mutation strategy of the original BSO for requiring a better exploitation. Besides, an alternate search pattern (ASP) strategy is designed for controlling the transformation between GBS and BSO to balance exploration and exploitation. Finally, two variants of BSO have been proposed based on the original BSO and a global-best BSO, and termed as ABSO and AGBSO, respectively. The proposed ABSO and AGBSO are tested on a number of widely used benchmark optimization problems. The comparative analysis shows that ASP strategy can significantly improve the performance of BSO in terms of solution quality and population diversity. Additionally, AGBSO can be considered as a state-of-the-art BSO among all its variants. The source code of all proposed methods can be found at https://toyamaailab.github.io/sourcedata.html.
FL-MGVN: Federated learning for anomaly detection using mixed gaussian variational self-encoding network

Anomalous data are such data that deviate from a large number of normal data points, which often have negative impacts on various systems. Current anomaly detection technology suffers from low detection accuracy, high false alarm rate and lack of labeled data. Anomaly detection is of great practical importance as an effective means to detect anomalies in the data and provide important support for the normal operation of various systems. In this paper, we propose an anomaly detection classification model that incorporates federated learning and mixed Gaussian variational self-encoding networks, namely MGVN. The proposed MGVN network model first constructs a variational self-encoder using a mixed Gaussian prior to extracting features from the input data, and then constructs a deep support vector network with the mixed Gaussian variational self-encoder to compress the feature space. The MGVN finds the minimum hypersphere to separate the normal and abnormal data and measures the abnormal fraction by calculating the Euclidean distance between the data features and the hypersphere center. Federated learning is finally incorporated with MGVN (FL-MGVN) to effectively address the problems that multiple participants collaboratively train a global model without sharing private data. The experiments are conducted on the benchmark datasets such as NSL-KDD, MNIST and Fashion-MNIST, which demonstrate that the proposed FL-MGVN has higher recognition performance and classification accuracy than other methods. The average AUC on MNIST and Fashion-MNIST reached 0.954 and 0.937, respectively.
Intraday volume-return nexus in cryptocurrency markets: Novel evidence from cryptocurrency classification

This paper analyses the volume-return relationships across the top 30 most traded cryptocurrencies from February 2018 to July 2019 using high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which the cryptocurrency is built, and consensus algorithm, among others. We identify significant bidirectional causalities between trading volume and returns at different high-frequency intervals; however, those linkages are weakening with decreasing data frequencies. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency markets providing useful insights from cryptocurrency categorisation.