Reviewerly/exHarmony huggingface dataset:
Blockchain and Artificial Intelligence for Business Transformation Toward Sustainability

Artificial intelligence and blockchain are considered among the most well-known technologies. Blockchain is a secure, distributed, and immutable database shared by all parties in a distributed network, in which transaction data can be recorded and is easy to audit. Artificial Intelligence represents today’s paradigm shift, and simultaneously promotes scientific progress and industry development. Combining these two technologies has many potentials, especially when they are related to companies, and the economy. A comprehensive overview of these two emerging fields, and the importance of their integration, is strongly needed. This chapter gives an overview of artificial intelligence technology, then, the importance of the integration of artificial intelligence and blockchain technologies is discussed. Also, the potential of their integration in the business field is discussed to transform businesses toward digitization, and enhance several processes, such as cross-border trade and transactions’ payments inside the business environment. Besides, this chapter discusses the effect of their integration on the circular economy for helping to reach a zero-waste ecosystem, on the alternative money system leading to the transformation of the current financial system, on the token economy in providing secured decentralized digital platforms, and finally, provides conclusions and future works about what are the expectations from their integration.
Analysis of security and privacy issues of information management of big data in B2B based healthcare systems

The healthcare industry may benefit from the implementation of a system that facilitates safe and efficient data transmission if business-to-business (B2B) methods were more widely adopted and used. Many aspects of healthcare, like as patient access to physicians, the efficiency of information trans- mission, and the quality of care received, stand to benefit from this development. However, a primary issue for the development of B2B-based healthcare systems is the inability to handle a large quantity of data generated by smart devices. in this context, in this paper, we analysis the issues related to big data in B2B-based healthcare sector. First, we give the background and the advantages of using big data technology in the B2B-based healthcare sector in general; then we discuss the security aspects of the big data. Then, we discuss different security techniques proposed by the researchers to maintain the security of B2B-based health care sector.
Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision Making

Decision Support Systems (DSS) based on Machine Learning (ML) often aim to assist lay decision-makers, who are not math-savvy, in making high-stakes decisions. However, existing ML-based DSS are not always transparent about the probabilistic nature of ML predictions and how uncertain each prediction is. This lack of transparency could give lay decision-makers a false sense of reliability. Growing calls for AI transparency have led to increasing efforts to quantify and communicate model uncertainty. However, there are still gaps in knowledge regarding how and why the decision-makers utilize ML uncertainty information in their decision process. Here, we conducted a qualitative, think-aloud user study with 17 lay decision-makers who interacted with three different DSS: 1) interactive visualization, 2) DSS based on an ML model that provides predictions without uncertainty information, and 3) the same DSS with uncertainty information. Our qualitative analysis found that communicating uncertainty about ML predictions forced participants to slow down and think analytically about their decisions. This in turn made participants more vigilant, resulting in reduction in over-reliance on ML-based DSS. Our work contributes empirical knowledge on how lay decision-makers perceive, interpret, and make use of uncertainty information when interacting with DSS. Such foundational knowledge informs the design of future ML-based DSS that embrace transparent uncertainty communication.
End-to-End Dual-Branch Network Towards Synthetic Speech Detection

Synthetic speech attacks bring more threats to Automatic Speaker Verification (ASV) systems, thus many synthetic speech detection (SSD) systems have been proposed to help the ASV system resist synthetic speech attacks. However, existing SSD systems still lack the generalization ability for the attacks generated by unknown synthesis algorithms. This letter proposes an end-to-end ensemble system, namely Dual-Branch Network, in which linear frequency cepstral coefficients (LFCC) and constant Q transform (CQT) are used as the input of two branches respectively. In addition, four fusion strategies are compared for the fusion of two branches to obtain an optimal one; multi-task learning and convolutional block attention module (CBAM) are introduced into the Dual-Branch Network to help the network learn the common forgery features from different forgery types of speech and enhance the representation power of learned features. Experimental results on the ASVspoof 2019 logical access (LA) dataset demonstrate that the proposed system outperforms existing state-of-the-art systems on both t-DCF and EER scores and has good generalization for unknown forgery types of synthetic speech.
Laplacian Twin Support Vector Machine With Pinball Loss for Semi-Supervised Classification

Semi-supervised learning utilizes labeled data and the geometric information in the unlabeled data to construct a model whereas supervised learning makes use of the only label data. So, semi-supervised learning establishes a more reasonable classifier. In recent years, the Laplacian support vector machine (Lap-SVM) has received a lot of interest in the framework of semi-supervised classification. To develop the performance of Lap-SVM, Laplacian twin support vector machine (Lap-TSVM) has shown exceptional performance as an addition to improve the computational complexity. However, dealing with noise sensitivity and instability for resampling due to its hinge loss function is still a challenge. In this paper, we provide a novel Laplacian twin support vector machine by combining pinball loss function, termed as Lap-PTSVM, to effectively handle the aforementioned problems. As a result, it improves a better generalization ability of the classifier. Several experiments have been performed on artificial and UCI datasets. The results show that the proposed model has noise insensitivity comparable to the Lap-TSVM and has a great generalization performance. Furthermore, non-parametric statistical test are also conducted to justify the competitive performance of Lap-PTSVM.
FLIS: Clustered Federated Learning Via Inference Similarity for Non-IID Data Distribution

Conventional federated learning (FL) approaches are ineffective in scenarios where clients have significant differences in the distributions of their local data. The Non-IID data distribution in the client data causes a drift in the local model updates from the global optima, which significantly impacts the performance of the trained models. In this paper, we present a new algorithm called FLIS that aims to address this problem by grouping clients into clusters that have jointly trainable data distributions. This is achieved by comparing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inference similarity</i> of client models. Our proposed framework captures settings where different groups of users may have their own objectives (learning tasks), but by aggregating their data with others in the same cluster (same learning task), superior models can be derived via more efficient and personalized federated learning. We present experimental results to demonstrate the benefits of FLIS over the state-of-the-art approaches on the CIFAR-100/10, SVHN, and FMNIST datasets. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/MMorafah/FLIS</uri> .
RLAT: Multi-hop temporal knowledge graph reasoning based on Reinforcement Learning and Attention Mechanism

Temporal knowledge graphs (TKGs) have been widely used in artificial intelligence but are usually incomplete. Recently, researchers have proposed reasoning methods to infer missing relations in TKGs. TKG reasoning is mainly divided into single-hop reasoning and multi-hop reasoning. Multi-hop reasoning takes the semantics of facts into account. However, multi-hop reasoning methods of TKGs lack a path memory component, and the reasoning results depend on good training. In addition, different adjacent feature information is assigned the same weight, which makes it difficult to distinguish their importance to the central entity. We propose a multi-hop reasoning model combining Reinforcement Learning with the ATtention mechanism (RLAT) to solve the above limitations. First, we use LSTM and attention mechanism as memory components, which are helpful to train multi-hop reasoning paths. Second, an attention mechanism with an influence factor is proposed. This mechanism measures the influence of neighbor information and provides different feature vectors. The strategy function obtained makes the agent focus on occurring relations with high frequency and then reasons multi-hop reasoning paths with higher correlation. Experimental results demonstrate that our approach outperforms most metrics compared to recent efforts.
Quantum support vector machine without iteration

Quantum algorithms can enhance machine learning in different aspects. The quantum support vector machine was proposed to improve the performance, in which the Swap Test plays a crucial role in realizing the classification. However, as the Swap Test is destructive, the quantum support vector machine must be repeated in preparing qubits and manipulating operations. This paper proposes a quantum support vector machine based on the amplitude estimation (AE-QSVM) which gets rid of the constraint of repetitive process and saves the quantum resources. At first, a generalized quantum amplitude estimation is introduced in which the initial state can be arbitrary instead of being |0〉. Then, AE-QSVM is trained by the quantum singular value decomposition and a query sample is classified by the generalized quantum amplitude estimation. In AE-QSVM, a high accuracy can be achieved by adding auxiliary qubits instead of repeating the algorithm. The time and space complexity of AE-QSVM are reduced compared with other algorithms. Finally, we ran experiments on the IBM's quantum computer and experimental results demonstrate that classification with a 95% probability of success only uses 12 qubits.
Addressing the Cold-Start Problem in Recommender Systems Based on Frequent Patterns

Recommender systems aim to forecast users’ rank, interests, and preferences in specific products and recommend them to a user for purchase. Collaborative filtering is the most popular approach, where the user’s past purchase behavior consists of the user’s feedback. One of the most challenging problems in collaborative filtering is handling users whose previous item purchase behavior is unknown, (e.g., new users) or products for which user interactions are not available, (e.g., new products). In this work, we address the cold-start problem in recommender systems based on frequent patterns which are highly frequent in one set of users, but less frequent or infrequent in other sets of users. Such discriminant frequent patterns can distinguish one target set of users from all other sets. The proposed methodology, first forms different clusters of old users and then discovers discriminant frequent patterns for each different such cluster of users and finally exploits the latter to hallucinate the purchase behavior of new users. We also present empirical results to demonstrate the efficiency and accuracy of the proposed methodology.
Robust deep learning object recognition models rely on low frequency information in natural images

Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.
An Improved Black Widow Optimization Algorithm for Engineering Constrained Optimization Problems

In solving engineering constrained optimization problems, the conventional black widow optimization algorithm (BWOA) has some shortcomings such as insufficient robustness and slow convergence speed. Therefore, an improved black widow optimization algorithm (IBWOA) is proposed by combining methods of double chaotic map, Cauchy center of gravity inverse difference mutation and golden sine guidance strategy. Firstly, the quality of the initial population of the BWOA is improved based on the double chaotic map; Secondly, in order to make full use of the difference information between the current and the optimal position thus improve optimization accuracy, the golden sine algorithm (Gold-SA) is introduced to update the position of the black widow individuals; Finally, the Cauchy barycenter reverse differential mutation operator is employed to increase the diversity of the population, avoid local optimization thus improve the global search ability of the algorithm. In addition, the global convergence characteristics of the IBWOA are analyzed based on the Markov process and the convergence probability reaches 1 for the globally optimal solution. The performance of the proposed IBWOA was evaluated based on eight continuous / discrete hybrid engineering optimization problems and typical benchmark functions. The results show that the improved BWOA can improve the search accuracy, convergence speed and robustness effectively comparing with some other conventional optimization algorithms.
S<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3190" altimg="si24.svg"><mml:msup><mml:mrow /><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> map: Semisupervised aspect-based sentiment analysis with masked aspect prediction

Aspect-based sentiment analysis (ABSA) refers to a fine-grained task of detecting the sentiment polarities of sentences at the aspect level. To resolve this task, ABSA training samples must be annotated with aspect words and the corresponding sentiment polarities. However, collecting such fine-grained training samples is expensive and time-consuming. Therefore the available ABSA training samples are often scarce. To break the data scarcity challenge of ABSA, we investigate semi-supervised aspect-based sentiment analysis (SemiABSA), which trains ABSA models using a limited amount of expensive labeled sentences and more unlabeled-yet-cheaper sentences. We propose a novel SemiABSA framework, namely semi-supervised aspect-based sentiment analysis with masked aspect prediction (S3 map), built on the self-training paradigm. We form pseudo-aspect words and pseudo-sentiment polarities for unlabeled sentences and improve model training. Specifically, a BERT-encoder-based masked aspect prediction (MAP) task achieves the pseudo-aspect words generation. Based on S3 map, we thoroughly investigate the potential of SemiABSA from various perspectives. The empirical results show that S3 map can consistently improve performance by leveraging unlabeled sentences, even those from different domains.
Global path conflict detection algorithm of multiple agricultural machinery cooperation based on topographic map and time window

Path planning is one of the crucial problems of multi-machine cooperative navigation. In the context of regional farmland operation, multiple agricultural machines often need to complete multiple tasks together. Studies on global path conflict detection algorithms based on topographic maps and time windows have been conducted to solve the global path conflict problem of multiple agricultural machinery cooperation in the farmland operation environment. First, the global path conflict problem of multiple agricultural machinery cooperation was analyzed. The constraints of time window conflict detection and the time parameters to be considered in calculating the time window were proposed. Then, the global path preplanning was performed based on a topographic map and Dijkstra algorithm. According to the path preplanning results, the global path conflict was detected based on the time window. Finally, the global path conflict detection algorithm was simulated on MATLAB with the topographic map of Zhuozhou Experimental Farm as an example. The global path preplanning results show that the four paths overlapped in different degrees at nodes 1, 2, 7, 11, 15, 20, and 27. Path 3 and Path 1 were inclusive interval-type conflicts, and Path 4 and Path 1 were node-type conflicts. Path 3 adopted the change path strategy after the conflicting paths were replanned. The path was changed from [15-11-7-2-1-5-10-14-18-31] to [15-21-24-18-31]. Path 4 adopted the waiting strategy, and its paths did not change. Although the four paths still had different degrees of path overlap, there were no time conflict in the overlapping part of the replanned path. The simulation results show that the global path optimization and path conflict detection of multiple agricultural machinery cooperation could be realized based on the topographic map and time window. Moreover, the conflict resolution strategy with the least time could be obtained to achieve a safe, efficient, and conflict-free global path of multiple agricultural machinery cooperation. Thus, a foundation for further solving the path planning of multiple agricultural machinery cooperation in the complex operation environment of regional farmland is laid.
VABDC-Net: A framework for Visual-Caption Sentiment Recognition via spatio-depth visual attention and bi-directional caption processing

People are becoming accustomed to posting images and captions on social media platforms to express their opinions. Hence, Visual-Caption Sentiment Recognition (VCSR) has been a subject of growing attention recently. Thus, the correlation between visual and caption modalities is crucial for VCSR. However, most recent VCSR strategies concatenate features from the visual and caption modalities with the help of pre-trained deep learning models containing millions of trainable parameters without adding a dedicated attention module, ultimately leading to less desirable results. Motivated by this observation, we have proposed a novel model VABDC-Net, that integrates an attention module with the convolutional neural network to focus on the most relevant information from the visual modality and attentional tokenizer-based method to extract the most relevant contextual information from the caption modality. Demanding to this dire need, the following are the significant contributions of our experimentation: (1) an attentional tokenizer-based bi-directional caption branch to retrieve useful textual features from the captions, (2) an attentional visual branch to retrieve appropriate visual features, and (3) a cross-domain feature fusion to merge multi-modal features and predict sentiment. Thorough experimentation on two benchmark datasets, Twitter-15, with an accuracy of 83.80%, and Twitter-17, with an accuracy of 72.42%, indicates that our technique outperforms existing methods for VCSR.
An ensemble face recognition mechanism based on three-way decisions

The explainable human–computer interaction (HCI) is about designing approaches capable of using cognitive characteristics like humans. One such characteristic is human vision and its accuracy. The accuracy measures the trust in that system. Therefore, improving accuracy in the authorization with identification process is a primary concern for a visual-based explainable human–computer interaction system. In this article, we propose a three-way decision based ensembled face recognition mechanism called E3FRM. The E3FRM uses a three-way approach to determine the match cases and the respective worth of the captured image with the match cases. Features are extracted using PCA/FLD, and the ensembled face recognition algorithms utilize the extracted features to process the image. Ensemble Face recognition approaches find the match cases based on a given threshold. Finally, the three-way decision model evaluates the suitability of the captured image for acceptance, rejection, or deferred cases with a dual verification mechanism. Experimental results on well-known eighteen datasets suggest improvements in commonly used metrics of F1, Accuracy and Recall by up to 0.8% to 12.8%, 1% to 9.6% and 1.2% to 13.9%, respectively, in comparison to the state-of-the-art methods available, including SPCA +, ML-EM, FLDA-SVD, DMMA, Fast-DMMA, LU, LPP, TDL, KCFT, RBF + DT, and NMF. Furthermore, the proposed approach is comparatively analyzed with ensembled face recognition methods that result in an outperformed F1, Accuracy and Recall by up to 1.1% to 10.3%, 0.1% to 7.3% and 0.9% to 10.5%, respectively. These results suggest that the proposed model may improve face recognition accuracy and the resulting trust in the machines.
Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace

In an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm.