Reviewerly/exHarmony huggingface dataset:
Towards Blockchain-Based Energy Trading: A Smart Contract Implementation of Energy Double Auction and Spinning Reserve Trading

The decentralization of power generation driven by the rise in the adoption of distributed energy resources paves the way for a new paradigm in grid operations. P2P energy trading is beneficial to the grid as well as the connected peers. A blockchain-based smart contract is well suited to transparently facilitate trades between energy consumers and producers without the services of intermediaries. In this paper, Ethereum-based smart contracts that facilitate double energy auction and spinning reserve trading are developed with Solidity, compiled, and deployed within the Remix IDE. Willing energy sellers/buyers submit offers/bids to a contract that implements the double auction procedure. In order to fulfil energy supply obligations, sellers are also able to purchase spinning reserves via another smart contract. The smart contracts’ effectiveness in performing the auction procedure and making payments is confirmed using an energy/reserve market scenario. The proposed scheme encourages further adoption of distributed energy resources and participation in local P2P energy trading.
CAN Bus Intrusion Detection Based on Auxiliary Classifier GAN and Out-of-distribution Detection

The Controller Area Network (CAN) is a ubiquitous bus protocol present in the Electrical/Electronic (E/E) systems of almost all vehicles. It is vulnerable to a range of attacks once the attacker gains access to the bus through the vehicle’s attack surface. We address the problem of Intrusion Detection on the CAN bus and present a series of methods based on two classifiers trained with Auxiliary Classifier Generative Adversarial Network (ACGAN) to detect and assign fine-grained labels to Known Attacks and also detect the Unknown Attack class in a dataset containing a mixture of (Normal + Known Attacks + Unknown Attack) messages. The most effective method is a cascaded two-stage classification architecture, with the multi-class Auxiliary Classifier in the first stage for classification of Normal and Known Attacks, passing Out-of-Distribution (OOD) samples to the binary Real-Fake Classifier in the second stage for detection of the Unknown Attack class. Performance evaluation demonstrates that our method achieves both high classification accuracy and low runtime overhead, making it suitable for deployment in the resource-constrained in-vehicle environment.
NLP-Based Digital Forensic Analysis for Online Social Network Based on System Security

Social media evidence is the new topic in digital forensics. If social media information is correctly explored, there will be significant support for investigating various offenses. Exploring social media information to give the government potential proof of a crime is not an easy task. Digital forensic investigation is based on natural language processing (NLP) techniques and the blockchain framework proposed in this process. The main reason for using NLP in this process is for data collection analysis, representations of every phase, vectorization phase, feature selection, and classifier evaluation. Applying a blockchain technique in this system secures the data information to avoid hacking and any network attack. The system's potential is demonstrated by using a real-world dataset.
Diagnosis of breast cancer for modern mammography using artificial intelligence

The diagnosis of breast cancer, one of the most common types of cancer worldwide, is still a challenging task. Localisation of the breast mass and accurate classification is crucial in detecting breast cancer at an early stage. In machine learning-based classification models, performance is dependent on the accuracy of extracted features and is susceptible to saturation problems. Deep learning methods are currently used to learn self-regulating top-level features and achieve remarkable accuracy. It has long been recognised that mammography is competent for the early detection of cancer cells. Thus the technique of image segmentation and artificial intelligence can be applied to the initial stage diagnosis of breast cancer. The proposed method is composed of two major approaches. In the first, the transfer learning method is employed. In the second, convolution neural network architecture is constructed, and its hyper-parameters are adjusted to achieve accurate classification. The result indicates that the proposed methods achieve significant accuracy for MIAS (95.95%), DDSM (99.39%), INbreast (96.53%), and combined datasets (92.27%). Comparison of results of the proposed approach with current schemes demonstrates its efficiency.
Overview of Blockchain Oracle Research

Whereas the use of distributed ledger technologies has previously been limited to cryptocurrencies, other sectors—such as healthcare, supply chain, and finance—can now benefit from them because of bitcoin scripts and smart contracts. However, these applications rely on oracles to fetch data from the real world, which cannot reproduce the trustless environment provided by blockchain networks. Despite their crucial role, academic research on blockchain oracles is still in its infancy, with few contributions and a heterogeneous approach. This study undertakes a bibliometric analysis by highlighting institutions and authors that are actively contributing to the oracle literature. Investigating blockchain oracle research state of the art, research themes, research directions, and converging studies will also be highlighted to discuss, on the one hand, current advancements in the field and, on the other hand, areas that require more investigation. The results also show that although worldwide collaboration is still lacking, various authors and institutions have been working in similar directions.
Robust stochastic configuration networks for industrial data modelling with Student’s-t mixture distribution

Data collected from industrial sites commonly contains outliers or noise that obey unknown distributions, making it challenging to establish an accurate data-driven model. Therefore, this paper proposes a novel robust stochastic configuration network based on a Student’s-t mixture distribution (termed as SM-RSC). Firstly, a stochastic configuration algorithm is employed to determine the number of hidden nodes, the input weights and biases. Secondly, the maximum a posteriori (MAP) estimate is used to evaluate the output weights of the SCN learner model under the assumption that outliers or noises obey the Student’s-t mixture distribution. Because the output weights cannot be solved directly due to the unknown hyper-parameters of the mixture distribution, we apply the well-known expectation–maximization (EM) algorithm for optimizing the hyper-parameters of the mixture distribution and update the output weights iteratively. The proposed algorithm is evaluated by a function approximation, four benchmark datasets, and a case study on industrial data modelling for a waste incineration process. The results show that SM-RSC performs favorably compared with other methods.
Constructing multi-layer classifier ensembles using the Choquet integral based on overlap and quasi-overlap functions

Ensembles of classifiers have been receiving much attention lately, they consist of a collection of classifiers that process the same information and their output is combined in some manner. The combination method is probably the most important part in a ensemble of classifiers however, many works found in literature focus mostly on the classification step, using simple approaches on the combination step, such as majority voting. In this paper, we propose a new combination method based on a generalization of discrete Choquet integrals, combining it with quasi overlap functions to aggregate the outputs of classifiers in an ensemble. We also tested the proposed combination approach in a simple ensemble against other methods in literature to verify if there was a significant gain in performance.
Development of a risk index for cross-border data movement

Cross-border data transmission in the biomedical area is on the rise, which brings potential risks and management challenges to data security, biosafety, and national security. Focusing on cross-border data security assessment and risk management, many countries have successively issued relevant laws, regulations, and assessment guidelines. This study aims to provide an index system model and management application reference for the risk assessment of the cross-border data movement. From the perspective of a single organization, the relevant risk assessment standards of several countries are integrated to guide the identification and determination of risk factors. Then, the risk assessment index system of cross-border data flow is constructed. A case study of risk assessment in 358 biomedical organizations is carried out, and the suggestions for data management are offered. This study is condusive to improving security monitoring and the early warning of the cross-border data flow, thereby realizing the safe and orderly global flow of biomedical data.
Extracting keyframes of breast ultrasound video using deep reinforcement learning

Ultrasound (US) plays a vital role in breast cancer screening, especially for women with dense breasts. Common practice requires a sonographer to recognize key diagnostic features of a lesion and record a single or several representative frames during the dynamic scanning before performing the diagnosis. However, existing computer-aided diagnosis tools often focus on the final diagnosis process while neglecting the influence of the keyframe selection. Moreover, the lesions could have highly-irregular shapes, varying sizes, and locations during the scanning. The recognition of diagnostic characteristics associated with the lesions is challenging and also faces severe class imbalance. To address these, we proposed a reinforcement learning-based framework that can automatically extract keyframes from breast US videos of unfixed length. It is equipped with a detection-based nodule filtering module and a novel reward mechanism that can integrate anatomical and diagnostic features of the lesions into keyframe searching. A simple yet effective loss function was also designed to alleviate the class imbalance issue. Extensive experiments illustrate that the proposed framework can benefit from both innovations and is able to generate representative keyframe sequences in various screening conditions.
Deep multi-view semi-supervised clustering with sample pairwise constraints

Multi-view clustering has attracted much attention thanks to the capacity of multi-source information integration. Although numerous advanced methods have been proposed in past decades, most of them generally overlook the significance of weakly-supervised information and fail to preserve the feature properties of multiple views, thus resulting in unsatisfactory clustering performance. To address these issues, in this paper, we propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimizes three kinds of losses during networks finetuning, including multi-view clustering loss, semi-supervised pairwise constraint loss and multiple autoencoders reconstruction loss. Specifically, a KL divergence based multi-view clustering loss is imposed on the common representation of multi-view data to perform heterogeneous feature optimization, multi-view weighting and clustering prediction simultaneously. Then, we innovatively propose to integrate pairwise constraints into the process of multi-view clustering by enforcing the learned multi-view representation of must-link samples (cannot-link samples) to be similar (dissimilar), such that the formed clustering architecture can be more credible. Moreover, unlike existing rivals that only preserve the encoders for each heterogeneous branch during networks finetuning, we further propose to tune the intact autoencoders frame that contains both encoders and decoders. In this way, the issue of serious corruption of view-specific and view-shared feature space could be alleviated, making the whole training procedure more stable. Through comprehensive experiments on eight popular image datasets, we demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.
Asymmetric cross–modal hashing with high–level semantic similarity

• To give reliable supervisory signals for the hash learning procedure, we develop a simple but effective supervised cross-modal hash learning framework to learn more discriminative hash codes and preserve the semantic similarity . An effective iterative alternative optimization scheme is developed to solve the NP-hard optimization problem, the time consuming and space complexity of our approach is O(n). • We propose a novel cross-modal hashing approach, which can effectively embed high level semantic similarity into the learning process of hash codes. A two-stage hash coding learning strategy is designed to optimize learning hash codes and hash functions respectively; our strategy effectively reduces the information loss in the process of instances mapping hash codes. To some extent, it improves the hash code learning rate of new samples, so our strategy can be extended to large-scale multi-mode information retrieval. • We propose a novel semantic-enhanced scheme to make full leverage the label information and gain more powerful hash functions, our approach can generate more discriminative hash codes for new instances via powerful hash functions. we conduct comprehensive experiments on several datasets, experimental results demonstrate the superior effectiveness of our approach outperforms several state-of-the-art hashing models on either the retrieval accuracy or the hash learning efficiency. Cross-modal hashing aims at using modality content to retrieve semantically relevant objects of different modalities, so cross-modal retrieval has attracted much attention. To effectively exploit the discriminative label information and retain more semantic information in the process of hash learning, we propose a novel cross-modal hashing method , named high-level semantic similarity analysis hashing (HSSAH) for cross-modal retrieval. To reduce time complexity and enhance discriminant ability in hash codes, HSSAH constructs an asymmetric high-level semantic similarity learning framework to replace the binary semantic similarity matrix. Moreover, the developed HSSAH is a two-stage approach, and a semantic-enhanced scheme is proposed in the second stage, which fully leverages the label information to gain more powerful hash functions. We conducted comprehensive experiments on three benchmark datasets to evaluate the performance of HSSAH. Experimental results show that HSSAH can achieve significantly better retrieval precision and outperforms several state-of-the-art approaches.
Single image denoising via multi-scale weighted group sparse coding

Nonlocal self-similarity (NSS) property of natural images, which means that the structure of the image sub-patches will appear repeatedly within a certain area, has been widely exploited as an effective prior to establishing various models in image denoising task. However, most of the existing NSS-based denoising models exploit the NSS prior in single scale only, and for some of the image patches that do not appear repeatedly, undesirable ringing artifacts will occur in the restored image, and even the image content may be lost. Considering the fact that NSS exists both within the same scale and across different scales, in order to better restore the structure and the edges of images contaminated by noise, we propose, in this paper, a novel multi-scale weighted group sparse coding model (MS-WGSC) for image denoising, wherein the patch groups are constructed using multi-scale NSS priors. Furthermore, an alternating minimization method is proposed to obtain the solution for our model. Extensive experiments are conducted that demonstrate the competitiveness of the proposed model compared with that of state-of-the-art methods not only in terms of the quantitative metrics such as PSNR and SSIM, but also in perceptual quality.
Explaining Link Prediction Systems based on Knowledge Graph Embeddings

Link Prediction (LP) aims at tackling Knowledge Graph incompleteness by inferring new, missing facts from the already known ones. The rise of novel Machine Learning techniques has led researchers to develop LP models that represent Knowledge Graph elements as vectors in an embedding space. These models can outperform traditional approaches and they can be employed in multiple downstream tasks; nonetheless, they tend to be opaque, and are mostly regarded as black boxes. Their lack of interpretability limits our understanding of their inner mechanisms, and undermines the trust that users can place in them. In this paper, we propose the novel Kelpie explainability framework. Kelpie can be applied to any embedding-based LP models independently from their architecture, and it explains predictions by identifying the combinations of training facts that have enabled them. Kelpie can extract two complementary types of explanations, that we dub necessary and sufficient. We describe in detail both the structure and the implementation details of Kelpie, and thoroughly analyze its performance through extensive experiments. Our results show that Kelpie significantly outperforms baselines across almost all scenarios.
Dual-Channel and Hierarchical Graph Convolutional Networks for document-level relation extraction

Document-level relation extraction aims to infer complex semantic relations among entities in an entire document. Compared with the sentence-level relation extraction, document-level relational facts are expressed by multiple mentions across the sentences in a long-distance, requiring excellent reasoning. In this paper, we propose Dual-Channel and Hierarchical Graph Convolutional Networks (DHGCN), which constructs three graphs in token-level, mention-level, and entity-level to model complex interactions among different semantic representations across the document. Based on the multi-level graphs, we apply the Graph Convolutional Network (GCN) for each level to aggregate the relevant information scattered throughout the document for better inferring the implicit relations. Moreover, we propose a dual-channel encoder to capture structural and contextual information simultaneously, which also supplies the contextual representation for the higher layer to avoid losing low-dimension information. Our DHGCN yields significant improvements over the state-of-the-art methods by 2.75, 5.5, and 3.5 F1 on DocRED, CDR, and GDA, respectively, which are popular document-level relation extraction datasets. Furthermore, to demonstrate the effectiveness of our method, we evaluate DHGCN on a fine gained clinical document-level dataset Symptom-Acupoint Relation (SAR) proposed by ourselves and available at https://github.com/QiSun123/SAR. The experimental results illustrate that DHGCN is able to infer more valuable relations among entities in the document.
MXene‐Based Broadband Ultrafast Nonlinear Activator for Optical Computing

Abstract Optical neural networks (ONNs) are particularly advantageous owing to their inherent parallelism and low energy consumption. However, one of the obstacles to the implementation of ONNs is the lack of optical nonlinearity. In this study, optical nonlinear activators for ONNs are prepared by combining Ti 3 C 2 T x MXene with microfibers and their principles are verified. Activation functions obtained from experimental measurements are used to simulate multiclassification and super‐resolution reconstruction tasks with performance comparable to that of activation functions commonly used in computers. Four necessary criteria are proposed and validated for evaluating the performance of the nonlinear activator: recovery time, deviation from linearity, the activation function close to identity mapping, and reconfigurability of the configuration. Theoretically, the nonlinear activator can compute 100 times faster than commonly used electronic computers and can be used as a nonlinear activation unit for ONNs to help the integration of ONNs with artificial intelligence.
Multi-class nucleus detection and classification using deep convolutional neural network with enhanced high dimensional dissimilarity translation model on cervical cells

Advanced cervical screening via liquid-based cytology (LBC)/Pap smear is a highly efficient precancerous cell detection tool based on cell image analysis, in which cells are classified as normal/abnormal. This paper outlines the drawbacks by introducing a new framework for the accurate classification of cervical cells. The proposed methodology comprises three phases: segmentation, localization of nucleus, and classification. In the segmentation phase, we develop a hybrid system that incorporates two binary image patches obtained by a 19-layered convolutional neural network (ConvNet) model with an enhanced deep high dimensional dissimilarity translation (HDDT) based conspicuous segmentation. To get the relevant information from binary patched images, a technique called optimum semantic similarity selective search (OSS-SS) is proposed that returns the localized RGB patched image. A pre-trained ResNet-50 model is retrained using transfer learning on localized patched images in the classification phase. Following that, the selected features from the average pool and fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) approach. Finally, these combined features are fed into a multi-class weighted kernel extreme learning machine (WKELM) classifier via a sparse multicanonical correlation (SMCCA) method. Three datasets (SIPaKMed, CRIC, and Harlev) are used to evaluate the segmentation and classification task. The proposed approach obtained an accuracy of 99.12 %, specificity of 99.45 %, sensitivity of 99.25 % with an execution time 99.6248 on SIPaKMed. The experimental analysis indicate that our model is more effective than existing techniques.
Do Men Have No Need for “Feminist” Artificial Intelligence? Agentic and Gendered Voice Assistants in the Light of Basic Psychological Needs

Artificial Intelligence (AI) is supposed to perform tasks autonomously, make competent decisions, and interact socially with people. From a psychological perspective, AI can thus be expected to impact users' three Basic Psychological Needs (BPNs), namely (i) autonomy, (ii) competence, and (iii) relatedness to others. While research highlights the fulfillment of these needs as central to human motivation and well-being, their role in the acceptance of AI applications has hitherto received little consideration. Addressing this research gap, our study examined the influence of BPN Satisfaction on Intention to Use (ITU) an AI assistant for personal banking. In a 2×2 factorial online experiment, 282 participants (154 males, 126 females, two non-binary participants) watched a video of an AI finance coach with a female or male synthetic voice that exhibited either high or low agency (i.e., capacity for self-control). In combination, these factors resulted either in AI assistants conforming to traditional gender stereotypes (e.g., low-agency female) or in non-conforming conditions (e.g., high-agency female). Although the experimental manipulations had no significant influence on participants' relatedness and competence satisfaction, a strong effect on autonomy satisfaction was found. As further analyses revealed, this effect was attributable only to male participants, who felt their autonomy need significantly more satisfied by the low-agency female assistant, consistent with stereotypical images of women, than by the high-agency female assistant. A significant indirect effects model showed that the greater autonomy satisfaction that men, unlike women, experienced from the low-agency female assistant led to higher ITU. The findings are discussed in terms of their practical relevance and the risk of reproducing traditional gender stereotypes through technology design.