Reviewerly/exHarmony huggingface dataset:
A blockchain-based lightweight identity authentication scheme for the IEDs of security and stability control system

As an important part of the second defense line of the power system, the Security and Stability Control System (SSCS) is of great significance to ensure the reliable operation of the power system. However, SSCS still lacks an effective security mechanism and is easily accessed by attackers, thus posing a threat to the stable and reliable operation of the power system. To tackle this issue, we propose a blockchain-based identity authentication scheme for Intelligent Electronic Devices (IEDs) of SSCS. We first propose an identity authentication system model for IEDs and design the deployment of consortium chain nodes on IEDs, with architectural characteristics of SSCS and the working scenario of IEDs taken into consideration. The consortium chain is used to store credentials required for authentication, ensuring that they are tamper-proof. We combine IP address, port number and physical ID, and propose the unique identification of IEDs, with a data structure designed for the identification. We also propose a lightweight identity authentication method based on renewable hash chains, with hash chains used as one-time authentication passwords, and introduce a renewal mechanism of hash chains. Further, the detailed processes of registration and authentication phase are designed. Finally, the security analysis shows that our identity authentication scheme can resist various attacks, and the feasibility of our scheme is verified by experiments.
MMPCANet: An Improved PCANet for Occluded Face Recognition

Principal Component Analysis Network (PCANet) is a lightweight deep learning network, which is fast and effective in face recognition. However, the accuracy of faces with occlusion does not meet the optimal requirement for two reasons: 1. PCANet needs to stretch the two-dimensional images into column vectors, which causes the loss of essential image spatial information; 2. When the training samples are few, the recognition accuracy of PCANet is low. To solve the above problems, this paper proposes a multi-scale and multi-layer feature fusion-based PCANet (MMPCANet) for occluded face recognition. Firstly, a channel-wise concatenation of the original image features and the output features of the first layer is conducted, and then the concatenated result is used as the input of the second layer; therefore, more image feature information is used. In addition, to avoid the loss of image spatial information, a spatial pyramid is used as the feature pooling layer of the network. Finally, the feature vector is sent to the random forest classifier for classification. The proposed algorithm is tested on several widely used facial image databases and compared with other similar algorithms. Our experimental results show that the proposed algorithm effectively improves the efficiency of the network training and the recognition accuracy of occluded faces under the same training and testing datasets. The average accuracies are 98.78% on CelebA, 97.58% on AR, and 97.15% on FERET.
Fact-Driven Abstractive Summarization by Utilizing Multi-Granular Multi-Relational Knowledge

Abstractive summarization generates a concise summary to capture the key ideas of the source text. This task underpins important applications like information retrieval, document comprehension, and event tracking. While much progress has been achieved, state-of-the-art summarization approaches often fail to generate high-quality summaries to reproduce factual details accurately. One of the key limitations of existing solutions is that they are primarily concerned about extracting facts from the source text but overlook other crucial factual information, such as the related time, locations, reasons, consequences, purposes, participants and involved parties. Furthermore, the current summarization frameworks are inadequate in modeling the complex semantic relations among facts and the corresponding factual information, leaving much room for improvement. This paper presents <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFSum</small> , a novel summarization framework for exploiting multi-grained factual information to improve text summarization. To this end, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFSum</small> constructs an individual fine-grained factual graph with multiple relations among facts and the corresponding factual information. It employs a fact-driven graph attention network to integrate multi-granular factual representations at the encoding stage. It then uses a hybrid pointer network to retrieve factual pieces from the graph for the summary generation. We evaluate the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFSum</small> by applying it to two real-world datasets. Experimental results show that the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FFSum</small> consistently outperforms a state-of-the-art approach across evaluation datasets.
Blockchain Enabled Optimal Lightweight Cryptography Based Image Encryption Technique for IIoT

Industrial Internet of Things (IIoT) and Industry 4.0/5.0offer several interconnections between machinery, equipment, processes, and personnel in diverse application areas namely logistics, supply chain, manufacturing, transportation, and healthcare.The conventional security-based solutions in IIoT environment get degraded due to the third parties.Therefore, the recent blockchain technology (BCT) can be employed to resolve trust issues and eliminate the need for third parties.Therefore, this paper presents a novel blockchain enabled secure optimal lightweight cryptography based image encryption (BC-LWCIE) technique for industry 4.0 environment.In addition, the BC-LWCIE technique involves the design of an optimal LWC based hash function with optimal key generation using chicken swarm optimization (CSO) algorithm.Moreover, the CSO algorithm derives a fitness function with the maximization of peak signal to noise ratio (PSNR).The BC-LWCIE technique stores the cryptographic pixel values of the encrypted image in the BCT to ensure secrecy in the IIoT environment.In order to highlight the enhanced security performance of the BC-LWCIE technique, a series of simulations were carried out and the results demonstrated the betterment of the BC-LWCIE technique over the recent techniques.
Multi-scale sparse representation-based shadow inpainting for retinal OCT images

Inpainting shadowed regions cast by superficial blood vessels in retinal optical coherence tomography (OCT) images is critical for accurate and robust machine analysis and clinical diagnosis. Traditional sequence-based approaches such as propagating neighboring information to gradually fill in the missing regions are cost-effective. But they generate less satisfactory outcomes when dealing with larger missing regions and texture-rich structures. Emerging deep learning-based methods such as encoder-decoder networks have shown promising results in natural image inpainting tasks. However, they typically need a long computational time for network training in addition to the high demand on the size of datasets, which makes it difficult to be applied on often small medical datasets. To address these challenges, we propose a novel multi-scale shadow inpainting framework for OCT images by synergically applying sparse representation and deep learning: sparse representation is used to extract features from a small amount of training images for further inpainting and to regularize the image after the multi-scale image fusion, while convolutional neural network (CNN) is employed to enhance the image quality. During the image inpainting, we divide preprocessed input images into different branches based on the shadow width to harvest complementary information from different scales. Finally, a sparse representation-based regularizing module is designed to refine the generated contents after multi-scale feature aggregation. Experiments are conducted to compare our proposal versus both traditional and deep learning-based techniques on synthetic and real-world shadows. Results demonstrate that our proposed method achieves favorable image inpainting in terms of visual quality and quantitative metrics, especially when wide shadows are presented.
<scp>SubtStream</scp>: Online subtractive stream clustering algorithm

Abstract Real‐time stream data processing has gained high importance with the rapid rise of big data trends in different areas such as social media, finance, business, science, and bioinformatics. Stream data can be characterized as fast, unstable, and big data sets. Due to these properties of stream data, it cannot be processed effectively with traditional algorithms. Just like stream data processing, clustering is also a difficult task. However, researchers have attempted to classify stream data by modifying traditional algorithms or designing new ones. So, in previous studies, incremental methods were used for clustering the stream data. This paper highlights the need to develop an efficient real‐time clustering algorithm for data streams in the presence of concept high drift and an adaptive algorithm for different dimensions. The proposed clustering algorithm, SubtStream, combines decremental (subtractive property) and incremental (additivity property) strategies to overcome the high drift. It also introduces a new dimension‐based approach to adopt the dimension change. We use three radius parameters, Predefined User Parameter, Proactive Adaptive Parameter, and Reactive Adaptive Parameter, to achieve adaptability. The proposed method, SubtStream, showed better performance on synthetic and real data sets.
Adaptive multi-modal fusion hashing via Hadamard matrix

Hashing plays an important role in information retrieval, due to its low storage and high speed of processing. As an effective multi-modal representation learning method, multi-modal hashing has received particular attention. Most of the existing multi-modal hashing methods adopt the fixed weighting factors to fuse multiple modalities for any query data, which cannot capture the variation among different queries. Besides, there are too much hyper-parameters in their models while it is time-consuming and labor-intensive to determine the proper parameters. The limitations may significantly hinder their promotion in practical applications. In this paper, we propose a simple, yet effective method that is inspired by the Hadamard matrix. On the one hand, our proposed method that involves a very few hyper-parameters is flexible. On the other hand, the complementary information between multi-modal data and the semantic discrimination information are preserved well in the hash codes. Extensive experimental results on four benchmark datasets show that the proposed framework is effective and achieves superior performance compared to state-of-the-art methods.
Feature Balance for Fine-Grained Object Classification in Aerial Images

Fine-grained object classification (FGOC) focuses on identifying subcategories of objects, which is crucial in military and civilian. Existing FGOC methods primarily focus on high-resolution aerial images, limiting their application on low-resolution (LR) FGOC that is a more realistic setting, especially on resource-constrained satellite devices. It is more challenging to deal with LR FGOC since objects’ details are blurred or missing. Addressing this issue, we make the first attempt to explore LR FGOC and propose a novel pipeline based on two technical insights: 1) feature balance strategy discriminatively integrates super-resolution weak and strong detailed presentations into coarse features of LR aerial images, achieving a feature balance to avoid that the weak detailed presentations are inhibited by the strong ones and 2) iterative interaction mechanism alternately refines feature details of the discriminative ship regions and optimizes the performance of FGOC. Moreover, we build a low-resolution fine-grained object (LFS) dataset to promote further study and evaluation. Extensive experiments on the proposed LFS dataset and the other three object datasets of DOTA, FS23, and HRSC2016 demonstrate that our method outperforms state-of-the-art algorithms. Dataset and code are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wdzhao123/FBNet</uri> .
Should explainability be a fifth ethical principle in AI ethics?

It has been recently claimed that explainability should be added as a fifth principle to AI ethics, supplementing the four principles that are usually accepted in Bioethics: Autonomy, Beneficence, Nonmaleficence and Justice. We propose here that with regard to AI, on the one hand explainability is indeed a new dimension of ethical concern that should be paid attention to, while on the other hand, explainability in itself should not necessarily be considered an ethical "principle". We think of explainability rather (i) as an epistemic requirement for taking into account ethical principles, but not as an ethical principle in itself; (ii) as an ethical demand that can be derived from ethical principles. We do agree that explainability is a key demand in AI Ethics, with practical importance for stakeholders to take into account; but we argue that it should not be considered as a fifth ethical principle, to maintain a philosophical consistency in the organization of AI ethical principles.
Reference framework “HOGO” for cybersecurity in SMEs based on ISO 27002 and 27032

As information and communication technologies are empowered in organizations, they are also victims of attacks in cyberspace, generating the need to protect the most important asset, information. For this reason, it is important to develop the &#x201C;HOGO&#x201D; reference framework based on the good practices of ISO 27002 and the security controls of ISO 27032 for cybersecurity in SMEs. The results of the research show the benefits of the implementation of the reference framework &#x201C;HOGO&#x201D; in SMEs, applying good practices related to internet security, critical infrastructures for information, network security, and information security.
Face Recognition Method Based on Residual Convolution Neural Network

Abstract With the advancement of information technology and societal growth, social security has become more important than ever. Face recognition, as compared to other traditional recognition methods like fingerprint recognition, palm recognition, etc, has the benefit that it is contact less, and now it is becoming one among the most prominent technologies in development. Although there are numerous recognition systems that use DNNs in the field of facial expression recognition, their accuracy and practicality are still insufficient for real-world applications. A facial recognition approach based on Resnet 152 v2 has been proposed in this work. In this paper, a residual learning approach is presented to make the training of networks that are far deeper than previously employed networks easier. The proposed method, employs the AT&amp;T face dataset, and supposing that normalization and segmentation are complete, we concentrate on the subtask of person verification and recognition, demonstrating performance using a testing database comprising illumination, pose, expression and occlusion variations. SoftMax is the activation function that has been used, which adjusts the output sum up to one allowing it to be understood as probabilities. Then, the model would generate a judgment depending on which option has a strong likelihood. This system employs Adam as an optimizer to control the learning rate through training and categorical cross entropy as its loss function. The proposed approach has a 97 percent face recognition accuracy on AT&amp;T dataset, showing its efficacy after a significant number of analyses and experimental verification.
Image Segmentation with a Priori Conditions: Applications to Medical and Geophysical Imaging

In this paper, we propose a method for semi-supervised image segmentation based on geometric active contours. The main novelty of the proposed method is the initialization of the segmentation process, which is performed with a polynomial approximation of a user defined initialization (for instance, a set of points or a curve to be interpolated). This work is related to many potential applications: the geometric conditions can be useful to improve the quality the segmentation process in medicine and geophysics when it is required (weak contrast of the image, missing parts in the image, non-continuous contour…). We compare our method to other segmentation algorithms, and we give experimental results related to several medical and geophysical applications.
Context-Based Question Answering System with Suggested Questions

Question Answering and Question Generation are well-researched problems in the field of Natural Language Processing and Information Retrieval. This paper aims to demonstrate the use of novel transformer-based models like BERT, AIBERT, and DistilBERT for Question Answering System and the t5 model for Question Generation. The Question Generation task is integrated with the Question Answering System to suggest relevant questions from the input context using the transfer learning-based model. The question generation model generates questions from the context input by the user and uses different models like DistilBERT, RoBERTa for getting answers from the context. Suggested questions are ranked using BM25 scores to show the most relevant question-answer pairs on the top. The input context can be given as PDF or image(extract texts from image).
Verified programs can party

Operating system (OS) extensions are more popular than ever. For example, Linux BPF is marketed as a "superpower" that allows user programs to be downloaded into the kernel, verified to be safe and executed at kernel hook points. So, BPF extensions have high performance and are often placed at performance-critical paths for tracing and filtering.
An Efficient Design of Scalable Reversible Multiplier with Testability

A new architecture of 4-bit reversible multiplier with scalability factor of order [Formula: see text] is presented in this paper. The design procedure is based on a unique method of gates placement, which produces parity preserving circuits. This property facilitates graceful testing and full coverage of single-bit faults at lower overhead. The circuit is designed and implemented on the top of reversible analyser for obtaining operating costs in terms of number of wires, gate cost, quantum cost, garbage output and ancilla input. Testable implementation of recently reported multiplier circuits has also been performed using the existing method of testing under the same platform. This work achieved a reduction of up to 33% in operating costs when all the parameters are combined together.
Efficient subhypergraph matching based on hyperedge features

Hypergraphs consist of vertices and hyperedges that can connect multiple vertices. Since hypergraphs can effectively simulate complex intergroup relationships between entities, they have a wide range of applications such as computer vision and bioinformatics. In this paper, we study the subhypergraph matching problem, which is one of the most challenging problems in the processing of the hypergraphs. We aim to extract all subhypergraph isomorphism embeddings of a query hypergraph q in a large data hypergraph D. The existing methods on subgraph matching are designed for the ordinary graphs, which typically achieve the goal by three phases, i.e., filtering candidate vertex sets, refining candidates, and then enumeration final results in some matching order. However, such a design cannot be trivially extended to efficiently handle hypergraphs due to the inherent difference between ordinary graphs and hypergraphs. This motivates us to enhance the performance by exploiting hyperedge features, such as the typical intersections and inclusion relations between hyperedges.
A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model

There is a growing interest in topic modeling to decipher the valuable information embedded in natural texts. However, there are no studies training an unsupervised model to automatically categorize the social networks (SN) messages according to personality traits. Most of the existing literature relied on the Big 5 framework and psychological reports to recognize the personality of users. Furthermore, collecting datasets for other personality themes is an inherent problem that requires unprecedented time and human efforts, and it is bounded with privacy constraints. Alternatively, this study hypothesized that a small set of seed words is enough to decipher the psycholinguistics states encoded in texts, and the auxiliary knowledge could synergize the unsupervised model to categorize the messages according to human traits. Therefore, this study devised a dataless model called Seed-guided Latent Dirichlet Allocation (SLDA) to categorize the SN messages according to the PEN model that comprised Psychoticism, Extraversion, and Neuroticism traits. The intrinsic evaluations were conducted to determine the performance and disclose the nature of texts generated by SLDA, especially in the context of Psychoticism. The extrinsic evaluations were conducted using several machine learning classifiers to posit how well the topic model has identified latent semantic structure that persists over time in the training documents. The findings have shown that SLDA outperformed other models by attaining a coherence score up to 0.78, whereas the machine learning classifiers can achieve precision up to 0.993. We also will be shared the corpus generated by SLDA for further empirical studies.
Logics with Group Announcements and Distributed Knowledge: Completeness and Expressive Power

Abstract Public announcement logic (PAL) is an extension of epistemic logic with dynamic operators that model the effects of all agents simultaneously and publicly acquiring the same piece of information. One of the extensions of PAL, group announcement logic (GAL), allows quantification over (possibly joint) announcements made by agents. In GAL, it is possible to reason about what groups can achieve by making such announcements. It seems intuitive that this notion of coalitional ability should be closely related to the notion of distributed knowledge, the implicit knowledge of a group. Thus, we study the extension of GAL with distributed knowledge, and in particular possible interaction properties between GAL operators and distributed knowledge. The perhaps surprising result is that, in fact, there are no interaction properties, contrary to intuition. We make this claim precise by providing a sound and complete axiomatisation of GAL with distributed knowledge. We also consider several natural variants of GAL with distributed knowledge, as well as some other related logic, and compare their expressive power.