Reviewerly/exHarmony huggingface dataset:
“Check, Check, Check, We Got Those” – Catalogue Use in Information Security Risk Management

Information Security Risk Management (ISRM) is fundamental in most organisations today. The literature describes ISRM as a complex activity, and one way of addressing this is to enable knowledge reuse in the shape of catalogues. Catalogues in the ISRM domain can contain lists of, e.g. assets, threats and security controls. In this paper, we focus on three aspects of catalogue use. Why we need catalogues, how catalogue granularity is perceived, and how catalogues help novices in practice. As catalogue use is not yet a widespread practice in the ISRM, we have selected a domain where catalogues are a part of the ISRM work. In this case, the Air Traffic Management (ATM) domain uses a methodology that includes catalogues and is built on ISO/IEC 27005. The results are based on data collected from 19 interviews with ATM professionals that are either experts or novices in ISRM. With this paper, we nuance the view on what catalogues can contribute with. For example, consistency, coherency, a starting point and new viewpoints. At the same time, we identify the need to inform about the aim of the catalogues and the limitations that come with catalogue use in order to leverage the use – especially from a novice perspective.
End-to-end translation of human neural activity to speech with a dual–dual generative adversarial network

In a recent study of auditory evoked potential (AEP) based brain–computer interface (BCI), it was shown that, with an encoder–decoder framework, it is possible to translate human neural activity to speech (T-CAS). Current encoder–decoder-based methods achieve T-CAS often with a two-step approach where the information is passed between the encoder and decoder with a shared vector of reduced dimension, which, however, may result in information loss. In this paper, we propose an end-to-end model to translate human neural activity to speech (ET-CAS) by introducing a dual–dual generative adversarial network (Dual-DualGAN) for cross-domain mapping between electroencephalogram (EEG) and speech signals. In this model, we bridge the EEG and speech signals by introducing transition signals which are obtained by cascading the corresponding EEG and speech signals in a certain proportion. We then learn the mappings between the speech/EEG signals and the transition signals. We also develop a new EEG dataset where the attention of the participants is detected before the EEG signals are recorded to ensure that the participants have good attention in listening to speech utterances. The proposed method can translate word-length and sentence-length sequences of neural activity to speech. Experimental results show that the proposed method significantly outperforms state-of-the-art methods on both words and sentences of auditory stimulus.
EDRL: Entropy-guided disentangled representation learning for unsupervised domain adaptation in semantic segmentation

Deep learning-based approaches are excellent at learning from large amounts of data, but can be poor at generalizing the learned knowledge to testing datasets with domain shift, i.e., when there exists distribution discrepancy between the training dataset (source domain) and the testing dataset (target domain). In this paper, we investigate unsupervised domain adaptation (UDA) techniques to train a cross-domain segmentation method which is robust to domain shift, eliminating the requirement of any annotations on the target domain.To this end, we propose an Entropy-guided Disentangled Representation Learning, referred as EDRL, for UDA in semantic segmentation. Concretely, we synergistically integrate image alignment via disentangled representation learning with feature alignment via entropy-based adversarial learning into one network, which is trained end-to-end. We additionally introduce a dynamic feature selection mechanism via soft gating, which helps to further enhance the task-specific feature alignment. We validate the proposed method on two publicly available datasets: the CT-MR dataset and the multi-sequence cardiac MR (MS-CMR) dataset.On both datasets, our method achieved better results than the state-of-the-art (SOTA) methods. Specifically, on the CT-MR dataset, our method achieved an average DSC of 84.8% when taking CT as the source domain and MR as the target domain, and an average DSC of 84.0% when taking MR as the source domain and CT as the target domain.Results from comprehensive experiments demonstrate the efficacy of the proposed EDRL model for cross-domain medical image segmentation.
EPPSQ: Achieving efficient and privacy-preserving statistics queries over encrypted data in smart grids

Outsourcing encrypted data and query services to clouds have been widely adopted by data owners, such as in the energy, healthcare, and transportation sectors, usually for economic considerations. However, this is usually prone to data privacy breaches. In smart grids, although many privacy-preserving data query solutions have been presented in the literature, they either suffer from low query efficiencies or limited capabilities of statistics queries (say the aggregation, variance, or extrema only). To mitigate this gap, in this paper we propose an efficient and privacy-preserving statistics query scheme over encrypted data in smart grids, coined EPPSQ, which can achieve diverse statistics queries in an efficient and privacy-preserving way. Specifically, we first develop a public key encryption based secure data collection protocol for data owners. Then, we design a two-server model based privacy-preserving statistics query protocol for data requesters. In addition, four algorithms respectively for securely computing the statistics (i.e., max/min, variance, mean, and count) over the encrypted data in smart grids are crafted. Security analysis shows that the proposed EPPSQ scheme can effectively guarantee data privacy while outsourcing the encrypted data and query services in smart grids. Further, extensive experiments demonstrate that the proposed EPPSQ scheme outperforms existing CKKS-based or BFV-based studies in terms of computational efficiency.
KE-X: Towards subgraph explanations of knowledge graph embedding based on knowledge information gain

Over the past years, knowledge graph embedding approaches have proven effective for knowledge graph completion tasks. However, most existing models are either built on a certain embedding space or black-box neural networks, making it hard to access explanations for prediction results and resulting in limited explainability. In this paper, we propose to leverage information entropy to quantify the importance of explanation candidates, then build a framework KE-X, for explaining results from knowledge graph embedding approaches by generating explainable subgraphs. Specifically, by performing a modified message passing mechanism on a partially masked knowledge subgraph and maximizing knowledge information gain, KE-X can extract the most valuable subgraph explanation for a link prediction query. To evaluate KE-X, we conduct experiments on three real-world knowledge graphs with two representative KGE models, TransE and DistMult. Both quantitative and case study results show that our framework can extract high-quality explanations.
Analyzing Cybersecurity Definitions for Non-experts

Current definitions of cybersecurity are not standardized and are often targeted towards cybersecurity experts and academics. There has been little evaluation about the appropriateness and understandability of these definitions for non-experts (individuals without cybersecurity expertise). This poses a challenge for practitioners and researchers when trying to communicate the meaning and importance of cybersecurity to non-experts. We take an initial step towards addressing this challenge by building a corpus of cybersecurity definitions likely to be encountered by non-experts, unlike prior efforts that only consider definitions from authoritative sources. We observed several issues that may impede non-experts’ understanding, including cybersecurity definitions: being inconsistent in describing what cybersecurity is and does; often using overly-technical terminology; and varying greatly in the components of cybersecurity (e.g., objects of protection, who is responsible, threats) included in the definitions. Our findings illustrate the full landscape of cybersecurity definitions and provide a basis for investigating which definitions and terminology may be best for non-experts.
Aircraft target type recognition technology based on deep learning and structure feature matching

In order to solve the problems with the traditional aircraft target type recognition algorithm, such as difficulty in feature selection, weak generalization ability, slow recognition speed, and low recognition accuracy, this paper put forward a new method that could detect and recognize aircraft targets in aerial images quickly and accurately. The aircraft targets in the images were detected rapidly and located through YOLOv3-tiny, and after image denoising, shadow detection, and positioning, then we used the Sobel operator to calculate the edge gradient of the target; the image of the aircraft target was segmented by using the region growth method, and then the principal component analysis (PCA)was used to obtain the central axis of the aircraft target. The projected distance from the edge contour to the central axis was sampled at equal intervals along the direction of the central axis, and its ratio to the length of the central axis was calculated to construct the feature vector. Finally, the Spearman rank correlation method was used to match the feature vectors to realize the recognition of the aircraft type. Experiments showed that the proposed method had strong adaptability and small computation and could quickly detect and accurately recognize aircraft targets in aerial images.
Horizontal federated differential evolution algorithm for the optimization of fuzzy clusters

In this study, we are concerned with the optimization of fuzzy clustering (Fuzzy C-Means) on the basis of a collection of distributed datasets without violating data confidentiality and security. The optimization of fuzzy clusters is realized using the differential evolution algorithm in a federated learning environment. Fuzzy clustering plays an important role in revealing the underlying structure of a given dataset. However, traditional iterative method is easy to get stuck at local optimum. With the growing concerning on data confidentiality and security, how to reveal the underlying structure of the data that are stored locally across different sites is becoming an urgent problem. In order to overcome these two obstacles, we propose a federated differential evolution algorithm to realize fuzzy clustering. We augment the well-known differential evolution algorithm such that it can work in a federated learning environment to ensure local data privacy. The design practice of the federated differential evolution is elaborated on by highlighting its effectiveness in finding the optimal fuzzy clusters on the basis of distributed datasets. The performance of the proposed method is compared with traditional fuzzy clustering algorithm. Experimental studies completed on a series of real-world datasets coming from machine learning repository are reported to demonstrate the superiority of the proposed algorithm.
MHANet: Multi-scale hybrid attention network for crowd counting

Crowd counting aims to estimate the number, density, and distribution of crowds in an image. The current mainstream approach, based on CNN, has been highly successful. However, CNN is not without its flaws. Its limited receptive field hampers the modeling of global contextual information, and it struggles to effectively handle scale variation and background complexity. In this paper, we propose a Multi-scale Hybrid Attention Network called MHANet to solve crowd counting challenges more effectively. To address the issue of scale variation, we have developed a Multi-scale Aware Module (MAM) that incorporates multiple sets of dilated convolutions with varying dilation rates. The MAM significantly improves the network’s ability to extract information at multiple scales. To tackle the problem of background complexity, we have introduced a Hybrid Attention Module (HAM) that combines spatial attention and channel attention. The HAM effectively directs attention to the crowd region while suppressing background interference, resulting in more accurate counting. MHANet has been extensively experimented on four benchmark datasets and compared against state-of-the-art algorithms. It consistently achieves superior performance in terms of the MAE evaluation metric. MHANet outperforms the current state-of-the-art methods by margins of 1.9%, 5.4%, 0.4%, and 0.8% on the ShanghaiTech Part_A, ShanghaiTech Part_B, UCF-QNRF, and UCF_CC_50 datasets, respectively. Furthermore, a series of ablation experiments targeting MAM and HAM were conducted in this paper, and the experimental results fully demonstrate that MAM and HAM can effectively address the challenges of scale variation and background complexity, ultimately enhancing the accuracy and robustness of the network.
Natural deduction calculi for classical and intuitionistic S5

We propose an indexed natural deduction system for the modal logic S5, ideally following Wansing's previous work in the context of tableaux sequents. The system, given both in the classical and intuitionistic versions (called N5c and N5i respectively), is designed to match as much as possible the structure and properties of the standard system of natural deduction for first-order logic, exploiting the formal analogy between modalities and quantifiers. We study a (syntactical) normalization theorem for both N5c and N5i and its main consequences, the sub-formula principle and the consistency theorem. In particular, we propose an intuitionistic encoding of classical S5 (via a suitable extension of the Gödel translation for first-order classical logic). Moreover, via the BHK interpretation of intuitionistic proofs, we propose a suitable Curry–Howard isomorphism for N5i. By translation into the natural deduction system given by Galmiche and Salhi in [(2010b). Label-free proof systems for intuitionistic modal logic is5. In E. M. Clarke & A. Voronkov (Eds.), Logic for programming, artificial intelligence, and reasoning (pp. 255–271). Springer Berlin Heidelberg.], we prove the equivalence of N5i w.r.t. an Hilbert-style axiomatization of IS5. However, when considering the sheer provability of labelled formulas, our system is comparable to the one presented by Simpson in [(1993). The proof theory and semantics of intuitionistic modal logic [PhD thesis], University of Edinburgh, UK.]. Nevertheless, it remains uncertain whether it is feasible to establish a translation between the corresponding derivations.
Implementation of Smart NFC Door Access System for Hotel Room

Security remains a top priority for those users in the hotel, even with the advent of innovative technological advances. This is because many tragic incidents, such as theft and crime, have occurred with unrestricted access. This paper proposes an intelligent door access system that would allow hotel guests to authenticate into their rooms without resorting to traditional closeness access methods. Therefore, research was conducted to solidify the understanding and refine the capabilities of the proposed system. This project aims to promote high-security aspects access system technology, which is Near-Field Communication with the use of application that have the function of simulated smart keys for explicit validation access. A Host-Card Emulator opens the opportunities for efficient financial benefit and the launch of a protective mechanism in the post-pandemic period. The suggested method was statistically and analytically accessed on hotel guests and staff from various hotels in Malaysia. The proposed system is a contactless NFC access control system that employs smartphone Host Card Emulation application technology to reduce the need for appropriate physical access, enhance security, and publicize the use of mobile access systems in the hospitality industry.
(In)Visible Participants

What should we understand about designing against infrastructures of oppression and marginalization? Research articles and social technologies produced by our design community have focused on procedures for action. Through this technicist approach, we have often disregarded the experiences of the participants of this type of design process, excluding their voices from institutionalized design knowledge, and thus replicating their invisibility. To overcome this, it is necessary to understand what characterizes people’s experiences and drives their participation, how these processes and practices interweave with their lives, and to bring their voices to the core of our research outcomes. These reflections emerge out of interviews with the participants of a design project in a Brazilian favela. The need for a more complex and multilayered perspective for design processes against infrastructures of oppression and marginalization emerges, together with the understanding of desire and affection, as critical factors in the process of unmaking them.
Modeling the assessment of a region’s innovative potential

The paper is devoted to the design and modeling of the system for assessing a region’s innovative potential to justify management decisions in public authorities. The purpose of the study is to substantiate the feasibility of using digital tools and artificial intelligence to formalize and visualize the assessment results. The use of a multi-criteria system for assessing the level of a region’s development, which includes four areas, characterized by 46 indicators, is difficult to interpret to justify management decisions. Based on the theory of fuzzy logic, logical programming methods focused on the application of a system of facts and rules, formal knowledge representation languages for describing subject areas and methodology for developing expert systems for complex problem solving, taking into account the empirical knowledge of experts, a model for assessing the innovative potential of a region is proposed. A case study of one of the Russian regions is illustrated by graphs of 3D visualization of the optimal combination of parameters of the level of innovative development. The formed triad of sustainable and safe development makes it possible to display the behavior of the regional innovation development index as a whole on the basis of social, economic and environmental indicators of sustainability.
Robust Fisher-Regularized Twin Extreme Learning Machine with Capped L1-Norm for Classification

Twin extreme learning machine (TELM) is a classical and high-efficiency classifier. However, it neglects the statistical knowledge hidden inside the data. In this paper, in order to make full use of statistical information from sample data, we first come up with a Fisher-regularized twin extreme learning machine (FTELM) by applying Fisher regularization into TELM learning framework. This strategy not only inherits the advantages of TELM, but also minimizes the within-class divergence of samples. Further, in an effort to further boost the anti-noise ability of FTELM method, we propose a new capped L1-norm FTELM (CL1-FTELM) by introducing capped L1-norm in FTELM to dwindle the influence of abnormal points, and CL1-FTELM improves the robust performance of our FTELM. Then, for the proposed FTELM method, we utilize an efficient successive overrelaxation algorithm to solve the corresponding optimization problem. For the proposed CL1-FTELM, an iterative method is designed to solve the corresponding optimization based on re-weighted technique. Meanwhile, the convergence and local optimality of CL1-FTELM are proved theoretically. Finally, numerical experiments on manual and UCI datasets show that the proposed methods achieve better classification effects than the state-of-the-art methods in most cases, which demonstrates the effectiveness and stability of the proposed methods.
A Novel Process of Shoe Pairing Using Computer Vision and Deep Learning Methods

Abstract The industrialisation of the footwear recycling processes is a major issue in the European Union—particularly in view of the fact that at least 90% of shoes consumed in western economies are ultimately sent to landfill. This requires new AI-empowered technologies that enable detection, classification, pairing, and quality assessment in a viable automatic process. This article discusses automatic shoe pairing, which comprises two sequential stages: a) deep multiview shoe embedding (compact representation of multiview data); and b) clustering of shoes’ embeddings with a fixed similarity threshold to return sets of possible pairs. Each shoe in our pipeline is represented by multiple images that are collected in industrial darkrooms. We present various approaches to shoe pairing—from fully unsupervised ones based on image descriptors to supervised ones that rely on deep neural networks—to identify the most effective one for this highly specific industrial task. The article also explains how the selected method can be improved by hyperparameter tuning, massive increases in training data, and data augmentation.
Creep Properties of a Viscoelastic 3D Printed Sierpinski Carpet-Based Fractal

In this paper, the phenomenon of creep compliance and the creep Poisson’s ratio of a 3D-printed Sierpinski carpet-based fractal and its bulk material (flexible resin Resione F69) was experimentally investigated, as well as the quantification of the change in the viscoelastic parameters of the material due to the fractal structure. The samples were manufactured via a vat photopolymerization method. The fractal structure of the samples was based on the Sierpinski carpet at the fourth iteration. In order to evaluate the response of both the fractal and the bulk material under the creep phenomenon, 1 h-duration tensile creep tests at three constant temperatures (20, 30 and 40 °C) and three constant stresses (0.1, 0.2 and 0.3 MPa) were conducted. A digital image correlation (DIC) technique was implemented for strain measurement in axial and transverse directions. From the results obtained, the linear viscoelastic behavior regime of the fractal and the bulk material was identified. The linear viscoelastic parameters of both fractal and bulk materials were then estimated by fitting the creep Burgers model to the experimental data to determine the effect of the fractal geometry on the viscoelastic properties of the samples. Overall, it was found that the reduction in stiffness induced by the fractal porosity caused a more viscous behavior of the material and a reduction in its creep Poisson’s ratio, which means an increase in the compliance of the material.
Development of a Web-Based Strategic Management Expert System Using Knowledge Graphs

In this paper, we present the development of a Web-based expert system, StrategyAdvisor Cloud, to support strategic management decision-making. The system was developed using a multistage methodology that builds upon knowledge graphs, where knowledge acquisition and rule base construction by project members with different roles, capabilities, and skills can be facilitated through customized visual languages. The methodology systematizes knowledge acquisition and knowledge representation for each stage, coupled with algorithms for the transformation of knowledge graphs between successive stages. The developed expert system and the development process are described in detail in the paper and its supplement, to serve as guidance in the development of similar systems in future.