Reviewerly/exHarmony huggingface dataset:
Impossibilities in Succinct Arguments: Black-Box Extraction and More

The celebrated result by Gentry and Wichs established a theoretical barrier for succinct non-interactive arguments (SNARGs), showing that for (expressive enough) hard-on-average languages, we must assume non-falsifiable assumptions. We further investigate those barriers by showing new negative and positive results related to the proof size. Along the way, we identify a class of non-trivial languages, which we dub “trapdoor languages”, that can bypass these impossibility results.
Contextualised segment-wise citation function classification

Much effort has been made in the past decades to citation function classification, but noteworthy issues exist. Annotation difficulty resulted in limited data size, especially for minority classes, and inadequate representativeness of the underlying scientific domains. Concerning algorithmic classification, state-of-the-art deep learning-based methods are flawed by generating a feature vector for the whole citation context (or sentence) and failing to exploit the full realm of citation modelling options. Responding to these issues, this paper studied contextualised citation function classification. Specifically, a large new citation context dataset was created by merging and re-annotating six datasets about computational linguistics. A variety of strong SciBERT-based citation function classification models were proposed, and new states of the art were achieved. Through deeper performance analysis, this study focused on answering several research questions about the effective ways of performing citation function classification. More specifically, the study justified the necessity of modelling in-text citations in context and confirmed the superiority of doing citation function classification at citation (segment) level. A particular emphasis was placed on in-depth per-class performance analysis to understand whether citation function classification is robust enough to suit various popular downstream applications and what further efforts are required to meet such analytic needs. Finally, a naïve ensemble classifier was proposed, which greatly improved citation function classification performance.
Women Economic Empowerment during Covid-19 Crisis and Transition in BRICS Countries

The paper reviews the BRICS policies in rebuilding economies equity and fairness. All economies are affected by the global pandemic, which has damaged most economies and led to a worldwide public health emergency. The paper adopts a qualitative approach by exploring secondary sources to analyse the role of the BRICS community on women's economic empowerment during the Covid-19 pandemic. It presents a descriptive narrative in the context of the study. The findings project that the BRICS community still lacks strategic policy implementation that integrates women into the BRICS economy. As a result, there is a massive gap in women's economic representativeness and the need to promote access and affordable policies and education-related initiatives that enhance gender inclusion. It recommends that BRICS communities ensure closer cooperation with various stakeholders, national organisations, private businesses, and multinationals to promote gender equality in their respective economies.
DEGAN: Decompose-Enhance-GAN Network for Simultaneous Low-Light Image Lightening and Denoising

Images taken in low-light situations frequently have a significant quality reduction. Taking care of these degradation problems in low-light images is essential for raising their visual quality and enhancing high-level visual task performance. However, because of the inherent information loss in dark images, conventional Retinex-based approaches for low-light image enhancement frequently fail to accomplish real denoising. This research introduces DEGANet, a revolutionary deep-learning framework created particularly for improving and denoising low-light images. To overcome these restrictions, DEGANet makes use of the strength of a Generative Adversarial Network (GAN). The Decom-Net, Enhance-Net, and an Adversarial Generative Network (GAN) are three linked subnets that make up our novel Retinex-based DEGANet architecture. The Decom-Net is in charge of separating the reflectance and illumination components from the input low-light image. This decomposition enables Enhance-Net to effectively enhance the illumination component, thereby improving the overall image quality. Due to the complicated noise patterns, fluctuating intensities, and intrinsic information loss in low-light images, denoising them presents a significant challenge. By incorporating a GAN into our architecture, DEGANet is able to effectively denoise and smooth the enhanced image as well as retrieve the original data and fill in the gaps, producing an output that is aesthetically beautiful while maintaining key features. Through a comprehensive set of studies, we demonstrate that DEGANet exceeds current state-of-the-art methods in both terms of image enhancement and denoising quality.
Effects of Different Full-Reference Quality Assessment Metrics in End-to-End Deep Video Coding

Visual quality assessment is often used as a key performance indicator (KPI) to evaluate the performance of electronic devices. There exists a significant association between visual quality assessment and electronic devices. In this paper, we bring attention to alternative choices of perceptual loss function for end-to-end deep video coding (E2E-DVC), which can be used to reduce the amount of data generated by electronic sensors and other sources. Thus, we analyze the effects of different full-reference quality assessment (FR-QA) metrics on E2E-DVC. First, we select five optimization-suitable FR-QA metrics as perceptual objectives, which are differentiable and thus support back propagation, and use them to optimize an E2E-DVC model. Second, we analyze the rate–distortion (R-D) behaviors of an E2E-DVC model under different loss function optimizations. Third, we carry out subjective human perceptual tests on the reconstructed videos to show the performance of different FR-QA optimizations on subjective visual quality. This study reveals the effects of the competing FR-QA metrics on E2E-DVC and provides a guide for further future study on E2E-DVC in terms of perceptual loss function design.
Forgery Detection for Anti-Counterfeiting Patterns Using Deep Single Classifier

At present, anti-counterfeiting schemes based on the combination of anti-counterfeiting patterns and two-dimensional codes is a research hotspot in digital anti-counterfeiting technology. However, many existing identification schemes rely on special equipment such as scanners and microscopes; there are few methods for authentication that use smartphones. In particular, the ability to classify blurry pattern images is weak, leading to a low recognition rate when using mobile terminals. In addition, the existing methods need a sufficient number of counterfeit patterns for model training, which is difficult to acquire in practical scenarios. Therefore, an authentication scheme for an anti-counterfeiting pattern captured by smartphones is proposed in this paper, featuring a single classifier consisting of two modules. A feature extraction module based on U-Net extracts the features of the input images; then, the extracted feature is input to a one-class classification module. The second stage features a boundary-optimized OCSVM classification method. The classifier only needs to learn positive samples to achieve effective identification. The experimental results show that the proposed approach has a better ability to distinguish the genuine and counterfeit anti-counterfeiting pattern images. The precision and recall rate of the approach reach 100%, and the recognition rate for the blurry images of the genuine anti-counterfeiting patterns is significantly improved.
Multi-Attribute NMS: An Enhanced Non-Maximum Suppression Algorithm for Pedestrian Detection in Crowded Scenes

Removing duplicate proposals is a critical process in pedestrian detection, and is usually performed via Non-Maximum Suppression (NMS); however, in crowded scenes, the detection proposals of occluded pedestrians are hard to distinguish from duplicate proposals, making the detection results inaccurate. In order to address the above-mentioned problem, the authors of this paper propose a Multi-Attribute NMS (MA-NMS) algorithm, which combines density and count attributes in order to adaptively adjust suppression, effectively preserving the proposals of occluded pedestrians while removing duplicate proposals. In order to obtain the density and count attributes, an attribute branch (ATTB), which uses a context extraction module (CEM) to extract the context of pedestrians, and then, concatenates the context with the features of pedestrians in order to predict both the density and count attributes simultaneously, is also proposed. With the proposed ATTB, a pedestrian detector, based on MA-NMS, is constructed for pedestrian detection in crowded scenes. Extensive experiments are conducted using the CrowdHuman and CityPersons datasets, and the results show that the proposed method outperforms mainstream methods on AP (average precision), Recall, and MR−2 (log-average miss rate), sufficiently validating the effectiveness of the proposed MA-NMS algorithm.
Detection of Human Visceral Leishmaniasis Parasites in Microscopy Images from Bone Marrow Parasitological Examination

Visceral Leishmaniasis (VL) is a neglected disease that affects between 50,000 and 90,000 new cases annually worldwide. In Brazil, VL causes about 3500 cases/per year. This chronic disease can lead to death in 90% of untreated cases. Thus, it is necessary to study safe technologies for diagnosing, treating, and controlling VL. Specialized laboratories carry out the VL diagnosis, and this step has a significant automation power through methods based on computational tools. The gold standard for detecting VL is the microscopy of material aspirated from the bone marrow to search for amastigotes. This work aims to assist in detecting amastigotes from microscopy images using deep learning techniques. The proposed methodology consists of segmenting the Leishmania parasites in the images, precisely indicating the location of the amastigotes in the image. In the detection of VL parasites, in this methodology, a Dice of 80.4% was obtained, Intersection over Union (IoU) of 75.2%, Accuracy of 99.1%, Precision of 81.5%, Sensitivity of 72.2%, Specificity of 99.6%, and Area under the Receiver Operating Characteristics Curve (AUC) of 86.5%. The results are promising and demonstrate that deep learning models trained with images of microscopy slides of biological material can precisely help the specialist detect VL in humans.
IESRGAN: Enhanced U-Net Structured Generative Adversarial Network for Remote Sensing Image Super-Resolution Reconstruction

With the continuous development of modern remote sensing satellite technology, high-resolution (HR) remote sensing image data have gradually become widely used. However, due to the vastness of areas that need to be monitored and the difficulty in obtaining HR images, most monitoring projects still rely on low-resolution (LR) data for the regions being monitored. The emergence of remote sensing image super-resolution (SR) reconstruction technology effectively compensates for the lack of original HR images. This paper proposes an Improved Enhanced Super-Resolution Generative Adversarial Network (IESRGAN) based on an enhanced U-Net structure for a 4× scale detail reconstruction of LR images using NaSC-TG2 remote sensing images. In this method, in-depth research has been performed and consequent improvements have been made to the generator and discriminator within the GAN network. Specifically, before introducing Residual-in-Residual Dense Blocks (RRDB), in the proposed method, input images are subjected to reflective padding to enhance edge information. Meanwhile, a U-Net structure is adopted for the discriminator, incorporating spectral normalization to focus on semantic and structural changes between real and fake images, thereby improving generated image quality and GAN performance. To evaluate the effectiveness and generalization ability of our proposed model, experiments were conducted on multiple real-world remote sensing image datasets. Experimental results demonstrate that IESRGAN exhibits strong generalization capabilities while delivering outstanding performance in terms of PSNR, SSIM, and LPIPS image evaluation metrics.
Evaluating the Effectiveness of the Earthquake Early Warning Message in China: An Affordance Perspective Using Immersive Virtual Reality

The early earthquake warning (EEW) system is essential for mitigating the effects of seismic incidents. However, in China, the design of EEW messages has not received much attention. This study employs affordance theory to examine the effectiveness of the EEW message generated by the Institute of Care-Life (ICL) in China, specifically by investigating four aspects of affordances: functional, cognitive, sensory, and emotional affordance. With 68 participants, we conducted an immersive virtual reality experiment. The results revealed that the ICL EEW message has a strong emotional affordance but inadequate functional, cognitive, and sensory affordance. These data provide recommendations for enhancing EEW messages, which could result in better interaction during earthquakes in China. This study investigated the viability of immersive virtual reality as a research tool for EEW. It increases understanding of the elements that determine the effectiveness of EEW communications, leading to better preparedness and response measures, reducing the impact of earthquakes and saving lives and property.
Organisational characteristics as antecedents of enterprise security risk management adoption in Kenya’s accredited universities

ABSTRACTABSTRACTAcademic institutions today are experiencing a legion of security risks that are increasingly impeding their mission of producing high-quality graduates, guarding reason and educational integrity, and ultimately advancing human civilisation. The Enterprise Security Risk Management (ESRM) model represents a potential solution to the dynamic threats that accredited universities face today. However, ESRM adoption is largely absent, slow, and inconsistent in Kenya’s accredited universities. Only a few attempts have been made to understand what drives security risk management adoption in these universities. Based on seven in-depth interviews with Chief Security Officers across larger accredited universities in Kenya and utilising an overarching innovation adoption model, we examine the organisational predictors of ESRM adoption in these universities. Within the limitations of this study, the data collected highlight the fundamental role of university executive commitment, security governance, and security risk management training in enhancing the diffusion of ESRM systems in universities. We have discussed managerial implications and suggested future research directions.KEYWORDS: Enterprise security risk management (ESRM)ESRM adoptionuniversity security risk managementsecurity governanceuniversity executive commitment AcknowledgementsThis article is part of the Master of Forensics and Security Management thesis at the Institute of Criminology, Forensics, and Security Management, Dedan Kimathi University, Kenya. Special thanks to all the thesis advisors from the university, including Dr. Consolata Ndung’u –Thuranira, for helpful feedback and guidance. Thanks also to the security executives that agreed to participate in this study.Disclosure statementNo potential conflict of interest was reported by the author(s).Declaration of conflicting interestsThe authors declare no conflicts of interest in regard to research, authorship, and/or publication of this article.
Investigating the influence of governance determinants on reporting cybersecurity incidents to police: Evidence from Canadian organizations’ perspectives

Government agencies and standard setters require organizations operating in critical infrastructure sectors to disclose cybersecurity incidents, yet little is known about whether organizations report these incidents to law enforcement. This study examines this issue based on data from the 2017–2021 periods of the Canadian Survey of Cybersecurity and Cybercrime administered to Canadian organizations. We assessed the effects of governance determinants along with cyber incidents and their impacts using partial least squares equation modelling to identify the relationships between these factors and cybersecurity incidents reported to police services. To conceptualize these relationships, we developed a framework based on resource-dependence theory, protection motivation theory, and previous empirical evidence. The overall governance determinants as well as the impacts of the incidents explained 51% of the intention to report cybersecurity incidents to police, and the intensity of the impacts explained 30% of these intentions to signal incidents to law enforcement. The results also revealed that the intensity of cyber incident impacts dictates the attitudes of organizations towards reporting digital attacks. This study makes a significant theoretical contribution to the information security literature and has practical implications for standard setters and government agencies that aim to combat cybersecurity incidents.
C-MWCAR: Classification Based on Multiple Weighted Class Association Rules

Classification is a very important task in data mining and pattern analysis, which have been widely used to solve various real-world problems. To obtain better classification performance, in this paper, we propose a novel classification framework based on multiple weighted class association rules (C-MWCAR), whose key idea is to transform the association among features into a set of class association rules (CARs), then classify unknown instances based on the CARs obtained. Concretely, C-MWCAR consists of a dictionary order-based CAR mining algorithm (DOCMA), a branch-based CAR selection algorithm (BCSA), and a multiple weighted CARs-based classifier (MWCC). Specifically, DOCMA mines the complete set of CARs, from which BCSA further selects a representative and concise set of CARs based on the distribution, coverage, and redundancy of the mined CARs. When classifying an unknown instance, MWCC picks out a set of CARs that are most similar to the given instance and computes the weighted importance of those CARs. Finally, the class label of the given instance will be determined by the similarities between the instance and the CARs and the weighted importance of the CARs. Furthermore, we apply the proposed C-MWCAR to a real-world classification task, i.e., hypertension diagnosis, based on a real dataset of 128 subjects. Experimental results indicate that C-MWCAR outperforms four baseline methods and achieves 93.3%, 93.8%, and 92.7% in terms of accuracy, sensitivity, and specificity, respectively.
A Reuse-Oriented Visual Smart Contract Code Generator for Efficient Development of Complex Multi-Party Interaction Scenarios

Multi-party interaction scenarios usually involve interaction and collaboration between multiple participants, such as business negotiation, teamwork, etc. In multi-party interaction scenarios, trust mechanisms are needed to ensure the use of rights and the fulfillment of obligations. The use of blockchain and smart contracts to establish a decentralized trust mechanism is a solution. The development process of smart contracts may require experts in different fields to design them together, and it is difficult for non-experts to understand smart contract code. The traditional method of smart contract development is for the developer to manually write the smart contract code, which may lead to low development efficiency and the inability of the scene participants to understand the smart contract code. To address this challenge, we propose a reuse-oriented visual smart contract generator to efficiently develop complex multi-party interaction scenarios. This paper proposes a model based on BPMN that uses smart contracts to express the process of multi-party interaction so that the parties in various domains understand the business requirements. Then, a multi-layer reusable library is proposed and used to generate a visual solidity code. We also integrate code compilation, security checking, and deployment into the target blockchain environment for testing. The function of the tool is verified in the case of flight delay insurance, which shows that it can aid the automated development of smart contracts for multi-party interaction scenarios. At the same time, the feasibility of the tool is verified by five cases selected from the literature, which shows that it can aid the development of smart contracts to achieve a high degree of reusability.
Integrating blockchain capabilities in an omnichannel healthcare system: A dual theoretical perspective

Abstract The COVID‐19 pandemic opened a market for omnichannel healthcare. While some patients trusted the hybrid mode of consultancy, others lacked trust in the online mode of consultancy. The purpose of the study is to understand the extent to which blockchain can influence the swift trust of stakeholders and the usage intention of omnichannel healthcare systems (OHS). We used a quantitative approach by surveying 250 patients that rendered services from OHS by drawing upon the lens of organizational information processing theory (OIPT) and swift trust theory (STT). We test the hypotheses using the PLS‐SEM technique. The results show blockchain technology can significantly affect the adoption of omnichannel healthcare by reinforcing swift trust in OHS and its continued usage.
Forensic experts' view of forensic‐ready software systems: A qualitative study

Abstract Software engineers widely acknowledge the inclusion of security requirements in the early stages of the development process. However, the need to prepare the software for the failure of the implemented security controls and subsequent investigation of the incident is often not discussed. Forensic‐ready software systems represent an evolution of secure systems being designed for the eventual digital forensic investigation. However, their exact properties remain largely unexplored, beyond preliminary high‐level conceptualizations of requirements and capabilities. Further obstacles hindering the adoption of forensic‐ready software systems are the different priorities and goals of involved parties and a gap in the digital forensics expertise of software engineers. In this paper, we conduct an empirical qualitative study identifying the problems and needs of forensic readiness while framing the notion of an ideal forensic‐ready software system and how it should treat potential evidence. To this end, we conducted semisupervised interviews with digital forensics experts on their idea, experience, and suggestions. The results provide insights into the needs of the experts to facilitate the definition of correct requirements towards forensic‐ready software systems to support the anticipated investigations properly.