Reviewerly/exHarmony huggingface dataset:
Exploring the value of feminist theory in understanding digital crimes: Gender and cybercrime types

Abstract Do men and women perceive cybercrime types differently? This article draws on the distinction between socio‐economic and psychosocial cybercrime proposed by Lazarus (2019) to investigate whether men and women hold different perceptions of digital crimes across these two dimensions. Informed by the synergy between feminist theory and the Tripartite Cybercrime Framework (TCF), our survey examined respondents’ differential perceptions of socio‐economic cybercrime (online fraud) and psychosocial cybercrime (cyberbullying, revenge porn, cyberstalking, online harassment) among men and women in the United Kingdom. The results revealed that women considered psychosocial cybercrime worse than men. Conversely, we found no differences between men and women with regard to socio‐economic cybercrime. The article concludes that psychosocial cybercrimes are more gendered than socio‐economic cybercrime, suggesting problems with the meaning of ‘cyber‐enabled crimes’, and substantiating the synergy between the TCF and feminist perspectives.
Smart contract-based caching and data transaction optimization in mobile edge computing

The emergence of mobile edge computing can provide technical support for the development of Internet services and for coping with massive data traffic, thereby reducing network latency and ensuring efficient network operations and service delivery. However, the mobile edge computing environment is prone to data loss and privacy leakage, and data security and reliability cannot be guaranteed. The application of blockchain technology ensures the stability and dependability of data caching and transactions. In order to guarantee the security of data caching in the mobile edge computing environment and minimize the response time of caching servers, this paper proposes a decentralized data caching strategy in the mobile edge computing environment. The strategy uses a greedy algorithm to make the transmission delay of the requested content as small as possible under the multiple constraints of the storage space of each server and whether the content is cached on the server. To address the problem that data trading platforms are unable to afford the computation and storage of massive amounts of data and cannot guarantee that the data will not be leaked, this paper proposes a secure decentralized data transaction program. The scheme promotes the increase of transaction turnover rate and improves the revenue of both participating parties by establishing a buyer–seller matching algorithm. According to the experimental results, the proposed data caching strategy can improve the cache hit rate and reduce the transmission delay; the proposed data transaction solution can increase the revenue of both the data holder and the data buyer.
AuSR2: Image watermarking technique for authentication and self-recovery with image texture preservation

This paper presents an image watermarking technique for authentication and self-recovery called AuSR2. The AuSR2 scheme partitions the cover image into 3 × 3 non-overlapping blocks. The watermark data is embedded into two Least Significant Bit (LSB), consisting of two authentication bits and 16 recovery bits for each block. The texture of each block is preserved in the recovery data. Thus, each tampered pixel can be recovered independently instead of using the average block. The recovery process may introduce the tamper coincidence problem, which can be solved using image inpainting . The AuSR2 implements the LSB shifting algorithm to increase the imperceptibility by 2.8%. The experimental results confirm that the AuSR2 can accurately detect the tampering area up to 100%. The AuSR2 can recover the tampered image with a PSNR value of 38.11 dB under a 10% tampering rate. The comparative analysis proves the superiority of the AuSR2 compared to the existing schemes.
The association and mediation role of Food and Nutrition Literacy (FNLIT) with eating behaviors, academic achievement and overweight in 10–12 years old students: a structural equation modeling

Abstract Background Food and nutrition literacy is a key factor in shaping healthy dietary behaviors and may result in decreasing decrease the prevalence of overweight. Empirical research on food and nutrition literacy and its outcomes is limited, especially among children and adolescents. Thus, this study investigates the link between Food and Nutrition Literacy (FNLIT) with eating behaviors, academic performance, and overweight in 10–12 years old students in Tehran, Iran. Methods This study was performed through two phases: 1) Proposing a conceptual model of the relationship between FNLIT and its determinants and outcomes, based on the existing evidence and previous models, and 2) Testing the proposed FNLIT model through a cross-sectional study on 803 primary school students (419 boys and 384 girls, from 34 public and 10 private primary schools), aged 10–12 years using structural equation modeling. Demographic, socio-economic, and household food security characteristics were collected by interviewing the students and their mothers/caregivers using a questionnaire. FNLIT was measured by a self-administered, locally designed, and validated questionnaire. Results The fit indices suggested a reasonably adequate fit of the data to the hypothesized model (χ 2 /df = 2.03, p &lt; 0.001, goodness of fit index (GFI) = 0.90, adjusted goodness of fit index (AGFI) = 0.88, comparative fit index (CFI) = 0.91, incremental fit index (IFI) = 0.91, root mean square error of approximation (RMSEA) = 0.04, standardized root mean residual (SRMR) = 0.06). SES was directly and positively related to FNLIT and its subscale in students. FNLIT score had a positive direct (non-mediated) relationship with healthy eating behavior and academic performance. This pattern was strongly reversed in unhealthy eating behavior. There was a full mediation relationship between FNLIT and overweight/obesity via healthy eating behaviors. SES predicted academic performance partially through the mediating effect of Food Label Literacy (FLL). The results indicated that despite the direct relationship between SES and academic performance, an indirect but negative relationship existed with food insecurity. The finding also revealed the fully mediating role of Food Choice Literacy (FCL) in the relationship between demographic factors and healthy eating behaviors. Our study also found that Interactive Food and Nutrition Literacy (IFNL) protected unhealthy eating behaviors, and FCL predicted healthy eating behaviors in children. Conclusion Our study draws attention to FNLIT, especially the skills domain, including IFNL, FCL, and FLL, as the most important determinant of healthy eating behavior, academic performance, and weight status in school-age children reduces social inequalities in children’s development. To ensure an adequate level of FNLIT, educators should assess and plan to enhance food literacy skills in children and adolescents.
Collaborative Navigation and Manipulation of a Cable-Towed Load by Multiple Quadrupedal Robots

This letter tackles the problem of robots collaboratively towing a load with cables to a specified goal location while avoiding collisions in real time. The introduction of cables (as opposed to rigid links) enables the robotic team to travel through narrow spaces by changing its intrinsic dimensions through slack/taut switches of the cable. However, this is a challenging problem because of the hybrid mode switches and the dynamical coupling among multiple robots and the load. Previous attempts at addressing such a problem were performed offline and do not consider avoiding obstacles online. In this letter, we introduce a cascaded planning scheme with a parallelized centralized trajectory optimization that deals with hybrid mode switches. We additionally develop a set of decentralized planners per robot, which enables our approach to solve the problem of collaborative load manipulation online. We develop and demonstrate one of the first collaborative autonomy framework that is able to move a cable-towed load, which is too heavy to move by a single robot, through narrow spaces with real-time feedback and reactive planning in experiments.
A survey of moving object detection methods: A practical perspective

Moving object detection is the foundation of research in many computer vision fields. In recent decades, a number of detection methods have been proposed. Relevant surveys mainly focused on the detection accuracy, while different practical detection tasks were not considered. However, in different application tasks, training modes and requirements are completely different. The purpose of this survey is to classify and evaluate recent moving object detection methods from a practical perspective. Two main types of practical application tasks are considered: the detection of seen scenes and the detection of unseen scenes. In the survey, two practical application tasks are defined, corresponding recent moving object detection technologies are reviewed, and future directions are suggested to provide references for researchers and technicians when choosing suitable algorithms in practical work.
Multi-attention deep neural network fusing character and word embedding for clinical and biomedical concept extraction

• Local and global self-attention mechanisms are used for character embedding. • CNN with multi-size filters are used to extract character information for NER . • A cross-attention method that fuses character and word embedding for NER is proposed • A modified Mogrifier LSTM is presented to improve the performance of NER. • Proposed methods integrated with a transformer-based model achieve good performance. Clinical and biomedical concept extraction is critical in medical analysis using clinical and biomedical documents from professional literature, EHRs and PHRs. Named entity recognition (NER) accurately marks essential information in the literature based on the characteristics of the target entity, providing a method for extracting clinical and biomedical concepts. The performance of NER is heavily embedding-dependent, so recent studies have proposed the method of generating word embedding from character-level information, which can strengthen the representation ability for word embedding. In this paper, we present a novel neural network model including an attention mechanism network and a convolutional neural network (CNN) to further improve character-level embedding. First, an attention mechanism is applied simultaneously to the local and global character embedding. Then, a CNN with multi-size filters is used to extract more information from the character level, which can capture more meaningful features from words with various lengths. In addition, a cross-attention method is used to leverage the interaction between word embedding and character embedding to generate the final word representation. Finally, we modified Mogrifier LSTM to make it suitable for NER tasks and integrated it into our model. Experimental results show that our method is effective and that the model performs better than the baseline models. We also apply our methods proposed in this paper to the transformer-based model and obtain a 90.36 F1-score on NCBI-Disease.
Delivering Trustworthy AI through Formal XAI

The deployment of systems of artificial intelligence (AI) in high-risk settings warrants the need for trustworthy AI. This crucial requirement is highlighted by recent EU guidelines and regulations, but also by recommendations from OECD and UNESCO, among several other examples. One critical premise of trustworthy AI involves the necessity of finding explanations that offer reliable guarantees of soundness. This paper argues that the best known eXplainable AI (XAI) approaches fail to provide sound explanations, or that alternatively find explanations which can exhibit significant redundancy. The solution to these drawbacks are explanation approaches that offer formal guarantees of rigor. These formal explanations are not only sound but guarantee irredundancy. This paper summarizes the recent developments in the emerging discipline of formal XAI. The paper also outlines existing challenges for formal XAI.
Webformer

Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.
A novel image encryption scheme based on chaotic apertured fractional Mellin transform and its filter bank

A novel image encryption scheme based on chaotic apertured fractional Mellin transform and its filter bank has been proposed in this paper. A nonlinear chaotic apertured fractional Mellin transform (ACFrMT) and its filter bank (ACFrMT-FB) are proposed, where a pair of complementary chaotic apertures is introduced into FrMT to form filter bank and a special algorithm is employed to solve the problem of data redundancy in filter banks. In order to improve the sensitivity of chaotic aperture, two random phases based on chaotic aperture (RPCA) are generated as phase masks. The proposed encryption algorithm not only provides huge key space, but also greatly improves the sensitivities of fractional order p and wavelength λ. The maximum values of those sensitivity can reach 10-16 and 10-22. The experiment results prove that the proposed encryption scheme is efficient and secure. The results are: sensitivity attack analysis (> 94.77%), differential attack analysis (average NPCR =99.6120% and average UACI = 33.4888%), entropy values (average value 7.99928), correlation coefficient values (almost 0), and cipher key space analysis (at least 1.677×2341).
SAFNet: A deep spatial attention network with classifier fusion for breast cancer detection

Breast cancer is a top dangerous killer for women. An accurate early diagnosis of breast cancer is the primary step for treatment. A novel breast cancer detection model called SAFNet is proposed based on ultrasound images and deep learning. We employ a pre-trained ResNet-18 embedded with the spatial attention mechanism as the backbone model. Three randomized network models are trained for prediction in the SAFNet, which are fused by majority voting to produce more accurate results. A public ultrasound image dataset is utilized to evaluate the generalization ability of our SAFNet using 5-fold cross-validation. The simulation experiments reveal that the SAFNet can produce higher classification results compared with four existing breast cancer classification methods. Therefore, our SAFNet is an accurate tool to detect breast cancer that can be applied in clinical diagnosis.
Secret image encryption based on chaotic system and elliptic curve cryptography

The advent of internet has been a boon to society. It has helped in information retrieval and is a great help in communication. Nowadays, communication comprises digital information in images, videos, GIFs, and many other formats. Amongst the digital data, images are one of the most transmitted data. Therefore, the security of image data has gained the attention of researchers. This paper addresses the drawbacks of Abdelfatah's method, a block-based image encryption scheme based on a chaotic system and elliptic curve cryptography. One of the issues is caused when multiple pixels are grouped to form large integers, which turns out to be more than the modulo prime of the elliptic curve leading to incorrect decryption. Another issue is to transfer extra information for the number of pixels that are grouped. In order to overcome, an algorithm is proposed that uses the largest 512 bits elliptic curve prime modulo along with confusion and diffusion operation using a chaotic system. Security and statistical analysis are conducted to substantiate the strength of the proposed enciphering method.
Deep Learning and Transfer Learning for Malaria Detection

Infectious disease malaria is a devastating infectious disease that claims the lives of more than 500,000 people worldwide every year. Most of these deaths occur as a result of a delayed or incorrect diagnosis. At the moment, the manual microscope is considered to be the most effective equipment for diagnosing malaria. It is, on the other hand, time-consuming and prone to human error. Because it is such a serious global health issue, it is important that the evaluation process be automated. The objective of this article is to advocate for the automation of the diagnosis process in order to eliminate the need for human intervention in the process. Convolutional neural networks (CNNs) and other deep-learning technologies, such as image processing, are being utilized to evaluate parasitemia in microscopic blood slides in order to enhance diagnostic accuracy. The approach is based on the intensity characteristics of Plasmodium parasites and erythrocytes, which are both known to be variable. Images of infected and noninfected erythrocytes are gathered and fed into the CNN models ResNet50, ResNet34, VGG-16, and VGG-19, which are all trained on the same dataset. The techniques of transfer learning and fine-tuning are employed, and the outcomes are contrasted. The VGG-19 model obtained the best overall performance given the parameters and dataset that were evaluated.
Community-based Co-design across Geographic Locations and Cultures: Methodological Lessons from Co-design Workshops in South Africa

In this article, we report on methodological reflections that emerged during community-based co-design workshops exploring maternal and child health challenges across four provinces in rural and urban South Africa. Based on these workshops, we present how cultural norms such as hlonipha facilitated learning opportunities for participants' engagement with gender norms and relations with community leaders and senior members of the community. We also describe stark contrasts with regards to cultural norms between rural and urban sites and their implications for co-design activities. By reflecting on the multi-linguistic, social, and cultural challenges conducting workshops across geographic locations, we discuss how cultural nuances influenced co-design workshops and artefacts. Adjustments to the rhythms of the workshops and co-design activities enabled "transferability" between rural and urban settings, and across diverse cultures, regardless of whether these settings include members from the same cultures.
Adversarial Attack and Defense Strategies of Speaker Recognition Systems: A Survey

Speaker recognition is a task that identifies the speaker from multiple audios. Recently, advances in deep learning have considerably boosted the development of speech signal processing techniques. Speaker or speech recognition has been widely adopted in such applications as smart locks, smart vehicle-mounted systems, and financial services. However, deep neural network-based speaker recognition systems (SRSs) are susceptible to adversarial attacks, which fool the system to make wrong decisions by small perturbations, and this has drawn the attention of researchers to the security of SRSs. Unfortunately, there is no systematic review work in this domain. In this work, we conduct a comprehensive survey to fill this gap, which includes the development of SRSs, adversarial attacks and defenses against SRSs. Specifically, we first introduce the mainstream frameworks of SRSs and some commonly used datasets. Then, from the perspectives of adversarial example generation and evaluation, we introduce different attack tasks, the prior knowledge of attacks, perturbation objects, perturbation constraints, and attack effect evaluation indicators. Next, we focus on some effective defense strategies, including adversarial training, attack detection, and input refactoring against existing attacks, and analyze their strengths and weaknesses in terms of fidelity and robustness. Finally, we discuss the challenges posed by audio adversarial examples in SRSs and some valuable research topics in the future.
Efficient and Privacy-Preserving Blockchain-Based Multifactor Device Authentication Protocol for Cross-Domain IIoT

Industrial Internet of Things (IIoT) has emerged as a prospective technology that improves the productivity and automation level for industrial applications. Devices from cooperative IIoT domains will communicate and collaborate on the increasingly complicated manufacturing tasks. To secure cross-domain device collaborations, we propose combining the blockchain with multifactor authentication. Because the multifactor authentication conforms to IIoT devices’ operation modes and brings higher security levels, and the blockchain technology contributes to building trust among different domains. However, this combined usage still has limitations in terms of the potential loss of factor attack, the storage overhead on the blockchain, and the contradiction between efficiency and privacy preservation. Motivated by these facts, in this article, we develop a privacy-preserving blockchain-based multifactor device authentication protocol for cross-domain IIoT. Specifically, multiple factors are additionally encoded by the hardware fingerprint into random numbers, before being transformed into key materials. The blockchain only stores each domain’s dynamic accumulator, which accumulates derived key materials for devices, thereby reducing the overhead. Moreover, the on-chain accumulator is leveraged to efficiently verify the unlinkable identities of cross-domain IIoT devices. The security of our protocol is formally proved, and the security features and functionalities are, respectively, discussed. A proof-of-concept prototype was implemented to prove the efficiency and reliability. The comparison results indicate that the on-chain storage is greatly reduced. Finally, the smart contract’s performance was evaluated to show scalability.