Reviewerly/exHarmony huggingface dataset:
Privacy preserving content distribution framework for multidistributor DRM systems

Abstract Growing network technology and computing facilities present greater opportunities for the e‐commerce industry to sell digital content on an easily assessable platform. However, digital content creators need help due to digital content easily copying and redistributing without quality degradation. Digital rights management (DRM) systems are built up to deal with content piracy and effective distribution. However, traditional two‐party DRM systems are client–server architecture, which may create a bottleneck situation for large networks. At the same time, a multidistributor DRM (MD‐DRM) system can offer content distribution more effectively. However, as the number of distributors increases, chances of security and privacy vulnerability may increase. Thus, a MD‐DRM system requires with an effective mechanism of privacy‐preserving and secure content distribution. We proposed a privacy‐preserving and authorized content distribution framework that remains compatible with multiple distributors and single‐distributor architecture to achieve basic security attributes. Besides, the proposed scheme does not breach the key parameters of accountability. This mechanism makes traitor identification possible without violating the privacy rights of authorized consumers in multidistributor architecture.
Intelligent recognition of English fuzzy text and teaching based on parallel projection and region expansion

The most important and difficult point of English fuzzy text is how to effectively improve boundary segmentation and character classification. Traditional fuzzy text recognition models are often difficult to accurately identify the characteristics of fuzzy English text. In order to improve the effect of English fuzzy text recognition, based on machine learning technology, this paper combines parallel projection and region expansion technology to construct an English fuzzy text intelligent recognition system. Moreover, based on the idea of region growth, this paper expands the triangular facets of the same processing area onto the same plane, then recalculates the associated texture points, and uses the rotation and translation method to wrap the two-dimensional vector texture on the surface of the three-dimensional model, which effectively solves the problem of entanglement of the two-dimensional vector map on the three-dimensional model when the texture information is insufficient, and realizes the intelligent recognition of fuzzy English characters with the support of the intelligent database. In addition, this paper designs a controlled experiment to analyze the recognition effect. The research results show that the model constructed in this paper works well.
Using Design Thinking to Build Skills for Working with Agile Methodologies in a Digitalized Environment

This article discusses the design thinking as a tool for developing skills in working with flexible methodologies in the face of rapid changes associated with digitalization. In the era of the digital economy, adaptability is a key characteristic that determines the competitiveness of a company. Agile methodologies are becoming more and more common in product development and project management. In this regard, a question rises of how to prepare modern specialists to work with flexible agile methodologies, instill the appropriate values, and prepare them for teamwork in conditions of uncertainty and risk. One of the possible tools is design thinking, a solution generation technique that supports the values of agile methodology. The main purpose of this article is to review and validate design thinking as a tool for developing the skills necessary to work with agile methodologies. The practical significance of this article lies in revealing the possibilities and potential of design thinking and encouraging to use this technique in the educational process. The article considers the concept of agile methodologies, their principles and features, discusses the role of design thinking as an approach for learning to work with agile methodologies. Analytical materials published in the systems Google Scholar and EBSCO were used to conduct the study. Through a selection of international literature, the article provides the results of case studies, theoretical reflections and reports to improve understanding of the context, opportunities, experiences, application effects, disadvantages and advantages of the design thinking approach in business and education. The study showed that the use of design thinking in the educational process improves the skills of students that are important for working with flexible methodologies. The benefits and challenges of design thinking as an approach that immerses students in the basics of flexible methodologies were identified.
Cardiac MRI Tagline Extraction Based on Diffeomorphic Active Contour Algorithm

Cardiac tagged Magnetic Resonance Imaging (MRI) is readily available in clinical and preclinical scanners and can be used to quantitatively characterize cardiac motion. Because tag lines fade towards the end of cardiac cycle, accurately extracting their location over the full cardiac cycle is particularly challenging. In this work, we present a geometric flow approach to the segmentation of tag lines in cardiac MRI. This algorithm is based on the diffeomorphic active contour algorithm that evolves straight lines placed on the tagged MR images to match taglines. We evaluated the accuracy of the algorithm using the Hausdorff distance, with errors of $$1.62 \text { } (\pm 0.21 \text { SD})$$ pixel units for horizontal tag lines and $$1.79 \text { } (\pm 0.62 \text { SD})$$ pixel units for vertical ones generated from long axis cardiac images. Our algorithm performed well in the presence of noise or large deformations.
Data Comics for Business Process Visualization

Two third-party studies, related to the legal and veterinarian sector, discuss the level of engagement and business process comprehension acquired by subjects who used Data Comics against other different kind of media. These studies consist of developing quantitative research following two different approaches. In the first one only one media was used: comics; and includes a pre-test and a post-test evaluation. In the second approach the participants were exposed to different kind of media, but only a post-test evaluation was developed. The review debates some strengths and weaknesses of data comics, as well as the results of the studies. The aim of the review was to evaluate the effectiveness of visualization to explain business processes against different kinds of materials, the results were positive in favor of Data Comics.
Advancing Primary Education through Active Teaching Methods and ICT for Increasing Knowledge

This article is based on enhancing online interaction while integrating it with face-to-face interaction and promoting access to and development of information and communication technologies (ICT). The aim was to implement digital tools for researching and developing school assignments. The study employed a mixed method approach, incorporating qualitative–quantitative techniques—such as surveys and observations—to analyze documents related to the investigation. The article elucidates the conceptual and theoretical framework, methodological criteria, and interdisciplinary approach that aligns with the investigated group’s profile. The applicability of the proposed methodology was demonstrated in practice, indicating its effectiveness in reaching a superior level of understanding, competence, and behavior. The outcomes substantiate that applying active tools and methodologies grounded in the knowledge of society can reinforce the notion that education is the cornerstone of social development, including ICT.
ADD-UNet: An Adjacent Dual-Decoder UNet for SAR-to-Optical Translation

Synthetic aperture radar (SAR) imagery has the advantages of all-day and all-weather observation. However, due to the imaging mechanism of microwaves, it is difficult for nonexperts to interpret SAR images. Transferring SAR imagery into optical imagery can better improve the interpretation of SAR data and support the further fusion research of multi-source remote sensing. Methods based on generative adversarial networks (GAN) have been proven to be effective in SAR-to-optical translation tasks. To further improve the translation results of SAR data, we propose a method of an adjacent dual-decoder UNet (ADD-UNet) based on conditional GAN (cGAN) for SAR-to-optical translation. The proposed network architecture adds an adjacent scale of the decoder to the UNet, and the multi-scale feature aggregation of the two decoders improves structures, details, and edge sharpness of generated images while introducing fewer parameters compared with UNet++. In addition, we combine multi-scale structure similarity (MS-SSIM) loss and L1 loss as loss functions with cGAN loss together to help preserve structures and details. The experimental results demonstrate the superiority of our method compared with several state-of-the-art methods.
Is Bitcoin gathering dust? An analysis of low-amount Bitcoin transactions

Abstract In the Bitcoin protocol, dust refers to small amounts of currency that are lower than the fee required to spend them in a transaction. Although “economically irrational”, dust is commonly used for achieving unconventional side effects, rather than exchanging value. For instance, dust might be linked to on-chain services or to malicious activity, such as dust attacks attempting to break users’ pseudonymity. To study this phenomenon, this paper presents an in depth analysis of Bitcoin transactions involving dust, showing how dust is created and consumed. We identify the top dust creators and consumers and discuss how consumption has evolved over time. Finally, we use the data to identify transactions suspected of being part of dust attacks and quantify their impact on address deanonymization. Our results show that dust is mainly related to on-chain betting services. Transactions likely to be part of dust attacks are a minority of dust creating transactions but, despite their relatively low number, they have a disproportionately high effect on helping attackers to break address pseudonymity.
Does subnetting and port hardening influence human adversarial decisions? An investigation via a HackIT tool

Prior research in cyber deception has investigated the effectiveness of the timing of deception on human decisions using simulation tools. However, there exists a gap in the literature on how the availability of subnets and port-hardening influence human decisions to attack a system. We tested the influence of subnets and port-hardening on human attack decisions in a simulated environment using the HackIT tool. Availability of subnets (present/absent) within a network and port-hardening (easy-to-attack/hard-to-attack) were varied across four between-subject conditions (N = 30 in each condition): with-subnet with easy-to-attack, with-subnet with hard-to-attack, without-subnet with easy-to-attack, and without-subnet with hard-to-attack. In with-subnet conditions, 40 systems were connected in a hybrid topology network with ten subnets connected linearly, and each subnet contained four connected systems. In without-subnet conditions, all 40 systems were connected in a bus topology. In hard-to-attack (easy-to-attack) conditions, the probabilities of successfully attacking real systems and honeypots were kept low (high) and high (low), respectively. In an experiment, human participants were randomly assigned to one of the four conditions to attack as many real systems as possible and steal credit card information. Results revealed a significant decrease in the proportion of real system attacks in the availability of subnetting and port hardening within the network. Also, more honeypots were attacked in with-subnet conditions than without-subnet conditions. Moreover, a significantly lower proportion of real systems were attacked in the port-hardened condition. This research highlights the implications of subnetting and port-hardening with honeypots to reduce real system attacks. These findings are relevant in developing advanced intrusion detection systems trained on hackers' behavior.
Cybersecurity and Blockchain

Blockchain technology provides unprecedented security to sensitive data to both businesses and individuals; it has become one of the most famous methods for securing data through a decentralized system. Cybercrime is a serious threat to enterprises as hackers are devising new techniques and implementing them to execute cybercrime. Blockchain technology has the potential to give higher security, which has no limit to keeping important and sensitive information and records. Blockchain is the technology that could go a long way in fighting this and keeping digital information safe and secured. Blockchain technology strengthens cyber security for different areas of industries and plays a vital role in making data more secure with its decentralized technology. Through this chapter, the authors will get to know how blockchain technology helps in providing cyber security to the different sectors of industries with its advantages and disadvantages of blockchain. The author also explores the role of blockchain in cybersecurity and the future benefits of blockchain technology to strengthen cybersecurity.
Participatory monitoring in farmer field school program through Whatsapp among indigenous farmers in rural Sarawak, Malaysia

This paper aims to assess the use of WhatsApp Group for participatory monitoring in a System of Rice Intensification – Farmer Field School (SRI-FFS) program involving the rice farming community in the rural Bidayuh village of Tebaro in Sarawak, Malaysia. Descriptive analysis was performed on a 182-day WhatsApp Group chat taking place from 17 September 2019 to 16 March 2020. The data were analyzed using two online softwares to generate data visualizations. The study revealed that the strengths of participatory monitoring using WhatsApp Group were found in the principles of flexibility and methodologically eclectic. For flexibility, four factors indicated were technical sharing, personal sharing, comments on current events, and the addition of new members. The methodologically eclectic elements were identified as messages in the forms of texts, media, emoji and links. Limitations for participatory monitoring were in the elements of participation and negotiation due to the role-based group structure. To effectively promote the use of digital platforms, agricultural policies must promote inclusiveness and prioritize equitable access to ICT devices and facilities. Current findings show that social media can enhance active multi-stakeholder participation but should be complemented with non-ICT means of communication for rural farming communities.
Near-Optimal Learning of Tree-Structured Distributions by Chow and Liu

We provide finite sample guarantees for the classical Chow–Liu algorithm [Chow and Liu, IEEE Trans. Inform. Theory, 14 (1968), pp. 462–467] to learn a tree-structured graphical model of a distribution. For a distribution on and a tree on nodes, we say is an -approximate tree for if there is a -structured distribution such that is at most more than the best possible tree-structured distribution for . We show that if itself is tree-structured, then the Chow–Liu algorithm with the plug-in estimator for mutual information with independent and identically distributed samples outputs an -approximate tree for with constant probability. In contrast, for a general (which may not be tree-structured), samples are necessary to find an -approximate tree. Our upper bound is based on a new conditional independence tester that addresses an open problem posed by Canonne et al. [Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, ACM, 2018, pp. 735–748]: we prove that for three random variables each over , testing if is 0 or is possible with samples. Finally, we show that for a specific tree , with samples from a distribution over , one can efficiently learn the closest -structured distribution in KL divergence by applying the add-1 estimator at each node.
Building a pedagogic spellchecker for L2 learners of Spanish

Abstract In 2016, Lawley proposed an easy-to-build spellchecker specifically designed to help second language (L2) learners in their writing process by facilitating self-correction. The aim was to overcome the disadvantages to L2 learners posed by generic spellcheckers (GSC), such as that embedded in Microsoft Word. Drawbacks include autocorrection, misdiagnoses, and overlooked errors. With the aim of imparting explicit L2 spelling knowledge, this correcting tool does not merely suggest possible alternatives to the detected error but also provides explanations of any relevant spelling patterns. Following Lawley’s (2016) recommendations, the present study developed a prototype computer-based pedagogic spellchecker (PSC) to aid L2 learners in self-correcting their written production in Spanish. First, a corpus was used to identify frequent spelling errors of Spanish as a foreign language (SFL) learners. Handcrafted feedback was then designed to tackle the commonest misspellings. To subsequently evaluate this PSC’s efficacy in error detection and correction, another learner Spanish corpus was used. Sixty compositions were analysed to determine the PSC’s capacity for error recognition and feedback provision in comparison with that of a GSC. Results indicate that the PSC detected over 90% of the misspellings, significantly outperforming the GSC in error detection. Both provided adequate feedback on two out of three detected errors, but the pedagogic nature of the former has the added advantage of facilitating self-learning (Blázquez-Carretero &amp; Woore, 2021). These findings suggest that it is feasible to develop spellcheckers that provide synchronous feedback, allowing SFL learners to confidently self-correct their writing while saving time and effort on the teacher’s part.
Algorithms patrolling content: where’s the harm?

ABSTRACTABSTRACTAt the heart of this paper is an examination of the colloquial concept of a ‘shadow ban’. It reveals ways in which algorithms on the Facebook platform have the effect of suppressing content distribution without specifically targeting it for removal, and examines the consequential stifling of users’ speech. It reveals how the Facebook shadow ban is implemented by blocking dissemination of content in News Feed. The decision-making criteria are based on ‘behaviour’, a term that relates to activity of the page that is identifiable through patterns in the data. It’s a technique that is rooted in computer security, and raises questions about the balance between security and freedom of expression. The paper is situated in the field of responsibility of online platforms for content moderation. It studies the experience of the shadow ban on 20 UK-based Facebook Pages over the period from November 2019 to January 2021. The potential harm was evaluated using human rights standards and a comparative metric produced from Facebook Insights data. The empirical research is connected to recent legislative developments: the EU’s Digital Services Act and the UK’s Online Safety Bill. Its most salient contribution may be around ‘behaviour’ monitoring and its interpretation by legislators.KEYWORDS: Online platform accountabilityshadow bansdigital services actonline safety billbehaviour monitoring AcknowledgementsThe author wishes to acknowledge the kind support of Scientists for EU and Dr Mike Galsworthy, who called on me to help some of the Page administrators, and drew my attention to the issue of shadow bans. It was this work that established the foundations of the research which became this paper. I subsequently came into contact with other Page administrators who were experiencing the same issues and reinforced my conclusions. This study could not have happened without their assistance (and persistence!), including Beth Linton, Adam Brady, Peter Packham, Syd Cottle, and Mark Cunliffe. I would also like to thank Joe McNamee for his thoughtful feedback, and Professor Robin Mansell, Professor Juliet Lodge and Dr Miranda Mowbray for their insights.Disclosure statementNo potential conflict of interest was reported by the author(s).
“What's Your Name Again?”: How Race and Gender Dynamics Impact Codesign Processes and Output

Creating technology products using codesign techniques often results in higher end-user engagement compared to expert-driven designs. Codesign sessions are typically structured in flexible and informal ways to achieve equal design partnerships, especially in adult-child interactions. This generally leads to better design output, however, it may also increase the enactment of socially constructed stereotypes and biases in ways that negatively affect the experiences of racial minorities and girls/women in design spaces. We codesigned a video game with a K-5 afterschool program located in a working-class, rural, predominantly white county over 20 weeks. We uncover ways that the codesign process and different activity types can create a permissive environment for enacting behaviors that are harmful to minorities. We discuss ways to manage and restructure codesign programs to be more conducive for children and adults from diverse backgrounds, ultimately leading to healthier design partnerships.
Double graphs regularized multi-view subspace clustering

In recent years, there has been an increasing interest in multi-view subspace clustering (MSC). However, existing MSC methods fail to take full advantage of the local geometric structure in each manifold throughout the data flow, which is essential for clustering. To remedy this drawback, in this paper, a novel Double Graphs Regularized Multi-view Subspace Clustering (DGRMSC) method is proposed, which aims to harness both global and local structural information of multi-view data in a unified framework. Specifically, DGRMSC first learns a latent representation to exploit the global complementary information of multiple views. Based on the learned latent representation, we learn a self-representation to explore its global cluster structure. Further, Double Graphs Regularization (DGR) is performed on both latent representation and self-representation to take advantage of their local manifold structures simultaneously. Then, we design an iterative algorithm to solve the optimization problem effectively. Comprehensive experiments on several popular multi-view datasets demonstrate the effectiveness of the proposed method.
Domain embedding transfer for unequal RGB-D image recognition

Most recent unsupervised domain adaptation (UDA) approaches concentrate on single RGB source to single RGB target task. They have to face the real-world scenario, where the source domain can be collected from multiple modalities, e.g., RGB data and depth data. Our work focuses on a more practical and challenging scenario which recognizes RGB images by learning from RGB-D data under the label inequality scenario. We are confronted with three challenges: multiple modalities in the source domain, domain shifting problem and unequal label numbers. To address the aforementioned settings, a novel method, referred to as Domain depth Embedding Transfer (DdET) is proposed, which takes advantage of the depth data in the source domain and handles the domain distribution mismatch under label inequality scenario simultaneously. We conduct comprehensive experiments on five cross domain image classification tasks and observe that DdET can perform favorably against state-of-the-art methods, especially under label inequality scenario.