Reviewerly/exHarmony huggingface dataset:
Secure User Authentication Protocol for Roaming Services in Mobile Networks Using Blockchain

Increase in wireless devices made mobile communication pervasive. Global Mobile Networks (GLOMONET) provision the roaming service to accomplish this, where mobile users must experience secure and seamless roaming services over multiple foreign agents. The main objective of network providers is to have mutually authenticated, secured, and lightweight service to guard mobile user’s data and privacy. Many interesting roaming authentication protocols have been proposed to achieve the security and privacy of mobile users in traditional communication networks. But they all suffer from one or another known security attack with the fact that current mobile networks are prone to attacks. Blockchain technology offers its advantages to establish a secure connection and authentication by safeguarding mobile user information and privacy with its immutable nature. The study shows that limited work has been done in space protocol design for GLOMONET using blockchain technology and the main goal of the protocol is to maintain security for transactional data and privacy of the mobility users along with anonymity property. In this article, soulbound tokens are used to issue credentials between the mobile user and Home Agent (HA) by serving as a secure and decentralized form of digital identity. The idea behind using soulbound tokens for issuing credentials is to create a tamper-proof and easily verifiable system that reduces the reliance on centralized authorities for identity verification. In addition, the smart contracts for user authentication have been implemented through solidity programming and the security strength of the proposed protocol is verified through a formal verification tool called AVISPA (Automated Validation of Internet Security Protocols and Applications).
Efficient Feature Selection in High Dimensional Data Based on Enhanced Binary Chimp Optimization Algorithms and Machine Learning

Abstract Feature selection with the highest performance accuracy is the biggest win for multidimensional data. The Chimpanzee Optimization Algorithm (ChOA) serves as a crucial technique for dealing with multidimensional global optimization issues. However, ChOA often lacks fast convergence and good selection of sensitive attributes leading to poor performance. To address these issues, most significant features were selected using two variants of ChOA called BChimp1 and BChimp2 (BChimp1 and BChimp are available at : https://www.mathworks.com/matlabcentral/fileexchange/133267-binary-chimpoptimization-algorithm-forfeatures-selection . September 22, 202). BChimp1 selects the optimal solution from the four best possible solutions and it applies a stochastic crossover on four moving solutions to deeply speed-up convergence level. BChimp2 uses the sigmoid function to select the significant features. Then, these features were trained using six-well known classifiers. The proposed techniques tend to select the most significant features, speed up the convergence rate and decrease training time for high-dimensional data. 23 standard datasets with six well-known classifiers were employed to assess the performance of BChimp1 and BChimp2. Experimental results validate the efficiency of BChimp1 and BChimp2 in enhancing accuracy by 83.83% and 82.02%, and reducing dimensionality by 42.77% and 72.54%, respectively. However, time-evaluation results of BChimp1 and BChimp2 in all datasets showed fast convergence and surpassed current optimization algorithms such as PSO, GWA, GOA, and GA.
R-PreNet: Deraining Network Based on Image Background Prior

Single image deraining (SID) has shown its importance in many advanced computer vision tasks. Although many CNN-based image deraining methods have been proposed, how to effectively remove raindrops while maintaining background structure remains a challenge that needs to be overcome. Most of the deraining work focuses on removing rain streaks, but in heavy rain images, the dense accumulation of rainwater or the rain curtain effect significantly interferes with the effective removal of rain streaks, and often introduces some artifacts that make the scene more blurry. In this paper, a novel network architecture, R-PReNet, is introduced for single image denoising with an emphasis on preserving the background structure. The framework effectively exploits the cyclic recursive structure inherent in PReNet. Additionally, the residual channel prior (RCP) and feature fusion modules have been incorporated, enhancing denoising performance by emphasizing background feature information. Compared with the previous methods, this approach offers notable improvement in rainstorm images by reducing artifacts and restoring visual details.
SpeechTripleNet: End-to-End Disentangled Speech Representation Learning for Content, Timbre and Prosody

Disentangled speech representation learning aims to separate different factors of variation from speech into disjoint representations. This paper focuses on disentangling speech into representations for three factors: spoken content, speaker timbre, and speech prosody. Many previous methods for speech disentanglement have focused on separating spoken content and speaker timbre. However, the lack of explicit modeling of prosodic information leads to degraded speech generation performance and uncontrollable prosody leakage into content and/or speaker representations. While some recent methods have utilized explicit speaker labels or pre-trained models to facilitate triple-factor disentanglement, there are no end-to-end methods to simultaneously disentangle three factors using only unsupervised or self-supervised learning objectives. This paper introduces SpeechTripleNet, an end-to-end method to disentangle speech into representations for content, timbre, and prosody. Based on VAE, SpeechTripleNet restricts the structures of the latent variables and the amount of information captured in them to induce disentanglement. It is a pure unsupervised/self-supervised learning method that only requires speech data and no additional labels. Our qualitative and quantitative results demonstrate that SpeechTripleNet is effective in achieving triple-factor speech disentanglement, as well as controllable speech editing concerning different factors.
Enhanced Frequency-Guided Method with Particle Swarm Optimization for the Phase Recovery of a Single Interferogram

Frequency-guided sequential demodulation (FSD) is a technique used to recover the phase of closed fringe patterns when only a single interferogram is available. FSD is a sequential method that follows simple and intuitive processing steps for interferogram demodulation, guided by the frequency of the fringes. The objective of this work is to apply an optimizer block that feeds from the first stages of the FSD and make feedback in such a manner as to optimize the search for guided frequencies. This approach significantly reduces demodulation errors, especially when dealing with high-frequency fringe patterns. The results of using the FSD with and without optimizers are compared. The optimizers used are the Harmonic Search Optimization (HSO) as well as the Particle Swarm Optimization (PSO). Results show that FSD with PSO delivers better results compared with other methods here analyzed.
FashionDiff: A Controllable Diffusion Model Using Pairwise Fashion Elements for Intelligent Design

The process of fashion design involves creative expression through various methods, including sketch drawing, brush painting, and choices of textures and colors, all of which are employed to characterize the originality and uniqueness of the designed fashion items. Despite recent advances in intelligence-driven fashion design, the complexity of the diverse elements of a fashion item, such as its texture, color and shape, which are associated with the semantic information conveyed, continues to present challenges in terms of generating high-quality fashion images as well as achieving a controllable editing process. To address this issue, we propose a unified framework, FashionDiff, that leverages the diverse elements in fashion items to generate new items. Initially, we collected a large number of fashion images with multiple categories and created pairwise data in terms of sketch and additional data, such as brush areas, textures, or colors. To eliminate semantic discrepancies between these pairwise datasets, we introduce a feature modulation fusion (FMFusion) process, which enables interactive communication among different images, allowing them to be fused into latent spaces characterized by different resolutions. In order to produce high-quality editable fashion images, we develop a generator based on a state-of-the-art diffusion model called FD-ControlNet, which integrates latent spaces into different layers of the generator to generate ready-to-wear fashion items. Qualitative and quantitative experimental results demonstrate the effectiveness of our proposed method, and suggest that our model can offer flexible control over the generated images in terms of sketches, brush areas, textures, and colors.
Experimental optimization of charging quantum batteries through a catalyst system

Quantum batteries are quantum systems that potentially serve as fuel for other quantum devices by receiving and supplying energy. In this Letter, we investigate the charging processes of a state-of-the-art quantum battery made of a harmonic oscillator, which can store infinite amounts of energy in principle. We experimentally simulate the charging process using a high-dimensional photonic quantum system with multiple concatenated interferometers. Our experiments demonstrate that both the stored and extractable energy of the quantum battery can be significantly improved by introducing a catalyst system. This charging protocol has the same performance as optimized the frequency of the external field in the direct charging protocol but is more convenient without the requirement of probing the global frequency of the charge-battery system. From the viewpoint of experimental innovation, we propose a general method to deterministically implement an arbitrary trace-preserving nonunitary channel and realize it experimentally. Our work shows the potential of this interesting quantum battery and sheds light on experimental investigations of quantum batteries.
Semantic Similarity Based on Taxonomies

The evaluation of the semantic similarity of concepts organized according to taxonomies is a long-standing problem in computer science and has attracted great attention from researchers over the decades. In this regard, the notion of information content plays a key role, and semantic similarity measures based on it are still on the rise. In this review, we address the methods for evaluating the semantic similarity between either concepts or sets of concepts belonging to a taxonomy that, often, in the literature, adopt different notations and formalisms. The results of this systematic literature review provide researchers and academics with insight into the notions that the methods discussed have in common through the use of the same notation, as well as their differences, overlaps, and dependencies, and, in particular, the role of the notion of information content in the evaluation of semantic similarity. Furthermore, in this review, a comparative analysis of the methods for evaluating the semantic similarity between sets of concepts is provided.
LeARn Flags: Assessment of the learning curve of inexperienced users with gesture interactions with hands-free augmented reality

Hands-free augmented reality (HAR) is intrinsically different from mobile augmented reality (MAR), as the interactions with the digital or holographic content happen with HAR in 3D space. For novel users, this method of interaction can be difficult to learn, as learned affordances for interacting with digital content are mostly based on screen interactions by touch or keyboard and mouse. Available HAR devices, such as HoloLens 2 and Meta Quest Pro, are still quite expensive and unobtainable for consumers, which is why most people have no prior experience with them. In this work in progress, we use a game prototype: LeARn Flags for Meta Quest Pro to observe differences between experienced (N=5) and inexperienced (N=5) users with HAR. The learning curve is observable in our findings, which suggests that this method with some improvements is viable for the assessment of the training requirements for novel users. Based on our findings, we suggest that a 4-5-minute tutorial phase is required for pinch gesture for novel users.
Engaging the General Public in Speculation on Human-Building Interaction Futures at a Pop-up Event: A Design Fiction Approach

The blend of novel technologies and built environments gave rise to the emerging field of human-building interaction (HBI) and introduced fresh forms of spatial experience. Civic involvement and engagement are paramount to HBI research and outreach, yet remain under-explored when speculating its future. Additionally, the various forms and functioning of such interfaces remain enigmatic for the general public. To address this disparity, we present our design fiction approach aimed at engaging citizens in speculating on HBI futures at a pop-up event. Through the pop-up format, visitors are initiated to the event’s subject matter through an exhibition and then invited to participate in a design fiction activity. We report on our approach through an implemented use case with 26 participants. Based on our learnings from organising, designing, and implementing this approach, we discuss the strengths, opportunities, and limitations of our work for other researchers interested in involving the general public in speculation.
Towards Trustworthy and Understandable AI: Unraveling Explainability Strategies on Simplifying Algorithms, Appropriate Information Disclosure, and High-level Collaboration

Human-centered artificial intelligence (AI) has garnered significant attention. Explainability strategies based on the concept of explainable AI (XAI) are comprehensive sets of techniques and principles that help users establish understandable and trustworthy AI systems. However, existing explainability strategies still face numerous challenges in enabling users to understand AI system decisions better. This literature review aims to explore how to overcome these challenges through simplified algorithms, appropriate information disclosure, and high-level collaboration, thereby offering future research direction for building AI systems that are trustworthy and understandable to users.
Nature and Natural Rhythm in the Digital World - What's in It for Child–Computer Interaction?: A narrative literature review and an initial design framework

Nature and the natural world are important for the well-being of humans, a position that has been acknowledged in research within various disciplines. Research has also shown humans are alienated from nature, which is of great concern, especially for children, whose well-being should be society's top priority. Child–computer interaction (CCI) research is among the disciplines that should examine nature and natural rhythm in children's lives from the perspective of digital technology. We build a multidisciplinary theoretical framework on nature and natural rhythm with which to review the extant CCI literature. We discuss the state of the art and identify paths for future work. We show that in CCI research, being in nature and catering to nature have been addressed, while less attention has been paid to children being part of nature and its natural rhythm. We discuss how CCI research and design can acknowledge and better support such aspects.
Exploring the potential for Smart City technology for Women's Safety

Safety for women in cities, in particular in mega-cities, is a major issue, given current approaches to city planning frequently neglect women’s perspectives, resulting in cities where women and female-read people cannot move freely without being constantly worried about safety. Through a co-design approach, we explored the potential for Smart City technology to create urban environments that are safe for women. We ran a set of online co-design sessions, starting with three mixed and then eight city-specific sessions with female-identifying persons from Berlin, Melbourne and Cairo. Through these, requirements of women for smart city technology were explored and analysed in connection to the contexts of the respective cities. We found that in terms of practical solutions, ideas are always bound to the local context of the city, to routines of women’s lifestyle, as well as to cultural specifics of the place. Nevertheless, general requirements for Smart City solutions across all cities similarly concern “Preventive measures”, “Reactive measures”, “Reporting support” and “Systematic changes”.
Misuse of digital assets: current legislation, challenges and recommendations

In 2020, the Republic of Serbia adopted the Law on Digital Assets (Official Gazette of the Republic of Serbia no. 153/2020). Legal regulation of the issuance, use and circulation of digital assets is present in a very small number of European countries. Although the 2020 European Union Proposal for a Regulation on Markets in Crypto-assets encourages the regulation of the crypto-assets market at the national level of member states, the question can be raised whether the available mechanisms and means are effective enough in preventing the abuse of digital assets and in protecting the integrity of crypto-assets market. Bearing in mind that the international standards in the field of abuse prevention have been adopted recently, while some are still in the process of being established, we start from the assumption that it will be necessary to additionaly improve both legislation and institutional capacities in the mentioned area at the national level. In the first part of the paper, we first point out the emerging forms of abuse of digital assets, and then we will look at the international standards that define potential prevention mechanisms. After that, the legislation of the Republic of Serbia in the field of prevention of abuses related to digital assets and the crypto-assets market will be analysed. Therefore, the method of content analysis and the dogmatic method dominate in this paper. By applying the mentioned methodology, we try to give recommendations for improving the mechanisms of prevention of abuses at the national level.
Designers' Expectations from Virtual Product Experience in Metaverse

Virtual spaces and immersive environments have been the focal point of HCI research for years. However, the Metaverse poses new challenges and opportunities for user experience in the industry and literature. One significant gap lies in understanding the expectations and perspectives of architects, product designers, and fashion designers regarding their desire to use the Metaverse for exploring new interactions with users. In this study, we explore the anticipations of 34 designers from various design fields, uncovering valuable insights into how these professionals envision integrating the Metaverse into their work and its potential impact on user engagement. Through semi-structured interviews, we identified three critical themes that are crucial to designers’ engagement with the Metaverse: (1) Ambient experiences, which delve into the environmental aspects of design within the Metaverse; (2) Representation of Avatars, which examines the importance of customizable and diverse user representations, embodiment, and user interaction; and finally, (3) The design of collaborative interaction in these virtual spaces. Our study contributes to the HCI literature by providing a unique lens into designers’ views of the Metaverse, reporting insights from non-Metaverse user designers, and suggest future directions.
Exploring the intersection of Design and Biology in today’s industry.

Abstract This article examines the intersection of design and biology in today’s industry. The study is divided into two stages, with the first stage exploring the terminology and definitions associated with the commonly used term ‘biomaterial’ through a comprehensive search and qualitative analysis. The results suggest the need to reconsider the labelling of materials, moving away from the generic term ‘biomaterial’ towards more specific descriptors such as ‘biobased’, ‘biodegradable’, or ‘biomanufactured’. By enhancing the understanding and communication surrounding biological material streams, this study contributes to the field. Additionally, the study identifies that the focus lies more in the design process associated with these materials rather than their materiality alone, as exemplified by the four identified lenses: ‘origin’, ‘production’, ‘use’, and ‘end-of-life’. This aligns with the concept of ‘biodesign’, which aims to reestablish a synergistic relationship between mankind and nature. This resulted in the suggestion of a new biodesign model comprised of these four lenses. The findings of the first stage prompted the initiation of the ongoing second stage, which aims to uncover the motives and design processes underlying biodesign practices by conducting extensive expert interviews and qualitative analysis. The ultimate objective is to identify the gaps that impede the professional implementation of biodesign in today’s industry. The authors plan to publish the findings of this ongoing research in a future academic publication.
Novel learning framework for optimal multi-object video trajectory tracking

With the rapid development of Web3D, virtual reality, and digital twins, virtual trajectories and decision data considerably rely on the analysis and understanding of real video data, particularly in emergency evacuation scenarios. Correctly and effectively evacuating crowds in virtual emergency scenarios are becoming increasingly urgent. One good solution is to extract pedestrian trajectories from videos of emergency situations using a multi-target tracking algorithm and use them to define evacuation procedures. To implement this solution, a trajectory extraction and optimization framework based on multi-target tracking is developed in this study. First, a multi-target tracking algorithm is used to extract and preprocess the trajectory data of the crowd in a video. Then, the trajectory is optimized by combining the trajectory point extraction algorithm and Savitzky–Golay smoothing filtering method. Finally, related experiments are conducted, and the results show that the proposed approach can effectively and accurately extract the trajectories of multiple target objects in real time. In addition, the proposed approach retains the real characteristics of the trajectories as much as possible while improving the trajectory smoothing index, which can provide data support for the analysis of pedestrian trajectory data and formulation of personnel evacuation schemes in emergency scenarios. Further comparisons with methods used in related studies confirm the feasibility and superiority of the proposed framework.