Reviewerly/exHarmony huggingface dataset:
Authentication Methods for Quantum Key Distribution: Challenges and Perspectives

In this article, we investigate the issue of authenticating the classical post-processing components of a QKD protocol through an insecure channel. Our analysis starts with state-of-the-art solutions and subsequently introduces an authentication scheme aiming at both Information Theoretic Security and resiliency from particular denial-of-service attacks targeting the authentication procedure. The proposed strategy combines -Almost Strongly Universal hashing for unconditional protection and computationally-secure Message Authentication Codes for DoS detection capability. Specifically, we consider the protocol security in a typical QKD scenario, taking into account partially weak authentication keys. Moreover, we add practical considerations related to authentication parameters and experimentally evaluate the performance of the proposed system in terms of computational complexity and key consumption in a reasonable use case.
A Brief Overview of Quantum Advances and Impact on Economics

This paper provides an overview of the economic impact of quantum technologies. The goal is not to be exhaustive but rather to describe an up-to-date panel of stakes, challenges and figures that remains accessible to non-expert readers who wish to understand the priorities of the so-called quantum revolution. In a first part, the paper reminds the fundamentals of quantum physics (though not entering into technical details). Then in section 2, the benefits of different quantum technologies are challenged and/or illustrated. Section 3 puts in light the main steps that are required in order to economically benefit from the quantum; three axis are identified: education of skilled people, strategy for innovation ecosystem, and standardization. Section 4 concludes by giving some figures on funding efforts made by countries around the world and by pointing out some limitations to overcome.
Distributed Graph Algorithms: From Local Data to Global Solutions

As data scales increase, traditional centralized graph algorithms struggle to meet modern computational demands. Distributed graph algorithms, which parallelize data processing across multiple computing nodes, have significantly improved the efficiency of handling large-scale graph data. This report explores the principles, application scenarios, key technologies, and challenges of distributed graph algorithms, aiming to provide a comprehensive perspective from local data to global solutions. With the rapid development of computer networks and big data technologies, solving large-scale graph data problems has become a hot research topic. Distributed graph algorithms can solve problems without global information and offer new solutions for processing massive graph structures. This report introduces the basic concepts, key technologies, and challenges of distributed graph algorithms and discusses methods for achieving global solutions starting from local data through case analyses.
Primate of the Issue: Red Ruffed Lemur

The primate of the issue is the red ruffed lemur (Varecia rubra).
Exploration and Evaluation of PFA (Psychological First Aid): Potential for Development of PFA Online Services in Academic Setting

In Indonesia, PFA (Psychological First Aid) services are primarily employed in distress situations; however, a relatively small number are used in crisis situations, mainly in academic settings through the use of digital media, or what is commonly referred to as PFA online services. Teman Sehati, a PFA online service provided by UIN Prof. K.H. Saifuddin Zuhri Purwokerto, will be addressed in this study. In order to encourage the use of mental health services in academic settings, this research attempts to present an overview of PFA online services. The CIPPO approach (Context Evaluation, Input Evaluation, Process Evaluation, Product Evaluation, and Outcome) was used in the research to apply qualitative evaluation. Research collection methods include documenting, interviewing, and observation. Sorting, classifying, summarising, indexing, and researching primary and secondary sources are all methods used in data analysis. The results of the study indicate that since rooms, networks, and electronic devices serve as the main method of maintaining the service, it is necessary to increase the availability of reliable internet. Every semester, increasing numbers of individuals are in need of this service due to a variety of issues, including physiologic disorders, bullying, financial difficulties, sexual harassment, work-related issues, psychological disorders, academic difficulties, romantic issues, family issues, and personal issues. The online PFA service at UIN Prof. K.H. Saifuddin Zuhri Purwokerto is deemed highly helpful by 92% of service users. Online PFA services can aid in treating students' mental health, despite certain technological challenges; for this reason, it is crucial to implement them in the academic setting.
Health Communication as Apo-Mediation. The Impact of Communication Design on Health Prevention and Perception

Considering the paradigmatic evolution of the concept of health into physical, psychological, and social well-being and its relational dimension determined by the continuous exchange between individuals, Design for Health Communication has developed new services, tools, and languages. This discussion paper aims to highlight the pivotal role of Communication Design in ensuring accessible content for health prevention and developing tools to inform citizens regarding medical issues and pro-health attitudes and behaviour. As evidence of the research conducted on the sanitary districts of Piacenza, the definition of guidelines for accessible sanitary networks and social actors in the territory redefines the designer’s responsibility as a bridge between citizens, doctors, and community in a broader sense, to ensure that the information for a better Health Literacy is both widespread and effective.
Quantum-Safe Cryptography from BSI’s Perspective

We discuss how the German Federal Office for Information Security (BSI) views the migration to quantum-safe cryptographic solutions in agile and hybrid systems. In particular, the perspective of BSI on Post-Quantum-Cryptography and on Quantum Key Distribution are explained, and existing gaps are pointed out.
Group Key Exchange: Living on the Edge with a Quantum Adversary

Quantum computing has redefined the assumed complexity of different computational tasks, which implies the need for new cryptographic tools that can be considered secure even in the presence of entities that may be able to execute quantum algorithms. In particular, this poses significant challenges for the adaptation of cryptographic implementations, which are particularly relevant in the IoT scenario, where resources are especially limited. In this work, we report on a simple cryptographic protocol for group key establishment, where n &gt; 2 devices interact to establish a key to secure their communication. Our design can actually be seen as a so-called key transport, where a small set of devices cooperatively choose a key that is subsequently distributed to all users over an insecure (and somewhat unstable) network using only cryptographic tools that are claimed to be post-quantum, i.e., resistant to quantum attacks. We give a theoretical description and also describe our experimental results implementing our protocol within networks up to 16 nodes based on ARM Cortex-M4. We believe that this design is a first proof-of-concept of a post-quantum group key exchange protocol in the IoT scenario.
POTENTIAL USES OF BLOCKCHAINS IN HUMAN RESOURCES

Blockchain technology offers businesses many opportunities for more efficient operation and safer data management. It also becomes easy tostore and share employee data, while the blockchain guarantees that it does not fall into unauthorized hands. The management of financial transactions and the automation of the payment process are also a great advantage for businesses, which can manage the payment of wages andbenefits more efficiently. Another area of application of blockchain technology is the creation of more efficient workflows that can improve productivity and reduce costs. The management of work schedules and optimized work processes will also be easier with the help of the blockchain, so businesses can become more efficient and effective. In this article, the relationship between HR and blockchains was explored through a meta-analysis based on available related publications.
Bayesian view on the training of invertible residual networks for solving linear inverse problems

Abstract Learning-based methods for inverse problems, adapting to the data's inherent structure, have become ubiquitous in the last decade. Besides empirical investigations of their often remarkable performance, an increasing number of works address the issue of theoretical guarantees. Recently, [3] exploited invertible residual networks (iResNets) to learn provably convergent regularizations given reasonable assumptions. They enforced these guarantees by approximating the linear forward operator with an iResNet. Supervised training on relevant samples introduces data dependency into the approach. An open question in this context is to which extent the data's inherent structure influences the training outcome, i.e., the learned reconstruction scheme. Here, we address this delicate interplay of training design and data dependency from a Bayesian perspective and shed light on opportunities and limitations. We resolve these limitations by analyzing reconstruction-based training of the inverses of iResNets, where we show that this optimization strategy introduces a level of data-dependency that cannot be achieved by approximation training. We further provide and discuss a series of numerical experiments underpinning and extending the theoretical findings.
Moving museums into the Metaverse

In recent years, the fusion of advanced technologies with virtual reality has opened new cultural preservation and engagement avenues. This dissertation explores the innovative application of Neural Radiance Fields (NeRF) technology in transcending the boundaries of physical museums and transporting their treasures into the metaverse. While classical computer vision has seen substantial progress, a developing intersection exists between NeRF and cultural heritage preservation. This study bridges this gap by introducing an approach that amalgamates NeRF techniques with the rich cultural wealth of museums.The conventional museum experience is extended into the metaverse through a novel methodology that leverages NeRF’s capabilities. The core objective is to enable individuals to explore digitized museum artifacts with unparalleled realism. NeRF technology captures intricate visual details and enables immersive interactions by rendering scenes with volumetric precision, transforming how cultural artifacts are experienced and understood.This dissertation delves into the technical intricacies of integrating NeRF technology into the metaverse. The implementation involves the reconstruction of 3D artifact models. The results underscore the potential of NeRF to reshape the cultural heritage landscape by bridging the gap between traditional museums and the boundless possibilities of the metaverse.
Adopting E-Learning to Facilitate English Teaching and Learning in Kuwait

This research investigates the implications of adopting e-learning on the teaching and speaking proficiency of English among teachers in Kuwaiti public schools, particularly in the wake of the COVID-19 pandemic. Although many private schools in Kuwait had already transitioned to digital learning platforms, public schools lagged behind. This study used interviews and questionnaires to glean insights from teachers, with a particular focus on their experiences and perceptions. Initial findings suggest that while the shift to online learning introduced challenges, it also offered significant opportunities to improve teaching methodologies and English-speaking capabilities. Despite initial anxieties regarding the transition, e-learning proved to be a flexible and convenient medium, prompting a revaluation of its role in Kuwaiti public high school education. The research underscores the significance of understanding the strengths and weaknesses of e-learning in the English curriculum, especially as the world grapples with the ongoing challenges of the COVID-19 pandemic.
Generation multi-scroll chaotic attractors using composite sine function and its application in image encryption

Abstract In this paper, a composite sine function is proposed and applied in a chaotic system, which is capable of generating definite number of chaotic attractors. The proposed composite sine function possesses infinite breakpoints, but it can produce a fixed number of scrolls by adjusting its parameters. Compared to other chaotic systems with multiple scrolls chaotic attractors, the realization circuit of the chaotic system with compound sine function allows obtaining different numbers of scrolls by adjusting only one resistance value. As a result, the circuit structure remains unchanged despite variations in the number of scrolls. Various analytical methods are applied to study the dynamical behaviors of the proposed chaotic system, including Lyapunov exponent, equilibrium point, bifurcation diagram,algorithm. Furthermore, phase diagram, spectral entropy and C0-based on the analysis of dynamical characteristics, theelectronic circuits of the proposed system are given on Multisim circuit simulationsoftware, and the multi-scroll chaotic attractors exhibit consistency with the numericalsimulation results. Finally, we incorporated the proposed chaotic system into a DNA coding algorithm for image encryption, and this method exhibits excellent encryption efficiency and high level of security.&amp;#xD;
Quantum machine learning for image classification

Abstract Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two Quantum Machine Learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a Hybrid Quantum Neural Network with parallel quantum circuits, enables the execution of computations even in the Noisy Intermediate-Scale Quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a Hybrid Quantum Neural Network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.
A 3D map construction method for autonomous vehicles in unknown environment

Environment map construction is one of the key research directions of autonomous driving technology. Autonomous vehicle driving in unknown environments need to detect obstacles and identify passable areas. Traditional methods usually use two-dimensional planar maps without vertical information, and cannot exclude the influence of dynamic obstacles. In order to reflect the unknown environment information more accurately, this paper proposes a 3D map construction method based on Apollo autonomous driving system. Firstly, the point cloud data and pose data were obtained by the sensors mounted on the vehicle. Secondly, the 3D environment space was divided into different grids by the occupancy grid map algorithm. Then, the coordinates of different state grids are updated by ray casting algorithm. Finally, the Point Pillar algorithm is used to identify the dynamic obstacles and eliminate the residual shadows to obtain a more accurate 3D map. The experimental results show that the proposed method can effectively construct the unknown environment map, accurately reflect the three-dimensional information in the environment, and avoid the influence of dynamic obstacles.
Linguistic Violence on Women: Representation of Women in Ritual Killing Discourse on Nigerian Social Media Spaces

Language can be used to represent people in a humiliating and defaming way that violates their humanity. In the discourse of ritual killing on social media, Nigerian women are often blamed for their misfortunes of victimization through ritual killing and are further abused verbally. This study analyzes the representation of linguistic violence on women through evaluating the discourses on ritual killings on Nigerian social media space. It adopts Norman Fairclough’s model of critical discourse analysis and qualitative research design. After observing the social media platforms in Nigeria for a period of three months, from January-March, 2022, the researcher purposively collected twenty (20) data from Facebook and twelve (12) from Twitter, but sampled ten (10) textual data from Facebook and two (2) from Twitter. Findings indicated that women are blamed and defamed in the discursive representation of ritual killings in Nigeria. By implication, the perpetrators (mostly men) are excused and the patriarchal subjugation of women are sustained. Keywords: linguistic violence, critical discourse analysis, violence, ritual killing, social media
Regional pragmatic Variation in French: A contrastive study of complaint realizations in Cameroon and France

This study examined and compared complaints by speakers of French in Cameroon and in France. Although complaints have been extensively analyzed, to date, little attention has been devoted to complaints across regional varieties of French. The aim of this study was to fill this knowledge and research gap by analyzing strategies used by speakers of Cameroon French and Hexagonal French to complain in three situations. The study is at the intersection of variational and postcolonial pragmatics and it is based on data provided by 20 Cameroonian and 19 French university students, who were asked to fill a DCT questionnaire. The results reveal some similarities in both French varieties regarding the use of complex complaint utterances. However, many differences were found with respect to preferences for specific complaint strategies, external modifiers, internal modification devices and address terms.
Inertial Parameter Identification for Closed-Loop Mechanisms: Adaptation of Linear Regression for Coordinate Partitioning

Abstract This study investigates the use of linear-regression-based identification in rigid multibody system applications. A multibody system model, originally described with differential-algebraic equations, is transformed into a set of ordinary differential equations using coordinate partitioning. This allows the identification framework (where the system is described with ordinary differential equations) to be applied to rigid multibody systems described with non-minimal coordinates. The methodology is demonstrated via numerical and experimental validation on a slider-crank mechanism. The results show that the presented methodology is capable of accurately identifying the system's inertial parameters even with a short motion trajectory used for training. The presented linear-regression-based identification approach opens new opportunities to develop more accurate multibody models. The resulting updated multibody models can be considered especially useful for state-estimation and the control of multibody systems.
Robust Clustering Based Automated Video Shot Boundary Detection Using Hand-Crafted and Deep Feature Fusion

International Journal on Artificial Intelligence ToolsAccepted Papers No AccessRobust Clustering Based Automated Video Shot Boundary Detection Using Hand-Crafted and Deep Feature FusionR. Mishra, P.N. Chopkar, V. Moyal, D.S. Marotkar, and V.R. KapurR. Mishra, P.N. Chopkar Search for more papers by this author , V. Moyal Search for more papers by this author , D.S. Marotkar Search for more papers by this author , and V.R. Kapur Search for more papers by this author https://doi.org/10.1142/S0218213024500106Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out Notable Titles in Artificial Intelligence. FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 30 June 2023 Accepted 12 February 2024 PDF download
English Foreign Language Teachers’ Perception on The Use of Information and Communication Technology in Teaching English at SMKN 3 Langsa

This study aimed to explore English as a Foreign Language (EFL) teachers' perceptions of Information and Communication Technology (ICT) use in the English language teaching and learning process. The main purpose of this article is to explore the challenges of teacher face in using ICT. Two experienced teachers, recognized for their ICT integration, selected as the research subjects at SMK Negeri 3 Langsa. Employing a qualitative approach with a phenomenological design, data was collected through semi-structured interview. The findings reveal that both teachers acknowledge the positive role of ICT in enhancing teaching efficiency, student engagement, and diversifying learning resources. Despite encountering technical challenges, such as network disruptions, both teachers advocate for continuous teacher training, increased investment in ICT infrastructure, and innovative research to optimize ICT integration in English language teaching, ultimately contributing to a more effective learning environment.
TNT Board: An Interactive Electronic Board Game

Abstract In summer 2020, a research and enrichment program funded by a Student Engagement, Retention, and Success (SERS) grant from the Tennessee Board of Regents took place in the form of online/remote delivery. The goal of the program was to improve the GPA and retention rate of underrepresented and minority students by engaging them in multidisciplinary and collaborative summer projects. This paper presents the project carried out by one of the student groups, in which two students in Mechatronics Engineering and one student in Computer Science worked together remotely and designed and implemented an interactive board game using Arduino Mega and various other electronics and mechanical parts. The board consists of six games and is fun to play for all age groups. All student participants had no prior experience with Arduino, but with the help from the faculty mentor, they were able to learn it quickly and complete the project within 4 weeks. Although each student was primarily responsible for two games, the project was a true multidisciplinary collaboration: the computer science student led the efforts of setting up GitHub for software programming and source management whereas the mechatronics engineering students conducted mechanical design for the board. In this paper, we will present the mechanical, electrical, and software design of the board game. The bill of materials and the details of remote collaboration will also be included. On the educational front, we discuss a couple of efforts that helped the students: (i) We designed an introductory training lesson to introduce Arduino to them and (ii) We utilized project-based learning techniques to encourage them to learn new things along the way.