Reviewerly/exHarmony huggingface dataset:
Distributed Graph Algorithms: From Local Data to Global Solutions

Authors:
raw_author_name: Jiaheng Zhang

Abstract: 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.
Exploration and Evaluation of PFA (Psychological First Aid): Potential for Development of PFA Online Services in Academic Setting

Authors:
raw_author_name: Frida ‘Atiqotun Nurul ‘Amalia

Abstract: 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.
Quantum-Safe Cryptography from BSI’s Perspective

Authors:
raw_author_name: Tobias Hemmert
raw_affiliation_string: Federal Office for Information Security, Germany

Abstract: 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

Authors:
raw_author_name: María Isabel González Vasco
raw_affiliation_string: Universidad Carlos III de Madrid, Spain
raw_author_name: Agoney Moreno
raw_affiliation_string: Arquimea Research Center, Tenerife, Spain
raw_author_name: Ángel L. Pérez Del Pozo
raw_affiliation_string: Universidad Rey Juan Carlos, Spain
raw_author_name: Jorge Portilla
raw_affiliation_string: Universidad Politécnica de Madrid, Spain
raw_author_name: Jaime Señor
raw_affiliation_string: Universidad Politécnica de Madrid, Spain

Abstract: 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.
Bayesian view on the training of invertible residual networks for solving linear inverse problems

Authors:
raw_author_name: Clemens Arndt
raw_author_name: Sören Dittmer
raw_author_name: Nick Heilenkötter
raw_author_name: Judith Nickel

Abstract: 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.
Adopting E-Learning to Facilitate English Teaching and Learning in Kuwait

Authors:
raw_author_name: Ahmad A. Alenezi

Abstract: 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

Authors:
raw_author_name: Zixuan Wang

Abstract: 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

Authors:
raw_author_name: Basil Kyriacou
raw_affiliation_string: Terra Quantum AG, Kornhausstrasse 25, St. Gallen, 9000, SWITZERLAND
raw_author_name: Alexey Melnikov
raw_affiliation_string: Terra Quantum AG, Kornhausstrasse 25, St. Gallen, 9000, SWITZERLAND

Abstract: 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

Authors:
raw_author_name: Yu Peng
raw_affiliation_string: Dalian Univ. of Technology (China)

Abstract: 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.
Inertial Parameter Identification for Closed-Loop Mechanisms: Adaptation of Linear Regression for Coordinate Partitioning

Authors:
raw_author_name: Thijs Willems
raw_affiliation_string: E2E Lab, Flanders Make at KU Leuven Department of Mechanical Engineering, KU Leuven Leuven, 3001 Belgium
raw_author_name: Frank Naets
raw_affiliation_string: E2E Lab, Flanders Make at KU Leuven Department of Mechanical Engineering, KU Leuven Leuven, 3001 Belgium

Abstract: 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

Authors:
raw_author_name: R. Mishra
raw_author_name: V. Moyal

Abstract: 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

Authors:
raw_author_name: None Nurul Fitri

Abstract: 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

Authors:
raw_author_name: Cen Li
raw_affiliation_string: Middle Tennessee State University

Abstract: 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.
Machine Learning and Cryptanalysis: An In-Depth Exploration of Current Practices and Future Potential

Authors:
raw_author_name: Ajeet Singh
raw_author_name: Kaushik Bhargav Sivangi
raw_author_name: Appala Naidu Tentu

Abstract: The rapidly evolving landscape of cryptanalysis necessitates an urgent and detailed exploration of the high-degree non-linear functions that govern the relationships between plaintext, key, and encrypted text. Historically, the complexity of these functions has posed formidable challenges to cryptanalysis. However, the advent of deep learning, supported by advanced computational resources, has revolutionized the potential for analyzing encrypted data in its raw form. This is a crucial development, given that the core principle of cryptosystem design is to eliminate discernible patterns, thereby necessitating the analysis of unprocessed encrypted data. Despite its critical importance, the integration of machine learning, and specifically deep learning, into cryptanalysis has been relatively unexplored. Deep learning algorithms stand out from traditional machine learning approaches by directly processing raw data, thus eliminating the need for predefined feature selection or extraction. This research underscores the transformative role of neural networks in aiding cryptanalysts in pinpointing vulnerabilities in ciphers by training these networks with data that accentuates inherent weaknesses alongside corresponding encryption keys. Our study represents an investigation into the feasibility and effectiveness of employing machine learning, deep learning, and innovative random optimization techniques in cryptanalysis. Furthermore, it provides a comprehensive overview of the state-of-the-art advancements in this field over the past few years. The findings of this research are not only pivotal for the field of cryptanalysis but also hold significant implications for the broader realm of data security.
POSTER: RadiK: Scalable Radix Top-K Selection on GPUs

Authors:
raw_author_name: Yinghan Li
raw_affiliation_string: Alibaba Group, Beijing, China
raw_author_name: Yingda Chen
raw_affiliation_string: Alibaba Group, Seattle, United States

Abstract: By identifying the k largest or smallest elements in a set of data, top-k selection is critical for modern high-performance databases and machine learning systems, especially with large data volumes. However, previous studies on its GPU implementation are mostly merge-based and rely heavily on the high-speed but size-limited on-chip memory, thereby resulting in a restricted upper bound on k. This paper introduces RadiK, a highly optimized GPU-parallel radix top-k selection that is scalable with k, input length, and batch size. With a carefully designed optimization framework targeting high memory bandwidth and resource utilization, RadiK supports far larger k than the prior art, achieving up to 2.5× speedup for non-batch queries and up to 4.8× speedup for batch queries. We also propose a lightweight refinement that strengthens the robustness of RadiK against skewed distributions by adaptively scaling the input elements.
Three-Dimensional Double Random-Phase Encryption for Simultaneous Two-Primary Data

Authors:
raw_author_name: Jae-Young Jang
raw_affiliation_string: Department of Optometry, Eulji University, 553 Sanseong-daero, Sujeong-gu, Seongnam-si 13135, Kyonggi-do, Republic of Korea
raw_author_name: Myungjin Cho
raw_affiliation_string: School of ICT, Robotics, and Mechanical Engineering, Institute of Information and Telecommunication Convergence (IITC), Hankyong National University, 327 Chungang-ro, Anseong 17579, Kyonggi-do, Republic of Korea

Abstract: In this paper, we propose a three-dimensional (3D) optical encryption technique for simultaneous two-primary data using double random-phase encryption (DRPE). In conventional DRPE, the primary data can be encrypted through two different random phase masks optically. Thus, its speed is the same as the speed of light. However, in this method, each primary dataset can be decrypted by the individual key data. For simultaneous two primary dataset such as stereo images or multi-view images, a new encryption technique is required. Therefore, in this paper, we encrypt the simultaneous two different primary datasets by DRPE. In our method, the first and second primary data are regarded as the amplitude and phase with single key data for encryption. To verify the feasibility of our method, we implement the simulation and measure the performance metrics such as thw peak signal to noise ratio (PSNR) and the peak sidelobe ratio (PSR). As a result, PSNR values of two-dimensional decryption results for the first (“LENA” text) and second (lena image) primary data by our proposed method with the correct and incorrect key data are 311.0139, 41.9609, 12.0166, and 7.4626, respectively, since the first primary data are lossless, and the second primary data are lossy. For 3D reconstruction, PSR values of the first and second primary data are 914.2644 and 774.1400, respectively.