Reviewerly/exHarmony huggingface dataset:
Determining the effect of a floating point on the Falcon digital signature algorithm security

Authors:
raw_author_name: Oleksandr Potii
raw_affiliation_string: State Service of Special Communications and Information Protection of Ukraine, Ukraine
raw_author_name: Olena Kachko
raw_affiliation_string: Kharkiv National University of Radio Electronics; Institute of Information Technologies PrJSC, Ukraine
raw_author_name: Serhii Kandii
raw_affiliation_string: V. N. Karazin Kharkiv National University; Institute of Information Technologies PrJSC, Ukraine

Abstract: The object of research is digital signatures. The Falcon digital signature scheme is one of the finalists in the NIST post-quantum cryptography competition. Its distinctive feature is the use of floating-point arithmetic. However, floating-point arithmetic has so-called rounding noise, which accumulates during computations and in some cases may lead to significant changes in the processed values. The work considers the problem of using rounding noise to build attacks on implementation. The main result of the study is a novel attack on implementation, which enables the secret key recovery. This attack differs from existing attacks in using two separately secure implementations with different computation orders. As a result of the analysis, the conditions under which secret key recovery is possible were revealed. The attack requires 300,000 signatures and two implementations to recover key. The probability of successful attack ranges from 70 % to 76 %. This probability is explained by the structure of the Gaussian sampling algorithm used in the Falcon digital signature. At the same time, a necessary condition for conducting an attack is identical seed during signature generation. This condition makes the attack more theoretical than practical since the correct implementation of the Falcon makes probability of two identical seeds negligible. However, the possible usage of floating-point noise shows potential existence of additional attack vectors for the Falcon that should be covered in security models. The results could be used in the construction of digital signature security models and their implementation in existing information and communication systems
LiMAML: Personalization of Deep Recommender Models via Meta Learning

Authors:
raw_author_name: Wang, Ruofan
raw_author_name: Srivastava, Gaurav
raw_author_name: Wang, Tianqi
raw_author_name: Ouyang, Yunbo
raw_author_name: Nigam, Aastha
raw_author_name: Gupta, Aman
raw_author_name: Borisyuk, Fedor
raw_author_name: Keerthi, Sathiya
raw_author_name: Muralidharan, Ajith

Abstract: In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a diverse array of members. In this work, we introduce an innovative meta-learning solution tailored to the personalization of models for individual members and other entities, coupled with the frequent updates based on the latest user interaction signals. Specifically, we leverage the Model-Agnostic Meta Learning (MAML) algorithm to adapt per-task sub-networks using recent user interaction data. Given the near infeasibility of productionizing original MAML-based models in online recommendation systems, we propose an efficient strategy to operationalize meta-learned sub-networks in production, which involves transforming them into fixed-sized vectors, termed meta embeddings, thereby enabling the seamless deployment of models with hundreds of billions of parameters for online serving. Through extensive experimentation on production data drawn from various applications at LinkedIn, we demonstrate that the proposed solution consistently outperforms the baseline models of those applications, including strong baselines such as using wide-and-deep ID based personalization approach. Our approach has enabled the deployment of a range of highly personalized AI models across diverse LinkedIn applications, leading to substantial improvements in business metrics as well as refreshed experience for our members.
Community Service Data Visualization at PT Surya Data Infocreasi in Retail Companies X, Y and Z

Authors:
raw_author_name: None Yosia
raw_affiliation_string: Matana University

Abstract: This Community Service (PKM) is carried out at PT Surya Data Infokreasi with a focus on data visualization using the Power BI platform. This service aims to increase the efficiency of understanding customer data and the company's business performance. Design and implement interactive reports using various Power BI visualizations such as graphs, maps, and dynamic tables. Interactive slicers and filters are applied to give users flexibility in exploring data in greater depth or detail. The main focus of visualization is sales analysis, customer trends, and inventory monitoring. Selection of performance metrics including total sales, customer returns, and profit analysis. The implication of this service is to help facilitate understanding of data in terms of patterns, trends and information contained in the data.
CLLMs: Consistency Large Language Models

Authors:
raw_author_name: Kou, Siqi
raw_author_name: He, Zhezhi
raw_author_name: Deng, Zhijie
raw_author_name: Zhang, Hao

Abstract: Parallel decoding methods such as Jacobi decoding show promise for more efficient LLM inference as it breaks the sequential nature of the LLM decoding process and transforms it into parallelizable computation. However, in practice, it achieves little speedup compared to traditional autoregressive (AR) decoding, primarily because Jacobi decoding seldom accurately predicts more than one token in a single fixed-point iteration step. To address this, we develop a new approach aimed at realizing fast convergence from any state to the fixed point on a Jacobi trajectory. This is accomplished by refining the target LLM to consistently predict the fixed point given any state as input. Extensive experiments demonstrate the effectiveness of our method, showing 2.4$\times$ to 3.4$\times$ improvements in generation speed while preserving generation quality across both domain-specific and open-domain benchmarks.
Gradient Cuff: Detecting Jailbreak Attacks on Large Language Models by  Exploring Refusal Loss Landscapes

Authors:
raw_author_name: Hu, Xiaomeng
raw_author_name: Chen, Pin-Yu
raw_author_name: Ho, Tsung-Yi

Abstract: Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer. To reduce harm and misuse, efforts have been made to align these LLMs to human values using advanced training techniques such as Reinforcement Learning from Human Feedback (RLHF). However, recent studies have highlighted the vulnerability of LLMs to adversarial jailbreak attempts aiming at subverting the embedded safety guardrails. To address this challenge, this paper defines and investigates the Refusal Loss of LLMs and then proposes a method called Gradient Cuff to detect jailbreak attempts. Gradient Cuff exploits the unique properties observed in the refusal loss landscape, including functional values and its smoothness, to design an effective two-step detection strategy. Experimental results on two aligned LLMs (LLaMA-2-7B-Chat and Vicuna-7B-V1.5) and six types of jailbreak attacks (GCG, AutoDAN, PAIR, TAP, Base64, and LRL) show that Gradient Cuff can significantly improve the LLM's rejection capability for malicious jailbreak queries, while maintaining the model's performance for benign user queries by adjusting the detection threshold.
Manajemen Pembelajaran Terintegrasi dalam Model Pembelajaran Blended Learning, Learning Management System

Authors:
raw_author_name: Muhammad Firman
raw_affiliation_string: Universitas Linggabuana PGRI Sukabumi
raw_author_name: R.Supyan Sauri
raw_affiliation_string: Universitas Islam Nusantara Bandung, Indonesia
raw_author_name: Iim Wasliman
raw_affiliation_string: Universitas Islam Nusantara Bandung, Indonesia

Abstract: The primary objective of this research is to examine the impact of the blended learning model based on the Learning Management System (LMS) on both student learning outcomes and motivation. Within the realm of education, students often encounter various challenges, such as a lack of enthusiasm, boredom, and difficulties in grasping learning materials due to the limited variety in teaching and learning approaches. To address these issues, this study adopts a survey method employing a quantitative approach. The study encompasses all students at STKIP PGRI Sukabumi, with a sample size of 100 students chosen through the Simple Random Sampling technique. Data collection is conducted via a questionnaire distributed through Google Forms. The analysis involves multiple linear regression utilizing the SPSS 23 program, and the findings are as follows: 1. The LMS-based blended learning model demonstrates a significant influence on learning outcomes, as indicated by the t-count value of 6.835, surpassing the t-table value of 1.984. 2. Learning motivation is identified as a significant factor affecting learning outcomes, with a t-count value of 10.216, exceeding the t-table value of 1.984. 3. Both the LMS-based blended learning model and learning motivation collectively exhibit a substantial impact on learning outcomes, evidenced by an F-count value of 59.851, surpassing the F-table value of 3.089. In summary, the study concludes that the integration of the LMS-based blended learning model and enhanced learning motivation plays a pivotal role in advancing student learning outcomes.
In ternet of Things (IoT) relies on Wireless Technology for its major Functionalities

Authors:
raw_author_name: Joseph Mart
raw_affiliation_string: Austin Peay State University, Clarksville, TN, United States

Abstract: Abstract: A decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This article presents the changes that the IoT has undertaken since its conception and research on how wireless technological advances have shaped it and fostered the arising of derived names suitable to specific domains. In this article, we provide a study of some wireless communication standards, evaluating their main features and behaviours in terms of various metrics, including the transmission time, data coding efficiency, complexity, and power consumption. It is believed that the comparison presented in this paper would benefit application engineers in selecting an appropriate protocol.
Research on Chaotic Image Encryption Based on Fibonacci-ILogistic-IHenon

Authors:
raw_author_name: Yong Yang
raw_author_name: Xuan Chen
raw_author_name: Jiaying Xu
raw_author_name: Yuxia Li

Abstract: To further improve the security of image encryption, we propose an image encryption algorithm that combines Fibonacci with improved logistic and improved Henon chaotic mapping, i.e., the Fibonacci-ILogistic-IHenon algorithm. The algorithm addresses that the existing logistic chaotic algorithm is controlled by the parameter resulting in an uneven distribution, and the Henon chaotic algorithm may have the problem of periodic orbits. Using the mode-taking and sinusoidal functions for optimization, the output sequence region generated by the logistic chaotic mapping is adjusted to the input of the Henon chaotic mapping, which enables the construction of a new chaotic system (ILogistic-IHenon). In the image encryption implementation, Fibonacci is used to cure the chaos, and ILogistic-IHenon is utilized to generate chaotic sequences to complete the encryption. In the simulation experiments, we choose the improved logistic encryption algorithm, the improved Henon encryption algorithm, and the improved logistic-ent encryption algorithm as the comparison algorithms, and the results illustrate that the Fibonacci-ILogistic-IHenon algorithm has better performance in image encryption statistical analysis, encryption speed, pixel correlation analysis, information entropy analysis, differential attack analysis and other metrics.
Rendezvous problem for chains of kinematic points

Authors:
raw_author_name: Yufeng Lu
raw_affiliation_string: School of Mathematical Sciences and Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

Abstract: Abstract This paper studies the dynamic of a chain of infinitely many kinematic points guaranteeing the uniform convergence in the sense. A sufficient condition and some necessary conditions for infinite many kinematic points rendezvousing are given in terms of the spectrum and the invariant subspaces of the dynamic operator. The necessary and sufficient condition for finitely many kinematic points rendezvousing is characterized by the eigenvalues and the invariant subspaces of the dynamic operator. The estimate on the rate of the rendezvous is derived based on the spectrum of the dynamic operator.
THE STRUCTURE OF INFORMATION AND EDUCATION ENVIRONMENT OF A MEDICAL UNIVERSITY

Authors:
raw_author_name: Наталя Іванькова

Abstract: The authors examine the process of transformation of the educational infrastructure of medical universities during the transition to a mixed form of studying while COVID – 19 quarantine. Analysis of scientific publications on the digital transformation of the higher education system has shown the effectiveness of a hybrid model for organizing a virtual learning environment, which combines the ground-based infrastructure of the university and can include private and public cloud services. Hybrid model allows providing appropriate technological conditions for quality organization of teachers' work in online mode of distance learning system. Cloud services allow organizing a virtual group educational environment - a virtual classroom and a personal environment for students. The authors analyzed the changes in the infrastructure of Zaporizhzhia State Medical University in the process of digital transformation of the educational process during the period of COVID-19 quarantine, which became the ground for the development of a structural model of the information and educational environment of the university. The work focuses on the fact that one of the features of the educational process in medical universities is the usage of special learning tools: software and hardware systems for examination of a patient's condition, technical means of telemedicine, classroom self-training sessions for Licensing Integrated Exams “KROK” 1,2,3 on the basis of licensed software, etc. The authors conducted a systematic analysis and identified the technological components of the unified information educational environment of medical HEIs, namely: corporate computer network; workplaces of participants in the development of online courses (designer, lecturer of videoconferencing, moderator of video lectures or webinars, designer of video processing; specialized modeling systems; simulation complexes; software and hardware complexes for processing biological signals; specialized classrooms: computer room of the library, lecture halls for remote video lectures with interactive communication, lecture halls for interactive lectures, media centers for webinars and telemedicine consultations; cloud services . The proposed structure of information and educational environment of a medical university was implemented in the educational process of Zaporizhzhia State Medical University which made possible the creation of a cloud-oriented educational environment. The usage of hybrid cloud model allowed to solve the issue of integration of virtual environments of departments, in particular, territorially remote ones, and the ground infrastructure of the university.
CHALLENGES FOR UNIVERSITIES TO ONLINE TECHNOLOGIES IMPLEMENTATION IN THE CONDITIONS OF WAR IN UKRAINE

Authors:
raw_author_name: Yuliia Sytnykova
raw_author_name: Anastasiia Ptushka

Abstract: The experience of online education in Ukraine during wartime is unique. After the Russian-Ukrainian war beginning, in a month of so-called "holidays", in spring 2022, from April, 1, by the order of the Ministry of Education and Science of Ukraine and the rectors' orders of universities, online education started. The classes were online, and it was difficult to overcome the war stress, but the experience of the pandemic taught everyone to work with online technologies such as Zoom, Meet, and Google Classroom. Teaching students online was carried out as online as offline with the university platforms like Moodle and others for offline learning, they contained all educational materials from the previous two years of the pandemic, tasks, quizzes, and tests. The purpose of the research is to identify pedagogical, psychological, and technical challenges for teaching university students during the war in Ukraine with the survey method. The survey was conducted online with 65 university lecturers and 122 students from 5 universities in Kharkiv, Ukraine. The pedagogical, psychological, and technical difficulties were analyzed. The questions were aimed at studying such aspects as 1) pedagogical and psychological difficulties: the teachers/students' readiness for online teaching/learning, 2) pedagogical difficulties: the procedures of online teaching/learning and technologies, in particular, methods, forms, means of giving/mastering knowledge, 3) psychological and technical difficulties faced both by teachers and students. The results are as follows. The acquired teaching and learning experience with online technologies helped to establish online education in wartime positively in general. The main pedagogical difficulties were overcome with basic methods (online and offline), teaching forms such as lectures, seminars, and practical classes online, and offline, and, finally, different tools/means/platforms (video/audio recordings, corresponding emails, social networks, Moodle platform, etc.). The psychological state of both teachers and students in the circumstances of online education was also examined. The most common psychological difficulties with online learning were connected with external ones as constant shelling, and air alarms as with internal ones as stress, anxiety, worries, depression, burnout, sadness, and frustration. The technical difficulties were connected to blackouts, lack of Internet connection, power outages, evacuation, changes in living and working conditions, relocations, and the impossibility of using laboratory equipment. The conclusion is despite all online difficulties, the quality of teaching did not change significantly, thanks to the clear understanding that it is a necessity to teach and learn online.
THE ELECTRONIC SYSTEM FOR THE MANAGEMENT OF UNIVERSITY EDUCATIONAL PROGRAMS

Authors:
raw_author_name: Yevhen Palamarchuk
raw_author_name: Olena Kovalenko

Abstract: The article presents the research results of the electronic system for managing university educational programs (EP). The analysis of scientific works showed that higher educational institutions need the introduction of integrated information systems. Such systems are the technical basis of the electronic university and have functionality for managing education and providing communications to university management. An analysis of the main processes of EP management was performed, and interested parties, their roles, and participation in the methods were determined. The proposed mathematical model is the basis of management for introducing the EP, its development, and implementation control based on quality parameters. The software module is a part of the university's management process support system and the JetIQ of Vinnytsia National Technical University's (VNTU's) comprehensive information system. For the analysis of the subject area and the formation of optimal management processes, the methodological recommendations of the National Agency for Higher Education Quality Assurance (NAQA), data export and import system for the Unified State Electronic Database on Education Issues, the experience, and recommendations of experts from the department of quality assurance of education of VNTU, experts and guarantors of educational programs were used. Precedent models are formed about such role scenarios – applicant, student, teacher, guarantor, management, expert, stakeholder (employer). Such models made it possible to create not only the software module for the management of educational programs (QEd – Quality Educational) but also to determine the conditions for publication on the main website of VNTU of educational programs and documentation of their development, syllabi on the foremost showcase and jet-sites of the department, to create scenarios for automatic quality control of the EP. The result of the creation of the software module for the management of educational programs is a system of structured information regarding the development and implementation of the educational program, which allows the guarantor and specialists of the department to ensure the quality of education by use special containers for documentation and interested parties to use public up-to-date information on the university's websites.
Container Security in Cloud Environments

Authors:
raw_author_name: Joseph Mart
raw_affiliation_string: Austin Peay State University, Clarksville, TN, United States

Abstract: A bstract: The widespread adoption of containers in modern software applications has introduced new challenges to security and integrity. Containers, known for their lightweight and portable nature, facilitate agile deployment across diverse environments. However, this popularity has led to security risks such as vulnerabilities in container images, misconfigurations, and insecure runtime environments. This paper addresses these challenges by proposing automated and robust security techniques integrated into continuous integration and continuous development pipelines. The work emphasizes the importance of a solid security policy, container image scanning, orchestration security, and runtime monitoring. The study also identifies specific issues faced by the DevSecOps community and proposes initial fixes to fortify container security. In the cloud environment, containers play a pivotal role in application deployment by sharing the same OS kernel, reducing resource requirements, and minimizing start-up times. Despite their advantages, weak container isolation poses security challenges, including privilege escalation and information leaks. To mitigate these concerns, the paper conducts an in-depth analysis of existing access control mechanisms for container security. It discusses challenges in architecture modeling and presents use cases for fulfilling security requirements, encompassing container, inter-container, and host protection. The work emphasizes the need for both software and hardware solutions to enhance container security. Containers have emerged as a lightweight alternative to virtual machines, supporting microservices architecture. The container market is growing rapidly, but security concerns remain a significant barrier to adoption. This paper surveys existing literature on container security, categorizing it into four use cases: protecting containers from internal applications, inter-container protection, safeguarding the host from containers, and defending containers froma malicious or semi-honest host. The analysis reveals that software-based solutions, leveraging Linux kernel features and security modules, address the first three use cases, while the last use case relies on hardware-based solutions. The paper concludes with highlighting open research problems and future directions to guide further exploration in container security.