检索结果-内蒙古大学图书馆

Securing Automated Insulin Delivery Systems: A Review of Security Threats and Protectives Strategies

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Niu, Yuchen Lam, Siew-Kei College of Computing and Data Science Nanyang Technological University 50 Nanyang Ave 639798 Singapore

Automated insulin delivery (AID) systems have emerged as a significant technological advancement in diabetes care. These systems integrate a continuous glucose monitor, an insulin pump, and control algorithms to automate insulin delivery, reducing the burden of self-management and offering enhanced glucose control. However, the increasing reliance on wireless connectivity and software control has exposed AID systems to critical security risks that could result in life-threatening treatment errors. This review first presents a comprehensive examination of the security landscape, covering technical vulnerabilities, legal frameworks, and commercial product considerations, and an analysis of existing research on attack vectors, defence mechanisms, as well as evaluation methods and resources for AID systems. Despite recent advancements, several open challenges remain in achieving secure AID systems, particularly in standardising security evaluation frameworks and developing comprehensive, lightweight, and adaptive defence strategies. As one of the most widely adopted and extensively studied physiologic closed-loop control systems, this review serves as a valuable reference for understanding security challenges and solutions applicable to analogous medical systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Closed loop control systems

Peer Tutoring and its Impact on Academic Achievements

学校读者我要写书评

暂无评论

Peer Tutoring and its Impact on Academic Achievements

IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Aqeel Manahil Hassan Affaf Ghani Manahil Zaheer Bilal Anjum Faisal Bukhari Department of Data Science Faculty of Computing and Information Technologys University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504465

ISBN: (纸本)9798331504472

Peer tutoring is gaining attention rapidly nowadays, providing a learning environment where students have the opportunity to learn and grow through collaboration and effective communication. This study focuses on the impact of peer tutoring by collecting data from different students as well as surveys and then analyzing the pre and post-tutoring assessments to determine the impact of peer tutoring. It employs statistical techniques to study the impact of peer tutoring on both tutee and tutor, their self-confidence, and the overall academic achievement gained through tutoring.

关键词： Training Surveys Job shop scheduling Statistical analysis Collaboration Cultural differences Socioeconomics

Advanced Techniques in Network Traffic Analysis: Utilizing Wireshark For In-Depth Live data Packet Inspection And Information Capture 2

学校读者我要写书评

暂无评论

Advanced Techniques in Network Traffic Analysis: Utilizing W...

2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Chowhan, Santhosh Saxena, Abhilash Kumar University Department of Data Analytics and Mathematical Sciences Karnataka Bangalore India College of Computing Science and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350369960

The escalating frequency and sophistication of cyber-attacks have placed a spotlight on the importance of Quality of Service (QoS) and robust network security mechanisms. Effective traffic analysis and distribution are critical for maintaining the integrity of network applications and safeguarding data. Our research focuses on the development and assessment of a network intrusion detection system (NIDS) that utilizes the advanced packet analysis capabilities of Wireshark, a renowned network protocol analyzer. The system is designed to enhance network security by enabling more efficient and accurate data collection through network monitoring tools. This serves as the foundation for detecting and thwarting a wide range of malicious activities, including malware and spyware incursions, within network traffic. The innovation of our approach lies in its integration of packet sniffing technology with AI-driven analysis techniques. By applying Machine Learning algorithms to the data captured by Wireshark, our NIDS is capable of identifying patterns indicative of intrusive behavior, which might elude traditional detection methods. We present a comprehensive evaluation of our system, showcasing its efficacy in real-time intrusion detection and its potential for integration into existing network infrastructures. This study contributes to the ongoing discourse in cybersecurity, offering a sophisticated tool in the battle against digital threats and reinforcing the role of intelligent systems in protecting network environments © 2023 IEEE.

关键词： data Packet Analysis Techniques Network Security and Forensics Network Traffic Analysis packets Real-Time Network Monitoring Wireshark for data Packet Inspection

Using Intelligent Texts in a Computer science Classroom: Findings from an iTELL Deployment 8

学校读者我要写书评

暂无评论

Using Intelligent Texts in a Computer Science Classroom: Fin...

8th Educational data Mining in Computer science Education Workshop, CSEDM 2024

作者： Crossley, Scott Choi, Joon Suh Morris, Wesley Holmes, Langdon Joyner, David Gupta, Vaibhav Peabody College Vanderbilt University NashvilleTN United States College of Computing Georgia Institute of Tech AtlantaGA United States Data Science Institute Vanderbilt University NashvilleTN United States

This study assesses the efficacy of intelligent texts to help students in a computer science class understand and process information about computational thinking and programming. The intelligent texts used in this study were taken from an introductory programming textbook. The texts were ingested into an intelligent text format using the Intelligent Texts for Enhanced Language Learning (iTELL) framework, which converts any type of machine-readable text into an interactive, intelligent text. iTELL asks students to complete constructed response items and summaries, which are scored automatically by large language models (LLMs) specifically trained to generate scores to inform qualitative feedback to students. Survey results indicated that students responded positively to the constructed response and summary items and felt both items helped them learn. An analysis of delta value gain scores between pre-tests and post-tests for students that used iTELL and those that did not use iTELL indicated that iTELL students showed increased learning gains. Regression analyses showed that delta scores for the iTELL students were predicted by the number of scrolls, word scores on summaries, and pre-test proficiency level (low/high). The results indicate that intelligent texts may help computer science students better learn material than traditional texts. © 2024 Copyright for this paper by its authors.

关键词： Regression analysis

Finding a Shortest M-link Path in a Monge Directed Acyclic Graph

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wan, Joy Z. Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

关键词： Directed graphs

Uncovering the Internet’s Hidden Values: An Empirical Study of Desirable Behavior Using Highly-Upvoted Content on Reddit

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Goyal, Agam Lambert, Charlotte Jain, Yoshee Chandrasekharan, Eshwar Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

A major task for moderators of online spaces is norm-setting, essentially creating shared norms for user behavior in their communities. Platform design principles emphasize the importance of highlighting norm-adhering examples and explicitly stating community norms. However, norms and values vary between communities and go beyond content-level attributes, making it challenging for platforms and researchers to provide automated ways to identify desirable behavior to be highlighted. Current automated approaches to detect desirability are limited to measures of prosocial behavior, but we do not know whether these measures fully capture the spectrum of what communities value. In this paper, we use upvotes, which express community approval, as a proxy for desirability and examine 16,000 highly-upvoted comments across 80 popular sub-communities on Reddit. Using a large language model, we extract values from these comments across two years (2016 and 2022) and compile 64 and 72 macro, meso, and micro values for 2016 and 2022 respectively, based on their frequency across communities. Furthermore, we find that existing computational models for measuring prosociality were inadequate to capture on average 82% of the values we extracted. Finally, we show that our approach can not only extract most of the qualitatively-identified values from prior taxonomies, but also uncover new values that are actually encouraged in practice. Our findings highlight the need for nuanced models of desirability that go beyond preexisting prosocial measures. This work has implications for improving moderator understanding of their community values and provides a framework that can supplement qualitative approaches with larger-scale content analyses. © 2024, CC BY.

关键词：

Polycystic Ovary Syndrome Prediction through CNN based Image Analysis: A Deep Learning Based Approach

学校读者我要写书评

暂无评论

Polycystic Ovary Syndrome Prediction through CNN based Image...

International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Yazhini Karthik Sruthi R Sujithra M Department of Computing - Data Science Coimbatore Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Polycystic Ovary Syndrome (PCOS) is a prevalent hormonal disorder affecting women during their reproductive years. It is characterized by irregular menstrual cycles, excessive hair growth, and the presence of cysts in the ovaries. The diagnosis of PCOS typically involves a combination of clinical evaluations and laboratory tests. The paper proposes a novel approach using Convolutional Neural Networks (CNNs) for predicting PCOS through the analysis of ultrasound images. The dataset comprises images obtained from ovarian ultrasound scans of both PCOS-positive and PCOS-negative individuals. These images undergo preprocessing and augmentation techniques such as rescaling, shearing, zooming, flipping, and rotation to enhance the diversity of the training data. Multiple CNN-based models with varying architectures are constructed, altering parameters such as the number of convolutional and pooling layers, activation functions, and other hyperparameters. The models are trained and validated using a split of the dataset into training and validation sets, with metrics like loss and accuracy monitored during training to assess model performance. Upon completion of training, the models are tested using unseen data to evaluate their predictive capabilities. The results demonstrate that deep learning models trained on ultrasound images can accurately predict the presence or absence of PCOS. Such models hold promise in aiding healthcare professionals with early diagnosis and management of PCOS, ultimately improving patient care and outcomes.

关键词： Training Deep learning Ultrasonic imaging Neural networks Training data Medical services Predictive models Convolutional neural networks Shearing Monitoring

SLM-Mod: Small Language Models Surpass LLMs at Content Moderation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhan, Xianyang Goyal, Agam Chen, Yilun Chandrasekharan, Eshwar Saha, Koustuv Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

Large language models (LLMs) have shown promise in many natural language understanding tasks, including content moderation. However, these models can be expensive to query in real-time and do not allow for a community-specific approach to content moderation. To address these challenges, we explore the use of open-source small language models (SLMs) for community-specific content moderation tasks. We fine-tune and evaluate SLMs (less than 15B parameters) by comparing their performance against much larger open- and closed-sourced models in both a zero-shot and few-shot setting. Using 150K comments from 15 popular Reddit communities, we find that SLMs outperform zero-shot LLMs at content moderation—11.5% higher accuracy and 25.7% higher recall on average across all communities. Moreover, few-shot in-context learning leads to only a marginal increase in the performance of LLMs, still lacking compared to SLMs. We further show the promise of cross-community content moderation, which has implications for new communities and the development of cross-platform moderation techniques. Finally, we outline directions for future work on language model based content moderation.1 © 2024, CC BY.

关键词： Structured Query Language

Optimal and Feasible Contextuality-based Randomness Generation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Liu, Yuan Ramanathan, Ravishankar School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong

Semi-device-independent (SDI) randomness generation protocols based on Kochen-Specker contextuality offer the attractive features of compact devices, high rates, and ease of experimental implementation over fully device-independent (DI) protocols. Here, we investigate this paradigm and derive four results to improve the state-of-art. Firstly, we introduce a family of simple, experimentally feasible orthogonality graphs (measurement compatibility structures) for which the maximum violation of the corresponding non-contextuality inequalities allows to certify the maximum amount of log2 d bits from a qudit system with projective measurements for d ≥ 3. We analytically derive the Lovasz theta and fractional packing number for this graph family, and thereby prove their utility for optimal randomness generation in both randomness expansion and amplification tasks. Secondly, a central additional assumption in contextuality-based protocols over fully DI ones, is that the measurements are repeatable and satisfy an intended compatibility structure. We frame a relaxation of this condition in terms of Ε-orthogonality graphs for a parameter Ε > 0, and derive quantum correlations that allow to certify randomness for arbitrary relaxation Ε ∈ [0, 1). Thirdly, it is well known that a single qubit is non-contextual, i.e., the qubit correlations can be explained by a non-contextual hidden variable (NCHV) model. We show however that a single qubit is almost contextual, in that there exist qubit correlations that cannot be explained by Ε-faithful NCHV models for small Ε > 0. Finally, we point out possible attacks by quantum and general consistent (non-signalling) adversaries for certain classes of contextuality tests over and above those considered in DI scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Qubits