arXiv - CS - Emerging Technologies最新文献_第6页

Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response 在网络事件响应期间进行强化学习以实现高效和有效的恶意软件调查

arXiv - CS - Emerging Technologies Pub Date : 2024-08-04 DOI: arxiv-2408.01999

Dipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev

{"title":"Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response","authors":"Dipo Dunsin, Mohamed Chahine Ghanem, Karim Ouazzane, Vassil Vassilev","doi":"arxiv-2408.01999","DOIUrl":"https://doi.org/arxiv-2408.01999","url":null,"abstract":"This research focused on enhancing post-incident malware forensic\u0000investigation using reinforcement learning RL. We proposed an advanced MDP post\u0000incident malware forensics investigation model and framework to expedite post\u0000incident forensics. We then implement our RL Malware Investigation Model based\u0000on structured MDP within the proposed framework. To identify malware artefacts,\u0000the RL agent acquires and examines forensics evidence files, iteratively\u0000improving its capabilities using Q Table and temporal difference learning. The\u0000Q learning algorithm significantly improved the agent ability to identify\u0000malware. An epsilon greedy exploration strategy and Q learning updates enabled\u0000efficient learning and decision making. Our experimental testing revealed that\u0000optimal learning rates depend on the MDP environment complexity, with simpler\u0000environments benefiting from higher rates for quicker convergence and complex\u0000ones requiring lower rates for stability. Our model performance in identifying\u0000and classifying malware reduced malware analysis time compared to human\u0000experts, demonstrating robustness and adaptability. The study highlighted the\u0000significance of hyper parameter tuning and suggested adaptive strategies for\u0000complex environments. Our RL based approach produced promising results and is\u0000validated as an alternative to traditional methods notably by offering\u0000continuous learning and adaptation to new and evolving malware threats which\u0000ultimately enhance the post incident forensics investigations.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracting Object Heights From LiDAR & Aerial Imagery 从激光雷达和航空图像中提取物体高度

arXiv - CS - Emerging Technologies Pub Date : 2024-08-02 DOI: arxiv-2408.00967

Jesus Guerrero

引用次数: 0

Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment 通过噪声门贝叶斯网络为计算思维能力评估建立基于评分标准的学习者模型

arXiv - CS - Emerging Technologies Pub Date : 2024-08-02 DOI: arxiv-2408.01221

Giorgia Adorni, Francesca Mangili, Alberto Piatti, Claudio Bonesana, Alessandro Antonucci

{"title":"Rubric-based Learner Modelling via Noisy Gates Bayesian Networks for Computational Thinking Skills Assessment","authors":"Giorgia Adorni, Francesca Mangili, Alberto Piatti, Claudio Bonesana, Alessandro Antonucci","doi":"arxiv-2408.01221","DOIUrl":"https://doi.org/arxiv-2408.01221","url":null,"abstract":"In modern and personalised education, there is a growing interest in\u0000developing learners' competencies and accurately assessing them. In a previous\u0000work, we proposed a procedure for deriving a learner model for automatic skill\u0000assessment from a task-specific competence rubric, thus simplifying the\u0000implementation of automated assessment tools. The previous approach, however,\u0000suffered two main limitations: (i) the ordering between competencies defined by\u0000the assessment rubric was only indirectly modelled; (ii) supplementary skills,\u0000not under assessment but necessary for accomplishing the task, were not\u0000included in the model. In this work, we address issue (i) by introducing dummy\u0000observed nodes, strictly enforcing the skills ordering without changing the\u0000network's structure. In contrast, for point (ii), we design a network with two\u0000layers of gates, one performing disjunctive operations by noisy-OR gates and\u0000the other conjunctive operations through logical ANDs. Such changes improve the\u0000model outcomes' coherence and the modelling tool's flexibility without\u0000compromising the model's compact parametrisation, interpretability and simple\u0000experts' elicitation. We used this approach to develop a learner model for\u0000Computational Thinking (CT) skills assessment. The CT-cube skills assessment\u0000framework and the Cross Array Task (CAT) are used to exemplify it and\u0000demonstrate its feasibility.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141937236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing Quantum Circuit Depth Reduction with Ancilla Qubits in MCX Gates 用 MCX 栅极中的 Ancilla Qubits 分析量子电路深度缩减情况

arXiv - CS - Emerging Technologies Pub Date : 2024-08-02 DOI: arxiv-2408.01304

Ahmad Bennakhi, Paul Franzon, Gregory T. Byrd

引用次数: 0

The Energy Cost of Artificial Intelligence of Things Lifecycle 人工智能物联网生命周期的能源成本

arXiv - CS - Emerging Technologies Pub Date : 2024-08-01 DOI: arxiv-2408.00540

Shih-Kai Chou, Jernej Hribar, Mihael Mohorčič, Carolina Fortuna

{"title":"The Energy Cost of Artificial Intelligence of Things Lifecycle","authors":"Shih-Kai Chou, Jernej Hribar, Mihael Mohorčič, Carolina Fortuna","doi":"arxiv-2408.00540","DOIUrl":"https://doi.org/arxiv-2408.00540","url":null,"abstract":"Artificial intelligence (AI)coupled with existing Internet of Things (IoT)\u0000enables more streamlined and autonomous operations across various economic\u0000sectors. Consequently, the paradigm of Artificial Intelligence of Things (AIoT)\u0000having AI techniques at its core implies additional energy and carbon costs\u0000that may become significant with more complex neural architectures. To better\u0000understand the energy and Carbon Footprint (CF) of some AIoT components, very\u0000recent studies employ conventional metrics. However, these metrics are not\u0000designed to capture energy efficiency aspects of inference. In this paper, we\u0000propose a new metric, the Energy Cost of AIoT Lifecycle (eCAL) to capture the\u0000overall energy cost of inference over the lifecycle of an AIoT system. We\u0000devise a new methodology for determining eCAL of an AIoT system by analyzing\u0000the complexity of data manipulation in individual components involved in the\u0000AIoT lifecycle and derive the overall and per bit energy consumption. With eCAL\u0000we show that the better a model is and the more it is used, the more energy\u0000efficient an inference is. For an example AIoT configuration, eCAL for making\u0000$100$ inferences is $1.43$ times higher than for $1000$ inferences. We also\u0000evaluate the CF of the AIoT system by calculating the equivalent CO$_{2}$\u0000emissions based on the energy consumption and the Carbon Intensity (CI) across\u0000different countries. Using 2023 renewable data, our analysis reveals that\u0000deploying an AIoT system in Germany results in emitting $4.62$ times higher\u0000CO$_2$ than in Finland, due to latter using more low-CI energy sources.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141881507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic Generation of Behavioral Test Cases For Natural Language Processing Using Clustering and Prompting 利用聚类和提示自动生成自然语言处理行为测试用例

arXiv - CS - Emerging Technologies Pub Date : 2024-07-31 DOI: arxiv-2408.00161

Ying Li, Rahul Singh, Tarun Joshi, Agus Sudjianto

引用次数: 0

An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification 基于支持向量机的可解释视觉变换器与迁移学习相结合的高效干旱压力识别技术

arXiv - CS - Emerging Technologies Pub Date : 2024-07-31 DOI: arxiv-2407.21666

Aswini Kumar Patra, Ankit Varshney, Lingaraj Sahoo

{"title":"An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification","authors":"Aswini Kumar Patra, Ankit Varshney, Lingaraj Sahoo","doi":"arxiv-2407.21666","DOIUrl":"https://doi.org/arxiv-2407.21666","url":null,"abstract":"Early detection of drought stress is critical for taking timely measures for\u0000reducing crop loss before the drought impact becomes irreversible. The subtle\u0000phenotypical and physiological changes in response to drought stress are\u0000captured by non-invasive imaging techniques and these imaging data serve as\u0000valuable resource for machine learning methods to identify drought stress.\u0000While convolutional neural networks (CNNs) are in wide use, vision transformers\u0000(ViTs) present a promising alternative in capturing long-range dependencies and\u0000intricate spatial relationships, thereby enhancing the detection of subtle\u0000indicators of drought stress. We propose an explainable deep learning pipeline\u0000that leverages the power of ViTs for drought stress detection in potato crops\u0000using aerial imagery. We applied two distinct approaches: a synergistic\u0000combination of ViT and support vector machine (SVM), where ViT extracts\u0000intricate spatial features from aerial images, and SVM classifies the crops as\u0000stressed or healthy and an end-to-end approach using a dedicated classification\u0000layer within ViT to directly detect drought stress. Our key findings explain\u0000the ViT model's decision-making process by visualizing attention maps. These\u0000maps highlight the specific spatial features within the aerial images that the\u0000ViT model focuses as the drought stress signature. Our findings demonstrate\u0000that the proposed methods not only achieve high accuracy in drought stress\u0000identification but also shedding light on the diverse subtle plant features\u0000associated with drought stress. This offers a robust and interpretable solution\u0000for drought stress monitoring for farmers to undertake informed decisions for\u0000improved crop management.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Sovereign Identity for Consented and Content-Based Access to Medical Records using Blockchain 使用区块链以自主身份同意和基于内容访问医疗记录

arXiv - CS - Emerging Technologies Pub Date : 2024-07-31 DOI: arxiv-2407.21559

Marie Tcholakian, Karolina Gorna, Maryline Laurent, Hella Kaffel Ben Ayed, Montassar Naghmouchi

{"title":"Self-Sovereign Identity for Consented and Content-Based Access to Medical Records using Blockchain","authors":"Marie Tcholakian, Karolina Gorna, Maryline Laurent, Hella Kaffel Ben Ayed, Montassar Naghmouchi","doi":"arxiv-2407.21559","DOIUrl":"https://doi.org/arxiv-2407.21559","url":null,"abstract":"Electronic Health Records (EHRs) and Medical Data are classified as personal\u0000data in every privacy law, meaning that any related service that includes\u0000processing such data must come with full security, confidentiality, privacy and\u0000accountability. Solutions for health data management, as in storing it, sharing\u0000and processing it, are emerging quickly and were significantly boosted by the\u0000Covid-19 pandemic that created a need to move things online. EHRs makes a\u0000crucial part of digital identity data, and the same digital identity trends --\u0000as in self sovereign identity powered by decentralized ledger technologies like\u0000Blockchain, are being researched or implemented in contexts managing digital\u0000interactions between health facilities, patients and health professionals. In\u0000this paper, we propose a blockchain-based solution enabling secure exchange of\u0000EHRs between different parties powered by a self-sovereign identity (SSI)\u0000wallet and decentralized identifiers. We also make use of a consortium IPFS\u0000network for off-chain storage and attribute-based encryption (ABE) to ensure\u0000data confidentiality and integrity. Through our solution, we grant users full\u0000control over their medical data, and enable them to securely share it in total\u0000confidentiality over secure communication channels between user wallets using\u0000encryption. We also use DIDs for better user privacy and limit any possible\u0000correlations or identification by using pairwise DIDs. Overall, combining this\u0000set of technologies guarantees secure exchange of EHRs, secure storage and\u0000management along with by-design features inherited from the technological\u0000stack.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141866683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

CultureVo: The Serious Game of Utilizing Gen AI for Enhancing Cultural Intelligence CultureVo：利用创人工智能提升文化智能的严肃游戏

arXiv - CS - Emerging Technologies Pub Date : 2024-07-30 DOI: arxiv-2407.20685

Ajita Agarwala, Anupam Purwar, Viswanadhasai Rao

引用次数: 0

Introducing a new hyper-parameter for RAG: Context Window Utilization 为 RAG 引入一个新的超参数：上下文窗口利用率

arXiv - CS - Emerging Technologies Pub Date : 2024-07-29 DOI: arxiv-2407.19794

Kush Juvekar, Anupam Purwar

引用次数: 0