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检索条件"任意字段=19th International Multiconference on Advanced Computer Systems"

共 590 条记录，以下是1-10 订阅

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PROV2VEC: Learning Provenance Graph Representation for Anomaly Detection in computer systems 24

PROV2VEC: Learning Provenance Graph Representation for Anoma...

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19th international Conference on Availability, Reliability, and Security (ARES)

作者： Bhattarai, Bibek Huang, H. Howie George Washington Univ GraphLab Washington DC 20052 USA

ISBN: (纸本)9798400717185

Modern cyber attackers use advanced zero-day exploits, highly targeted spear phishing, and other social engineering techniques to gain access, and also use evasion techniques to maintain a prolonged presence within the victim network while working gradually towards the objective. To minimize damage, detecting these advanced Persistent threats as early in the campaign as possible is crucial. this paper proposes, PROV2VEC, a system for the continuous monitoring of enterprise host's behavior to detect attackers' activities. It leverages the data provenance graph built using system event logs to get complete visibility into the execution state of an enterprise host and the causal relationship between system entities. It proposes a novel provenance graph kernel to obtain the canonical representation of the system behavior, which is compared against its historical behaviors and that of other hosts to detect the deviation from the norm. these representations are used in several machine learning models to evaluate their ability to capture the underlying behavior of an endpoint host. We have empirically demonstrated that the provenance graph kernel produces a much more compact representation compared to existing methods while improving prediction ability.

关键词： Provenance Graph Machine Learning Anomaly Detection

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Human Technology Frontier: A Retrospective and Challenges for the Future in the Era of Artificial Intelligence 19th

Human Technology Frontier: A Retrospective and Challenges fo...

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19th international Conference on Dependability of computer systems (DepCoS-RELCOMEX)

作者： Rozenblit, Jerzy W. Univ Arizona Dept Elect & Comp Engn Tucson AZ 85721 USA

ISBN: (纸本)9783031618567;9783031618574

As intelligent systems and technologies mature, a shift in the modality of use is occurring, namely computer-based systems are no longer an assistive extension of the human operator but are an inherent part of human endeavors. this, on the one hand, leads to augmented human performance, while on the other hand, presents challenges in managing the boundaries between human versus machine control, over- and under-reliance on automation, and artificial intelligence (AI). Social impacts such as dramatic changes in work paradigms, outsourcing, and human interactions are more and more pronounced. the presentation will explore the evolution of smart systems from fundamental models to advanced technologies today. It will discuss the human impact of the new advances on engineering, medicine, other endeavors, and the society at large. It will debate the question of "how much better off are we?" living in the technology driven universes and our interactions with the ubiquitous computer-based worlds.

关键词： High technologies Artificial Intelligence Human computer interactions

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A Comparative Study of YOLO Models for Enhanced Vehicle Detection in Complex Aerial Scenarios 19

A Comparative Study of YOLO Models for Enhanced Vehicle Dete...

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19th international Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Azis, Huzain Nasrullah Abdullah, Munaisyah Ismail, Suriana Purnawansyah Syafie, Lukman Department of Computer Science Universitas Muslim Indonesia Makassar Indonesia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

the use of Unmanned Aerial Vehicles (UAVs) in aerial imaging is expanding rapidly, particularly in traffic monitoring and intelligent transportation systems. Detecting small and occluded vehicles in aerial images poses significant challenges due to varying resolutions and obstructions like buildings or trees. this study seeks to enhance vehicle detection accuracy by improving You Only Look Once (YOLO) models, with a focus on small and occluded object detection. Utilizing the COWC-M dataset and advanced data augmentation techniques such as Mosaic Augmentation, this research evaluates multiple YOLO variants. the YOLOv8-L model achieved the highest mAP50 score of 0.9899, demonstrating superior detection accuracy for small objects. Additionally, the YOLOv10-L model outperformed others with the best mAP50-95 score of 0.8715, indicating strong results across different intersection-over-union (IoU) ranges. Compared to YOLO-RTUAV, which achieved an mAP50 of 0.9353, the newer YOLO models provide significant improvements in both precision and recall. these findings contribute to the development of highly efficient, real-time vehicle detection systems suitable for large-scale surveillance applications in complex environments. © 2025 IEEE.

关键词： Aerial photography

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2024 IEEE 19th international Conference on the Perspective Technologies and Methods in MEMS Design, MEMSTECH 2024 - Proceedings

2024 IEEE 19th International Conference on the Perspective T...

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19th IEEE international Conference on the Perspective Technologies and Methods in MEMS Design, MEMSTECH 2024

ISBN: (纸本)9798350378627

the proceedings contain 31 papers. the topics discussed include: Bilow – knee-prothesis study;electrodynamic modeling the motion devices of MEMS;application of bayes theory model to determine the optimal variant of technological process for production of MEMS components;the monitoring of automated platforms for testing Internet of things devices;simulation of parameters and modes of wave propagation in electromechanical systems using methods of inverse problems theory;advanced computer-aided optimization of quartz resonator-based sensors;improvement of the control system of the robotic mobile platform;segmentation of medical images using deep learning and texture enhancement based on fractional derivative operators;modelling of the dynamic process in the microbeam of the MEMS resonators;exploring the potential of cellulose acetate membranes for precise gas permeation in microfluidic devices;and investigation of magnetic properties of cryogenically cooled electrolyte pure iron powder.

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Classical Machine Learning and Large Models for Text-Based Emotion Recognition 19

Classical Machine Learning and Large Models for Text-Based E...

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19th international Conference on Future Networks and Communications, FNC 2024 / 21st international Conference on Mobile systems and Pervasive Computing, MobiSPC 2024 / 14th international Conference on Sustainable Energy Information Technology, SEIT 2024

作者： Esfahani, Seyed Hamed Noktehdan Adda, Mehdi Department of Mathematics and Computer Science University of Quebec RimouskiQC Canada

In the era of digital communication, detecting emotions has become crucial for applications ranging from customer service to mental health assessment. this study examines emotion recognition in text through various machine learning techniques, from traditional machine learning techniques, to advanced large language models (LLMs) such as BERT, Falcon 7B, and Mistral 7B. the results reveal that the fine-tuned Mistral 7B model is the most precise, achieving an accuracy of 76%. Furthermore, Support Vector Machine attained an accuracy of 64%. © 2024 the Authors. Published by Elsevier B.V.

关键词： Adversarial machine learning

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DynKDD: Dynamic Graph-Based Network Intrusion Detection Using Graph Neural Networks 19

DynKDD: Dynamic Graph-Based Network Intrusion Detection Usin...

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19th international Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Mir, Aabid A. Zuhairi, Megat F. Musa, Shahrulniza Namoun, Addallah Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia AI Centre Faculty of Computer and Information Systems Islamic University of Madinah Madinah Saudi Arabia

ISBN: (纸本)9798331507817

the inability to capture the temporal dynamics of network interactions limits traditional intrusion detection systems (IDSs) in detecting sophisticated threats that evolve over time. this research introduces DynKDD, a dynamic graph-based version of the NSL-KDD dataset, designed to capture the temporal relationships between network entities and their interactions. the proposed solution leverages Graph Neural Networks (GNNs) to detect network intrusions by analysing both the spatial and temporal evolution of network traffic. the methodology involves transforming the static NSL-KDD dataset into a series of time-ordered graph snapshots by assigning synthetic timestamps and constructing dynamic graphs. the snapshots represent the evolution of network interactions, enabling advanced dynamic graph learning models to process and detect intrusions. Five state-of-the-art GNN models, Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Graph Sample and Aggregation (GraphSAGE), Graph Isomorphism Networks (GIN) and Graph Auto-Encoders (GAE), were trained and tested on the DynKDD dataset, and their performance was evaluated using metrics such as accuracy, precision, recall, and F1-score. Evaluation results demonstrate that GraphSAGE consistently outperforms other models, achieving an accuracy of 95.80% on the DynKDD dataset, a notable improvement over the static NSL-KDD dataset. Other models follow a similar trend with a significant improvement on the constructed DynKDD dataset. the results validate the superiority of dynamic graph-based intrusion detection systems in identifying evolving network threats. With its ability to model evolving cyberattacks, DynKDD holds immense potential for real-world applications in protecting critical infrastructure from advanced and persistent threats, offering a transformative approach to network intrusion detection. © 2025 IEEE.

关键词： Network intrusion

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Leveraging Large Language Models for Smart Pharmacy systems: Enhancing Drug Safety and Operational Efficiency 19

Leveraging Large Language Models for Smart Pharmacy Systems:...

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19th international Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Osheba, Abdelrahman Abou-El-Ela, Ahmed Adel, Osama Maaod, Youssef Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Adverse drug reactions (ADRs) remain a crucial challenge in healthcare systems, highly contributing to patient mortality. We present an innovative smart pharmacy system that utilizes advanced large language models (LLMs) to enhance drug safety and pharmacy operational efficiency. Our system integrates real-time data from patient's prescriptions, medication databases, and electronic health records (EHR) to automate the detection of potential drug interactions, optimizing clinical decision-making and reducing ADR-related risks. the system's architecture is designed to easily manage prescription processing for patients, inventory management and control, and Pharmacist consultations through an intelligent chatbot interface. Key features include real-time tracking of medication expiration and inventory levels, an interaction checker API for identifying and mitigating risky drug combinations, and an LLM-powered chatbot for accurate data analysis and visualization. By combining advanced computational techniques with AI-driven insights, our smart pharmacy system not only improves medication safety but also eases pharmacy operations. this transformative approach holds the potential to significantly reduce ADR-related hospital admissions and enhance overall healthcare delivery. Our research underscores the vital role of AI and LLMs in modern pharmacy practice, offering an inclusive solution that integrates easily into existing healthcare infrastructures. the proposed smart pharmacy system can be plugged into hospital EHR systems to automatically track patient medications. © 2025 IEEE.

关键词： Electronic health record

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Best-Practices-Based Training for Improving Cybersecurity in Power Grids 19th

Best-Practices-Based Training for Improving Cybersecurity i...

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19th international Workshop on Data Privacy Management, DPM 2024, 8th international Workshop on Cryptocurrencies and Blockchain Technology, CBT 2024 and 10th Workshop on the Security of Industrial Control systems and of Cyber-Physical systems, CyberICPS 2024 which were held in conjunction with the 29th European Symposium on Research in computer Security, ESORICS 2024

作者： Kavallieratos, Georgios Amro, Ahmed Gkioulos, Vasileios Stamatescu, Grigore Rantos, Kostantinos Lagkas, thomas Demertzis, Konstantinos Paterakis, Fotis Lekidis, Alexios Dalamagkas, Christos Kotsiuba, Igor Katsikas, Sokratis Norwegian University of Science and Technology Gjøvik Norway TÜV Austria Romania 139B Calea Plevnei Bucharest060011 Romania Department of Automation and Industrial Informatics University Politehnica of Bucharest 313 Splaiul Independentei Bucharest060042 Romania Department of Informatics Democritus University of Thrace Kavala65404 Greece School of Science Department of Computer Science Kavala Campus International Hellenic University Kavala65404 Greece Athens Greece Innovation Hub of Public Power Corporation S.A. Attica Greece Durham University Durham United Kingdom

ISBN: (纸本)9783031823619

the increased financial value of the power grids and the multitude of potential attackers, including such with advanced capabilities, makes the enhancement of cybersecurity in power grids very important. In order to strengthen the cybersecurity posture of the power grids, it is necessary to foster the development of cybersecurity skills and knowledge of the personnel working in them. To this end, this work proposes a systematic process that leverages cybersecurity best practices to define training content for personnel employed in power grids. To illustrate the workings of the proposed approach, best practices against phishing, spoofing, denial of service, and unauthorized activities are presented. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Phishing

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19th international Conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

19th International Conference on Artificial Intelligence: Me...

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19th international Conference on Artificial Intelligence: Methodology, systems, and Applications, AIMSA 2024

ISBN: (纸本)9783031815416

the proceedings contain 18 papers. the special focus in this conference is on Artificial Intelligence: Methodology, systems, and Applications. the topics include: Cross-Lingual Style Transfer TTS for High-Quality Machine Dubbing;an Approach to Discovering, Tracking Over Time, and Summarizing Publicly Available Information on a Given Topic;reinforcement Learning Control of Cart Pole System with Spike Timing Neural Network Actor-Critic Architecture;Predictive and Explainable Modelling in Economics on the Case Study of Remittance Prediction Using the NeuDen AI Computational Architecture;deep Learning for Multi-class Diagnosis of thyroid Disorders Using Selective Features;advanced CNN-SVM Machine Learning Techniques for Facial Skin Ultrasound Image Analysis;Testing the NEAT Algorithm on a PSPACE-Complete Problem;investigating the Regularization of Deep Neural Networks for Affect Recognition with Relevance-Guided Local Explanations;Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation;Combining Graph NN and LLM for Improved Text-Based Emotion Recognition;a Novel Study on Modelling and Adaptive Optimal Control of a Tubular Reactor Based on Gaussian Processes;converging Dimensions: Information Extraction and Summarization through Multisource, Multimodal, and Multilingual Fusion;enhancing Question Answering in Lecture Videos with a Multimodal Retrieval-Augmented Generation Framework;agent-Based Simulation Leveraging Declarative Modeling for Efficient Resource Allocation in Emergency Scenarios;enhancing Security in Federated Learning: Detection of Synchronized Data Poisoning Attacks;Clinical and Acquisition Data for Optimizing MGMT Methylation Status Prediction: A Comprehensive Ensemble Strategy Emphasizing Non-invasive Approaches.

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Grid-Based Decompositions for Spatial Data Under Local Differential Privacy 19th

Grid-Based Decompositions for Spatial Data Under Local Diff...

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作者： Balioglu, Berkay Kemal Khodaie, Alireza Taweel, Ameer Gursoy, M.Emre Department of Computer Engineering Koç University Istanbul Turkey

ISBN: (纸本)9783031823480

Local differential privacy (LDP) has recently emerged as a popular privacy standard. With the growing popularity of LDP, recent works have applied LDP to spatial data, and grid-based decompositions have been a common building block in DP and LDP. In this paper, we study three grid-based decomposition methods for spatial data under LDP: Uniform Grid (UG), PrivAG, and AAG. UG is a static approach that consists of equal-sized cells. To enable data-dependent decomposition, PrivAG was proposed by Yang et al. (2022). To advance the state-of-the-art in adaptive grids, this paper proposes the advanced Adaptive Grid (AAG) method. For each grid cell, following the intuition that the cell’s intra-cell density distribution will be affected by its neighbors, AAG performs uneven cell divisions depending on the neighboring cells’ densities. We experimentally compare UG, PrivAG, and AAG using three real-world location datasets, varying privacy budgets, and query sizes. Results show that AAG provides higher utility than PrivAG, demonstrating the superiority of our proposed approach. Furthermore, when the grid size is chosen optimally in UG, AAG still beats UG for small queries, but UG beats AAG for large (coarse-grained) queries. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Differential privacy

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