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Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1

作者： Dezhi Yang Guoxian Yu Jun Wang Jinglin Zhang Carlotta Domeniconi School of Software Shandong University Jinan Shandong China School of Control Science and Engineering Shandong University Jinan Shandong China Department of Computer Science George Mason University Fairfax VA USA

ISBN: (纸本)9798400712456

A fundamental problem in many science domains is learning the causal structure of a system from observed data. The observed data canonically come from multiple environments (i.e. different times, locations, and measurements), and causal models may have unobserved shifts. Although the causal graphs can be identified by modeling the distribution changes among different environments, existing solutions can only learn causal structures when given environmental information. In contrast, we propose a causal discovery approach (CausalSME) which automatically identifies pseudo environments and unobserved distribution shifts. Specifically, CausalSME learns a causal model containing unobserved variables, which can correct the distribution shifts with mixed environments. The heart of CausalSME is a variational autoencoder that infers shifted causal effects of unobserved variables and guides the identification of environment information. It further divides the shifted samples by the identified environments to jointly learn an invariant causal model. We prove the structure identifiability of CausalSME with the causal additive model. In our extensive experiments we show that CausalSME achieves state-of-the-art performance.

关键词： adversarial learning

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SirLLM: Streaming Infinite Retentive LLM

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

As Large Language Models (LLMs) become increasingly prevalent in various domains, their ability to process inputs of any length and maintain a degree of memory becomes essential. However, the one-off input of overly long texts is limited, as studies have shown that when input lengths exceed the LLMs' pre-trained text length, there is a dramatic decline in text generation capabilities. Moreover, simply extending the length of pre-training texts is impractical due to the difficulty in obtaining long text data and the substantial memory consumption costs this would entail for LLMs. Recent efforts have employed streaming inputs to alleviate the pressure of excessively long text inputs, but this approach can significantly impair the model's long-term memory capabilities. Motivated by this challenge, we introduce Streaming Infinite Retentive LLM (SirLLM), which allows LLMs to maintain longer memory during infinite-length dialogues without the need for fine-tuning. SirLLM utilizes the Token Entropy metric and a memory decay mechanism to filter key phrases, endowing LLMs with both long-lasting and flexible memory. We designed three distinct tasks and constructed three datasets to measure the effectiveness of SirLLM from various angles: (1) DailyDialog;(2) Grocery Shopping;(3) Rock-Paper-Scissors. Our experimental results robustly demonstrate that SirLLM can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. When having a coversation, "A sir could forget himself," but SirLLM never does! Our code is publicly available at https://***/Zoeyyao27/SirLLM. © 2024, CC BY.

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Deepinfusion: A Dynamic Infusion Based-Neuro-Symbolic Ai Model for Segmentation of Intracranial Aneurysms

SSRN

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SSRN 2023年

作者： Abdullah, Iram Javed, Ali Malik, Khalid Mahmood Malik, Ghaus Department of Software Engineering University of Engineering & Technology Punjab Taxila47040 Pakistan Department of Computer Science & Engineering Oakland University RochesterMI48309 United States Department of Neurosurgery Henry Ford Hospital DetroitMI48202 United States

The detection and segmentation of cerebral aneurysms is a crucial step in the development of a clinical decision support system for estimating aneurysm rupture risk. However, accurately identifying and segmenting regions of interest in 2D medical images is often challenging, particularly when using deep learning (DL) methods on small datasets with limited annotated data. The accuracy of DL approaches is often affected by the availability of large, annotated training datasets that are required for effective deep learning. Additionally, when using DL to differentiate aneurysms from arterial loops in 2D DSA images, DL can fail to detect aneurysms in areas where dye concentration is low. To address these issues and enhance the reliability and accuracy of aneurysm detection and segmentation methods, incorporating medical expert-advised, hand-crafted features can provide a clinical perspective to DL methods. This approach can help to improve the performance of DL methods by providing additional information that is not captured in the data. To this end, a novel Neuro-symbolic AI-based DeepInfusion model is proposed which allows for the infusion of human intellect through hand-crafted features into deep neural networks (DNNs), thus combining the strengths of DL with the knowledge and expertise of medical professionals. The proposed approach includes a novel technique for dynamic layer selection and feature weight adjustment during the model infusion process. The performance of the DeepInfusion model is evaluated on an in-house prepared dataset of 409 DSA images, and experimental results demonstrate the effectiveness of the proposed method for the segmentation of cerebral aneurysms. The model achieves an accuracy of 99.98%, an F1-score of 93.23%, and an IOU- score of 96.02% on unseen DSA images. The model is also tested on two publicly available datasets of Kvasir-SEG polyp and DRIVE for vessel segmentation of retinal images. The results show a significant improvement compar

关键词： Deep neural networks

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A novel three-phase primary-side control topology for high-power IPT system

A novel three-phase primary-side control topology for high-p...

引用

Wireless Power Week (WPW)

作者： Zhihao He Duleepa Thrimawithana Martin Neuburger Grant Covic Department of Electrical Computer and Software Engineering University of Auckland Auckland New Zealand Faculty of Mobility and Technology Esslingen University of Applied Sciences Goppingen Germany

There is a recognised need for developing multiphase inductive power transmission (IPT) systems to shorten the charging time in the wireless charging field. Researchers have not treated the converter needed to drive the multi-phase coils in much detail. This paper proposes a three-phase integrated boost multilevel converter (TP-IBMC) based on a multi-phase IPT system. There are advantages of multi-level converters and the boost function in TP-IBMC. Using a digitized modulation scheme, it can output a square wave voltage with variable amplitude to regulate the power in the primary. The TP-IBMC includes three bridge arms (a, b, c), each of which contains a DC inductor to achieve energy storage and multiple sub-modules to realize variable levels. The number of sub-modules can be increased or decreased. This paper establishes a three-phase IPT model considering the mutual inductance between phases and discusses the capacitor voltage balancing strategy. A primary-side control IPT simulation model compliant with the SAE J2954 WPT 3 standard is built to verify that TP-IBMC can regulate output power through a digitised modulation scheme, under the fluctuations of coupling factor (i.e., 0.14 - 0.31) and battery voltage (i.e., 280 V - 420 V).

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Enhancing Medical Image-Based Diagnostics Through the Application of Convolutional Neural Networks Techniques

Enhancing Medical Image-Based Diagnostics Through the Applic...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Haewon Byeon Rajit Nair V. Mahalakshmi Mohammed I. Khalaf Bharat Kaushik Mohammad Shabaz Department of AI and Software Inje University Gimhae Republic of Korea School of Computing Science & Engineering VIT Bhopal University Bhopal India Department of Computer Science College of Engineering and computer science Jazan University Jazan Saudi Arabia Department of computer science Al Maarif University College Al Anbar Iraq Masters University of Northern Iowa Lowa USA Model Institute of Engineering and Technology Jammu J&K India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Perioperative nutrition evaluation is crucial for patient care and resource efficiency in healthcare. This work proposes a deep learning-based perioperative nutrition evaluation tool that leverages sophisticated machine learning to improve predictions and assist clinicians in making better choices. As surgical techniques become more complex and medical technology progresses, a thorough postoperative nutrition study becomes more important than ever. An extensive review demonstrates that the recommended strategy performs better than other machine learning techniques. Our model's accuracy, precision, memory, and F1-score let it predict surgical outcomes more accurately. The higher AVC-ROC and AVC-PR values of the suggested approach show that it performs particularly well. Its sensitivity-specificity balance is demonstrated by this. Our approach generates fewer errors, according to the confusion matrix analysis, which makes it reliable for healthcare decisions. Our work enhances critical healthcare by assessing nutrition with state-of-the-art techniques.

关键词： Fluctuations Reviews Hospitals Measurement uncertainty Surgery Medical services Predictive models

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A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions

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IEEE Communications Surveys and Tutorials 2025年

作者： Boateng, Gordon Owusu Sami, Hani Alagha, Ahmed Elmekki, Hanae Hammoud, Ahmad Mizouni, Rabeb Mourad, Azzam Otrok, Hadi Bentahar, Jamal Muhaidat, Sami Talhi, Chamseddine Dziong, Zbigniew Guizani, Mohsen Khalifa University KU6G Research Center Department of Computer Science Abu Dhabi United Arab Emirates Ecole de Technologie Superieure Department of Software and IT Engineering Montreal Canada Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Concordia University Concordia Institute for Information Systems Engineering MontrealQC Canada Ecole de Technologie Superieure Department of Electrical Engineering Montreal Canada Department of Computer Science Abu Dhabi United Arab Emirates Khalifa University KU6G Research Center Department of Computer and Information Engineering Abu Dhabi United Arab Emirates Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada Mohamed Bin Zayed University of Artificial Intelligence Department of ML Abu Dhabi United Arab Emirates

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unparalleled capabilities in various Natural Language Processing (NLP) tasks and generating context-aware insights, offering transformative potential for automating diverse communication NSM tasks. Contrasting existing surveys that consider a single network domain, this survey investigates the integration of LLMs across different communication network domains, including mobile networks and related technologies, vehicular networks, cloud-based networks, and fog/edge-based networks. First, the survey provides foundational knowledge of LLMs, explicitly detailing the generic transformer architecture, general-purpose and domain-specific LLMs, LLM model pre-training and fine-tuning, and their relation to communication NSM. Under a novel taxonomy of network monitoring and reporting, AI-powered network planning, network deployment and distribution, and continuous network support, we extensively categorize LLM applications for NSM tasks in each of the different network domains, exploring existing literature and their contributions thus far. Then, we identify existing challenges and open issues, as well as future research directions for LLM-driven communication NSM, emphasizing the need for scalable, adaptable, and resource-efficient solutions that align with the dynamic landscape of communication networks. We envision that this survey serves as a holistic roadmap, providing critical insights for leveraging LLMs to enhance NSM. © 1998-2012 IEEE.

关键词： Resource allocation

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A Secured and Optimized Broadcast Authentication Scheme for the Internet of Medical Things

A Secured and Optimized Broadcast Authentication Scheme for ...

引用

Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Syed Waleed Riaz Fazlullah Khan Insaf Ullah Bandar Alshawi Ryan Alturki Mohammad Wedyan Computer Science Department Abdul Wali Khan University Mardan KPK Pakistan Faculty of Science and Engineering School of Computer Science University of Nottingham Ningbo China Ningbo Zhejiang China Institute for Analytics and Data Science University of Essex Colchester UK Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Software Engineering College of Computing Umm Al-Qura University Makkah Saudi Arabia Department of Computer Sciences Faculty of Information Technology and Computer Sciences Yarmouk University (YU) Irbid Jordan

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

This Internet of Medical Things (IoMT), facilitates the medical stop regarding real-time monitoring of patients, medical emergency management, remote surgery, patient information management, medical equipment, drug monitoring, etc. However, IoMT devices communicate with each other in an open environment that makes them vulnerable to a wide range of malicious threats from malicious entities. To protect the devices and their associated data, we designed a broadcast authentication scheme for IoMT devices using identity-based public key cryptography that exploits the lightweight features of the Hyper-Elliptic Curve (HEC). We then performed the security analysis based on the Random Oracle Model (ROM), in which we have proved that the proposed scheme is unforgeable under the hardiness of the hyperelliptic curve discrete logarithm problem. The proposed scheme is analyzed in terms of computational and communication overhead and the experimental results justify the superiority of the proposed work in comparison to the existing schemes.

关键词： Performance evaluation Computational modeling Authentication Surgery Internet of Medical Things Public key cryptography Real-time systems Information management Monitoring Read only memory

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Language-driven Grasp Detection with Mask-guided Attention

arXiv

引用

arXiv 2024年

作者： Van Vo, Tuan Vu, Minh Nhat Huang, Baoru Vuong, An Le, Ngan Vo, Thieu Nguyen, Anh FPT Software AI Center Viet Nam Automation & Control Institute TU Wien Austria GmbH Austria Imperial College London United Kingdom Department of Computer Science & Computer Engineering University of Arkansas United States National University of Singapore Singapore Department of Computer Science University of Liverpool United Kingdom

Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we propose a new method for language-driven grasp detection with mask-guided attention by utilizing the transformer attention mechanism with semantic segmentation features. Our approach integrates visual data, segmentation mask features, and natural language instructions, significantly improving grasp detection accuracy. Our work introduces a new framework for language-driven grasp detection, paving the way for language-driven robotic applications. Intensive experiments show that our method outperforms other recent baselines by a clear margin, with a 10.0% success score improvement. We further validate our method in real-world robotic experiments, confirming the effectiveness of our approach. © 2024, CC BY.

关键词： Semantic Segmentation

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Transforming Healthcare Diagnostics With Tensorized Attention and Continual Learning on Multi-Modal Data

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IEEE Transactions on Consumer Electronics 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Khan, Muhammad Attique Shabaz, Mohammad Wu, Zongze AlHammadi, Dina Abdulaziz Liu, Weixiang Algamdi, Shabbab Ali Li, Yang Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Prince Mohammad Bin Fahd University Department of AI Alkhobar Saudi Arabia Model Institute of Engineering and Technology J and K Jammu India Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Information Systems Riyadh11671 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Science and Engineering Department of Software Engineering Al Kharj Saudi Arabia Shihezi University College of Mechanical and Electrical Engineering Shihezi832003 China

Analyzing multi-modal medical data in the setting of uncertain healthcare situations continues to be a major topic in medical image analysis and healthcare big data. Traditional machine learning algorithms are severely hampered by inaccurate data fusion, a lack of adaptability to changing patient data, and challenges managing uncertainty. These difficulties are made worse by complicated medical images and diverse data sources, which results in less accurate diagnosis and worse-than-ideal healthcare choices. To tackle these urgent problems, this paper suggests two new approaches: Continual Learning using Progressive Neural Networks (PNNs) and Tensorized Attention Mechanism for Data Fusion. The Tensorized Attention Mechanism improves multi-modal data fusion by using dynamic, task-specific attention to improve feature alignment across modalities, and the PNNs framework uses continual learning, memory augmentation, and domain adaptation to ensure robust learning under data uncertainty. We test these methods on a variety of multi-modal datasets, such as MIMIC-IV, CheXpert, MOST, OAI, and Heart Murmur, which offer a comprehensive representation of medical data from clinical reports, chest X-rays, heart murmurs, and other heterogeneous data sources. Our experimental results show notable improvements in diagnostic performance, with notable results like a CFI of 0.10, a KR score of 90.4%, and an MMC score of 0.097, indicating superior generalization and robustness across domains. Healthcare AI applications could be revolutionized by the use of specialized losses, such as Conditional Variational Autoencoder (CVAE), Adversarial Contrastive Learning (ACL), Reciprocal Regularization, and domain adaptation losses, which are essential for preventing forgetting and guaranteeing learning stability across shifting data streams. © 1975-2011 IEEE.

关键词： Sensor data fusion

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed System

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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Computing (ICOCO), IEEE International Conference on

作者： Muntadher Saadoon Siti Hafizah Ab. Hamid Hazrina Sofian Hamza Altarturi Nur Nasuha Zati Hakim Azizul Haqi Khalid Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Computer and Communication Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism.

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