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2023 Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2023

作者： Pereira, C. Niemann, U. Braun, A. Mengoni, M. Tüting, T. Preim, B. Meuschke, M. Department of Simulation and Graphics University of Magdeburg Germany Department of Computer Science University of Magdeburg Germany University Dermatology Clinic University Hospital Magdeburg Germany

ISBN: (纸本)9783038682165

We present a visual analytics system to support treatment decisions in late-stage Melanoma patients. With the aim of improving patient outcomes, personalized treatment decisions based on individual characteristics and medical histories are crucial. The research focuses on the design and development of a visual analytics system tailored specifically for tumor boards, where multidisciplinary teams collaborate to make informed decisions. By leveraging a comprehensive database containing treatment and tumor stage progression information from over 1100 patients, the system provides healthcare professionals with a holistic overview and facilitates the analysis of individual cases as well as comparisons between multiple patients. The distinction between tumor board preparation systems and systems used during discussions is emphasized to ensure user-centric design and usability. Through the use of visual analytics techniques, complex relationships between treatment outcomes, temporal features, and patient-specific factors are explored, enabling clinicians to identify patterns and trends that may impact treatment decisions. The findings of this research contribute to the growing field of visual analytics in healthcare and have the potential to enhance treatment decision-making and patient care in late-stage cancer scenarios. © 2023 The Authors.

关键词： Tumors

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UpDown: Combining Scalable Address Translation with Locality Control

UpDown: Combining Scalable Address Translation with Locality...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Wang, Yuqing Perarnau, Swann Chien, Andrew A. University of Chicago Department of Computer Science Chicago United States Argonne National Laboratory Chicago United States

ISBN: (纸本)9798350355543

Global shared memories of petabytes are increasingly useful for applications, but traditional page-based techniques do not scale (limited reach), and scalable techniques such as segments fail to provide needed locality control. We propose a novel two-level translation scheme, UpDown, that provides compact, efficient access control to distributed segments of arbitrary size and data layout control, solving problems of reach and data locality. UpDown's novel two-level structure separates access control from data layout, allowing applications to manage locality cheaply, without privileged *** evaluate UpDown against page-based systems, using big data computations. Our results show that UpDown is scalable and provides effective global data locality management. UpDown's translation states are an average of ~620 billion times smaller total and ~130 billion times smaller local translation state. Simulations with synthetic traces show that the two-level translation scheme enables fast, user-level management of data locality in a scalable parallel machine. © 2024 IEEE.

关键词： address translation global address space page translation parallel machine process isolation segment translation virtual memory

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Exploiting discourse relations to produce Arabic extracts

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International Journal of Reasoning-based Intelligent Systems 2022年第2-3期14卷 130-143页

作者： Lagrini, Samira Azizi, Nabiha Redjimi, Mohammed Labged Laboratory Computer Science Department Badji Mokhtar University P.O. Box 12 Annaba23000 Algeria LICUS Laboratory Computer Science Department University 20 Aout 1955 Skikda21000 Algeria

Text summarisation is one of the interesting tools for a quick and optimal exploitation of the huge amount of online textual documents. Several approaches have been proposed to date to produce extractive summaries in Arabic. However, in most cases, the linguistic qualities of the generated summary are not satisfactory. In this paper, we attempt to overcome this limitation by proposing a new approach for single-document summarisation that combines a discourse analysis following the rhetorical structure theory (RST) framework and a score-based method. Unlike traditional RST-based approaches, the proposed approach relies on exploiting intra-sentence discourse relations instead of text discourse structure to produce a primary summary. Then, each sentence within the primary summary is evaluated based on a combination of statistical and linguistic features to produce the final summary considering user compression rate. The proposed approach was evaluated under Essex Arabic Summaries Corpus (EASC) using ROUGE-1 and ROUGE-2 measures, and compared against other existing methods. A human evaluation was also conducted in order to assess the linguistic qualities of generated summaries. Experimental results are very encouraging and prove that, exploiting discourse relations is very useful to produce Arabic extractive summaries with good linguistic qualities. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Text processing

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Tunable multicolor luminescence in vanadates from yttrium to indium with enhanced luminous efficiency and stability for its application in WLEDs and indoor photovoltaics

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Nano Research 2023年第8期16卷 11486-11494页

作者： Yaoyang Zhang Jing Mao Peifen Zhu Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMissouri 65211USA

The preparation of high-efficiency phosphor is the key to the construction of white light-emitting diode(WLED)devices and their application in indoor *** with YVO_(4),InVO_(4)is not suitable as the host material of lanthanide ions because of its strong ***,the work focused on combining the broadband emission from InVO_(4)and the red luminescence from YVO_(4):Eu^(3+)to obtain enhanced and stable multicolor *** band structure,density of state,and optical properties were studied by density functional *** spectral configuration of YVO_(4):In^(3+)/Eu^(3+)with(112)surface appears to be broadening and redshifts with increasing layer *** the In^(3+)concentration is 3.5 mol%,the YVO_(4):30%Eu^(3+)/In^(3+)emits the strongest *** Judd-Ofelt parameterΩ2 of YVO_(4):In^(3+)/Eu^(3+)increases with increaing In^(3+)concentration,indicating that the symmetry *** adjusting In^(3+)/Eu^(3+)contents,the YVO_(4):In^(3+)/Eu^(3+)not only can emit white light with a color rendering index of 95,but also can be used as high-efficiency red phosphor to build WLED devices with blue emitting N/Tb codoped carbon quantum dots(CQDs-N:Tb^(3+))and green emitting MOF:Tb^(3+)(MOF=metal organic framework),for which the color rendering index can also reach 95 and the color temperature is 5549 *** manufactured WLED devices were further used to excite the silicon solar cell and make it show good photoelectric characteristics.

关键词： luminescence enhancement multicolor stability density functional theory(DFT)calculation white light-emitting diodes(WLEDs) indoor photovoltaics

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Counter Data Paucity through Adversarial Invariance Encoding: A Case Study on Modeling Battery Thermal Runaway

Counter Data Paucity through Adversarial Invariance Encoding...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tabassum, Anika Allu, Srikanth Kannan, Ramakrishnan Muralidhar, Nikhil Oak Ridge National Laboratory United States Stevens Institute of Technology Department of Computer Science United States

ISBN: (纸本)9798350362480

Lithium-ion batteries, widely used for their durability and high energy storage, face the risk of internal short circuits leading to catastrophic thermal runaway events. These events, triggered by external stimuli like mechanical loads, pose safety concerns in applications such as electric vehicles. Detecting and understanding thermal runaway events is crucial, but physics-driven models struggle to explain the non-linear evolution of battery temperature during these events, considering factors like material composition and state-of-charge. Due to the rarity of these events and the cost of data collection, we propose a deep learning (DL) model to predict battery temperature responses during thermal runaway. The challenge lies in the scarcity of data, making traditional DL models prone to overfitting and learning low-quality representations of the complex *** approach introduces a novel few-shot architecture that incorporates an adversarially governed invariant encoding process. This architecture aims to distill "invariant"relationships by addressing distributional shifts in data across various battery properties, facilitating the detection of thermal runaway events. Specifically, our results demonstrate that deep learning models conditioned on these "invariant"representations outperform state-of-the-art baselines, achieving a remarkable 96.8% performance improvement in terms of the popular metric MAPE. This framework presents a promising direction for enhancing battery safety modeling, particularly in the context of rare and complex events like thermal runaway. Our code and code and dataset used for the paper are public. © 2024 IEEE.

关键词： State of charge

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Advancing Cybersecurity with LLMs: A Comprehensive Review of Intrusion Detection Systems and Emerging Applications 7

Advancing Cybersecurity with LLMs: A Comprehensive Review of...

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7th International Conference on Informatics and Applied Mathematics, IAM 2024

作者： Djallel, Hamouda Ferrag, Mohamed Amine Nadjette, Benhamida Hamid, Seridi Labstic Laboratory Department of Computer Science Guelma University B.P. 401 Guelma24000 Algeria

The rapid advancements in Transformers and Large Language Models (LLMs) have significantly transformed the landscape of cybersecurity, particularly in Intrusion Detection Systems (IDS). These models offer enhanced detection accuracy, scalability, and adaptability, surpassing traditional approaches in identifying and mitigating sophisticated cyber threats. This survey explores the integration of LLMs in IDS by addressing six key research dimensions: foundational methodologies, comparative performance with classical techniques, challenges in interpretability, practical applications, emerging trends, and directions for future research. By synthesizing the latest advancements, this work aims to provide a comprehensive framework for understanding the role of LLMs in strengthening cybersecurity and fostering innovation in network security and anomaly detection. © 2024 Copyright for this paper by its authors.

关键词： Network intrusion

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Diversified and compatible web APIs recommendation based on game theory in IoT

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Digital Communications and Networks 2024年第4期10卷 1198-1209页

作者： Wenwen Gong Huiping Wu Xiaokang Wang Xuyun Zhang Yawei Wang Yifei Chen Mohammad R.Khosravi College of Information and Electrical Engineering China Agricultural UniversityBeijing100000China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyShouguang262700China Department of Computer Science St.Francis Xavier UniversityAntigonishNSCanada Department of Computing Macquarie UniversitySydneyNSW2122Australia Department of Computer Engineering Persian Gulf UniversityBushehr***Iran Department of Electrical and Electronic Engineering Shiraz University of TechnologyShiraz71557-13876Iran

With the ever-increasing popularity of Internet of Things(IoT),massive enterprises are attempting to encapsulate their developed outcomes into various lightweight Web Application Programming Interfaces(APIs)that can be accessible *** this context,finding and writing a list of existing Web APIs that can collectively meet the functional needs of software developers has become a promising approach to economically and easily develop successful mobile ***,the number and diversity of candidate IoT Web APIs places an additional burden on application developers’Web API selection decisions,as it is often a challenging task to simultaneously ensure the diversity and compatibility of the final set of Web APIs *** this challenge and latest successful applications of game theory in IoT,a Diversified and Compatible Web APIs Recommendation approach,namely DivCAR,is put forward in this *** of all,to achieve API diversity,DivCAR employs random walk sampling technique on a pre-built“API-API”correlation graph to generate diverse“API-API”correlation ***,with the diverse“API-API”correlation subgraphs,the compatible Web APIs recommendation problem is modeled as a minimum group Steiner tree search problem.A sorted set of multiple compatible and diverse Web APIs are returned to the application developer by solving the minimum group Steiner tree search *** last,a set of experiments are designed and implemented on a real dataset crawled from *** results validate the effectiveness and efficiency of our proposed DivCAR approach in balancing the Web APIs recommendation diversity and compatibility.

关键词： Internet of things Web APIs recommendation Game theory Diversity and compatibility

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Ensemble deep learning of CNN vs vision transformers for brain lesion classification on MRI images 7

Ensemble deep learning of CNN vs vision transformers for bra...

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7th International Conference on Informatics and Data-Driven Medicine, IDDM 2024

作者： Laribi, Norelhouda Gaceb, Djamel Touazi, Fayçal Rezoug, Abdellah Sahad, Abdelmoumen Reggai, Massine Omar LIMOSE laboratory Computer science Department University M'hamed Bougara Independence Avenue Boumerdes35000 Algeria

Deep learning models, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have achieved state-of-the-art performance in the classification of brain lesions on MRI images. However, the complexity of this type of images requires CNNs to use deeper architectures with more parameters to effectively capture their high-dimensional features and subtle variations. On one side, ViTs provide a different approach to tackle this challenge, but they require larger datasets and more computational costs. On the other side, ensemble deep learning techniques, such as Bagging, Stacking, and Boosting, can help mitigate these limitations by combining multiple CNN models. This study explores these methods and compares them to evaluate their accuracy and efficiency using three approaches: CNN-based transfer learning, ViT-based transfer learning, and ensemble deep learning techniques, such as Bagging, Stacking, and Boosting based on XGBoost, AdaBoost methods. The experiments, carried out on four MRI image datasets with different levels of complexity and types of brain lesions, show that the combination of CNNs with ensemble techniques offers very competitive performances to those of individual CNNs and ViTs, with interesting improvements compared to already existing approaches. © 2024 CEUR-WS. All rights reserved.

关键词： Convolutional neural networks

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Protecting machine learning systems using blockchain: solutions, challenges and future prospects

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Multimedia Tools and Applications 2024年 1-28页

作者： Hajlaoui, Rejab Dhahri, Salah Mahfoudhi, Sami Moulahi, Tarek Alotibi, Gaseb Department of Computer and Informtion Sciences College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Laboratory of Advanced Multifunctional Materials and Technological Applications Faculty of Science and Technology of Sidi Bouzid University of Kairouan Sidi Bouzid Tunisia Department of Information and Technology Management Mediterranean School of Business South Mediterranean University Tunis Tunisia Department of Information Technology College of Computer Qassim University Buraidah Saudi Arabia Department of Computers and Information Technology Alwajah College University of Tabuk Tabuk Saudi Arabia

Machine learning-based systems have emerged as the primary means for achieving the highest levels of productivity and efficiency. They have become the most influential competitive factor for many technologies and business companies such as Cloud AI Companies (Google, Facebook…) and Health Care AI Companies (Tempus, Nanox …). However, privacy and security issues have become the biggest challenge facing all ML applications. These threats are known as Adversarial Machine Learning (AML). Their main goal is to maliciously manipulate training data and model parameters to obtain misleading results. To overcome these challenges, several research studies have proven that Blockchain, which relies on cryptographic technologies, constitutes a promising solution for securing ML applications. It provides high guarantees to make the system scalable, reliable and more secure. In this survey, we provide an overview of ML applications and the most hostile attacks they face. Then, we define the blockchain technique and its distinctive features. After that, we conduct a comprehensive and detailed survey of the latest and best blockchain-based research to address security issues in ML applications (integrity, confidentiality and availability). We analyze, compare and comment on all the proposed solutions. In the end, we discuss in detail the various challenges that hinder the adoption of blockchain technology and we extract the most important scientific trends that will serve as guidance and support for researchers in their future works. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Preventing the Popular Item Embedding Based Attack in Federated Recommendations 40

Preventing the Popular Item Embedding Based Attack in Federa...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Zhang, Jun Li, Huan Rong, Dazhong Zhao, Yan Chen, Ke Shou, Lidan Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Aalborg University Department of Computer Science Aalborg Denmark

ISBN: (纸本)9798350317152

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense. © 2024 IEEE.

关键词： Embeddings

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