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Feasibility Analysis and Performance Optimization of the Conflict Test Algorithms for Searching Eviction Sets 26th

Feasibility Analysis and Performance Optimization of the Con...

引用

26th International Conference on Information Security and Cryptology (ICISC)

作者： Li, Zhenzhen Xue, Zihan Song, Wei Chinese Acad Sci Key Lab Cyberspace Secur Def Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9789819712373;9789819712380

Cache side-channel attacks have been widely utilized as an intermediate step in some comprehensive attacks. Eviction sets, especially the minimal eviction sets, are essential components of the conflict-based cache side-channel attacks. It is important to develop efficient search algorithms that incur the lowest latency with the highest success rate. Several fast search algorithms have been proposed in recent years, among which conflict test (CT) achieves the highest success rate with the lowest latency. In this paper, we have conducted the first systematic feasibility analysis of the CT algorithm. Besides failing on the commonly known cache architectures where the last-level cache (LLC) is exclusive or non-inclusive, CT is also found and verified failing on two inclusive LLC architectures if it is running in single-core mode. We have further explored three optimizations for improving the speed performance of the CT algorithm, two of which are newly proposed in this paper.

关键词： computer micro-architecture Cache architecture Cache side-channel attack Eviction set construction

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A Bayesian Interpretation of Fuzzy C-Means 1

引用

Fuzzy Logic and Technology, and Aggregation Operators 13th Conference of the European Society for Fuzzy Logic and Technology, EUSFLAT 2023, and 12th International Summer School on Aggregation Operators, AGOP 2023

作者： Mencar, Corrado Castiello, Ciro Department of Computer Science University of Bari Aldo Moro Bari Italy

ISBN: (数字)9783031399657

ISBN: (纸本)9783031399640

In Explainable Artificial Intelligence, the interpretation of the decisions provided by a model is of primary importance. In this context, we consider Fuzzy C-Means (FCM), which is a clustering algorithm that induces a model from data by assigning, to each data-point, a degree of membership to each cluster such that the sum of memberships is one. A fuzzification parameter is also used to tune the degree of fuzziness of clusters. The distribution of membership degrees suggests an interpretation of membership degrees within the Probability Theory. This paper shows that the membership degrees resulting from FCM can be interpreted as posterior probabilities derived from a Bayesian model, which assumes that data are generated through a specific probability density function. The results give a clear interpretation of the membership degrees of FCM, as well as its fuzzification parameter, within a sound theoretical framework, and shed light on possible extensions of the algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Clustering algorithms

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Joint Multiple Efficient Neighbors and Graph Learning for Multi-view Clustering 1

引用

10th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2023

作者： Sadjadi, Fatemeh Torra, Vicenç Department of Applied Mathematics Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Kerman117-76315 Iran Department of Computing Science MIT Building Umeå University Västerbotten Umeå901 87 Sweden

ISBN: (数字)9783031467813

ISBN: (纸本)9783031467806

Graph-based multi-view clustering is a popular method for identifying informative graphs for e.g. computer vision applications. Nevertheless, optimizing sparsity and connectivity simultaneously is challenging. Multi-view clustering (MC) integrates complementary information from different views. However, most existing methods introduce noise or ignore relevant data structures. This paper introduces a Joint multiple efficient neighbors and Graph (JMEG) learning method for MC. Our approach includes a post-processing technique to optimize sparsity and connectivity by means of identifying neighbors efficiently. JMEG also uses partition space and consensus graph learning to uncover data structures efficiently. Experiments show that JMEG outperforms state-of-the-art methods with negligible additional computation cost. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data structures

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Adaptivity Gap for Influence Maximization with Linear Threshold Model on Trees 1

引用

17th International Joint Conference on Theoretical computer science - Frontier of Algorithmic Wisdom, IJTCS-FAW 2023

作者： Tao, Yichen Wang, Shuo Yang, Kuan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Zhiyuan College Shanghai Jiao Tong University Shanghai200240 China John Hopcroft Center for Computer Science Shanghai Jiao Tong University Shanghai200240 China

ISBN: (数字)9783031393440

ISBN: (纸本)9783031393433

We address the problem of influence maximization within the framework of the linear threshold model, focusing on its comparison to the independent cascade model. Previous research has predominantly concentrated on the independent cascade model, providing various bounds on the adaptivity gap in influence maximization. For the case of a (directed) tree (in-arborescence and out-arborescence), [CP19] and [DPV23] have established constant upper and lower bounds for the independent cascade model. However, the adaptivity gap of this problem on the linear threshold model is not so extensively studied as on the independent cascade model. In this study, we present constant upper bounds for the adaptivity gap of the linear threshold model on trees. Our approach builds upon the original findings within the independent cascade model and employs a reduction technique to deduce an upper bound of 4e2e2-1 for the in-arborescence scenario. For out-arborescence, the equivalence between the two models reveals that the adaptivity gap under the linear threshold model falls within the range of [ee-1,2], as demonstrated in [CP19] under the independent cascade model. © 2023, Springer Nature Switzerland AG.

关键词： Stream flow

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The Smart Coverage Path Planner for Autonomous Drones Using TSP and Tree Selection 1

引用

9th International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

作者： Sundarrajan, M. Jothi, Akshya Prabakar, D. Kadry, Seifedine SRM Institute of Science and Technology Chennai India Department of Applied Data Science Noroff University College Kristiansand Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (数字)9783031440847

ISBN: (纸本)9783031440830

The problem of efficiently covering an area with a limited number of sensors or cameras is an important research topic in various fields such as surveillance, monitoring, and inspection. This problem can be posed as a Traveling Salesman Problem (TSP), where the optimal path needs to be found to visit all the coverage points and return to the starting point. However, the computational complexity of solving TSP increases exponentially with the number of coverage points, making it impractical for large-scale applications. This paper proposes a novel approach to solving the coverage problem as a TSP by incorporating tree selection algorithms to improve efficiency and accuracy. The proposed approach represents the study area as a graph, with each node representing a coverage point and the edges representing the distance between them. To reduce computational complexity, we use various tree selection algorithms to select the best possible starting point for the TSP algorithm. To evaluate the effectiveness of the proposed approach, we conduct simulations using different scenarios with varying numbers of coverage points. The simulation results demonstrate that the proposed approach significantly improves the quality of coverage while reducing computational complexity compared to traditional TSP algorithms. Moreover, the proposed approach can be applied in a wide range of real-world applications, such as surveillance, monitoring, or inspection, where efficient and accurate coverage of an area is essential. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Drones

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Enhancing Question Answering in lecture Videos with a Multimodal Retrieval-Augmented Generation Framework 19th

Enhancing Question Answering in Lecture Videos with a Mul...

引用

19th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

作者： Tanner, Thomas Marfurt, Andreas Oǧul, Hasan Østfold University College Halden Norway Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland

ISBN: (纸本)9783031815416

This paper addresses the challenges of searching and extracting information from lecture videos as found on online platforms. With the increasing popularity of educational videos, learners are faced with the growing challenge of video search and finding relevant sequences within videos to answer questions. This research describes the development of a framework that relies on Retrieval-Augmented Generation (RAG) to address this challenge with a focus on the nature of lecture videos, which usually rely heavily on audio data (voice of the lecturer) and image data (lecture slides) that can partially be extracted as text. The resulting artifact allows a user to ask a question based on a corpus of videos and receive an answer based on the videos’ contents, along with information regarding the most relevant videos and sequences within the corpus. This research illustrates that a RAG system leveraging textual context from lecture videos is effective at improving question answering accuracy compared to relying solely on a standalone Large Language Model (LLM) without contextual enhancements. Further findings revealed that boolean answer correctness can aid in optimizing hyperparameters, that the independent retrieval of text from audio and visual modalities outperforms more complex strategies, and that retrieval followed by re-ranking outperforms other strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Question answering

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Privacy Preserving CSI Fingerprint Device-Free Localization 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Tianxin Zhao, Lingjun Dai, Zeyang Lin, Liang Huang, Huakun School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China School of Software Engineering Sun Yat-sen University Zhuhai China Greater Bay Area Institue of Precision Medicine Guangzhou China Guangdong Provincial Key Laboratory of Building Energy Efficiency and Application Technologies Guangzhou University Guangzhou China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

Device-Free Localization (DFL) method based on wireless signal fingerprint is a mainstream indoor location method at present. The main concerns in this field are location accuracy and complexity of computing, while the problem of privacy protection is easily neglected. Since the leakage of wireless signal data is likely leading to the leakage of location information, this paper proposes a privacy-preserving Channel State Information(CSI) fingerprint device-free localization method based on federated learning(PPDFL). Through federated learning, each client trains a local model using the collected local data and then transmits the trained parameters to the server. The server consolidates these parameters and then distributes the resulting parameters to each client for local model’s updating. After several rounds of training and updating, each client ends up with a fully trained model. Compared with the existing DFL methods, this method would not share the data of each client, and only transmits model parameters between server and client, thus achieving the effect of privacy protection. Several experiments are performed to prove the effectiveness of the proposed PPDFL. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Channel state information

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Requirements Analysis to Support Equal Participation in Hybrid Collaboration Settings in Higher Education 1

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10th International Conference on Learning and Collaboration Technologies (LCT) Held as Part of the 25th International Conference on Human-computer Interaction (HCII)

作者： Avdullahu, Arlind Herrmann, Thomas Rummel, Nikol Ruhr Univ Bochum Educ Psychol & Technol Res Grp Bochum Germany Ruhr Univ Bochum Bochum Germany

ISBN: (数字)9783031345500

ISBN: (纸本)9783031345494;9783031345500

In the transition from COVID-19 back to regular life, hybrid collaboration has gained increased attention. Currently, hybrid collaboration settings in higher education are not sufficient to support equal participation between co-located and remote students. In a first step, we conducted a literature review on the characteristics of co-located, remote, and hybrid collaboration settings involved in successful collaboration. The characteristics of these three types of collaboration were then used to identify design elements for hybrid collaboration settings that support equal participation in small-group work in higher education. In a second step, we designed an initial hybrid collaboration setting for higher education and implemented it in an exploratory study to identify additional features that support equal participation and to analyze collaboration between co-located and remote participants. From the results of this analysis, we identified 16 requirements to support equal participation in hybrid collaboration settings in higher education, which can serve as the basis for implementing and evaluating proposed solutions in a next step.

关键词： Requirements engineering computer-supported collaborative learning Hybrid collaboration Equal participation

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A Novel MLLMs-Based Two-Stage Model for Zero-Shot Multimodal Sentiment Analysis 21st

A Novel MLLMs-Based Two-Stage Model for Zero-Shot Multimoda...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Lu, Heng-Yang Li, Xiao-Fei School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9789819601189

Multimodal Sentiment Analysis (MSA) is a popular research field in natural language processing and affective computing. Recent works have shown the feasibility of Multimodal Large Language Models (MLLMs) for zero-shot MSA. However, we observe that existing MLLMs-based work has the following limitations. (1) The responses do not contain the required labels. (2) There exists the bias brought by the order of label options in prompts. Moreover, for some ambiguous instances, it is difficult to answer correctly by directly asking MLLMs. Thus, we propose a novel MLLMs-based Two-stage Model (M2S) to help distinguish those hard samples. The first stage aims to select hard samples using our proposed probability distribution-based method. The second stage aims to predict sentiment polarity for hard samples by using auxiliary image content with our proposed ensembled method. We also design two new prompts with three kinds of label option orders for both fine-grained and coarse-grained MSA tasks. Experiments on five MSA datasets with different granularities show that our M2S method has substantial improvements by 6% accuracy on average over the state-of-the-art method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Zero-shot learning

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Trimodal Fusion Network Combined Global-Local Feature Extraction Strategy and Spatial-Frequency Fusion Strategy 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Yao, Danyang Wen, Jinyu Chen, Amei Fang, Meie Wei, Xinhua Pan, Zhigeng School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Department of Radiology Second Affiliated Hospital of South China University of Techonology Guangzhou China School of Artifical Intelligence Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9783031201028

ISBN: (纸本)9783031201011

Two or more images are fused into one to complement each other’s information, which is conducive to assisting doctors in locating disease types, conditions, and lesions. Most of the existing methods focus on the fusion of two modalities, and in fact, it is very common for the disease that requires the fusion of trimodal to assist diagnosis. This paper proposes a high-precision trimodal medical image fusion network. According to the information characteristics of anatomical images and functional images in medical images, we designed the global texture module (GTM) and the local detail module (LDM) for feature extraction simultaneously. The fusion strategy fully combined the advantages of spatial domain and frequency domain to retain more complete texture detail information and global contour information. At the same time, the multi-attention mechanism is adopted to extract more effective depth features and more accurate location information. Experimental results show that the proposed method is effective in both subjective vision and objective index evaluation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image fusion

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