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检索条件"任意字段=Workshop on New Trends in Search Computing/Workshop on Search Computing Challenges and Directions"

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Empirical evaluation of excluded middle vantage point forest on biological sequences workload 11

Empirical evaluation of excluded middle vantage point forest...

引用

Proceedings of the 1st workshop on new trends in Similarity search

作者： Xu, Weijia Thompson, Lee Parnell Miranker, Daniel P Texas Advanced Computing Center University of Texas Austin United States Department of Computer Sciences University of Texas Austin United States

ISBN: (纸本)9781450306126

We develop and evaluate a version of the excluded middle vantage point forest in support of range searches and load balancing for parallel queries. The algorithm is evaluated using a benchmark suite that includes real-world biological sequence workloads. Favorable results are demonstrated when comparing to the Multiple Vantage Point Tree and Spatial Approximation Tree algorithms with respect to sequential measures. We also demonstrate that the performance of this approach scales linearly up to at least 128 cores and outperforms a naïve distributed multiple vantage point forest approach when run in parallel. Copyright 2011 ACM.

关键词： Forestry

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AI Agent for Information Retrieval: Generating and Ranking 24

AI Agent for Information Retrieval: Generating and Ranking

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Zhang, Yongfeng Liu, Zhiwei Wen, Qingsong Pang, Linsey Liu, Wei Yu, Philip S. Rutgers State Univ New Brunswick NJ 08901 USA Salesforce AI Res Palo Alto CA USA Squirrel AI Learning Seattle WA USA Salesforce San Francisco CA USA Univ Technol Sydney Sydney NSW Australia Univ Illinois Chicago IL USA

ISBN: (纸本)9798400704369

The field of information retrieval has significantly transformed with the integration of AI technologies. AI agents, especially those leveraging LLMs and vast computational power, have revolutionized information retrieval, processing, and presentation. LLM agents, with advanced memory, reasoning, and planning capabilities, can perform complex tasks, engage in coherent conversations, and provide personalized responses. Despite these advancements, challenges such as ensuring relevance and accuracy, mitigating biases, providing real-time responses, and maintaining data security remain. This workshop aims to explore these challenges, share innovative solutions, and discuss future directions. It will provide a platform to bring together researchers, practitioners to discuss the latest theoretical advancements and practical implementations of AI agents in information retrieval. Topics include AI in search, recommendation, and personalization systems. By gathering a diverse group of experts, the workshop seeks to deepen the understanding of AI agents in information retrieval, advance the field, and enhance its societal impact. Participants will gain insights into cutting-edge research, emerging trends, and foster knowledge exchange and collaboration within the community.

关键词： Information Retrieval AI agent Large Language models (LLMs) Recommender Systems

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Next generation content networks: trends and challenges

Next generation content networks: Trends and challenges

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4th Edition of the UPGRADE-CN workshop on Use of P2P, GRID and Agents for the Development of Content Networks, UPGRADE-CN'09, Co-located with the 2009 International Symposium on High Performance Distributed computing Conference, HPDC'09

作者： Fortino, Giancarlo Mastroianni, Carlo Pathan, Mukaddim Vakali, Athena University of Calabria Italy ICAR-CNR Italy University of Melbourne Australia Aristotle University of Thessaloniki Greece

ISBN: (纸本)9781605585918

Content Delivery Networks (CDNs) [1] have emerged to overcome the inherent limitations of the Internet in terms of user perceived Quality of Service (QoS) when accessing Web content. They offer infrastructure and mechanisms to deliver content and services in a scalable manner, and enhance users' Web experience. However, modern applications do not just perform retrieval or access operations on content but also create content, modify and manage content, and actively place content at appropriate locations. In order to deal with such new requirements, along with the proliferation, formation, and consolidation of the CDN landscape, new forms of Content Networks (CNs) are coming into picture. Thus, distribution and management of content is introducing new challenges in this domain through raising new issues in the architecture, design and implementation of CNs. Moreover, the evolution of next-generation CNs in a large-scale heterogeneous environment demands for a paradigm shift within the research community in terms of the technologies used. Therefore, the integrated uses of existing emerging as well as stable technologies (e.g. agent, P2P, grid, data mining) are anticipated to augment the effectiveness and boost the efficiency of future CN infrastructures [2, 3, 4]. The aim of this panel session is to discuss about new ideas and results in the content networking domain. It will capture the state-of-the-art in content networking domain in terms of organizational structure, content distribution mechanisms, request redirection techniques, and performance measurement methodologies. It will also identify potential research directions and technologies that will drive innovations within this domain. In particular, the talks will be focused on agents for content management and delivery, P2P-based CNs, mobile multimedia CDNs, integration between CDNs and cloud computing, and content delivery on wireless networks.

关键词： Cloud computing

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Semantically-enabled virtual observatories

Semantically-enabled virtual observatories

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workshop on OWL: Experiences and directions, OWLED 2006

作者： McGuinness, Deborah Fox, Peter Cinquini, Luca West, Patrick Benedict, James Darnell, J. Anthony Garcia, Jose Middleton, Don McGuinness Associates Stanford CA 94305 United States Stanford University Stanford CA 94305 United States High Altitude Observatory National Center for Atmospheric Research Boulder CO 80307 United States Scientific Computing Division National Center for Atmospheric Research Boulder CO 80307 United States

We are developing a semantic data framework for virtual observatories. A Virtual Observatory provides online location, retrieval, and analysis services to a variety of heterogeneous scientific data sources. We employ semantic technologies to integrate data and provide "intelligent" services such as ontology-enhanced search, analysis, and data visualization. Our specific initial deployments are in the field of solar-terrestrial physics where we target atmospheric and solar researchers as end users. In this paper, we describe our general use case, our approach using OWL-DL and related tools, and our initial deployment. We describe what we have found as benefits and challenges using OWL-based semantic technologies in our efforts building an operational system. Our system is deployed in two scientific data collections with community usage migration starting now.

关键词： Data visualization

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International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014, Held at the 3rd World Congress on computing and Information Technology, WCIT

International Conference on Artificial Intelligence and Patt...

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International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2014

The proceedings contain 25 papers. The topics discussed include: cloud and mobile security: challenges and future research directions;DLP-technologies: new directions and trends;using fuzzy logic to evaluate trust in e-commerce;gamification of teaching and learning activity: prospect and challenges of mobile game-based learning;ComboSplit: combining various splitting criteria for building a single decision tree;text classification using computational model of the cerebral cortex;restricted Boltzmann machines for modeling businesses;variables selection for multiclass SVM using the multiclass radius margin bound;on the enumeration of frequent patterns in sequences;predicting movie incomes using search engine query data;best-parameterized sigmoid ELM for benign and malignant breast cancer detection;inference engine for classification of expert systems using keyword extraction technique;comparison of classifiers for retinal pathology images using surf and bag-of-words model;content based video quality control for wide-area video surveillance systems;line detection by centre and width estimation;and interactive versus passive 2D face spoofing detection.

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Group communication in real-time computing systems: issues and directions

Group communication in real-time computing systems: issues a...

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IEEE workshop on Future trends of Distributed computing Systems

作者： K.H. Kim University of California Irvine CA USA

Group communication in real-time computing systems has been a subject of research for almost two decades but it is not yet a mature technological field. The purpose of this paper is to review the main goals of developing group communication protocols and establish a practical framework of protocols toward meeting the goals. As a part of this, an attempt is made to clarify the relationship between group communication protocols and fault tolerance. Remaining major research issues and promising directions for solution search are also discussed.

关键词： Real time systems Multicast protocols Fault tolerance Proposals Delay Fault tolerant systems Maintenance Logic programming Telecommunication network reliability Operating systems

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Data Security and Privacy Concepts, Approaches, and Research directions 40

Data Security and Privacy Concepts, Approaches, and Research...

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40th Annual IEEE Computer Software and Applications Conference Symposium (COMPSAC) / Symposium on Software Engineering Technology and Applications (SETA)

作者： Bertino, Elisa Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA Purdue Univ CERIAS W Lafayette IN 47907 USA

ISBN: (纸本)9781467388450

Data are today an asset more critical than ever for all organizations we may think of. Recent advances and trends, such as sensor systems, IoT, cloud computing, and data analytics, are making possible to pervasively, efficiently, and effectively collect data. However for data to be used to their full power, data security and privacy are critical. Even though data security and privacy have been widely investigated over the past thirty years, today we face new difficult data security and privacy challenges. Some of those challenges arise from increasing privacy concerns with respect to the use of data and from the need of reconciling privacy with the use of data for security in applications such as homeland protection, counterterrorism, and health, food and water security. Other challenges arise because the deployments of new data collection and processing devices, such as those used in IoT systems, increase the data attack surface. In this paper, we discuss relevant concepts and approaches for data security and privacy, and identify research challenges that must be addressed by comprehensive solutions to data security and privacy.

关键词： Big Data Internet of Things Sensor Networks Data Confidentiality Data Trustworthiness Application Security

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10th Multi-Disciplinary International workshop on Artificial Intelligence, MIWAI 2016

引用

10th Multi-Disciplinary International workshop on Artificial Intelligence, MIWAI 2016

ISBN: (纸本)9783319493961

The proceedings contain 27 papers. The special focus in this conference is on Multi-disciplinary trends in Artificial Intelligence. The topics include: A hybrid text representation model for improving text categorization based on ***;an entailment procedure for Kleene answer set programs;an efficient Gaussian kernel based fuzzy-rough set approach for feature selection;an improvement of pattern-based information extraction using intuitionistic fuzzy sets;a hierarchical learning approach for finding multiple vehicle number plate under cluttered background;finding risk-averse shortest path with time-dependent stochastic costs;identification of relevant and redundant automatic metrics for MT evaluation;bankruptcy prediction using memetic algorithm;crowd simulation in 3D virtual environments;resolving the manufacturing cell design problem using the flower pollination algorithm;a new multiple objective cuckoo search for university course timetabling problem;from preference-based to multiobjective sequential decision-making;evolutionary analysis and computing of the financial safety net;learning to navigate in a 3d environment;computing sentiment scores of adjective phrases for Vietnamese;sentiment analysis using anaphoric coreference resolution and ontology inference and a GA-SSO based intelligent channel assignment approach for MR-MC wireless sensors networks.

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SLA-based task scheduling in cloud computing using randomized PSO algorithm

SLA-based task scheduling in cloud computing using randomize...

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2024 International Symposium on computing and Intelligent Systems, SCI 2024

作者： Lipsa, Swati Dash, Ranjan Kumar School of Computer Sciences Odisha University of Technology and Research Odisha Bhubaneswar751029 India

In SLA-based cloud computing environments, efficient task scheduling is critical for optimizing resource utilization and meeting performance objectives outlined in Service Level Agreements (SLAs). The existing literature highlights several limitations in this domain, including the complexity of scheduling tasks across heterogeneous Virtual Machines (VMs) while satisfying various constraints such as profit maximization or makespan minimization. These limitations underscore the need for innovative scheduling algorithms capable of addressing the dynamic nature of cloud environments. These challenges motivated us to develop a randomized Particle Swarm Optimization (PSO) algorithm designed specifically for the task scheduling problem in SLA-based cloud computing environments. This algorithm aims to efficiently allocate diverse tasks to available VMs while adhering to the critical constraints defined by the SLAs. The PSO algorithm employs randomized search strategies to efficiently explore the solution space and determine the optimal task for VM assignments. Further, to assess the effectiveness of the proposed scheduling algorithm, comparative analyses are performed against existing scheduling approaches such as Shortest Job First (SJF) and First Come First Serve (FCFS). These comparisons evaluate the performance, efficiency, and scalability of the randomized PSO algorithm in terms of profit maximization and makespan minimization. The outputs of this comparison provide valuable insights into the efficacy of the proposed algorithm in SLA-based cloud computing environments. Therefore, the findings of this study offer practical implications for enhancing the reliability, scalability, and performance of cloud-based services while ensuring compliance with SLA commitments and customer expectations. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Genetic algorithms

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Enabling spatial-visual search for geospatial image databases 5

Enabling spatial-visual search for geospatial image database...

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5th IEEE International Conference on Multimedia Big Data, BigMM 2019

作者： Shahabi, Cyrus University of Southern California Computer Science Department SAL 300 Los AngelesCA90007 United States

ISBN: (纸本)9781728155272

Due to the ubiquity of GPS-equipped cameras, captured photos are automatically tagged with camera location (referred to as geo-tagged images) at the acquisition time. Subsequently, web and mobile applications are emerging that provide search capabilities for images that are similar to a given query image and within a given geographical region (referred to as spatial-visual search). In this talk, I focus on two research challenges to enable spatial-visual search. First, I will discuss the need for new index structures to expedite image search based on both spatial and visual relevance. Hence, I present a generic index structure which supports organizing the images using their spatial and visual properties, and also supports an adaptive distribution of images based on different local partitioning either spatially or visually. Second, I discuss the challenge of inferring a location for the legacy images that are collected without spatial metadata. To utilize and support spatial-visual search for such images, there is a need for an accurate image localization technique for non-geo-tagged images. Moreover, even for geo-tagged images, the camera location of an image can be different from the location of the scene depicted in the image (referred to as scene location) rendering its native localization inaccurate. To address this problem, I present a framework for image scene localization using a CNN-based classification. I conclude by showing how spatial-visual search can be used for retrieving images to be analyzed for various smart-city applications in Los Angeles (e.g., street cleanliness and graffiti recognition). © 2019 IEEE.

关键词： Location

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