998 resources related to Keyword search
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2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
ICSE is the premier forum for researchers to present and discuss the most recent innovations,trends, outcomes, experiences, and challenges in the field of software engineering. The scopeis broad and includes all original and unpublished results of empirical, conceptual, experimental,and theoretical software engineering research.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
IECON is focusing on industrial and manufacturing theory and applications of electronics, controls, communications, instrumentation and computational intelligence.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...
Research on the fundamental contributions and the mathematics behind information forensics, information seurity, surveillance, and systems applications that incorporate these features.
IEEE Intelligent Systems, a bimonthly publication of the IEEE Computer Society, provides peer-reviewed, cutting-edge articles on the theory and applications of systems that perceive, reason, learn, and act intelligently. The editorial staff collaborates with authors to produce technically accurate, timely, useful, and readable articles as part of a consistent and consistently valuable editorial product. The magazine serves software engineers, systems ...
Delicate Balance: Technics, Culture and Consequences, 1989
2011 Eighth Web Information Systems and Applications Conference, 2011
Keyword search is a wildly popular way for querying XML document. However, the increasing volume of XML data poses new challenges to keyword search processing. Parallel database is an efficient solution for this problem. In this paper, we study the problem of effective keyword search for SLCA (Smallest lower common ancestor) in parallel XML databases. We propose two efficient algorithm ...
2017 International conference of Electronics, Communication and Aerospace Technology (ICECA), 2017
Keyword search query processing is considered as the most promising way of information retrieval over XML data in present days as it relieves user from understanding complex schemas of XML document and writing difficult queries using XPath and XQuery. Till date various query processing techniques have been proposed to get meaningful results through keyword search using LCA (Lowest Common Ancestor) ...
2010 International Conference on Machine Vision and Human-machine Interface, 2010
This paper we present a method for computing similarity between different concepts. Ontology has the good hierarchical structure of concepts and the support of logical reasoning, and semantic information can be realized through the semantic relationship of concepts. Ontology technology can be well applied to information retrieval. Ontology-based information retrieval is different from the traditional keyword search. Semantic retrieval can ...
IEEE Transactions on Dependable and Secure Computing, None
Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi- owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. ...
Learning from Katrina: Search and Rescue Robots for Natural Disasters
New Immersive Mediums - The Search For Egg Yolk: IEEE VICS 2018
LDA to Find User Archetypes for Search & Matching
Computer-Assisted Audiovisual Language Learning
IEEE Xplore: Insider Tips to Improve Your Productivity - Part 1
Dynamic Selection of Evolutionary Algorithm Operators Based on Online Learning and Fitness Landscape Metrics
An Introduction to Computational Intelligence in Multi-Criteria Decision-Making: The Intersection of Search, Preference Tradeoff
Keeping Up With IEEE Xplore: Insider Tips to Improve Your Productivity
Bari-Bari-II: Jack-Up Rescue Robot with Debris Opening Function
Some Recent Work in Computational Intelligence for Software Engineering
MyComputer: You Choose, We Deliver
IEEE Xplore: Insider Tips to Improve Your Productivity - Part 4
IEEE Xplore: Search vs. Research
Why is COLLABORATION better with IEEE Collabratec?
Playing Games with Computational Intelligence
New Results from the G2 Axion Dark Matter Experiment - Applied Superconductivity Conference 2018
Shaping Smarter Cities: The Technical Son Returns
IEEE Xplore: Insider Tips to Improve Your Productivity - Part 2
Micro-Apps 2013: Environment Simulation for Counter-IED Jammer Test
Keyword search is a wildly popular way for querying XML document. However, the increasing volume of XML data poses new challenges to keyword search processing. Parallel database is an efficient solution for this problem. In this paper, we study the problem of effective keyword search for SLCA (Smallest lower common ancestor) in parallel XML databases. We propose two efficient algorithm SONB (Scan once with no buffer) and MSOP (Merge strategy based on ordered partition) to compute the SLCA efficiently in the parallel environment. We have performed an extensive experimental study and the results show that our proposed approach achieves high efficiency for the keyword search.
Keyword search query processing is considered as the most promising way of information retrieval over XML data in present days as it relieves user from understanding complex schemas of XML document and writing difficult queries using XPath and XQuery. Till date various query processing techniques have been proposed to get meaningful results through keyword search using LCA (Lowest Common Ancestor) semantic under tree based approach. Amongst many such LCA based techniques which have been proposed to get more accurate and meaningful results SLCA (Smallest LCA) and ELCA (Exclusive LCA), have considered being the most popular ones. However due to AND-semantic constraints of LCA based techniques, SLCA or ELCA results into NULL for keyword queries involving missing elements and provides unintended results if the technique returns root element of the document. To address these issues, we propose an effective XML keyword query processing technique. In this paper we present the proposed technique based on ELCA query semantic which returns the meaningful results when ELCA based technique results into NULL or document root element thereby providing better information discovery over XML data. The proposed technique can also be applied to SLCA based techniques to get similar SLCA based meaningful results.
This paper we present a method for computing similarity between different concepts. Ontology has the good hierarchical structure of concepts and the support of logical reasoning, and semantic information can be realized through the semantic relationship of concepts. Ontology technology can be well applied to information retrieval. Ontology-based information retrieval is different from the traditional keyword search. Semantic retrieval can be realized because Ontology knowledge base strengthens the intrinsic link of the concepts and the implied and unclear information can be deduced through logical reasoning. Our results show that the general framework is feasible and this method is applicable across different concepts.
Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi- owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. In addition, due to privacy concerns on access policies, most existing schemes are vulnerable to off-line keyword-guessing attacks if the keyword space is of polynomial size. Furthermore, it is difficult to identify malicious users who leak the secret keys when more than one data user has the same subset of attributes. In this paper, we present a privacy-preserving CP-ABKS system with hidden access policy in Shared Multi-owner setting (basic ABKS-SM system), and demonstrate how it is improved to support malicious user tracing (modified ABKS-SM system). We then prove that the proposed ABKS-SM systems achieve selective security and resist off-line keyword-guessing attack in the generic bilinear group model. We also evaluate their performance using real-world datasets.
Search operations have become quite indispensable in recent days and loads of research are being organized to store and process the indices required for search operations in a simple and effective manner. Whenever indices are stored, the space it occupies and the ease of access are to be taken care of. This paper briefly deals with the existing system - the inverted index, and discusses the limitations in using such a system. The paper then introduces a new and effective way to store and process indices, namely using interval lists which drastically reduces the storage space and improves the access time. The paper then introduces basic search algorithms that uses indices stored as interval lists. The paper also discusses the basic differences between the existing and proposed system and analyses the various scenarios where each system comes in handy.
Cloud data storage (Storage as a Service) is an important service of cloud computing it can also be referred as Infrastructure as a Service (IaaS). The data storage security problem is an important aspect of Quality of Service (QoS). In the existing cloud storage, the user stores his information in the encrypted format, preserving privacy. When a user wants to retrieve files through keyword search, the cloud storage server returns all matched data, which forces the user to decrypt all returned data. Decrypting all the returned data will result in depletion of CPU time and results in increased memory utilization. This paper proposes an efficient semantic secure keyword based search (ESSKS) scheme, which retrieves exact information needed by the user, ensuring that the same keyword does not always produce the same result, in user querying, reducing computational and communication overhead. This security mechanism also addresses the data integrity problem.
Chord is a common circular P2P model with high routing efficiency and stable data inquiring. This paper proposes the Hot-Chord algorithm according to the fact that hot resources have occupied the majority of searching in the P2P system. Hot-Chord algorithm constructs an inner chord dynamically by using the nodes which hot resources is locating, to decrease the inquiring space; Nodes join or leave the inner chord adaptively according to the variation of resources searching rate. On the basis of theoretical analysis and experimental simulation, this paper certifies that the Hot-Chord algorithm has not only the better searching efficiency in hot resources inquiring but also the lower the average number of hops in finding resources than the original Chord and some improved algorithms.
Keyword search is an inherent example for searching linked data sources on the web. The proposed work tries to route keywords only to relevant sources. This would also reduce the high cost of processing keyword search queries over all sources. This proposed work also tries to compute top-k routing places based on their potentials to contain results for a given keyword query. The number of potential results may increase exponentially with the number of sources and links between them. Most of the results may be not necessary especially when they are not relevant to the user. The proposed work uses to solve the problem of keyword search over large number of linked and structured data source by using keyword expansion.
There is an increasingly number of worldwide available content on the Web, which could be reused to support learning processes. However, Web content is getting more and more structured as multimedia files and reusing it implies on using the whole file, which limits its applicability and the return on investment of its development. So, an interesting approach is the segmentation of content, structuring it on reusable segments. Furthermore, once created, these segments can be reused in an easy way in different educational contexts. This paper discusses the segmentation process and presents the results obtained from a case study conducted using a prototype to evaluate this approach.
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