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4th International Conference on information systems and Computer Networks, ISCON 2019

作者： Tuli, Shreshth Basumatary, Nipam Buyya, Rajkumar CLOUDS Laboratory School of Computing and Information Systems University of Melbourne Australia Department of Computer Science and Engineering Delhi India Madras India

ISBN: (纸本)9781728136516

Data-intensive applications are growing at an increasing rate and there is a growing need to solve scalability and high-performance issues in them. By the advent of cloud computing paradigm, it became possible to harness remote resources to build and deploy these applications. In recent years, new set of applications and services based on Internet of Things (IoT) paradigm, require to process large amount of data in very less time. Among them surveillance and object detection have gained prime importance, but cloud is unable to bring down the network latencies to meet the response time requirements. This problem is solved by Fog computing which harnesses resources in the edge of the network along with remote cloud resources as required. However, there is still a lack of frameworks that are successfully able to integrate sophisticated software and applications, especially deep learning, with fog and cloud computing environments. In this work, we propose a framework to deploy deep learning-based applications in fog-cloud environments to harness edge and cloud resources to provide better service quality for such applications. Our proposed framework, called EdgeLens, adapts to the application or user requirements to provide high accuracy or low latency modes of services. We also tested the performance of the software in terms of accuracy, response time, jitter, network bandwidth and power consumption and show how EdgeLens adapts to different service requirements. © 2019 IEEE.

关键词： Object detection

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Relevance Search over Knowledge Graph with User-entity Interaction Data 12

Relevance Search over Knowledge Graph with User-entity Inter...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Li, Jianyu Yue, Kun Yunnan University School of Information Science and Engineering China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350340181

Relevance search is to find a list of entities in a knowledge graph (KG), which are associative to a query entity. However, many entities are not linked in KG but are actually associated by user interactions. To this end, we propose a joint weighting function to evaluate the entity associations from both KG and user-entity interaction data simultaneously. Upon the subgraph extracted from KG w.r.t the query entity, we obtain the associative entities by calculating their association degrees. Experimental results show that the effectiveness of our method outperforms other competitors. © 2023 IEEE.

关键词： Association reactions

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Towards a framework definition for learning process engineering supported by an adaptive learning system

Towards a framework definition for learning process engineer...

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2012 IEEE International Conference on Technology Enhanced Education, ICTEE 2012

作者： Bayounes, Walid Saâdi, Ines Bayoudh Kinshuk Ghézala Ben, Henda RIADI Research Laboratory ENSI Manouba University Manouba Tunisia School of Computing and Information Systems Athabasca University Athabasca AB Canada

ISBN: (纸本)9781457707261

The work presented in this paper is related to the area of learning process engineering (LPE) which focuses on learning process construction supported by an adaptive learning system. This area has emerged in response to an increasing awareness that existing learning processes are not well suited to the needs of the learners, the teachers, the tutors, the system administrators and the system designers. We propose a faceted framework to understand and classify issues in learning process construction. This latter identifies four different and complementary viewpoints. Each view allows us to capture a particular aspect of the learning process. In order to study, understand and classify a particular view of LPE in its diversity, we associate a set of facets with each view. While a facet allows an in-depth description of one specific aspect of LPE, the views show the variety and diversity of these aspects. © 2012 IEEE.

关键词： Process engineering

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Integration of global and local information in videos for key frame extraction 11

Integration of global and local information in videos for ke...

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11th IEEE International Conference on information Reuse and Integration, IRI 2010

作者： Liu, Dianting Shyu, Mei-Ling Chen, Chao Chen, Shu-Ching Department of Electrical and Computer Engineering University of Miami Coral Gables FL 33124 United States Distributed Multimedia Information Systems Laboratory School of Computing and Information Sciences Florida International University Miami FL 33199 United States

ISBN: (纸本)9781424480975

Key frame extraction methods aim to obtain a set of frames that can efficiently represent and summarize video contents and be reused in many video retrieval-related applications. An effective set of key frames, viewed as a high-quality summary of the video, should include the major objects and events of the video, and contain little redundancy and overlapped content. In this paper, a new key frame extraction method is presented, which not only is based on the traditional idea of clustering in the feature extraction phase but also effectively reduces redundant frames using the integration of local and global information in videos. Experimental results on the TRECVid 2007 test video dataset have demonstrated the effectiveness of our proposed key frame extraction method in terms of the compression rate and retrieval precision. © 2010 IEEE.

关键词： Integration

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and cloud computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) cloud computing Diagnosis Digital Health Edge computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Attribute-Based Signature with Policy-and-Endorsement Mechanism

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Journal of Computer Science & Technology 2010年第6期25卷 1293-1304页

作者：王怀习朱岩冯荣权 Stephen S.Yau LMAM School of Mathematical SciencesPeking University Electronic Engineering Institute Institute of Computer Science and Technology Peking University Key Laboratory of Network and Software Security Assurance (Peking University) Ministry of Education Information Assurance Center and School of Computing Informatics and Decision Systems Engineering Arizona State University

In this paper a new signature scheme,called Policy-Endorsing Attribute-Based Signature,is developed to correspond with the existing Ciphertext-Policy Attribute-Based *** signature provides a policy-and-endorsement *** this mechanism a single user,whose attributes satisfy the predicate,endorses the *** signature allows the signer to announce his endorsement using an access policy without having to reveal the identity of the *** security of this signature,selfless anonymity and existential unforgeability,is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear map groups.

关键词： cryptography fine-grained access control attribute-based signature policy-and-endorsement selfless anonymity existential unforgeability

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Some thoughts on the legal background of the continuously increasing privacy risk in information systems and on how to deal with it

Some thoughts on the legal background of the continuously in...

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24th IEEE International Conference on Advanced information Networking and Applications Workshops, WAINA 2010

作者： Quirchmayr, Gerald Wills, Chrsitopher C. University of Vienna Department of Distributed and Multimedia Systems Vienna Austria University of South Australia School of Computer and Information Science Mawson Lakes Australia Kingston University of London Faculty of Computing Information Systems and Mathematics Kingston Upon Thames United Kingdom

ISBN: (纸本)9780769540191

This paper tries to give an overview of the rising concerns raised by the of privacy protection that is associated with the European Data Retention Directive. After relating this risk to the requirements as stated in the European Data Protection Directive, an approach for a security-integrated view on the design of information systems is discussed. The paper then finishes with an outlook on the possible impact on IT governance. © 2010 IEEE.

关键词： information systems

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Impact of Corpus Diversity and Complexity on NER Performance

Impact of Corpus Diversity and Complexity on NER Performance

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2013 Australasian Language Technology Association Workshop, ALTA 2013

作者： Shmanina, Tatyana Zukerman, Ingrid Yepes, Antonio Jimeno Cavedon, Lawrence Verspoor, Karin Nicta Victoria Research Laboratory Melbourne Australia Clayton School of Information Technology Monash University Australia Department of Computing and Information Systems University of Melbourne Australia

We describe a cross-corpora evaluation of disease mention recognition for two annotated biomedical corpora: the Human Variome Project Corpus and the Arizona Disease Corpus. Our analysis of the performance of a state-of-the-art NER tool in terms of the characteristics and annotation schema of these corpora shows that these factors significantly affect performance. © Australasian Language Technology Association Workshop 2013, ALTA 2013 - Proceedings. All rights reserved.

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Probabilistic Inference Based Incremental Graph Index for Similarity Search on Social Networks 19th

Probabilistic Inference Based Incremental Graph Index for S...

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19th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Lu, Tong Qi, Zhiwei Yue, Kun Duan, Liang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9783031545276

To find k neighbor users on social networks, the efficient approximate nearest neighbor search (ANNS) is useful. Existing graph index methods have shown attractive performance, but suffer from inaccuracy w.r.t. unindexed queries. To achieve both indexed and unindexed queries for graph-index methods, we propose an incremental graph index based method for ANNS on social networks. First, graph convolutional network based on attention mechanism is adopted to embed the social network into low-dimensional vector space, on which the graph index is constructed efficiently. To add the unindexed queries to the graph index incrementally, we propose Bayesian network (BN) learned from social interactions to represent dependency relations of unindexed queries and their neighbors, and perform probabilistic inferences in BN to infer the closest neighbors of unindexed queries. Extensive experiments show that our proposed method outperforms the state-of-the-art methods on both execution time and precision. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Vector spaces

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Strong Co-Location Pattern Mining Incorporating Multi-Path and Distance Decay Effects 7

Strong Co-Location Pattern Mining Incorporating Multi-Path a...

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7th International Conference on Algorithms, computing and Artificial Intelligence, ACAI 2024

作者： He, Yonggui Yang, Peizhong Wang, Lizhen Chen, Hongmei Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing School of Information Science and Engineering Kunming China

ISBN: (纸本)9798331529314

Spatial co-location pattern mining (SCPM) is a sub-field of data mining, which aims to discover the subset of spatial features whose instances are frequently located in proximate areas. SCPM has broad prospects in many applications, such as ecology, public health, smart cities, etc. In recent years, improving and applying SCPM technology with the constraints of road networks has emerged as a prominent research focus. However, existing studies solely focus on the shortest path between instances when assessing the proximity relationships, neglecting other proximity paths, and thus may overlook co-location patterns with strong associations. To address this issue, this paper introduces a novel metric called Strong Proximity Score, which integrates both multi-path proximity and distance decay effects to measure the strength of proximity relationships. Additionally, the Minimum First Search (MFS) algorithm is presented, which utilizes the strategy of minimum instance pair search to accelerate the calculation of Strong Proximity Score. Extensive experiments conducted on real datasets of points of interest demonstrate the superiority of the method based on Strong Proximity Score over traditional SCPM methods and confirm the efficiency of MFS algorithm. © 2024 IEEE.

关键词： Smart city

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