ORCID Profile
0000-0002-7873-1562
Current Organisation
Macquarie University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Other Artificial Intelligence | Information Systems | Computation Theory and Mathematics | Logics And Meanings Of Programs | Data Security | Interorganisational Information Systems and Web Services | Computer Software Not Elsewhere Classified | Knowledge Representation And Machine Learning | Artificial Intelligence and Image Processing | Distributed Computing | Data models storage and indexing | Web Technologies (excl. Web Search) | Programming Languages | Mathematical Logic And Formal Languages | Analysis Of Algorithms And Complexity | Expert Systems | Information Systems Management | Cognitive Science | Recommender systems | Other Information, Computing And Communication Sciences | Decision Support And Group Support Systems | Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) | Data management and data science | Global Information Systems
ANZSRC Socio-Economic Objective (SEO)
Information processing services | Computer software and services not elsewhere classified | Application tools and system utilities | Mathematical sciences | Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Communication services not elsewhere classified |
Related Links
Publications
Modelling Local and Global Dependencies for Next-Item Recommendations
Publisher: Springer International Publishing
Date: 2020
Special issue on trust and security in wireless sensor networks
Publisher: Wiley
Date: 20-05-2015
DOI: 10.1002/CPE.3497
A Survey of Authentication Schemes in Telecare Medicine Information Systems
Publisher: Springer Science and Business Media LLC
Date: 30-11-2016
DOI: 10.1007/S10916-016-0658-3
Abstract: E-Healthcare is an emerging field that provides mobility to its users. The protected health information of the users are stored at a remote server (Telecare Medical Information System) and can be accessed by the users at anytime. Many authentication protocols have been proposed to ensure the secure authenticated access to the Telecare Medical Information System. These protocols are designed to provide certain properties such as: anonymity, untraceability, unlinkability, privacy, confidentiality, availability and integrity. They also aim to build a key exchange mechanism, which provides security against some attacks such as: identity theft, password guessing, denial of service, impersonation and insider attacks. This paper reviews these proposed authentication protocols and discusses their strengths and weaknesses in terms of ensured security and privacy properties, and computation cost. The schemes are ided in three broad categories of one-factor, two-factor and three-factor authentication schemes. Inter-category and intra-category comparison has been performed for these schemes and based on the derived results we propose future directions and recommendations that can be very helpful to the researchers who work on the design and implementation of authentication protocols.
Behavior analysis in social networks: Challenges, technologies, and trends
Publisher: Elsevier BV
Date: 10-2016
Quantum direct secret sharing with efficient eavesdropping-check and authentication based on distributed fountain codes
Publisher: Springer Science and Business Media LLC
Date: 24-11-2013
Analysis of the Scalability and Stability of an ACO Based Routing Protocol for Wireless Sensor Networks
Publisher: IEEE
Date: 04-2015
DOI: 10.1109/ITNG.2015.44
Extending Temporal Logic Programming with Choice Predicates Non-determinism
Publisher: Oxford University Press (OUP)
Date: 1994
Towards a unified theory of intensional logic programming
Publisher: Elsevier BV
Date: 08-1992
Preface to the special issue on graph data management in online social networks
Publisher: Springer Science and Business Media LLC
Date: 16-01-2020
A Quantum Secret Sharing Scheme Using Orbital Angular Momentum onto Multiple Spin States Based on Fibonacci Compression Encoding
Publisher: IOP Publishing
Date: 10-2018
ESAS 2012: The Seventh IEEE International Workshop on Engineering Semantic Agent Systems
Publisher: IEEE
Date: 07-2012
Special Issue on the Design and Engineering of Cryptographic Solutions for Secure Information Systems
Publisher: Wiley
Date: 08-11-2012
DOI: 10.1002/SEC.646
Fundamental Issues in Mobile Healthcare Information Systems
Publisher: Hindawi Limited
Date: 2016
DOI: 10.1155/2016/6504641
Towards Context Sensitive Defeasible Rules
Publisher: Springer Berlin Heidelberg
Date: 2008
Weakly supervised learning for image keypoint matching using graph convolutional networks
Publisher: Elsevier BV
Date: 06-2020
Clustering techniques for minimizing object access time
Publisher: Springer Berlin Heidelberg
Date: 1998
DOI: 10.1007/BFB0057736
Analytic Tableaux for Verifying Agent Beliefs
Publisher: IEEE
Date: 12-2008
Joint Autonomous Resource Selection and Scheduled Resource Allocation for D2D-Based V2X Communication
Publisher: IEEE
Date: 06-2018
Incremental Graph Pattern Based Node Matching with Multiple Updates
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Analysis of microarray data to infer transcription regulation in the yeast cell cycle
Publisher: Inderscience Publishers
Date: 2010
Tumor attention networks: Better feature selection, better tumor segmentation
Publisher: Elsevier BV
Date: 08-2021
The Reactive-Causal Architecture: Radar Task Simulation
Publisher: Journal of Artificial Societies and Social Simulation
Date: 2012
DOI: 10.18564/JASSS.2079
SEC 2: A Secure and Energy Efficient Barrier Coverage Scheduling for WSN-Based IoT Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Graph learning based recommender systems
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS). GLRS mainly employ advanced graph learning approaches to model users’ preferences and intentions as well as items’ characteristics and popularity for Recommender Systems (RS). Differently from other approaches, including content based filtering and collaborative filtering, GLRS are built on graphs where the important objects, e.g., users, items, and attributes, are either explicitly or implicitly connected. With the rapid development of graph learning techniques, exploring and exploiting homogeneous or heterogeneous relations in graphs is a promising direction for building more effective RS. In this paper, we provide a systematic review of GLRS, by discussing how they extract knowledge from graphs to improve the accuracy, reliability and explainability of the recommendations. First, we characterize and formalize GLRS, and then summarize and categorize the key challenges and main progress in this novel research area.
A robust approach to finding trustworthy influencer in trust-oriented E-commerce environments
Publisher: Springer International Publishing
Date: 2016
An uncertain assessment compatible incentive mechanism for eliciting continual and truthful assessments of cloud services
Publisher: Springer International Publishing
Date: 2016
Social context-aware trust network discovery in complex contextual social networks
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 20-09-2012
Abstract: Trust is one of the most important factors for participants' decision-making in Online Social Networks (OSNs). The trust network from a source to a target without any prior interaction contains some important intermediate participants, the trust relations between the participants, and the social context, each of which has an important influence on trust evaluation. Thus, before performing any trust evaluation, the contextual trust network from a given source to a target needs to be extracted first, where constraints on the social context should also be considered to guarantee the quality of extracted networks. However, this problem has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers social contextual impact factors. These factors have significant influences on both social interaction between participants and trust evaluation. Then, we propose a new concept called QoTN (Quality of Trust Network) and a social context-aware trust network discovery model. Finally, we propose a Social Context-Aware trust Network discovery algorithm (SCAN) by adopting the Monte Carlo method and our proposed optimization strategies. The experimental results illustrate that our proposed model and algorithm outperform the existing methods in both algorithm efficiency and the quality of the extracted trust network.
A Convolutional Attention Network for Unifying General and Sequential Recommenders
Publisher: Elsevier BV
Date: 2022
Special issue on Information Visualization in Machine Learning and Applications
Publisher: Elsevier BV
Date: 04-2016
The interpreted system model of knowledge, belief, desire and intention
Publisher: ACM
Date: 08-05-2006
Secure sensors data acquisition and communication protection in eHealthcare: Review on the state of the art
Publisher: Elsevier BV
Date: 07-2018
TAP: A Two-Level Trust and Personality-Aware Recommender System
Publisher: Springer International Publishing
Date: 2021
An analytic tableau calculus for a temporalised belief logic
Publisher: Elsevier BV
Date: 12-2011
Finding K optimal social trust paths for the selection of trustworthy service providers in complex social networks
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.81
Contextual sub-network extraction in contextual social networks
Publisher: IEEE
Date: 08-2015
Design and deployment challenges in immersive and wearable technologies
Publisher: Informa UK Limited
Date: 17-01-2017
A Visual Approach for External Cluster Validation
Publisher: IEEE
Date: 2007
A Critical Analysis of ECG-Based Key Distribution for Securing Wearable and Implantable Medical Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2019
A constraint mechanism for knowledge specification of simulation systems based on temporal logic
Publisher: Springer Berlin Heidelberg
Date: 1997
A survey on session-based recommender systems
Publisher: Association for Computing Machinery (ACM)
Date: 18-07-2021
DOI: 10.1145/3465401
Abstract: Recommender systems (RSs) have been playing an increasingly important role for informed consumption, services, and decision-making in the overloaded information era and digitized economy. In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs. Different from other RSs such as content-based RSs and collaborative filtering-based RSs that usually model long-term yet static user preferences, SBRSs aim to capture short-term but dynamic user preferences to provide more timely and accurate recommendations sensitive to the evolution of their session contexts. Although SBRSs have been intensively studied, neither unified problem statements for SBRSs nor in-depth elaboration of SBRS characteristics and challenges are available. It is also unclear to what extent SBRS challenges have been addressed and what the overall research landscape of SBRSs is. This comprehensive review of SBRSs addresses the above aspects by exploring in depth the SBRS entities (e.g., sessions), behaviours (e.g., users’ clicks on items), and their properties (e.g., session length). We propose a general problem statement of SBRSs, summarize the ersified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research. Finally, we discuss new research opportunities in this exciting and vibrant area.
Uncovering generalized patterns of brand competition in China
Publisher: Emerald
Date: 11-11-2014
DOI: 10.1108/JPBM-04-2014-0557
Abstract: – The purpose of this research is to analyze brand competition in China using the Duplication of Purchase (DoP) law, with important implications for understanding Chinese buyer behavior in comparison with Western buyers. Discovered in the Western markets, the DoP law holds across a variety of product categories. – Multiple sets of new data are examined to extend past research in the application of the DoP law in Chinese buying behavior. This study draws on panel data and self-reported data, utilizing bootstrapping to identify partitions where excess sharing occurs. – This paper finds the DoP law holds across six categories (two personal care, two impulse categories and two durables), as well as over multiple years. Brands in China share customers with other brands in line with the market share of the competitor brand. There were few partitions where brands shared significantly more customers than expected. Partitions occur due to the same umbrella brand or ownership, and geographic location. – Areas for further research include extended replication in other categories, investigating partitions and whether a different consumer path to purchase occurs in China. – DoP can be applied across a wide range of categories in China to understand market structure. New entrants to China can use this approach to understand a category from a consumer behavior perceptive. DoP provides guidelines for marketers to identify competition and allocate resources appropriately. – This research provides a comprehensive, unparalleled examination across six very different categories of brand competition in China. This gives confidence in the robustness and generalizability of the results.
Classification of ISAR Ship Imagery Using Transfer Learning
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
The Reactive-Causal Cognitive Agent Architecture
Publisher: Springer Berlin Heidelberg
Date: 2010
The impact of human activity patterns on asymptomatic infectious processes in complex networks
Publisher: Elsevier BV
Date: 07-2012
Editing any version at any time: a consistency maintenance mechanism in Internet-based collaborative environments
Publisher: IEEE Comput. Soc
Date: 2002
Temporal Data Mining Using Hidden Periodicity Analysis
Publisher: Springer Berlin Heidelberg
Date: 2000
A learning ontology with metadata and user profiles for enhancing accessibility of resources
Publisher: IEEE
Date: 10-2016
XMask: An Enabled XML Management System
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11890393_5
A graph-based fault-tolerant approach to modeling QoS for IoT-based surveillance applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2021
An improved coding method of quantum key distribution protocols based on Fibonacci-valued OAM entangled states
Publisher: Elsevier BV
Date: 09-2017
A heuristic algorithm for trust-oriented service provider selection in complex social networks
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.47
Using Bayesian networks with hidden variables for identifying trustworthy users in social networks
Publisher: SAGE Publications
Date: 02-07-2019
Abstract: The popularity and broad accessibility of online social networks (OSNs) have facilitated effective communication among people, but such networks also pose potential risks that should not be ignored. Interaction through OSNs is complex and can be unsafe, as in iduals can be contacted by strangers at any time. This makes the notion of trust a crucial issue in the use of OSNs. However, compared with decision-making processes associated with whether to trust a stranger encountered in everyday life, this task is more difficult to address with regard to OSNs due to the lack of face-to-face communication and prior knowledge between people. In this article, trust evaluation is formalised as a classification problem. We demonstrate how user profiles and historical records can be organised into a logical structure based on Bayesian networks to recognise the trustworthy people without the need to build trust relationships in OSNs. This is possible when a more detailed description of features denoted by hidden variables is considered. We compare the performance of our method with those of six other machine learning methods using Facebook and Twitter datasets, and our results show that our method achieves higher values in accuracy, recall and F1 score.
Risk Analysis of Cloud Sourcing in Healthcare and Public Health Industry
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Managing Theories of Trust in Agent Based Systems
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11569596_47
A Dynamic Authentication Scheme for Hierarchical Wireless Sensor Networks
Publisher: Springer Berlin Heidelberg
Date: 2012
Foundations of linear-time logic programming
Publisher: Informa UK Limited
Date: 1995
Analysing Stream Authentication Protocols in Autonomous Agent-Based Systems
Publisher: IEEE
Date: 2006
DOI: 10.1109/DASC.2006.19
SETTRUST: Social Exchange Theory Based Context-Aware Trust Prediction in Online Social Networks
Publisher: Springer International Publishing
Date: 2019
Discovering trust networks for the selection of trustworthy service providers in complex contextual social networks
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/ICWS.2012.47
ADDROL: a method for on-line reorganization in a multisystem DBMS with shared disks
Publisher: IEEE Comput. Soc
Date: 2000
Correlation Matters: Multi-scale Fine-Grained Contextual Information Extraction for Hepatic Tumor Segmentation
Publisher: Springer International Publishing
Date: 2020
OWL 2 learn profile: an ontology sublanguage for the learning domain
Publisher: Springer Science and Business Media LLC
Date: 08-03-2016
A Visual Approach for Classification Based on Data Projection
Publisher: Springer Berlin Heidelberg
Date: 2012
A large-alphabet three-party quantum key distribution protocol based on orbital and spin angular momenta hybrid entanglement
Publisher: Springer Science and Business Media LLC
Date: 18-05-2018
Software clustering using automated feature subset selection
Publisher: Springer Berlin Heidelberg
Date: 2013
ESAS 2013: Message from the Workshop Organizers
Publisher: IEEE
Date: 07-2013
Analysis of authentication protocols in agent-based systems using labeled tableaux
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Cybersecurity and privacy issues for socially integrated mobile healthcare applications operating in a multi-cloud environment
Publisher: SAGE Publications
Date: 07-05-2017
Abstract: Social media has enabled information-sharing across massively large networks of people without spending much financial resources and time that are otherwise required in the print and electronic media. Mobile-based social media applications have overwhelmingly changed the information-sharing perspective. However, with the advent of such applications at an unprecedented scale, the privacy of the information is compromised to a larger extent if breach mitigation is not adequate. Since healthcare applications are also being developed for mobile devices so that they also benefit from the power of social media, cybersecurity privacy concerns for such sensitive applications have become critical. This article discusses the architecture of a typical mobile healthcare application, in which customized privacy levels are defined for the in iduals participating in the system. It then elaborates on how the communication across a social network in a multi-cloud environment can be made more secure and private, especially for healthcare applications.
Controllable quantum private queries using an entangled Fibonacci-sequence spiral source
Publisher: Elsevier BV
Date: 10-2015
CCCloud: Context-aware and credible cloud service selection based on subjective assessment and objective assessment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
From WannaCry to WannaDie: Security Trade-offs and Design for Implantable Medical Devices
Publisher: IEEE
Date: 09-2017
Modelling Bayesian attacker detection game in wireless networks with epistemic logic
Publisher: IEEE
Date: 2012
Modelling and manipulating multidimensional data in semistructured databases
Publisher: IEEE
Date: 2001
N2TM: A New Node to Trust Matrix Method for Spam Worker Defense in Crowdsourcing Environments
Publisher: Springer International Publishing
Date: 2019
Two types of dynamic quantum state secret sharing based on tensor networks states
Publisher: Elsevier BV
Date: 11-2021
Dynamic Theories of Trust for Secure Agent-Based Systems
Publisher: IEEE
Date: 04-2008
ESAS 2010 - The Fifth IEEE International Workshop on Engineering Semantic Agent Systems
Publisher: IEEE
Date: 07-2010
Optimization of XML Queries by Using Semantics in XML Schemas and the Document Structure
Publisher: Springer Berlin Heidelberg
Date: 2013
Dual compressible hybrid quantum secret sharing schemes based on extended unitary operations
Publisher: SPIE
Date: 28-05-2014
DOI: 10.1117/12.2052886
Efficient quantum key distribution using Fibonacci-number coding with a biased basis choice
Publisher: Elsevier BV
Date: 06-2018
Formalising theories of trust for authentication protocols
Publisher: Springer Science and Business Media LLC
Date: 04-10-2007
From Predefined Consistency to User-Centered Emergent Consistency in Real-Time Collaborative Editing Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2006
ISAR Ship Classification Using Transfer Learning
Publisher: IEEE
Date: 21-03-2022
Real-time event detection from the Twitter data stream using the TwitterNews+ Framework
Publisher: Elsevier BV
Date: 05-2019
A bio-inspired secure IPv6 communication protocol for Internet of Things
Publisher: IEEE
Date: 12-2017
An Improved Wavelet Analysis Method for Detecting DDoS Attacks
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/NSS.2010.23
An Application of Element Oriented Analysis Based Credit Scoring
Publisher: Springer Berlin Heidelberg
Date: 2010
Exploiting intra- and inter-session dependencies for session-based recommendations
Publisher: Springer Science and Business Media LLC
Date: 06-08-2022
Modelling and reasoning about multi-dimensional information
Publisher: Springer Berlin Heidelberg
Date: 1999
DOI: 10.1007/BFB0095136
A secure transaction environment for workflows in distributed systems
Publisher: IEEE Comput. Soc
Date: 2001
An efficient quantum blind digital signature scheme
Publisher: Springer Science and Business Media LLC
Date: 10-07-2017
A reverse‐engineering approach to subsystem structure identification
Publisher: Wiley
Date: 1993
Social context-aware trust prediction in social networks
Publisher: Springer Berlin Heidelberg
Date: 2014
A scalable approach for inferring transcriptional regulation in the yeast cell cycle
Publisher: ACM
Date: 08-2011
An ECG-based Secret Data Sharing scheme supporting emergency treatment of Implantable Medical Devices
Publisher: IEEE
Date: 09-2014
Solving Fuzzy Linear Regression with Hybrid Optimization
Publisher: Springer Berlin Heidelberg
Date: 2009
Fast and Accurate Lung Tumor Spotting and Segmentation for Boundary Delineation on CT Slices in a Coarse-to-Fine Framework
Publisher: Springer International Publishing
Date: 2019
ESAS 2011 - The Sixth IEEE International Workshop on Engineering Semantic Agent Systems
Publisher: IEEE
Date: 07-2011
Separating XHTML content from navigation clutter using DOM-structure block analysis
Publisher: ACM
Date: 06-09-2005
Reasoning about Dynamics of Trust and Agent Beliefs
Publisher: IEEE
Date: 09-2006
Joint mode selection and proportional fair scheduling for D2D communication
Publisher: IEEE
Date: 10-2017
Finding the optimal social trust path for the selection of trustworthy service providers in complex social networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
DOI: 10.1109/TSC.2011.58
A deep framework for Cross-Domain and Cross-System Recommendations
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of in idual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.
Subset selection classifier (SSC): A training set reduction method
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.130
A survey on real-time event detection from the Twitter data stream
Publisher: SAGE Publications
Date: 17-03-2017
Abstract: The proliferation of social networking services has resulted in a rapid growth of their user base, spanning across the world. The collective information generated from these online platforms is overwhelming, in terms of both the amount of content produced every moment and the ersity of topics discussed. The real-time nature of the information produced by users has prompted researchers to analyse this content, in order to gain timely insight into the current state of affairs. Specifically, the microblogging service Twitter has been a recent focus of researchers to gather information on events occurring in real time. This article presents a survey of a wide variety of event detection methods applied to streaming Twitter data, classifying them according to shared common traits, and then discusses different aspects of the subtasks and challenges involved in event detection. We believe this survey will act as a guide and starting point for aspiring researchers to gain a structured view on state-of-the-art real-time event detection and spur further research in this direction.
Novel quantum key distribution with shift operations based on Fibonacci and Lucas valued orbital angular momentum entangled states
Publisher: Elsevier BV
Date: 09-2020
Securing wireless medical implants using an ECG-based secret data sharing scheme
Publisher: IEEE
Date: 09-2015
Optimizing XML twig queries in relational systems
Publisher: ACM Press
Date: 2010
Securing session initiation protocol in voice over IP domain
Publisher: IEEE
Date: 12-2009
Leveraging deep preference learning for indexing and retrieval of biomedical images
Publisher: IEEE
Date: 05-2017
Evaluating cloud users’ credibility of providing subjective assessment or objective assessment for cloud services
Publisher: Springer Berlin Heidelberg
Date: 2014
A novel biomedical image indexing and retrieval system via deep preference learning
Publisher: Elsevier BV
Date: 05-2018
DOI: 10.1016/J.CMPB.2018.02.003
Abstract: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications.
BRINK: Initial theory on bounded rationality and inconsistent knowledge
Publisher: IEEE
Date: 08-2012
Leveraging adjusted user behavior in the detection and prevention of outgoing malicious SMSs in Android devices
Publisher: Elsevier BV
Date: 06-2016
Towards a Deep Attention-Based Sequential Recommender System
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Short-term androgen deprivation and radiotherapy for locally advanced prostate cancer: Results from the Trans-Tasman Radiation Oncology Group 96.01 randomised controlled trial
Publisher: Elsevier BV
Date: 11-2005
Round-robin-differential-phase-shift quantum key distribution based on wavelength division multiplexing
Publisher: IOP Publishing
Date: 04-09-2018
A reverse engineering environment based on spatial and visual software interconnection models
Publisher: ACM
Date: 11-1992
Proceedings of the 8th International Conference on Security of Information and Networks
Publisher: ACM
Date: 08-09-2015
DOI: 10.1145/2799979
Energy efficient military surveillance
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-05-2019
Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding
Publisher: Springer Science and Business Media LLC
Date: 12-08-2016
DOI: 10.1038/SREP31350
Abstract: With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m -bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding. To be exact, we employ entangled states prepared by m -bonacci sequences to detect eavesdropping. Meanwhile, we encode m -bonacci sequences in Lagrange interpolation polynomials to generate the shares of a secret with reverse Huffman-Fibonacci-tree coding. The advantages of the proposed scheme is that it can detect eavesdropping without joint quantum operations, and permits secret sharing for an arbitrary but no less than threshold-value number of classical participants with much lower bandwidth. Also, in comparison with existing quantum secret sharing schemes, it still works when there are dynamic changes, such as the unavailability of some quantum channel, the arrival of new participants and the departure of participants. Finally, we provide security analysis of the new hybrid quantum secret sharing scheme and discuss its useful features for modern applications.
Taking Levi Identity Seriously: A Plea for Iterated Belief Contraction
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11811220_26
Temporalised epistemic logic for reasoning about agent-based systems
Publisher: IEEE
Date: 2009
Message from the Workshop Organizers - ESAS 2014
Publisher: IEEE
Date: 07-2014
A hybrid quantum key distribution protocol based on extended unitary operations and fountain codes
Publisher: Springer Science and Business Media LLC
Date: 31-10-2014
A Top-Down Approach for Hierarchical Cluster Exploration by Visualization
Publisher: Springer Berlin Heidelberg
Date: 2010
Introduction to the special issue on advances in ontologies
Publisher: Wiley
Date: 07-2008
QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks.
Publisher: Public Library of Science (PLoS)
Date: 04-02-2016
Large-Capacity Three-Party Quantum Digital Secret Sharing Using Three Particular Matrices Coding
Publisher: IOP Publishing
Date: 11-2016
Knowledge Representation, Reasoning and Integration Using Temporal Logic with Clocks
Publisher: Springer Science and Business Media LLC
Date: 11-2008
INTRODUCTION: SPECIAL ISSUE ON KNOWLEDGE REPRESENTATION AND ONTOLOGY RESEARCH
Publisher: Informa UK Limited
Date: 02-02-2010
Dealing with Multiple Granularity of Time in Temporal Logic Programming
Publisher: Elsevier BV
Date: 11-1996
Locking without requesting a lock: A consistency maintenance mechanism in Internet-based real-time group editors
Publisher: Elsevier BV
Date: 07-2005
Finger-to-Heart (F2H): Authentication for Wireless Implantable Medical Devices
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
An Improved Binary Sequence Generation for Securing Wireless Body Area Networks
Publisher: IEEE
Date: 12-2015
Towards a Logical Basis for Modelling and Querying Multi-dimensional Databases
Publisher: Springer Berlin Heidelberg
Date: 2000
High-capacity (2,3) threshold quantum secret sharing based on asymmetric quantum lossy channels
Publisher: Springer Science and Business Media LLC
Date: 31-03-2020
A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Efficiently querying dynamic XML documents stored in relational database systems
Publisher: Inderscience Publishers
Date: 2011
A Review of Secure Routing Approaches for Current and Next-Generation Wireless Multimedia Sensor Networks
Publisher: SAGE Publications
Date: 2015
DOI: 10.1155/2015/524038
Abstract: Multimedia applications are gradually becoming an essential—and flourishing—part of our daily lives. The area of wireless sensor networks is not an exception to this trend, as multimedia data have attracted the attention of researchers. Their importance is due to the shift of focus from traditional scalar sensors to sensors equipped with multimedia devices, as well as to the next-generation wireless multimedia sensor networks (WMSNs). The highly sensitive nature of multimedia traffic makes data routing with security even more important in WMSNs. This paper discusses the challenges of secure routing in WMSNs, where most of the proposed works in the literature deal with routing and security as in idual issues. A critical and comprehensive review of state-of-the-art routing and security approaches for WMSNs is presented, followed by the discussion of their limitations and features. It is hoped that this extensive review and discussion ultimately identifies the future research directions and the open challenges of secure routing approaches in WMSNs.
A Survey on Trust Prediction in Online Social Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Probabilistic Rule Learning Systems
Publisher: Association for Computing Machinery (ACM)
Date: 03-05-2021
DOI: 10.1145/3447581
Abstract: This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative ex les, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.
A recursive temporal algebra and temporal completeness
Publisher: IEEE Comput. Soc. Press
Date: 1996
Chronolog(Z): linear-time logic programming
Publisher: IEEE Comput. Soc. Press
Date: 1993
A Critical Analysis of Mobility Management Related Issues of Wireless Sensor Networks in Cyber Physical Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Interactive Visual Classification of Multivariate Data
Publisher: IEEE
Date: 12-2012
AI 2007: Advances in Artificial Intelligence, 20th Australian Joint Conference on Artificial Intelligence, Gold Coast, Australia, December 2-6, 2007, Proceedings
Publisher: Springer Berlin Heidelberg
Date: 2007
VERSANT architecture: supporting high-performance object databases
Publisher: IEEE Comput. Soc
Date: 1998
Hybrid optimization with improved tabu search
Publisher: Elsevier BV
Date: 03-2011
Strong social component-aware trust sub-network extraction in contextual social networks
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.22
Ideas and Challenges for Securing Wireless Implantable Medical Devices: A Review
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2017
Message from the ESAS 2008 Workshop Organizers
Publisher: IEEE
Date: 2008
Embedding a timing system in TLC
Publisher: IEEE Comput. Soc
Date: 1998
High-capacity quantum key distribution using Chebyshev-map values corresponding to Lucas numbers coding
Publisher: Springer Science and Business Media LLC
Date: 19-08-2016
A 2-Stage Approach for Inferring Gene Regulatory Networks Using Dynamic Bayesian Networks
Publisher: IEEE
Date: 11-2009
DOI: 10.1109/BIBM.2009.87
A Visual Method for High-Dimensional Data Cluster Exploration
Publisher: Springer Berlin Heidelberg
Date: 2009
Intention2Basket
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: User purchase behaviours are complex and dynamic, which are usually observed as multiple choice actions across a sequence of shopping baskets. Most of the existing next-basket prediction approaches model user actions as homogeneous sequence data without considering complex and heterogeneous user intentions, impeding deep under-standing of user behaviours from the perspective of human inside drivers and thus reducing the prediction performance. Psychological theories have indicated that user actions are essentially driven by certain underlying intentions (e.g., diet and entertainment). Moreover, different intentions may influence each other while different choices usually have different utilities to accomplish an intention. Inspired by such psychological insights, we formalize the next-basket prediction as an Intention Recognition, Modelling and Accomplishing problem and further design the Intention2Basket (Int2Ba in short) model. In Int2Ba, an Intention Recognizer, a Coupled Intention Chain Net, and a Dynamic Basket Planner are specifically designed to respectively recognize, model and accomplish the heterogeneous intentions behind a sequence of baskets to better plan the next-basket. Extensive experiments on real-world datasets show the superiority of Int2Ba over the state-of-the-art approaches.
On the Analysis of On-Line Database Reorganization
Publisher: Springer Berlin Heidelberg
Date: 2000
The discovery of hierarchical cluster structures assisted by a visualization technique
Publisher: Springer Berlin Heidelberg
Date: 2010
A Multi-Versioning Scheme for Intention Preservation in Collaborative Editing Systems*
Publisher: Springer Science and Business Media LLC
Date: 28-05-2008
Unsupervised Learning Aided by Hierarchical Analysis in Knowledge Exploration
Publisher: IEEE
Date: 10-2006
A novel fused convolutional neural network for biomedical image classification
Publisher: Springer Science and Business Media LLC
Date: 12-07-2019
DOI: 10.1007/S11517-018-1819-Y
Abstract: With the advent of biomedical imaging technology, the number of captured and stored biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and biomedical institutions. Therefore, more robust biomedical image analysis technology is needed to meet the requirement of the diagnosis and classification of various kinds of diseases using biomedical images. However, the current biomedical image classification methods and general non-biomedical image classifiers cannot extract more compact biomedical image features or capture the tiny differences between similar images with different types of diseases from the same category. In this paper, we propose a novel fused convolutional neural network to develop a more accurate and highly efficient classifier for biomedical images, which combines shallow layer features and deep layer features from the proposed deep neural network architecture. In the analysis, it was observed that the shallow layers provided more detailed local features, which could distinguish different diseases in the same category, while the deep layers could convey more high-level semantic information used to classify the diseases among the various categories. A detailed comparison of our approach with traditional classification algorithms and popular deep classifiers across several public biomedical image datasets showed the superior performance of our proposed method for biomedical image classification. In addition, we also evaluated the performance of our method in modality classification of medical images using the ImageCLEFmed dataset. Graphical abstract The graphical abstract shows the fused, deep convolutional neural network architecture proposed for biomedical image classification. In the architecture, we can clearly see the feature-fusing process going from shallow layers and the deep layers.
Geospatial editing over a federated cloud geodatabase for the state of NSW
Publisher: ACM
Date: 02-11-2010
CTumorGAN
Publisher: Springer Science and Business Media LLC
Date: 28-03-2020
Fault-tolerant high-capacity quantum key distribution over a collective-noise channel using extended unitary operations
Publisher: Springer Science and Business Media LLC
Date: 21-03-2014
Introduction to the special issue on engineering semantic agent systems
Publisher: Wiley
Date: 11-2011
Survey on cybersecurity issues in wireless mesh networks based eHealthcare
Publisher: IEEE
Date: 09-2016
Querying Historical Data Over Multiple Time-Lines
Publisher: Springer Science and Business Media LLC
Date: 11-2008
Incremental Graph Pattern Based Node Matching
Publisher: IEEE
Date: 04-2018
A dispute arbitration protocol based on a peer-to-peer service contract management scheme
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.15
Mining Multidimensional Data through Element Oriented Analysis
Publisher: Springer Berlin Heidelberg
Date: 2008
Multiple ECG Fiducial Points-Based Random Binary Sequence Generation for Securing Wireless Body Area Networks
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2017
Privacy Preservation in e-Healthcare Environments: State of the Art and Future Directions
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Executing specifications of distributed computations with Chronolog(MC)
Publisher: ACM Press
Date: 1996
Corrections to “Risk Analysis of Cloud Sourcing in Healthcare and Public Health Industry”
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Sequential recommender systems
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years. Different from the conventional recommender systems (RSs) including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users’ preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations. In this paper, we provide a systematic review on SRSs. We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic. Finally, we discuss the important research directions in this vibrant area.
A least square approach for the detection and removal of outliers for fuzzy linear regressions
Publisher: IEEE
Date: 12-2010
Modeling multi-purpose sessions for next-item recommendations via mixture-channel purpose routing networks
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: A session-based recommender system (SBRS) suggests the next item by modeling the dependencies between items in a session. Most of existing SBRSs assume the items inside a session are associated with one (implicit) purpose. However, this may not always be true in reality, and a session may often consist of multiple subsets of items for different purposes (e.g., breakfast and decoration). Specifically, items (e.g., bread and milk) in a subsethave strong purpose-specific dependencies whereas items (e.g., bread and vase) from different subsets have much weaker or even no dependencies due to the difference of purposes. Therefore, we propose a mixture-channel model to accommodate the multi-purpose item subsets for more precisely representing a session. Filling gaps in existing SBRSs, this model recommends more erse items to satisfy different purposes. Accordingly, we design effective mixture-channel purpose routing networks (MCPRN) with a purpose routing network to detect the purposes of each item and assign it into the corresponding channels. Moreover, a purpose specific recurrent network is devised to model the dependencies between items within each channel for a specific purpose. The experimental results show the superiority of MCPRN over the state-of-the-art methods in terms of both recommendation accuracy and ersity.
Efficient quantum private queries based on quantum key distribution with pulse-position modulation
Publisher: IOP Publishing
Date: 26-03-2019
Towards an OWL 2 Profile for Defining Learning Ontologies
Publisher: IEEE
Date: 07-2014
Temporal Data Mining Using Hidden Markov-Local Polynomial Models
Publisher: Springer Berlin Heidelberg
Date: 2001
Dynamic (2, 3) Threshold Quantum Secret Sharing of Secure Direct Communication*
Publisher: IOP Publishing
Date: 04-2015
DOI: 10.1088/0253-6102/63/4/459
Abstract: In this paper, we show that a (2, 3) discrete variable threshold quantum secret sharing scheme of secure direct communication can be achieved based on recurrence using the same devices as in BB84. The scheme is devised by first placing the shares of smaller secret pieces into the shares of the largest secret piece, converting the shares of the largest secret piece into corresponding quantum state sequences, inserting nonorthogonal state particles into the quantum state sequences with the purpose of detecting eavesdropping, and finally sending the new quantum state sequences to the three participants respectively. Consequently, every particle can on average carry up to 1.5-bit messages due to the use of recurrence. The control codes are randomly prepared using the way to generate fountain codes with pre-shared source codes between Alice and Bob, making three participants can detect eavesdropping by themselves without sending classical messages to Alice. Due to the flexible encoding, our scheme is also dynamic, which means that it allows the participants to join and leave freely.
Introduction to the special issue on Next Generation Network Architectures, Protocols, Theory, Systems and Applications
Publisher: Springer Science and Business Media LLC
Date: 07-2013
A non-key based security scheme supporting emergency treatment of wireless implants
Publisher: IEEE
Date: 06-2014
Failed-tuple triggered blocking strategy for managing near real-time spatial data replication
Publisher: ACM
Date: 21-06-2010
Theories of trust for communication protocols
Publisher: Springer Berlin Heidelberg
Date: 2009
KPMCF: A Learning Model for Measuring Social Relationship Strength
Publisher: Springer Berlin Heidelberg
Date: 2013
Modeling accountable cloud services
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.58
In Honour of Bill Wadge
Publisher: Springer Science and Business Media LLC
Date: 11-2008
Intention Nets
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: Human behaviors are complex, which are often observed as a sequence of heterogeneous actions. In this paper, we take user choices for shopping baskets as a typical case to study the complexity of user behaviors. Most of existing approaches often model user behaviors in a mechanical way, namely treating a user action sequence as homogeneous sequential data, such as hourly temperatures, which fails to consider the complexity in user behaviors. In fact, users' choices are driven by certain underlying intentions (e.g., feeding the baby or relieving pain) according to Psychological theories. Moreover, the durations of intentions to drive user actions are quite different some of them may be persistent while others may be transient. According to Psychological theories, we develop a hierarchical framework to describe the goal, intentions and action sequences, based on which, we design Intention Nets (IntNet). In IntNet, multiple Action Chain Nets are constructed to model the user actions driven by different intentions, and a specially designed Persistent-Transient Intention Unit models the different intention durations. We apply the IntNet to next-basket prediction, a recent challenging task in recommender systems. Extensive experiments on real-world datasets show the superiority of our Psychology-inspired model IntNet over the state-of-the-art approaches.
Representing and Reasoning about Utilization of Cloud Computing as Bayesian Games with Epistemic Logic
Publisher: Elsevier BV
Date: 2013
Intelligent Agents and P2P Semantic Web
Publisher: IEEE
Date: 07-2007
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
A Hybrid Quantum Key Distribution Protocol for Tele-care Medicine Information Systems
Publisher: Springer Science and Business Media LLC
Date: 22-08-2017
Empirical Studies of ECG Multiple Fiducial-Points Based Binary Sequence Generation (MFBSG) Algorithm in E-Health Sensor Platform
Publisher: IEEE
Date: 11-2016
DOI: 10.1109/LCN.2016.053
Trust management and trust theory revision
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2006
Use of standard risk screening and assessment forms to prevent harm to older people in Australian hospitals: a mixed methods study
Publisher: BMJ
Date: 13-03-2017
DOI: 10.1136/BMJQS-2016-005867
Abstract: Standard risk screening and assessment forms are frequently used in strategies to prevent harm to older people in hospitals. Little is known about good practices for their use. Scope the preventable harms addressed by standard forms used to screen and assess older people and how standard forms are operationalised in hospitals across Victoria, Australia. Mixed methods study: (1) cross-sectional audit of the standard risk screening and assessment forms used to assess older people at 11 health services in 2015 (2) nine focus groups with a purposive s le of 69 participants at 9 health services. Descriptive analysis examined the number of items on forms, preventable harms assessed and sources of duplication. Qualitative thematic analysis of focus group data identified themes explaining issues commonly affecting how health services used the forms. 152 standard assessment forms from 11 Victorian health services included over 3700 items with 17% duplicated across multiple forms. Assessments of skin integrity and mobility loss (including falls) were consistently included in forms however, nutrition, cognitive state, pain and medication risks were inconsistent and continence, venous thromboembolism risk and hospital acquired infection from invasive devices were infrequent. Qualitative analyses revealed five themes explaining issues associated with current use of assessment forms: (1) comprehensive assessment of preventable harms (2) burden on staff and the older person, (3) interprofessional collaboration, (4) flexibility to in idualise care and (5) information management. Ex les of good practice were identified. Current use of standard risk screening and assessment forms is associated with a high burden and gaps in assessment of several common preventable harms that can increase risk to older people in hospital. Improvement should focus on streamlining forms, increased guidance on interventions to prevent harm and facilitating front-line staff to manage complex decisions.
Modal tableaux for verifying stream authentication protocols
Publisher: Springer Science and Business Media LLC
Date: 02-02-2008
Semantic Agent Systems
Publisher: Springer Berlin Heidelberg
Date: 2011
Resource Allocation for Underlay D2D Communication With Proportional Fairness
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2018
Hypothesis oriented cluster analysis in data mining by visualization
Publisher: No publisher found
Date: 2006
Efficiently Querying XML Documents Stored in RDBMS in the Presence of Dewey-Based Labeling Scheme
Publisher: Springer Berlin Heidelberg
Date: 2010
Three Approaches to Iterated Belief Contraction
Publisher: Springer Science and Business Media LLC
Date: 07-06-2011
HESIP: A Hybrid System for Explaining Sub-symbolic Predictions
Publisher: Springer International Publishing
Date: 2022
Tunable multi-party high-capacity quantum key distribution based on m-generalized Fibonacci sequences using golden coding
Publisher: Springer Science and Business Media LLC
Date: 11-08-2018
A dynamic deep trust prediction approach for online social networks
Publisher: ACM
Date: 30-11-2020
Cost-Effective Encryption-Based Autonomous Routing Protocol for Efficient and Secure Wireless Sensor Networks
Publisher: MDPI AG
Date: 31-03-2016
DOI: 10.3390/S16040460
The prediction of trust rating based on the quality of services using fuzzy linear regression
Publisher: IEEE
Date: 06-2011
Secure transfer of environmental data to enhance human decision accuracy
Publisher: Elsevier BV
Date: 10-2015
Social Context-Aware Trust Prediction: Methods for Identifying Fake News
Publisher: Springer International Publishing
Date: 2018
Efficient Random Key Based Encryption System for Data Packet Confidentiality in WSNs
Publisher: IEEE
Date: 07-2013
High-rate and high-capacity measurement-device-independent quantum key distribution with Fibonacci matrix coding in free space
Publisher: Springer Science and Business Media LLC
Date: 07-05-2018
Encryption for Implantable Medical Devices Using Modified One-Time Pads
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2015
Trust inference in complex trust-oriented social networks
Publisher: IEEE
Date: 2009
DOI: 10.1109/CSE.2009.248
TwitterNews+: A Framework for Real Time Event Detection from the Twitter Data Stream
Publisher: Springer International Publishing
Date: 2016
An overview of temporal and modal logic programming
Publisher: Springer-Verlag
Date: 1994
DOI: 10.1007/BFB0014004
Multi-Constrained Graph Pattern Matching in large-scale contextual social graphs
Publisher: IEEE
Date: 04-2015
Classifying high dimensional data by interactive visual analysis
Publisher: Elsevier BV
Date: 04-2016
Joint Power Control and Resource Allocation Mode Selection for Safety-Related V2X Communication
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Editorial: Special issue on security of information and networks
Publisher: Elsevier BV
Date: 06-2015
Fast and simple high-capacity quantum cryptography with error detection
Publisher: Springer Science and Business Media LLC
Date: 13-04-2017
DOI: 10.1038/SREP46302
Abstract: Quantum cryptography is commonly used to generate fresh secure keys with quantum signal transmission for instant use between two parties. However, research shows that the relatively low key generation rate hinders its practical use where a symmetric cryptography component consumes the shared key. That is, the security of the symmetric cryptography demands frequent rate of key updates, which leads to a higher consumption of the internal one-time-pad communication bandwidth, since it requires the length of the key to be as long as that of the secret. In order to alleviate these issues, we develop a matrix algorithm for fast and simple high-capacity quantum cryptography. Our scheme can achieve secure private communication with fresh keys generated from Fibonacci- and Lucas- valued orbital angular momentum (OAM) states for the seed to construct recursive Fibonacci and Lucas matrices. Moreover, the proposed matrix algorithm for quantum cryptography can ultimately be simplified to matrix multiplication, which is implemented and optimized in modern computers. Most importantly, considerably information capacity can be improved effectively and efficiently by the recursive property of Fibonacci and Lucas matrices, thereby avoiding the restriction of physical conditions, such as the communication bandwidth.
Partition-aware graph pattern based node matching with updates
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Modeling the dynamic trust of online service providers using HMM
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/ICWS.2013.68
An adaptive learning system using plug and play ontologies
Publisher: IEEE
Date: 08-2013
A Fibred Belief Logic for Multi-agent Systems
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11589990_6
Modeling User Demand Evolution for Next-Basket Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
A topic-sensitive trust evaluation approach for users in online communities
Publisher: Elsevier BV
Date: 04-2020
A temporal relational algebra based on multiple time-lines
Publisher: IEEE Comput. Soc
Date: 1999
A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images
Publisher: Elsevier BV
Date: 03-2017
DOI: 10.1016/J.CMPB.2016.12.019
Abstract: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. We first apply domain transferred deep convolutional neural network for building a deep model and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.
Special issue on logics for intelligent agents and multi-agent systems
Publisher: Elsevier BV
Date: 12-2011
An Engagement Model Based on User Interest and QoS in Video Streaming Systems
Publisher: Hindawi Limited
Date: 23-09-2018
DOI: 10.1155/2018/1398958
Abstract: With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the ergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model . Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.
Offer acceptance prediction of academic placement
Publisher: Springer Science and Business Media LLC
Date: 03-11-2015
2D medical image segmentation via learning multi-scale contextual dependencies
Publisher: Elsevier BV
Date: 06-2022
DOI: 10.1016/J.YMETH.2021.05.015
Abstract: Automatic medical image segmentation plays an important role as a diagnostic aid in the identification of diseases and their treatment in clinical settings. Recently proposed methods based on Convolutional Neural Networks (CNNs) have demonstrated their potential in image processing tasks, including some medical image analysis tasks. Those methods can learn various feature representations with numerous weight-shared convolutional kernels, however, the missed diagnosis rate of regions of interest (ROIs) is still high in medical image segmentation. Two crucial factors behind this shortcoming, which have been overlooked, are small ROIs from medical images and the limited context information from the existing network models. In order to reduce the missed diagnosis rate of ROIs from medical images, we propose a new segmentation framework which enhances the representative capability of small ROIs (particularly in deep layers) and explicitly learns global contextual dependencies in multi-scale feature spaces. In particular, the local features and their global dependencies from each feature space are adaptively aggregated based on both the spatial and the channel dimensions. Moreover, some visualization comparisons of the learned features from our framework further boost neural networks' interpretability. Experimental results show that, in comparison to some popular medical image segmentation and general image segmentation methods, our proposed framework achieves the state-of-the-art performance on the liver tumor segmentation task with 91.18% Sensitivity, the COVID-19 lung infection segmentation task with 75.73% Sensitivity and the retinal vessel detection task with 82.68% Sensitivity. Moreover, it is possible to integrate (parts of) the proposed framework into most of the recently proposed Fully CNN-based models, in order to improve their effectiveness in medical image segmentation tasks.
An exclusive version locking scheme in Internet-based real-time group editors
Publisher: IEEE Comput. Soc
Date: 2003
A Stratified Model for Short-Term Prediction of Time Series
Publisher: Springer Berlin Heidelberg
Date: 2010
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles
Publisher: ACM
Date: 08-03-2009
Cloud service selection based on the aggregation of user feedback and quantitative performance assessment
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/SCC.2013.92
Specifying Agent Beliefs for Authentication Systems
Publisher: IEEE
Date: 02-2007
Two Approaches to Iterated Belief Contraction
Publisher: Springer Berlin Heidelberg
Date: 2009
Context-aware cloud service selection based on comparison and aggregation of user subjective assessment and objective performance assessment
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.24
Supporting Distributed Collaborative Work with Multi-versioning
Publisher: IEEE
Date: 05-2006
Labeling XML Nodes in RDBMS
Publisher: Springer Berlin Heidelberg
Date: 2006
DOI: 10.1007/11610496_15
Temporal Data Mining Using Multilevel-Local Polynomial Models
Publisher: Springer Berlin Heidelberg
Date: 2000
An Adaptive Labeling Method for Dynamic XML Documents
Publisher: IEEE
Date: 08-2007
The reactive-causal architecture: Combining intentional notion and theories of needs
Publisher: IEEE
Date: 08-2008
The Reactive-Causal Architecture: Towards Development of Believable Agents
Publisher: Springer Berlin Heidelberg
Date: 2008
Context-aware trustworthy service evaluation and recommendation in Social Internet of Things
Publisher: Springer International Publishing
Date: 2018
A Tabu–Harmony Search-Based Approach to Fuzzy Linear Regression
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2011
A framework for exploiting parallelism in Chronolog
Publisher: IEEE
Date: 1995
The Impact of Discrimination on the Spread of Infectious Diseases in Complex Networks
Publisher: Springer Science and Business Media LLC
Date: 08-2014
Fine-grained tumor segmentation on computed tomography slices by leveraging bottom-up and top-down strategies
Publisher: SPIE
Date: 10-03-2020
DOI: 10.1117/12.2550511
Analyzing ant colony optimization based routing protocol against the hole problem for enhancing user’s connectivity experience
Publisher: Elsevier BV
Date: 10-2015
A comparison of key distribution schemes using fuzzy commitment and fuzzy vault within wireless body area networks
Publisher: IEEE
Date: 08-2015
Modeling accountable cloud services based on dynamic logic for accountability
Publisher: IGI Global
Date: 07-2015
Abstract: Cloud computing services have been increasingly considered by businesses as a viable option for reducing IT expenditure. However, there are often associated problems with unmanaged accountability. This paper first analyses the accountability properties of a cloud service and then proposes the accountable cloud service (ACS) model to address those problems. In addition, the authors argue that from an accountability perspective a cloud service is a proactive system that needs to be modeled differently from the traditional reactive systems. They extend traditional structural operational semantics to cater for modeling of actors as well as scenarios of inaction and exception in state transitions. This leads to the creation of a new form of a process algebra called Accountable Process Algebra (APA). They also propose an Obligation Flow Diagram (OFD) as a simple method for conflict resolution and verification for the ACS model. The ACS model enables obligation specification, validation, decomposition, machine-interpretation, monitoring and reasoning, and ultimately facilitates accountability in cloud service consumption. Using Amazon S3 service as a case study, they show how to address those known accountability problems by using our ACS model. Finally the authors discuss the applicability of their model to cloud services in general.
Modelling and manipulating multidimensional data in semistructured databases
Publisher: Springer Science and Business Media LLC
Date: 2001
Belief Erasure Using Partial Imaging
Publisher: Springer Berlin Heidelberg
Date: 2010
Answering Why-Questions Using Probabilistic Logic Programming
Publisher: Springer International Publishing
Date: 2019
Guest editorial
Publisher: Springer Science and Business Media LLC
Date: 2020
Temporal Belief-Change: κ-functions Approach
Publisher: Springer Berlin Heidelberg
Date: 2010
Provably secure three-party key agreement protocol using Chebyshev chaotic maps in the standard model
Publisher: Springer Science and Business Media LLC
Date: 11-04-2014
Joint Distributed and Centralized Resource Scheduling for D2D-Based V2X Communication
Publisher: IEEE
Date: 12-2018
Representation and Integration of Knowledge based on Multiple Granularity of Time using Temporal Logic
Publisher: IEEE
Date: 09-2006
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