ORCID Profile
0000-0002-5344-1884
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)
Information Systems | Interorganisational Information Systems and Web Services | Computer Software Not Elsewhere Classified | Pattern Recognition and Data Mining | Information Systems Management | Data Security | Data Format | Artificial Intelligence and Image Processing | Distributed Computing | Data models storage and indexing | Information Systems Management | Database Management | Web Technologies (excl. Web Search) | Recommender systems | Business Information Management (incl. Records, Knowledge and Information Management, and Intelligence) | Data management and data science
ANZSRC Socio-Economic Objective (SEO)
Information Processing Services (incl. Data Entry and Capture) | Application Tools and System Utilities | Application tools and system utilities | Electronic Information Storage and Retrieval Services | Information processing services | Computer software and services not elsewhere classified | Expanding Knowledge in the Information and Computing Sciences |
Related Links
Publications
Modelling Local and Global Dependencies for Next-Item Recommendations
Publisher: Springer International Publishing
Date: 2020
Positive distribution pollution
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 26-06-2023
Abstract: Positive Unlabeled (PU) learning, which has a wide range of applications, is becoming increasingly prevalent. However, it suffers from problems such as data imbalance, selection bias, and prior agnostic in real scenarios. Existing studies focus on addressing part of these problems, which fail to provide a unified perspective to understand these problems. In this paper, we first rethink these problems by analyzing a typical PU scenario and come up with an insightful point of view that all these problems are inherently connected to one problem, i.e., positive distribution pollution, which refers to the inaccuracy in estimating positive data distribution under very little labeled data. Then, inspired by this insight, we devise a variational model named CoVPU, which addresses all three problems in a unified perspective by targeting the positive distribution pollution problem. CoVPU not only accurately separates the positive data from the unlabeled data based on discrete normalizing flows, but also effectively approximates the positive distribution based on our derived unbiased rebalanced risk estimator and supervises the approximation based on a novel prior-free variational loss. Rigorous theoretical analysis proves the convergence of CoVPU to an optimal Bayesian classifier. Extensive experiments demonstrate the superiority of CoVPU over the state-of-the-art PU learning methods under these problems.
Auction-Based Cluster Federated Learning in Mobile Edge Computing Systems
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
A Context-Aware Trust-Oriented Influencers Finding in Online Social Networks
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/ICWS.2015.67
Predicting information diffusion using the inter- and intra-path of influence transitivity
Publisher: Elsevier BV
Date: 12-2023
Service-Oriented Computing – ICSOC 2016 Workshops
Publisher: Springer International Publishing
Date: 2017
Targeted aspects oriented topic modeling for short texts
Publisher: Springer Science and Business Media LLC
Date: 07-03-2020
Several fundamental issues for a distributed hypermedia document system supporting mobile hosts
Publisher: IEEE
Date: 1999
PumaMart: a parallel and autonomous agents based internet marketplace
Publisher: Elsevier BV
Date: 09-2004
Multiple QoT constrained social trust path selection in complex social networks
Publisher: IEEE
Date: 06-2012
Message from the General Chairs
Publisher: IEEE
Date: 06-2015
The roadmap of trust and trust evaluation in web applications and web services
Publisher: Springer New York
Date: 06-08-2014
A subjective probability based deductive approach to global trust evaluation in composite services
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.28
From representational state transfer to Accountable State Transfer architecture
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/ICWS.2010.56
Toward a Distributed Trust Management System for Misbehavior Detection in the Internet of Vehicles
Publisher: Association for Computing Machinery (ACM)
Date: 13-07-2023
DOI: 10.1145/3594637
Abstract: Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.
Toward scalable and privacy-preserving deep neural network via algorithmic-cryptographic co-design
Publisher: Association for Computing Machinery (ACM)
Date: 03-05-2022
DOI: 10.1145/3501809
Abstract: Deep Neural Networks (DNNs) have achieved remarkable progress in various real-world applications, especially when abundant training data are provided. However, data isolation has become a serious problem currently. Existing works build privacy-preserving DNN models from either algorithmic perspective or cryptographic perspective. The former mainly splits the DNN computation graph between data holders or between data holders and server, which demonstrates good scalability but suffers from accuracy loss and potential privacy risks. In contrast, the latter leverages time-consuming cryptographic techniques, which has strong privacy guarantee but poor scalability. In this article, we propose SPNN—a Scalable and Privacy-preserving deep Neural Network learning framework, from an algorithmic-cryptographic co-perspective. From algorithmic perspective, we split the computation graph of DNN models into two parts, i.e., the private-data-related computations that are performed by data holders and the rest heavy computations that are delegated to a semi-honest server with high computation ability. From cryptographic perspective, we propose using two types of cryptographic techniques, i.e., secret sharing and homomorphic encryption, for the isolated data holders to conduct private-data-related computations privately and cooperatively. Furthermore, we implement SPNN in a decentralized setting and introduce user-friendly APIs. Experimental results conducted on real-world datasets demonstrate the superiority of our proposed SPNN.
Cross-domain recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: To address the long-standing data sparsity problem in recommender systems (RSs), cross-domain recommendation (CDR) has been proposed to leverage the relatively richer information from a richer domain to improve the recommendation performance in a sparser domain. Although CDR has been extensively studied in recent years, there is a lack of a systematic review of the existing CDR approaches. To fill this gap, in this paper, we provide a comprehensive review of existing CDR approaches, including challenges, research progress, and prospects. Specifically, we first summarize existing CDR approaches into four types, including single-target CDR, single-target multi-domain recommendation (MDR), dual-target CDR, and multi-target CDR. We then present the definitions and challenges of these CDR approaches. Next, we propose a full-view categorization and new taxonomies on these approaches and report their research progress in detail. In the end, we share several promising prospects in CDR.
Incremental Graph Pattern Based Node Matching with Multiple Updates
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
REFER: Randomized Online Factor Selection Framework for portfolio Management
Publisher: Elsevier BV
Date: 08-2023
A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Price trust evaluation in e-service oriented applications
Publisher: IEEE
Date: 07-2008
MCS-GPM: Multi-Constrained Simulation Based Graph Pattern Matching in Contextual Social Graphs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Recurrent Collaborative Filtering for unifying general and sequential recommender
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2018
Abstract: General recommender and sequential recommender are two commonly applied modeling paradigms for recommendation tasks. General recommender focuses on modeling the general user preferences, ignoring the sequential patterns in user behaviors whereas sequential recommender focuses on exploring the item-to-item sequential relations, failing to model the global user preferences. In addition, better recommendation performance has recently been achieved by adopting an approach to combine them. However, previous approaches are unable to solve both tasks in a unified way and cannot capture the whole historical sequential information. In this paper, we propose a recommendation model named Recurrent Collaborative Filtering (RCF), which unifies both paradigms within a single model.Specifically, we combine recurrent neural network (the sequential recommender part) and matrix factorization model (the general recommender part) in a multi-task learning framework, where we perform joint optimization with shared model parameters enforcing the two parts to regularize each other. Furthermore, we empirically demonstrate on MovieLens and Netflix datasets that our model outperforms the state-of-the-art methods across the tasks of both sequential and general recommender.
Trust and risk evaluation of transactions with different amounts in Peer-to-Peer E-commerce environments
Publisher: IEEE
Date: 10-2006
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
Strong social component-aware trust sub-network extraction in contextual social networks
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.22
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.
On the design of a new trust model for mobile agent security
Publisher: Springer Berlin Heidelberg
Date: 2004
Message from the ATC 2014 General Chairs and Program Chairs
Publisher: IEEE
Date: 12-2014
Several structures for protecting the routes of mobile agents dispatched in parallel
Publisher: IEEE Comput. Soc
Date: 2002
Next-item recommendations in short sessions
Publisher: ACM
Date: 13-09-2021
Updates-Aware Graph Pattern based Node Matching
Publisher: IEEE
Date: 04-2020
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.
Contextual sub-network extraction in contextual social networks
Publisher: IEEE
Date: 08-2015
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
A trust vector approach to transaction context-aware trust evaluation in e-commerce and e-service environments
Publisher: IEEE
Date: 12-2012
ReputationPro
Publisher: Association for Computing Machinery (ACM)
Date: 23-01-2015
DOI: 10.1145/2697390
Abstract: In e-commerce environments, the trustworthiness of a seller is utterly important to potential buyers, especially when a seller is not known to them. Most existing trust evaluation models compute a single value to reflect the general trustworthiness of a seller without taking any transaction context information into account. With such a result as the indication of reputation, a buyer may be easily deceived by a malicious seller in a transaction where the notorious value imbalance problem is involved—in other words, a malicious seller accumulates a high-level reputation by selling cheap products and then deceives buyers by inducing them to purchase more expensive products. In this article, we first present a trust vector consisting of three values for contextual transaction trust (CTT). In the computation of CTT values, three identified important context dimensions , including Product Category, Transaction Amount, and Transaction Time, are taken into account. In the meantime, the computation of each CTT value is based on both past transactions and the forthcoming transaction. In particular, with different parameters specified by a buyer regarding context dimensions, different sets of CTT values can be calculated. As a result, all of these trust values can outline the reputation profile of a seller that indicates the dynamic trustworthiness of a seller in different products, product categories, price ranges, time periods, and any necessary combination of them. We name this new model ReputationPro . Nevertheless, in ReputationPro , the computation of reputation profile requires new data structures for appropriately indexing the precomputation of aggregates over large-scale ratings and transaction data in three context dimensions, as well as novel algorithms for promptly answering buyers’ CTT queries. In addition, storing precomputed aggregation results consumes a large volume of space, particularly for a system with millions of sellers. Therefore, reducing storage space for aggregation results is also a great demand. To solve these challenging problems, we first propose a new index scheme CMK-tree by extending the two-dimensional K-D-B-tree that indexes spatial data to support efficient computation of CTT values. Then, we further extend the CMK-tree and propose a CMK-tree RS approach to reducing the storage space allocated to each seller. The two approaches are not only applicable to three context dimensions that are either linear or hierarchical but also take into account the characteristics of the transaction-time model—that is, transaction data is inserted in chronological order. Moreover, the proposed data structures can index each specific product traded in a time period to compute the trustworthiness of a seller in selling a product. Finally, the experimental results illustrate that the CMK-tree is superior in efficiency of computing CTT values to all three existing approaches in the literature. In particular, while answering a buyer’s CTT queries for each brand-based product category, the CMK-tree has almost linear query performance. In addition, with significantly reduced storage space, the CMK-tree RS approach can further improve the efficiency in computing CTT values. Therefore, our proposed ReputationPro model is scalable to large-scale e-commerce Web sites in terms of efficiency and storage space consumption.
Reducing the upfront cost of private clouds with clairvoyant virtual machine placement
Publisher: Springer Science and Business Media LLC
Date: 2019
Dispatching multiple mobile agents in parallel for visiting e-shops
Publisher: IEEE Comput. Soc
Date: 2002
Trust evaluation in composite services selection and discovery
Publisher: IEEE
Date: 2009
DOI: 10.1109/SCC.2009.70
Building Internet marketplaces on the basis of mobile agents for parallel processing
Publisher: IEEE Comput. Soc
Date: 2002
CTumorGAN
Publisher: Springer Science and Business Media LLC
Date: 28-03-2020
Joint operator scaling and placement for Distributed Stream Processing applications in edge computing
Publisher: Springer International Publishing
Date: 2019
Towards Autonomous and Automatic Evaluation and Negotiation in Agent-Mediated Internet Marketplaces
Publisher: Springer Science and Business Media LLC
Date: 10-2005
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.
A novel model for contextual transaction trust computation with fixed storage space in e-commerce and e-service environments
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/SCC.2013.108
CrowdRec: Trust-Aware Worker Recommendation in Crowdsourcing Environments
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.10
A recommendation algorithm based on dynamic user preference and service quality
Publisher: IEEE
Date: 07-2018
A trust vector approach to service-oriented applications
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/ICWS.2008.80
A Counterfactual Collaborative Session-based Recommender System
Publisher: ACM
Date: 30-04-2023
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
Incremental Graph Pattern Based Node Matching
Publisher: IEEE
Date: 04-2018
Context-aware reviewer assignment for trust enhanced peer review
Publisher: Public Library of Science (PLoS)
Date: 19-06-2015
Domain Disentanglement with Interpolative Data Augmentation for Dual-Target Cross-Domain Recommendation
Publisher: ACM
Date: 14-09-2023
A meta-modeling framework to support accountability in business process modeling
Publisher: Springer Berlin Heidelberg
Date: 2009
Sequential/session-based recommendations
Publisher: ACM
Date: 06-07-2022
Mining e-commerce feedback comments for dimension rating profiles
Publisher: Springer Berlin Heidelberg
Date: 2013
Few-Shot Semantic Relation Prediction Across Heterogeneous Graphs
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-0100
Phd Symposium Track: Message from Chairs
Publisher: Springer Berlin Heidelberg
Date: 2013
A unified framework for cross-domain and cross-system recommendations
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Trust management in three generations of web-based social networks
Publisher: IEEE
Date: 2009
A heuristic algorithm for trust-oriented service provider selection in complex social networks
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.47
A decentralized collaborative approach to online edge user allocation in edge computing environments
Publisher: IEEE
Date: 10-2020
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.
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.
An adaptive news-driven method for CVaR-sensitive online portfolio selection in non-stationary financial markets
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2021
Abstract: CVaR-sensitive online portfolio selection (CS-OLPS) becomes increasingly important for investors because of its effectiveness to minimize conditional value at risk (CVaR) and control extreme losses. However, the non-stationary nature of financial markets makes it very difficult to address the CS-OLPS problem effectively. To address the CS-OLPS problem in non-stationary markets, we propose an effective news-driven method, named CAND, which adaptively exploits news to determine the adjustment tendency and adjustment scale for tracking the dynamic optimal portfolio with minimal CVaR in each trading round. In addition, we devise a filtering mechanism to reduce the errors caused by the noisy news for further improving CAND's effectiveness. We rigorously prove a sub-linear regret of CAND. Extensive experiments on three real-world datasets demonstrate CAND’s superiority over the state-of-the-art portfolio methods in terms of returns and risks.
A mobile agent based system for distributed database access on the internet
Publisher: IEEE
Date: 2000
Veracity-aware and event-driven personalized news recommendation for fake news mitigation
Publisher: ACM
Date: 25-04-2022
Message from the UFirst 2014 Symposium Chairs
Publisher: IEEE
Date: 12-2014
Efficient Query of Quality Correlation for Service Composition
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
KPMCF: A Learning Model for Measuring Social Relationship Strength
Publisher: Springer Berlin Heidelberg
Date: 2013
Two-dimensional trust rating aggregations in service-oriented applications
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
DOI: 10.1109/TSC.2010.39
A 2-phase evaluation model for agent-mediated Internet marketplaces
Publisher: IEEE Comput. Soc
Date: 2001
Context based trust normalization in service-oriented environments
Publisher: Springer Berlin Heidelberg
Date: 2010
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
Crowd Trust: A Context-Aware Trust Model for Worker Selection in Crowdsourcing Environments
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/ICWS.2015.26
A decentralized reactive approach to online task offloading in mobile edge computing environments
Publisher: Springer International Publishing
Date: 2020
Modeling accountable cloud services
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.58
Subjective trust inference in composite services
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 05-07-2010
Abstract: In Service-Oriented Computing (SOC) environments, the trustworthiness of each service is critical for a service client when selecting one from a large pool of services. The trust value of a service is usually in the range of [0,1] and is evaluated from the ratings given by service clients, which represent the subjective belief of these service clients on the satisfaction of delivered services. So a trust value can be taken as the subjective probability, with which one party believes that another party can perform an action in a certain situation. Hence, subjective probability theory should be adopted in trust evaluation. In addition, in SOC environments, a service usually invokes other services offered by different service providers forming a composite service. Thus, the global trust of a composite service should be evaluated based on complex invocation structures. In this paper, firstly, based on Bayesian inference, we propose a novel method to evaluate the subjective trustworthiness of a service component from a series of ratings given by service clients. Secondly, we interpret the trust dependency caused by service invocations as conditional probability, which is evaluated based on the subjective trust values of service components. Furthermore, we propose a joint subjective probability method to evaluate the subjective global trust of a composite service on the basis of trust dependency. Finally, we introduce the results of our conducted experiments to illustrate the properties of our proposed subjective global trust inference method.
A-MCTS
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
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.
The approaches to contextual transaction trust computation in e-Commerce environments
Publisher: Wiley
Date: 12-02-2014
DOI: 10.1002/SEC.839
Early Detection of Rumours on Twitter via Stance Transfer Learning
Publisher: Springer International Publishing
Date: 2020
A Quantitative Service Accountability Model
Publisher: IEEE
Date: 10-2009
Transaction similarity-based contextual trust evaluation in e-commerce and e-service environments
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.62
Space Reduction for Contextual Transaction Trust Computation in E-Commerce and E-Service Environments
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.97
A data-driven approach for constructing multilayer network-based service ecosystem models
Publisher: Springer Science and Business Media LLC
Date: 13-08-2023
Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Trust-Oriented Composite Service Selection and Discovery
Publisher: Springer Berlin Heidelberg
Date: 2009
Data science for next-generation recommender systems
Publisher: Springer Science and Business Media LLC
Date: 29-06-2023
A structured hypertext data model with versioning for engineering documents
Publisher: Springer Science and Business Media LLC
Date: 2003
On the detection of shilling attacks in federated collaborative filtering
Publisher: IEEE
Date: 09-2020
Efficient contextual transaction trust computation in E-commerce environments
Publisher: IEEE
Date: 06-2012
A formal service contract model for accountable SaaS and cloud services
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.85
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
Social context-aware trust inference for trust enhancement in social network based recommendations on service providers
Publisher: Springer Science and Business Media LLC
Date: 23-08-2013
The evaluation of situational transaction trust in e-service environments
Publisher: IEEE
Date: 2008
Modeling Dynamic Missingness of Implicit Feedback for Sequential Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
N2TM: A New Node to Trust Matrix Method for Spam Worker Defense in Crowdsourcing Environments
Publisher: Springer International Publishing
Date: 2019
Commtrust
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Cross-heterogeneity Graph Few-shot Learning
Publisher: ACM
Date: 21-10-2023
The prediction of trust rating based on the quality of services using fuzzy linear regression
Publisher: IEEE
Date: 06-2011
A mobile autonomous agent-based secure payment protocol supporting multiple payments
Publisher: IEEE
Date: 2005
DOI: 10.1109/IAT.2005.13
A study of securing route structures for mobile agents dispatched in parallel
Publisher: Inderscience Publishers
Date: 2004
Interaction trust evaluation in decentralized environments
Publisher: Springer Berlin Heidelberg
Date: 2004
Trust inference in complex trust-oriented social networks
Publisher: IEEE
Date: 2009
DOI: 10.1109/CSE.2009.248
Exploiting intra- and inter-session dependencies for session-based recommendations
Publisher: Springer Science and Business Media LLC
Date: 06-08-2022
A Dynamic Grouping-Based Trust Model for Mobile P2P Networks
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/SCC.2016.121
Multi-Constrained Graph Pattern Matching in large-scale contextual social graphs
Publisher: IEEE
Date: 04-2015
A Disclosure Framework for Service Accountability in SOA
Publisher: IEEE
Date: 2008
The study of trust vector based trust rating aggregation in service-oriented environments
Publisher: Springer Science and Business Media LLC
Date: 11-2011
CrowdDefense: A Trust Vector-Based Threat Defense Model in Crowdsourcing Environments
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/ICWS.2017.39
Partition-aware graph pattern based node matching with updates
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Social context-aware trust prediction in social networks
Publisher: Springer Berlin Heidelberg
Date: 2014
Modeling the dynamic trust of online service providers using HMM
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/ICWS.2013.68
Modeling User Demand Evolution for Next-Basket Prediction
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
A service computing manifesto
Publisher: Association for Computing Machinery (ACM)
Date: 24-03-2017
DOI: 10.1145/2983528
Abstract: Mapping out the challenges and strategies for the widespread adoption of service computing.
MSDS: A Novel Framework for Multi-Source Data Selection Based Cross-Network Node Classification
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Stratified and time-aware sampling based adaptive ensemble learning for streaming recommendations
Publisher: Springer Science and Business Media LLC
Date: 09-11-2021
Message from the SOSE 2018 TPC Co-Chairs
Publisher: IEEE
Date: 03-2018
Message from ICEBE 2009 program chairs: ICEBE 2009
Publisher: IEEE
Date: 10-2009
DOI: 10.1109/ICEBE.2009.6
Security and robustness enhanced route structures for mobile agents
Publisher: Springer Science and Business Media LLC
Date: 2003
Message from the General Chairs and Program Chairs
Publisher: IEEE
Date: 06-2014
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.
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
Enhancing grid security with trust management
Publisher: IEEE
Date: 2004
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
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
Exploiting two-faceted web of trust for enhanced-quality recommendations
Publisher: Elsevier BV
Date: 12-2013
Evaluating cloud users’ credibility of providing subjective assessment or objective assessment for cloud services
Publisher: Springer Berlin Heidelberg
Date: 2014
Employ a mobile agent for making a payment
Publisher: Hindawi Limited
Date: 2008
DOI: 10.1155/2008/402519
Abstract: The mobile agent paradigm offers flexibility and autonomy to e-commerce applications. But it is challenging to employ a mobile agent to make a payment due to the security consideration. In this paper, we propose a new agent-assisted secure payment protocol, which is based on SET payment protocol and aims at enabling the dispatched consumer-agent to autonomously sign contracts and make the payment on behalf of the cardholder after having found the best merchant, without the possibility of disclosing any secret to any participant. This is realized by adopting the Signature-Share scheme, and employing a Trusted Third Party (TTP). In the proposed protocol, the principle that each participant knows what is strictly necessary for his/her role is followed as in SET. In addition, mechanisms have been devised for preventing and detecting double payment, overspending and overpayment attacks. Finally the security properties of the proposed protocol are studied analytically. In comparison with other existing models, the proposed protocol is more efficient and can detect more attacks.
Reputation-oriented trustworthy computing in e-commerce environments
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
DOI: 10.1109/MIC.2008.84
Defending Blockchain Forking Attack by Delaying MTC Confirmation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Context-aware trustworthy service evaluation and recommendation in Social Internet of Things
Publisher: Springer International Publishing
Date: 2018
Double-wing mixture of experts for streaming recommendations
Publisher: Springer International Publishing
Date: 2020
Trust-oriented service provider selection in complex online social networks
Publisher: Springer New York
Date: 06-08-2014
Repeatable multi-dimensional virtual network embedding in cloud service platform
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Security and trust management in mobile agents
Publisher: IEE
Date: 2005
DOI: 10.1049/CP:20051473
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
A graph-based comprehensive reputation model
Publisher: Elsevier BV
Date: 10-2015
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.
Relationship network augmented web services clustering
Publisher: IEEE
Date: 07-2019
Trust enhanced security for mobile agents
Publisher: IEEE
Date: 2005
IEEE Access special section editorial
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
A graphical and attentional framework for dual-target cross-domain recommendation
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2020
Abstract: The conventional single-target Cross-Domain Recommendation (CDR) only improves the recommendation accuracy on a target domain with the help of a source domain (with relatively richer information). In contrast, the novel dual-target CDR has been proposed to improve the recommendation accuracies on both domains simultaneously. However, dual-target CDR faces two new challenges: (1) how to generate more representative user and item embeddings, and (2) how to effectively optimize the user/item embeddings on each domain. To address these challenges, in this paper, we propose a graphical and attentional framework, called GA-DTCDR. In GA-DTCDR, we first construct two separate heterogeneous graphs based on the rating and content information from two domains to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common users learned from both domains. Both steps significantly enhance the quality of user and item embeddings and thus improve the recommendation accuracy on each domain. Extensive experiments conducted on four real-world datasets demonstrate that GA-DTCDR significantly outperforms the state-of-the-art approaches.
Trust development in peerto-peer environments
Publisher: IGI Global
Date: 2010
DOI: 10.4018/978-1-61520-682-7.CH009
Abstract: In peer-to-peer (P2P) service-oriented environments, a peer may need to interact with unknown peers for the services or products provided. Thus the trust evaluation prior to and posterior to interactions becomes a very important issue, which may be based on other peers’ recommendations/evaluations. This chapter presents a dynamic peer trust evaluation model, which aims to measure responding peers’ recommendation trust, and hence filter out low credibility recommendations and obtain more accurate and objective trust values. In our model, prior to any interaction with an unknown peer (target peer), the mean trust value results from the evaluations (recommendations) given by responding peers. Posterior to interactions with the target peer, the trust values are aggregated from both responding peers’ recommendations and the requesting peer’s experience. On aggregating trust values, the weight to the requesting peer’s evaluation becomes bigger and bigger. Meanwhile, during this process, the credibility (recommendation trust) of each responding peer’s recommendation can be measured round by round. This helps filter out low credibility peers and improve the trust evaluation accuracy.
A Study of building internet marketplaces on the basis of mobile agents for parallel processing
Publisher: Springer Science and Business Media LLC
Date: 2002
Context-aware trust network extraction in large-scale trust-oriented social networks
Publisher: Springer Science and Business Media LLC
Date: 14-08-2017
Data science and artificial intelligence for responsible recommendations
Publisher: ACM
Date: 22-07-2021
Finding High-quality Item Attributes for Recommendation
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
An efficient multiple trust paths finding algorithm for trustworthy service provider selection in real-time Online Social Network environments
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.29
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