ORCID Profile
0000-0002-5344-1884
Current Organisation
Macquarie University
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
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.
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
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 |
Publisher: Springer International Publishing
Date: 2020
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/ICWS.2015.67
Publisher: Elsevier BV
Date: 12-2023
Publisher: Springer International Publishing
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 07-03-2020
Publisher: IEEE
Date: 1999
Publisher: Elsevier BV
Date: 09-2004
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 06-2015
Publisher: Springer New York
Date: 06-08-2014
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.28
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/ICWS.2010.56
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.
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.
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Elsevier BV
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2019
Publisher: IEEE
Date: 07-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
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.
Publisher: IEEE
Date: 10-2006
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.
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.22
Publisher: Springer International Publishing
Date: 2016
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.
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: IEEE
Date: 12-2014
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: ACM
Date: 13-09-2021
Publisher: IEEE
Date: 04-2020
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.
Publisher: IEEE
Date: 08-2015
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.81
Publisher: IEEE
Date: 12-2012
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.
Publisher: Springer Science and Business Media LLC
Date: 2019
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: IEEE
Date: 2009
DOI: 10.1109/SCC.2009.70
Publisher: IEEE Comput. Soc
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 28-03-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 10-2005
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.
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/SCC.2013.108
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.10
Publisher: IEEE
Date: 07-2018
Publisher: IEEE
Date: 09-2008
DOI: 10.1109/ICWS.2008.80
Publisher: ACM
Date: 30-04-2023
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/ICWS.2016.15
Publisher: IEEE
Date: 04-2018
Publisher: Public Library of Science (PLoS)
Date: 19-06-2015
Publisher: ACM
Date: 14-09-2023
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: ACM
Date: 06-07-2022
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-0100
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.47
Publisher: IEEE
Date: 10-2020
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.
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.
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.
Publisher: IEEE
Date: 2000
Publisher: ACM
Date: 25-04-2022
Publisher: IEEE
Date: 12-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2011
DOI: 10.1109/TSC.2010.39
Publisher: IEEE Comput. Soc
Date: 2001
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: IEEE
Date: 06-2012
DOI: 10.1109/ICWS.2012.47
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/ICWS.2015.26
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.58
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
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.
Publisher: Wiley
Date: 12-02-2014
DOI: 10.1002/SEC.839
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE
Date: 10-2009
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.62
Publisher: IEEE
Date: 06-2015
DOI: 10.1109/SCC.2015.97
Publisher: Springer Science and Business Media LLC
Date: 13-08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
Publisher: Springer Berlin Heidelberg
Date: 2009
Publisher: Springer Science and Business Media LLC
Date: 29-06-2023
Publisher: Springer Science and Business Media LLC
Date: 2003
Publisher: IEEE
Date: 09-2020
Publisher: IEEE
Date: 06-2012
Publisher: IEEE
Date: 07-2010
DOI: 10.1109/SCC.2010.85
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2015
Publisher: Springer Science and Business Media LLC
Date: 23-08-2013
Publisher: IEEE
Date: 2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2014
Publisher: ACM
Date: 21-10-2023
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 2005
DOI: 10.1109/IAT.2005.13
Publisher: Inderscience Publishers
Date: 2004
Publisher: Springer Berlin Heidelberg
Date: 2004
Publisher: IEEE
Date: 2009
DOI: 10.1109/CSE.2009.248
Publisher: Springer Science and Business Media LLC
Date: 06-08-2022
Publisher: IEEE
Date: 06-2016
DOI: 10.1109/SCC.2016.121
Publisher: IEEE
Date: 04-2015
Publisher: IEEE
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 11-2011
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/ICWS.2017.39
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Berlin Heidelberg
Date: 2014
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/ICWS.2013.68
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2023
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 09-11-2021
Publisher: IEEE
Date: 03-2018
Publisher: IEEE
Date: 10-2009
DOI: 10.1109/ICEBE.2009.6
Publisher: Springer Science and Business Media LLC
Date: 2003
Publisher: IEEE
Date: 06-2014
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
DOI: 10.1109/TSC.2011.58
Publisher: IEEE
Date: 2004
Publisher: IEEE
Date: 06-2013
DOI: 10.1109/SCC.2013.92
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.24
Publisher: Elsevier BV
Date: 12-2013
Publisher: Springer Berlin Heidelberg
Date: 2014
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.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2008
DOI: 10.1109/MIC.2008.84
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer New York
Date: 06-08-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2022
Publisher: IEE
Date: 2005
DOI: 10.1049/CP:20051473
Publisher: SPIE
Date: 10-03-2020
DOI: 10.1117/12.2550511
Publisher: Elsevier BV
Date: 10-2015
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.
Publisher: IEEE
Date: 07-2019
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
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.
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.
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Springer Science and Business Media LLC
Date: 14-08-2017
Publisher: ACM
Date: 22-07-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 06-2014
DOI: 10.1109/ICWS.2014.29
Start Date: 2023
End Date: 12-2025
Amount: $420,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 06-2015
End Date: 12-2017
Amount: $280,100.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2005
End Date: 10-2010
Amount: $178,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2018
End Date: 08-2021
Amount: $421,104.00
Funder: Australian Research Council
View Funded ActivityStart Date: 09-2020
End Date: 12-2024
Amount: $420,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 05-2018
End Date: 01-2022
Amount: $348,026.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2010
End Date: 12-2013
Amount: $180,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 04-2013
End Date: 12-2017
Amount: $210,000.00
Funder: Australian Research Council
View Funded Activity