Machine learning models for medical images can help physicians diagnose and manage diseases. However, due to the fact that medical image annotation requires a great deal of manpower and expertise, as well as the fact that clinical departments perform image annotation based on task orientation, there is the problem of having fewer medical image annotation data with more unlabeled data and having many datasets that annotate only a single organ. In this paper, we present UniMOS, the first universal framework for achieving the utilization of fully and partially labeled images as well as unlabeled images. Specifically, we construct a Multi-Organ Segmentation (MOS) module over fully/partially labeled data as the basenet and designed a new target adaptive loss. Furthermore, we incorporate a semi-supervised training module that combines consistent regularization and pseudo-labeling techniques on unlabeled data, which significantly improves the segmentation of unlabeled data. Experiments show that the framework exhibits excellent performance in several medical image segmentation tasks compared to other advanced methods, and also significantly improves data utilization and reduces annotation cost. Code and models are available at: https://github.com/lw8807001/UniMOS.

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2-D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images, such as rotations and reflections. To solve this problem, we propose a novel group equivariant segmentation framework by encoding those inherent symmetries for learning more precise representations. First, kernel-based equivariant operations are devised on each orientation, which allows it to effectively address the gaps of learning symmetries in existing approaches. Then, to keep segmentation networks globally equivariant, we design distinctive group layers with layer-wise symmetry constraints. Finally, based on our novel framework, extensive experiments conducted on real-world clinical data demonstrate that a group equivariant Res-UNet (called GER-UNet) outperforms its regular CNN-based counterpart and the state-of-the-art segmentation methods in the tasks of hepatic tumor segmentation, COVID-19 lung infection segmentation, and retinal vessel detection. More importantly, the newly built GER-UNet also shows potential in reducing the sample complexity and the redundancy of filters, upgrading current segmentation CNNs, and delineating organs on other medical imaging modalities.

Graph Pattern based Node Matching(GPNM) is to find all the matches of the nodes in a data graph$G_D$based on a given pattern graph$G_P$. GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both$G_P$and$G_D$are updated frequently. However, the existing GPNM methods either need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated$G_P$and$G_D$or incrementally perform the GPNM procedure for each of the updates, leading to low efficiency. Although the elimination relations between updates and partitions of data graphs are considered in the state-of-the-art method, it still suffers from low efficiency as only the labels of nodes are considered in the partitions. Therefore, there is a pressing need for a new method to efficiently deliver the node matching results on the updated graphs. In this paper, we propose a new Partition-aware GPNM algorithm, called P-GPNM, where we propose two new partition methods, i.e.,connection-based partitionanddensity-based partition. In these two methods, P-GPNM considers the dense connections between partitions and the inner connections inside a single partition, respectively. The experimental results on five real-world social graphs demonstrate that our proposed P-GPNM is much more efficient than the state-of-the-art GPNM methods.

Users’ purchase behaviors are complex and dynamic, which are usually driven by various personal demands evolving with time. According to psychology and economic theories, user demands can be satisfied with a sequence of purchase behaviors, resulting in a basket of items. However, most of the existing works simply predict the next basket from a shallow perspective of (purchase) sequence data modeling without deep insight into the underlying factors which drive user purchase behaviors. In fact, filling a basket with multiple items is a process to incrementally satisfy a user's demand. Therefore, the key challenges to predict a user's next basket lie in (1) how to track the changes of the user's demand, and (2) how to satisfy her demand at a given moment. To this end, we propose an Evolving DEmand SAtisfaction (EvoDESA) model to model a user's demand evolution for next-basket prediction. In EvoDESA, a demand evolution module learns the dynamics of user demand over a sequence of basket-purchase behaviors. Then, a next-basket planning module effectively packs an optimal combination of items to best satisfy the user's current demand. Extensive experiments on three real-world transaction datasets demonstrate the considerable superiority of EvoDESA over the state-of-the-art approaches.

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.

Node scheduling provides an effective way to prolong the network lifetime of Internet-of-Things (IoT) networks comprising of energy-constrained sensor nodes. Barrier scheduling is a special type of node scheduling scheme that targets IoT-based surveillance applications. An efficient barrier scheduling scheme must address the key Quality-of-Service (QoS) requirements of smart surveillance applications, such as coverage, connectivity, and energy efficiency. Moreover, such a scheme must be capable of dynamically adapting its execution strategy in the event of node failures caused due to faults arising out of unexpected battery depletion. This article proposes a fault-tolerant barrier scheduling scheme that satisfies the key QoS requirements of surveillance applications in the event of such faults. The approach is based on a novel fully weighted dynamic graph model. This article suggests two novel heuristics to guarantee fault tolerance and recovery. Extensive simulation studies are conducted to evaluate and compare the performance and effectiveness of this scheme with other such approaches.

Graph Pattern based Node Matching (GPNM) has been proposed to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has been increasingly adopted in many applications such as group finding and expert recommendation, in which data graphs are frequently updated overtime. Moreover, many typical pattern graphs frequently and repeatedly appear in users' queries in a short period of time, e.g., social graph searches on Facebook. To deliver a GPNM result in such applications, the existing GPNM methods have to perform an incremental GPNM procedure for each of the updates in the data graph, which is computationally expensive. To address this problem, in this paper, we first analyze the elimination relationships between multiple updates in GD and the hierarchical structure between these elimination relationships. Then, we generate an Elimination Hierarchy Tree (EH-Tree) to index the elimination relationships and propose an EH-Tree based GPNM method, called EHGPNM, considering the elimination relationships between multiple updates in GD. EH-GPNM first delivers the GPNM result of an initial query, and then delivers the GPNM result of a subsequent query, based on the initial GPNM result and the multiple updates of GD that occur between those two queries. The experimental results on five real-world social graphs demonstrate that our proposed EH-GPNM is much more efficient than the state-of-the-art GPNM methods.

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.

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.

Trust can be employed for finding reliable information in Online Social Networks (OSNs). Since users in OSNs may intentionally change their behavior over time (in some cases for deceiving other users), modeling (pair-wise) trust relations in such complex environment is a challenging task. However, most of the existing trust prediction approaches assume that trust relations are fixed over time and they fail to capture the dynamic behavior of users in OSNs. In this paper, we propose a dynamic deep trust prediction model. As the impact of incidental emotions on trust has been proven in psychology studies, in this paper, we also study this impact on our trust prediction approach. First, we propose a novel deep structure that incorporates users' emotions and their textual contents in OSNs. Second, we use embeddings to represent the users and their self-descriptions provided. Finally, considering different time windows, we dynamically predict pair-wise trust relations. To evaluate our approach, we collected a large twitter dataset. The evaluation results demonstrate the effectiveness of our approach compared to the state-of-the-art approaches.

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 diverse 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 diversity.

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.

Customer reviews are now increasingly available on Online Social Networks (OSNs) for a wide range of products and services. Trust in the review's author is a crucial basis for believing in the reliability of reviews generated on such networks. In this context, the main challenge is to predict the unknown trust relationship between two users. Existing trust prediction approaches fail to incorporate textual footprint of users. To address this challenge, we present a deep learning-based graph analytics model to predict trust relations in OSNs. We leverage and extend GraphSAGE, a method for computing node representations in an inductive manner, to develop a deep classifier. We present our experiment with datasets from review websites to train classifiers that predict trust relations between pairs of users, and highlight how our approach significantly improves the quality of predicted trust relations compared to the state-of-the-art approaches.

Any proposal to provide security for implantable medical devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a finger-to-heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD. Doctors can gain access to the IMD and perform emergency treatment by scanning the patient's finger tip instead of asking the patient for passwords/security tokens, thereby, achieving the necessary trade-off. In the scheme, an improved minutia-cylinder-code-based fingerprint authentication algorithm is proposed for the IMD by reducing the length of each feature vector and the number of query feature vectors. Experimental results show that the improved fingerprint authentication algorithm significantly reduces both the size of messages in transmission and computational overheads in the device, and thus, can be utilized to secure the IMD. Compared to existing electrocardiogram signal-based security schemes, the F2H scheme does not require the IMD to capture or process biometric traits in every access attempt since a fingerprint template is generated and stored in the IMD beforehand. As a result, the scarce resources in the IMD are conserved, making the scheme sustainable as well as energy efficient.

Incorporating accountability mechanisms in online services requires effective trust management and immutable, traceable source of truth for transaction evidence. The emergence of the blockchain technology brings in high hopes for fulfilling most of those requirements. However, a major challenge is to find a proper consensus protocol that is applicable to the crowdsourcing services in particular and online services in general. Building upon the idea of using blockchain as the underlying technology to enable tracing transactions for service contracts and dispute arbitration, this paper proposes a novel consensus protocol that is suitable for the crowdsourcing as well as the general online service industry. The new consensus protocol is called “Proof-of-Trust” (PoT) consensus; it selects transaction validators based on the service participants' trust values while leveraging RAFT leader election and Shamir's secret sharing algorithms. The PoT protocol avoids the low throughput and resource intensive pitfalls associated with Bitcoin' s “Proof-of-Work” (PoW) mining, while addressing the scalability issue associated with the traditional Paxos-based and Byzantine Fault Tolerance (BFT)-based algorithms. In addition, it addresses the unfaithful behaviors that cannot be dealt with in the traditional BFT algorithms. The paper demonstrates that our approach can provide a viable accountability solution for the online service industry.

Device-to-Device (D2D) technology based proximity services (ProSe) has been recently proposed to support Vehicle-to-Everything (V2X) communication. In this paper, we propose a joint resource scheduling scheme that caters to both distributed and centralized resource scheduling for D2D-based V2X communication under different network load conditions. In the proposed scheme, Vehicle User Equipments (V-UEs) can operate in either the scheduled resource allocation mode (mode 3) or the autonomous resource selection mode (mode 4). We then further divide mode 3 into dedicated mode and reuse mode. Since safety-critical information is very important for V2X communication, we guarantee the Quality of Service (QoS) of V2X communication by considering both ProSe Per- Packet Priority (PPPP) and communication link quality of V2X messages. Our objective is to maximize the overall information value of all V-UEs by joint resource scheduling of different resource allocation modes under different network load conditions while satisfying the minimum signal to interference noise ratio (SINR) requirements of both Pedestrian User Equipments (P-UEs) and V-UEs. Finally, extensive simulation results are presented to evaluate the efficacy of the proposed scheme.

Graph Pattern based Node Matching (GPNM) is to find all the matches of the nodes in a data graph GD based on a given pattern graph GP. GPNM has become increasingly important in many applications, e.g., group finding and expert recommendation. In real scenarios, both GP and GD are updated frequently. However, the existing GPNM methods need to perform a new GPNM procedure from scratch to deliver the node matching results based on the updated GP and updated GD, which consumes much time. Therefore, there is a pressing need for a novel method to efficiently deliver the node matching results. In this paper, we propose a novel INCremental GPNM method called INC-GPNM, where we first build up indices to incrementally maintain the shortest path length range between different label types in GD, and then identify the affected parts of GD in GPNM including nodes and edges w.r.t. the updates of GP and GD. Moreover, based on the index structure and our novel search strategies, INC-GPNM can efficiently deliver node matching results taking the updates of GP and GD as input, and can greatly save the query processing time with improved time complexity. The extensive experiments on five real-world social graphs demonstrate that our method greatly outperforms the state-of-the-art GPNM method in efficiency.

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 individual 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.

In this paper, we investigate the resource allocation problem for D2D-based Vehicle-to- Everything (V2X) communication. There are two resource allocation modes for V2X sidelink communication: autonomous resource selection and scheduled resource allocation which is further divided into 2 categories-the dedicated mode and the reuse mode. Considering that each V2X message is arranged with the independent ProSe Per-Packet Priority (PPPP) and V2X sidelinks require stringent latency and reliability, therefore, in this paper, we propose a new joint autonomous resource selection and scheduled resource allocation approach to maximize the sum of the information values of all users. Our proposed scheme not only considers the user's PPPP but also takes into account the user's communication reliability, while guaranteeing the quality of service (QoS) of both Pedestrian user equipments (P-UEs) and Vehicle user equipments (V-UEs). A comprehensive performance evaluation is conducted to demonstrate the efficacy of the proposed scheme.

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 divergence 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 anExtraction-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 aQoS 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.

In this paper, we propose a new joint mode selection and proportional fair scheduling scheme for Device-to-Device (D2D) communication in cellular networks. Our objective is to maximize the sum of all users' proportional fairness functions while guaranteeing the signal-to-interference-plus-noise ratio (SINR) requirements of both D2D and cellular links. In our scheme, we consider two communication mode options for D2D communication: the dedicated mode and the reuse mode. In order to improve the fairness of the D2D users (DUs), we allocate the dedicated radio resource to the D2D pair with a higher priority. Furthermore, we use the Hungarian Algorithm to implement the sub-optimal resource allocation for multiple D2D pairs in the reuse mode and their reuse cellular users (CUs). The simulation results demonstrate that both the system throughput and the system fairness experience significant increases in our proposed scheme.

The current century has brought an unimaginable growth in information and communications technology (ICT) and needs of enormous computing. The advancements in computer hardware and software particularly helped fuel the requirements of human beings, and revolutionized the smart products as an outcome. The advent of wearable devices from their development till successful materialisation has only taken less than a quarter of a century. The huge benefits of these smart wearable technologies cannot be fully enjoyed until and unless the reliability of a complete system is ensured. The reliability can be increased by the consistent advancements in hardware and software in parallel. User expectations actually are the challenges that keep the advancements alive while improving at an unmatchable pace. The future of wearable and other smart devices depends on whether they can provide a timely solution that is reliable, richer in resources, smaller in size, and cheaper in price. This paper addresses the threats and opportunities in the development and the acceptance of immersive and wearable technologies. The hardware and software challenges for the purpose of development are discussed to demonstrate the bottlenecks of the current technologies and the limitations that impose those bottlenecks. For the purpose of adoption, social and commercial challenges related to innovation and acceptability are discussed. The paper proposes guidelines that are expected to be applicable in several considerable applications of wearable technologies, for example, social networks, healthcare, and banking.

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.

Information and Communication Technologies (ICT) based applications for Ambient Assisted Living (AAL) help elderly or individual people living home alone. AAL system reliability is mostly based on the recent emerging class of network that is known as wireless mesh network (WiMesh). In WiMesh the information security is the most difficult problem to tackle because the medium is open to cyber-attacks. Moreover, when we talk about AAL where the complete personal information is digitized and stored, the need for implementation and maintenance of strict security measures is essential. In this article, we present a critical literature survey on communication security issues in e-health care environments. We highlight and explore the representative state of the art security prototypes for eHealthcare environments and also provide the details of their security characteristics. In addition, we discuss in detail the challenges and opportunities of these systems.

In Online Social Networks (OSNs), the important participants, the trust relations between participants, and the interaction contexts between participants greatly impact a participant's decision-making in many applications, such as service provider selection and crowdsourcing service invocation. However, predicting the trust between two unknown participants based on the whole large-scale social network can lead to very high computation costs. Thus, prior to trust prediction, extracting a small-scale sub-network containing the important participants and the corresponding contextual information with a high density could make the trust prediction more efficient and effective. However, extracting such a sub-network has been proved to be an NP-Complete problem. To address this challenging problem, we propose a strong social component-aware trust sub-network extraction model, So-BiNet, to search for near-optimal solutions effectively and efficiently. Our method can extract a trust sub-network without any decompression, which can in turn greatly save the search time of trust sub-network extraction. The experiments, conducted on four social network datasets, demonstrate that our approach can efficiently extract sub-networks covering important participants and contextual information while keeping a high density. Our approach is superior to the state-of-the-art approaches in terms of the quality of the sub-networks extracted within the same execution time.

Cloud computing has provided an attractive model for business service delivery. However, accountability aspects, mainly the monitoring of the execution of a service contract, liability assignment and dispute arbitration are still lacking. On the one hand, the traditional centralized monitoring and the trusted-third party (TTP) based arbitration solutions are not suitable for the distributed cloud environment. On the other hand, a decentralized solution also faces challenges in guaranteeing fairness, accuracy and sustainability. To address this issue, this paper firstly proposes an innovative service contract management scheme that facilitates the monitoring of the execution of a service contract in a peer-to-peer environment, inspired by the concept of blockchain in Bitcoin. Secondly, on top of the scheme it presents a novel dispute resolution protocol based on the Byzantine agreement and the commitment scheme. Thirdly, it identifies the optimal settings of the key parameters of the protocol through a set of experiments and scenario analysis, aiming to strike the balance of fairness, accuracy, incentive maximization for the honest arbiters and cost minimization for the overall arbitration process. With this approach, service participants can be held accountable in a truly distributed environment without the presence of a central authority, which increases businesses confidence on adopting cloud services.

Graph Pattern Matching (GPM) plays a significant role in social network analysis, which has been widely used in, for example, experts finding, social community mining and social position detection. Given a pattern graph GQand a data graph GD, a GPM algorithm finds those subgraphs, GM, that match GQin GD. However, the existing GPM methods do not consider the multiple constraints on edges in GQ, which are commonly exist in various applications such as, crowdsourcing travel, social network based e-commerce and study group selection, etc. In this paper, we first conceptually extend Bounded Simulation to Multi-Constrained Simulation (MCS), and propose a novel NP-Complete Multi-Constrained Graph Pattern Matching (MC-GPM) problem. Then, to address the efficiency issue in large-scale MC-GPM, we propose a new concept called Strong Social Component (SSC), consisting of participants with strong social connections. We also propose an approach to identify SSCs, and propose a novel index method and a graph compression method for SSC. Moreover, we devise a heuristic algorithm to identify MC-GPM results effectively and efficiently without decompressing graphs. An extensive empirical study on five real-world large-scale social graphs has demonstrated the effectiveness, efficiency and scalability of our approach.

Online Social Networks (OSNs) have become an integral part of daily life in recent years. OSNs contain important participants, the trust relations between participants, and the contexts in which participants interact with each other. All of these have a great influence on the prediction of the trust between a source participant and a target participant, which is important for a participant's decision-making process in many applications, such as seeking service providers. However, predicting the trust from a source participant to a target one based on the whole social network is not really feasible. Thus, prior to trust prediction, the extraction of a small-scale sub-network containing most of the important nodes and contextual information with a high density rate could make trust prediction more efficient and effective. However, extracting such a sub-network has been proved to be an NP-Complete problem. To address this challenging problem, we propose BiNet: a social context-aware trust sub-network extraction model to search for near-optimal solutions effectively and efficiently. In this model, we first capture important factors that affect the trust between participants in OSNs. Next, we define a utility function to measure the trust factors of each node in a social network. At last, we design a novel binary ant colony algorithm with newly designed initialization and mutation processes for sub-network extraction incorporating the utility function. The experiments, conducted on two popular datasets of Epinion and Slash dot, demonstrate that our approach can extract sub-networks covering important participants and contextual information while keeping a high density rate. Our approach is superior to the state-of-the-art approaches in terms of the quality of extracted sub-networks within the same execution time.

The fuzzy commitment scheme and the fuzzy vault scheme have been widely investigated by researchers in the distribution of symmetric keys within a Wireless Body Area Network (WBAN) for security purposes. Both schemes could use the same physiological signal (Electrocardiogram) for the same purpose (key distribution). To provide guidance to researchers, this paper conducts a comparative analysis of these two schemes to identify their similarities and differences, and contrast their relative merits and demerits. In the paper, we present their mathematical models first, and then compare the models step by step together with an analysis of their simulation performance. According to our analysis, we find that both the techniques follow common workflows to conceal a symmetric key in a transmitter and reveal the key in a receiver, respectively. On the other hand, the fuzzy commitment scheme has a more complicated process in obtaining ECG measurements than the fuzzy vault scheme; by contrast, its key concealing and revealing process is much simpler when compared to the fuzzy vault scheme. Besides a superior False Acceptance Rate (FAR) performance of the fuzzy commitment scheme, their False Rejection Rate (FRR) performance is comparable. Since the polynomial calculation and reconstruction are utilized in the fuzzy vault scheme, from the perspective of the computational complexity, the fuzzy commitment scheme is recommended for lightweight WBAN sensors.

[Abstract Not Available]

Due to the diversity and dynamic nature of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. This paper proposes CCCloud: a context-aware and credible cloud service selection model based on the comparison and aggregation of subjective assessments extracted from ordinary cloud consumers and objective assessments from quantitative performance testing parties. We propose a novel approach to evaluate cloud users' credibility, which not only can accurately evaluate how truthfully they assess cloud services, but also resist user collusion. In addition, in our model, objective assessments are used as benchmarks to filter out potentially biased subjective assessments, and then objective assessments and subjective assessments are aggregated to evaluate the overall performance of a cloud service. Furthermore, our model takes the contexts of objective assessments and subjective assessments into account. By calculating the similarity between different contexts, the benchmark level of objective assessments is dynamically adjusted according to context similarity, and the aggregated final scores of alternative cloud services are weighted by the similarity between the contexts of a potential cloud consumer and every testing party. This makes our cloud service selection model reflect potential cloud consumers' customized requirements more effectively. Finally, our proposed model is evaluated through the experiments conducted under different conditions. The experimental results demonstrate that our model significantly outperforms the existing work, especially in the resistance of user collusion.

Online social networks have been used for a variety of rich activities in recent years, such as investigating potential employees and seeking recommendations of high quality services and service providers. In such activities, trust is one of the most critical factors for the decision-making of users. In the literature, the state-of-the-art trust prediction approaches focus on either dispositional trust tendency and propagated trust of the pair-wise trust relationships along a path or the similarity of trust rating values. However, there are other influential factors that should be taken into account, such as the similarity of the trust rating distributions. In addition, tendency, propagated trust and similarity are of different types, as either personal properties or interpersonal properties. But the difference has been neglected in existing models. Therefore, in trust prediction, it is necessary to take all the above factors into consideration in modeling, and process them separately and differently. In this paper we propose a new trust prediction model based on trust decomposition and matrix factorization, considering all the above influential factors and differentiating both personal and interpersonal properties. In this model, we first decompose trust into trust tendency and tendency-reduced trust. Then, based on tendency-reduced trust ratings, matrix factorization with a regularization term is leveraged to predict the tendency-reduced values of missing trust ratings, incorporating both propagated trust and the similarity of users' rating habits. In the end, the missing trust ratings are composed with predicted tendency-reduced values and trust tendency values. Experiments conducted on a real-world dataset illustrate significant improvement delivered by our approach in trust prediction accuracy over the state-of-the-art approaches.

Many OWL ontologies for the learning and education domain have been reported in the literature. The preliminary analysis of those ontologies shows that they use similar constructors and axioms but they do not seem to be following a particular or common fragment (profile) of the OWL language. We analyzed a corpus of 10 representative learning and educational ontologies in order to identify the usage patterns of OWL constructors in those ontologies. Based on our findings and considering the fact that OWL is superseded by OWL 2, we propose OWL 2 Learn, a profile of OWL 2 that is expressive enough for specifying all those ontologies in our corpus with minor modifications. This profile can offer guidance on selecting appropriate constructors for developing an OWL 2 ontology for the learning and education domain.

The security of wireless communication module for Implantable Medical Devices (IMDs) poses a unique challenge that doctors in any qualified hospital should have the access to the IMDs for an emergency treatment while the IMD should be protected from adversaries during a patient's daily life. In this paper, we present a non-key based security scheme for the emergency treatment of IMDs, named the BodyDouble. This scheme employs an external authentication proxy embedded in a gateway to authenticate the identity of a programmer. The gateway here employs a transmitting antenna to send data and jamming signals. When an adversary launches attacks, the gateway jams the request signal to the IMD and authenticates its identity. The gateway will also pretend to be the wireless module of the IMD by establishing a communication link with the adversary so that the adversary is spoofed to communicate with the gateway instead of the IMD. For the emergency situation, the IMD can be accessed without using any cryptographic keys by simply powering off or removing the gateway. Simulation results show that this security scheme can protect the IMD from the adversary's attacks successfully, and resist the potential repeated attacks to prevent the battery depletion of the IMD.

This paper proposes a novel context-aware cloud service selection model based on the comparison and aggregation of subjective assessment extracted from cloud user feedback and objective assessment from quantitative performance testing. In this model, objective assessment provided by some professional testing parties is used as a benchmark to filter out potentially biased subjective assessment from cloud users, then objective assessment and subjective assessment are aggregated to evaluate the overall performance of cloud services according to potential cloud users' personalized requests. Moreover, our model takes the contexts of objective assessment and subjective assessment into account. By calculating the similarity between different contexts, the benchmark level of objective assessment is dynamically adjusted according to context similarity, which makes the following comparison and aggregation process more accurate and effective. After aggregation, the final results can quantitatively reflect the overall quality of cloud services. Finally, our proposed model is evaluated through the experiments executed in different conditions.

Cloud computing services have been increasingly considered by business as a viable option for reducing IT expenditure. The highly automated and agile nature of cloud services offer businesses low cost, high efficiency and flexibility benefits. However, there are often associated problems with unmanaged accountability such as lack of disclosure of service obligations, mechanisms for detection for obligation fulfilment or determination of liable party if an obligation is violated. This paper analyses the accountability properties of a cloud service and proposes an accountable cloud service (ACS) model to address those problems. The ACS model is underpinned by a hybrid logic system called Dynamic Logic for Accountability (DLA) extended from Dynamic Logic. ACS provides an intuitive notation for modeling service collaboration diagrams based on a reduced version of BPMN2.0 to capture the fulfillment of service obligations. We 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, decomposition, validation, machine-interpretation, monitoring and reasoning, and ultimately facilitates accountability in cloud service consumption. Using Amazon S3 service as a case study, we show how to address those known accountability problems using our ACS model. Finally we discuss the applicability of our model to cloud services in general.

Online trading takes place in a very complex environment full of uncertainty in which deceitful service providers or sellers may strategically change their behaviors to maximize their profits. The proliferation of deception cases makes it essential and challenging to model the dynamics of a service provider and predict the trustworthiness of the service provider in transactions. Recently, probabilistic trust models have been used to assist decision making in computing environments. Although the typical Hidden Markov Model (HMM) has been used to model a provider's behavior dynamics, existing approaches focus only on the outcomes or ignore the hidden characteristics of the HMM model. In this paper, we model the dynamic trust of service providers concerning a forthcoming transaction in light of as much information as we can consider, including the static features, such as the provider's reputation and item price, and the dynamic features, such as the latest profile changes of a service provider and price changes. Based on a service provider's historical transactions, we predict the trustworthiness of the service provider in a forthcoming transaction. In addition, the Mutual Information theories and the Principle Component Analysis method are leveraged to eliminate redundant information and combine essential features to form lower dimensional feature vectors. Furthermore, by adopting Vector Quantization techniques, we apply the discrete HMM in a more powerful way, in which all the features extracted from both contextual information and the rating of each transaction are treated as observations of HMM. We evaluate our approach empirically in order to study its performance. The experiment results illustrate that our approach significantly outperforms the state-of-the-art probabilistic trust methods in accuracy in the cases with complex changes.

Online Social networks have provided the infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers or the recommendation of files as services. In these applications, trust is one of the most important factors in decision making by a service consumer, requiring the evaluation of the trustworthiness of a service provider along the social trust paths from a service consumer to the service provider. However, there are usually many social trust paths between two participants who are unknown to one another. In addition, some social information, such as social relationships between participants and the recommendation roles of participants, has significant influence on trust evaluation but has been neglected in existing studies of online social networks. Furthermore, it is a challenging problem to search the optimal social trust path that can yield the most trustworthy evaluation result and satisfy a service consumer's trust evaluation criteria based on social information. In this paper, we first present a novel complex social network structure incorporating trust, social relationships and recommendation roles, and introduce a new concept, Quality of Trust (QoT), containing the above social information as attributes. We then model the optimal social trust path selection problem with multiple end-to-end QoT constraints as a Multiconstrained Optimal Path (MCOP) selection problem, which is shown to be NP-Complete. To deal with this challenging problem, we propose a novel Multiple Foreseen Path-Based Heuristic algorithm MFPB-HOSTP for the Optimal Social Trust Path selection, where multiple backward local social trust paths (BLPs) are identified and concatenated with one Forward Local Path (FLP), forming multiple foreseen paths. Our strategy could not only help avoid failed feasibility estimation in path selection in certain cases, but also increase the chances of delivering a near-optimal solution with high quality. The results of our experiments conducted on a real data set of online social networks illustrate that MFPB-HOSTP algorithm can efficiently identify the social trust paths with better quality than our previously proposed H_OSTP algorithm that outperforms prior algorithms for the MCOP selection problem.

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.

In collaborative networks operating over a wireless medium, to maximize connectivity of the network, two nodes that are not directly connected may need to communicate with one another through other nodes in the network. However in such networks, not all nodes are reliable. Therefore it may be n

This study proposes a visual approach for classification of multivariate data based on the enhanced separation feature of a visual technique, called Hypothesis-Oriented Verification and Validation by Visualization (HOV3). In this approach, the user first builds up a visual classifier from a training dataset based on its data projection plotted by HOV3with a statistical measurement of the training dataset on a 2d space where data points with the same class label are well grouped. Then the user classifies unlabeled data points by projecting them with the labeled data points of the visual classifier together in order to collect the unlabeled data points overlapped by the labeled ones. As a result, this study provides a method which is intuitive and easy to use for data classification by visualization.

Online Social Networks (OSNs) have provided an infrastructure for a number of emerging applications in recent years, e.g., for the recommendation of service providers, where trust is one of the most important factors for the decision-making of service consumers. In order to evaluate the trustworthiness of a service provider (i.e., the target) without any prior interaction with a service consumer (i.e., the source), the trust network from the source to the target need to be extracted firstly before performing any trust evaluation, as it 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. However, the network extraction has been proved to be NP-Complete. Towards solving this challenging problem, we first propose a complex contextual social network structure which considers some social contexts, having significant influences on both social interactions and trust evaluation between participants. 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 Heuristic Social Context-Aware trust Network discovery algorithm (H-SCAN) by adopting the K-Best-First Search (KBFS) method and our 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 networks.

In Online Social Networks (OSNs), participants can conduct rich activities, where trust is one of the most important factors for their decision making. This necessitates the evaluation of the trustworthiness between two unknown participants along the social trust paths between them based on the trust transitivity properties (i.e., if A trusts B and B trusts C, then A can trust C to some extent). In order to compute more reasonable trust value between two unknown participants, a critical and challenging problem is to make clear how and to what extent trust is transitive along a social trust path. To address this problem, we first propose a new complex social network structure that takes, besides trust, social relationships, recommendation roles and preference similarity between participants into account. These factors have significant influence on trust transitivity. We then propose a general concept, called Quality of Trust Transitivity (QoTT), that takes any factor with impact on trust transitivity as an attribute to illustrate the ability of a trust path to guarantee a certain level of quality in trust transitivity. Finally, we propose a novel Multiple QoTT Constrained Trust Transitivity (MQCTT) model. The results of our experiments demonstrate that our proposed MQCTT model follows the properties of trust and the principles illustrated in social psychology, and thus can compute more resonable trust values than existing methods that consider neither the impact of social aspects nor the properties of trust.

With the advent of service-oriented computing, the issue of trust and Quality of Service (QoS) have become increasingly important. In service-oriented environments, when there are a few service providers providing the same service, a service client would be keen to know the trustworthiness of each service provider in the forthcoming transaction. The trust rating of a delivered service from a service provider can be predicted according to a set of advertised QoS data collected by the trust management authority. Although trust and QoS are qualitative by nature, most data sets represent trust and QoS in the ordinal form for the sake of simplicity. This paper introduces a new approach based on Fuzzy Linear Regression Analysis (FLRA) to extract qualitative information from quantitative data and so use the obtained qualitative information for better modeling of the data. For verification purposes, the proposed approach can be applied for the trust prediction in the forthcoming transaction based on a set of advertised QoS in service-oriented environments.

In a service-oriented online social network consisting of service providers and consumers as participants, a service consumer can search trustworthy service providers via the social network between them. This requires the evaluation of the trustworthiness of a service provider along a potentially very large number of social trust paths from the service consumer to the service provider. Thus, a challenging problem is how to identify K optimal social trust paths that can yield the K most trustworthy evaluation results based on service consumers' evaluation criteria. In this paper, we first present a complex social network structure and a concept, Quality of Trust (QoT). We then model the K optimal social trust paths selection with multiple end-to-end QoT constraints as the Multiple Constrained K Optimal Paths (MCOP-K) selection problem, which is NP-Complete. For solving this challenging problem, based on Dijkstra's shortest path algorithm and our optimization strategies, we propose a heuristic algorithm H-OSTP-K with the time complexity of O(m+Knlogn). The results of our experiments conducted on a real dataset of online social networks illustrate that H-OSTP-K outperforms existing methods in the quality of identified social trust paths.

SCI-Expanded | YÖK-Özgeçmiş

We propose an unconstrained global continuous optimization method based on tabu search and harmony search to support the design of fuzzy linear regression (FLR) models. Tabu and harmony search strategies are used for diversification and intensification of FLR, respectively. The proposed approach offers the flexibility to use any kind of an objective function based on client's requirements or requests and the nature of the dataset and then attains its minimum error. Moreover, we elaborate on the error produced by this method and compare it with the errors resulting from the other known estimation methods. To study the performance of the method, three categories of datasets are considered: Numeric inputs-symmetric fuzzy outputs, symmetric fuzzy inputs-symmetric fuzzy outputs, and numeric inputs-asymmetric fuzzy outputs. Through a series of experiments, we demonstrate that in terms of the produced error with different model-fitting measurements, the proposed method outperforms or is Pareto-equivalent to the existing methods reported in the literature.

Online social networks are becoming increasingly popular and are being used as the means for a variety of rich activities. This demands the evaluation of the trustworthiness between two unknown participants along a certain social trust path between them in the social network. However, there are usually many social trust paths between participants. Thus, a challenging problem is finding which social trust path is the optimal one that can yield the most trustworthy evaluation result.In this paper, we first present a new complex social network structure and a new concept of Quality of Trust (QoT) to illustrate the ability to guarantee a certain level of trustworthiness in trust evaluation. We then model the optimal social trust path selection as a Multi-Constrained Optimal Path (MCOP) selection problem which is NP-Complete. For solving this problem, we propose an efficient approximation algorithm MONTE K based on the Monte Carlo method. The results of our experiments conducted on a real dataset of social networks illustrate that our proposed algorithm significantly outperforms existing approaches in both efficiency and the quality of selected social trust paths.

In this paper, we present a trust establishment and management framework for hierarchical wireless sensor networks. The wireless sensor network architecture we consider consists of a collection of sensor nodes, cluster heads and a base station arranged hierarchically. The framework encompasses schemes for establishing and managing trust between these different entities. We demonstrate that the proposed framework helps to minimize the memory, computation and communication overheads involved in trust management in wireless sensor networks. Our framework takes into account direct and indirect (group) trust in trust evaluation as well as the energy associated with sensor nodes in service selection. It also considers the dynamic aspect of trust by introducing a trust varying function which could be adjusted to give greater weight to the most recently obtained trust values in the trust calculation. The architecture also has the ability to deal with the inter-cluster movement of sensor nodes using a combination of certificate based trust and behaviour based trust.

Geospatial information changes continually, and geospatial datasets become outdated and unsuitable for decision support due to inadequate data quality. They are also costly to maintain. There is a growing demand for accurate and consistent geospatial information in critical sectors such as emergency response.Within the land fabric geospatial vector data context, the ownership of data is distributed vertically among many government agencies by thematic types and horizontally by different administrative domains. A geospatial transaction, that updates changes, say for example, the release of a portion of forest-land for residential development, could inherently span across multiple agencies, infringing the jurisdictional responsibilities of individual agencies who own the participating data layers. Unauthorized editing and even publishing of updated data takes place in different organizations, unaware of editing taking place in other agencies. While updating of geospatial datasets for improved accuracy and distribution takes place, the legal aspects are generally ignored.In the context of current jurisdictional structure, to avoid duplicate geospatial editing as well as to support legally binding transaction, a geodatabase must transcend organizational boundaries and become truly federated. In addition, it needs to support a mechanism for versioned geospatial editing and more importantly support commits that could span many days. In this paper, we present a versioned editing model for a geospatial cloud database environment. The paper also discusses a new data workflow paradigm that utilizes the salient features of cloud for updating geospatial data addressing the issues highlighted. This is followed by a succinct discussion of the controlled investigation of the paradigm for the case of Australia's NSW state.

In this paper, we propose a new approach for optimizing structural-join and twig queries for XML documents more effectively by utilizing the existing facilities of the relational database systems. Our approach is based on an enhanced structure of a compact Dewey-based labeling scheme. Further, we introduce techniques to make RDBMS more tree-aware without the need to modify the system kernel and without sacrificing space by exploiting the XML schema summary. Our techniques are portable and can be applied to any Dewey-based labeling technique. An extensive experimental evaluation confirms the performance benefits of our approach. In particular, we report on extended twig query performance tests between our system and other two well known XML management systems (eXist: native, MonetDB/XQuery: relational-based system).

Security and trust are fundamental challenges when it comes to the deployment of large wireless sensor networks. In this paper, we propose a novel hierarchical trust management scheme that minimizes communication and storage overheads. Our scheme takes into account direct and indirect (group) trust in trust evaluation as well as the energy associated with sensor nodes in service selection. It also considers the dynamic aspect of trust by introducing a trust varying function which could give greater weight to the most recently obtained trust values in the trust calculation. The proposed framework can be extended to such dynamic mobile inter-cluster wireless sensor network environments.

Wavelet Analysis method is considered as one of the most efficient methods for detecting DDoS attacks. However, during the peak data communication hours with a large amount of data transactions, this method is required to collect too many samples that will greatly increase the computational complexity. Therefore, the real-time response time as well as the accuracy of attack detection becomes very low. To address the above problem, we propose a new DDoS detection method called Modified Wavelet Analysis method which is based on the existing Isomap algorithm and wavelet analysis. In the paper, we present our new model and algorithm for detecting DDoS attacks and demonstrate the reasons of why we enlarge the Hurst's value of the self-similarity in our new approach. Finally we present an experimental evaluation to demonstrate that the proposed method is more efficient than the other traditional methods based on wavelet analysis.

The inference of gene regulatory networks (GRN) from microarrray data suffers from the low accuracy and the excessive computation time. Biological domain knowledge of the cellular process, from which the data is generated, is believed to be effective in addressing such challenges. In this paper, we have used two biological features of gene regulation of yeast cell cycle: 1) a high proportion of the cell cycle regulated genes are periodically expressed, and 2) genes are both co-expressed and co-regulated. Together with the computational implementation of these features, we have learnt regulators of both individual and co-expressed genes using dynamic Bayesian networks. The proposed 2-stage GRN model has been found to be more computationally efficient and topologically accurate compared to other existing models.

Mobile ad hoc networks (MANETs) are self-organizing and adaptive, and securing such networks is non-trivial. Most security schemes suggested for MANETs tend to build upon some fundamental assumptions regarding the trustworthiness of the participating hosts and the underlying networking systems without presenting any definite scheme for trust establishment. If MANET is to achieve the same level of acceptance as traditional wired and wireless network, then a formal specification of trust and a framework for trust management must become an intrinsic part of its infrastructure. The goal of this paper is to highlight issues relating to trust in MANETs and describe a context-aware, reputation-based approach for establishing trust that assesses the trustworthiness of the participating nodes in a dynamic and uncertain MANET environment.

Voice service is vulnerable to a number of attacks that can compromise the confidentiality, integrity and authenticity of voice communication. This paper describes the design of communication protocols for securing SIP based VOIP communication. It presents the architectural principles involved and the overall security solution comprising the design of secure extensions to SIP messages. Finally it evaluates the performance of the proposed scheme and presents some results.

The study of multiagent systems (MASs) focuses on systems in which many intelligent agents interact with each other using communication protocols. For example, an authentication protocol is used to verify and authorize agents acting on behalf of users to protect restricted data and information. After authentication, two agents should be entitled to believe that they are communicating with each other and not with intruders. For specifying and reasoning about the security properties of authentication protocols, many researchers have proposed the use of belief logics. Since authentication protocols are designed to operate in dynamic environments, it is important to model the evolution of authentication systems through time in a systematic way. We advocate the systematic combinations of logics of beliefs and time for modeling and reasoning about evolving agent beliefs in MASs. In particular, we use a temporal belief logic called TML (+) for establishing trust theories for authentication systems and also propose a labeled tableau system for this logic. To illustrate the capabilities of TML (+), we present trust theories for several well-known authentication protocols, namely, the Lowe modified wide-mouthed frog protocol, the amended Needham-Schroeder symmetric key protocol, and Kerberos. We also show how to verify certain security properties of those protocols. With the logic TML (+) and its associated modal tableaux, we are able to reason about and verify authentication systems operating in dynamic environments.

Presents the introductory welcome message from the conference proceedings.

Provides a listing of current committee members and society officers.

This special issue of the Expert Systems journal addresses research issues on ontology, an area that is receiving increased attention from researchers on the semantic web. According to Gruber (1993), an ontology is an explicit specification of a conceptualization, i.e. an abstract, simplified view of the world that includes the objects, concepts and the relationships between them in a domain of interest. The use of formal ontologies in knowledge systems has many advantages. It allows an unambiguous specification of the structure of knowledge in a domain, enables knowledge sharing and reuse and, consequently, makes automated reasoning about ontologies possible. In recent years, there has been a worldwide increase in the use of ontologies, both in industry and in research laboratories. This special issue presents the recent advances, both in theory and practical applications, of ontologies to a general audience and provides an opportunity for the broader expert systems community to become aware of current ontology research. There was an overwhelming interest in the general call for papers for the special issue. We received 24 high quality submissions from researchers in Argentina, Australia, Brazil, France, Iran, Japan, Spain, Taiwan, Republic of China, Turkey and the USA. Each submission was sent to at least two reviewers who are experts in ontology research and closely related areas. Although we judged many more submissions to be publishable, we could only include eight papers in the special issue due to time and space limitations. A few high quality papers that we could not accommodate in the special issue were referred to regular issues of Expert Systems. The submissions also included the revised and extended versions of a number of papers selected from among those presented at the Australasian Ontology Workshop (AOW 2006) (Orgun & Meyer, 2006). AOW 2006 was held on 5 December 2006 in conjunction with the 19th Australian Joint Conference on Artificial Intelligence in Hobart, Tasmania, Australia. The purpose of the AOW workshop series is to bring together ontology researchers from academia and industry in the Australasian region for interaction, discussion, sharing of results and initiation of new projects, and also to raise the awareness of the Australasian artificial intelligence community to state-of-the-art ontology research conducted in the region. This special issue is further testament to the vibrant ontology research conducted within the Australasian region, and its strong connections with the international ontology community. We trust that the breadth and diversity of the papers published in this special issue will foster further research on ontologies ranging from theoretical to practical issues and to applications. The special issue starts off with papers on interoperability in ontologies and ontology merging, alignment and integration. Semantic interoperability between ontologies is essential for enabling communication and sharing of information between heterogeneous systems. The paper by Orgun et al. surveys the main approaches for semantic interoperability between domain ontologies. The authors critically examine various approaches based on the underlying technology used, i.e. agent- or non-agent-based, the degree of automation and the use of intermediaries such as lexicons and meta-ontologies. Their conclusion is that if ontologies for the semantic web are to realize their full potential, it is important to work towards full automation of the semantic translation between ontologies. The paper by Li and Yang discusses a novel agent-based approach for ontology mapping and integration. Their main aim is to identify the main tasks of ontology mapping and integration and assign them to different agents, with the purpose of providing a runtime environment for dynamic ontology management. This work leverages agent technology, and it is a step towards the full automation envisioned in the paper by Orgun et al. The paper by Qazvinian et al. proposes an evolutionary approach based on genetic algorithms to extract an optimal mapping in ontology matching. The main idea here is to transform the ontology alignment problem into an optimization problem based on maximizing the overall similarity between entities among two ontologies. The paper by Hooijmaijers and Stumptner addresses how ontology integration can be enhanced by considering author information, trust and credibility. It is observed that, by annotating ontologies with author information and trust ratings, the user is provided with extra flexibility when making decisions based on queries to an integrated ontology. Trust is a key concept in agent-based systems operating in dynamic environments such as the semantic web (Golbeck et al., 2003) and it should not come as a surprise that it should also play an important role in ontology integration. The next two papers further explore the design and implementation of content languages for the semantic web (Berners-Lee et al., 2001). Effective communication between agents operating on behalf of humans is essential to realize the ultimate goal of the semantic web. The paper by Schwitter and Tilbrook proposes a novel approach to support the creation of meaningful web annotations in a controlled natural language. The authors advance the thesis that, rather than using a formal language, a well-defined controlled natural language enables human annotators to summarize the contents of a website better. Annotations are then transformed into a machine-processable form based on predicate logic and checked for consistency and informativeness for question-answering. The paper by Erdur and Seylan starts with the well-known OWL Web Ontology Language (Smith et al., 2004) and bases their content language for agent communication on a hybrid description logic which allows for the representation and reasoning about beliefs and intentions of agents. As a result, it is shown that agents can conform to the semantics of agent communication that is explained in terms of the mental states of the participants. The last two papers address different topics. The paper by Lefort, Taylor and Ratcliffe provides an empirical study of description logic reasoners in building and maintaining large part–whole ontologies such as those used in supply-chain management or reliability assessment in the aerospace industry. The study starts with the transformation of large-scale part–whole hierarchies into ontologies based on two best practice ontology engineering patterns, and then feeds them to a number of description logic reasoners for performance benchmarking. The empirical study shows that a particular ontology engineering pattern (the use of right-identity axioms supported by the EL+description logic) results in better reasoner performance. The paper by Valencia-García et al. addresses the difficult task of learning ontologies from natural language documents. The presented semi-automatic methodology is driven by knowledge engineering techniques such as incremental knowledge acquisition from domain experts and natural language techniques such as part-of-speech tagging. It supports multiple semantic relationships between concepts in an ontology and is able to detect inconsistencies in the resulting ontologies. Many individuals contributed to this special issue. First, we would like to thank the Editor-in-Chief of Expert Systems, Lucia Rapanotti, for her enthusiasm and continuing support for the special issue. Second, we are also indebted to the authors of the 24 submissions who responded to the call for papers in early 2007. This special issue would not have been possible without their submissions. Last but not the least, we would like express our appreciation to our reviewers; they generously donated their time and expertise in reading the submissions and providing very detailed and constructive comments for the authors; we would like to thank them all: Mike Bain (University of New South Wales, Australia) Richard Booth (Mahasarakham University, Thailand) Werner Ceusters (SUNY Buffalo, USA) Samir Chopra (CUNY Brooklyn, USA) Bob Colomb (University of Queensland, Australia) Stephen Cranefield (University of Otago, New Zealand) Anne Cregan (NICTA and University of New South Wales, Australia) Peter Eklund (University of Wollongong, Australia) Atilla Elçi (Eastern Mediterranean University, Turkey) Giorgos Flouris (FORTH, Greece) Vadim Gerasimov (CSIRO, Australia) Aurona Gerber (Meraka Institute, South Africa) Manolis Gergatsoulis (Ionian University, Greece) Aditya Ghose (University of Wollongong, Australia) Guido Governatori (University of Queensland, Australia) Warwick Graco (Australian Taxation Office, Australia) Fikret Gürgen (Boḡaziçi University, Turkey) Dennis Hooijmaijers (University of South Australia, Australia) Bo Hu (University of Southampton, UK) Laurent Lefort (CSIRO, Australia) Costas Mantratzis (University of Westminster, UK) Philippe Martin (Griffith University, Australia) Lars Mönch (University of Hagen, Germany) Abhaya Nayak (Macquarie University, Australia) Bhavna Orgun (Macquarie University, Australia) Maurice Pagnucco (University of New South Wales, Australia) Jeff Pan (University of Aberdeen, UK) Laurent Perrussel (IRIT – Université Toulouse, France) Anet Potgieter (University of Cape Town, South Africa) Quentin Reul (University of Aberdeen, UK) Debbie Richards (Macquarie University, Australia) Jennifer Sampson (NICTA, Australia) Rolf Schwitter (Macquarie University, Australia) Steven Shapiro (University of Leipzig, Germany) Kerry Taylor (CSIRO, Australia) Jean-Marc Thevenin (IRIT – Université Toulouse, France) Olga de Troyer (Vrije Universiteit Brussel, Belgium) Chao Wang (Universiy of Technology, Sydney, Australia) Wayne Wobcke (University of New South Wales, Australia) Pιnar Yolum (Boḡaziçi University, Turkey) Minjie Zhang (University of Wollongong, Australia) Mehmet A. Orgun Mehmet A. Orgun is an associate professor at Macquarie University, Sydney, Australia. He received his BSc and MSc degrees in computer science and engineering from Hacettepe University, Ankara, Turkey, and his PhD degree in computer science from the University of Victoria, Canada, in 1991. Prior to joining Macquarie University as a lecturer in September 1992, he worked as a postdoctoral research associate at the University of Victoria in the Rigi project on software reverse engineering. His current research interests include intelligent agents, temporal reasoning, knowledge discovery and reactive and distributed systems. He is co-founder of the Intelligent Systems Group at Macquarie University. He has authored and co-authored more than 120 peer-reviewed technical papers. He has received funding for his research programme from the Australian Research Council and Macquarie University. He serves on the editorial boards of the Journal of Universal Computer Science and the Open Cybernetics and Systemics Journal. He recently served as the workshop co-chair of the Second Australasian Ontology Workshop (AOW 2006) and the 2nd IEEE International Workshop on Engineering Semantic Agent Systems (ESAS 2007). He was the Program Committee co-chair of the 20th Australian Joint Conference on Artificial Intelligence (AI'07). He is also serving as the workshop co-chair of the 32nd Annual IEEE International Computer Software and Applications Conference (COMPSAC 2008). He is a senior member of the IEEE. Thomas Meyer Thomas Meyer is a principal researcher and research group leader of the Knowledge Systems Group at the Meraka Institute, Pretoria, South Africa. He was a senior researcher in the Knowledge Representation and Reasoning program at NICTA, Sydney, Australia, from 2003 to 2007. During that time he also had a conjoint appointment as associate professor in the School of Computer Science at the University of New South Wales, Sydney. Prior to that he held positions as associate professor in computer science at the University of Pretoria, senior lecturer in computer science at the University of South Africa, Pretoria, and postdoctoral research fellow in information systems at the University of Wollongong, Australia. He obtained a PhD in computer science from the University of South Africa in 1999. He is interested in the reasoning capabilities of agents, both human and artificial. His current research interests include reasoning about ontologies using description logics, non-standard inference, dealing with preferences, and constraints. Thomas has authored and co-authored more than 90 technical papers in peer-reviewed conferences and workshops. He is on the programme committee of numerous conferences and workshops, including the AAAI Conference on Artificial Intelligence and the European Conference on Artificial Intelligence. He is co-chair of the Australasian Ontology Workshop series, and publicity chair for KR 2008: Eleventh International Conference on Principles of Knowledge Representation and Reasoning.

Visualization can be very powerful in revealing cluster structures. However, directly using visualization techniques to verify the validity of clustering results is still a challenge. This is due to the fact that visual representation lacks precision in contrasting clustering results. To remedy this problem, in this paper we propose a novel approach, which employs a visualization technique called HOV (hypothesis oriented verification and validation by visualization) which offers a tunable measure mechanism to project clustered subsets and non-clustered subsets from a multidimensional space to a 2D plane. By comparing the data distributions of the subsets, users not only have an intuitive visual evaluation but also have a precise evaluation on the consistency of cluster structure by calculating geometrical information of their data distributions

Summary form only given. The semantic Web aims to address the limitations of the World Wide Web by providing content that can be interpreted by both humans and machines, thus enabling intelligent agents acting on behalf of humans and/or other agents to discover and reason with the available information in Berners-Lee, J. (2001). While there has been tremendous progress on the infrastructure development for the Semantic Web, it has been observed that comparable progress in the use of the agent technology is still lacking by Hendler, J. (2007). In the mean time, Peer-to-Peer (P2P) networks have been very successful by making available and redistributing huge amounts of data among millions of networked computers. This naturally leads to the concept of a distributed Semantic Web archive integrated with the existing Semantic Web that resides on a P2P network of user nodes (hence so-called P2P Semantic Web).

Cluster analysis is an important technique that has been used in data mining. However, cluster analysis provides numerical feedback making it hard for users to understand the results better; and also most of the clustering algorithms are not suitable for dealing with arbitrarily shaped data distributions of datasets. While visualization techniques have been proven to be effective in data mining, their use in cluster analysis is still a major challenge, especially in data mining applications with high-dimensional and huge datasets. This paper introduces a novel approach, Hypothesis Oriented Verification and Validation by Visualization, named HOV3, which projects datasets based on given hypotheses by visualization in 2D space. Since HOV3 approach is more goal-oriented, it can assist the user in discovering more precise cluster information from high-dimensional datasets efficiently and effectively.

The multi-version approach is useful in both synchronous and asynchronous groupware systems. This paper discusses the implementation of a real-time group editor that embodies our approaches and algorithms based on multi-versioning, which can preserve individual users' concurrent conflicting intentions in a consistent way. To highlight the distinct contributions of our work, we also present a detailed description of some novel features of the system

In stream authentication protocols used for large-scale data dissemination in autonomous systems, authentication is based on the timing of the publication of keys, and depends on trust of the receiver in the sender and belief on whether an intruder can have prior knowledge of a key before it is published by a protocol. Many existing logics and approaches have successfully been applied to specify other types of authentication protocols, but most of them are not appropriate for analysing stream authentication protocols. We therefore consider a fibred modal logic that combines a belief logic with a linear-time temporal logic which can be used to analyse time-varying aspects of certain problems. With this logical system one is able to build theories of trust for analysing stream authentication protocols, which can deal with not only agent beliefs but also the timing properties of an autonomous agent-based system

A theory of trust for a given system consists of a set of rules that describe trust of agents in the system. In a certain logical framework, the theory is generally established based on the initial trust of agents in the security mechanisms of the system. Such a theory provides a foundation for reasoning about agent beliefs as well as security properties that the system may satisfy. However, trust changes dynamically. When agents lose their trust or gain new trust in a dynamic environment, the theory established based on the initial trust of agents in the system must be revised, otherwise it can no longer be used for any security purpose. This paper investigates the factors influencing trust of agents and discusses how to revise theories of trust in dynamic environments. A methodology for revising and managing theories of trust for multiagent systems is proposed. This methodology includes a method for modeling trust changes, a method for expressing theory changes, and a technique for obtaining a new theory based on a given trust change. The proposed approach is very general and can be applied to obtain an evolving theory of trust for agent-based systems

In this paper, we introduce a basic conceptual model of real-time collaborative editing systems which differentiates the coordination aspect of actions (which do not exist in single-user systems) from the application-dependent semantic aspect of actions (i.e., those actions that happen in single-user systems). Based on this model, a new taxonomy is provided to analyze the existing consistency-maintenance mechanisms and highlight potential new mechanisms. Furthermore, a new user-centered consistency model is proposed, which focuses on dynamic consistency that is negotiable among end users rather than static consistency that is automatically determined by the system. In the mean time, some challenging research issues in real-time collaborative editing systems are outlined

This paper investigates the multi-version approach to consistency maintenance in Internet-based real-time collaborative editing systems. It proposes a new multi-versioning scheme that is able to preserve individual users' intentions while guaranteeing convergent document states. The scheme has been implemented in Java in a prototype called POLO.

Multidimensional information is pervasive in many computer applications including time series, spatial information, data warehousing, and visual data. While semistructured data or XML is becoming more and more popular for information integration and exchange, not much research work has been done in the design and implementation of semistructured database systems to manage multidimensional information efficiently. In this paper, dimension operators have been defined based on a multidimensional logic which we call ML(/spl omega/). It can be used in applications such as multidimensional spreadsheets and multidimensional databases usually found in decision support systems and data warehouses. Finally a multidimensional, an XML database system is prototyped and described in detail. Technologies such as XSL are used to transform or visualise data from different dimensions.

The paper describes the design of a model as well as an architecture to provide support for distributed advanced workflow transactions. We discuss the application of transaction concepts to activities that involve integrated execution of multiple tasks over different processes. This kind of application is described as transactional workflow. The classical commit protocol, used in many commercial systems, is not suitable for use in multilevel secure distributed workflow database systems that use a locking protocol for concurrency control. The reason is that it is not possible for a locking protocol to guarantee that read locks won't be released by a subtransaction during its window of uncertainty-the period after a participant has voted yes to commit, but before it receives the commit or abort decision from the coordinator, possibly resulting in nonserializable executions. A distinguishing feature of the proposed workflow transaction support system is the ability to manage the arbitrary distribution of business processes over multiple workflow management systems.

We consider the problem of optimizing the number of I/O operations by means of an effective clustering, with the objective to overcome deficiencies of existing solutions. Our main objective is to dynamically adapt physical database organizations, online, according to the access patterns of the users without adding significant overhead on processing workload. We propose a clustering schedule that may be convenient for any object database management systems (ODBMS); it combines flexibility, dynamics, and efficiency. The paper describes relative partial validation of our approach using Versant.

Benchmarks, such as the Cattel/001 benchmark, clearly demonstrate that even the slowest object databases are faster than the fastest relational databases when managing complex data structures. Other independently audited benchmarks, such as the University of Wisconsin's 007 benchmark, demonstrate that VERSANT is the clear, overall leader in ODBMS performance. We propose a clustering schedule that may be convenient for any ODBMS; it combines flexibility, dynamics, and efficiency. This paper describes relative partial validation of our approach using VERSANT. The research reported here takes steps towards supplying insights that should provide guidelines to database practitioners and researchers who are interested in applying high-performance, balanced client-server object database management systems relying on the data-shipping paradigm.

This article introduces a temporal deductive database system featuring a logic programming language and an algebraic front‐end. The language, called Temporal DATALOG, is an extension of DATALOG based on a linear‐time temporal logic in which the flow of time is modeled by the set of natural numbers. Programs of Temporal DATALOG are considered as temporal deductive databases, specifying temporal relationships among data and providing base relations to the algebraic front‐end. The minimum model of a given Temporal DATALOG program is regarded as the temporal database the program models intensionally. The algebraic front‐end, called TRA, is a point‐wise extension of the relational algebra upon the set of natural numbers. When needed during the evaluation of TRA expressions, slices of temporal relations over intervals can be retrieved from a given temporal deductive database by bottom‐up evaluation strategies. A modular extension of Temporal DATALOG is also proposed, through which temporal relations created during the evaluation of TRA expressions may be fed back to the deductive part for further manipulation. Modules therefore enable the algebra to have full access to the deductive capabilities of Temporal DATALOG and to extend it with nonstandard algebraic operators. This article also shows that the temporal operators of TRA can be simulated in Temporal DATALOG by program clauses.

In temporal logic programming, a stream can be specified by a single-valued, time-varying predicate which, at any given moment in time, represents the corresponding element in the stream. However, due to inherent non-determinism in logic programming, time-varying predicates do not necessarily represent single-valued relations at any given moment in time. Choice predicates are also time-varying predicates, but, in principle, they act like a dataflow node with multiple input lines which non-deterministically selects one of its inputs as output. Thus they are guaranteed to be single-valued at all moments in time, and they can be regarded as representing ‘non-deterministic’ streams. Users do not define choice predicates, they are supplied automatically for all predicates defined in temporal logic programs. Inputs to choice predicates are supplied by the corresponding predicates. When the connection between choice predicates and the corresponding predicates is established, we obtain non-Horn temporal logic programs as a result. The model-theoretic semantics of such a program is developed in terms of ‘minimal models’. However, the logical structure of the program dictates which minimal models are constructible from the program. We in particular discuss a characterization of constructible minimal models as limits of alternating chains of models obtained by applications of two new mappings NTP and CP. The paper also outlines a proof procedure for the temporal language Chronolog extended with choice predicates.

Abstract Reverse‐engineering is the process of extracting system abstractions and design information out of existing software systems. This process involves the identification of software artefacts in a particular subject system, the exploration of how these artefacts interact with one another, and their aggregation to form more abstract system representations that facilitate program understanding. This paper describes our approach to creating higher‐level abstract representations of a subject system, which involves the identification of related components and dependencies, the construction of layered subsystem structures, and the computation of exact interfaces among subsystems. We show how top‐down decompositions of a subject system can be (re)constructed via bottom‐up subsystem composition. This process involves identifying groups of building blocks (e.g., variables, procedures, modules, and subsystems) using composition operations based on software engineering principles such as low coupling and high cohesion. The result is an architecture of layered subsystem structures. The structures are manipulated and recorded using the Rigi system, which consists of a distributed graph editor and a parsing system with a central repository. The editor provides graph filters and clustering operations to build and explore subsystem hierarchies interactively. The paper concludes with a detailed, step‐by‐step analysis of a 30‐module software system using Rigi.