Since the emergence of Graph Neural Networks (GNNs), researchers have extensively investigated training on large-scale GNN training because of their success and wide usage in various domains including biological networks, finance, and recommendation systems. This work focuses on training largescale distributed GNNs, where partitioning massive graphs across multiple machines creates remote communication overhead that becomes a major scalability bottleneck. We introduce a policy-driven caching mechanism that prioritizes node features and embeddings based on access frequency and cross-partition fetch cost, significantly minimizing communication overhead without sacrificing accuracy. Our policies are based on analysis of Node Affinities (NAFs) during multi-hop neighborhood sampling that extend substantially beyond the graph partition boundaries. Analyzing NAFs not only alleviates the communication bottleneck but also provides a systematic mechanism to manage in-memory data effectively, prioritizing GPU storage for node features with high fetch latency. We present FLARE, a system designed to handle partitioned feature data while leveraging the NAFbased caching policy. FLARE substantially reduces both communication overhead and training convergence time. Extensive experiments on benchmark datasets show that training FLARE on a three-layer GCN, GAT, and GraphSAGE across eight GPU machines achieves up to 12.04× (8.12× on average) speedup over DistDGLv2, demonstrating substantial performance gains compared to state-of-the-art methods.

GPU-accelerated relational query execution engines have parallelized the execution of a pipeline, a sequence of operators. For the parallelization, the engines evenly partition the tuples in a table that will be scanned by the pipeline's first operator (a scan), and each thread executes the pipeline for the tuples in a partition. However, this approach leads to load imbalances since an operator returns a varying number of output tuples per input tuple, particularly under non-uniform data distributions such as skewed join key values. The load imbalances are classified into intra- and inter-warp load imbalances (intra-WLIs and inter-WLIs) since 1) threads are grouped into warps and 2) every thread in a warp evaluates the same operator for an input tuple concurrently following a single-instruction-multiple-thread manner. In contrast, threads in different warps can evaluate different operators concurrently. Although load balancing techniques have been proposed, however, they fail to solve the load imbalances on various workloads. In this paper, we propose a query execution engine, Themis, named after the deity of fairness, which symbolizes balanced workloads within our context. Themis minimizes intra-WLIs and inter-WLIs across various workloads. First, Themis minimizes intra-WLIs by redistributing tuples between the threads in a warp and making the threads evaluate an operator only when all of them hold inputs. Second, Themis mitigates the inter-WLIs by redistributing the tuples of warps with heavy workloads to idle warps. To check whether a warp's workload is heavy, we propose a method to approximate the sizes of warps' workloads. Based on these approximations, Themis adaptively adjusts the threshold for determining a warp's workload as heavy. In a recent benchmark JCC-H, which introduces skewed join key distributions to TPC-H, Themis significantly alleviates the inter-WLIs and intra-WLIs, outperforming the runner-up by up to 379x.

Depression is a serious psychiatric disorder that restricts an individuals ability to work properly in both their daily and professional lives. Usually, the diagnosis of depression often needs a thorough assessment by an expert. Recently, significant consideration has been given to automatic depression prediction for more reliable and efficient depression investigation. In this article, we propose a novel framework to estimate the depression level from video data by employing a two-stream deep spatiotemporal network. Our approach extracts spatial information using the Inception-ResNet-v2 network. In contrast, we introduce a volume local directional number (VLDN) based dynamic feature descriptor to capture facial motions. Then, the feature map obtained from the VLDN is fed into a convolutional neural network (CNN) to obtain more discriminative features. Additionally, we designed a multilayer bidirectional long short-term memory (Bi-LSTM) model to obtain temporal information by integrating the temporal median pooling (TMP) approach into the model. The TMP approach is employed on the temporal fragments of spatial and temporal features. Finally, extensive experimental analysis of two challenging datasets, AVEC2013 and AVEC2014, demonstrates that the proposed approach shows promising performance compared to the existing approaches for depression level prediction.

In recent years, dynamic scene understanding has gained attention from researchers because of its widespread applications. The main important factor in successfully understanding the dynamic scenes lies in jointly representing the appearance and motion features to obtain an informative description. Numerous methods have been introduced to solve dynamic scene recognition problem, nevertheless, a few concerns still need to be investigated. In this article, we introduce a novel multi-modal network for dynamic scene understanding from video data, which captures both spatial appearance and temporal dynamics effectively. Furthermore, two-level joint tuning layers are proposed to integrate the global and local spatial features as well as spatial and temporal stream deep features. In order to extract the temporal information, we present a novel dynamic descriptor, namely, Volume Symmetric Gradient Local Graph Structure ( VSGLGS ), which generates temporal feature maps similar to optical flow maps. However, this approach overcomes the issues of optical flow maps. Additionally, Volume Local Directional Transition Pattern ( VLDTP ) based handcrafted spatiotemporal feature descriptor is also introduced, which extracts the directional information through exploiting edge responses. Lastly, a stacked Bidirectional Long Short-Term Memory ( Bi-LSTM ) network along with a temporal mixed pooling scheme is designed to achieve the dynamic information without noise interference. The extensive experimental investigation proves that the proposed multi-modal network outperforms most of the state-of-the-art approaches for dynamic scene understanding.

Troubleshooting imperative programs at runtime is very challenging because the final optimized plan is quite different from the original design time model. In this demonstration, we present ProcAnalyzer, an expressive and intuitive tool for troubleshooting issues related to performance, code quality, and security. We propose end-to-end graph (E2EGraph) that provides a holistic view of design time, compile time, and runtime behavior so that end users and engine developers easily find the correlations between design time and runtime. ProcAnalyzer provides suggestions and visualization to find problematic statements through the E2EGraph.

Hybrid transactional and analytical processing (HTAP) systems like SAP HANA make it much simpler to manage both operational load and analytical queries without ETL, separate data warehouses, et al. To represent both transactional and analytical business logic in a single database system, stored procedures are often used to express analytical queries using control flow logic and DMLs. Optimizing these complex procedures requires a fair knowledge of imperative programming languages as well as the declarative query language. Therefore, unified optimization techniques considering both program and query optimization techniques are essential for achieving optimal query performance. In this paper, we propose a novel unified optimization technique for efficient iterative query processing. We present a notion of query motion that allows the movement of SQL queries in and out of a loop. Additionally, we exploit a new cost model that measures the quality of the execution plan with consideration for queries and loop iterations. We describe our experimental evaluation that demonstrates the benefit of our technique using both a standard decision support benchmark and real-world workloads. An extensive evaluation shows that our unified optimization technique enumerates plans that achieve performance improvements of up to an order of magnitude faster than plans generated by the existing loop-invariant code motion technique.

Action recognition from videos is an important area of computer vision research due to its various applications, ranging from visual surveillance to human-computer interaction. To address action recognition problems, this paper presents a framework that jointly models multiple complex actions and motion units at different hierarchical levels. We achieve this by proposing a generative topic model, namely, multi-label hierarchical Dirichlet process (ML-HDP). The ML-HDP model formulates the co-occurrence relationship of actions and motion units, and enables highly accurate recognition. In particular, our topic model possesses the three-level representation in action understanding, where low-level local features are connected to high-level actions via mid-level atomic actions. This allows the recognition model to work discriminatively. In our ML-HDP, atomic actions are treated as latent topics and automatically discovered from data. In addition, we incorporate the notion of class labels into our model in a semi-supervised fashion to effectively learn and infer multi-labeled videos. Using discovered topics and inferred labels, which are jointly assigned to local features, we present the straightforward methods to perform three recognition tasks including action classification, joint classification and segmentation of continuous actions, and spatiotemporal action localization. In experiments, we explore the use of three different features and demonstrate the effectiveness of our proposed approach for these tasks on four public datasets: KTH, MSR-II, Hollywood2, and UCF101.

Graph classification is an important topic in graph mining research since it has many applications, such as, social web mining, function prediction of molecules for drug design, XML document classification and anomaly detection in program flows. The key difficulty in graph classification lies in selecting a subset of optimal features from a huge number of structural features. The features need to be highly discriminative and small in numbers for better classification accuracy and running time. In this paper, we propose a novel feature selection framework that selects an optimal feature subset by removing redundant subgraphs. Topologically similar subgraphs have similar discriminative powers and coverage. We cluster these subgraphs and select one subgraph as a feature. We also propose an efficient topological similarity-based clustering method that guarantees the efficiency of our framework. Empirical results show that the proposed framework achieves significantly improved classification accuracy and running time in comparison with the state-of-the-art methods.

Automatic foreground object segmentation is a fascinating, a demanding research area, and an exigent problem in biomedical applications. Existing works cannot segment concave objects and completely dependent on initial curve that is initialized manually by the users, and must be closer to the object. Due to these limitations, most of them were considered as semi-automatic approaches. In this paper, we incorporated active contours (level-set) based on Bhattacharya distance to the Chan and Vese energy functional such that are not only minimized the differences within each region but also maximized the distance between the two regions as well. Compared with active contour snake, the proposed model gave more accurate results that segment the foreground objects automatically.

This paper presents an approach for scheduling U-Life care applications in the cloud computing environment based on virtual resources to support real-time services and to improve user Quality of Service (QoS) requirements. We design and develop an architecture called ULC3 (Ubiquitous Life Care Cloud Computing) that uses virtual resources provided by cloud computing to schedule U-Life care applications. The ULC3 is based on the concepts of cloud computing and wireless sensor networks. The architecture is very important and necessary to support create virtual clusters dynamically, deploys the required number of virtual machines (VMs) in potential computing resources to meet the application requirements, and to configure with the required software execution environment. Thus, the system can improve computation time, guarantee the QoS, and support real-time services. Finally, the results from the execute applications are provided to the end-users as a service.

This paper presents a Secured Wireless Sensor Network-integrated Cloud computing for u-Life Care (SC3). SC3 monitors human health, activities, and shares information among doctors, care-givers, clinics, and pharmacies in the Cloud, so that users can have better care with low cost. SC3 incorporates various technologies with novel ideas including; sensor networks, Cloud computing security, and activities recognition.

Trust-aware recommender system (TARS) suggests the worthwhile information to the users on the basis of trust. Existing works of TARS suffers from the problem that they need extra user efforts to label the trust statements. The authors propose a novel model named iTARS to improve the existing TARS by using the implicit trust networks: instead of using the effort-consuming explicit trust, the easy available user similarity information is used to generate the implicit trusts for TARS. Further analysis shows that the implicit trust network has the small-world topology, which is independent of its dynamics. The rating prediction mechanism of iTARS is based on the small worldness of the implicit trust network: the authors set the maximum trust propagation distance of iTARS approximately equals the average path length of the trust network's corresponding random network. Experimental results show that with the same computational complexity, iTARS is able to improve the existing TARS works with higher rating prediction accuracy and slightly worse rating prediction coverage.

Physical-activity recognition via wearable sensors can provide valuable information regarding an individual's degree of functional ability and lifestyle. In this paper, we present an accelerometer sensor-based approach for human-activity recognition. Our proposed recognition method uses a hierarchical scheme. At the lower level, the state to which an activity belongs, i.e., static, transition, or dynamic, is recognized by means of statistical signal features and artificial-neural nets (ANNs). The upper level recognition uses the autoregressive (AR) modeling of the acceleration signals, thus, incorporating the derived AR-coefficients along with the signal-magnitude area and tilt angle to form an augmented-feature vector. The resulting feature vector is further processed by the linear-discriminant analysis and ANNs to recognize a particular human activity. Our proposed activity-recognition method recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.

Key establishment plays a central role in authentication and encryption in wireless sensor networks, especially when they are mainly deployed in hostile environments. Because of the strict constraints in power, processing and storage, designing an efficient key establishment protocol is not a trivial task. Compared with public key cryptography, symmetric key cryptographic with key predistribution mechanism is more suitable for large-scale wireless sensor networks. Most of previous solutions have some issues on performance and security capabilities. In this paper, we propose a novel key predistribution model using pre-deployment knowledge and random values in pairwise key generation to take advantage in terms of network connectivity, memory cost, energy for transmission and strong resilience against node capture attacks.

For a learning task with a huge number of training instances, we sample some informative/important instances, which are then used for learning. Obtaining accurately labeling data is always difficult thus noise detection is required to filter out noises from sampled instances since the noises will degrade the learning performance. In this work, we propose to utilize unsampled instances to improve the performance of noise detection in sampled instances. Empirical study validates our idea that refined classifier can be achieved from noisy sampled instances by utilizing unsampled instances.

Knowledge constantly grows in scientific discourse and is revised over time by domain experts. The body of knowledge will get structured and refined as the communities of practice concerned with the field of knowledge develop a deeper understanding of issues. The knowledge model, as a result evolves to a new state to accommodate the new knowledge. Keeping trail of these changes in semantically rich and formally sound mechanism, has pragmatic advantages for providing the undo and redo facility and recover to a previous state of the knowledge body (i.e. ontology). In this research, we have developed and tested comprehensive methodological framework for change tracer. The ontology changes are captured and then stored in change history log (CHL) in conformance to change history ontology (CHO). The CHL is later used for reverting ontology to a previous consistent state and visualization of change effects on ontology. The system is compared with ChangesTab of Protege, a comprehensive evaluation of the accuracy of roll-back and roll-forward algorithm has been conducted over documentation ontology. The system is also tested over a standard dataset of OMV and high accuracy results are observed for both roll-back and roll-forward algorithms.

In this paper, we present a new approach to solving the distributed facility location problem using the recent modeling and computational methodology of factor graph and message-passing. We first formulate the problem as finding a valid network configuration that minimizes the overall cost. We then represent the problem using a factor graph, and derive simplified, localized, broadcast-based message-passing rules which can elect a near-optimal set of facility nodes in a few iterations. Simulation results for small-world network topologies show that the algorithm is able to achieve good convergence rate and approximation ratio, and scalable to the network size.

Recently, high utility pattern (HUP) mining is one of the most important research issues in data mining due to its ability to consider the nonbinary frequency values of items in transactions and different profit values for every item. On the other hand, incremental and interactive data mining provide the ability to use previous data structures and mining results in order to reduce unnecessary calculations when a database is updated, or when the minimum threshold is changed. In this paper, we propose three novel tree structures to efficiently perform incremental and interactive HUP mining. The first tree structure, Incremental HUP Lexicographic Tree ({\rm IHUP}_{{\rm {L}}}-Tree), is arranged according to an item's lexicographic order. It can capture the incremental data without any restructuring operation. The second tree structure is the IHUP Transaction Frequency Tree ({\rm IHUP}_{{\rm {TF}}}-Tree), which obtains a compact size by arranging items according to their transaction frequency (descending order). To reduce the mining time, the third tree, IHUP-Transaction-Weighted Utilization Tree ({\rm IHUP}_{{\rm {TWU}}}-Tree) is designed based on the TWU value of items in descending order. Extensive performance analyses show that our tree structures are very efficient and scalable for incremental and interactive HUP mining.

This paper proposes a prefix-tree structure, called CPS-tree (Compact Pattern Stream tree) that efficiently discovers the exact set of recent frequent patterns from high-speed data stream. The CPS-tree introduces the concept of dynamic tree restructuring technique in handling stream data that allows it to achieve highly compact frequency-descending tree structure at runtime and facilitates an efficient FP-growth-based [1] mining technique.

The performance of supervised learning could be improved when valuable data are selected for training. In this paper, we proposed three data selection methods based on fuzzy C-means algorithm. They are: center-based selection, border-based selection and bin-based selection. In center-based selection, the data with high degree of membership in each cluster are selected for training. In border-based selection, the data around the borders between clusters are selected. In bin-based selection, the data in each cluster are sorted based on their membership degrees. Then for each cluster, the sorted data are divided into bins. Finally, there is one data selected from each bin for training. The effects of them are empirically studied on a set of UCI data sets. Experimental results indicate that bin-based selection could effectively improve the performance of learning compared to randomly selecting training samples.

This paper proposes a novel method for data editing. The goal of data editing in instance-based learning is to remove instances from a training set in order to increase the accuracy of a classifier. To the best of our knowledge, although many diverse data editing methods have been proposed, this is the first work which uses semi-supervised learning for data editing. Wilson editing is a popular data editing technique and we implement our approach based on it. Our approach is termed semi-supervised nearest neighbor editing (SSNNE). Our empirical evaluation using 12 UCI datasets shows that SSNNE outperforms KNN and Wilson editing in terms of generalization ability.

Important applications of Wireless Ad Hoc Networks make them very attractive to attackers, therefore more research is required to guarantee the security for Wireless Ad Hoc Networks. In this paper, we proposed a transmission time based mechanism (TTM) to detect wormhole attacks - one of the most popular & serious attacks in Wireless Ad Hoc Networks. TTM detects wormhole attacks during route setup procedure by computing transmission time between every two successive nodes along the established path. Wormhole is identified base on the fact that transmission time between two fake neighbors created by wormhole is considerably higher than that between two real neighbors which are within radio range of each other. TTM has good performance, little overhead and no special hardware is required.

Networks make them very attractive to attackers, therefore more research is required to guarantee the security for Wireless Ad Hoc Networks. In this paper, we proposed a transmission time based mechanism (TTM) to detect wormhole attacks – one of the most popular & serious attacks in Wireless Ad Hoc Networks. TTM detects wormhole attacks during route setup procedure by computing transmission time between every two successive nodes along the established path. Wormhole is identified base on the fact that transmission time between two fake neighbors created by wormhole is considerably higher than that between two real neighbors which are within radio range of each other. TTM has good performance, little overhead and no special hardware required. TTM is designed specifically for Ad Hoc On-Demand Vector Routing Protocol (AODV) but it can be extended to work with other routing protocols.

Due to mobility, energy limitations, and unreliable wireless channels, applications running on mobile devices suffer from faults such as temporary disconnection and data loss. We, therefore, need a fault tolerance mechanism to guarantee their smooth working and performance. In this paper, we present a novel proxy-based uncoordinated checkpointing scheme with pessimistic message logging for efficient fault recovery in mobile Grid system. Simulation results show that this scheme is reliable, efficient and, at the sametime, consumes less network traffic.

The provision of embedding neural networks into software applications can enable variety of artificial intelligence systems for individual users as well as organizations. Previously, software implementation of neural networks remained limited to only simulations or application specific solutions. Tightly coupled solutions end up in monolithic systems and non reusable programming efforts. We adapt component based software engineering approach to effortlessly integrate neural network models into AI systems in an application independent way. As proof of concept, this paper presents componentization of three famous neural network models i) multi layer perceptron ii) learning vector quantization and iii) adaptive resonance theory family of networks.

In this work we present a new technique of facial-image retrieval using constrained independent component analysis (cICA). We have employed cICA for the online extraction of those independent components from the entire database which bear some similarity to the query-images. Instead of using any offline learning mechanism or feature extraction technique, our system works completely online. It can cater for queries formulated from both single and multiple examples, for achieving higher accuracy. For compound queries, instead of treating each query-image independently, the system is capable of finding images similar not only to the individual query-images, but also to their different combinations

Received signal strength (RSS) based location systems are potential candidates to enable indoor location aware services due to pervasively available wireless local area networks and hand held devices. Intrinsically RSS based positioning is a multi-class pattern recognition problem. Previous researches have shown that Visibility Matrix based approach of modular classifiers improves location accuracy but costs longer periods of training and testing in development life cycle. We present a context-aware fuzzy ArtMap neural network that provides competitive location accuracy in comparison with modular approach while leveraging online and incremental learning capabilities to location system development life cycle.

Reputation system is a way to maintain trust in dynamic environments by collecting, distributing and aggregating feedbacks about the service providers' past behaviors. Most existing reputation systems assume that raters evaluate the ratee by means of numerical values. However, raters sometimes cannot express their judgments with exact numerical values, especially when the raters have uncertain or ambiguous opinions on the ratee. Our paper introduces a novel reputation system based on the methodology of Computing with Words (CW), in which the ratings and reputations of computation are words and propositions drawn from a natural language instead of numerical values. Our reputation system has a sound mathematical basis. At the same time, it is convenient for the raters to express their judgments and simple for the participants to understand the integrated reputation.

The technology of multimedia content adaptation based upon the location of a target device can become the long expected killer application of ubiquitous computing. Easy to develop, lightweight, and robust location estimation is the core component of this technology. Until now, location estimation technology remains restricted to highly sophisticated hardware and networking infrastructure where semantics of the location information are defined and controlled by service providers. We aim to lower the technical and infrastructure barriers to allow general users to define and develop the semantically meaningful location systems. This paper presents a simple location estimation method to build radio beacon based location systems in the indoor environments. It employs an realtime learning approach which requires zero prior knowledge. The salient features of our method are low memory requirements and simple computations which make it desirable for location-aware multimedia systems functioning in distributed client-server settings as well as privacy sensitive applications residing on stand alone devices.

Compared to all emerging issues, privacy is probably the most prominent concern when it comes to judging the effects of a wide spread deployment of ubiquitous computing. On one hand, service providers want to authenticate legitimate users and make sure they are accessing their authorized services in a legal way. On the other hand, users prefer not to expose any sensitive information to anybody. They want to have complete control on their personal data, without being tracked down for wherever they are, whenever and whatever they do. In this paper, we introduce an anonymous identification authentication and access control scheme to secure interactions between users and services in ubiquitous environments. The scheme uses anonymous user ID, sensitive data sharing method, and account management to provide a lightweight authentication while keeping users anonymously interacting with the services in a secure and flexible way.

Activity recognition is a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from labeled activity samples. Since labeling samples requires human's efforts, most existing research in activity recognition focus on refining learning techniques to utilize the costly labeled samples as effectively as possible. However, few of them consider using the costless unlabeled samples to boost learning performance. In this work, we propose a novel semi-supervised learning algorithm named En-Co-training to make use of the unlabeled samples. Our algorithm extends the co- training paradigm by using ensemble method. Experimental results show that En-Co-training is able to utilize the available unlabeled samples to enhance the performance of activity learning with a limited number of labeled samples.

Hospital work is characterized by the need to manage multiple activities simultaneously, constant local mobility, frequently interruptions, and intense collaboration and communication. Hospital employees must handle a large amount of data that is often tied to specific work activities. This calls for a proper access control model. In this paper, we propose a novel approach, activity-based access control model (ACM). Unlike conventional approaches which exploit user identity/role information, ACM leverages user's activities to determine the access permissions for that user. In ACM, a user is assigned to perform a number of actions if s/he poses a set of satisfactory attributes. Access permissions to hospital information are granted according to user's actions. By doing this, ACM contributes a number of advantages over conventional models: (1) facilitates user's work; (2) reduces complexity and cost of access management. Though the design of ACM first aims to support clinical works in hospitals, it can be applied in other activity-centered environments.

Location Awareness is key capability of Context-Aware Ubiquitous environments. Received Signal Strength (RSS) based localization is increasingly popular choice especially for in-building scenarios after pervasive adoption of IEEE 802.11 Wireless LAN. Fundamental requirement of such localization systems is to estimate location from RSS at a particular location. Multipath propagation effects make RSS to fluctuate in unpredictable manner, introducing uncertainty in location estimation. Moreover, in real life situations RSS values are not available at some locations all the time making the problem more difficult. We employ Modular Multi Layer Perceptron (MMLP) approach to effectively reduce the uncertainty in location estimation system. It provides better location estimation results than other approaches and systematically caters for unavailable signals at estimation time.

Traditional authentication and access control are no longer suitable for ubiquitous computing paradigm. They are only effective if the system knows in advance which users are going to access and what their access rights are. Therefore, it calls for a novel security model. In this paper, we outline major security problems in ubiquitous computing and propose a new architecture, TBSI (Trust-based Security Infrastructure). In TBSI, trust and risk management plays a key role to support authentication and authorization to unknown users. Meanwhile, intrusion detection and home firewall are also integrated to make TBSI more robust. This paper is an extension of our previous work to give more detailed description and enhancement of the architecture. TBSI is on-going research project to support our context-aware middleware CAMUS.

A roaming user in ubiquitous environment should have access to different services anywhere anytime. In an infrastructure based smart environment, user acquires this facility from the middleware. Syntax based service discovery has proven to be inadequate for flexible interaction between user and the middleware; context based semantic matching is necessary. This paper shows the use of ontology to facilitate the semantic discovery of services in a ubiquitous middleware named Context Aware Middleware for Ubiquitous System (CAMUS).

Ubiquitous computing foresees a massively networked world supporting a population of diverse but cooperating mobile devices where trust relationships between entities are uncertain. Though there have been lots of effort focusing on trust for ubiquitous systems, they did not attach enough importance to uncertainty in their model. On the other hand, most of the works draw a general picture without a detailed computational model. In this paper, we present a trust model based on the vectors of trust values of different entities. The evaluation of trust depends upon the recommendation of peer entities common to the interacting entities. These recommendations are weighted according to the number and time of past interactions. Furthermore we present a method of handling false recommendations without introducing significant computational burden. The model can calculate trust between two entities in situations both in which there is past experience among the interacting entities and in which the two entities are communicating for the first time. Several tuning parameters are suggested which can be adjusted to meet the security requirement of a ubiquitous system.

Location based services are becoming essential feature of context-awareness in ubiquitous computing. Reflective distribute component programming model is proposed to systematically provide location to location based services (LBS). We integrate distributed component technology and reflection to develop localization capability as middleware service. Concept of meta object protocols (Reflection) is used in different way than traditional reflective mechanisms. It deals with the state of the component that is not inside the component rather resides outside of it, namely extrinsic. This component model provides the basis for middleware architecture to support location providing service at design, implementation and run time. We describe the methodology we used to build location-awareness as middleware service based upon our reflective component model.

Context aware middleware infrastructures have traditionally been implemented with a modular approach to allow different components to work cooperatively and supply context synthesis and provision services. In this paper, we discuss the important requirements that arise when such a middleware is deployed in a distributed environment and present the design and implementation of context aware middleware for ubiquitous systems (CAMUS) with which the authors have attempted to meet those requirements. Issues related to distributed coordination within the middleware in terms of component discovery and management and multiple context domains are also discussed.

Large-scale distributed systems, such as ubiquitous computing environments, require a service delivery mechanism in order to keep track of the vast set of services offered and make them available to interested clients. The amount of services and clients, their context, and loose coupling between them makes service delivery in ubiquitous environments different from other systems. This paper presents a solution to overcome these issues by utilizing the underlying ontology and semantics for service lookup. Access control over context data is also considered by specifying dynamic policies at the system and service level.

Many efficient data dissemination protocols for mobile sinks in large scale sensor networks are currently under developed by researchers. In this paper we propose CODE, a coordination-based data dissemination protocol for wireless sensor networks, CODE relies on grid structure and GAF protocol to achieve better energy consumption by establishing an efficient data dissemination path and turning off unnecessary nodes. Our simulation results show that CODE achieves more energy efficient and longer networks life time compared with other approaches while still handling efficient data delivery to mobile sinks.

Association rule mining often generates a huge number of rules, but a majority of them either are redundant or do not reflect the true correlation relationship among data objects. We re-examine this problem and show that two interesting measures, all-confidence (denoted as /spl alpha/) and coherence (denoted as /spl gamma/), both disclose genuine correlation relationships and can be computed efficiently. Moreover, we propose two interesting algorithms, CoMine(/spl alpha/) and CoMine(/spl gamma/), based on extensions of a pattern-growth methodology. Our performance study shows that the CoMine algorithms have high performance in comparison with their Apriori-based counterpart algorithms.

In a database management system (DBMS), partial rollback is an important mechanism for canceling only part of the operations executed in a transaction back to a savepoint. Partial rollback complicates buffer management because it should restore the state of the buffers as well as that of the database. Several relational DBMSs (RDBMSs) currently provide this mechanism using page buffers. However, object-oriented or object-relational DBMSs (OO/ORDBMSs) cannot utilize the partial rollback scheme of RDBMSs as is because, unlike RDBMSs, many of them use a dual buffer consisting of an object buffer and a page buffer. In this paper, we propose a thorough study of partial rollback schemes of OO/ORDBMSs with a dual buffer. First, we classify the partial rollback schemes of OO/ORDBMSs into a single buffer-based scheme and a dual buffer-based scheme by the number of buffers used to process rollback. Next, we propose four alternative partial rollback schemes: a page buffer-based scheme, an object buffer-based scheme, a dual buffer-based scheme using a soft log, and a dual buffer-based scheme using shadows. We then evaluate their performance through simulations. The results show that the dual buffer-based partial rollback scheme using shadows provides the best performance. Partial rollback in OO/ORDBMS has not been addressed in the literature; yet, it is a useful mechanism that must be implemented. The proposed schemes are practical ones that can be implemented in such DBMSs.

Spatial join finds pairs of spatial objects having a specific spatial relationship in spatial database systems. A number of spatial join algorithms have recently been proposed in the literature. Most of them, however, perform the join in the original space. Joining in the original space has a drawback of dealing with sizes of objects and thus has difficulty in developing a formal algorithm that does not rely on heuristics. In this paper, we propose a spatial join algorithm based on the transformation technique. An object having a size in the two-dimensional original space is transformed into a point in the four-dimensional transform space, and the join is performed on these point objects. This can be easily extended to n-dimensional cases. We show the excellence of the proposed approach through analysis and extensive experiments. The results show that the proposed algorithm has a performance generally better than that of the R*-based algorithm proposed by Brinkhoff et al. This is a strong indicating that corner transformation preserves clustering among objects and that spatial operations can be performed better in the transform space than in the original space. This reverses the common belief that transformation will adversely affect clustering. We believe that our result will provide a new insight towards transformation-based spatial query processing.

Spatial access methods (SAMs) are often used as clustering indexes in spatial database systems. Therefore, a SAM should have the clustering property both in the index and in the data file. In this paper we argue that corner transformation preserves the clustering property such that objects having similar sizes and positions in the original space tend to be placed in the same region in the transform space. We then show that SAMs based on corner transformation are able to maintain clustering both in the index and in the data file for storage systems with fixed object positions and propose the MBR-MLGF as an example to implement such an index. Extensive experiments comparing with the R*-tree show that corner transformation indeed preserves the clustering property. This result reverses the common belief that transformation will adversely affect the clustering.

Spatial join finds pairs of spatial objects having a specific spatial relationship in spatial database systems. Since spatial join is a fairly expensive operation, we need an efficient algorithm taking advantage of the characteristics of available spatial access methods. In this paper, we propose a spatial join algorithm using corner transformation and show its excellence through experiments. To the extent of authors' knowledge, the spatial join processing using corner transformation is new. In corner transformation, two regions in one file joined with two adjacent regions in the other file share a large common area. The proposed algorithm utilizes this property in order to reduce the number of disk accesses for spatial join. Experimental results show that the performance of the algorithm is generally better than that of the R*-tree based algorithm proposed by Brinkhoff et al. (1993. 1994). This is a strong indication that corner transformation is a promising category of spatial access methods and that spatial operations can be performed better in the transform space than in the original space. This reverses the common belief that transformation will adversely effect the clustering. We also briefly mention that the join algorithm based on corner transformation has a nice property of being amenable to parallel processing. We believe that our result will provide a new insight towards transformation-based processing of spatial operations.