Personalized fall detection models can significantly improve accuracy by adapting to individual motion patterns, yet their effectiveness is often limited by the scarcity of real-world fall data and the dominance of non-fall feedback samples. This imbalance biases the model toward routine activities and weakens its sensitivity to true fall events. To address this challenge, we propose a personalization framework that combines semi-supervised clustering with contrastive learning to identify and balance the most informative user feedback samples. The framework is evaluated under three retraining strategies, including Training from Scratch (TFS), Transfer Learning (TL), and Few-Shot Learning (FSL), to assess adaptability across learning paradigms. Real-time experiments with ten participants show that the TFS approach achieves the highest performance, with up to a 25% improvement over the baseline, while FSL achieves the second-highest performance with a 7% improvement, demonstrating the effectiveness of selective personalization for real-world deployment.

Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporatenew features while the old APIs get deprecated. Even though the importance of updating deprecated API usages with the recommended replacement APIs has been widely recognized, it is non-trivial to update the deprecated API usages. Therefore, the usages of deprecated APIs linger in Android apps and cause compatibility issues in practice. This paper introduces GUPPY, an automated approach that utilizes large language models (LLMs) to update Android deprecated API usages. By employing carefully crafted Chain-of-Thoughts prompts, GUPPY leverages GPT-4, one of the most powerful LLMs, to update deprecated-API usages, ensuring compatibility in both the old and new API levels. Additionally, GUPPY uses GPT-4 to generate tests, identify incorrect updates, and refine the API usage through an iterative process until the tests pass or a specified limit is reached. Our evaluation, conducted on 360 benchmark API usages from 20 deprecated APIs and an additional 156 deprecated API usages from the latest API levels 33 and 34, demonstrates GUPPY’s advantages over the state-of-the-art techniques.

Time-series data is widely used across domains but presents challenges due to high dimensionality and noise. This study proposes a hybrid feature selection approach to enhance forecasting accuracy by reducing irrelevant features. We first trained a 1D-CNN-based forecasting model using all features as a baseline. Then, we evaluated four feature selection methods: two traditional (Variance and Dynamic Mode Decomposition) and two XAI-based (SHAP and LIME). To address the limitations of individual methods, we introduced a hybrid approach combining Variance and LIME. Experiments on four diverse datasets, including EEG (seizure forecasting), Pole-balancing (pre-fall detection), Weather (meteorological forecasting), and Electricity (demand prediction), show that the hybrid method consistently outperformed all others. It achieved the highest gains in EEG (+7.34%), Pole-balancing (+9.02%), Weather (+8.62%), and Electricity (+6.72%), demonstrating robust, interpretable, and generalizable performance across tasks.

The lack of real-world data in clinical fields poses a major obstacle in training effective AI models for diagnostic and preventive tools in medicine. Generative AI has shown promise in increasing data volume and enhancing model training, particularly in computer vision and natural language processing (NLP) domains. However, generating physiological time-series data, a common type in medical AI applications, presents unique challenges due to its inherent complexity and variability. This paper introduces TransConv-DDPM, an enhanced generative AI method for biomechanical and physiological time-series data generation. The model employs a denoising diffusion probabilistic model (DDPM) with U-Net, multi-scale convolution modules, and a transformer layer to capture both global and local temporal dependencies. We evaluated TransConv-DDPM on three diverse datasets, generating both long and short-sequence time-series data. Quantitative comparisons against state-of-the-art methods, TimeGAN and Diffusion-TS, using four performance metrics, demonstrated promising results, particularly on the SmartFallMM and EEG datasets, where it effectively captured the more gradual temporal change patterns between data points. Additionally, a utility test on the SmartFallMM dataset revealed that adding synthetic fall data generated by TransConv-DDPM improved predictive model performance, showing a 13.64% improvement in F1-score and a 14.93% increase in overall accuracy compared to the baseline model trained solely on fall data from the SmartFallMM dataset. These findings highlight the potential of TransConv-DDPM to generate high-quality synthetic data for real-world applications.

Abstract Android dominates the mobile operating system market, yet ensuring the quality and reliability of Android applications remains a persistent challenge. The diversity of devices, screen sizes, and OS versions complicates testing, leading to fragmented adoption of best practices. Despite advancements in automated testing, there is Limited empirical evidence on how developers test Android applications and the extent to which existing tools and frameworks are utilized effectively. In this paper, we aim to investigate the current state of Android app testing, identifying key challenges, Limitations, and best practices. Specifically, we assess the adoption of automated testing, test coverage levels, and the impact of testing practices on software quality. We conduct an experimental study on 2965 open-source Android apps, examining the quantity and coverage of the tests used for open-source Android app development. We further conduct a survey to gather more insights in testing practices from Android app developers and testers. The results reveal a limited adoption of testing among Android app developers, a restricted range of testing tools and frameworks being used, and low code and API coverage in testing. This investigation shows that current Android app testing practices are lacking the use of automated testing tools and embarks on a need for more awareness and adoption of state-of-the-art testing tools and techniques.

We provide a retrospective on our article “QoS-Aware Middleware for Web Service Composition” published in the May 2004 issue of IEEE Transactions on Software Engineering. We start by defining the concept of Web service composition, and reviewing key paradigms emerging from early research on this topic. We then explain how the 2004 QoS article laid down a conceptual foundation that spawned a prolific line of research into the estimation and optimization of Quality of Service (QoS) properties of composite Web services. We close with a reflection on how the challenges tackled by the 2004 QoS article and other research on service composition are re-emerging in the field of Large Language Model (LLM) agents. We argue that the design of LLM agents raises challenges similar to those that the 2004 QoS article tackled, while bringing in new challenges due to the non-deterministic nature of LLMs and the fact that they consume and produce unstructured data.

AI-Powered Edge Computing is accelerating the integration of the cyber world with the ever-growing list of new physical IoT devices and will fundamentally change and empower the way humans interact with the world. In this paper, we prototyped and analyzed three edge computing architectures for running SmartFall, a real-time fall detection application that uses accelerometer data from the watch, to compare the trade-off in relationship to battery consumption, potential data loss, machine learning model's prediction accuracy, and latency in model inferencing. Our experiments show that running the machine learning prediction on the server using the TensorFlow native model format has achieved the best model accuracy with-out draining the battery power of the smartwatches. However, the optimal selection of the software architecture depends on the intended deployment environment, projected user numbers, users' privacy concerns, and network stability.

In an era where Android dominates the mobile operating system market, it is important to ensure high quality Android app delivery. In this paper, we delve into the essential need for effective Android app testing in a market characterized by diversity and widespread usage and empirically investigate testing practices for Android apps. We conduct an experimental study on 2965 open-source Android apps, examining the quantity and coverage of the tests used for open-source Android app development. We further conduct a survey to gather more insights in testing practices from Android app developers and testers. The results reveal a limited adoption of testing among Android app development, a restricted range of testing tools and frameworks being used, and low code and API coverage in testing. This investigation shows that current Android app testing practices are lacking the use of automated testing tools and embarks on a need for more awareness and adoption of state-of-the-art testing tools and techniques.

Human activity recognition (HAR) based on multi-modal approach has been recently shown to improve the accuracy performance of HAR. However, restricted computational resources associated with wearable devices, i.e., smartwatch, failed to directly support such advanced methods. To tackle this issue, this study introduces an end-to-end Vision-to-Sensor Knowledge Distillation (VSKD) framework. In this VSKD framework, only time-series data, i.e., accelerometer data, is needed from wearable devices during the testing phase. Therefore, this framework will not only reduce the computational demands on wearable devices, but also produce a learning model that closely matches the performance of the computational expensive multi-modal approach. In order to retain the local temporal relationship and facilitate visual deep learning models, we first convert time-series data to two-dimensional images by applying the Gramian Angular Field (GAF) based encoding method. We adopted multi-scale TRN with BN-Inception and ResNet18 as the teacher and student network in this study, respectively. A novel loss function, named Distance and Angle-wised Semantic Knowledge loss (DASK), is proposed to mitigate the modality variations between the vision and the sensor domain. Extensive experimental results on UTD-MHAD, MMAct, and Berkeley-MHAD datasets demonstrate the competitiveness of the proposed VSKD model which can be deployed on wearable devices.

Wearable sensor-based Human Action Recognition (HAR) has achieved remarkable success recently. However, the accuracy performance of wearable sensor-based HAR is still far behind the ones from the visual modalities-based system (i.e., RGB video, skeleton and depth). Diverse input modalities can provide complementary cues and thus improve the accuracy performance of HAR, but how to take advantage of multi-modal data on wearable sensor-based HAR has rarely been explored. Currently, wearable devices, i.e., smartwatches, can only capture limited kinds of non-visual modality data. This hinders the multi-modal HAR association as it is unable to simultaneously use both visual and non-visual modality data. Another major challenge lies in how to efficiently utilize multi-modal data on wearable devices with their limited computation resources. In this work, we propose a novel Progressive Skeleton-to-sensor Knowledge Distillation (PSKD) model which utilizes only time-series data, i.e., accelerometer data, from a smartwatch for solving the wearable sensor-based HAR problem. Specifically, we construct multiple teacher models using data from both teacher (human skeleton sequence) and student (time-series accelerometer data) modalities. In addition, we propose an effective progressive learning scheme to eliminate the performance gap between teacher and student models. We also designed a novel loss function called Adaptive-Confidence Semantic (ACS), to allow the student model to adaptively select either one of the teacher models or the ground-truth label it needs to mimic. To demonstrate the effectiveness of our proposed PSKD method, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD and MMAct datasets. The results confirm that the proposed PSKD method has competitive performance compared to the previous mono sensor-based HAR methods.

The majority of current smart health applications are deployed on a smartphone paired with a smartwatch. The phone is used as the computation platform or the gateway for connecting to the cloud while the watch is used mainly as the data sensing device. In the case of fall detection applications for older adults, this kind of setup is not very practical since it requires users to always keep their phones in proximity while doing the daily chores. When a person falls, in a moment of panic, it might be difficult to locate the phone in order to interact with the Fall Detection App for the purpose of indicating whether they are fine or need help. This paper demonstrates the feasibility of running a real-time personalized deep-learning-based fall detection system on a smartwatch device using a collaborative edge-cloud framework. In particular, we present the software architecture we used for the collaborative framework, demonstrate how we automate the fall detection pipeline, design an appropriate UI on the small screen of the watch, and implement strategies for the continuous data collection and automation of the personalization process with the limited computational and storage resources of a smartwatch. We also present the usability of such a system with nine real-world older adult participants.

The Internet of Things (IoT) is a rapidly growing system of physical sensors and connected devices, enabling advanced information gathering, interpretation, and monitoring. The realization of a versatile IoT edge computing framework will accelerate seamless integration of the cyber-world with new physical IoT devices, and will fundamentally change and empower the way humans interact with the world. While there are many cloud-based IoT computing frameworks, they cannot support the needs of IoT applications that require local processing and guarantee of consumer’s privacy. This article presents experimentation with the opensource plug-and-play IoT middleware, called Cordova Accesor Host. We demonstrated that Cordova Accessor Host supports the essential ingredients of the composition and reusability of IoT services using the accessor as the basic building block and adopting an accessor-module-plugin design pattern. The portability is demonstrated by using the same accessor for collecting sensor data from radically different IoT devices such as, wearables (e.g., smartwatches) and microcontrollers (e.g., Arduino). Our energy profiling experiments show that IoT services deployed using the Cordova Accessor Host consume around 35% less battery power than the same IoT services deployed in the native Android operating system.

This article presents an in-depth experimental study of Ensemble Deep Learning techniques on small datasets for the analysis of time-series data generated by wearable devices. Deep Learning networks generally require large datasets for training. In some health care applications, such as the real-time smartwatch-based fall detection, there are no publicly available, large, annotated datasets that can be used for training, due to the nature of the problem (i.e., a fall is not a common event). We conducted a series of offline experiments using two different datasets of simulated falls for training various ensemble models. Our offline experimental results show that an ensemble of Recurrent Neural Network (RNN) models, combined by the stacking ensemble technique, outperforms a single RNN model trained on the same data samples. Nonetheless, fall detection models trained on simulated falls and activities of daily living performed by test subjects in a controlled environment, suffer from low precision due to high false-positive rates. In this work, through a set of real-world experiments, we demonstrate that the low precision can be mitigated via the collection of false-positive feedback by the end-users. The final Ensemble RNN model, after re-training with real-world user archived data and feedback, achieved a significantly higher precision without reducing much of the recall in a real-world setting.

Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp , a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.

Volunteered Geographic Information, social media, and data from Information and Communication Technology are emerging sources of big data that contribute to the development and understanding of the spatiotemporal distribution of human population. However, the inherent anonymity of these crowd-sourced or crowd-harvested data sources lack the socioeconomic and demographic attributes to examine and explain human mobility and spatiotemporal patterns. In this paper, we investigate an Internet-based demographic data source, personal microdata databases publicly accessible on the World Wide Web (hereafter web demographics), as potential sources of aspatial and spatiotemporal information regarding the landscape of human dynamics. The objectives of this paper are twofold: (1) to develop an analytical framework to identify mobile population from web demographics as an individual-level residential history data, and (2) to explore their geographic and demographic patterns of migration. Using web demographics of Vietnamese–Americans in Texas collected in 2010 as a case study, this paper (1) addresses entity resolution and identifies mobile population through the application of a Cost-Sensitive Alternative Decision Tree (CS-ADT) algorithm, (2) investigates migration pathways and clusters to include both short- and long-distance patterns, and (3) analyze the demographic characteristics of mobile population and the functional relationship with travel distance. By linking the physical space at the individual level, this unique methodology attempts to enhance the understanding of human movement at multiple spatial scales.

The Internet of Things (IoT) provides the ability for humans and computers to learn and interact from billions of things that include sensors, actuators, services, and other Internet-connected objects. The realization of IoT systems will enable seamless integration of the cyber world with our physical world and will fundamentally change and empower human interaction with the world. A key technology in the realization of IoT systems is middleware, which is usually described as a software system designed to be the intermediary between IoT devices and applications. In this paper, we first motivate the need for an IoT middleware via an IoT application designed for real-time prediction of blood alcohol content using smartwatch sensor data. This is then followed by a survey on the capabilities of the existing IoT middleware. We further conduct a thorough analysis of the challenges and the enabling technologies in developing an IoT middleware that embraces the heterogeneity of IoT devices and also supports the essential ingredients of composition, adaptability, and security aspects of an IoT system.

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations among ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article presents DisCor-T , a novel graph-based approach for discovering underlying connections of things via mining the rich content embodied in the human-thing interactions in terms of user, temporal, and spatial information. We model this various information using two graphs, namely a spatio-temporal graph and a social graph. Then, random walk with restart (RWR) is applied to find proximities among things, and a relational graph of things (RGT) indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management and analytics. To demonstrate the utility of the proposed approach, we develop a flexible feature-based classification framework on top of RGT and perform a systematic case study. Our evaluation exhibits the strength and feasibility of the proposed approach.

The emerging Internet of Things (IoT) bridges the gap between the physical and the digital worlds, which enables a deeper understanding of user preferences and behaviors. The rich interactions and relations between users and things call for effective and efficient recommendation approaches to better meet users’ interests and needs. In this article, we focus on the problem of things recommendation in IoT, which is important for many applications such as e-Commerce and health care. We discuss the new properties of recommending things of interest in IoT, and propose a unified probabilistic factor based framework by fusing relations across heterogeneous entities of IoT, for example, user-thing relations, user-user relations, and thing-thing relations, to make more accurate recommendations. Specifically, we develop a hypergraph to model things’ spatiotemporal correlations, on top of which implicit things correlations can be generated. We have built an IoT testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

Trust management is one of the most challenging issues for the adoption and growth of cloud computing. The highly dynamic, distributed, and non-transparent nature of cloud services introduces several challenging issues such as privacy, security, and availability. Preserving consumers' privacy is not an easy task due to the sensitive information involved in the interactions between consumers and the trust management service. Protecting cloud services against their malicious users (e.g., such users might give misleading feedback to disadvantage a particular cloud service) is a difficult problem. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of cloud environments. In this article, we describe the design and implementation of CloudArmor, a reputation-based trust management framework that provides a set of functionalities to deliver trust as a service (TaaS), which includes i) a novel protocol to prove the credibility of trust feedbacks and preserve users' privacy, ii) an adaptive and robust credibility model for measuring the credibility of trust feedbacks to protect cloud services from malicious users and to compare the trustworthiness of cloud services, and iii) an availability model to manage the availability of the decentralized implementation of the trust management service. The feasibility and benefits of our approach have been validated by a prototype and experimental studies using a collection of real-world trust feedbacks on cloud services.

The last decade has witnessed a tremendous growth of web services as a major technology for sharing data, computing resources, and programs on the web. With increasing adoption and presence of web services, designing novel approaches for efficient and effective web service recommendation has become of paramount importance. Most existing web service discovery and recommendation approaches focus on either perishing UDDI registries, or keyword-dominant web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. It would be desirable for a system to recommend web services that align with users’ interests without requiring the users to explicitly specify queries. Recent research efforts on web service recommendation center on two prominent approaches:collaborative filteringandcontent-based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in web service recommendation. In this paper, we propose a novel approach that unifies collaborative filtering and content-based recommendations. In particular, our approach considers simultaneously both rating data (e.g., QoS) and semantic content data (e.g., functionalities) of web services using a probabilistic generative model. In our model, unobservable user preferences are represented by introducing a set of latent variables, which can be statistically estimated. To verify the proposed approach, we conduct experiments using 3,693 real-world web services. The experimental results show that our approach outperforms the state-of-the-art methods on recommendation performance.

Internet of Things (IoT) is an emerging paradigm where physical objects are connected and communicated over the Web. Its capability in assimilating the virtual world and the physical one offers many exciting opportunities. However, how to realize a smooth, seamless integration of the two worlds remains an interesting and challenging topic. In this paper, we showcase an IoT prototype system that enables seamless integration of the virtual and the physical worlds and efficient management of things of interest (TOIs), where services and resources offered by things can be easily monitored, visualized, and aggregated for value-added services by users. This paper presents the motivation, system design, implementation, and demonstration scenario of the system.

The Internet of Things (IoT) is a rapidly growing system of physical sensors and connected devices, enabling an advanced information gathering, interpretation and monitoring. However, IoT must be supported by a middleware that allows IoT consumers and IoT application developers to interact in a

With the fast emerging Internet of Things (IoT), effectively and efficiently searching and selecting the most related things of a user’s interest is becoming a crucial challenge. In the IoT era, human interactions with things are taking place at a new level in ubiquitous computing. These interaction

Finding relevant Web services and composing them into value-added applications is becoming increasingly important in cloud and service based marketplaces. The key problem with current approaches to finding relevant Web services is that most of them only provide searches over a discrete set of features using exact keyword matching. We demonstrate in this paper that by utilizing well known indexing scheme such as inverted file and R-tree indexes over Web services attributes, the Earth Mover's Distance (EMD) algorithm can be used efficiently to find partial matches between a query and a database of Web services.

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web-based services, physical things are becoming an integral part of the emerging ubiquitous Web. In this paper, we focus on the things recommendation problem in Internet of Things (IoT). In particular, we propose a unified probabilistic based framework by fusing information across relationships between users (i.e., users'social network) and things (i.e., things correlations) to make more accurate recommendations. The proposed approach not only inherits the advantages of the matrix factorization, but also exploits the merits of social relationships and thing-thing correlations. We validate our approach based on an Internet of Things platform and the experimental results demonstrate its feasibility and effectiveness.

Trust management of cloud services is emerging as an important research issue in recent years, which poses significant challenges because of the highly dynamic, distributed, and non-transparent nature of cloud services. This paper describes Cloud Armor, a platform for credibility-based trust management of cloud services. The platform provides a crawler for automatic cloud services discovery, an adaptive and robust credibility model for measuring the credibility of feedbacks, and a trust-based recommender to recommend the most trustworthy cloud services to users. This paper presents the motivation, system design, implementation, and a demonstration of the Cloud Armor platform.

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Correlation discovery for ubiquitous things is critical for many important applications such as things search, recommendation, annotation, classification, clustering, composition, and management. In this paper, we propose a novel approach for discovering things correlation based on user, temporal, and spatial information captured from usage events of things. In particular, we use a spatio-temporal graph and a social graph to model things usage contextual information and user-thing relationships respectively. Then, we apply random walks with restart on these graphs to compute correlations among things. This correlation analysis lays a solid foundation and contributes to improved effectiveness in things management. To demonstrate the utility of our approach, we perform a systematic case study and comprehensive experiments on things annotation.

Over the past few years, Cloud computing has been receiving much attention as a new computing paradigm for providing flexible and on-demand infrastructures, platforms and software as services. In Cloud computing, challenges in searching cloud services need to be renewed due to a number of unique characteristics of cloud services such as the dynamic, diverse services offering at different levels, as well as the lack of standardized description languages. In this paper, we present the Cloud Service Crawler Engine that we used to collect metadata of 5, 883 valid cloud services through search engines after parsing more than a half million possible links. Based on the collected data, we conducted a set of statistical analysis and present the results in this paper. These statistical results offer an overall view on the current status of cloud services. Some most intriguing findings from our investigation include: i) the scarcity of standardization in Cloud computing, and ii) little evidence on a strong support of Cloud computing from the established service-oriented computing (SOC) technologies. Our findings provide some further insights on improving cloud services discovery and the datasets collected from this study will be valuable to the research community.

A drawback of existing scientific workflow systems is the lack of support to domain scientists in designing and executing their own scientific workflows. Many domain scientists avoid developing and using workflows because the basic objects of workflows are too low-level and high-level tools and mechanisms to aid in workflow construction and use are largely unavailable. In our research, we are prototyping higher-level abstractions and tools to better support scientists in their workflow activities. Specifically, we are developing generic actors that provide abstract interfaces to specific functionality, workflow templates that encapsulate workflow and data patterns that can be reused and adapted by scientists, and context-awareness mechanisms to gather contextual information from the workflow environment on behalf of the scientist. To evaluate these scientist-centered abstractions on real problems, we apply them to construct and execute scientific workflows in the specific domain area of groundwater modeling and analysis.

The need for integration of all types of client and server applications that were not initially designed to interoperate is gaining popularity. One of the reasons for this popularity is the capability to quickly reconfigure a composite application for a task at hand, both by changing the set of components and the way they are interconnected. Service-Oriented Architecture (SOA) has recently become a popular platform in the IT industry for building such composite applications with the integrated components being provided as Web services. A key limitation of solely Web-service-based integration is that it requires extra programming efforts when integrating non-Web service components, which is not cost-effective. Moreover, with the emergence of new standards, such as Open Service Gateway Initiative (OSGi), the components used in composite applications have grown to include more than just Web services. Our work enables progressive composition of non-Web-service-based components such as portlets, Web applications, native widgets, legacy systems, and Java Beans. Further, we proposed a novel application of semantic annotation together with the standard semantic Web matching algorithm for finding sets of functionally equivalent components out of a large set of available non-Web-service-based components. Once such a set is identified, the user can drag and drop the most suitable component into an Eclipse-based composition canvas. After a set of components has been selected in such a way, they can be connected by data-flow arcs, thus forming an integrated, composite application without any low-level programming and integration efforts. We implemented and conducted extensive experimental study on the above progressive composition framework on IBM's Lotus Expeditor, an extension of an SOA platform called the Eclipse Rich Client Platform (RCP) that complies with the OSGi standard.

Context-aware Web services are currently emerging as an important technology for building innovative context-aware applications. Unfortunately, context-aware Web services are still difficult to build. This paper describes ContextServ, a platform for rapid development of context-aware Web services. ContextServ adopts model-driven development where context-aware Web services are specified using ContextUML, a UML based modeling language. The platform also offers a set of automated tools for generating and deploying executable implementations of context-aware Web services. This paper presents the motivation, system design, implementation, and usage of ContextServ.

Web services composition has been an active research area over the last few years. However, the technology is still not mature yet and several research issues need to be addressed. In this paper, we describe the design of CCAP, a system that provides tools for adaptive service composition and provisioning. We introduce a composition model where service context and exceptions are configurable to accommodate needs of different users. This allows for reusability of a service in different contexts and achieves a level of adaptiveness and contextualization without recoding and recompiling of the overall composed services. The execution semantics of the adaptive composite service is provided by an event-driven model. This execution model is based on Linda Tuple Spaces and supports real-time and asynchronous communication between services. Three core services, coordination service, context service, and event service, are implemented to automatically schedule and execute the component services, and adapt to user configured exceptions and contexts at run time. The proposed system provides an efficient and flexible support for specifying, deploying, and accessing adaptive composite services. We demonstrate the benefits of our system by conducting usability and performance studies.

Music information processing has become very important due to the ever-growing amount of music data from emerging applications. In this demonstration,we present a novel approach for generating small but comprehensive music descriptors to facilitate efficient content music management (accessing and retrieval, in particular). Unlike previous approaches that rely on low-level spectral features adapted from speech analysis technology, our approach integrates human music perception to enhance the accuracy of the retrieval and classification process via PCA and neural networks. The superiority of our method is demonstrated by comparing it with state-of-the-art approaches in the areas of music classification query effectiveness, and robustness against various audio distortion/alternatives.

In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare our approach with state of the art techniques and demonstrate its effectiveness on content-based music retrieval. In addition, extensive experimental study illustrates its effectiveness and robustness against various kinds of audio alteration.

With advances in multimedia technology, image data with various formats is is becoming available at an explosive rate from various domain applications. How to efficiently organise and access them has been an extremely important issue and enjoying growing attention. In this paper, we present results from experimental studies investigating performance of image classification for a novel dimension reduction scheme with hybrid architecture. We demonstrate that not only can the method provide superior quality of classification accuracy with various machine learning based classifier but also substantially speed up training and categorisation process. Moreover, it is fairly robust against various kinds of visual distortions and noises.

In this paper, we present a novel feature extraction method facilitating efficient content-based music retrieval and classification, called InMAF. The goal of our approach is to allow straightforward incorporation of multiple musical features, such as timbral texture, pitch and rhythm structure, into a single low dimensional vector that is effective for retrieval and classification. Unlike earlier approaches that used only acoustic properties as the basis for retrieval, our approach can easily incoporate human music perception to improve accuracy of retrieval and classification process. The superiority of our method is demonstrated by comparing it with state-of-the-art approaches in the areas of music classification (using a variety of machine learning algorithms), query effectiveness and robustness against audio distortion.

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.

No abstract available.

InfoSleuth is an agent-based system for information discovery and retrieval in a dynamic, open environment. Brokering in InfoSleuth is a matchmaking process, recommending agents that provide services to agents requesting services. This paper discusses InfoSleuth's distributed multibroker design and implementation. InfoSleuth's brokering function combines reasoning over both the syntax and semantics of agents in the domain. This means the broker must reason over explicitly advertised information about agent capabilities to determine which agent can best provide the requested services. Robustness and scalability issues dictate that brokering must be distributable across collaborating agents. Our multibroker design is a peer-to-peer system that requires brokers to advertise to and receive advertisements from other brokers. Brokers collaborate during matchmaking to give a collective response to requests initiated by nonbroker agents. This results in a robust, scalable brokering system.

The development of new services through the integration of existing ones has gained a considerable momentum as a means to create and streamline business-to-business collaborations. Unfortunately, as Web services are often autonomous and heterogeneous entities, connecting and coordinating them in order to build integrated services is a delicate and time-consuming task. In this paper, we describe the design and implementation of a system through which existing Web services can be declaratively composed, and the resulting composite services can be executed following a peer-to-peer paradigm, within a dynamic environment. This system provides tools for specifying composite services through. statecharts, data conversion rules, and provider selection, policies. These specifications are then translated into XML documents that can be interpreted by peer-to-peer inter-connected software components, in order to provision the composite service without requiring a central authority.

Operation support systems (OSSs) integrate software components and network elements to automate the provisioning and monitoring of telecommunications services. This paper illustrates Telcordia's Cable OSS and shows how customers may use this OSS to provision IP and telephone services over the cable infrastructure. Telcordia's Cable OSS is a process-based application, i.e. a collection of flows, specialized components (e.g. a billing system, a call agent soft switch, network services and elements, cable modems, etc.) and corresponding adaptors that are integrated, coordinated and monitored using CMI (Collaboration Management Infrastructure), Telcordia's advanced process-based integration technology. Customers interact with the Cable OSS by using Web or IVR (interactive voice response) interfaces.

Autonomy of operations combined with decentralized management of data gives rise to a number of heterogeneous databases or information systems within an enterprise. These systems are often incompatible in structure as well as content and, hence, difficult to integrate. Depsite heterogeneity, the unity of overall purpose within a common application domain, nevertheless, provides a degree of semantic similarity that manifests itself in the form of similar data structures and common usage patterns of existing information systems. This article introduces a conceptual integration approach that exploits the similarity in metalevel information in existing systems and performs metadata mining on database objects to discover a set of concepts that serve as a domain abstraction and provide a conceptual layer above existing legacy systems. This conceptual layer is further utilized by an information reengineering framework that customizes and packages information to reflect the unique needs of different user groups within the application domain. The architecture of the information reengineering framework is based on an object-oriented model that represents the discovered concepts as customized application objects for each distinct user group.

InfoSleuth is an agent based system for information discovery and retrieval in a dynamic, open environment. The paper discusses InfoSleuth's multi broker design and implementation. InfoSleuth's brokering function combines reasoning over both the syntax and semantics of agents in the domain. The broker must reason over explicitly advertised information about agent capabilities to determine which agent can best provide the requested services. Brokering in InfoSleuth is a match making process, recommending agents that provide services to agents requesting services. Robustness and scalability issues dictate that brokering must be distributable across collaborating processes. Our multibroker design is a peer-to-peer system that requires brokers to advertise to and receive advertisements from other brokers. Brokers collaborate during match making to give a collective response to requests initiated by non broker agents. This results in a robust, scalable brokering system.

Spatial relationships are important ingredients for expressing constraints in retrieval systems for pictorial or multimedia databases. We have proposed a unified representation for spatial relationships, 2D Projection Interval Relationships (2D-PIR), that integrates both directional and topological relationships. We develop techniques for similarity retrieval based on the 2D-PIR representation, including a method for dealing with rotated and reflected images.

The specification of communication behavior is fundamental in developing interoperable transactions. In particular, the temporal ordering of messages exchanged between different communicating agents must be declaratively specified and verified in order to guarantee consistency of data in the various component systems. This paper shows that by expressing communication constraints in propositional temporal logic, the tableau method can be applied to construct a dependency graph. If the specification is correct, this method guarantees that all possible execution paths satisfying the specification will be generated. The declarative specification and verification of communication constraints in interoperable transactions is demonstrated using the classic business trip. It is argued that the specification formalism provides an improvement over the Flexible Transaction Model.

Transaction modelling, which involves capturing the dynamic properties of an organization and is viewed as a necessary component in data modelling is discussed. A specification-language-transaction schema based on ACM/PCM specification language is implemented as the transaction-modelling tool. The benefit of such a modeling tool is discussed and its ability to extend the static scheme automatically to support the intended transactions is illustrated.>

Transaction modeling, which involves capturing the dynamic properties of an organization, is seen as a necessary component in data modeling. A specification language-transaction schema based on the ACM/PCM specification language-is implemented as the transaction modeling tool. The benefit of such a modeling tool is discussed and, in particular, illustrated by its capability to extend the static schema automatically in order to support the intended transactions.>