Advancements in fitness tracking systems have revolutionised cycling by making health fitness related data more accessible. The cycling power is an essential indicator when it comes to actual measurement of absolute efforts and performance of athletes and coaches. Unfortunately, cycling power requires a specialist power meter sensor to measure, which adds significant cost to an already expensive sport. This study investigates the potential of machine learning techniques to predict average cycling power without the need for a power meter. Unlike traditional physics-based models, which rely on fixed equations, regression models can learn complex, data-driven patterns to improve accuracy and generalisability. Five regression models — multiple linear regression, random forest regression, XGBoost regression, support vector regression, and bayesian ridge regression were trained on two distinct yet comparable datasets. One dataset was from an online source and the other sourced from cycling clubs local to city of Edinburgh, Scotland. Feature selection and hyper-parameter tuning were performed to optimise each model. Support vector regression model, which has previously missing from literature in similar applications, emerged as the best performing model — achieving a mean R2score of 0.91. These results advance the field of cycling power prediction by identifying a more accurate regression model while also providing a comparative analysis with other regression techniques.

Fog computing is paving the way for time-sensitive and energy-efficient IoT applications by placing computation closer to end devices. However, sustainability concerns due to its increasing energy demands and associated carbon emissions stem from the expansion of fog infrastructure. Despite numerous offloading strategies that focus on energy consumption and makespan, green computing and its broader environmental impact are often overlooked. To address this gap, we propose a carbon-aware multi-objective optimisation model that integrates three metaheuristic algorithms - GWO, CSA, and SSA - to balance makespan and energy consumption while minimising$C O_{2}$emissions. In our proposed framework, node suitability is evaluated based on a customised energy model designed to prevent the overuse of non-renewable energy sources and maintain system responsiveness. Our framework advances foglevel scheduling by integrating a hybrid energy model, unlike prior cloud-focused carbon-aware approaches. The optimisation process simultaneously considers three objectives - energy, makespan, and carbon emissions - through a hybrid metaheuristic algorithm. Simulation results using the LEAF simulator indicate that our framework achieves an average improvement in energy consumption, makespan, and$C O_{2}$emissions of approximately$18 \%, 23 \%$, and 20 %, respectively, compared to EEAIOT-EFC and MOHHOSSA. These results show the viability of integrating carbon awareness into fog-cloud task offloading for green computing.

In the dynamic landscape of network security, where cyberattacks continuously evolve, robust and adaptive detection mechanisms are essential, particularly for safeguarding Internet of Things (IoT) networks. This paper introduces an advanced anomaly detection model that utilizes Artificial Intelligence (AI) to identify network anomalies based on traffic features, explaining the most influential factors behind each detected anomaly. The model integrates domain knowledge stored in a knowledge graph to verify whether the detected anomaly constitutes a legitimate attack. Upon validation, the model identifies which core cybersecurity principles—Confidentiality, Integrity, or Availability (CIA)—are violated by mapping influential feature values. This is followed by an alignment with the MITRE ATT&CK framework to provide insights into potential attack tactics, techniques, and intelligence-driven countermeasures. By leveraging explainable AI (XAI) and incorporating expert domain knowledge, our approach bridges the gap between complex AI predictions and human-understandable decision-making, thereby enhancing both detection accuracy and result interpretability. This transparency facilitates faster responses and real-time decision-making while improving adaptability to new, unseen cyber threats. Our evaluation on network traffic datasets demonstrates that the model not only excels in detecting and explaining anomalies but also achieves an overall detection accuracy of 0.97 with the integration of domain knowledge for attack legitimacy. Furthermore, it provides 100% accuracy for threat intelligence based on the MITRE ATT&CK framework, ensuring that security measures are verifiable, actionable, and ultimately strengthen IoT environment defenses by delivering real-time threat intelligence and responses, thus minimizing human response time.

The advancement of mobile multimedia communications, 5G, and Internet of Things (IoT) has led to the widespread use of edge devices, including sensors, smartphones, and wearables. This has generated in a large amount of distributed data, leading to new prospects for deep learning. However, this data is confined within data silos and contains sensitive information, making it difficult to be processed in a centralized manner, particularly under stringent data privacy regulations. Federated learning (FL) offers a solution by enabling collaborative learning while ensuring privacy. Nonetheless, data and device heterogeneity complicate FL implementation. This research presents a specialized FL algorithm for heterogeneous edge computing. It integrates a lightweight grouping strategy for homogeneous devices, a scheduling algorithm within groups, and a Split Learning (SL) approach. These contributions enhance model accuracy and training speed, alleviate the burden on resource-constrained devices, and strengthen privacy. Experimental results demonstrate that the GSFL outperforms FedAvg and SplitFed by 6.53× and 1.18×. Under experimental conditions with \(\alpha=0.05\) , representing a highly heterogeneous data distribution typical of extreme Non-IID scenarios, GSFL showed better accuracy compared to FedAvg by 10.64%, HACCS by 4.53%, and Cluster-HSFL by 1.16%. GSFL effectively balances privacy protection and computational efficiency for real-world applications in mobile multimedia communications.

Summary As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM, called KGLSTM is used to achieve mid‐long term mixed sequential mode RT forecasting. In the proposed edge‐cloud framework, distributed intelligent sockets are regarded as edge nodes to collect, analyze and upload data to cloud services for further processing. The proposed KGLSTM network adopts a double branch structure. One branch extracts the data characteristics of mid‐short term time‐series data through an encoding–decoding LSTM module; the other branch extracts the data features of long term timing data through an adapted GCN. GCN is used to extract spatial correlations between different nodes. In addition, by combining a dynamic weighted loss function, the accuracy of peak forecasting is effectively improved. Finally, through various experimental indicators, this article shows that KGLSTM and weighted KGLSTM have achieved significant performance improvement over recent methods in mid‐long term time‐series forecasting and peak forecasting.

Fake news is a prevalent issue in modern society, leading to misinformation, and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awareness and understanding of news, while also effectively mitigating the dissemination of fake news. However, news credibility assessment meets challenges when processing large-scale and constantly growing data, due to insufficient and unreliable labels and standards, and diversity and semantic ambiguity of news contents. Recently, machine learning models have been well developed to address these issues, but suffer from limited effectiveness. A unified framework is also required for them to represent various entities and relationships involved in news stories. This article proposes an entity ontology-based knowledge graph network (EKNet) to leverage knowledge graphs and entity frameworks for news credibility assessment. The model utilizes the information from knowledge graphs by combining entities and relationships from news and knowledge graphs. Experimental results show that the EKNet has advantages in evaluating news credibility over existing methods. Specifically, compared to several strong baselines, the model demonstrates a significant performance improvement in scores across various tasks. Which indicates that using the EKNet to address the challenges in news credibility assessment is highly effective and can conduct better performance for the problem of fake news in the social media environment.

The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA).As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability.In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets.Our approach supports flexible skill configurations based on the target domain to boost performance.To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer.We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion.Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-ofthe-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.

Google Lighthouse is a tool made by Google for auditing web pages performance, accessibility, SEO, and best practices with the intention of improving the quality of the websites. This allows software developers to understand areas of improvement within a website. However, accessibility becomes an issue when it comes to rapidly handling multiple input for generating performance report consisting different formats with better accuracy. In this work, a test on set of improvements to enhance the reference Google Lighthouse architecture was conducted to enable it to process multiple file input types with the focus of producing different output format including better accuracy. Multiple experimental runs involving different size of file input were conducted by comparing the results from using the modified Lighthouse with the original Lighthouse. The results proves the feasibility of the enhancement and can further improve the accuracy of performance audit report with known Lighthouse performance metrics.

Abstract Energy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect, it also helps to conserve energy for future use. Over the years, several methods for energy forecasting have been proposed, all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment. This research, however, proposes the uses of Deep Neural Network (DNN) for energy forecasting on mobile devices at the edge of the network. This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery. Nevertheless, the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them. Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source (D-RES) network. Moreover, a novel grid control algorithm that uses the forecasting model to administer a well-coordinated and effective control for renewable energy sources (RESs) in the electrical network is designed. The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network. The model was trained using a dataset from a solar power generation company in Belgium (elis) and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations. The performance of each architecture was evaluated using the mean square error (MSE) and the r-square.

Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three-hidden-layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k-nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large-scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

At present, the study of upper‐limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper‐limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three‐channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine‐ (FSM‐) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

The growing trend of sharing news/contents, through social media platforms and the World Wide Web has been seen to impact our perception of the truth, altering our views about politics, economics, relationships, needs and wants. This is because of the growing spread of misinformation and disinformation intentionally or unintentionally by individuals and organizations. This trend has grave political, social, ethical, and privacy implications for society due to 1) the rapid developments in the field of Machine Learning (ML) and Deep Learning (DL) algorithms in creating realistic-looking yet fake digital content (such as text, images, and videos), 2) the ability to customize the content feeds and to create a polarized so-called "filter-bubbles" leveraging the availability of the big-data. Therefore, there is an ethical need to combat the flow of fake content. This paper attempts to resolves some of the aspects of this combat by presenting a high-level overview of TRUSTD, a blockchain and collective signature based ecosystem to help content creators in getting their content backed by the community, and to help users judge on the credibility and correctness of these contents.

Semantic technology can provide a bridge between smart applications and Internet of Things (IoT) to enable possible integration and interoperability of data produced by heterogeneous devices. In IoT, data quality plays an important role when it comes to interfacing sensor readings with real-time applications at the basic atomic level. Popular techniques of machine learning and point-based calibrations are inadequate due to inability to perform semantic reasoning and interoperability on sensor streams even in real time. In this paper, a layered software framework based on semantic technologies is developed to maintain the consistency of data streams produced by physical sensors that interprets measurements as numeric values. The framework shows how semantic modelling and reasoning can be applied to validate the consistency of data streams while placing emphasis on the temporal characteristics of the stream. The evaluation of the approach involves analysing the effects of different Resource Description Format(RDF) data serializations on the response times of the reasoning engine and throughput of continuous semantic stream query execution. The outcome of experiments indicates the semantic framework as a promising approach for stream validation in Smart Spaces and other related IoT domains.

Smart Spaces currently benefits from Internet of Things (IoT) infrastructures in order to realise its objective. In many cases, it demonstrates this through certain automated applications that relies on sensor streams that comes with some uncertainties in measurements. However, these sensor data tend to be uncertain or fault-prone due to the faults of the sensor either themselves or the wireless sensor networks. Sometimes, the extreme operating condition of the sensor can be a contributing factor to the uncertainty. The proposed approach provides a software framework that aims at homogenising, annotating and reasoning over these data. The framework consists of four layers that utilizes the semantic process involving a domain ontology and reasoning process to deliver improved quality data streams to applications. This will allow for early detection of missing data points and enhancing the accuracy of decisions and actions in such spaces.

Blurred vision images caused by rainy weather can negatively influence the performance of outdoor vision systems. Therefore, it is necessary to remove rain streaks from single image. In this work, a multiscale generative adversarial network- (GAN-) based model is presented, called DR-Net, for single image deraining. The proposed architecture includes two subnetworks, i.e., generator subnetwork and discriminator subnetwork. We introduce a multiscale generator subnetwork which contains two convolution branches with different kernel sizes, where the smaller one captures the local rain drops information, and the larger one pays close attention to the spatial information. The discriminator subnetwork acts as a supervision signal to promote the generator subnetwork to generate more quality derained image. It is demonstrated that the proposed method yields in relatively higher performance in comparison to other state-of-the-art deraining models in terms of derained image quality and computing efficiency.

Cloud service brokerage and related management and marketplace concepts have been identified as key concerns for future cloud technology development and research. Cloud service management is an important building block of cloud architectures that can be extended to act as a broker service layer between consumers and providers, and even to form marketplace services. We present a three-pronged classification and comparison framework for broker platforms and applications. A range of specific broker development concerns like architecture, programming and quality are investigated. Based on this framework, selected management, brokerage and marketplace solutions will be compared, not only to demonstrate the utility of the framework, but also to identify challenges and wider research objectives based on an identification of cloud broker architecture concerns and technical requirements for service brokerage solutions. We also discuss emerging cloud architecture concerns such as commoditisation and federation of integrated, vertical cloud stacks.

In today’s world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions. Cognitive IoE is a new state-of-art computing paradigm for interconnecting and controlling network objects in context-aware perception-action cycle for our cognitive needs. The interconnected objects (sensors, RFID, network objects etc.) behave as agents to learn, think and adapt situations according to dynamic contextual environment with no or minimum human intervention. One most important recent research problem is “how to recognize inhabitant activity patterns from the observed sensors data”. In this paper, we proposed a two level classification model named as ACM (Ambient Cognition Model) for inhabitant’s activities pattern recognition, using Hidden Markov Model based probabilistic model and subtractive clustering classification method. While subtractive clustering separates similar activity states from non-similar activity states, a HMM works as the top layer to train systems for temporal-sequential activities to learn and predict inhabitant activity pattern proactively. The proposed ACM framework plays a significant role to identify user activity intention in more proactive manner such as routine, location, social activity intentions in smart home scenario. The experimental results have been performed on Matlab simulation to evaluate the efficiency and accuracy of proposed ACM model.

in this paper, we proposed a new type of decisionmaking system to achieve the intelligent goal for automated smart environments.The artificial intelligence techniques, used as building blocks to understand inhabitant activity patterns.The collected information fused to a central inference engine based on Hidden Markov model and Fuzzy rules for taking appropriate actions to communicate and control various home appliances.We proposed a novel CASH (cognitive automated smart home) architecture, based on the Hidden Markov Model and the fuzzy rule based system.The Hidden Markov Model and fuzzy rules are well equipped to address the spatio-temporal activity pattern recognition problem and to trigger appropriate task execution rules.

Abstract With the resource‐constrained nature of mobile devices and the resource‐abundant offerings of the cloud, several promising optimisation techniques have been proposed by the green computing research community. Prominent techniques and unique methods have been developed to offload resource intensive tasks from mobile devices to the cloud. Although these schemes address similar questions within the same domain of mobile cloud application (MCA) optimisation, evaluation is tailored to the scheme and also solely mobile focused, thus making it difficult to clearly compare with other existing counterparts. In this work, we first analyse the existing/commonly adopted evaluation technique, then with the aim to fill the above gap, we propose the behaviour‐driven full‐tier green evaluation approach, which adopts the behaviour‐driven concept for evaluating MCA performance and energy usage—ie, green metrics. To automate the evaluation process, we also present and evaluate the effectiveness of a resultant application program interface and tool driven by the behaviour‐driven full‐tier green evaluation approach. The application program interface is based on Android and has been validated with Elastic Compute Cloud instance. Experiments show that Beftigre is capable of providing a more distinctive, comparable, and reliable green test results for MCAs.

Requirements ontology offers a mechanism to map requirements for cloud computing services to cloud computing resources. Multiple stakeholders can capture and map knowledge in a flexible and efficient manner. The major contribution of the paper is the definition and development of an ontology for cloud computing requirements. The approach views each user requirement as a semantic intelligence task that maps and delivers it as cloud services. Requirements are modelled as tasks designed to meet specific requirements, problem domains that the requirements exist in, and problem-solving methods which are generic mechanisms to solve problems. A meta-ontology for cloud computing is developed and populated with ontology fragments on to which cloud computing requirements can be mapped. A critical analysis of the usage of ontologies in the requirements process is made and a case study is described that demonstrates the approach in a real-world application. The conclusion is that problem-solving ontologies provide a useful mechanism for the specification and reuse of requirements in the cloud computing environment.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Abstract A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large‐scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial–temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel‐based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi‐objective algorithm called time and space optimized NSGA‐II (TS‐NSGA‐II). Experiment results have shown that compared with the original load‐balancing scheme, our approach can save approximate 47–55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6%improvement over the original scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Pricing Intelligence as a service (PINaaS) can be seen as the brokerage of user requirements to pricing of available resources in a cloud computing environment. Users of both public and private clouds have to consider the price of services they consume. Current cloud pricing approaches require the service consumer to calculate their own prices by interpreting high level requirements. This paper will examine issues seen in cloud service pricing and propose an ontological problem-solving approach which manages cloud pricing automatically at semantic level as "pricing intelligence". A case study of Amazon EC2 pricing has been carried out to provide a practical demonstration and validation of the approach.

Along with the green attentions and efforts on energy efficiency of computer hardware and embedded systems, software relevant sustainability issues are becoming increasingly focused, as a part of Green Information Technology (GIT). Undoubtedly, software evolution capability is a vital part in achieving such sustainability, as software that is not adaptable will become obsolete sooner or later. Internetware is currently one of the prevailing paradigms of software evolution, known as its autonomous, cooperative, situational, evolvable, emergent and trustworthy. In this paper, we propose an approach of evolution focusing on sustainability for Internetware software entities. Targeting certain software systems which cannot actively control their functions, service components or attached actuators energy-efficiently, the proposed transformation mechanism can intelligently implement power management adaptations using Web Ontology Language (OWL) -based user behavioral profiles and reasoning mechanisms. In the effort of increasing the sustainability, the transformation consequently enables a greener, predicted and rapidly adapted evolution.

In a pervasive environment, it is essential for computing applications to be context-aware. However, one of the major challenges is the establishment of a generic and dynamic context model. Many different approaches to modeling the context exist, but an application- and domain-agnostic context model, that captures various types of context information and the dependencies between them, that could be reused and shared by different applications, and that can be dynamically changed when a shift in focus occurs, is missing. Therefore, we are interested in defining a structure for the dynamic management of context information. This paper describes our notion of context and proposes a distributed context management architecture that supports the development of context-aware applications. It presents CANDEL, a generic context information representation framework that considers the context as a dynamic product line composed of context primitives (CPs). Frame based software product line techniques are used together with OWL ontology to define CPs and to dynamically generate the current context model. Further, using Petri-Nets, we also show how this framework will be used to support context-aware adaptive pervasive applications.

Context-awareness and adaptability are important and desirable properties of service-based processes designed to provide personalized services. Most of the existing approaches focus on the adaptation at the process instance level which involves extending the standard Business Process Execution Language (BPEL) and its engine or creating their own process languages (e.g. However, the approach proposed here aims to apply an adaptation to processes modeled or developed without any adaptation possibility in mind and independently of specific usage contexts. In addition, most of the existing approaches tackle the adaptation on the process instance or definition levels by explicitly specifying some form of variation points. This, however, leads to a contradiction between how the architect logically views and interprets differences in the process family and the actual modeling constructs through which the logical differences must be expressed. We introduce the notion of an evolution fragment and evolution primitive to capture the variability in a more logical and independent way. Finally, the proposed approach intends to support the viewpoint of context-aware adaptation as a crosscutting concern with respect to the core “business logic” of the process. In this way, the design of the process core can be decoupled from the design of the adaptation logic. To this end, we leverage ideas from the domain of model-driven development (MDD) and generative programming.

Context-aware computing is widely accepted as a promising paradigm to enable seamless computing. Several middlewares and ontology-based models for describing context information have been developed in order to support context-aware applications. However, the context variability, which refers to the possibility to infer or interpret different context information from different perspectives, has been neglected in the existing context modeling approaches. This paper presents an approach for context-aware software development based on a flexible product line based context model which significantly enhances reusability of context information by providing context variability constructs to satisfy different application needs.

The evolving concepts of mobile computing, context-awareness, and ambient intelligence are increasingly influencing user's experience of services. Therefore, the goal of this paper is to provide an overview of recent developments and implementations of middleware-based pervasive systems, and to explore major challenges of implementing such systems. This paper also provides a comprehensive access to the literature of the emerging approaches and design strategies of middleware for providing users with personalized services taking into consideration their preferences and the overall operating context. Middleware systems were categorized according to their internal coordination model.

Presents the introductory welcome message from the conference proceedings.

The quality of component-based systems highly depends on how effectively testing is carried out. To achieve the maximal testing effectiveness, this paper presents a product line based aspect oriented approach to unit testing. The aspect product line facilitates the automatic creation of aspect test cases that deal with specific quality requirements. An expandable repository of reusable aspect test cases has been developed. A prototype tool is built to verify and lever up the approach.

This discussion concerns 'metrics'. More specifically, we discuss quantitative metrics for evaluating Internet applications: what should we quantify, monitor and analyse in order to characterise, evaluate and develop Internet applications based on reusing existing Internet applications,which is widely available in the Internet. Due to the distinctive evolution nature of Internet applications, assessing quality of software will provide ease and higher accuracy for Web developers. However, there is a great gap between the rapid development of Internet applications and the slow speed of developing corresponding metric measures. To tackle this issue, we look into measuring the quality of Internet applications and enable Web developers to enhance the quality of their programs and identify reusable components from Internet-based resources.

Mismatches between pre-qualified existing components and the particular reuse context in applications are often inevitable and have been a major hurdle of component reusability and smooth composition. Although component adaptation has acted as a key solution of eliminating these mismatches, existing practices are either only capable for adaptation at a rather simple level, or requires too much intervention from software engineers. This paper presents a highly automated approach to component adaptation at adequately deep level. Its aspect-oriented nature makes the approach particularly suitable for constructing highly dependable component-based software. The adaptability and automation is achieved in an aspect-oriented component adaptation framework by generating and then applying the adaptation aspects under designed weaving process according to specific adaptation requirements. An expandable library of reusable adaptation aspects at multiple abstraction levels has been developed. A prototype tool is developed to scale up the approach