Cheng Xu, Changhong Jin, Yingjie Niu, Nan Yan, Yuke Mei, Shuhao Guan, Liming Chen, Tahar Kechadi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Log anomaly detection (LAD) is of paramount importance to enhance the reliability and stability of software systems. Current state-of-the-art LAD suffers a significant performance degradation when dealing with consistently evolving log events caused by system updates. To build a reliable LAD model under the context of log data evolution, we propose an incremental learning-based method for LAD, namely ilLog, to avoid catastrophic forgetting of previously learned knowledge while continuously updating the model for better detection when processing the evolving log events. In particular, we design a novel entropy-driven sorting algorithm for real log sample replay, which enables the preservation of old knowledge via storing representative samples with discrete sequence features from previous tasks. Additionally, we introduce a Halton-based low discrepancy sequence to better approximate the sliced Cram´ er distance between the probability distributions of two models, thus enhancing the model learning capability. Based on a standard incremental learning protocol setting, we evaluate the newly proposed ilLog method on three publicly available datasets. Experimental results demonstrate that our approach achieves the best performance compared to SOTA LAD methods and models by applying existing IL-based methods in evolving software systems.

Wireless Rechargeable Sensor Networks (WRSNs) promise to address the limited energy resource issue for sensor nodes through wireless power transfer technology. However, WRSNs are vulnerable to various security threats, such as compromised node attack and malicious mobile charger (MC) attack, which can disrupt the charging process and degrade charging efficiency. In this work, we investigate the eneRgy conversionEfficiency maximization problem unDer chargIng attackS(REDIS). We propose a blockchain-based framework that employs a lightweight multi-layer storage approach tailored for resource-constrained sensor nodes and features consensus algorithms that validate charging transactions. Furthermore, we introduce a validation node selection strategy that integrates consensus execution with charging scheduling, reducing energy consumption, and improving energy efficiency. Extensive simulations and experiments validate the effectiveness of our framework, improving energy efficiency by 30% and as much as 5 times in networks without attacks and those under full attacks, respectively.

Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep learning models, such as convolutional neural networks, RNNs, and transformers, their extensive parameters often pose significant computational and memory constraints, rendering them less suitable for resource-constrained mobile health applications. This study introduces HARMamba, an innovative lightweight and versatile HAR architecture that combines selective bidirectional state-space model and hardware-aware design. To optimize real-time resource consumption in practical scenarios, HARMamba employs linear recursive mechanisms and parameter discretization, allowing it to selectively focus on relevant input sequences while efficiently fusing scan and recompute operations. The model employs independent channels to process sensor data streams, dividing each channel into patches and appending classification tokens to the end of the sequence. It utilizes position embedding to represent the sequence order. The patch sequence is subsequently processed by HARMamba Block, and the classification head finally outputs the activity category. The HARMamba Block serves as the fundamental component of the HARMamba architecture, enabling the effective capture of more discriminative activity sequence features. HARMamba outperforms contemporary state-of-the-art frameworks, delivering comparable or better accuracy with significantly reducing computational and memory demands. Its effectiveness has been extensively validated on four publicly available data sets, namely, PAMAP2, WISDM, UNIMIB SHAR, and UCI. The F1 scores of HARMamba on the four data sets are 99.74%, 99.20%, 88.23%, and 97.01%, respectively.

Traditional deep learning methods struggle to simultaneously segment, recognize, and forecast human activities from sensor data. This limits their usefulness in many fields, such as healthcare and assisted living, where real-time understanding of ongoing and upcoming activities is crucial. This article introduces P2LHAP, a novel Patch-to-Label Seq2Seq framework that tackles all three tasks in an efficient single-task model. P2LHAP divides sensor data streams into a sequence of “patches,” served as input tokens, and outputs a sequence of patch-level activity labels, including the predicted future activities. A unique smoothing technique based on surrounding patch labels, is proposed to identify activity boundaries accurately. Additionally, P2LHAP learns patch-level representation by sensor signal channel-independent Transformer encoders and decoders. All channels share embedding and Transformer weights across all sequences. Evaluated on the three public datasets, P2LHAP significantly outperforms the state-of-the-art in all three tasks, demonstrating its effectiveness and potential for real-world applications.

Log anomaly detection (LAD) is essential to ensure the safe and stable operation of Cyeber-physical systems. Although current LAD methods exhibit significant potential in addressing challenges posed by unstable log events and temporal sequence patterns, their limitations in detection efficiency and generalization ability present a formidable challenge when dealing with evolving systems. To construct a real-time and reliable online log anomaly detection model, we propose OMLog, a semi-supervised online meta-learning method, to effectively tackle the distribution shift issue caused by changes in log event types and frequencies. Specifically, we introduce a maximum mean discrepancy-based distribution shift detection method to identify distribution changes in unseen log sequences. Depending on the identified distribution gap, the method can automatically trigger online fine-grained detection or offline fast inference. Furthermore, we design an online learning mechanism based on meta-learning, which can effectively learn the highly repetitive patterns of log sequences in the feature space, thereby enhancing the generalization ability of the model to evolving data. Extensive experiments conducted on two publicly available log datasets, HDFS and BGL, validate the effectiveness of the OMLog approach. When trained using only normal log sequences, the proposed approach achieves the F1-Score of 93.7% and 64.9%, respectively, surpassing the performance of the state-of-the-art (SOTA) LAD methods and demonstrating superior detection efficiency.

Abstract With the rapid proliferation of the Internet of Things (IoT), robust trust management has become imperative to ensure security in these IoT systems. Prior machine learning approaches to IoT trust management have exhibited suboptimal performance, failing to capture the dynamic behaviour and complex discriminative features of IoT devices. To address these challenges, we design an evocative trust management scheme for user’s authentication based on Dempster–Shafer’s evidence theory, which can persuade the normal activities of IoT device systems. We establish a set of discriminating features to predict the trustworthiness of a network node by assessing its observed behaviours. These behaviours being assessed encompass several characteristics such as throughput, delay, jitter and network latency. As such, nodes that demonstrate elevated data transmission rates and have anomalous traffic patterns could potentially be categorised as untrustworthy. In addition, the existence of persistently high latency will impede trust prediction algorithms, and this will consequently alter the overall behaviour of the node, ultimately affecting the trustworthiness of the entire network. We also design a framework for the fusion of evidence based on the belief degree and reputation-based evidence to avoid misclassification resulting in evidence conflicting. The resultant fusion outcomes are transformed into category labels which serve as the prediction outcome of the multi-classifier ensemble scheme. We evaluate our proposed scheme with and without the best discriminative features on performance metrics including accuracy, precision, recall, F1-score, detection rate and false alarm rate. Comprehensive experiments on a transformed UNSW-NB15 dataset demonstrate the better performance of our proposed framework, especially in the application of evidence conflicting.

Hybrid human–artificial intelligence (H-AI) in the metaverse is facing a growing trend in theoretical research and practical applications. Based on 101 academic papers published from 1996–2024, this article identifies research themes using thematic analysis, technological analysis, comparative analysis, and knowledge integration. The results show that academic interest and attention to H-AI have gradually increased since 2020. Through the results of thematic analysis, this article synthesizes five applications of H-AI domains: industry field, medical field, entertainment field, transportation field, and other fields. By analyzing the impact of H-AI on the integration of the virtual and real world, the role of H-AI in both the virtual and real worlds and the improvements over artificial intelligence will be outlined. This article also identifies challenges and responses that need further attention: disputes over responsibility ownership, bubble issues, lack of trust, and high-cost issues. This article looks ahead to add new skills to the metaverse platform, drive the metaverse as the next wave of the digital economy, promote the new field of metaverse combined with traditional, increase metaverse assistance to people with disabilities, and promote the systematization of the metaverse. This article helps enhance researchers' and practitioners' understanding of H-AI in the metaverse while raising awareness about the current research frontiers and potential future directions.

Log-based anomaly detection (LAD) is one of the dominant approaches to improving the reliability and security of software systems. Presently, despite the efficacy demonstrated by state-of-the-art LAD approaches in processing static log events, their performance significantly degrades when confronting changes of log event types from system updates. To construct a reliable LAD model that could adapt well to the evolution of log data, we propose a method grounded in semi-supervised domain adaptation on the rationale of incremental log anomaly detection dubbed as SSDALog, which dynamically updates the model utilizing limited labeled samples to reconcile distributional shifts between evolving and historical data. Specifically, the proposed approach addresses the issue through two primary mechanisms: (i) creation of a cross-domain mixup algorithm, which computes the feature salience of log discrete sequences through occlusion strategy, thus enhancing the adaptability of the model to unknown patterns by mixing evolving features; and (ii) design of an incremental semi-supervised domain adaptation training framework based on noisy label learning to obtain a robust feature extractor, thus improving the generalization ability of the detection model. We empirically assess the efficacy of the SSDALog approach across two publicly available datasets. The experimental results show that our method outperforms the SOTA LAD approach, particularly for evolving systems.

Time series anomaly detection is crucial for improving the security and reliability of Cyber-Physical systems (CPS). While significant progress has been made, existing methods struggle to learn discriminative representations from multivariate time series with complex interactions and noise. To address this challenge, we propose a semi-supervised anomaly detection method based on denoising-aware contrastive learning, namely SSDCL, which can achieve robust performance for CPS anomaly detection using limited supervision. Specifically, we first design a similarity combination data augmentation algorithm to handle complex interactions among continuous sensor measurements and discrete actuator states. Furthermore, we develop a denoising hierarchical contrastive loss function that mitigates data noise interference while ensuring discriminative spatio-temporal representation. To validate the effectiveness of SSDCL, we conducted empirical evaluations on three publicly available CPS time series datasets including PUMP, SWaT and WADI. The experimental results show that the proposed method achieves F1 Score of 97.5%, 93.0%, and 74.4%, respectively, outperforming the state-of-the-art (SOTA) CPS anomaly detection methods.

The massive generation of time-series data by large-scale Internet of Things (IoT) devices necessitates the exploration of more effective models for multivariate time-series forecasting. In previous models, there was a predominant use of the channel dependence (CD) strategy (where each channel represents a univariate sequence). Current state-of-the-art (SOTA) models primarily rely on the channel independence (CI) strategy. The CI strategy treats channel multichannel series as separate single-channel series, expanding the data set to improve generalization performance and avoiding interchannel correlation that disrupts long-term features. However, the CI strategy faces the challenge of interchannel correlation forgetting. To address this issue, we propose an innovative Mixed Channels strategy, combining the data expansion advantages of the CI strategy with the ability to mitigate interchannel correlation forgetting. Based on this strategy, we introduce MCformer, a multivariate time-series forecasting model with mixed channel features. The model blends a specific number of channels, leveraging an attention mechanism to effectively capture interchannel correlation information when modeling long-term features. Experimental results demonstrate that the Mixed Channels strategy outperforms pure CI strategy in multivariate time-series forecasting tasks.

Using portable tools to monitor and identify daily activities has increasingly become a focus of digital healthcare, especially for elderly care. One of the difficulties in this area is the excessive reliance on labeled activity data for corresponding recognition modeling. Labeled activity data is expensive to collect. To address this challenge, we propose an effective and robust semi-supervised active learning method, which combines the mainstream semi-supervised learning method with expert collaboration. Our method takes a user's trajectory as the only input. In addition, this novel method uses expert collaboration to judge the valuable samples further to enhance its performance. Our method relies on very few semantic activities, outperforms all baseline activity recognition methods, and is close to the performance of supervised learning methods. On the adlnormal dataset with 200 semantic activities data, our work achieved an accuracy of 89.07%, and supervised learning has 91.77%. Our ablation study validated the components in our method using a query strategy and a data fusion approach.

Abstract Motivation to engage in learning is essential for learning performance. Learners’ motivation is traditionally assessed using self-reported data, which is time-consuming, subjective, and interruptive to their learning process. To address this issue, this paper proposes a novel framework for multimodal assessment of learners’ motivation in e-learning environments with the ultimate purpose of supporting intelligent e-learning systems to facilitate dynamic, context-aware, and personalized services or interventions, thus sustaining learners’ motivation for learning engagement. We investigated the performance of the machine learning classifier and the most and least accurately predicted motivational factors. We also assessed the contribution of different electroencephalogram (EEG) and eye gaze features to motivation assessment. The applicability of the framework was evaluated in an empirical study in which we combined eye tracking and EEG sensors to produce a multimodal dataset. The dataset was then processed and used to develop a machine learning classifier for motivation assessment by predicting the levels of a range of motivational factors, which represented the multiple dimensions of motivation. We also proposed a novel approach to feature selection combining data-driven and knowledge-driven methods to train the machine learning classifier for motivation assessment, which has been proved effective in our empirical study at selecting predictors from a large number of extracted features from EEG and eye tracking data. Our study has revealed valuable insights for the role played by brain activities and eye movements on predicting the levels of different motivational factors. Initial results using logistic regression classifier have achieved significant predictive power for all the motivational factors studied, with accuracy of between 68.1% and 92.8%. The present work has demonstrated the applicability of the proposed framework for multimodal motivation assessment which will inspire future research towards motivationally intelligent e-learning systems.

A trust management system (TMS) is an integral component of any Internet of Things (IoT) network. A reliable TMS must guarantee the network security, data integrity, and act as a referee that promotes legitimate devices, and punishes any malicious activities. Trust scores assigned by TMSs reflect devices’ reputations, which can help predict the future behaviors of network entities and subsequently judge the reliability of different entities in the IoT networks. Many TMSs have been proposed in the literature, these systems are designed for small-scale trust attacks and can deal with attacks where a malicious device tries to undermine TMS by spreading fake trust reports. However, these systems are prone to large-scale trust attacks. To address this problem, in this article, we propose a TMS for large-scale IoT systems called Trust2Vec, which can manage trust relationships in large-scale IoT systems and can mitigate large-scale trust attacks that are performed by hundreds of malicious devices. Trust2Vec leverages a random-walk network exploration algorithm that navigates the trust relationship among devices and computes trust network embeddings, which enables it to analyze the latent network structure of trust relationships, even if there is no direct trust rating between two malicious devices. To detect large-scale attacks, such as self-promoting and bad-mouthing, we propose a network embeddings community detection algorithm that detects and blocks communities of malicious nodes. The effectiveness of Trust2Vec is validated through large-scale IoT network simulation. The results show that Trust2Vec can achieve up to 94% mitigation rate in various network settings.

Contrastive learning is an emerging and important self-supervised learning paradigm that has been successfully applied to sensor-based human activity recognition (HAR) because it can achieve competitive performance relative to supervised learning. Contrastive learning methods generally involve instance discrimination, which means that the instances are regarded as negatives of each other, and thus their representations are pulled away from each other during the training process. However, instance discrimination could cause overclustering, meaning that the representations of instances from the same class could be overly separated. To alleviate this overclustering phenomenon, we propose a new contrastive learning framework to select negatives by clustering in HAR, which is named clustering for contrastive learning in human activity recognition (ClusterCLHAR). First, ClusterCLHAR clusters the instance representations, and for each instance, only those from different clusters are regarded as negatives. Second, a new contrastive loss function is proposed to mask the same-cluster instances from the negative pairs. We evaluate ClusterCLHAR on three popular benchmark data sets: 1) USC-HAD; 2) MotionSense; and 3) UCI-HAR, using the mean F1-score as an evaluation metric for downstream tasks. The experimental results show that ClusterCLHAR outperforms all the state-of-the-art methods applied to HAR in self-supervised learning and semi-supervised learning.

Abstract Convolutional- and Transformer-based backbone architecture are two dominant, widely accepted, models in computer vision. Nevertheless, it is still a challenge, thus a focus of research, to decide which backbone architecture performs better, and under which circumstances. In this paper, we conduct an in-depth investigation into the differences of the macroscopic backbone design of the CNN and Transformer models with the ultimate purpose of developing new models to combine the strengths of both types of architectures for effective image classification. Specifically, we first analyze the model structures of both models and identified four main differences, then we design four sets of ablation experiments using the ImageNet-1K dataset with an image classification problem as an example to study the impacts of these four differences on model performance. Based on the experimental results, we derive four observations as rules of thumb for designing a vision model backbone architecture. Informed by the experiment findings, we then conceive a novel model called CMNet which marries the experiment-proved best design practices of CNN and Transformer architectures. Finally, we carry out extensive experiments on CMNet using the same dataset against baseline classifiers. Initial results prove CMNet achieves the highest top-1 accuracy of 80.08% on the ImageNet-1K validation set, this is a very competitive value compared to previous classical models with similar computational complexity. Details of the implementation, algorithms and codes, are publicly available on Github: https://github.com/Arwin-Yu/CMNet .

It is commonly agreed that a recommender system should use not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., incidental information) to deal with the problem of data sparsity and cold start. The knowledge graph (KG), due to its expressive structural and semantic representation capabilities, has been increasingly used for capturing auxiliary information for recommender systems, such as the recent development of graph neural network (GNN) based models for KG-aware recommendation. Nevertheless, these models have the shortcoming of insufficient node interactions or improper node weights during information propagation, which limits the performance of recommender systems. To address this issue, we propose a Multi-dimension Interaction based attentional Knowledge Graph Neural Network (MI-KGNN) for enhanced KG-aware recommendation. MI-KGNN characterizes similarities between users and items through information propagation and aggregation in knowledge graphs. As such, it can optimize the updating direction of node representation by fully exploring multi-dimension interactions among nodes during information propagation. In addition, MI-KGNN introduces a dual attention mechanism, which allows users and items to jointly determine the weight of neighbor nodes. As a result, MI-KGNN can effectively capture and represent both structural (i.e., the topology of interactions) and semantic information (i.e., the weight of interactions) in the knowledge graph. Experimental results show that the proposed model significantly outperforms baseline methods for top-K recommendation. Specifically, the recall rate is increased by 5.78%, 6.66%, and 3.22% on three public datasets, compared with the best performance of existing methods.

Vehicular social networking is an emerging application of the Internet of Vehicles (IoV) which aims to achieve seamless integration of vehicular networks and social networks. However, the unique characteristics of vehicular networks, such as high mobility and frequent communication interruptions, make content delivery to end-users under strict delay constraints extremely challenging. In this paper, we propose a social-aware vehicular edge computing architecture that solves the content delivery problem by using some vehicles in the network as edge servers that can store and stream popular content to close-by end-users. The proposed architecture includes three main components: 1) the proposed social-aware graph pruning search algorithm computes and assigns the vehicles to the shortest path with the most relevant vehicular content providers. 2) the proposed traffic-aware content recommendation scheme recommends relevant content according to its social context. This scheme uses graph embeddings in which the vehicles are represented by a set of low-dimension vectors (vehicle2vec) to store information about previously consumed content. Finally, we propose a deep reinforcement learning (DRL) method to optimise the content provider vehicle distribution across the network. The results obtained from a real-world traffic simulation show the effectiveness and robustness of the proposed system when compared to the state-of-the-art baselines.

Research on human mobility drives the development of economy and society. How to predict when and where one will go accurately is one of the core research questions. Existing work is mainly concerned with performance of mobility prediction models. Since accuracy of predict models does not indicate whether or not one’s mobility is inherently easy to predict, there has not been a definite conclusion about that to what extent can our predictions of human mobility be accurate. To help solve this problem, we describe the formalized definition of predictability of human mobility, propose a model based on additive Markov chain to measure the probability of exploration, and further develop an information theory based method for quantifying the predictability considering exploration of human mobility. Then, we extend our method by using mutual information in order to measure the predictability considering external influencing factors, which has not been studied before. Experiments on simulation data and three real-world datasets show that our method yields a tighter upper bound on predictability of human mobility than previous work, and that predictability increased slightly when considering external factors such as weather and temperature.

With the convergence of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been extended to new domains. In particular, social computing was significantly influenced by the recent advances of artificial intelligence (AI). However, the conventional technologies of AI have various drawbacks in dealing with complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI), which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows some advantages over the conventional AI. This article firstly reviews the latest research progresses of AI in social computing. Secondly, it summarizes typical challenges AI faces in social computing, which motivate the necessity to introduce H-AI to tackle social-oriented problems. Finally, we discuss the concept of H-AI and propose a holistic architecture of H-AI in social computing, which consists of three layers: object layer, intelligent processing layer, and application layer. The proposed architecture shows that H-AI has significant advantages over AI in solving social problems.

Network traffic prediction is essential for network management and resource scheduling within Web information systems. However, existing prediction methods have difficulty fitting mutation values in traffic time-series data and are still inadequate in terms of precision. Here we describe a method for prediction using multimodal web traffic data. The method creates multi-dimensional time series on request traffic, response traffic, and abnormal code traffic, and uses the rich information contained in the different sequences in the preceding time window to make inferences about the traffic scale in subsequent time windows. In addition, we propose an improved algorithm based on the Gated Recurrent Unit (GRU) to reduce the prediction error. The algorithm introduces the residual structure into a stacked multi-layer recurrent network structure and uses the Mogrifier structure to interact the information before it is fed to the gating unit. The experimental results show that the improved method leads to a further reduction in the error between the predicted and true values, providing high usability in the field of network traffic prediction.

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

With the development of the cloud-based Internet of Things (IoT), people and things can request services, access data, or control actuators located thousands of miles away. The entity authentication of the remotely accessed devices is an essential part of the security systems. In this vein, physical unclonable functions (PUFs) are a hot research topic, especially for generating random, stable, and tamper-resistant fingerprints. This article proposes a lightweight, robust static random access memory (SRAM)-PUF-based entity authentication scheme to guarantee that the accessed end devices are trustable. The proposed scheme uses challenge-response pairs (CRPs) represented by reordered memory addresses as challenges and the corresponding SRAM cells' startup values as responses. The experimental results show that our scheme can efficiently authenticate resources-constrained IoT devices with a low computation overhead and small memory capacity. Furthermore, we analyze the SRAM-PUF by testing the PUF output under different environmental conditions, including temperature and magnetic field, in addition to exploring the effect of writing different values to the SRAM cells on the stability of their startup values.

Gesture recognition is key to enabling natural human-computer interactions. Existing approaches based on wireless sensing focus on accurate identification of arm gesture types. It remains a challenge to recognize and profile the details of arm gestures for precise interaction control. In addition, current approaches have strict positioning requirements between radars and users, making them difficult for real-world deployment. In this article, we adopt the multisensor approach and present gesture-radar-a dual Doppler radar-based gesture recognition and profiling system, which can capture subtle arm gestures with less positioning or environmental dependence. Gesture-radar uses two vertically placed Doppler radars to collect complementary sensing data of gestures, based on which cross-analysis can be performed for gesture recognition and profiling. Specifically, we first propose a two-stage classification model and enhance the signal proximity matching method by applying constraint functions to the DTW algorithm, aiming to improve the accuracy of gesture type recognition. Afterward, we establish and analyze unique features from the time-frequency spectrogram, which can be used to characterize in-depth gesture details, e.g., the angle or range of an arm movement. Experimental results show that gesture-radar achieves up to 93.5% average accuracy for gesture type recognition, and over 80% precision for profiling gesture details. This proves that the proposed approach is viable and can work in real-world environments.

Abstract The use of Convolutional Neural Networks (CNNs) as a feature learning method for Human Activity Recognition (HAR) is becoming more and more common. Unlike conventional machine learning methods, which require domain-specific expertise, CNNs can extract features automatically. On the other hand, CNNs require a training phase, making them prone to the cold-start problem. In this work, a case study is presented where the use of a pre-trained CNN feature extractor is evaluated under realistic conditions. The case study consists of two main steps: (1) different topologies and parameters are assessed to identify the best candidate models for HAR, thus obtaining a pre-trained CNN model. The pre-trained model (2) is then employed as feature extractor evaluating its use with a large scale real-world dataset. Two CNN applications were considered: Inertial Measurement Unit (IMU) and audio based HAR. For the IMU data, balanced accuracy was 91.98% on the UCI-HAR dataset, and 67.51% on the real-world Extrasensory dataset. For the audio data, the balanced accuracy was 92.30% on the DCASE 2017 dataset, and 35.24% on the Extrasensory dataset.

It is still a challenge to detect anomalous events in video sequences in the field of computer vision due to heavy object occlusions, varying crowded densities and complex situations. To address this, we propose a novel human-machine cooperative approach which uses human feedback on anomaly confirmation to inform and enhance video anomaly detection. Specifically, we analyze the spatio-temporal characteristics of sequential frames of a video from the appearance and motion perspective from which spatial and temporal features are identified and extracted. We then develop a convolutional autoencoder neural network to compute an abnormal score based on reconstruction errors. In this process, a group of experts will provide human feedback to a certain proportion of classified frames to be incorporated into the model, and also the final judgment for the event anomalies for training and classification. The proposed approach is evaluated on 3 publicly available surveillance datasets, showing improved accuracy and competitive performance (93.7% AUC) with respect to the best performance (90.6% AUC) of the state-of-the-art approaches. The approach has not been previously seen to the best of our knowledge.

E-Learning systems can support real-time monitoring of learners’ learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users’ motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students’ motivation. Existing research has rarely attempted to model dyslexic users’ motivation in e-learning context from a comprehensive perspective. The present work has conceived a hybrid approach, namely, combining the strengths of qualitative and quantitative analysis methods, to motivation modeling. It examines a variety of factors that affect dyslexic students’ motivation to engage in e-learning systems from psychological, behavioral, and technical perspectives, and establishes their interrelationships. Specifically, the study collects data from a multi-item Likert-style questionnaire to measure relevant factors for conceptual motivation modeling. It then applies both covariance-based (CB-SEM) and variance-based structural equation modeling (PLS-SEM) approaches to determine the quantitative mapping between dyslexic students’ continued use intention and motivational factors, followed by discussions about theoretical findings and design instructions according to our motivation model. Our research has led to a novel motivation model with new constructs of Learning Experience, Reading Experience, Perceived Control, and Perceived Privacy. From both the CB-SEM and PLS-SEM analyses, results on the total effects have indicated consistently that Visual Attractiveness, Reading Experience, and Feedback have the strongest effects on continued use intention.

Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise. We propose a new approach to SAD where we treat it as a two-dimensional multilabel image classification problem. To classify the audio segments, we compute their Short-time Fourier Transform spectrograms and classify them with a Convolutional Recurrent Neural Network (CRNN), traditionally used in image recognition. Our CRNN uses a sigmoid activation function, max-pooling in the frequency domain, and a convolutional operation as a moving average filter to remove misclassified spikes. On the development set of Task 1 of the 2019 Fearless Steps Challenge, our system achieved a decision cost function (DCF) of 2.89%, a 66.4% improvement over the baseline. Moreover, it achieved a DCF score of 3.318% on the evaluation dataset of the challenge, ranking first among all submissions.

The Internet of Things (IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking, we propose the concept of software-defined device (SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open IoT system architecture which decouples upper-level applications from the underlying physical devices (Physical-D) through the SDD mechanism. A logically centralized controller is designed to conveniently manage Physical-D and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining, and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared Physical-D.

User behaviors in online social networks convey not only literal information but also one’s emotional attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this article, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model (MERM) to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor, and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%.

Sleep positions have an impact on sleep quality and therefore need to be further analyzed. Current research on position tracking includes only the four basic positions. In the context of wearable devices, energy efficiency is still an open issue. This research presents a way to detect eight positions with higher granularity under energy efficient constraints. Generalized Matrix Learning Vector Quantization is used, as it is a fast and appropriate method for environments with limited computation resources, and has not been seen for this kind of application before. The overall model trained on individuals performs with an averaged accuracy of 99.8%, in contrast to an averaged accuracy of 83.62% for grouped datasets. Real world application gives an accuracy of around 98%. The results show that energy efficiency will be feasible, as performance stays similar for lower sampling rate. This is a step towards a mobile solution which gives more insight in person's sleep behaviour.

Ambient Assisted Living (AAL) technologies create intelligent systems to assist the aging population for a healthier and safer life in their living environment. Such systems usually offer context-aware, personalized and adaptive services. However, these kinds of systems make extensive and intensive use of personal data, which makes privacy protection a critical issue. In this paper, we propose a framework for privacy modeling computation and management for AAL within Smart Homes. We analyze the privacy features in the smart home that affect the privacy of the users. Based on these features a metric is developed to compute the sensitivity of the collected information and consequently the potential privacy risk. A simple implementation of the proposed framework is then applied to a real world smart home living environment at Great Northern Haven, in which data were collected and the framework was evaluated. This study offers an effective and practical approach to evaluate the privacy risk of users and proposes a metric that can be used for access control and recommendation of privacy settings to the users of the AAL environments.

Mobile crowd photographing (MCP) is an emerging area of interest for researchers as the built-in cameras of mobile devices are becoming one of the commonly used visual logging approaches in our daily lives. In order to meet diverse MCP application requirements and constraints of sensing targets, a multifacet task model should be defined for a generic MCP data collection framework. Furthermore, MCP collects pictures in a distributed way in which a large number of contributors upload pictures whenever and wherever it is suitable. This inevitably leads to evolving picture streams. This paper investigates the multiconstraint-driven data selection problem in MCP picture aggregation and proposes a pyramid-tree (PTree) model which can efficiently select an optimal subset from the evolving picture streams based on varied coverage needs of MCP tasks. By utilizing the PTree model in a generic MCP data collection framework, which is called CrowdPic, we test and evaluate the effectiveness, efficiency, and flexibility of the proposed framework through crowdsourcing-based and simulation-based experiments. Both the theoretical analysis and simulation results indicate that the PTree-based framework can effectively select a subset with high utility coverage and low redundancy ratio from the streaming data. The overall framework is also proved flexible and applicable to a wide range of MCP task scenarios.

The papers in this special section focus on cyber-physical man-machine systems with particular emphasis on situation, activity, and goal awareness deployed in these systems. Recent advances in sensing technologies, the Internet of Things, pervasive computing, smart environments have transformed traditional embedded ICT systems into an ecosystem of interconnected and collaborating smart objects, devices, embedded systems, and most importantly humans. Such systems, often referred to as cyber-physical systems (CPS), are usually human user-driven or user-centered, and are aimed at providing people and businesses with a wide range of innovative applications and services. For example, a “smart home” can monitor and analyze the daily activities of its inhabitants, usually the elderly or individuals with disabilities, so that personalized context-aware assistance can be provided. A “smart city” can monitor, manage, and potentially control all basic city functionalities such as transport, energy supply, and waste collection, at a higher level of automation by collecting and harnessing sensor data across a large geographic expanse. In order to respond in real-time to an individual user’s specific needs in dynamic and complex situations, and to support ergonomics and user-friendliness through consideration of human factors such as privacy, dignity, and behavior characteristics, cyber-physical human–machine systems need to be aware of the physical environment and human participant behavior. This awareness enables effective and fast feedback loops between sensing and actuation, possibly with cognitive and learning capabilities adapting to participant preferences, capabilities, and the modality of human–machine interaction as well as dynamic situations.

The global population is aging; projections show that by 2050, more than 20% of the population will be aged over 64. This will lead to an increase in aging related illness, a decrease in informal support, and ultimately issues with providing care for these individuals. Assistive smart homes provide a promising solution to some of these issues. Nevertheless, they currently have issues hindering their adoption. To help address some of these issues, this study introduces a novel approach to implementing assistive smart homes. The devised approach is based upon an intention recognition mechanism incorporated into an intelligent agent architecture. This approach is detailed and evaluated. Evaluation was performed across three scenarios. Scenario 1 involved a web interface, focusing on testing the intention recognition mechanism. Scenarios 2 and 3 involved retrofitting a home with sensors and providing assistance with activities over a period of 3 months. The average accuracy for these three scenarios was 100%, 64.4%, and 83.3%, respectively. Future will extend and further evaluate this approach by implementing advanced sensor-filtering rules and evaluating more complex activities.

This paper presents a group-aware mobile crowd sensing system called MobiGroup, which supports group activity organization in real-world settings. Acknowledging the complexity and diversity of group activities, this paper introduces a formal concept model to characterize group activities and classifies them into four organizational stages. We then present an intelligent approach to support group activity preparation, including a heuristic rule-based mechanism for advertising public activity and a context-based method for private group formation. In addition, we leverage features extracted from both online and offline communities to recommend ongoing events to attendees with different needs. Compared with the baseline method, people preferred public activities suggested by our heuristic rule-based method. Using a dataset collected from 45 participants, we found that the context-based approach for private group formation can attain a precision and recall of over 80%, and the usage of spatial-temporal contexts and group computing can have more than a 30% performance improvement over considering the interaction frequency between a user and related groups. A case study revealed that, by extracting the features such as dynamic intimacy and static intimacy, our cross-community approach for ongoing event recommendation can meet different user needs.

Micro-blog has been increasingly used for the public to express their opinions, and for organizations to detect public sentiment about social events or public policies. In this article, we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, multi-media elements, and hierarchical structure of Chinese “Weibo.” Based on the analysis, we propose a novel approach and develop associated theoretical and technological methods to address these problems. These include a new sentiment word mining method based on three wording metrics and point-wise information, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements as auxiliary affective factors. We evaluate our new word discovery and sentiment detection methods on a real-life Chinese micro-blog dataset. Initial results show that our new diction can improve sentiment detection, and they demonstrate that our multi-level rule set method is more effective, with the average accuracy being 10.2% and 1.5% higher than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualization techniques to study the relationships between online sentiment and real life. The visualization of detected sentiment can help depict temporal patterns and spatial discrepancy.

In today's world of internet connected devices and smart phones, it has become effortless to create and consume vast amounts of information. This is particularly the case with photographs, with vast amounts being created and shared online every day. Never-the-less, it still remains a challenge to discover the "right" information for the appropriate purpose. This paper describes and discusses the testing and evaluation of the MediaPlace system with the well-known dataset YFCC-100M, which contains 48 million geospatial geotagged photographs, from Flickr produced by Yahoo. MediaPlace is a system which we have developed to automatically enrich geotagged photographs with semantic geospatial information derived from several online geospatial datasets.

Micro-blog has been increasingly used for the public to express their opinions, and for organisations to detect public sentiment about social events. In contrast to the effort and progress made in English-based micro-blog analysis, research on Chinese micro-blog received relatively little attention. In this paper we examine and identify the key problems of this field, focusing particularly on the characteristics of innovative words, emoticon elements and hierarchical structure of Chinese “Weibo”. Based on the analysis we propose and develop associated theoretical and technological methods to address these problems. These include the development of new sentiment word mining method based on three wording standards and point-wise metrics, a rule set model for analyzing sentiment features of different linguistic components, and the corresponding methodology for calculating sentiment on multi-granularity considering emoticon elements. We use original Chinese tweets from a dataset of Sina Weibo to test and evaluate our new word discovery and sentiment detection methods. Initial results show that our new diction can improve sentiment detection, and demonstrate that our multi-level rule set method is more effective by giving 10.2% and 1.5% higher average accuracy than two existing methods for Chinese micro-blog sentiment analysis. In addition, we exploit visualisation techniques to study the relationships between online sentiment and real life, which can help depict the correlation between public emotions and events.

Activity models play a critical role for activity recognition and assistance in ambient assisted living. Existing approaches to activity modeling suffer from a number of problems, e.g., cold-start, model reusability, and incompleteness. In an effort to address these problems, we introduce an ontology-based hybrid approach to activity modeling that combines domain knowledge based model specification and data-driven model learning. Central to the approach is an iterative process that begins with “seed” activity models created by ontological engineering. The “seed” models are deployed, and subsequently evolved through incremental activity discovery and model update. While our previous work has detailed ontological activity modeling and activity recognition, this paper focuses on the systematic hybrid approach and associated methods and inference rules for learning new activities and user activity profiles. The approach has been implemented in a feature-rich assistive living system. Analysis of the experiments conducted has been undertaken in an effort to test and evaluate the activity learning algorithms and associated mechanisms.

This paper introduces an ontology-based integrated framework for activity modeling, activity recognition and activity model evolution. Central to the framework is ontological activity modeling and semantic-based activity recognition, which is supported by an iterative process that incrementally improves the completeness and accuracy of activity models. In addition, the paper presents a service-oriented architecture for the realization of the proposed framework which can provide activity context-aware services in a scalable distributed manner. The paper further describes and discusses the implementation and testing experience of the framework and services in the context of smart home based assistive living.

Improvements and portability of technologies and smart devices has enabled rapid growth in the amount of user generated media such as photographs and videos. Whilst various media generation and management systems exist it still remains a challenge to discover the "right" metadata information for the right purpose. This paper describes an approach to extract relevant geospatial information through reverse geocoding in addition to cross-referencing several public geospatial data sources. The extracted geospatial information can be used to enable enrichment of media with rich semantic geo-metadata and therefore enable improved organisation and searching of the media. Central to the system is the cross-referencing and data fusion of several public geospatial datasets to determine the most relevant Points of Interest and extract their relevant features. These relevant Points of Interest and features are subsequently used to annotate media with human readable information, leading to enriched media repositories. The system has been implemented as a client/server architecture, with a web interface for the client front end and Java for the backend server side processing. Details of the implementation are discussed. Testing in a scenario has been undertaken and a discussion of the testing technique and results is presented. The initial results show the system to be effective at fusing several public geospatial datasets and extracting relevant geo-metadata.

Ubiquitous computing aims to enable and support anywhere, anytime, context-aware applications. Sensing, interpretation and integration of events, behaviors and environmental states have been keys to the success of such ubiquitous systems. Over the past two decades, there has been a constant shift of sensor observation modeling, representation, interpretation and usage, namely from low-level raw observation data and their direct/hardwired usage, data aggregation and fusion, to high-level formal context modeling and context-based computing. It is envisioned that this trend will continue towards a further higher level of abstraction, allowing situation, activity and goal modeling, representation and inference, thus realizing the vision of ubiquitous computing. The proposed "mini-track" workshop intends to bring together researchers and practitioners from relevant fields to present and disseminate the latest accomplished and/or ongoing research on Situation, Activity and Situation Awareness (SAGAware) and their novel application in ubiquitous computing. It aims to facilitate knowledge transfer and synergy, bridge gaps between different research communities/groups, lay down foundation for common purposes, and help identify opportunities and challenges for interested researchers and technology and system developers.

Activity modelling is required to support activity recognition and further to provide activity assistance for users in smart homes. Current research in knowledge-driven activity modelling has mainly focused on single activities with little attention being paid to the modelling of composite activities such as interleaved and concurrent activities. This paper presents a hybrid approach to composite activity modelling by combining ontological and temporal knowledge modelling formalisms. Ontological modelling constructors, i.e. concepts and properties for describing composite activities, have been developed and temporal modelling operators have been introduced. As such, the resulting approach is able to model both static and dynamic characteristics of activities. Several composite activity models have been created based on the proposed approach. In addition, a set of inference rules has been provided for use in composite activity recognition. A concurrent meal preparation scenario is used to illustrate both the proposed approach and associated reasoning mechanisms for composite activity recognition.

Current clinical methods for the assessment of Parkinson's disease suffer from inconvenience, infrequency and subjectivity. WiiPD is an approach for the objective home based assessment of Parkinson's disease which utilizes the intuitive and sensor rich Nintendo Wii Remote. Combined with an electronic patient diary, a suite of mini-games, a metric analyzer, and a visualization engine, we propose that this system can complement existing clinical practice by providing objective metrics gathered frequently over extended periods of time. In this paper we detail the approach and introduce a series of metrics deemed capable of quantifying the severity of tremor and bradykinesia in those with Parkinson's disease. The system has been tested on a 71 year old participant with Parkinson's disease over a period of 15 days, a 72 year old control user without Parkinson's disease, and a group of 8 young adults. Results indicate a clear correlation between patient self rating scores of tremor severity and metric values obtained, in addition to clear differences in metrics obtained from each user group. These results suggest that this approach is capable of indicating the presence and severity of the motor symptoms of Parkinson's disease that affect arm motor control.

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes. The approach goes beyond the traditional data-centric methods for activity recognition in three ways. First, it makes extensive use of domain knowledge in the life cycle of activity recognition. Second, it uses ontologies for explicit context and activity modeling and representation. Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition. In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies. We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process. Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition. The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory. We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios. An average activity recognition rate of 94.44 percent was achieved and the average recognition runtime per recognition operation was measured as 2.5 seconds.

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

This report summarizes the International Workshop on Situation, Activity and Goal Awareness held at the 13th ACM International Conference on Ubiquitous Computing, on September 18 in Beijing, China.

The number of intelligent environment implementations such as smart homes is set to increase dramatically within the next 40 years. This is predicted using forecasts of demographic data which indicates an expansion of the aged population. It has also been predicted that governments will struggle to meet the demand for resources such as sensor technology due to costs. Optimisation of limited resources involves physically positioning devices to maximise pertinent data gathering potential. Currently the most utilised methodology of distributing limited spatial detection sensors such as pressure mats within smart homes is via ad-hoc deployments performed by a human being. In this study idiosyncratic inhabitant spatial-frequency data was processed using a Pure Random Search (PRS) algorithm to uncover probabilistic future regions of interest, alluding to optimal sensor distributions under resource constraint. With PRS a null hypothesis was stated: `using lower iteration stopping criteria produce less optimal sensor distributions than when using higher iteration stopping criteria'. A student t-test between 1000 and 5000 iterations was statistically significant at 5% (p = 0.016852) whereby the null hypothesis was rejected. Similar results were obtained between other iteration criteria. These data demonstrate that the iteration stopping criterion is not as critical as sensor size or number of sensors; and that comparable results could be obtained when lower stopping parameters are specified when using PRS.

Smart Homes (SH) have emerged as a realistically viable solution capable of providing technology-driven assistive living for the elderly and disabled. Nevertheless, it still remains a challenge to provide situation-aware assistance for those in need in their Activity of Daily Living (ADL). This paper introduces a systematic approach to providing situation-aware ADL assistances in a smart home environment. The approach makes use of semantic technologies for sensor data modeling, fusion and management, thus creating machine understandable and processable situational data. It exploits intelligent agents for interpreting and reasoning semantic situational (meta)data to enhance situation-aware decision support. We analyze the nature and issues of SH-based healthcare for cognitively deficient inhabitants. We discuss the ways in which semantic technologies enhance situation comprehension. We describe a cognitive agent for realizing high-level cognitive capabilities such as prediction and explanation. We outline the implementation of a prototype assistive system and illustrate the proposed approach through simulated and real-time ADL assistance scenarios in the context of situation aware assistive living.

Knowledge incorporation is one challenge in e- Commerce automated negotiation. In this paper, we describe a model of B2B negotiation using knowledge. We classify the types of knowledge namely general knowledge and negotiation knowledge, in the negotiation process. A methodology that uses knowledge bead (KB) and meta-KB as knowledge representation that would be suitable for the design of automated negotiation systems is discussed. An experimental prototype demonstrates that by incorporating knowledge into automated negotiation yields improved results.

Knowledge has become increasingly important to support intelligent process automation and collaborative problem solving in large-scale science over the Internet. This paper addresses distributed knowledge management, its approach and methodology, in the context of grid application. We start by analyzing the nature of grid computing and its requirements for knowledge support; then, we discuss knowledge characteristics and the challenges for knowledge management on the grid. A semantic Web-based approach is proposed to tackle the six challenges of the knowledge lifecycle - namely, those of acquiring, modeling, retrieving, reusing, publishing, and maintaining knowledge. To facilitate the application of the approach, a systematic methodology is conceived and designed to provide a general implementation guideline. We use a real-world Grid application, the GEODISE project, as a case study in which the core semantic Web technologies such as ontologies, semantic enrichment, and semantic reasoning are used for knowledge engineering and management. The case study has been fully implemented and deployed through which the evaluation and validation for the approach and methodology have been performed

Provenance is becoming increasingly important in service-oriented distributed computing environments in which services are dynamically discovered and composed into workflows for problem solving, and disbanded later. This paper proposes a semantic Web service (SWS) based approach to creating and exploiting rich provenance data - the so-called augmented provenance. Augmented provenance enhances conventional provenance data with extensive metadata and semantics, enabling large scale sharing and deep reuse. We present a general architecture for the approach and discuss mechanisms for modelling, capturing, recording and querying provenance data. The approach has been applied to a real world application in which tools and GUIs are developed to facilitate provenance management. Application experiences are discussed

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e‐Science. The framework uses ontology as the conceptual backbone and adopts the service‐oriented computing paradigm for information‐ and knowledge‐level computation. We further present a semantics‐based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e‐Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology‐enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported.

The future success of Grid-enabled e-Science depends on the availability of semantic/knowledge-rich resources on the Grid, i.e., the so-called semantic Grid. This requires not only novel knowledge modelling and representation formalisms but also a shift of knowledge acquisition and population from a limited number of specialised knowledge engineers to resource providers. To this end we have developed a lightweight ontology-enabled tool, "Function Annotator", to support resource providers in capturing and publishing resource semantics and knowledge. Function Annotator takes a different line to most knowledge acquisition tools in that it is designed for use by resource providers, probably in the absence of a knowledge engineer. Its aim is to facilitate large scale knowledge population on the Grid. Function Annotator is built on the state-of-the-art of semantic web technologies, such as ontologies, OWL, instance store and DL-based reasoning, thus ensuring flexibility and scalability on the Grid. This paper describes the tool's role in a Grid-oriented resource lifecycle, its underlying technologies and implementation. It also illustrates the usage of the tool in the context of engineering design search and optimisation.

We are developing a Grid Enabled Optimisation and Design Search system (GEODISE). It offers grid-based access to a state-of-the-art collection of optimisation and design search tools, industrial strength analysis codes, and distributed computing and data resources.