Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Non‐functional requirements (NFRs) address important issues in software systems, and are vital in successful software. The NFR problems in a system reflect the complexity of that system. Ideally, NFRs are systematically investigated to determine the aspects that may be harmonious or cause conflicts so that conflicts should be detected as early as possible. The work aims to extend the scopes of the NFRs framework and softgoal interdependency graph (SIG) for modelling and analysing NFRs. A formal ontological approach to modelling NFR interactions is proposed, and a core NFR ontology is developed in the ontology web language. The mechanisms involved in NFR interactions with SIG models (through interdependencies in the softgoals) are investigated and formalised. The NFRs are first analyses using a semantic modelling process to provide evaluation criteria. Next, correlation rules for implicit softgoal interdependencies are proposed from knowledge of the domain, and rules for indirect semantic correlations are proposed by extending the delta evaluation method. Reasonable rules in the semantic web environment are defined to allow conflicts between NFRs in SIG instance models to be detected and analysed. A concepts proof is performed to illustrate tool support of the proposed method, and validate the rules for conflict detection.

The widespread adoption of Transformers in deep learning, serving as the core framework for numerous large-scale language models, has sparked significant interest in understanding their underlying mechanisms. However, beginners face difficulties in comprehending and learning Transformers due to its complex structure and abstract data representation. We present TransforLearn, the first interactive visual tutorial designed for deep learning beginners and non-experts to comprehensively learn about Transformers. TransforLearn supports interactions for architecture-driven exploration and task-driven exploration, providing insight into different levels of model details and their working processes. It accommodates interactive views of each layer's operation and mathematical formula, helping users to understand the data flow of long text sequences. By altering the current decoder-based recursive prediction results and combining the downstream task abstractions, users can deeply explore model processes. Our user study revealed that the interactions of TransforLearn are positively received. We observe that TransforLearn facilitates users' accomplishment of study tasks and a grasp of key concepts in Transformer effectively.

Software vulnerabilities represent one of the most pressing threats to computing systems. Identifying vulnerabilities in source code is crucial for protecting user privacy and reducing economic losses. Traditional static analysis tools rely on experts with knowledge in security to manually build rules for operation, a process that requires substantial time and manpower costs and also faces challenges in adapting to new vulnerabilities. The emergence of pre-trained code language models has provided a new solution for automated vulnerability detection. However, code pre-training models are typically based on token-level large-scale pre-training, which hampers their ability to effectively capture the structural and dependency relationships among code segments. In the context of software vulnerabilities, certain types of vulnerabilities are related to the dependency relationships within the code. Consequently, identifying and analyzing these vulnerability samples presents a significant challenge for pre-trained models. In this paper, we propose a data flow embedding technique to enhance the performance of pre-trained models in vulnerability detection tasks, named DFEPT, which provides effective vulnerability data flow information to pre-trained models. Specifically, we parse data flow graphs (DFG) from function-level source code, and use the data type of the variable as the node characteristics of the DFG. By applying graph learning techniques, we embed the data flow graph and incorporate relative positional information into the graph embedding using sine positional encoding to ensure the completeness of vulnerability data flow information. Our research shows that DFEPT can provide effective vulnerability semantic information to pre-trained models, achieving an accuracy of 64.97% on the Devign dataset and an F1-Score of 47.9% on the Reveal dataset. Compared with the pre-trained model that is only fine-tuned, the performance increases by 1.96%-17.26%.

Background subtraction is a fundamental problem of computer vision, which is usually the first step of video analytics to extract the interesting region. Most previously available region-based background subtraction methods ignore the similarity between the pixels, meaning that the information gained from the pixels that do not contribute, or even contribute negatively to understanding an image, is taken into account. A new background subtraction model based on random superpixel segmentation under multiple scales is proposed. A custom region segmentation area is replaced with a superpixel segmentation area that uses similarity characteristics for pixels in the superpixel area. The compactness of the pixels in the same superpixel area means that the pixels positively contribute to understanding an image compared with when using custom region pixels. Superpixel segmentation is performed using the random simple linear iterative cluster method. Taking random samples during the superpixel segmentation process produces the Matthew effect, thus improving the robustness and efficiency of the model. Multi-scale superpixel segmentation is therefore guaranteed to give more accurate results. Standard benchmark experiments using the proposed approach produced encouraging results compared with the results given by previously available algorithms.

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.