In medical image analysis, computed tomography (CT) is essential for organ segmentation and computer-aided diagnosis. However, existing methods relying on single modalities (e.g., CT or MRI) face two issues: (1) similar grayscale characteristics among organs cause ambiguous boundaries, and (2) lacking semantic guidance limits organ discrimination. To overcome these challenges, this study proposes a CLIP-based semantic fusion method. The CLIP text encoder converts label text into semantic vectors, while SwinUNETR extracts image features. A DOKAN fusion network then achieves orthogonal fusion and cross-modal semantic alignment. Experiments on BTCV and AMOS datasets show Dice score gains of 1.81 % and 2.28 %, with over 4 % improvement in adrenal gland segmentation. The proposed method effectively bridges the semantic gap and advances multimodal fusion for intelligent diagnosis systems.

Role-based similarity search, predicated on the topological structure of graphs, is a highly effective and widely applicable technique for various real-world information extraction applications. Although the prominent rolebased similarity algorithm, RoleSim, successfully provides the automorphic (role) equivalence of similarity between pairs of nodes, it does not effectively differentiate nodes that exhibit exact automorphic equivalence but differ in terms of structural equivalence within a given graph. This limitation arises from disregarding most adjacency similarity information between pairs of nodes during the RoleSim computation. To address this research gap, we propose a novel single-source role similarity search algorithm, named FaRS, which employs the top Γ maximum similarity matching technique to capture more information from the classes of neighboring nodes, ensuring both automorphic equivalence and structural equivalence of role similarity. Furthermore, we establi sh the convergence of FaRS and demonstrate its adherence to various axioms, including uniqueness, symmetry, boundedness, and triangular inequality. Additionally, we introduce the Opt FaRS algorithm, which optimizes the computation of FaRS through two acceleration components: path extraction tracking and precomputation (P-speedup and Out-speedup approach). Experimental results on real datasets demonstrate that FaRS and Opt FaRS outperform baseline algorithms in terms of both accuracy and efficiency.

Abstract To support artificial intelligence (AI)‐involved tasks offloaded from the mobile devices (MDs), it is necessary to equip the Unmanned Aerial Vehicle (UAV) with custom‐made co‐processor (CP) for handling AI workloads in multi‐UAV‐empowered Edge Intelligence. Existing CPU‐oriented task scheduling algorithm cannot apply to the CPU+CP heterogeneous architecture. In this backdrop, this paper first formulates the joint service function placement, collaborative task scheduling, UAV deployment, and MD position determination problem as a Mixed Integer Non‐Linear Programming problem. Then, an alternating optimization‐based algorithm is put forward to derive a sub‐optimal solution of the problem utilizing Differential Evolution and Greedy‐based Hungarian algorithms. A series of experiments are conducted to evaluate the performance of the proposal. Results show that authors' proposal can achieve an overall revenue that is roughly 50% higher than those of existing methods.

As a newly proposed computational paradigm of membrane computing, cP systems are used to solve several NP-complete and PSPACE-complete problems in linear or sub-linear time theoretically. Most cP systems proposed in previous studies lack of automated verification support. In this paper, we present cPV, the first software implementation for cP system simulation and verification. cPV offers multiple features, which include modelling, simulation, automated verification of properties such as absence of deadlock, confluence, termination, determinism, and goal reachability. As an extensible framework, modules in cPV are loosely coupled, where new verification algorithms, reduction techniques, and property specifications can be easily extended. To evaluate cPV, we constructed two benchmark datasets that cover several important aspects of cP systems. The experimental results demonstrated effective automatic verification support to the membrane computing problem domain.

Considerable research has recently focused on integrating cyber-physical systems in a social context. However, several challenges remain concerning appropriate methodologies, frameworks and techniques for supporting socio-cyber-physical collaboration. Existing systems do not recognize how cyber-physical resources can be socially connected so that they interact in collaborative decision-making like humans. Furthermore, the lack of semantic representations for heterogeneous cyber-social-collaborative networks limits integration, interoperability and knowledge discovery from their underlying data sources. Semantic Web ontology models can help to overcome this limitation by semantically describing and interconnecting cyber-physical objects and human participants in a social space. This research addresses the establishment of both cyber-physical and human relationships and their interactions within a social-collaborative network. We discuss how nonhuman resources can be represented as socially connected nodes and utilized by software agents. A software agent-centric Semantic Social-Collaborative Network (SSCN) is then presented that provides functionality to represent and manage cyber-physical resources in a social network. It is supported by an extended ontology model for semantically describing human and nonhuman resources and their social interactions. A software agent has been implemented to perform some actions on behalf of the nonhuman resources to achieve cyber-physical collaboration. It is demonstrated within a real-world decision support system, GRiST (www.egrist.org), used by mental-health services in the UK.

Program monitoring aims at making sure the functionalities of the software are always correctly performed during runtime. Semantic Web provides a context enriched framework for data representation and manipulation. This paper proposed the use of ontological rules and reasoning engines to monitor the dynamic behaviours of computer systems in handling of exceptional circumstances, both positive and negative, that occur at runtime within the software processes. A prototype framework was proposed on how to integrate the rule based monitoring technique together with the targeted system. To validate the proposed solution, a light control system case study together with the Unity game engine were used to develop a simulation environment for the evaluation purpose. Compared to existing solutions, the approach outlined can provide an effective software behavioural monitoring outcome.

With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue.The current software production has been largely depended on manual code development.Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product.This paper explores the possibility of generating code and assertion constraints from formal design models and use them to verify the implementation.We translate Z formal models into their OCL counter-parts and Java assertions.With the help of existing tools, we demonstrate various checking at different levels to enhance correctness.

With the growing in size and complexity of modern computer systems, the need for improving the quality at all stages of software development has become a critical issue. The current software production has been largely dependent on manual code development. Despite the slow development process, the errors introduced by the programmers contribute to a substantial portion of defects in the final software product. This paper investigates the synergy of generating code and assertion constraints from formal design models and use them to verify the implementation. We translate Z formal models into their OCL counterparts and Java assertions. With the help of existing tools, we demonstrate various checkings at different levels to enhance correctness.

This article did research on multi-target tracking system based on the classic kinetics in Wireless Sensor Networks (WSN), a distributed Cell tracking model is brought up. The whole WSN was layered by Virtual Grid Architecture (VGA)[1][2]. When a target appeared in a grid, local aggregators (LA) aroused all nodes in the eight adjacent grids to compose a Cell to track target. A Cell election rule is proposed: when a target is escaping from the current Cell, based on the classical laws of kinetics and the historical track information, the position and velocity of the target can be estimated when it crossed the edge of Cell and the next Cell can be elected. Multi-Cell sequence can track multitarget concurrently. The concepts of main target and subtarget are introduced. When multi-target were space-time overlapped, a single Cell may have several main targets and sub-targets. The tracking algorithms are designed for them separately. This model can manage the problems like wrong association and missing target. The simulation showed the approach is effective.

Semistructured data has become more and more prominent in the fast growing areas of web information technology. XML has been used as a standard format for semistructured data in representing and exchanging information in various applications. However, the lack of formality and verification support in the design of a good semistructured data model may hinder its development. For example, redundant data in XML must be removed or minimized to avoid inconsistent and inefficient information processing. Normalization algorithms have been developed to overcome these problems by transforming the schema of a semistructured document into a better form. Therefore, it is essential to ensure that a transformed schema model preserves the same information that its original form holds. In this paper, we present an approach to investigate and verify the no-data-loss property of semistructured data normalization. We encode the verification criteria in the Semantic Web Rule Language (SWRL) and make use of its ontology reasoning engine to provide automated support for the checking process. In summary, our approach not only investigates the information preserving aspect of semistructured data normalization, but also provides a scalable and automated solution towards the problem.

In this paper, we present a new technique, called stream projected ouliter detector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique in a number of aspects. First, SPOT employs a novel window-based time model and decaying cell summaries to capture statistics from the data stream. Second, sparse subspace template (SST), a set of top sparse subspaces obtained by unsupervised and/or supervised learning processes, is constructed in SPOT to detect projected outliers effectively. Multi-Objective genetic algorithm (MOGA) is employed as an effective search method in unsupervised learning for finding outlying subspaces from training data. Finally, SST is able to carry out online self- evolution to cope with dynamics of data streams. This paper provides details on the motivation and technical challenges of detecting outliers from high-dimensional data streams, present an overview of SPOT, and give the plans for system demonstration of SPOT.

Semantic Web services have been one of the most significant research areas within the semantic Web vision, and have been recognized as a promising technology that exhibits huge commercial potential. Current semantic Web service research focuses on defining models and languages for the semantic markup of all relevant aspects of services, which are accessible through a Web service interface. The Web service modelling ontology (WSMO) is one of the most significant semantic Web service framework proposed to date. To support the standardization and tool support of WSMO, a formal semantics of the language is highly desirable. As there are a few variants of WSMO and it is still under development, the semantics of WSMO needs to be formally defined to facilitate easy reuse and future development. In this paper, we present a formal object-Z semantics of WSMO. Different aspects of the language have been precisely defined within one unified framework. This model provides a formal unambiguous specification, which can be used to develop tools and facilitate future development.

In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training data generation, anomaly classification and false positive reduction are proposed in this paper as well. Experimental results demonstrate that SPOT is effective in detecting anomalies from network data streams and outperforms existing anomaly detection methods.

As the Semantic Web is an open, complex and constantly evolving medium, it is the norm, but not exception that information at different sites is incomplete/inconsistent. This poses challenges for the engineering and development of agent systems on the Semantic Web since autonomous software agents need to understand, process and aggregate this information. Ontology language OWL provides core language constructs to semantically markup resources on the Semantic Web. However, as OWL was designed on top of (a subset of) classic predicate logic, it lacks the ability to reason about inconsistent/incomplete information. Belief Augmented Frames (BAF) is a frame-based logic system that associates with each frame/slot a supporting and a refuting belief value. In this paper, we propose a new ontology language BOWL (Belief-augmented OWL) by integrating OWL DL and BAF to incorporate the notion of confidence. The BOWL is paraconsistent, hence it can perform useful reasoning services in the presence of inconsistencies/incompleteness. We define the abstract syntax and semantics of BOWL by extending those of OWL. One example in the sensor fusion domain is presented to demonstrate the application of BOWL.

Semantic Web services, one of the most significant research areas within the semantic Web vision, has attracted increasing attention from both the research community and industry. The Web service modelling ontology (WSMO) has recently been proposed as an enabling framework for the total/partial automation of the tasks (e.g., discovery, selection, composition, mediation, execution, monitoring, etc.) involved in both intra- and inter-enterprise integration of Web services. To support the standardization and tool support of WSMO, a formal semantics of the language is highly desirable. As there are a few variants of WSMO and it is still under development, the semantics of WSMO needs to be formally defined to facilitate easy reuse and future development. In this paper, we present a formal object-Z semantics of WSMO. Different aspects of the language have been precisely defined within one unified framework. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in this paper, can be used to identify and eliminate errors presented in existing documentation.

The design of an embedded control system starts with an investigation of properties and behaviors of the process evolving within its environment, and an analysis of the requirement for its safety performance. In early stages, system requirements are often specified as scenarios of behavior using sequence charts for different use cases. This specification must be precise, intuitive and expressive enough to capture different aspects of embedded control systems. As a rather rich and useful extension to the classical message sequence charts, Live Sequence Charts (LSC), which provide a rich collection of constructs for specifying both possible and mandatory behaviors, are very suitable for designing an embedded control system. However, it is not a trivial task to realize a high-level design model in executable program codes effectively and correctly. This paper tackles the challenging task by providing a mapping algorithm to automatically synthesize SystemVerilog programs from given LSC specifications.

To support standardization and tool support of OWL-S, a formal semantics of the language is highly desirable. In this paper, we present a formal Object-Z semantics of OWL-S. This model not only provides a formal unambiguous model which can be used to develop tools and facilitate future development, but as demonstrated in the paper, can be used to identify and eliminate errors in the current documentation.

Detecting outlying subspaces is a relatively new research problem in outlier-ness analysis for high-dimensional data. An outlying subspace for a given data point p is the subspace in which p is an outlier. Outlying subspace detection can facilitate a better characterization process for the detected outliers. It can also enable outlier mining for highdimensional data to be performed more accurately and efficiently. In this paper, we proposed a new method using genetic algorithm paradigm for searching outlying subspaces efficiently. We developed a technique for efficiently computing the lower and upper bounds of the distance between a given point and its kth nearest neighbor in each possible subspace. These bounds are used to speed up the fitness evaluation of the designed genetic algorithm for outlying subspace detection. We also proposed a random sampling technique to further reduce the computation of the genetic algorithm. The optimal number of sampling data is specified to ensure the accuracy of the result. We show that the proposed method is efficient and effective in handling outlying subspace detection problem by a set of experiments conducted on both synthetic and real-life datasets.

The problem of gene specific co-regulation discovery is that, for a particular gene of interest, identify its closely coregulated genes and the associated subsets of experimental conditions in which such co-regulations occur. The coregulations are local in the sense that they occur in some subsets of full experimental conditions. In this paper, we propose an innovative method for finding gene specific coregulations using genetic algorithm (GA). Two novel ad hoc GAs, the single-stage and two-stage progressive GA, are proposed. They are called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in the preceding window position, enabling them to achieve better accuracy than the nonprogressive algorithm. Experimental results with real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific coregulations

Research on features has received much attention in the domain engineering community. Feature modeling plays an important role in the design and implementation of complex software systems. However, the presentation and analysis of feature models are still largely informal. There is also an increasing need for methods and tools that can support automated feature model analysis. This paper presents a formal engineering approach to the specification and verification of feature models. A formal semantics for the feature modeling language is defined using first-order logic. It provides a precise and rigorous formal interpretation for the graphical notation. In addition, further validation of the semantics using the Z/EVES theorem prover is presented. Finally, we demonstrate that the consistency of a feature model and its configurations can be automatically verified by encoding the semantics into the Alloy Analyzer. A case study of the Key Word in Context (KWIC) index systems feature model is presented to illustrate the verification process.

Semantic Web, the next generation of Web, gives data well-defined and machine-understandable meaning so that they can be processed by remote intelligent agents cooperatively. Ontology languages are the building blocks of Semantic Web as they prescribe how data are defined and related. The existing reasoning and verification tools for Semantic Web are improving however still elementary. We believe that Semantic Web can be a novel application domain for software modeling languages and tools. Z is a formal modeling language for specifying software systems and Z/EVES is a proof tool for Z. In this paper, we firstly present Z semantics for ontology language DAML+OIL. This semantic model is embedded as a Z section daml2zin Z/EVES, which serves as an environment for checking and verifying Web ontologies. Then we present a tool for automatically transforming ontology documents into the specialized Z codes understood by Z/EVES. Finally, we use a recent real application, the military plan ontologies, to demonstrate the different reasoning tasks that Z/EVES can perform. Furthermore, undiscovered errors in the original ontologies were found by Z/EVES and some of these errors are even beyond Semantic Web modeling and reasoning capabilities.

The understanding of Semantic Web documents is built upon ontologies that define concepts and relationships of data. Hence, the correctness of ontologies is vital. Ontology reasoners such as RACER and FaCT have been developed to reason ontologies with a high degree of automation. However, complex ontology-related properties may not be expressible within the current web ontology languages, consequently they may not be checkable by RACER and FaCT. We propose to use the software engineering techniques and tools, i.e., Z/EVES and Alloy Analyzer, to complement the ontology tools for checking Semantic Web documents.In this approach, Z/EVES is first applied to remove trivial syntax and type errors of the ontologies. Next, RACER is used to identify any ontological inconsistencies, whose origins can be traced by Alloy Analyzer. Finally Z/EVES is used again to express complex ontology-related properties and reveal errors beyond the modeling capabilities of the current web ontology languages. We have successfully applied this approach to checking a set of military plan ontologies.

The challenge for system specification is how to visually and precisely capture static, dynamic and real-time system properties in a highly structured way. Timed Communicating Object-Z (TCOZ) is an integrated formal notation that build on Object-Z's strengths in modeling complex data structures, and on Timed CSP's strengths in modeling real-time interactions. In this paper, we demonstrate approaches of using XML/XSL as a transformation tool to visualize TCOZ models into various UML diagrams and to animate TCOZ specifications with a multi-paradigm programming language-Oz.

This paper presents the XML/XSL approach to the developmentofaweb environment for the formal specification language Object-Z. The projection techniques and tools from Object-Z (in XML) to UML (in XMI) are developed using XSL Transformations (XSLT). Furthermore, Object-Z (itself) is used to specify and design the essential functionalities of the web environment and the projection tools to UML. In a sense, the paper also demonstrates a formal approach to modeling web applications. Keywords Object-Z, XML/XSL/XMI, UML 1.