Intrusion Detection Systems (IDS) play a vital role in network security, yet signature-based methods are limited by high false positive rates (FPR) and inability to detect novel threats. Recent AI-based approaches offer improved adaptability, but most rely on flow-level or statistical features, constraining their ability to analyze sophisticated payload-based attacks. To address these challenges, we present a dual-path IDS framework: Xavier-CMAE, a lightweight model using Hex2Int tokenization and Xavier initialization, achieves 99.9718% accuracy and a 0.0182% FPR without pre-training; and LLM-CMAE, which leverages pre-trained LLM tokenizers for enhanced detection, achieves 99.9696% accuracy and a 0.0194% FPR at higher computational cost. Experimental results on the CIC-IDS2017 dataset reveal a distinct trade-off between efficiency and Contextually Adept and Scalable (CAS) power, indicating that a modular approach may enable both real-time scalability and in-depth threat analysis. This work advances AI-powered intrusion detection by (1) introducing a modular, payload-centric dual-path architecture that combines lightweight and CAS detection for adaptive, layered security; (2) demonstrating that Xavier-CMAE achieves real-time scalability and state-of-the-art accuracy without embedding pre-training; and (3) exploring the effectiveness and future potential of integrating pre-trained LLM tokenizers for nuanced, selective threat analysis and robust IDS design.

In winter 2025, an observational experiment was conducted at Mahidol University, Thailand, to investigate how lighting colour influences individuals’ perceptions of religious imagery. The study examined how participants emotionally and symbolically respond to a pensive Korean Buddha statue when presented under different colored lighting conditions. We experimented with 116 participants who viewed the statue inside a black box illuminated by five colors-red, yellow, blue, green, and white-in randomized order. After each exposure, participants completed semantic differential scales measuring formality, authenticity, sacredness, memorability, perceived value, and durability. White lighting received the highest ratings for sacredness, perceived value, and authenticity; yellow excelled in memorability and sacredness; blue scored lowest on authenticity and formality; red was near-neutral. These findings inform the design of religious spaces and the presentation of sacred artifacts.

ABSTRACT Healthcare robots are cyberphysical systems designed to assist older adults and reduce the burden on healthcare professionals and family caregivers. These robots can perform various tasks, including delivering medications on time, promoting physical activity, and cultivating social connections by contacting family and friends. As the global population ages, healthcare robots are emerging as critical technology to support older adults and alleviate the burden on healthcare systems. However, their widespread adoption is hindered by significant challenges, including inflexible hard‐coded functionalities, high development and training costs, and a common lack of cultural adaptability. Extended reality (XR) and digital twin (DT) technologies offer a transformative approach to overcome these hurdles by enabling safe, scalable, and cost‐effective virtual training environments. This paper presents a systematic review of the literature at the intersection of XR, DT, and healthcare robotics, adhering to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. We not only present a thematic analysis of current research but also identify key fundamental challenges in existing robot training methodologies and analyse the field's evolutionary trends through visualisations. Based on our synthesis, we propose a conceptual framework and guidelines for developing future healthcare robots that are not only technologically advanced but also personalised, empathetic, and culturally sensitive. The purpose of this survey is to provide a roadmap for researchers and practitioners to take advantage of these convergent technologies, with the intention of creating a more effective and humane healthcare ecosystem for ageing populations around the world.

ABSTRACT In this paper, we introduce Traversal Learning (TL), a novel approach designed to address the problem of decreased quality encountered in popular distributed learning (DL) paradigms such as Federated Learning (FL), Split Learning (SL) and SplitFed Learning (SFL). Traditional FL often suffers an accuracy drop during aggregation due to its averaging function, while SL and SFL face increased loss due to the independent gradient updates on each split network. TL adopts a unique strategy where the model traverses the nodes during forward propagation (FP) and performs backward propagation (BP) at the orchestrator, effectively implementing centralised learning (CL) principles within a distributed environment. The orchestrator is tasked with generating virtual batches and planning the model's sequential node visits during FP, aligning them with the ordered index of the data within these batches. We conducted experiments on six datasets representing diverse characteristics across various domains. Our evaluation demonstrates that TL is on par with classic CL approaches in terms of accurate inference, thereby offering a viable and robust solution for DL tasks. TL outperformed other DL methods and improved accuracy by 7.85% for independent and identically distributed (IID) datasets, macro F1‐score by 1.06% for non‐IID datasets, accuracy by 2.05% for text classification and AUC by 1.41% and 2.82% for medical and financial datasets, respectively. By effectively preserving data privacy while maintaining performance, TL represents a significant advancement in DL methodologies.

This study explores the impact and challenges of new user behaviors in the Web 3.0 environment on distributed networks. The traditional Chord algorithm allows nodes to freely join and leave the network by hashing their IP addresses, and publishing and storing resources through the same hash function. When the keys of the resources are unique, the resources will be evenly distributed across each node, thereby achieving load balancing. However, in cases where many identical resources are published, this method leads to specific nodes bearing too much load, causing performance bottlenecks and resource concentration issues. In Web 3.0, when the nodes use the resource’s topic as the key to publish resources, as the topic’s popularity increases, the number of nodes using the same key as the publishing node and the nodes with demand for the topic resources will also increase. In the traditional Chord algorithm, the same key will be managed by the same node. The node responsible for the key needs to save the routing information of all related nodes and cope with a large number of resource requests for it. To address these issues, this paper proposes a new variant of the Chord algorithm, which uses two different Chord rings for resource clustering: one based on the hash of resource names and the other based on the hash of IP addresses. This method allows us to allocate resources more effectively, ensuring each node bears a reasonable load share according to capacity. This paper will present the design principles of this method and validate its effectiveness in improving resource distribution and reducing the problem of single-point overload through experiments.

Collecting personal data from various sources and using it for machine learning (ML) is prevalent. However, there are increasing concerns about the monopolization and potential breach of private data by greedy and malicious organizations. Interest in Web 3.0 systems is on the rise as an alternative. These systems aim to guarantee the self-sovereignty of personal data in a decentralized setting. Users can share data with others directly for fair compensation. Nevertheless, malicious remote users can still violate the integrity and confidentiality of personal data. Therefore, this paper proposes a novel method of preventing unwanted leakage and counterfeiting of the private data lent on the premise of remote users. This paper focuses on the decentralized nature of Web 3.0 to leverage existing personal storage so that the burden of collecting secure data is relieved. Data owners create a lightweight Docker container to encapsulate their private data sources. The data owners generate another container to be deployed on a remote premise for taking and executing any ML algorithms remote users create. Between the containers forming a distributed trusted execution environment (TEE), data are read through a secure channel. Since the TEE is strictly controlled by the data owner, no malicious ML application can leak or breach the private information. This paper explains the engineering details of how this new method is realized.

Software-Defined Networking not only addresses the shortcoming of traditional network technologies in dealing with frequent and immediate changes in cloud data centers but also made network resource management open and innovation-friendly. To further accelerate the innovation pace, accessible and easy-to-learn testbeds are required which estimate and measure the performance of network and host capacity provisioning approaches simultaneously within a data center. This is a challenging task and is often costly if accomplished in a physical environment. Thus, a lightweight and scalable simulation environment is necessary to evaluate the network allocation capacity policies while avoiding such a complicated and expensive facility. This paper introduces CloudSimSDN, a simulation framework for SDN-enabled cloud environments based on CloudSim. This paper develops and presents the overall architecture and features of the framework and provides several use cases. Moreover, we empirically validate the accuracy and effectiveness of CloudSimSDN through a number of simulations of a cloud-based three-tier web application.

Reconfiguring a topology is an important management technique to sustain high efficiency and robustness of an overlay. But, the problem of transforming the overlay from an old topology to a newly refined topology, at runtime, has received relatively little attention. The key challenge is to minimize the disruption that can be caused by topology transformation operations. Excessive disruption can be costly and harmful and thus it may hamper the decision to migrate to a better topology. To address this issue, we solve a problem of finding an appropriate sequence of steps to transform a topology that incurs the least service disruption. We refer to this problem as an incremental topology transformation (ITT) problem. The ITT problem can be formulated as an automated planning problem and can be solved with numerous off-the-shelf planning techniques. However, we found that state-of-the-art domain-independent planning techniques did not scale to solve large ITT problem instances. This shortcoming motivated us to develop a suite of planners that use novel domain-specific heuristics to guide the search for a solution. We empirically evaluated our planners on a wide range of topologies. Our results illustrate that our planners offer a viable solution to a diversity of ITT problems. We envision that our approach could eventually provide a compelling addition to the arsenal of techniques currently employed by the administrators of distributed overlay networks.

We introduce the ideas of developing a distributed event-based system for screening and monitoring the activities that can lead to the production of hazardous chemicals such as explosive materials and narcotic drugs. This system is built on top of the state-of-the-art content-based publish/subscribe messaging substrate that processes complex chemical events in a robust and scalable way. This system also leverages the chemical information encoded in semantic chemistry networks to identify any undesirable chemical reactions.

Content-based publish/subscribe overlays offer a scalable messaging substrate for various event-based distributed systems. In an enterprise environment where service level agreements(SLAs) are strictly enforced, maintaining high availability and efficiency of the broker overlay is critical. To support these requirements, a set of three primitive operations are proposed to allow arbitrary transformations of an overlay to an optima lone, and two additional primitives are developed to enable ondemand adjustments when there are permanent or transient failures. Both sets of primitive operations minimize disruption by preserving message delivery guarantees even as the overlay topology changes, requiring no overhead when the overlay is not being modified, operating on a fixed neighborhood of brokers regardless of the size of the overlay, and completing quickly under a variety of conditions.

In service-oriented architectures (SOA), independently developed Web services can be dynamically composed. However, the composition is prone to producing semantically conflicting interactions among the services. For example, in an interdepartmental business collaboration through Web services, the decision by the marketing department to clear out the inventory might be inconsistent with the decision by the operations department to increase production. Resolving semantic conflicts is challenging especially when services are loosely coupled and their interactions are not carefully governed. To address this problem, we propose a novel distributed service choreography framework. We deploy safety constraints to prevent conflicting behavior and enforce reliable and efficient service interactions via federated publish/subscribe messaging, along with strategic placement of distributed choreography agents and coordinators to minimize runtime overhead. Experimental results show that our framework prevents semantic conflicts with negligible overhead and scales better than a centralized approach by up to 60%.

Abstract This paper is a case study of the effectiveness of component‐oriented development for enhancing both productivity and performance for parallel programs. A process for converting monolithic applications into semantically composable components is described. The supporting software, the P‐COM2 compositional compiler, is briefly described. The componentized version of Sweep3D is described. Productivity is illustrated by composing different instances of the Sweep3D code through automated composition of components using P‐COM2. These instances, each of which targets improving performance for some execution environment or problem case, are examples of a family of instances which are composable from a modest set of components. It is found that customization of componentized codes by component‐level adaptation may yield substantial performance improvement for specific execution environments. We identify and explain some of the benefits of component‐oriented development for high‐performance parallel systems. Copyright © 2006 John Wiley & Sons, Ltd.