Established in 1982, Computers & Security (COSE) was the first journal dedicated to the technical and organizational dimensions of information security. This study examines COSE’s development from 1982 to 2024 through a bibliometric retrospective based on 4,405 Scopus-indexed documents, complemented by selected Web of Science analyses. Following the SPAR-4-SLR protocol, we combine publication and citation indicators with co-citation, bibliographic coupling, and keyword co-occurrence analyses using VOSviewer and bibliometrix . The results show four phases of publication growth, with the strongest expansion after 2016. Citation patterns indicate a sustained influence in network security, intrusion detection, privacy, malware analysis, and behavioral information security, while recent clusters point to growing attention to adversarial machine learning, blockchain, and cyber-physical systems. Author, institutional, and country-level patterns also suggest a broadening contributor base, with increased participation from Asian research systems in the most recent period. The network and keyword analyses show that COSE has retained links to its foundational security themes while incorporating newer socio-technical and AI-related topics. By mapping publication performance, intellectual structure, and topical change, the study provides a longitudinal account of COSE’s role in cybersecurity research and offers evidence that may support future editorial planning, author positioning, and research-policy discussions.

Decision Support Systems (DSS) is a leading international journal dedicated to decision support system research and practice, with the aim of exploring theoretical and technical advancements to facilitate enhanced decision making in industry, commerce, government, and other business settings. The journal published its first issue in 1985, and in 2025, celebrates its 40th anniversary. Motivated by this special event, this paper develops a comprehensive bibliometric analysis to present a lifetime overview of the development characteristics and leading trends of DSS journal between 1985 and 2023. By using the bibliographic data collected from the Scopus and Web of Science Core Collection databases, this study analyzes the publication and citation structure of the journal and investigates a wide range of issues including the most cited papers, the most cited documents by the journal's publications, the citing articles, the most productive and influential authors, institutions and countries/territories, and the most popular keywords and topics. Moreover, this work also graphically maps the bibliographic material by using the visualization of similarities (VOS) viewer software. In the graphical analysis, several bibliometric techniques in terms of co-citation, bibliographic coupling, and co-occurrence of author keywords are adopted. The results accentuate the significant growth and impact of DSS journal throughout its lifetime. It is expected that the journal will continue to grow its international reputation and disseminate knowledge in decision support, information systems, and business area, providing an efficient mechanism for researchers around the world to keep abreast with advances in the scientific community.

Established in 1963 under the title Information Storage and Retrieval, the journal adopted its current name, Information Processing & Management (IPM), in 1975, reflecting a broadening scope aligned with computational and cognitive developments in information science. This study uses data from Web of Science and Scopus databases to deliver a longitudinal, multi-perspective bibliometric and science mapping analysis of IPM’s evolution from 1963 to 2023. Employing co-citation analysis, bibliographic coupling, keyword co-occurrence, and thematic mapping via VOSviewer and Bibliometrix, the analysis delineates the structural, conceptual, and topical transformation of the journal content. Co-citation networks uncover foundational cores in information retrieval, relevance theory, and evaluation methodologies, while also revealing temporal shifts toward natural language processing, deep learning, and social media analytics. Bibliographic coupling identifies coherent intellectual clusters centered on GNN-based recommendation systems, blockchain-secured infrastructures, and sentiment-aware retrieval frameworks. Keyword co-occurrence and topic evolution trajectories illustrate the journal’s recent pivot toward transformer models, misinformation detection, ethical AI, and interdisciplinary convergence across cognitive science, machine learning, and computational linguistics. Regional co-word analysis underscores epistemological diversity and geographic differentiation across North America, Europe, and East Asia. Productivity and influence metrics highlight the ascent of East Asian institutions and the emergence of globally distributed citation impact. Finally, SciVal-based topic and topic cluster analyses reveal the journal’s role in advancing highly cited research (as measured by FWCI) in areas such as ABSA, multi-view clustering, and health informatics. This work not only charts IPM’s conceptual landscape and disciplinary diffusion but also provides actionable intelligence on the journal’s strategic positioning within the broader information and computational sciences.

Recipient's absorptive capacity (ACAP) is a barrier to knowledge transfer in organizations. The technology readiness (TR) dimensions measure an individual's technological beliefs and aligns with the individual's ACAP. The purpose of this research is to study if technological beliefs have a causal effect onto individual learning capability and behaviour. University's knowledge transfer makes them an ideal context for this research. Through surveying individuals and conducting statistical analysis, the authors provide empirical evidence that there is a causal effect from the TR dimensions to individuals ACAP and their technological learning behaviour at the individual level. The findings could potentially help leverage technology to address said recipient's ACAP. It would also benefit the development of new technologies, in particular in e-learning and tailoring pedagogy.

Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant pre-processing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early evidence shows that it has satisfactory performance compared to conventional information extraction methods such as conditional random field and bi-directional recurrent neural network, for micro learning services.

In the credit risk domain, lenders frequently face situations where there is no, or limited historical lending outcome data.It generally results in limited or unaffordable credit for some individuals and small businesses.Transfer learning can potentially reduce this limitation, by leveraging knowledge from related domains, with sufficient outcome data.We investigated the potential for applying transfer learning across various credit domains, for example, from the credit card lending and debt consolidation domain into the small business lending domain.

The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint and focus and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develop a generic process model and then evaluate and refine the model through three case studies and domain expert reviews. This research benefits academics and practitioners alike by underpinning a substrate for constructing, standardising, maintaining, and sharing bespoke cloud migration models that can be applied to given cloud adoption scenarios.

Open learning is a rising trend in the educational sector and it attracts millions of learners to be engaged to enjoy massive latest and free open education resources (OERs). Through the use of mobile devices, open learning is often carried out in a micro learning mode, where each unit of learning activity is commonly shorter than 15 minutes. Learners are often at a loss in the process of choosing OER leading to their long term objectives and short term demands. Our pilot work, namely MLaaS, proposed a smart system to deliver personalized OER with micro learning to satisfy their real-time needs, while its decision-making process is scarcely supported due to the lack of historical data. Inspired by this, MLaaS now embeds a new solution to tackle the cold start problem, by opening up a brand new profile for each learner and delivering them the first resources in their fresh start learning journey. In this paper, we also propose an ontology-based mechanism for learning prediction and recommendation.

Learning path in online learning systems refers to a sequence of learning objects which are designated to help the students in improving their knowledge or skill in particular subjects or degree courses. In this paper, we review the recent research on learning path adaptation to pursue two goals, first is to organize and analyze the parameter of adaptation in learning path; the second is to discuss the challenges in implementing learning path adaptation. The survey covers the state of the art and aims at providing a comprehensive introduction to the learning path adaptation for researchers and practitioners.

In this paper, we describe research results arising from a technology transfer exercise on agent-oriented requirements engineering with an industry partner. We introduce two improvements to the state-of-the-art in agent-oriented requirements engineering, designed to mitigate two problems experienced by ourselves and our industry partner: (1) the lack of systematic methods for agent-oriented requirements elicitation and modelling; and (2) the lack of prescribed deliverables in agent-oriented requirements engineering. We discuss the application of our new approach to an aircraft turnaround simulator built in conjunction with our industry partner, and show how agent-oriented models can be derived and used to construct a complete requirements package. We evaluate this by having three independent people design and implement prototypes of the aircraft turnaround simulator, and comparing the three prototypes. Our evaluation indicates that our approach is effective at delivering correct, complete, and consistent requirements that satisfy the stakeholders, and can be used in a repeatable manner to produce designs and implementations. We discuss lessons learnt from applying this approach.

Huge collections of data have been created in recent years. Cloud computing has been widely accepted as the next-generation solution to addressing data-proliferation problems. Because of the explosion in digital data and the distributed nature of the cloud, as well as the increasingly large number of providers in the market, providing efficient cost models for composing data-intensive services will become central to this dynamic market. The location of users, service composers, service providers, and data providers will affect the total cost of service provision. Different providers will need to make decisions about how to price and pay for resources. Each of them wants to maximize its profit as well as retain its position in the marketplace. Based on our earlier work, this paper addresses the effect of data intensity and the communication cost of mass data transfer on service composition, and proposes a service selection algorithm based on an enhanced ant colony system for data-intensive service provision. In this paper, the data-intensive service composition problem is modeled as an AND/OR graph, which is not only able to deal with sequence relations and switch relations, but is also able to deal with parallel relations between services. In addition, the performance of the service selection algorithm is evaluated by simulations.

Software agents are highly autonomous, situated and interactive software components. They autonomously sense their environment and respond accordingly. Agents behaviours are often constrained by by real time constraints such as the time in which the agent is expected to respond .i.e. time needed for a task to complete. Failing to meet such a constraint can result in a task being not achieved. This possibly causes an agent or a system to fail, depending on how critical the task is to the agent or system as a whole. Our research aims at identifying and modelling real time constraints in the early phase of analysis which helps in creating a more reliable and robust system.

Due to rapid social development in Asia, sports events have grown larger and many new countries are also hosting them for their first time. In addition to required increase in expenditures and more efficient management, various instances of inadequate planning highlighted the needs for more effective and better sustainable structures to support knowledge transfer between organizers, from one event to the next. The research presented in this paper aims to facilitate the deployment of systematic knowledge management practices to sports event management, to enable sustainable planning. The research in this paper synthesizes is carried out on the Malaysian Games as an example of a sports event management. Furthermore, we introduce knowledge management (KM) framework that was developed based on studies and observations of processes and activities in this organization. The focus is on knowledge that is key to the success of the Malaysian Games and that which can be used to the development of the organization and in future games.

In some areas of software engineering research, there are several metamodels claiming to capture the main issues. Though it is profitable to have variety at the beginning of a research field, after some time, the diversity of metamodels becomes an obstacle, for instance to the sharing of results between research groups. To reach consensus and unification of existing metamodels, metamodel-driven software language engineering can be applied. This paper illustrates an application of software language engineering in the agent-oriented software engineering research domain. Here, we introduce a relatively generic agent-oriented metamodel whose suitability for supporting modeling language development is demonstrated by evaluating it with respect to several existing methodology-specific metamodels. First, the metamodel is constructed by a combination of bottom-up and top-down analysis and best practice. The concepts thus obtained and their relationships are then evaluated by mapping to two agent-oriented metamodels: TAO and Islander. We then refine the metamodel by extending the comparisons with the metamodels implicit or explicit within five more extant agent-oriented approaches: Adelfe, PASSI, Gaia, INGENIAS, and Tropos. The resultant FAML metamodel is a potential candidate for future standardization as an important component for engineering an agent modeling language.

In any modelling activity, a framework to determine the maturity of a developed model before its use is highly advantageous. Such a framework would save modellers expensive time in many areas of information systems. It would also lower the risk of users relying on an incomplete or inaccurate model. In this paper, we develop a framework which uses internal inconsistencies as a quantitative indicator for estimating the completeness and correctness of a model as it is cooperatively evolved. Whilst internal inconsistencies are due to bad fit between different parts of a model, we argue that they are also correlated with how the evolved model fits with the "world". This argument underpins our framework to evaluate integrated models. Contributions of this paper are three folds: firstly, it presents a theoretically grounded framework for integrating models. We extend an existing incremental modelling framework, NRDR, which represents multiple hierarchical restricted domains (MHRD), with automatic concept integration to allow NRDR to deal with multiple experts. Secondly, we couple this integration framework with a theoretically grounded monitoring process to assess the quality of the cooperatively developed model. Thirdly, we illustrate an initial empirical study of our evaluation and integration framework in a computer hardware administration domain. We capture and integrate computer hardware models from several experts and we use our modelling evaluation framework to evaluate the resultant cooperative model.

Ripple down rules (RDR) is an incremental knowledge acquisition (KA) methodology, where a knowledge base (KB) is constructed as a collection of rules with exceptions. Nested ripple down rules (NRDR) is an extension of this methodology which allows the expert to enter her/his own domain concepts and later refine these concepts hierarchically. In this paper we show similarities between incremental knowledge acquisition and database schema evolution, and propose to use the F2 object-oriented database management system (OODBMS) to implement an NRDR knowledge based system. We use the existing non-standard features of F2 and show how multiple instantiation and object migration (known as multiobjects feature in F2), and schema evolution capabilities in F2 easily accommodate all the update mechanisms required to incrementally build an NRDR KB. We illustrate our approach with a KA session.