bluuebunny/crossref_metadata_2025 huggingface dataset:
A finite-valued solver for disjunctive fuzzy answer set programs
Authors: Mushthofa Mushthofa  , Schockaert Steven  , De Cock Martine
Published: February 2025

Fuzzy Answer Set Programming (FASP) is a declarative programming paradigm which extends the flexibility and expressiveness of classical Answer Set Programming (ASP), with the aim of modeling continuous application domains. In contrast to the availability of efficient ASP solvers, there have been few attempts at implementing FASP solvers. In this paper, we propose an implementation of FASP based on a reduction to classical ASP. We also develop a prototype implementation of this method. To the best of our knowledge, this is the first solver for disjunctive FASP programs. Moreover, we experimentally show that our solver performs well in comparison to an existing solver (under reasonable assumptions) for the more restrictive class of normal FASP programs.
How much trust is enough to trust? A market-adaptive trust threshold setting for e-marketplaces
Authors: Noorian Zeinab  , Mohkami Mohsen  , Vassileva Julita
Published: February 2025

The inherent uncertainties of open marketplaces motivate the design of reputation systems to facilitate buyers in finding honest feedback from other buyers (advisers). Defining the threshold for an acceptable level of honesty of advisers is very important, since inappropriately set thresholds would filter away possibly good advice, or the opposite – allow malicious buyers to badmouth good services. However, currently, there is no systematic approach for setting the honesty threshold. We propose a self-adaptive honesty threshold management mechanism based on PID feedback controller. Experimental results show that adaptively tuning the honesty threshold to the market performance enables honest buyers to obtain higher quality of services in comparison with static threshold values defined by intuition and used in previous work.
Symmetry Breaking for Exact Solutions in Adjustable Robust Optimisation
Authors: Prestwich S.D.  , Laumanns M.  , Kawas B.
Published: February 2025

One of the key unresolved challenges in Adjustable Robust Optimisation is how to deal with large discrete uncertainty sets. In this paper we present a technique for handling such sets based on symmetry breaking ideas from Constraint Programming. In earlier work we applied the technique to a pre-disaster planning problem modelled as a two-stage Stochastic Program, and we were able to solve exactly instances that were previously considered intractable and only had approximate solutions. In this paper we show that the technique can also be applied to an adjustable robust formulation that scales up to larger instances than the stochastic formulation. We also describe a new fast symmetry breaking heuristic that gives improved results.
On Combinatorial Actions and CMABs with Linear Side Information
Authors: Shleyfman Alexander  , Komenda Antonín  , Domshlak Carmel
Published: February 2025

Online planning algorithms are typically a tool of choice for dealing with sequential decision problems in combinatorial search spaces. Many such problems, however, also exhibit combinatorial actions, yet standard planning algorithms do not cope well with this type of “the curse of dimensionality”. Following a recently opened line of related work on combinatorial multi-armed bandit (CMAB) problems, we propose a novel CMAB planning scheme, as well as two specific instances of this scheme, dedicated to exploiting what is called linear side information. Using a representative strategy game as a benchmark, we show that the resulting algorithms very favorably compete with the state-of-the-art.
Optimisation for the Ride-Sharing Problem: a Complexity-based Approach
Authors: Simonin Gilles  , O'Sullivan Barry
Published: February 2025

The dial-a-ride problem is a classic challenge in transportation and continues to be relevant across a large spectrum of applications, e.g. door-to-door transportation services, patient transportation, etc. Recently a new variant of the dial-a-ride problem, called ride-sharing, has received attention due to emergence of the use of smartphone-based applications that support location-aware transportation services. The general dial-a-ride problem involves complex constraints on a time-dependent network. In ride-sharing riders (resp. drivers) specify transportation requests (resp. offers) between journey origins and destinations. The two sets of participants, namely riders and drivers, have different constraints; the riders have time windows for starting and finishing the journey, while drivers have a starting time window, a destination, and a vehicle capacity. The challenge is to maximise the overall utility of the participants in the system which can be defined in a variety of ways. In this paper we study variations of the ride-sharing problem, under different notions of utility, from a computational complexity perspective, and identify a number of tractable and intractable cases. These results provide a basis for the development of efficient methods and heuristics for solving problems of real-world scale.
Quantifying the Completeness of Goals in BDI Agent Systems
Authors: Thangarajah John  , Harland James  , Morley David N.  , Yorke-Smith Neil
Published: February 2025

Given the current set of intentions an autonomous agent may have, intention selection is the agent's decision which intention it should focus on next. Often, in the presence of conflicts, the agent has to choose between multiple intentions. One factor that may play a role in this deliberation is the level of completeness of the intentions. To that end, this paper provides pragmatic but principled mechanisms for quantifying the level of completeness of goals in a BDI-style agent. Our approach leverages previous work on resource and effects summarization but we go beyond by accommodating both dynamic resource summaries and goal effects, while also allowing a non-binary quantification of goal completeness. We demonstrate the computational approach on an autonomous robot case study.
Verifying CTL* Properties of GOLOG Programs over Local-Effect Actions
Authors: Zarrieß Benjamin  , Claßen Jens
Published: February 2025

GOLOG is a high-level action programming language for controlling autonomous agents such as mobile robots. It is defined on top of a logic-based action theory expressed in the Situation Calculus. Before a program is deployed onto an actual robot and executed in the physical world, it is desirable, if not crucial, to verify that it meets certain requirements (typically expressed through temporal formulas) and thus indeed exhibits the desired behaviour. However, due to the high (first-order) expressiveness of the language, the corresponding verification problem is in general undecidable. In this paper, we extend earlier results to identify a large, non-trivial fragment of the formalism where verification is decidable. In particular, we consider properties expressed in a first-order variant of the branching-time temporal logic CTL*. Decidability is obtained by (1) resorting to the decidable first-order fragment C2as underlying base logic, (2) using a fragment of GOLOG with ground actions only, and (3) requiring the action theory to only admit local effects.
False-name-proof Combinatorial Auction Design via Single-minded Decomposition
Authors: Zhao Dengji  , Luo Siqi  , Todo Taiki  , Yokoo Makoto
Published: February 2025

This paper proposes a new approach to building false-name-proof (FNP) combinatorial auctions from those that are FNP only with single-minded bidders, each of whom requires only one particular bundle. Under this approach, a general bidder is decomposed into a set of single-minded bidders, and after the decomposition the price and the allocation are determined by the FNP auctions for single-minded bidders. We first show that the auctions we get with the single-minded decomposition are FNP if those for single-minded bidders satisfy a condition called PIA. We then show that another condition, weaker than PIA, is necessary for the decomposition to build FNP auctions. To close the gap between the two conditions, we have found another sufficient condition weaker than PIA for the decomposition to produce strategy-proof mechanisms. Furthermore, we demonstrate that once we have PIA, the mechanisms created by the decomposition actually satisfy a stronger version of false-name-proofness, called false-name-proofness with withdrawal.
Predicting Agents' Behavior by Measuring their Social Preferences
Authors: Cheng Kan-Leung  , Zuckerman Inon  , Nau Dana  , Golbeck Jennifer
Published: February 2025

There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated stochastic games. In such situations, each agent's performance may depend greatly on how well it can predict the other agents' preferences and behavior. For use in making such predictions, we adapt and extend the Social Value Orientation (SVO) model from social psychology, which provides a way to measure an agent's preferences for both its own payoffs and those of the other agents.
A deductive approach to the identification and description of clusters in Linked Open Data
Authors: Colucci Simona  , Giannini Silvia  , Donini Francesco M.  , Di Sciascio Eugenio
Published: February 2025

We propose an approach for inferring clusters in collections of RDF resources based on the features shared by their descriptions. The approach grounds on an algorithm for computing Common Subsumers in RDF proposed in a previous research work. The clustering service introduced here returns not only a possible partition of resources in a collection, but also a description of the knowledge shared within each cluster, in terms of (generalized) RDF triples.
Embedding a Card Game Language into a General Game Playing Language
Authors: Kowalski Jakub
Published: February 2025

We make a link between a specialized context free language expressing the rules of variety of card games, called CGDL, and the most known general-purpose game description language GDL-II. We present a systematic translation from CGDL to GDL-II, prove that the translation is correct, and analyze the complexity of resulting code in both theoretical and empirical way.
Integration of Temporal abstraction and Dynamic Bayesian Networks for Coronary Heart Diagnosis
Authors: Orphanou Kalia  , Stassopoulou Athena  , Keravnou Elpida
Published: February 2025

Temporal data abstraction (TA) is a set of techniques aiming to abstract time-points into higher-level interval concepts and to detect significant trends in both low-level data and abstract concepts. Dynamic Bayesian networks (DBNs) are temporal probabilistic graphical models that model temporal processes, temporal relationships between events and state changes through time. In this paper, we propose the integration of TA methods with DBNs in the context of medical decision-support systems, by presenting an extended DBN model. More specifically, we demonstrate the derivation of temporal abstractions which are used for building the network structure. We also apply machine learning algorithms to learn the parameters of the model through data. The model is applied for diagnosis of coronary heart disease using as testbed a longitudinal dataset. The classification accuracy of our model evaluated using the evaluation metrics of Precision, Recall and F1-score, shows the effectiveness of our proposed system.
Predicting Players Behavior in Games with Microtransactions
Authors: Pluskal Ondřej  , Šedivý Jan
Published: February 2025

This paper focuses on predicting player behaviour in two-player games with microtransactions. Typically the games are for free and companies generate their revenue by selling in-game goods. We show creation of a users behaviour model, which are then used in a recommendation system increasing in-game goods purchases. We focus on learning techniques in a novel way, predicting the time of purchases rather than the most likely product to be purchased. The player model is based on in-game signals, such as players success, curiosity, social interactions etc. We had access to a Pool Live Tour game dataset made by Geewa. We report promising results in predicting the purchase events.
Key Issues on Assuring the Safe Operation of Heath IT Systems
Authors: Despotou George  , Jones Richard  , White Sean  , Ryan Mark  , Kelly Tim
Published: February 2025

The paper discusses the motivation behind the safety assurance of health IT, lists pertinent standards and regulation, as well as discussing prominent challenges in the field of health I safety.
3D texture analysis of MR images to improve classification of paediatric brain tumours: a preliminary study
Authors: Fetit Ahmed E.  , Novak Jan  , Peet Andrew C.  , Arvanitis Theodoros N.
Published: February 2025

Brain and central nervous system (CNS) tumours form the second most common group of cancers in children in the UK, accounting for 27% of all childhood cancers. Initial assessment of tumours from MRI scans is usually performed qualitatively, via radiologists' visual inspection. However, different brain tumours do not always demonstrate clear differences in physical appearance, so a diagnosis is usually made via histopathological examination of biopsy samples taken through surgery. This gives rise to the need for accurate, yet non-invasive diagnostic aids. In a previous study, we demonstrated the potential of MRI texture analysis in capturing quantitative information about paediatric brain tumours. In this work, we carry out a preliminary investigation on the use of 3D (volumetric) texture analysis of T1 and T2-weighted MR images in order to classify paediatric brain tumours. We then compare its performance with the traditional 2D texture analysis approach. Our preliminary findings are very encouraging and show that 3D textural features are capable of capturing more discriminative information about the tumours than the traditional 2D approach. However, it remains necessary to expand the work further to include larger cohorts and additional modalities.
Quality of Care as an Emergent Phenomenon out of a Small-World Network of Relational Actors
Authors: Fiorini Rodolfo  , De Giacomo Piero  , Marconi Pier Luigi  , L'Abate Luciano
Published: February 2025

In Healthcare Decision Support System, the development and evaluation of effective “Quality of Care” (QOC) indicators, in simulation-based training, are key feature to develop resilient and antifragile organization scenarios. Is it possible to conceive of QOC not only as a result of a voluntary and rational decision, imposed or even not, but also as an overall system “emergent phenomenon” out of a small-world network of relational synthetic actors, endowed with their own personality profiles to simulate human behaviour (for short, called “subjects”)? In order to answer this question and to observe the phenomena of real emergence we should use computational models of high complexity, with heavy computational load and extensive computational time. Nevertheless, De Giacomo's Elementary Pragmatic Model (EPM) intrinsic self-reflexive functional logical closure enables to run simulation examples to classify the outcomes grown out of a small-world network of relational subjects fast and effectively. Therefore, it is possible to take note and to learn of how much strategic systemic interventions can induce context conditions of QOC facilitation, which can improve the effectiveness of specific actions, which otherwise might be paradoxically counterproductive also. Early results are so encouraging to use EPM as basic block to start designing more powerful Evolutive Elementary Pragmatic Model (E2PM) for real emergence computational model, to cope with ontological uncertainty at system level.
Optimising computerised alerts within electronic medication management systems: A synthesis of four years of research
Authors: Baysari Melissa T.  , Westbrook Johanna I.  , Richardson Katrina  , Day Richard O.
Published: February 2025

Most studies evaluating the effect of computerised alerts embedded in electronic medication management systems (eMMS) on prescribing behavior demonstrate positive and often substantial effects. But many studies also report that doctors override computerised alerts, sometimes up to 95% of the time. Alert fatigue, due to excessive numbers of alerts being presented, is the primary reason for alerts being overridden. This paper summarises and sythesises a program of research undertaken to determine whether doctors working in a teaching hospital in Sydney, Australia, were experiencing alert fatigue, and to identify and implement strategies for alleviating alert fatigue. We synthesise several published studies adopting a variety of data collection methods (observation of prescribers as they interact with the eMMS, interviews with users, review of alerts generated in eMMS, and a Delphi technique) to present four key lessons learnt. These are: 1) the fewer alerts the better; 2) context of use matters; 3) people use systems in unexpected ways; and 4) user feedback is invaluable.
Mobile learning: A workforce development strategy for nurse supervisors
Authors: Mather Carey  , Cummings Elizabeth
Published: February 2025

Digital technology provides opportunities for using mobile learning strategies in healthcare environments. To realise the vision of the National Workforce Development Strategy there needs to be innovation of health professionals to further develop knowledge and skills of clinical supervisors to access and gain an understanding of the value of mobile learning at the workplace. The use of digital technology by clinical supervisors was explored in 2012 as part of a teaching development grant to evaluate the use of Web 2.0 technology to develop a community of practice about clinical supervision. Prior to developing the virtual network of clinical supervisors, feedback about the use of Web 2.0 technology by clinicians was sought via an online survey. Over 90% of respondents used social media, 85% understood what a blog and wiki were and approximately half of the respondents used smart phones. More than one-third indicated they would participate in a virtual community of practice and would like to receive information about clinical facilitation at least once per week. Findings indicate both inhibitors and opportunities for workforce development within healthcare environments that need to be addressed. Support of graduate-ready nurses can be achieved through an integrated outlook that enables health professionals within organisations to undertake mobile learning in situ. A flexible and collaborative approach to continuing professional development within organisations could enhance practice development and could positively impact on workforce development.
Describing Localized Diseases in Medical Ontology: An FMA-based Algorithm
Authors: Charlet Jean  , Mazuel Laurent  , Declerck Gunnar  , Miroux Patrick  , Gayet Pierre
Published: February 2025

ONTOLURGENCES is a termino-ontological resource developed to index and retrieve information in electronic Emergency Medical Record. In this project, we improved the ontology coverage to accommodate both anatomical and pathophysiological concepts in emergency medicine. This work lead to the automatic addition of 3,470 concepts and their underlying semantic formalization. In our method, we reuse and select the anatomical concepts relevant to emergency from FMA: To capture the anatomical specific concepts, (i) we involved Emergency practitioners and identified the key concepts from this domain; (ii) we applied an automatic algorithm to define the semantic relationships and integrated the result in the existing ontology.
A Comprehensive Clinical Research Database based on CDISC ODM and i2b2
Authors: Meineke Frank A.  , Stäubert Sebastian  , Löbe Matthias  , Winter Alfred
Published: February 2025

We present a working approach for a clinical research database as part of an archival information system. The CDISC ODM standard is target for clinical study and research relevant routine data, thus decoupling the data ingest process from the access layer. The presented research database is comprehensive as it covers annotating, mapping and curation of poorly annotated source data. Besides a conventional relational database the medical data warehouse i2b2 serves as main frontend for end-users. The system we developed is suitable to support patient recruitment, cohort identification and quality assurance in daily routine.
Addressing the Challenge of Encoding Causal Epidemiological Knowledge in Formal Ontologies: A Practical Perspective
Authors: Okhmatovskaia Anya  , Shaban-Nejad Arash  , Lavigne Maxime  , Buckeridge David L.
Published: February 2025

The paper presents an overview of approaches to encoding uncertain causal knowledge in formal ontologies and demonstrates how these approaches can be used in a semantic-driven application for public health using the Population Health Record (PopHR) platform as an example.
Towards IHE Profiles for e-Supply in the Healthcare Domain
Authors: Schneider Gerd  , Heidenreich Georg  , Van De Sand Lasse  , Teixeira Jose  , Rathmer Alfons  , Böckmann Britta  , Otten Hubert  , Bergh Björn  , Thun Sylvia
Published: February 2025

Procurement and supply belong to the crucial supporting processes in hospitals. By providing the right products at the right time in the right quantity in the right place, these processes are of major importance for patient safety and the success of the primary hospital process cure and care. Though of such an importance, the hospital's internal and external procurement and supply processes are still not performed seamlessly by information systems, due to a variety of electronic standards for procurement and supply. These different standards and missing guidelines for electronic procurement and supply in the healthcare domain lead to little or no interoperability between the participating procurement and supply systems. Consequences can be delayed or wrong supply, and increased costs. To overcome this deficiency this work describes the derivation of a common guideline for the implementation of electronically enabled supply processes in the healthcare domain based upon the identification of commonly used electronic standards and detailed specifications of procurement processes in hospital facilities.
Computer Based Extraction of Phenoptypic Features of Human Congenital Anomalies from the Digital Literature with Natural Language Processing Techniques
Authors: Karakülah Gökhan  , Dicle Oğuz  , Koşaner Özgün  , Suner Aslı  , Birant Çağdaş Can  , Berber Tolga  , Canbek Sezin
Published: February 2025

The lack of laboratory tests for the diagnosis of most of the congenital anomalies renders the physical examination of the case crucial for the diagnosis of the anomaly; and the cases in the diagnostic phase are mostly being evaluated in the light of the literature knowledge. In this respect, for accurate diagnosis, ,it is of great importance to provide the decision maker with decision support by presenting the literature knowledge about a particular case. Here, we demonstrated a methodology for automated scanning and determining of the phenotypic features from the case reports related to congenital anomalies in the literature with text and natural language processing methods, and we created a framework of an information source for a potential diagnostic decision support system for congenital anomalies.
Link Prediction in a MeSH Co-occurrence Network: Preliminary Results
Authors: Kastrin Andrej  , Rindflesch Thomas C.  , Hristovski Dimitar
Published: February 2025

Literature-based discovery (LBD) refers to automatic discovery of implicit relations from the scientific literature. Co-occurrence associations between biomedical concepts are commonly used in LBD. These co-occurrences can be represented as a network that consists of a set of nodes representing concepts and a set of edges representing their relationships (or links). In this paper we propose and evaluate a methodology for link prediction of implicit connections in a network of co-occurring Medical Subject Headings (MeSH®). The proposed approach is complementary to, and may augment, existing LBD methods. Link prediction was performed using Jaccard and Adamic-Adar similarity measures. The preliminary results showed high prediction performance, with area under the ROC curve of 0.78 and 0.82 for the two similarity measures, respectively.
Machine learning-based detection of chemical risk
Authors: Grabar Natalia  , Wandji Tchami Ornella  , Maxim Laura
Published: February 2025

Chemical risk appears with chemical substances that are dangerous for human or animal health, or for environment, such as with Bisphenol A and phtalates. Chemical risk causes several severe health disorders and is particularly dangerous for human health. Specific agencies are involved in the verification of the suitability of products and goods to be marketed. For this, a large amount of scientific and institutional literature is manually analyzed to study the current knowledge on the associated chemical risk. We propose to use machine learning and dedicated classification for the automatic detection of chemical risk statements. We test several algorithms and features and obtain between 0.60 and 0.95 F-measure.
Balancing the need for big data and patient data privacy – an IT infrastructure for a decentralized emergency care research database
Authors: Ahlbrandt Janko  , Brammen Dominik  , Majeed Raphael W.  , Lefering Rolf  , Semler Sebastian C.  , Thun Sylvia  , Walcher Felix  , Röhrig Rainer
Published: February 2025

Emergency rooms of hospitals provide care to a lot of patients and have great impact on their outcome, so researching the quality of care seems important. Research using registries has yielded impressive results in other areas of medicine. However centralized data-storage has its pitfalls, especially regarding data privacy. We therefore drafted an IT infrastructure that uses decentralized storage to ensure data privacy, but still enables data transfer between participating hospitals. It implements an independent information broker to ensure anonymity of patients. Still it provides a way for researchers to request data and hospitals to contribute data on an opt-in basis. Although not an entirely new approach, the emphasis on data privacy throughout the design is a novel aspect providing a better balance between the need for big sample sizes and patient privacy.
Health Information Research Platform (HIReP) – An Architecture Pattern
Authors: Schreiweis Björn  , Schneider Gerd  , Eichner Theresia  , Bergh Björn  , Heinze Oliver
Published: February 2025

Secondary use or single source is still far from routine in healthcare, although lots of data are available either structured or unstructured. As data are stored in multiple systems, using them for biomedical research is difficult. Clinical data warehouses already help overcoming this issue, but currently they are only used for certain parts of biomedical research. A comprehensive research platform based on a generic architecture pattern could increase the benefits of existing data warehouses for both patient care and research by meeting two objectives: serving as a so called single point-of-truth and acting as a mediator between them strengthening interaction and close collaboration. Another effect is to reduce boundaries for the implementation of data warehouses. Taking further settings into account the architecture of a clinical data warehouse supporting patient care and biomedical research needs to be integrated with biomaterial banks and other sources. This work provides a solution conceptualizing a comprehensive architecture pattern of a Health Information Research Platform (HIReP) derived from use cases of the patient care and biomedical research domain. It serves as single IT infrastructure providing solutions for any type of use case.
eduCRATE- A Virtual Hospital Architecture
Authors: Stoicu-Tivadar Lăcrimioara  , Stoicu-Tivadar Vasile  , Berian Dorin  , Drăgan Simona  , Şerban Alexandru  , Şerban Corina
Published: February 2025

eduCRATE is a complex project proposal which aims to develop a virtual learning environment offering interactive digital content through original and integrated solutions using cloud computing, complex multimedia systems in virtual space and personalized design with avatars. Compared to existing similar products the project brings the novelty of using languages for medical guides in order to ensure a maximum of flexibility. The Virtual Hospital simulations will create interactive clinical scenarios for which students will find solutions for positive diagnosis and therapeutic management. The solution based on cloud computing and immersive multimedia is an attractive option in education because is economical and it matches the current working style of the young generation to whom it addresses.
Determinants of Eligibility and Use of Ehealth for Cardiac Rehabilitation Patients: Preliminary Results
Authors: Wiggers Anne-Marieke  , Peek Niels  , Kraaijenhagen Roderik  , Jaspers Monique
Published: February 2025

To foster implementation of eHealth in care practice, it is important to gain insight into the factors that influence acceptance. The aim of this study was to identify determinants of eligibility and use for completing an electronic needs assessment for cardiac rehabilitation. We analysed the influence of age, gender, diagnosis, health literacy, quality of life scores, and depression and anxiety scores. Among 240 patients entering CR in two clinics, 101 patients were deemed eligible to use the system by their CR professional, of which 75 (74.2%) actually used it. Only 50% of the patients who had an acute coronary syndrome and 20% of the patients with chronic heart failure were deemed eligible. Furthermore, there was a decreasing trend towards usage in female patients, patients with symptoms of anxiety, and patients with a better quality of life. In the future we will continue to explore barriers and success factors as experienced by the patients and the participating clinics to optimize the system.
Teaching Bioinformatics to Engineers
Authors: Mihalas George I.  , Tudor Anca  , Paralescu Sorin  , Andor Minodora  , Stoicu-Tivadar Lacramioara
Published: February 2025

The paper refers to our methodology and experience in establishing the content of the course in bioinformatics introduced to the school of “Information Systems in Healthcare” (SIIS), master level. The syllabi of both lectures and laboratory works are presented and discussed.
Designing and Conducting Low-Cost In-Situ Clinical Simulations: A Methodological Approach
Authors: Kushniruk Andre W.  , Borycki Elizabeth M.
Published: February 2025

The need to develop more effective and usable systems in healthcare has led to increasing adoption of usability engineering methods. Primary among these methods has been usability testing, which involves observing representative users while they carry out representative tasks using health information systems. However, such testing may not capture the cognitive and social contexts of use of systems in real settings, necessitating the use of clinical simulations. Such simulations involve testing representative users carrying out representative tasks, systems in realistic and representative environments. In this paper we describe our work in both reducing the cost of simulation testing while at the same time increasing the fidelity of the simulations. We describe an approach which involves conducting simulations within real work environments and contexts as a way to achieve a high level of ecological validity. The stages involved in setting up such low-cost in-situ clinical simulations are detailed in this paper.
User Satisfaction and Experience with a Telehealth System for the Danish TeleCare North Trial: A Think-Aloud Study
Authors: Lilholt Pernille Heyckendorff  , Heiden Sisse  , Hejlesen Ole K.
Published: February 2025

The aim was to evaluate a redesigned version of Telekit – a telehealth system developed for the Danish TeleCare North Trial. Telekit is used in the management of care in patients diagnosed with chronic obstructive pulmonary disease (COPD). This paper summarises the experience and the feedback received from six COPD-participants in terms of usability and satisfaction. Participants were asked to think-aloud while performing some system specific tasks. After each session, participants completed a post-test questionnaire. The think-aloud test was recorded, and notes from the tests were categorised and analysed. All tasks were completed by participants. Difficulties were observed concerning monitoring of measurements and use of the touchscreen. User feedback was mainly positive, and nearly all participants perceived Telekit as very easy to use. The study provides important insight regarding use of Telekit by patients suffering from a chronic illness and increased understanding about, how similar systems can more effectively be used in such home health initiatives.
Integration of Safety Risk Assessment Techniques into Requirement Elicitation
Authors: Yeow Eileen  , Chiam Yin Kia
Published: February 2025

Incomplete and incorrect requirements may cause the safety-related software systems to fail to achieve their safety goals. It is crucial to ensure software safety by identifying proper software safety requirements during the requirements elicitation activity. Practitioners apply various Safety Risk Assessment Techniques (SRATs) to identify, analyze and assess safety risk. Nevertheless, there is a lack of guidance on how appropriate SRATs and safety process can be integrated into requirements elicitation activity to bridge the gap between the safety and requirements engineering practices. In this research, we proposed an Integration Framework that integrates safety activities and techniques into existing requirements elicitation activity.
Hybridization of Hidden Markov Model and Case Based Reasoning for Time Series Forecasting
Authors: Zahari Azuanda Azlina  , Jaafar Jafrezal
Published: February 2025

In the past few years, tremendous studies have been made to examine the accuracy of time series forecasting that provide the foundation for decision models in foreign exchange data. This study proposes a novel approach of Hidden Markov Model and Case Based reasoning for time series forecasting. This paper compares the proposed method with the technical models; moving average convergence/divergence model (MACD), William's percent range, and naïve strategy for short-term trading decision. HMM is trained by using forward-backward or Baum-Welch algorithm and the likelihood value is used to predict future exchange rate price. The forecasting accuracy has been measured according to Root Mean Square Error (RMSE). The statistical performance of all techniques is investigated in testing of EUR/USD exchange rate time series over the period of October 2010 to November 2013. The preliminary results indicate that the new approach of HMM produce the lowest RMSE compared to the benchmark models. Further study is to adopt Case Based reasoning to further improve the forecasting results.
Extracting Significant Features from Virtual Histology to Detect Vulnerable Plaque
Authors: Rezaei Zahra  , Selamat Ali  , Taki Arash  , Mohd Rahim Mohd Shafry  , Abdul Kadir Mohammed Rafiq
Published: February 2025

One of the major challenges and concerns of researchers is an early detection and diagnosis of thin-cap-fibro-atheroma or vulnerable plaque to prevent the sudden heart events. Recently, Virtual Histology (VH) as a new approach based on spectral analysis of Intravascular Ultrasound (IVUS) provides color coded of coronary tissue maps. In IVUS-VH image, plaque's components can be discriminated based on echogenicity. Nonetheless; available methods do not provide clinical relevant information about the pattern of plaque structure, plaque composition, and geometric position of each components, location or distribution of plaque components toward the lumen border. In this paper, we proposed a new method of feature extraction that plays a decisive role in vulnerable plaque detection including NCCL (Necrotic Core in Contact with the Lumen), DCCL (Dense Calcium in Contact with the Lumen), Confluent NC and Confluent DC.
A Matrix Approach in System Dynamics Simulations: The Case of Flow Shop Layout Architecture in PTO Production Environment
Authors: Ascione Monica  , Centobelli Piera  , Converso Guseppe  , Santillo Liberatina C.
Published: February 2025

This paper arises from the need to give an answer to a problem of optimization of industrial production in conditions of high rigidity of production constraints. In particular, the production architecture is that of a set of work centers arranged in a typical flow shop. This architecture presents the inability to change the order of machining operations. This implies that the problems of optimization by the latter were always resolved by a classical approach to scheduling. The approach presented in this work uses a System Dynamics simulation model that in despite of an apparent greater complexity if compared to a classical approach, allows, basing on the use of matrices, to identify at each instant the position and the confluence of each single order, and also to decide in a dynamic way operations planning at each work center. This approach has been implemented in a case study but shows to be enough general to be applicable to any company.
On the Properties of the Relation between Argumentation Semantics and Argumentation Inference Operators
Authors: Simari Guillermo R.
Published: February 2025

The problem of finding properties that characterizes the relation between argumentation semantics and argumentation inference operators has beginning to surface in the last years. Several works have addresses this concern proposing different “postulates” that reflect the intuitions in this respect. Argumentative reasoning is by nature defeasible and that distinct feature must have a deep influence on the resulting constrains. We believe that the very essence of argumentation should affect the manner in which the required properties are described.
Splitting Abstract Dialectical Frameworks
Authors: Linsbichler Thomas
Published: February 2025

Among the abundance of generalizations of abstract argumentation frameworks, the formalism of abstract dialectical frameworks (ADFs) proved to be powerful in modelling various argumentation problems. Implementations of reasoning tasks that come within ADFs struggle with their high computational complexity. Thus methods simplifying the evaluation process are required. One such method is splitting, which was shown to be an effective optimization technique in other nonmonotonic formalisms. We apply this approach to ADFs by providing suitable techniques for directional splitting (allowing links only from the first to the second part of the splitting) under all the standard semantics of ADFs as well as preliminary results on general splitting.
NoDE: A Benchmark of Natural Language Arguments
Authors: Cabrio Elena  , Villata Serena
Published: February 2025

In the latest years, natural models of argumentation and argument mining are becoming more and more important topics in the argumentation community. Given this tendency, there is the need to produce standard datasets on which natural language approaches to argumentation can be evaluated. In this paper, we present NoDE, a benchmark of natural language arguments composed of three datasets, built from different textual sources and annotated highlighting positive and negative connections between arguments.
Towards Argumentation for Statistical Model Selection
Authors: Sassoon Isabel  , Keppens Jeroen  , McBurney Peter
Published: February 2025

The increase in routine clinical data collection coupled with an expectation to exploit this in support of evidence based decision making creates the requirement for a system to support clinicians in this analysis. This paper looks at applying argumentation to this problem, by collating all the relevant statistical approaches and their assumptions into a statistical knowledge base and then representing the model selection process through argumentation. This will form the foundation for the development of a prototype that will enable clinicians to answer their research questions with no statistics, informatics or administrative support.
Mathematical Foundations for Participation Ontologies
Authors: Chui Carmen  , Grüninger Michael
Published: February 2025

The notion of participation as a relation between objects, activities, and time has been axiomatized in various ontologies. In this paper, we focus on three of these ontologies – PSL-Core, Gangemi's axioms, and DOLCE. We provide a verification of these participation ontologies by introducing ontologies for new classes of mathematical structures known as incidence bundles and incidence foliations. The new mathematical ontologies serve as reusable ontology design patterns for participation, and also are the basis for mappings between the different participation ontologies. Finally, we illustrate the concept of ontology transfer through the use of these ontology design patterns.
Events and Activities: Is there an Ontology behind BPMN?
Authors: Sanfilippo Emilio M.  , Borgo Stefano  , Masolo Claudio
Published: February 2025

In the context of business process modelling, the Business Process Model and Notation (BPMN) is a de-facto standard with more than 70 commercial tools that currently support its use. Amongst its main modelling constructs, BPMN includes activities and events. However, the focus of the standard is on providing an intuitive graphical language, rather than formal semantics specifications. This results in semantic ambiguities regarding the interpretation of its modelling constructs. We investigate whether the main building blocks of BPMN commit to an ontological theory of the domain entities at hand, eventually clarifying this commitment by the approach of ontological analysis.
The Ontology of Group Agency
Authors: Porello Daniele  , Bottazzi Emanuele  , Ferrario Roberta
Published: February 2025

We present an ontological analysis of the notion of group agency developed by Christian List and Philip Pettit. We focus on this notion as it allows us to neatly distinguish groups, organizations, corporations – to which we may ascribe agency – from mere aggregates of individuals. We develop a module for group agency within a foundational ontology and we apply it to organizations.
Corpus Guided Sense Cluster Analysis: a methodology for ontology development (with examples from the spatial domain)
Authors: Bennett Brandon  , Cialone Claudia
Published: February 2025

The paper explores problems in ontology construction that arise due to the complex mapping between language and meaning. A new methodology is proposed, which combines a definitional approach using formal logic, with corpus-based statistical analysis of the use of terminology in natural language text. Underlying the approach is a semantic theory in which the notion of sense cluster plays a central role. Rather than having a single precise definition, the referent of a conceptual term is taken to be a sense cluster, modelled by a probability distribution over a set of precise definitions. This style of semantic specification pays heed to insights into the nature of language coming from philosophers such as Wittgenstein and his followers; but it also provides a framework supporting rigorous formal ontology development, which is often regarded as incompatible with the view of language suggested by Wittgenstein (in his later works).
An Ontological Analysis of the ISO/IEC 24744 Metamodel
Authors: Ruy Fabiano B.  , Falbo Ricardo A.  , Barcellos Monalessa P.  , Guizzardi Giancarlo
Published: February 2025

This paper presents an ontological analysis of the Software Engineering Metamodel for Development Methodologies (SEMDM), provided by the ISO/IEC 24744 Standard. An ISO initiative intends to use SEMDM as source of an ontology for standards harmonization purposes, and the ontological analysis can help to ensure the required semantics. This analysis is done in the light of the Unified Foundational Ontology (UFO). As result, we present some of the problems identified in SEMDM, as well as alternative model fragments solving them.
Ontology-based Data Management for the Italian Public Debt
Authors: Antonioli Natalia  , Castanò Francesco  , Coletta Spartaco  , Grossi Stefano  , Lembo Domenico  , Lenzerini Maurizio  , Poggi Antonella  , Virardi Emanuela  , Castracane Patrizia
Published: February 2025

In this paper we present an ontology-based data management (OBDM) project concerning the Italian public debt domain, carried out within a joint collaboration between Sapienza University of Rome and the Department of Treasury of the Italian Ministry of Economy and Finance. We discuss the motivations at the basis of this project and present the main characteristics of the ontology we have built. We also describe the mechanisms we used to link the ontology to the actual data and the tools we have adopted for supporting ontology development and maintenance, as well as exploiting OBDM services. Finally, we provide a thorough evaluation of the ontology we produced and discuss in detail the role that it plays within the whole information system of the ministry department responsible for managing Italian public debt data.
An Ontology for the τ-theory of Enterprise Engineering
Authors: Dietz Jan  , Aveiro David  , Pombinho João  , Hoogervorst Jan
Published: February 2025

Enterprise engineering (EE) is a growing scientific discipline that takes the increasingly needed construction-oriented point of view, and aims to provide synthetic knowledge for letting enterprises more effectively and continuously adapt to threats and challenges. In this paper we present the τ-theory, being developed to support EE and consisting in the clarification of a series of foundational concepts of the EE domain like: system, model, subject, object, function, purpose and value, among others. Such clarification is then systematized in an ontology, together with an instantiation to validate it.
A Sideways Look at Upper Ontologies
Authors: Grüninger Michael  , Hahmann Torsten  , Katsumi Megan  , Chui Carmen
Published: February 2025

This paper explores an alternative vision for upper ontologies which is more effective at facilitating the sharability and reusability of ontologies. The notion of generic ontologies is characterized through the formalization of ontological commitments and choices. Ontology repositories are used to modularize ontologies so that any particular upper ontology is equivalent to the union of a set of generic ontologies. In this way, upper ontologies are not replaced but rather integrated with other theories in the ontology repository.
Coreference Resolution in Latvian
Authors: Znotiņš Artūrs
Published: February 2025

Coreference resolution (CR) is a current problem in natural language processing (NLP) research and it is a key task in applications such as question answering, text summarization and information extraction for which text understanding is of crucial importance. This paper describes a work in progress for improving Latvian coreference resolution that includes further experiments with the rule based LVCoref system, enlarging existing coreference corpus and the first efforts to adapt machine learning methods. LVCoref system now reaches 58.0% F-score using predicted mentions and 76.5% F-score if gold entity mentions are used.
Towards Large Vocabulary Automatic Speech Recognition for Latvian
Authors: Salimbajevs Askars  , Pinnis Mārcis
Published: February 2025

In this paper, the authors present the results of ongoing research on Large Vocabulary Automatic Speech Recognition for the Latvian language. The paper describes the initial acoustic model, phoneme set, filler and noise models, and grapheme-to-phoneme modelling. The second part of this work is focused on language modelling. Different word and class-based n-gram models are evaluated in terms of perplexity and word error rate in a speech recognition task. The authors also train a recurrent neural network language model and use it for n-best rescoring.