bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of an Online Platform to Support the Network of Caregivers of People with Dementia
Authors: Verwey Renée  , van Berlo Miranda  , Duymelinck Saskia  , Willard Sarah  , van Rossum Erik
Published: February 2025

In the Netherlands, care technology is used insufficiently to support people with dementia, their family and professional caregivers. In this project we integrate a range of services and applications into an online platform, with the aim to strengthen these networks and to support communication between their members. The prototype of the platform was made in an iterative user centered way. Semi structured (group) interviews were conducted to specify the requirements. The platform consists of ‘cubes’ with information about dementia (care), video communication options, a calendar and a care plan. The first prototype of the platform was valued by the participants, but privacy matters and registration issues were pointed out when using a shared care plan. Additional applications to monitor health and safety will be integrated in the second prototype. This prototype will be tested on its usability, feasibility and desirability during a pilot study in spring 2016.
Social Media Use Among Nurses: Literature Review
Authors: Cordoş Ariana Anamaria  , Bolboacă Sorana D.
Published: February 2025

Aim: The scope of the research was to increase the understanding of social media's influence among nurses while highlighting gaps in the literature and areas for further research.
Gamified Design for Health Workshop
Authors: Giunti Guido
Published: February 2025

Increasing lifespans for chronic disease sufferers means a population of young patients who require lifestyle intervention from an early age. For multiple sclerosis (MS) patients, social problems begin with the decline of cognitive skills and their quality of life is affected.
User-Centric eHealth Tool to Address the Psychosocial Effects of Sickle Cell Disease
Authors: Issom David-Zacharie  , Hartvigsen Gunnar  , Bonacina Stefano  , Koch Sabine  , Lovis Christian
Published: February 2025

Sickle cell disease (SCD) is the most prevalent blood inherited disorder in the world. Patients suffer from several chronic issues, comorbidities and high-mortality rates. Despite its prevalence, the disease remains largely ignored. A literature review was conducted and a questionnaire was sent to patients in order to understand the potential of e-health tools to support people with SCD. Additionally, focus groups have been conducted to detail respondents' answers. The results showed that patients felt isolated and misunderstood. They also highlighted patients' wishes for a social network able to make them feel less scattered. Using participatory-design techniques, we designed a prototype of user-centric interface for an online self-supportive SCD patient community. The mock-ups include chatrooms, forums and videoconferences capabilities. They illustrate how SCD patients' social networking and caregivers-patient relationship needs could be met. Future work will focus on the implementation and evaluation of the system.
Public Health Information Systems: Priorities and Practices for Successful Deployments
Authors: Pearce Martin
Published: February 2025

A fast paced workshop designed for senior public health decision makers and clinical leaders implementing information systems to support delivery of public health programs. The tutorial will introduce public health information systems and provide best practices for implementing solutions related to immunization, communicable disease case management and outbreak management.
Nursing and eHealth: Are We Preparing Our Future Nurses as Automatons or Informaticians?
Authors: Honey Michelle  , Procter Paula M.  , Wilson Marisa L.  , Moen Anne  , Dal Sasso Grace T.M.
Published: February 2025

The Education Working Group of IMIA NI present this thought provoking panel where the changing and challenging role of nursing will be explored within the information intensive eHealth arena. The session will be of interest to any nurse as the discussion will be driven by the objective of trying to understand how best to prepare nurses to be actively engaged in information and communication technology (ICT) developments that enhance care assessment, delivery, evaluation and audit. As a balance, the discussion will consider the increasing emergence of ‘nursing by numbers’ where risk assessment tools are used in an automatic way leaving little room for individual evidenced based care.
Update: NI Scope and Standards of Practice, Competencies, and Certification
Authors: Bickford Carol J.
Published: February 2025

The new 2015 ANA Nursing Informatics: Scope and Standards of Practice, Second Edition, includes a slightly revised definition of nursing informatics, totally revised scope of practice statement, and updated standards and accompanying competencies. The competencies are now leveled for informatics nurses and informatics nurse specialists. This professional resource informs practice, education, research, administration, and the certification process. The presenters will provide details about the development of the latest NI scope and standards of practice and the contemporary ANCC certification process to stimulate group discussion and promote development of a personal action plan to integrate such content into nursing and informatics practice. The target audience includes all registered nurses, informatics nurses, informatics nurse specialists, faculty, and administrators.
Developing a Framework for Teaching Nursing Informatics Internationally
Authors: Madsen Inge  , Cummings Elizabeth  , Borycki Elizabeth M.  , Lacroix Paulette
Published: February 2025

Information technology systems in healthcare have resulted in transformation of work practices. Nurses need knowledge, skills, judgment and understanding of the importance of informatics from the commencement of their training. This interactive workshop will look at developing a framework for common core content, teaching methodologies and program structures in the integration of nursing informatics in undergraduate programs. The workshop format will provide a forum for international discussion on this serious challenge faced by nursing schools everywhere. The outcome of this workshop will be the development of a framework that may be applied in teaching nursing informatics internationally.
From a Suspect Victim to the Holmes: The Unexpected Value of a Home-Made Mobile Chemotherapy Medication Administration System
Authors: Kuo Ming-Chuan  , Chang Polun
Published: February 2025

Nurse used to be the first one to be investigated in a drug adverse event. Our newly hospital-wide implemented home-made mobile chemotherapy medication support system, which has released our nurses from the traditional heavy 2-nurse-double-checking loading, was unexpectedly used to protect our nurses from being suspected in a recent event of over delivery of infusion. The outcome turned us to reexamine the device maintenance and test protocols.
Developing Evidence-Based Care Standards and a Decision-Making Support System for Pain Management
Authors: Feng Rung-Chuang  , Chang Polun
Published: February 2025

Pain is a crucial sign and symptom in hospitalised patients. This paper describes how a medical centre created a knowledge-based, computerised pain management decision-making process to support nurses in personalising preventive interventions based on patient requirements.
Call for Increased Patient Support Focus: Review and Evaluation of Mobile Apps for Tuberculosis Prevention and Treatment
Authors: Iribarren Sarah  , Schnall Rebecca
Published: February 2025

Tuberculosis (TB) remains a major global public health problem and is a leading killer due to an infectious disease. Mobile applications (apps) could support TB prevention and treatment. App stores were searched and of the 1332 reviewed 24 met our inclusion criteria. For each app 11 functionalities were assessed. The majority were targeted towards clinicians (n=17), few patient focused (n=4). Most had fewer than 4 functions out of 11, inform and record being the highest. Peer reviewed publications were identified for 2 of the apps and 3 apps in testing stage were found in the grey literature. Apps for TB prevention and treatment had minimal functionality, primarily targeted clinicians, and focused on information or data collection. None were for patient self-management of care and treatment or to improve patient-provider interactions. Identifying TB patient needs and involving them in the design phase is recommended.
Event Categories on the Semantic Web and Their Relationship/Object Distinction
Authors: Hanzal Tomáš  , Svátek Vojtěch  , Vacura Miroslav
Published: February 2025

Events are a recurring topic in ontological modeling and the diversity of their encoding in the semantic web ontological language OWL is immense. We provide a lightweight comparative survey of approaches to event modeling in both foundational and semantic web ontologies, and build upon it a tentative system of four categories of what is commonly called ‘event’. A substantial part of the categorization has to do with the distinction between an object and an relationship, as conceived in the lightweight ontological background modeling language PURO.
Ontological Model for EHR Interoperability
Authors: Bouanani-Oukhaled Zahra  , Verdier Christine  , Dupuy-Chessa Sophie  , Fouladi Karan  , Breda Laurent
Published: February 2025

The main purpose of this paper is to design a data model for Electronic Health Records which main goal is to enable cooperation of various heterogeneous health information systems. We investigate the interest of the meta-ontologies proposed in [1] by instantiating it with real data. We tested the feasibility of our model on real anonymous medical data provided by the Médibase Systèmes company.
Applications and Developments of Telemedicine in Greece
Authors: Tsioumanis Vasileios  , Mangita Andrianna  , Diomidous Marianna
Published: February 2025

The need to transmit medical information with images, data and voice from distance via telecommunications has come to our knowledge from ages. The topic has a new interest because of the progress in technology of telecommunications and information systems. The current article makes an effort to inform the reader in a simple way about what Telemedicine is and where it can be used. In this paper is presented the definition and the purpose of telemedicine, followed by the services provide through telemedicine applications. Finally, is analyzed the use of telemedicine in Greece its advantages, benefits and disadvantages, concluding with a final evaluation of it.
Do Travellers Accept m-Health Technologies? A Literature Review
Authors: Gallos Parisis  , Kolokathi Aikaterini  , Minou Ioannis  , Mantas John
Published: February 2025

A literature review was conducted to present if travellers accept the mobile healthcare (m-Health) technologies. The research was run on international scientific databases using certain keywords to cover the content of the review. From the online search results, 21 publications were selected which combine the “m-Health”, “travellers” and “technology acceptance” topics. The majority of the papers are using the Technology Acceptance Model (or a variation of it) on their methodology. Only few papers discuss about the above three topics simultaneously. All of the publications highlight the acceptance of the mobile technologies by their users. Based on this, it can be assumed that travellers may accept the m-Health applications. Further investigation is needed on this subject to give more accurate results.
Access and Use: Improving Digital Multimedia Consumer Health Information
Authors: Thomas Alex
Published: February 2025

This project enabled novel organisational insight into the comparative utility of a portfolio of consumer health information content, by measuring patterns of attrition (abandonment) in content use. The project used as a case study the event activity log of a fully automated digital information kiosk, located in a community health facility. Direct measurements of the duration of content use were derived from the user interface activity recorded in the kiosk log, thus avoiding issues in using other approaches to collecting this type of data, such as sampling and observer bias. The distribution patterns of 1,383 durations of observed abandonments of use for twenty-eight discrete modules of health information content were visualised using Kaplan-Meir survival plots. Clear patterns of abandonment of content use were exhibited. The method of analysis is cost-effective, scalable and provides deep insight into the utility of health promotion content. The impact on the content producers, platform operators and service users is to improve organisational learning and thus increase the confidence in stakeholders that the service is continuously delivering high quality health and wellbeing benefits.
Leveraging the EHR to Facilitate Efficient Surgical Audit: A Case Study from a Ophthalmic Private Practice
Authors: Connell Ben  , Ward Trevor
Published: February 2025

Surgeons agree over the importance of surgical audit associated with their practice especially given the benefits to patient care. Medical registration also requires audit as part of their requirements. Concerns over inefficiencies that can arise from duplication in data entry, led Eye Surgery Associates, a 16 doctor practice over 3 locations in Melbourne, to develop a new data entry and analysis system to facilitate audit as well as to maintain the practice's health records system in a way that addresses these inefficiencies. They did this in conjunction with IT professionals. Surgeons in our practice are highly positive about the new system which has attracted interest and likely future participation from the broader surgeon community.
Classification of Movement of People with Parkinsons Disease Using Wearable Inertial Movement Units and Machine Learning
Authors: Ireland David  , Wang Ziwei  , Lamont Robyn  , Liddle Jacki
Published: February 2025

In this work, inertial movement units were placed on people with Parkinsons disease (PwPD) who subsequently performed a standard test of walking endurance (six-minute walk test – 6MWT). Five devices were placed on each the limbs and small of the back. These devices captured the acceleration and rotational motion while the person walked as far as they can in six minutes. The wearable devices can objectively indicate the pattern and rhythmicity of limb and body movements. It is possible that this data, when subject to machine learning could provide additional objective measures that may support clinical observations related to the quality of movement. The aim of this work is two fold. First, to identify the most useful features of the captured signals; second, to identify the accuracy of using these features to predict the severity of PD as measured by standard clinical assessment.
Higher-Order Correlation Coefficient Analysis for EEG-Based Brain-Computer Interface
Authors: Liu Ye  , Zhao Qibin  , Chen Min  , Zhang Liqing
Published: February 2025

Electroencephalogram (EEG) based brain-computer interface (BCI) has been proved to be an effective communication way between human brain and external devices. In order to effectively recover the cortical dynamics from the EEG signals and improve the classification performance, plenty of studies focused on constructing subject-specific spatial and spectral filters, achieving considerable improvement in classification accuracy. However, almost all the approaches aimed to find one common subspace for projection of all the samples in different classes. Studies have shown that active channels and frequency information were not only subject-dependent but also class-dependent. Thus the variety of class-dependent spatial and spectral characteristics can provide further discriminative information for classification. In this paper, we proposed a tensor-based method which attempted to seek individual spatial and spectral subspaces for each class by which each class was projected into its own subspace separately such that they were easily to be classified. Finally, we added a regularization term in this model to avoid overfitting. We evaluated the effectiveness and robustness of the proposed method on two different datasets including one widely-used benchmark EEG dataset collected from healthy subjects and one self-collected EEG dataset collected from stroke patients. The results demonstrated its superior performance.
Emotion Analysis as a Regression Problem – Dimensional Models and Their Implications on Emotion Representation and Metrical Evaluation
Authors: Buechel Sven  , Hahn Udo
Published: February 2025

Emotion analysis (EA) and sentiment analysis are closely related tasks differing in the psychological phenomenon they aim to catch. We address fine-grained models for EA which treat the computation of the emotional status of narrative documents as a regression rather than a classification problem, as performed by coarse-grained approaches. We introduce Ekman's Basic Emotions (BE) and Russell and Mehrabian's Valence-Arousal-Dominance (VAD) model—two major schemes of emotion representation following opposing lines of psychological research, i.e., categorical and dimensional models—and discuss problems when BEs are used in a regression approach. We present the first natural language system thoroughly evaluated for fine-grained emotion analysis using the VAD scheme. Although we only employ simple BOW features, we reach correlation values up until r = .65 with human annotations. Furthermore, we show that the prevailing evaluation methodology relying solely on Pearson's correlation coefficient r is deficient which leads us to the introduction of a complementary error-based metric. Due to the lack of comparable (VAD-based) systems, we, finally, introduce a novel method of mapping between VAD and BE emotion representations to create a reasonable basis for comparison. This enables us to evaluate VAD output against human BE judgments and, thus, allows for a more direct comparison with existing BE-based emotion analysis systems. Even with this, admittedly, error-prone transformation step our VAD-based system achieves state-of-the-art performance in three out of six emotion categories, out-performing all existing BE-based systems but one.
Summary Information for Reasoning About Hierarchical Plans
Authors: de Silva Lavindra  , Sardina Sebastian  , Padgham Lin
Published: February 2025

Hierarchically structured agent plans are important for efficient planning and acting, and they also serve (among other things) to produce “richer” classical plans, composed not just of a sequence of primitive actions, but also “abstract” ones representing the supplied hierarchies. A crucial step for this and other approaches is deriving precondition and effect “summaries” from a given plan hierarchy. This paper provides mechanisms to do this for more pragmatic and conventional hierarchies than in the past. To this end, we formally define the notion of a precondition and an effect for a hierarchical plan; we present data structures and algorithms for automatically deriving this information; and we analyse the properties of the presented algorithms. We conclude the paper by detailing how our algorithms may be used together with a classical planner in order to obtain abstract plans.
Evolutionary Agent-Based Modeling of Past Societies' Organization Structure
Authors: Chliaoutakis Angelos  , Chalkiadakis Georgios
Published: February 2025

In this work, we extend a generic agent-based model for simulating ancient societies, by blending, for the first time, evolutionary game theory with multiagent systems' self-organization. Our approach models the evolution of social behaviours in a population of strategically interacting agents corresponding to households in the early Minoan era. To this end, agents participate in repeated games by means of which they exchange utility (corresponding to resources) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the progressive adoption of the most successful agent strategies. Agent population is not fixed, but fluctuates over time. The particularity of the domain necessitates that agents in our games receive non-static payoffs, in contrast to most games studied in the literature; and that the evolutionary dynamics are formulated via assessing the perceived fitness of the agents, defined in terms of how successful they are in accumulating utility. Our results show that societies of strategic agents that self-organize via adopting the aforementioned evolutionary approach, demonstrate a sustainability that largely matches that of self-organizing societies of more cooperative agents; and that strategic cooperation is in fact, in many instances, an emergent behaviour in this domain.
Scaling Structure Learning of Probabilistic Logic Programs by MapReduce
Authors: Riguzzi Fabrizio  , Bellodi Elena  , Zese Riccardo  , Cota Giuseppe  , Lamma Evelina
Published: February 2025

Probabilistic Logic Programming is a promising formalism for dealing with uncertainty. Learning probabilistic logic programs has been receiving an increasing attention in Inductive Logic Programming: for instancethe system SLIPCOVER learns high quality theories in a variety of domains. HoweverSLIPCOVER is computationally expensivewith a running time of the order of hours. In order to apply SLIPCOVER to Big Data, we present SEMPRE, for “Structure lEarning by MaPREduce”, that scales SLIPCOVER by following a MapReduce strategy, directly implemented with the Message Passing Interface.
Transfer of Reinforcement Learning Negotiation Policies: From Bilateral to Multilateral Scenarios
Authors: Efstathiou Ioannis  , Lemon Oliver
Published: February 2025

Trading and negotiation dialogue capabilities have been identified as important in a variety of AI application areas. In prior work, it was shown how Reinforcement Learning (RL) agents in bilateral negotiations can learn to use manipulation in dialogue to deceive adversaries in non-cooperative trading games. In this paper we show that such trained policies can also be used effectively for multilateral negotiations, and can even outperform those which are trained in these multilateral environments. Ultimately, it is shown that training in simple bilateral environments (e.g. a generic version of “Catan”) may suffice for complex multilateral non-cooperative trading scenarios (e.g. the full version of Catan).
Substantive Irrationality in Cognitive Systems
Authors: Bisquert Pierre  , Croitoru Madalina  , Dupin de Saint-Cyr Florence  , Hecham Abdelraouf
Published: February 2025

In this paper we approach both procedural and substantive irrationality of artificial agent cognitive systems and consider that when it is not possible for an agent to make a logical inference (too expensive cognitive effort or not enough knowledge) she might replace certain parts of the logical reasoning with mere associations.
Speech Emotion Recognition Using Voiced Segment Selection Algorithm
Authors: Gu Yu  , Postma Eric  , Lin Hai-Xiang  , van den Herik Jaap
Published: February 2025

Speech emotion recognition (SER) poses one of the major challenges in human-machine interaction. We propose a new algorithm, the Voiced Segment Selection (VSS) algorithm, which can produce an accurate segmentation of speech signals. The VSS algorithm deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification. The finding shows that the VSS method is a more accurate algorithm for voiced segment detection. Therefore, it has potential to improve performance of emotion recognition from speech.
Shape Invariant Formulation for Change Point Models in Multiple Dimensions
Authors: Glass Jesse  , Obradovic Zoran
Published: February 2025

Artificial intelligence has achieved superhuman performance in a variety of tasks. Unfortunately, this is often done without interpretable methods. In medicine, it is not sufficient to have an algorithm with maximum accuracy. The methods must be evaluated by experts. Our motivating task is to detect features of the eye using unlabeled data. We detect features using a Bayesian changepoint model. Changepoint detection can perform object recognition when applied to two dimensional feature spaces such as images. We present a formula for detecting any shape with the changepoint model. We then extend it to multiple features in order to capture the color of an object. The work presented here has the ability to incorporate prior information, the ability to handle images of varying granularity, can provide confidence estimates on object features in an image. It will serve as the foundation for a classification method with interpretable results.
Decoupling a Resource Constraint Through Fictitious Play in Multi-Agent Sequential Decision Making
Authors: de Nijs Frits  , Spaan Matthijs T.J.  , de Weerdt Mathijs M.
Published: February 2025

When multiple independent agents use a limited shared resource, they need to coordinate and thereby their planning problems become coupled. We present a resource assignment strategy that decouples agents using marginal utility cost, allowing them to plan individually. We show that agents converge to an expected cost curve by keeping a history of plans, inspired by fictitious play. This performs slightly better than a state-of-the-art best-response approach and is significantly more scalable than a preallocation Mixed-Integer Linear Programming formulation, providing a good trade-off between performance and quality.
Boolean Negotiation Games
Authors: Bulling Nils  , Hindriks Koen V.
Published: February 2025

We propose a new strategic model of negotiation, called Boolean negotiation games. Our model is inspired by Boolean games and the alternating offers model of bargaining. It offers a computationally grounded model for studying properties of negotiation protocols in a qualitative setting. Boolean negotiation games can yield agreements that are more beneficial than stable solutions (Nash equilibria) of the underlying Boolean game.
Stochastic Area Pooling for Generic Convolutional Neural Network
Authors: Deng Zhidong  , Wang Zhenyang  , Wang Shiyao
Published: February 2025

This paper proposes a novel SAPNet model that incorporates a stochastic area pooling (SAP) method with a generic stacked T-shaped CNN architecture. In our SAP method, pooling area is randomly transformed and max pooling operation is then conducted on such areas, which are no longer regular identical fixed upright squares. It can be viewed as feature-level augmentation, substantially reducing model parameters while keeping generalization ability of CNN almost unchanged. Furthermore, we present a generic CNN architecture that structurally resembles three stacked T-shaped cubes. In such architecture, the number of kernels in convolutional layer preceding any pooling layer is doubled and all learnable weight layers are combined with batch normalization and dropout with a small ratio. Finally, on CIFAR-10, CIFAR-100, MNIST, and SVHN datasets, the experimental results show that our SAPNet requires fewer parameters than regular CNN models and still achieves superior recognition performances for all the four benchmarks.
An Intelligent System for Personalized Conference Event Recommendation and Scheduling
Authors: Gunawan Aldy  , Lau Hoong Chuin  , Varakantham Pradeep  , Wang Wenjie
Published: February 2025

Many conference mobile apps today lack the intelligent feature to automatically generates optimal schedules based on delegates' preferences. This entails two major challenges: (a) identifying preferences of users; and (b) given the preferences, generating a schedule that optimizes his preferences. In this paper, we specifically focus on academic conferences, where users are prompted to input their preferred keywords. Our key contribution is an integrated conference scheduling agent that automatically recognizes user preferences based on keywords, provides a list of recommended talks and optimizes user schedule based on these preferences. To demonstrate the utility of our integrated conference scheduling agent, we first demonstrated the app in the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2015) and conducted a survey to collect some data, which are used to verify the results presented in this paper. It is able to provide well calibrated results with respect to precision, accuracy and recall. We also tested the app in the 2015 WI-IAT International Conference (Singapore). The android and web-based apps have been demonstrated and deployed in AAMAS 2016 (Singapore) with positive responses from the users.
Combining Deterministic and Nondeterministic Search for Optimal Journey Planning Under Uncertainty
Authors: Kishimoto Akihiro  , Botea Adi  , Daly Elizabeth
Published: February 2025

Optimal multi-modal journey planning under uncertainty is a challenging problem, due in part to an increased branching factor generated by nondeterministic actions. Deterministic search, which ignores all uncertainty, can be much faster, but deterministic plans lack correctness and optimality guarantees in the uncertainty-aware domain.
Efficient Computation of Exact IRV Margins
Authors: Blom Michelle  , Teague Vanessa  , Stuckey Peter J.  , Tidhar Ron
Published: February 2025

Computing the margin of victory (MOV) in an Instant Runoff Voting (IRV) election is NP-hard. In an IRV election with winning candidate w, the MOV defines the smallest number of cast votes that, if modified, result in the election of a candidate other than w. The ability to compute such margins has significant value. Arguments over the correctness of an election outcome usually rely on the size of the electoral margin. Risk-limiting audits use the size of this margin to determine how much post-election auditing is required. We present an efficient branch-and-bound algorithm for computing exact margins that substantially improves on the current best-known approach. Although exponential in the worst case, our algorithm runs efficiently in practice, computing margins in instances that could not be solved by the current state-of-the-art in a reasonable time frame.
Online Adaptation of Deep Architectures with Reinforcement Learning
Authors: Ganegedara Thushan  , Ott Lionel  , Ramos Fabio
Published: February 2025

Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoiding loss of prior knowledge and more efficient learning. However, adaptation to changes in the data distribution (also known as covariate shift) needs to be performed without compromising past knowledge already built in into the model to cope with voluminous and dynamic data. In this paper, we propose an online stacked Denoising Autoencoder whose structure is adapted through reinforcement learning. Our algorithm forces the network to exploit and explore favourable architectures employing an estimated utility function that maximises the accuracy of an unseen validation sequence. Different actions, such as Pool, Increment and Merge are available to modify the structure of the network. As we observe through a series of experiments, our approach is more responsive, robust, and principled than its counterparts for non-stationary as well as stationary data distributions. Experimental results indicate that our algorithm performs better at preserving gained prior knowledge and responding to changes in the data distribution.
Abstraction-Based Verification of Infinite-State Reactive Modules
Authors: Belardinelli Francesco  , Lomuscio Alessio
Published: February 2025

We introduce the formalism of infinite-state reactive modules to reason about the strategic behaviour of autonomous agents in a setting where data are explicitly exhibited in the systems description and in the specification language. Technically, we endow reactive modules with an infinite domain of interpretation for individual variables, and introduce FO-ATL, a first-order version of alternating time temporal logic, for the specification of properties of interest. We show that their verification is decidable for classes of data types of interest. This result is proved by defining a first-order version of alternating bisimulations and finite bisimilar abstractions. We illustrate the formal machinery by applying it to English and sealed bid auctions. In particular, we show that strategic properties of agents in auctions, including manipulability and collusion, can be expressed and verified in this framework.
On the Construction of High-Dimensional Simple Games
Authors: Olsen Martin  , Kurz Sascha  , Molinero Xavier
Published: February 2025

Voting is a commonly applied method for the aggregation of the preferences of multiple agents into a joint decision. If preferences are binary, i.e., “yes” and “no”, every voting system can be described by a (monotone) Boolean function χ:{0,1}n→{0,1}. However, its naive encoding needs 2nbits. The subclass of threshold functions, which is sufficient for homogeneous agents, allows a more succinct representation using n weights and one threshold. For heterogeneous agents, one can represent χ as an intersection of k threshold functions. Taylor and Zwicker have constructed a sequence of examples requiringand provided a construction guaranteeing. The magnitude of the worst-case situation was to be determined by Elkind et al. in 2008, but the analysis unfortunately turned out to be wrong. Here we uncover a relation to coding theory that allows the determination of the minimum number k for a subclass of voting systems. As an application, we give a construction for k≥2n−o(n), i.e., there is no gain from a representation complexity point of view.
An Extension of the Owen-Value Interaction Index and Its Application to Inter-Links Prediction
Authors: Szczepański Piotr L.  , Michalak Tomasz P.  , Rahwan Talal  , Wooldridge Michael
Published: February 2025

Link prediction is a key problem in social network analysis: it involves making suggestions about where to add new links in a network, based solely on the structure of the network. We address a special case of this problem, whereby the new links are supposed to connect different communities in the network; we call it the interlinks prediction problem. This is particularly challenging as there are typically very few links between different communities. To solve this problem, we propose a local node-similarity measure, inspired by the Owen-value interaction index—a concept developed in cooperative game theory and fuzzy systems. Although this index requires an exponential number of operations in the general case, we show that our local node-similarity measure is computable in polynomial time. We apply our measure to solve the inter-links prediction problem in a number of real-life networks, and show that it outperforms all other local similarity measures in the literature.
On the Computation of Top-k Extensions in Abstract Argumentation Frameworks
Authors: Jabbour Said  , Raddaoui Badran  , Sais Lakhdar  , Salhi Yakoub
Published: February 2025

Formal argumentation has received a lot of attention during the last two decades, since abstract argumentation framework provides the basis for various reasoning problems in Artificial Intelligence. Unfortunately, the exponential number of its possible semantics extensions makes some reasoning problems intractable in this framework. In this paper, we investigate the pivotal issue of efficient computation of acceptable arguments called extensions according to a given semantics. In particular, we address this aspect by applying a strategy of how to use preferences at the semantics level in order to determine what are “desirable” outcomes of the argumentation process. Then, we present a new approach for computing the Top-k extensions of an abstract argumentation framework, according to a user-specified preference relation. Indeed, an extension is a Top-k extension for a given semantics if it admits less than k extensions preferred to it with respect to a preference relation. Our experiments on various datasets demonstrate the effectiveness and scalability of our approach and the accuracy of the proposed enumeration method.
Strategy Representation and Reasoning in the Situation Calculus
Authors: Xiong Liping  , Liu Yongmei
Published: February 2025

Strategy representation and reasoning has been one of the most active research areas in AI and multi-agent systems. Representative strategic logics are ATL and the more expressive Strategy Logic SL which reasons about strategies explicitly. In this paper, by a simple extension of the situation calculus with a strategy sort, we develop a general framework for strategy representation and reasoning for complete information games. This framework can be used to compactly represent both concurrent and turn-based possibly infinite game structures, specify the internal structure of strategies, reason about strategies explicitly, and reason about strategic abilities of coalitions under commitments to strategy specifications. We show that our framework is strictly more expressive than SL, and inspired by the work of De Giacomo et al. on bounded action theories, give a decidable fragment of our framework.
Exploiting MUS Structure to Measure Inconsistency of Knowledge Bases
Authors: Jabbour Said  , Sais Lakhdar
Published: February 2025

Measuring inconsistency is recognized as an important research issue for quantifying and handling inconsistencies in knowledge bases. Several logic-based inconsistency measures have been proposed. Minimal unsatisfiable and maximal satisfiable subsets are at the heart of the syntactic measures, while semantic inconsistency measures are often based on some paraconsistent semantics. In order to design interesting measures faithful to human rationality, many properties have been introduced to reach this goal. In this paper, we propose a new property called sub-additivity allowing to push further the ability to reorder knowledge bases according to their inconsistency degree. After pointing out the limitations of several measures to satisfy the sub-additivity property, we present a new measure based on a fine exploitation of the internal structure of the knowledge base, namely the structure of its associated minimal unsatisfiable subsets. Then, we show how its computation can be formulated as a nonlinear optimization problem. Finally, we prove that the new measure satisfies all the required properties while highlighting its interesting features.
Entailment Processing for Large RDF Data Sets Using GPU
Authors: Chantrapornchai Chantana  , Choksuchat Chidchanok  , Haidl Michael  , Gorlatch Sergei
Published: February 2025

In the Semantic Web, the Resource Description Framework (RDF) has become the standard representation to describe Internet resources. RDF data is structured in triples comprising a subject, a predicate and an object: the predicate defines the relation between subject and object. The RDF Schema (RDFS) extends raw RDF data with a standardized vocabulary to allow for entailment, e.g., type inheritance or type inference. Processing large RDF data sets, which are commonly stored as text files, is a time-intensive task: querying and entailment on RDF data requires a huge amount of computational power and storage. We propose TripleID – a framework for RDF querying and entailment processing. TripleID provides a novel, compressed file format for RDF data and utilizes Graphics Processing Units (GPUs) for accelerated, highly parallelized data processing. We demonstrate the advantages of our framework on real-world RDF data: TripleID reduces storage size for RDF data by up to 75% and accelerates querying and entailment processing up to 40 times as compared to the state-of-the-art tools that use conventional CPUs.
Patient's Perspective on Quality of Teleconsultation Services
Authors: Thijssing Leonie  , Tensen Esmée  , Jaspers Monique
Published: February 2025

Objective: Patient satisfaction with teleconsultation services can increase their acceptance. Validated and standardized questionnaires to measure the quality aspects of teleconsultation relevant from the patients' perspective are not available yet. We aim to develop such a questionnaire.
Wearable Technology in Medicine: Machine-to-Machine (M2M) Communication in Distributed Systems
Authors: Schmucker Michael  , Yildirim Kemal  , Igel Christoph  , Haag Martin
Published: February 2025

Smart wearables are capable of supporting physicians during various processes in medical emergencies. Nevertheless, it is almost impossible to operate several computers without neglecting a patient's treatment. Thus, it is necessary to set up a distributed network consisting of two or more computers to exchange data or initiate remote procedure calls (RPC). If it is not possible to create flawless connections between those devices, it is not possible to transfer medically relevant data to the most suitable device, as well as to control a device with another one. This paper shows how wearables can be paired and what problems occur when trying to pair several wearables. Furthermore, it is described as to what interesting scenarios are possible in the context of emergency medicine/paramedicine.
Assessment of Automatically Exported Clinical Data from a Hospital Information System for Clinical Research in Multiple Myeloma
Authors: Torres Viviana  , Cerda Mauricio  , Knaup Petra  , Löpprich Martin
Published: February 2025

Purpose: An important part of the electronic information available in Hospital Information System (HIS) has the potential to be automatically exported to Electronic Data Capture (EDC) platforms for improving clinical research. This automation has the advantage of reducing manual data transcription, a time consuming and prone to errors process. However, quantitative evaluations of the process of exporting data from a HIS to an EDC system have not been reported extensively, in particular comparing with manual transcription. In this work an assessment to study the quality of an automatic export process, focused in laboratory data from a HIS is presented.
A Dynamic Approach to Support Interoperability for Medical Reports Using DICOM SR
Authors: Matos Pedro  , Bastiao Silva Luis A.  , Godinho Tiago Marques  , Costa Carlos
Published: February 2025

The standardization of data structures for clinical observations in medical imaging environments is a relatively recent effort. DICOM standard defines a set of supplements for different medical reports denominated as Structured Reports (SR). In 2013, Integrating the Healthcare Enterprise (IHE) also followed this trend by publishing the profile Management of Radiology Report Templates (MRRT). However, the generalized adoption of these normalized reports has been delayed due to several factors. In fact, numerous medical institutions still use proprietary formats that do not promote sharing and remote access. New strategies to incentivise the adoption of normalized report templates are needed to make them interoperable between distinct applications. This article proposes a new method to automatically generate DICOM SR from distinct data sources. It encompasses a flexible mapping schema that can be used with distinct medical imaging modalities. Our ultimate goal is to encourage the usage of DICOM SR by providing an effortless method to convert proprietary formats into standard ones. Moreover, the developed methods can be also used for supporting IHE MRRT profiles, making the reports accessible across different information systems and institutions.
Using Frequent Item Set Mining and Feature Selection Methods to Identify Interacted Risk Factors – The Atrial Fibrillation Case Study
Authors: Li Xiang  , Liu Haifeng  , Du Xin  , Hu Gang  , Xie Guotong  , Zhang Ping
Published: February 2025

Disease risk prediction is highly important for early intervention and treatment, and identification of predictive risk factors is the key point to achieve accurate prediction. In addition to original independent features in a dataset, some interacted features, such as comorbidities and combination therapies, may have non-additive influence on the disease outcome and can also be used in risk prediction to improve the prediction performance. However, it is usually difficult to manually identify the possible interacted risk factors due to the combination explosion of features. In this paper, we propose an automatic approach to identify predictive risk factors with interactions using frequent item set mining and feature selection methods. The proposed approach was applied in the real world case study of predicting ischemic stroke and thromboembolism for atrial fibrillation patients on the Chinese atrial fibrillation registry dataset, and the results show that our approach can not only improve the prediction performance, but also identify the comorbidities and combination therapies that have potential influences on TE occurrence for AF.
Optimizing Digital Health Informatics Interventions Through Unobtrusive Quantitative Process Evaluations
Authors: Gude Wouter T.  , van der Veer Sabine N.  , de Keizer Nicolette F.  , Coiera Enrico  , Peek Niels
Published: February 2025

Health informatics interventions such as clinical decision support (CDS) and audit and feedback (A&F) are variably effective at improving care because the underlying mechanisms through which these interventions bring about change are poorly understood. This limits our possibilities to design better interventions. Process evaluations can be used to improve this understanding by assessing fidelity and quality of implementation, clarifying causal mechanisms, and identifying contextual factors associated with variation in outcomes. Coiera describes the intervention process as a series of stages extending from interactions to outcomes: the “information value chain”. However, past process evaluations often did not assess the relationships between those stages. In this paper we argue that the chain can be measured quantitatively and unobtrusively in digital interventions thanks to the availability of electronic data that are a by-product of their use. This provides novel possibilities to study the mechanisms of informatics interventions in detail and inform essential design choices to optimize their efficacy.
How to Present Evidence-Based Usability Design Principles Dedicated to Medication-Related Alerting Systems to Designers and Evaluators? Results from a Workshop
Authors: Marcilly Romaric  , Monkman Helen  , Villumsen Sidsel  , Kaufman David  , Beuscart-Zephir Marie-Catherine
Published: February 2025

Medication alerting system use errors and lack of adoption are often attributed to usability issues. Previous work has used evidence from the literature to reveal usability principles specific to medication alerting systems and identify potential consequences of violating these principles. The current study sought to explore how best to convey these principles to designers and evaluators of these systems to facilitate their work. To this aim, a workshop with 19 participants was used to generate ideas and opinions on how to deliver these topic-specific design principles in a way that would be most helpful for them. Participants generated ideas for how (e.g., a collaborative, continuously updated forum) and what (e.g., illustrations, checklists, evidence sources and strength, consequences of violations) information is most useful to disseminate usability principles for medication alerting systems. Participants, especially designers, expressed desire to use these principles in practice and avoid previously documented mistakes and therefore make design and evaluation of these systems more effective and efficient. Those insights are discussed in terms of feasibility and logistical challenges to developing the proposed documentation). To move this work forward, a more collaborative approach of Human Factors specialists in medical informatics is necessary.