bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Verification of Golog Programs via Predicate Abstraction
Authors: Mo Peiming  , Li Naiqi  , Liu Yongmei
Published: February 2025

Golog is a logic programming language for high-level agent control. In a recent paper, we proposed a sound but incomplete method for automatic verification of partial correctness of Golog programs where we give a number of heuristic methods to strengthen given formulas in order to discover loop invariants. However, our method does not work on arithmetic domains. On the other hand, the method of predicate abstraction is widely used in the software engineering community for model checking and partial correctness verification of programs. Intuitively, the predicate abstraction task is to find a formula consisting of a given set of predicates to approximate a given first-order formula. In this paper, we propose a method for automatic verification of partial correctness of Golog programs which use predicate abstraction as a uniform method to strengthen given formulas. We implement a system based on the proposed method, conduct experiments on arithmetical domains and examples from the paper by Li and Liu. Also, we apply our method to the verification of winning strategies for combinatorial games.
On Metric Temporal Description Logics
Authors: Gutiérrez-Basulto Víctor  , Jung Jean Christoph  , Ozaki Ana
Published: February 2025

We introduce metric temporal description logics (mTDLs) as combinations of the classical description logic ALC with (a) LTLbin, an extension of the temporal logic LTL with succinctly represented intervals, and (b) metric temporal logic MTL, extending LTLbinwith capabilities to quantitatively reason about time delays. Our main contributions are algorithms and tight complexity bounds for the satisfiability problem in these mTDLs: For mTDLs based on (fragments of) LTLbin, we establish complexity bounds ranging from EXPTIME to 2EXPSPACE. For mTDLs based on (fragments of) MTL interpreted over the naturals, we establish complexity bounds ranging from EXPSPACE to 2EXPSPACE.
The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks
Authors: Percy Chris  , d'Avila Garcez Artur S.  , Dragičević Simo  , França Manoel V.M.  , Slabaugh Greg  , Weyde Tillman
Published: February 2025

Responsible gambling is a field of study that involves supporting gamblers so as to reduce the harm that their gambling activity might cause. Recently in the literature, machine learning algorithms have been introduced as a way to predict potentially harmful gambling based on patterns of gambling behavior, such as trends in amounts wagered and the time spent gambling. In this paper, neural network models are analyzed to help predict the outcome of a partial proxy for harmful gambling behavior: when a gambler “self-excludes”, requesting a gambling operator to prevent them from accessing gambling opportunities. Drawing on survey and interview insights from industry and public officials as to the importance of interpretability, a variant of the knowledge extraction algorithm TREPAN is proposed which can produce compact, human-readable logic rules efficiently, given a neural network trained on gambling data. To the best of our knowledge, this paper reports the first industrial-strength application of knowledge extraction from neural networks, which otherwise are black-boxes unable to provide the explanatory insights which are crucially required in this area of application. We show that through knowledge extraction one can explore and validate the kinds of behavioral and demographic profiles that best predict self-exclusion, while developing a machine learning approach with greater potential for adoption by industry and treatment providers. Experimental results reported in this paper indicate that the rules extracted can achieve high fidelity to the trained neural network while maintaining competitive accuracy and providing useful insight to domain experts in responsible gambling.
Patient Centered Event Representation for the Treatment of Multifactorial Diseases: Current Progress and Challenges
Authors: Deng Yihan  , Gaebel Jan  , Denecke Kerstin
Published: February 2025

Events such as clinical interventions, adverse drug events are one of the basic semantic units in the clinical workflow and are the foundation of the pathway representation. Current research has mainly concentrated on event recognition through concept mapping using from medical ontologies (UMLS, SNOMED CT) and gene relation detection in the biological context. However, the analysis of the patient status and the interaction between the patient status and a context event is still at the primary stage. In order to realize an efficient personalized treatment design and pathway planning, the correlation between a patient status and different types of clinical events should be analyzed. In this paper, we will provide a summary of the current research progress in clinical event detection in the biomedical domain and compare two approaches of event acquisition: an event schema produced using a guideline-based method and an expert-based annotation. We will apply the approaches to generate a structured annotation corpus and a special case of an event schema based on the complication classification and risk management in treatment of laryngeal cancer.
Awareness, Attitude and Readiness of Clinical Staff Towards Telemedicine: A Study in Mashhad, Iran
Authors: Sheikhtaheri Abbas  , Sarbaz Masoumeh  , Kimiafar Khalil  , Ghayour Masoumeh  , Rahmani Soudabeh
Published: February 2025

A questionnaire was used to survey Iranian clinical staff about telemedicine. The score for awareness was 13 ± 5.5 out of 35 and indicated low awareness about telemedicine. Only 43.7% stated they had heard about teleconsultation. The figure for tele-monitoring was 20.1%. Awareness about other types of telemedicine services was even lower. The most frequently-used sources of information about telemedicine were friends (51.4%) and public media (30.3%). Attitudes were generally positive about telemedicine (63.42 ± 9.5 out of 95). It was found that a significant positive correlation exists between attitude and awareness (p = 0.027). In conclusion, Iranian clinical staff has little knowledge about telemedicine services; however, they have a positive perception of this type of service. Providing appropriate education and information resources to them is necessary.
Synchronising Mobile Electronic Case Report Forms with a Central Database
Authors: Müller Thomas H.
Published: February 2025

The increased dissemination of powerful mobile computing devices with access to a variety of wireless communication channels has benefited on-line capture of medical research data many areas. Yet, there is still a need for off-line capture, especially, when the availability of on-line connectivity cannot be established in advance. The clinical study “BreathEase” presents such a case, because it calls for extensive interviews conducted at the patient's home by visiting study staff. On the other, patient data also needed to be collected in an outpatient clinic. Furthermore, these activities needed to be co-ordinated according to protocol guidelines via a stationary system. Hence, the interview questionnaire data captured off-line needed to be merged with a stationary central database. For this purpose, an existing electronic data capture system (DBFORM) was conFred to include some clinical trial management features and complemented with a module to synchronize data in the mobile and stationary databases. This paper describes the requirements, the procedure, and its implementation. While it is shown that off-line capture followed by on-line synchronization is feasible, certain ambiguities may arise that should be addressed by technical and/or other means.
Transcription of Case Report Forms from Unstructured Referral Letters: A Semantic Text Analytics Approach
Authors: Abidi Syed Sibte Raza  , Singh Abhinav Kumar  , Christie Sean
Published: February 2025

In this paper we present a framework for the semi-automatic extraction of medical entities from referral letters and use them to transcribe a case report form. Our framework offers the functionality to: (a) extract the medical entity from the unstructured referral letters, (b) classify them according to their semantic type, and (c) transcribe a case report form based on the extracted information from the referral letter. We take a semantic text analytics approach where SNOMED-CT ontology is used to both classify referral concepts and to establish semantic similarities between referral concepts and CRF elements. We used 100 spine injury referral letters, and a standard case report form used by Association of Dalhousie Neurosurgeons, Dalhousie University.
Automatic Extraction of Drug Adverse Effects from Product Characteristics (SPCs): A Text Versus Table Comparison
Authors: Lamy Jean-Baptiste  , Ugon Adrien  , Berthelot Hélène
Published: February 2025

Background: Potential adverse effects (AEs) of drugs are described in their summary of product characteristics (SPCs), a textual document. Automatic extraction of AEs from SPCs is useful for detecting AEs and for building drug databases. However, this task is difficult because each AE is associated with a frequency that must be extracted and the presentation of AEs in SPCs is heterogeneous, consisting of plain text and tables in many different formats.
Suggesting Missing Relations in Biomedical Ontologies Based on Lexical Regularities
Authors: Quesada-Martínez Manuel  , Fernández-Breis Jesualdo Tomás  , Karlsson Daniel
Published: February 2025

The number of biomedical ontologies has increased significantly in recent years. Many of such ontologies are the result of efforts of communities of domain experts and ontology engineers. The development and application of quality assurance (QA) methods should help these communities to develop useful ontologies for both humans and machines. According to previous studies, biomedical ontologies are rich in natural language content, but most of them are not so rich in axiomatic terms. Here, we are interested in studying the relation between content in natural language and content in axiomatic form. The analysis of the labels of the classes permits to identify lexical regularities (LRs), which are sets of words that are shared by labels of different classes. Our assumption is that the classes exhibiting an LR should be logically related through axioms, which is used to propose an algorithm to detect missing relations in the ontology. Here, we analyse a lexical regularity of SNOMED CT, congenital stenosis, which is reported as problematic by the SNOMED CT maintenance team.
Estimating the Ratio of Patients with a Certain Disease Between Hospitals for the Allocation of Patients to Clinical Trials Using Health Insurance Claims Data in Japan
Authors: Takeda Toshihiro  , Mihara Naoki  , Murata Taizo  , Shimai Yoshie  , Okada Katsuki  , Manabe Shiro  , Matsumura Yasushi
Published: February 2025

In clinical trials, investigating the ratio of patients with each disease who are treated in a hospital is important for determining the number of patients who are allocated to hospitals. The Japanese health insurance claims data includes standardized disease and medicine data. However, the disease data has some problems in terms of reliability, because the healed diseases are sometimes not deleted or because a disease that a patient does not actually have is registered to claim the cost of the examination. On the other hand, therapeutic medicines are administered to target particular diseases. In this study, we developed a system for estimating the number of patients with each disease using the disease data and the therapeutic medicine data. We converted the ICD-10 code to a 4-grade classification code so that we could predict the diseases in the shallow layer (e.g. gastrointestinal disease) when it was difficult to predict the precise diseases in the deep layer (e.g. gastric ulcers). A table showing the disease code and the corresponding therapeutic medicine code was provided by the Japan Pharmaceutical Information Center (JAPIC). We calculated the disease probability score from the diseases and therapeutic medicines and recorded the predicted disease. For the system evaluation, we used the health insurance claims data from Osaka University Hospital for January 2015. A total of 58,526 diseases were predicted from the health insurance claims data of 18,393 patients. One hundred twenty patients were randomly extracted for use in a chart review that was performed by an expert physician. Two hundred twenty-four of 329 predicted diseases, were correctly predicted; 56 were reasonably predicted, and 49 were incorrectly predicted. The main disease was correctly predicted in 71 patients. In conclusion, we could estimate the number of patients with each disease using the health insurance claims data with a certain degree of accuracy.
The Future Impact of Healthcare Services Digitalization on Health Workforce: The Increasing Role of Medical Informatics
Authors: Lapão Luís Velez
Published: February 2025

The digital revolution is gradually transforming our society. What about the effects of digitalization and Internet of Things in healthcare? Among researchers two ideas are dominating, opposing each other. These arguments will be explored and analyzed. A mix-method approach combining literature review with the results from a focus group on eHealth impact on employment is used. Several experts from the WHO and from Health Professional Associations contributed for this analysis. Depending on the type of service it will entail reductions or more need of healthcare workers, yet whatever the scenario medical informatics will play an increasing role.
Citizens' Access to Their Digital Health Data in Eleven Countries – A Comparative Study
Authors: Nohr Christian  , Wong Ming Chao  , Turner Paul  , Almond Helen  , Parv Liisa  , Gilstad Heidi  , Koch Sabine  , Harðardóttir Guðrún Auður  , Hyppönen Hannele  , Marcilly Romaric  , Sheik Aziz  , Day Karen  , Kushniruk Andre
Published: February 2025

Governments around the world are actively promoting citizens electronic access to their health data as one of a number of ways to respond to the challenges of health care delivery in the 21st century. While numerous approaches have been utilized it is evident from cross-country comparisons that there are different conceptualizations of: both the expected and desired roles for citizens in the management of their own health; the benefits that will be delivered by citizen access and how these benefits should be measured and benchmarked over-time. This paper presents comparative analyses of the methods by which citizens are provided with access to their own health data across 11 countries. The paper aims to stimulate debate on electronic citizen access to health data and the challenges of measuring benefit as well as reflection on capacity of different citizens to engage with e-health.
Using Interactive 3D PDF for Exploring Complex Biomedical Data: Experiences and Solutions
Authors: Newe Axel  , Becker Linda
Published: February 2025

The Portable Document Format (PDF) is the most commonly used file format for the exchange of electronic documents. A lesser-known feature of PDF is the possibility to embed three-dimensional models and to display these models interactively with a qualified reader. This technology is well suited to present, to explore and to communicate complex biomedical data. This applies in particular for data which would suffer from a loss of information if it was reduced to a static two-dimensional projection. In this article, we present applications of 3D PDF for selected scholarly and clinical use cases in the biomedical domain. Furthermore, we present a sophisticated tool for the generation of respective PDF documents.
Publishing Biomedical Predication Repository About MeSH Co-Occurrences in MEDLINE
Authors: Miñarro-Giménez Jose Antonio  , Martínez Manuel Quesada  , Fernández-Breis Jesualdo Tomás  , Schulz Stefan
Published: February 2025

The construction and publication of predications form scientific literature databases like MEDLINE is necessary due to the large amount of resources available. The main goal is to infer meaningful predicates between relevant co-occurring MeSH concepts manually annotated from MEDLINE records. The resulting predications are formed as subject-predicate-object triples. We exploit the content of MRCOC file to extract the MeSH indexing terms (main headings and subheadings) of MEDLINE. The predications were inferred by combining the semantic predicates from SemMedDB, the clustering of MeSH terms by their associated MeSH subheadings and the frequency of relevant terms in the abstracts of MEDLINE records. The inferring process also obtains and associates a weight to each generated predication. As a result, we published the generated dataset of predications using the Linked Data principles to make it available for future projects.
Nonparametric Segment Detection
Authors: van Rossum Anne C.  , Lin Hai Xiang  , Dubbeldam Johan  , van den Herik H. Jaap
Published: February 2025

In computer and robotic vision point clouds from depth sensors have to be processed to form higher-level concepts such as lines, planes, and objects. Bayesian methods formulate precisely prior knowledge with respect to the noise and likelihood of points given a line, plane, or object. Nonparametric methods also formulate a prior with respect to the number of those lines, planes, or objects. Recently, a nonparametric Bayesian method has been proposed to perform optimal inference simultaneously over line fitting and the number of lines. In this paper we propose a nonparametric Bayesian method for segment fitting. Segments are lines of finite length. This requires 1.) a prior for line segment lengths: the symmetric Pareto distribution, 2.) a sampling method that handles nonconjugacy: an auxiliary variable MCMC method. Results are measured according to clustering performance indicators, such as the Rand Index, the Adjusted Rand Index, and the Hubert metric. Surprisingly, the performance of segment recognition is worse than that of line recognition. The paper therefore concludes with recommendations towards improving Bayesian segment recognition in future work.
Human Robot Collaboration to Reach a Common Goal in an Assembly Process
Authors: Akkaladevi Sharath Chandra  , Plasch Matthias  , Pichler Andreas  , Rinner Bernhard
Published: February 2025

Enabling robotic systems to collaborate with humans is a challenging task, on different levels of abstraction. Such systems need to understand the context under which they operate, by perceiving, planning and reasoning to team up with a human. The robotic system should also have perspective taking capabilities in order to efficiently collaborate with the human. In this work an integrated cognitive architecture for human robot collaboration, that aims to develop perspective taking capabilities using human preferences, is proposed. This is achieved by developing a ‘mental model’ that takes human preferences, the knowledge of the task (including the objects), and the capabilities of the human and the robot. This mental model forms the basis of the cognitive architecture, to perceive, reason and plan in the human-robot collaborative scenario. The robotic platform guided by the cognitive architecture, performs ‘picking’, ‘showing’, ‘placing’ and ‘handover’ actions on real world objects (of interest in the assembly process) in coordination with the human. The goal is to answer the ‘how’ (how a manipulation action should be carried out by the robot in a dynamically changing environment) and the ‘where’ (where the manipulation action should take place) of the assembly process considering/given varying human preferences. We show that the proposed cognitive architecture is capable of answering these questions through various experiments and evaluation.
Formal and Data Association Aware Robust Belief Space Planning
Authors: Pathak Shashank  , Soudjani Sadegh  , Indelman Vadim  , Abate Alessandro
Published: February 2025

State-of-the-art belief space planning (BSP) approaches assume data association to be solved or given. Some of the current authors have recently proposed a relaxation of this assumption, resulting in a more general framework of belief space planning where data association is incorporated within the belief (DA-BSP). Unfortunately, this can quickly become intractable under non-myopic planning. In this work, we seek to harness recent approaches in formal methods (specifically, linear temporal logic in the context of planning under uncertainty), to obtain formal-DA-BSP, an approach that incorporates high-level domain knowledge, to obtain more tractable planning. Thanks to generalised form of specification, the framework can also incorporate other complexities including explicit collision probability and determining planning horizon. The initial concepts are shown in an abstracted example of a robot janitor lost in one of the two floors.
Can We Build Inclusion?
Authors: Kirkeby Inge Mette  , Grangaard Sidse
Published: February 2025

Inclusion of children with special needs in kindergartens and preschools may be approached from different angles. This paper raises the question of whether the physical framework of kindergartens makes any difference for daily life at the kindergarten at all, and whether it can support inclusion of some children with special needs. Hence the title – can we build inclusion? In the literature of Universal Design, accommodation and design features seldom reflect the less visible disabilities. The paper is based on a research project initiated to investigate how more or less space influences daily pedagogical practice in general. Twelve interviews were conducted with experienced teachers from twelve different kindergartens with different amounts of space, varying from a ratio of 2.1 m2play area per child to 5.5 m2. The results indicated that, for a group of children with special needs in particular, the amount of space is crucial. This group consisted of children who were socially very extrovert, and who maybe were noisy, easily provoked, and quick to get involved in arguments with other children. Alternatively, children in the group were very restrained and withdrawn in social interaction. Based on the answers in the interviews, we found support for answering the question in the title in the affirmative; we can build inclusion! This is because the teachers' experience indicated that, if there was sufficient space per child, there were fewer conflicts and the children managed to stay in the same activity for a much longer period. Sufficient space made it possible to divide the children into smaller groups, and use any secluded space. Therefore, it was much easier for other children to include some children with special needs. Accordingly, we can say that, sufficient space per child and an adequate layout and furnishing of the kindergarten is an advantage for all children. This is a clear example of Universal Design in which architectural solutions that are good for one specific group are also beneficial for all children.
Upgrading Existing Buildings to Universal Design. What Cost-Benefit Analyses Can Tell Us
Authors: Aslaksen Finn
Published: February 2025

This article is based on a project aimed at finding the benefits of different measures to upgrade existing public buildings and outdoor areas to be accessible for all. The study was initiated by The Ministry of Children and Equality. The ministry asked for a study of benefits based on a stated preferences (SP) method and an easy-to-complete calculation tool for CBA. In the project 18 commonly used measures and their typical costs were identified. The benefits of each measure were analysed in a stated preference study. The SP analyses included 9 multiple choices in 4 different sequences in an Internet based survey with 800 respondents. The project concluded that it is possible to use stated preferences survey to identify the respondent's valuation of measures to improve accessibility in existing buildings. Some of the measures have a high cost-/benefit ratio. The project report including the calculation manual is based on the average valuation for each measure. But in the background analyses (not referred in the report) there are also some analyses of valuations for target groups for the various measures. The target groups were defined for each measure based on information about the respondents' abilities and use of technical aids. The analyses presented in this paper indicate how valuation varies between the target groups and the average population. This is named the measures profile. Some measures have benefits for the target group that are only twice as high as for the average citizen while another type of measures has high benefits only the target group. The first type which has a wide profile often has high overall socioeconomic benefits, while the last group with a narrow profile more often has low overall socioeconomic benefits, but may be very important for certain user groups and therefore essential for the elimination of discrimination and exclusion of those groups.
On the Effectiveness of Automated Configuration in Abstract Argumentation Reasoning
Authors: Cerutti Federico  , Vallati Mauro  , Giacomin Massimiliano
Published: February 2025

In this paper we investigate the impact of automated configuration techniques on the ArgSemSAT solver—runner-up of the ICCMA 2015—for solving the enumeration of preferred extensions. Moreover, we introduce a fully automated method for varying how argumentation frameworks are represented in the input file, and evaluate how the joint configuration of frameworks and ArgSemSAT parameters can have a remarkable impact on performance. Our findings suggest that automated configuration techniques lead to improved performances in argumentation solvers, an important message for participants to the forthcoming competition.
Argumentation for Machine Learning: A Survey
Authors: Cocarascu Oana  , Toni Francesca
Published: February 2025

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future.
Quantifying the Difference Between Argumentation Semantics
Authors: Doutre Sylvie  , Mailly Jean-Guy
Published: February 2025

Properties of argumentation semantics have been widely studied in the last decades. However, there has been no investigation on the question of difference measures between semantics. Such measures turn helpful when the semantics associated to an argumentation framework may have to be changed, in a way that ensures that the new semantics is not too dissimilar from the old one. Three main notions of difference measures between semantics are defined in this paper. Some of these measures are shown to be distances or semi-distances.
Degrees of “in”, “out” and “undecided” in Argumentation Networks
Authors: Gabbay Dov M.  , Rodrigues Odinaldo
Published: February 2025

The traditional 3-valued semantics of an argumentation frameworkidentifies arguments that are “in”, “out” and “undecided”. Yet, it has long been recognised by the community that some elements can be at different degrees in each of these categories [1,2,3]. For example, Dung's semantics can only classify some elements as “out”, but cannot reflect how much “out” they really are or if elements are “in” are they as much “in” as elements which are not attacked at all? In this paper we shall use a numerical approach to give a measure of “in”, “out” and “undecided” to the nodes of a network. We shall devise equations which allow for solutions that reflect these distinctions.
Assigning Likelihoods to Interlocutors' Beliefs and Arguments
Authors: Hosseini Seyed Ali  , Modgil Sanjay  , Rodrigues Odinaldo
Published: February 2025

This paper proposes mechanisms for agents to model other agents' beliefs and arguments, thus enabling agents to anticipate their interlocutors' arguments in dialogues, which in turn facilitates strategising and the use of enthymemes. In contrast with existing works on “opponent modelling” that treat arguments as abstract entities, the likelihood that an interlocutor can construct an argument is derived from the likelihoods that it possesses the beliefs required to construct the argument. We therefore address how a modelling agent can quantify the certainty that its interlocutor possesses beliefs, based on the modeller's previous dialogues, and the membership of its interlocutor in communities.
Argument Analytics
Authors: Lawrence John  , Duthie Rory  , Budzynska Katarzyna  , Reed Chris
Published: February 2025

Rapid growth in the area of argument mining has resulted in an ever increasing volume of analysed argument data. Being able to store information about arguments people make in favour or against different opinions, decisions and actions is a highly valuable resource, yet extremely challenging for sense-making. How, for example, can an analyst quickly check whether in a corpus of citizen dialogue people tend to rather agree or disagree with new policies proposed by the department of transportation; how can she get an insight into the interactions typical of this specific dialogical context; how can the general public easily see which presidential candidate is currently winning the debate by being able to successfully defend his arguments? In this paper, we propose Argument Analytics – a suite of techniques which provide interpretation of, and insight into, large-scale argument data for both specialist and general audiences.
Assessing Weight of Opinion by Aggregating Coalitions of Arguments
Authors: Rajendran Pavithra  , Bollegala Danushka  , Parsons Simon
Published: February 2025

Argument mining promises to be able to extract information from unstructured text that can help us to understand that text. This paper suggests a novel way to use such information once it has been extracted. Attack and support relations between arguments from a set of test texts are identified, the strength of the arguments is computed based on the relations, and arguments are grouped into coalitions. The resulting set of arguments is then used to predict the weight of opinion in new text, by identifying arguments whose weight has been computed, and aggregating these weights. Our approach is evaluated on a corpus of hotel reviews, and compared with an existing method of predicting the sentiment of reviews.
Gödel Fuzzy Argumentation Frameworks
Authors: Wu Jiachao  , Li Hengfei  , Oren Nir  , Norman Timothy J.
Published: February 2025

In this paper we combine fuzzy set theory and argumentation to facilitate the use of fuzzy arguments and attacks. Unlike many existing approaches, our work does not require the use of any parameters, bringing it closer to Dung's work in spirit. We begin by introducing Fuzzy Argumentation Frameworks, and specialise them using the Gödel t-norm. We then examine this framework's properties and show that the standard Dung extensions are obtained, though the stable semantics coincide with the preferred. Finally, we examine the relationship between our framework and Dung's original system, as well as the existing fuzzy frameworks, describing where they overlap and differ.
From Arguments to Constraints on a Bayesian Network
Authors: Bex Floris  , Renooij Silja
Published: February 2025

In this paper, we propose a way to derive constraints for a Bayesian Network from structured arguments. Argumentation and Bayesian networks can both be considered decision support techniques, but are typically used by experts with different backgrounds. Bayesian network experts have the mathematical skills to understand and construct such networks, but lack expertise in the application domain; domain experts may feel more comfortable with argumentation approaches. Our proposed method allows us to check Bayesian networks given arguments constructed for the same problem, and also allows for transforming arguments into a Bayesian network structure, thereby facilitating Bayesian network construction.
Interactive Optic Disk Segmentation via Discrete Convexity Shape Knowledge Using High-Order Functionals
Authors: Escorcia-Gutierrez José  , Torrents-Barrena Jordina  , Romero-Aroca Pedro  , Valls Aida  , Puig Domènec
Published: February 2025

Diabetic Retinopathy (DR) has become nowadays a considerable world-wide threat due to increased growth of blind people at early ages. From the engineering viewpoint, the detection of DR pathologies (microaneurysms, hemorrhages and exudates) through computer vision techniques is of prime importance in medical assistance. Such methodologies outperform traditional screening of retinal color fundus images. Moreover, the identification of landmark features as the optic disk (OD), fovea and retinal vessels is a key pre-processing step to detect the aforementioned potential pathologies. In the same vein, this paper works with the well-known Convexity Shape Prior algorithm to segment the main anatomical structure of the retina, the OD. At first, some pre-processing techniques such as the Contrast Limited Adaptive Histogram Equalization (CLAHE) and Brightness Preserving Dynamic Fuzzy Histogram Equalization (BPDFHE) are applied to enhance the image contrast and eliminate the artifacts. Subsequently, several morphological operations are performed to improve the post-segmentation of the OD. Finally, blood vessels are extracted through a novel fusion of the average, median, Gaussian and Gabor wavelet filters.
Diabetic Retinopathy Detection Through Image Analysis Using Deep Convolutional Neural Networks
Authors: de La Torre Jordi  , Valls Aida  , Puig Domenec
Published: February 2025

Diabetic Retinopathy is one of the main causes of blindness and visual impairment for diabetic population. The detection and diagnosis of the disease is usually done with the help of retinal images taken with a mydriatric camera. In this paper we propose an automatic retina image classifier that using supervised deep learning techniques is able to classify retinal images in five standard levels of severity. In each level different irregularities appear on the image, due to micro-aneurisms, hemorrages, exudates and edemas. This problem has been approached before using traditional computer vision techniques based on manual feature extraction. Differently, we explore the use of the recent machine learning approach of deep convolutional neural networks, which has given good results in other image classification problems. From a training dataset of around 35000 human classified images, different convolutional neural networks with different input size images are tested in order to find the model that performs the best over a test set of around 53000 images. Results show that it is possible to achieve a quadratic weighted kappa classification score over 0.75 not far from human expert reported scores of 0.80.
Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light
Authors: Garcia Jose Antonio  , Masip David  , Sbragaglia Valerio  , Aguzzi Jacopo
Published: February 2025

Automated video and image analysis can be a very efficient tool to behavior study, especially in hard access environments for researchers. The understanding of this social behavior can play a key role in the sustainable design of control policies of many species. This paper proposes the use of computer vision algorithms to identify and track, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior. This emergent behavior is modulated by the day-night cycle, but social interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-of-the-art computer vision algorithms to distinguish and track the different animals' movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community.
Overview of the Development of Language Resources and Technologies in Lithuania (2012–2015)
Authors: Utka Andrius  , Amilevičius Darius  , Krilavičius Tomas  , Vitkutė-Adžgauskienė Daiva
Published: February 2025

The paper presents an overview of recent advances of language technologies in Lithuania. It is shown that the development of Lithuanian language resources and technologies can be divided into three stages: the first (2004–2012), the second (2012–2015), and the third (2016–2020). The paper focuses on the second stage of development, which is labelled as the systematic breakthrough. The paper contains separate sections on the policy of language technologies, research infrastructures and international collaboration, language resources, and tools.
Recognition of ICD-10 Codes by Combining Two Recognizers
Authors: Ratkevičius Kastytis  , Paškauskaitė Gintarė  , Bartišiūtė Gintarė
Published: February 2025

Paper deals with the recognition of disease codes and with the hybrid recognition technology. It is impossible so far to recognize about 15000 various diseases, but each disease can be identified by its code, consisting of one letter and some digits. The appropriate Lithuanian names were selected for each letter and their recognition accuracy together with Lithuanian digit names recognition accuracy was investigated. By the hybrid approach we assume the combination of two different recognizers to achieve higher recognition accuracy. The first recognizer was HTK-based Lithuanian recognizer, the second one – the Spanish language recognizer adapted to the Lithuanian language. The experimental results show that a hybrid decision-making rule learned by “random forest” classifier decreases the recognition error of Lithuanian digits names speech corpus by 74.1% and the recognition error of Lithuanian names speech corpus by 76.7% compared with HTK-based Lithuanian recognizer when the speaker is unknown.
Polite Interactions with Robots
Authors: Benotti Luciana  , Blackburn Patrick
Published: February 2025

We sketch an inference architecture that permits linguistic aspects of politeness to be interpreted; we do so by applying the ideas of politeness theory to the SCARE corpus of task-oriented dialogues, a type of dialogue of particular relevance to robotics. The fragment of the SCARE corpus we analyzed contains 77 uses of politeness strategies: our inference architecture covers 58 of them using classical AI planning techniques; the remainder require other forms of means-ends inference. So by the end of the paper we will have discussed in some detail how to interpret automatically different forms of politeness — but should we do so? We conclude with some brief remarks on the issues involved.
Making Place for Social Norms in the Design of Human-Robot Interaction
Authors: Brinck Ingar  , Balkenius Christian  , Johansson Birger
Published: February 2025

We argue that social robots should be designed to behave similarly to humans, and furthermore that social norms constitute the core of human interaction. Whether robots can be designed to behave in human-like ways turns on whether they can be designed to organize and coordinate their behavior with others' social expectations. We suggest that social norms regulate interaction in real time, and agents rely on dynamic information about their own and others' attention, intention and emotion to perform social tasks.
How to Count Multiple Personal-Space Intrusions in Social Robot Navigation
Authors: Lindner Felix
Published: February 2025

One aspect of social robot navigation is to avoid personal space intrusions. Computationally, this can be achieved by introducing social costs into a robot's path planner's objective function. This article tackles the normative question of how robots should aggregate social costs incurred by multiple personal-space intrusions. Of particular interest is the question whether numbers should count, i.e., whether a robot ought to intrude into one person's personal space in order to avoid intruding into multiple personal spaces. This work proposes four different modes of aggregation of the costs of intrusions into personal space, discusses some of the philosophical arguments, and presents results from a pilot study.
Co-Designing Child-Robot Interactions
Authors: Robert David  , van den Bergh Victor
Published: February 2025

This workshop at Robophilosophy 2016 will explore approaches for designing robots for children that incorporate the needs and preferences of children, themselves. An interdisciplinary panel of child-robot interaction (CRI) researchers will introduce workshop participants to projects in Denmark, Russia and the United States of America that have used differing approaches for collecting children's attitudes and ideas about robots, including speculative co-design, quantitative survey-scales, linguistic analysis, and long-term research studies in educational contexts. Workshop participants will also engage in a group, roboethics imaginative exercise with the International Archive of Children's Robot Designs housed at the Center for Children's Speculative Design (a concurrent exhibition on display at Robophilosophy 2016). The published outcomes of this workshop will inform an evolving draft of recommendations and responsible strategies to be shared with scholars, robotics industry thought leaders and international policy makers concerned with the ethical implications of social robots designed to specifically target and impact the lives of children worldwide.
Robots as Confederates: How Robots Can and Should Support Research in the Humanities
Authors: Fischer Kerstin
Published: February 2025

This paper addresses the use of robots in experimental research for the study of human language, human interaction, and human nature. It is argued that robots make excellent confederates that can be completely controlled, yet which engage human participants in interactions that allow us to study numerous linguistic and psychological variables in isolation in an ecologically valid way. Robots thus combine the advantages of observational studies and of controlled experimentation.
Adaptive Techniques for Extracting Mental Activity Phases from Heart Beat Rate Streams
Authors: Dubatovka Alina  , Mikhailova Elena  , Zotov Mikhail  , Novikov Boris
Published: February 2025

The paper presents algorithms for automatic detection of non-stationary periods of cardiac rhythm during professional activity. While working and subsequent rest operator passes through the phases of mobilization, stabilization, work, recovery and the rest. The amplitude and frequency of non-stationary periods of cardiac rhythm indicates the human resistance to stressful conditions. We introduce and analyze a number of algorithms for non-stationary phase extraction: the different approaches to phase preliminary detection, thresholds extraction and final phases extraction are studied experimentally.
Using Linguistic Graph Similarity to Search for Sentences in News Articles
Authors: Schouten Kim  , Frasincar Flavius
Published: February 2025

With the volume of daily news growing to sizes too big to handle for any individual human, there is a clear need for effective search algorithms. Since traditional bag-of-words approaches are inherently limited since they ignore much of the information that is embedded in the structure of the text, we propose a linguistic approach to search called Destiny in this paper. With Destiny, sentences, both from news items and the user queries, are represented as graphs where the nodes represent the words in the sentence and the edges represent the grammatical relations between the words. The proposed algorithm is evaluated against a TF-IDF baseline using a custom corpus of user-rated sentences. Destiny significantly outperforms TF-IDF in terms of Mean Average Precision, normalized Discounted Cumulative Gain, and Spearman's Rho.
A Framework for Improving Web Application User Interfaces Through Immediate Evaluation
Authors: Marenkov Jevgeni  , Robal Tarmo  , Kalja Ahto
Published: February 2025

More and more web applications are being migrated from desktop platforms to platforms for smartphones and tablets. Usability and User experience are highly different on desktop and portable devices. Every minor modification of user interface (UI) could lead to violations of usability guidelines, e.g. changing the link color or font size could lead to lower usability for people with disabilities. Empirical usability evaluation methods are the approaches that help to verify the usability conformance to guidelines. Nevertheless, there are number of obstacles preventing from using such methods widely. The purpose of our study is to propose a conceptual model and corresponding framework with methodology including category specific metrics for immediate automatic usability evaluation of web application user interfaces during design and implementation phase. We address the gap between usability evaluation and development stage of UI by providing immediate feedback to developers.
Metamodel Specialization for Diagram Editor Building
Authors: Kalnins Audris  , Barzdins Janis
Published: February 2025

Domain-specific diagram editor building environments nowadays as a rule involve some usage of metamodels. However normally the metamodel alone is not sufficient to define an editor. Frequently the metamodel just defines the abstract syntax of the domain, mappings or transformations are required to define the editor functionality for diagram building. Another approach [8] is based on a fixed type metamodel describing the possible diagram elements, there an editor definition consists of an instance of this metamodel to be executed by an engine. However there typically a number of functionality extensions in a transformation language is required. The paper offers a new approach based on metamodel specialization – by just creating subclasses. First the permitted metamodel specialization based on standard UML class diagrams and OCL is precisely defined. A universal metamodel and an associated universal engine for the diagram editor domain is described, then it is shown how a specific editor definition can be obtained by specializing this metamodel. Examples of a flowchart editor and UML class diagram editor are given.
Neuronal Ceroid-Lipofuscinoses
Authors: Rakheja Dinesh  , Bennett Michael J.
Published: February 2025

The neuronal ceroid-lipofuscinoses (NCLs) are a group of recessively inherited diseases characterized by progressive neuronal loss, accumulation of intracellular lipofuscin-like autofluorescent storage material with distinctive ultrastructural appearance, and clinical signs and symptoms of progressive neurodegeneration. Initially classified by the age of onset of clinical signs and symptoms as well as the ultrastructural morphology of the storage material, the growing family of NCLs can now also be biologically classified by their underlying genetic defects. For a few NCLs, we now understand the functions of the proteins encoded by the NCL genes, which has enabled refining of the diagnostic algorithms and ignited hopes for finding effective treatments in the future. Ongoing research into understanding the functions of the remainder of the NCL proteins as well as continuing advancements in technology (such as massively-parallel gene sequencing) has and will continue to inform the clinical approach, diagnostic algorithms, and treatment strategies.