bluuebunny/crossref_metadata_2025 huggingface dataset:
Combining Bootstrapping and Feature Selection for Improving a Distributional Thesaurus
Authors: Ferret Olivier
Published: February 2025

Work about distributional thesauri has now widely shown that the relations in these thesauri are mainly reliable for high frequency words and for capturing semantic relatedness rather than strict semantic similarity. In this article, we propose a method for improving such a thesaurus through its re-balancing in favor of middle and low frequency words. This method is based on a bootstrapping mechanism: a set of positive and negative examples of semantically related words are selected in a unsupervised way from the results of the initial measure and used for training a supervised classifier. This classifier is then applied for reranking the initial semantic neighbors. We evaluate the interest of this reranking for a large set of English nouns with various frequencies.
A Bayesian Multiple Kernel Learning Framework for Single and Multiple Output Regression
Authors: Gönen Mehmet
Published: February 2025

Multiple kernel learning algorithms are proposed to combine kernels in order to obtain a better similarity measure or to integrate feature representations coming from different data sources. Most of the previous research on such methods is focused on classification formulations and there are few attempts for regression. We propose a fully conjugate Bayesian formulation and derive a deterministic variational approximation for single output regression. We then show that the proposed formulation can be extended to multiple output regression. We illustrate the effectiveness of our approach on a single output benchmark data set. Our framework outperforms previously reported results with better generalization performance on two image recognition data sets using both single and multiple output formulations.
Large-scale Interactive Ontology Matching: Algorithms and Implementation
Authors: Jiménez-Ruiz Ernesto  , Cuenca Grau Bernardo  , Zhou Yujiao  , Horrocks Ian
Published: February 2025

In this paper we present the ontology matching system LogMap 2, a much improved version of its predecessor LogMap. LogMap 2 supports user interaction during the matching process, which is essential for use cases requiring very accurate mappings. Interactivity, however, imposes very strict scalability requirements; we are able to satisfy these requirements by providing real-time user response even for large-scale ontologies. Finally, LogMap 2 implements scalable reasoning and diagnosis algorithms, which minimise any logical inconsistencies introduced by the matching process.
A Ranking Semantics for First-Order Conditionals
Authors: Kern-Isberner Gabriele  , Thimm Matthias
Published: February 2025

Usually, default rules in the form of conditional statements are built on propositional logic, representing classes of individuals by propositional variables, as in “Birds fly, but penguins don't”. Only few approaches have addressed the problem of giving formal semantics to first-order conditionals that allow (nonmonotonic) inferences both for classes and for individuals. In this paper, we present a semantics for first-order conditionals that is based on ordinal conditional (or ranking) functions which are well-known in the area of propositional default reasoning and makes use of representative individuals to establish conditional relationships. We generalize the c-representation approach of [8] for inductive reasoning with first-order conditionals, and evaluate our approach via benchmark examples and a catalogue of general properties.
A Path-Optimal GAC Algorithm for Table Constraints
Authors: Lecoutre Christophe  , Likitvivatanavong Chavalit  , Yap Roland H.C.
Published: February 2025

Filtering by Generalized Arc Consistency (GAC) is a fundamental technique in Constraint Programming. Recent advances in GAC algorithms for extensional constraints rely on direct manipulation of tables during search. Simple Tabular Reduction (STR), which systematically removes invalid tuples from tables, has been shown to be a simple yet efficient approach. STR2, a refinement of STR, is considered to be among the best filtering algorithms for positive table constraints. In this paper, we introduce a new GAC algorithm called STR3 that is specifically designed to enforce GAC during search. STR3 can completely avoid unnecessary traversal of tables, making it optimal along any path of the search tree. Our experiments show that STR3 is much faster than STR2 when the average size of the tables is not reduced drastically during search.
Updating inconsistent Description Logic knowledge bases
Authors: Lenzerini Maurizio  , Fabio Savo Domenico
Published: February 2025

Finding an appropriate semantics for task of updating an inconsistent knowledge base is a challenging problem. In this paper, we consider knowledge bases expressed in Description Logics, and focus on ABox inconsistencies, i.e., the case where the TBox is consistent, but the whole knowledge base is not. Our first contribution is the definition of a new semantics for updating an inconsistent Description Logic knowledge base with both the insertion and the deletions of a set of ABox assertions. We then concentrate on the
Game-theoretic Approach to Adversarial Plan Recognition
Authors: Lisý Viliam  , Píbil Radek  , Stiborek Jan  , Bošanský Branislav  , Pěchouček Michal
Published: February 2025

We argue that the problem of adversarial plan recognition, where the observed agent actively tries to avoid detection, should be modeled in the game theoretic framework. We define the problem as an imperfect-information extensive-form game between the observer and the observed agent. We propose a novel algorithm that approximates the optimal solution in the game using Monte-Carlo sampling. The experimental evaluation is performed on a synthetic domain inspired by a network security problem. The proposed method produces significantly better results than several simple baselines on a practically large domain.
Diagnosing Delays in Multi-Agent Plans Execution
Authors: Micalizio Roberto  , Torta Gianluca
Published: February 2025

The paper introduces the notion of Temporal Multi-Agent Plan (TMAP) and proposes a methodology, based on Simple Temporal Problems (STP), for detecting and diagnosing action execution delays. Actions are characterized by a finite set of behavioral modes, and each behavioral mode is a continuous interval of possible durations of the action. Nominal modes represent the expected durations, whereas faulty modes represent delays. Solving such diagnostic problems requires to find an assignment of modes to the actions that is consistent with the received observations and maximizes the likelihood of the delayed durations. An implementation of the approach and some preliminary experimental results are also discussed.
Large-scale Parallel Stratified Defeasible Reasoning
Authors: Tachmazidis Ilias  , Antoniou Grigoris  , Flouris Giorgos  , Kotoulas Spyros  , McCluskey Lee
Published: February 2025

We are recently experiencing an unprecedented explosion of available data coming from the Web, sensors readings, scientific databases, government authorities and more. Such datasets could benefit from the introduction of rule sets encoding commonly accepted rules or facts, application- or domain-specific rules, commonsense knowledge etc. This raises the question of whether, how, and to what extent knowledge representation methods are capable of handling huge amounts of data for these applications. In this paper, we consider inconsistency-tolerant reasoning in the form of defeasible logic, and analyze how parallelization, using the MapReduce framework, can be used to reason with defeasible rules over huge datasets. We extend previous work by dealing with predicates of arbitrary arity, under the assumption of stratification. Moving from unary to multi-arity predicates is a decisive step towards practical applications, e.g. reasoning with linked open (RDF) data. Our experimental results demonstrate that defeasible reasoning with millions of data is performant, and has the potential to scale to billions of facts.
A Probabilistic Semantics for abstract Argumentation
Authors: Thimm Matthias
Published: February 2025

Classical semantics for abstract argumentation frameworks are usually defined in terms of extensions or, more recently, labelings. That is, an argument is either regarded as accepted with respect to a labeling or not. In order to reason with a specific semantics one takes either a credulous or skeptical approach, i.e. an argument is ultimately accepted, if it is accepted in one or all labelings, respectively. In this paper, we propose a more general approach for a semantics that allows for a more fine-grained differentiation between those two extreme views on reasoning. In particular, we propose a probabilistic semantics for abstract argumentation that assigns probabilities or degrees of belief to individual arguments. We show that our semantics generalizes the classical notions of semantics and we point out interesting relationships between concepts from argumentation and probabilistic reasoning. We illustrate the usefulness of our semantics on an example from the medical domain.
Relation Mining in the Biomedical Domain using Entity-level Semantics
Authors: Tymoshenko Kateryna  , Somasundaran Swapna  , Prabhakaran Vinodkumar  , Shet Vinay
Published: February 2025

This work explores the use of semantic information from background knowledge sources for the task of relation mining between medical entities such as diseases, drugs, and their functional effects/actions. We hypothesize that the semantics of medical entities, and the information about them in different knowledge sources play an important role in determining their interactions and can thus be exploited to infer relations between these entities. We capture entities' semantics using a number of resources such as Wikipedia, UMLS Semantic Network, MEDCIN, MeSH and SNOMED. De-pending on coverage and specificity of the resources, and features of interest, different classifiers are learnt. An ensemble based approach is then used to fuse together individual predictions. Using a human-curated ontology as the gold standard, the proposed approach has been used to recognize ten medical relations of interest. We show that the proposed approach achieves substantial improvements in both coverage and performance over a distant supervision based baseline that uses sentence-level information. Finally, we also show that even a simple ensemble approach that combines all the semantic information is able to get the best coverage and performance.
A Novel Way to Connect BnB-ADOPT
Authors: Gutierrez Patricia  , Meseguer Pedro
Published: February 2025

Combining BnB-ADOPT+, with AC and FDAC levels of soft arc consistency (SAC) improves efficiency for optimal DCOP solving. However, it seems difficult in distributed context to achieve the higher consistency level EDAC, especially considering privacy. As alternative, we propose DAC by token passing. Agents receiving a token ask neighbors for cost extensions. When deletions or Cφ increments occur, the token is passed to neighbor agents. This strategy turns out to be more efficient than FDAC when combined with BnB-ADOPT+, improving communication and specially computation.
Statistical Disclosure Limitation of Health Data Based on Pk-Anonymity
Authors: Kimura Eizen  , Chida Koji  , Ikarashi Dai  , Hamada Koki  , Ishihara Ken
Published: February 2025

The Act for the Protection of Personal Information in Japan considers as personal information any quasi-identifier that may be used to obtain information that identifies individuals through comparisons with datasets. Studies using health records are not widely conducted because of the concern regarding the safety of anonymized health records. To increase the safety of such records, we used the Pk-anonymity method. In this method, attributes are probabilistically randomized and then reconstructions are performed on the basis of statistical information from perturbed data. Hence, it is expected to provide more precise statistics and more reliably preserve privacy than the traditional “k-anonymity” method. We anonymized health records, performed cross tabulation, and assessed the error rate using original data. This study shows that the Pk-anonymity method can be used to perform safety statistical disclosures with low error rates, even in small cases.
MInD: Moving in the Dark
Authors: Pepino A.  , Sicignano G.  , Rovani M.  , D'Addio G.
Published: February 2025

Visually-impaired people can develop several unhealthy behaviors, including the lack of physical activity or sports, due to the incomplete maturity in the control of their body in the space. This research focuses on the creation of an “exergame” – a videogame especially designed to stimulate physical exercise – that, through voice commands, allows the visually impaired users to practice physical activity and train their abilities. This tool has been developed starting from an existing dance-game, by generating some appropriate interfaces that also involve the sensory channel of sight. Our research aims to study the effects related to this exergame on the motor control mechanism in a blind children's group, aged between 8 and 13 years: it focuses also on the joint use of movement analysis systems and of videogames in order to stimulate the physical activity in these subjects.
RAVEL: Retrieval And Visualization in ELectronic health records
Authors: Thiessard Frantz  , Mougin Fleur  , Diallo Gayo  , Jouhet Vianney  , Cossin Sébastien  , Garcelon Nicolas  , Campillo Boris  , Jouini Wassim  , Grosjean Julien  , Massari Philippe  , Griffon Nicolas  , Dupuch Marie  , Tayalati Fayssal  , Dugas Edwige  , Balvet Antonio  , Grabar Natalia  , Pereira Suzanne  , Frandji Bruno  , Darmoni Stefan  , Cuggia Marc
Published: February 2025

Because of the ever-increasing amount of information in patients' EHRs, healthcare professionals may face difficulties for making diagnoses and/or therapeutic decisions. Moreover, patients may misunderstand their health status. These medical practitioners need effective tools to locate in real time relevant elements within the patients' EHR and visualize them according to synthetic and intuitive presentation models. The RAVEL project aims at achieving this goal by performing a high profile industrial research and development program on the EHR considering the following areas: (i) semantic indexing, (ii) information retrieval, and (iii) data visualization. The RAVEL project is expected to implement a generic, loosely coupled to data sources prototype so that it can be transposed into different university hospitals information systems.
Secondary Use of Clinical Data in Healthcare Providers – an Overview on Research, Regulatory and Ethical Requirements
Authors: Wiesenauer Matthias  , Johner Christian  , Röhrig Rainer
Published: February 2025

Hospital providers, physicians and researchers are interested in a cross-institutional use of their data for clinical research. This interest has led to the question whether the scientific potential of the data stored in so many different systems can be unfolded by the establishment of a cross-institutional medical data warehouse. The aim of this paper is to describe the ethical and regulatory requirements and to develop a solution architecture considering technical and organisational aspects. The present paper uses a structured approach to collect user requirements. The requirements are discussed with legal experts. The work was complemented by extended literature research. An essential requirement is the cross-institutional merging of the data. Here, aspects of data protection as the informed consent, or transparency must be considered. In addition it is essential to protect the researchers through transparency from accusations on publication bias. Technical and organisational solutions in combination of data protection, and data security enable an operation of a central medical data warehouse in compliance with the law. The usage of this infrastructure for research can contribute to an improvement of the treatment quality, and patient safety if there is an appropriate transparency. This contributes to innovation and added value of a hospital group.
Archetype based search in an IHE XDS Environment
Authors: Rinner Christoph  , Kohler Michael  , Hübner-Bloder Gudrun  , Saboor Samrend  , Ammenwerth Elske  , Duftschmid Georg
Published: February 2025

To prevent information overload of physicians when accessing EHRs we introduce a method to extend the IHE XDS profile metadata-based search towards a content-based search. Detailed queries are created based on predefined information needs mapped to ISO/EN 13606 Archetypes. They are aggregated to a metadata-based query to retrieve all relevant documents, which are then analyzed for the desired contents. The results are presented in a tabular form. The content-based search in IHE-XDS could be implemented efficiently and was found helpful by the evaluating physicians.
Envisioning Electronic Health Record Systems as Change Management: The Experience of an English Hospital Joining the National Programme for Information Technology
Authors: Takian Amirhossein
Published: February 2025

The historical National Programme for Information Technology (NPfIT) in England was the most expensive (~$20billion) and ambitious politically-driven IT-based transformations of public services ever undertaken. Nation-wide implementation of integrated electronic health record (EHR) systems in hospitals was at the heart of the NPfIT (~$10billion). We conducted the first longitudinal, prospective, and sociotechnical case study implementation and adoption of national EHRs implementations in 12 ‘early adopter’ hospitals across England. This paper reports the arrival, implementation process, and stakeholders' experiences of one EHR software (Millennium) at a National Health Service's (NHS) general hospital participating in NPfIT, hereafter called Alpha. From the outset, Alpha envisioned the implementation of EHR as a practice of change management to improve its performance. This vision attributed to the establishment of a ‘design authority’ at Alpha, including users from various capacities and levels. The ‘design authority’ was perceived a key contributor to appropriate (compared to other hospitals we studied) clinical engagement and bottom-up approach to deploying EHR. Through conducting several hundreds of group and individual workflow familiarization, Alpha adopted a novel approach to training staff on EHR software. This led to greater local configuration and high sense of ownership among users, which transformed work practices towards overall better performance of the hospital. Contrary to painful and turbulent experiences of EHR implementation via NPfIT route in the English hospitals, this in-depth case study revealed the importance of vision (change management) and insightful leadership in ‘working out’ EHR. We advocate envisioning EHRs as change management endeavors to enhance their complex, multi-dimensional, and sociotechnical adoption in healthcare settings.
New Approaches to Linking Clinical Guidelines to Virtual Patients
Authors: Kononowicz Andrzej A.  , Hege Inga  , Krawczyk Paweł  , Zary Nabil
Published: February 2025

An often reported challenge of evidence-based medicine concerns increasing use of clinical guidelines in practice. One of the proposed improvements is to promote guidelines by presenting them in conjunction with virtual patients. Three approaches to linking clinical guidelines to virtual patients are presented in this paper: (1) guidelines as a source for generating virtual patients; (2) guidelines hyper-flowchart as a virtual patient progress indicator; (3) guidelines flowchart reconstruction as a learning activity in virtual patient systems. The scenarios have been preliminarily evaluated using two demonstrator applications: Bit Pathways and CASUS. Challenges and direction for further development are proposed.
Sub-exercise Generation Support Environment for Logical Understanding of Mathematics
Authors: Nogami Yusuke  , Kojiri Tomoko
Published: February 2025

Mathematical exercises consist of several sub-exercises. When studying mathematics using an example exercise (example), some learners memorize all theorems used in the example, while others consider unknown derivation steps logically and memorize only new theorems. The latter learning method is effective, since the amount of memorization is small and learners can consider the reasons for applying newly appearing theorems. The aim of this research is to construct a learning environment that supports learners in generating sub-exercises from an example in order to foster logical thinking about mathematics. By finding known theorems from the example, learners are able to notice relations between exercises. In our environment, suggestions are provided if learners are not able to derive the sub-exercises. In addition, examples are given in the appropriate order to allow learners to easily notice the known theorems.
LMT: A Lightweight Logical Framework for Multi-agent Systems
Authors: Crossley John N.  , Cruz Lito Perez
Published: February 2025

Logics Modulo Theories, LMT, is a logical framework specially designed to support the work of multi-agent systems. It is a system that allows local logics to communicate through a global system which has its own logic. (The method has similarities with, but goes beyond, SMT, Satisfiability Modulo Theories.) In two earlier papers we considered a variety of logics used to represent ontologies (e.g. first order logic, description logic) as the local logics and at the global level we used a propositional logic. However, at that time, we only gently hinted how this applies to multi-agent systems and we lacked quantification capabilities in the upper logic. In this paper we present full blown support for multi-agent system, the use of the modal logic S5 at the global level. The following are the theses of this paper : a) when used for combining logics, in comparison to other methods, LMT produces a more elegant and simpler logical system which is appropriate for multi-agent work, b) the ideal properties of its component logics, namely – soundness and completeness transfer to the resulting logic, c) the proofs for these are very straightforward because LMT uses well-established technology.
Finding the Best Source of Information by means of a Socially-enabled Search Engine
Authors: Fosci Paolo  , Psaila Giuseppe
Published: February 2025

In the era of Web 2.0, users strongly contribute to blogs, providing their opinions and useful information about specific topics; typically, users that are interested in a topic looks for related blogs in pull mode. But social networks have become even more effective in disseminating opinions and information in push mode, because users directly receive tweets and posts.
Modeling and Managing Uncertainty in Concurrent Database Transactions
Authors: Cuzzocrea Alfredo  , Decker Hendrik  , MuÑOz-EscoÍ Francesc D.
Published: February 2025

In this paper, we provide preliminary results on the emerging problem of modeling and managing uncertainty in concurrent database transactions. This work thus completes several previous studies that, by the contrary, have not considered the specific case of concurrent transactions, which may worse the uncertainty of database management activities beyond to the simplest case of isolated transactions. Indeed, as we demonstrate in this paper, inconsistency tolerance of integrity management, constraint checking and repairing easily scales up to concurrent transactions in a natural way, and query answering in concurrent transactions over uncertain data remains certain in the presence of uncertainty
Aspects of Curry Algebras, Computability, Constructibility, and Topological Spaces
Authors: Minoro Abe Jair  , Nakamatsu Kazumi  , Akama Seiki
Published: February 2025

This paper illustrates some applications of the notion of Curry algebras. Formerly introduced as a concept to study algebraic version of some non-classical systems, such structures also generalize some fundamental logical notions as computability, construtibility, and topological spaces. A brief study of Curry algebras Cn and algebra Cω is also discussed.
The Development of a Knowledge-based Electricity Power Station Condenser Management System.
Authors: Prickett Paul  , Davies Gwyn  , Grosvenor Roger
Published: February 2025

This paper presents an approach to the monitoring one of the most critical elements of plant functioning within an electricity power station. It centres on research aimed at improving the efficient operation of the condenser stage of the generation cycle. This utilises the process optimisation data supplied by the condenser manufacturer in combination with the control signals issued by the programmable logic controller to manage the plant. The condenser performance data is transformed into an embedded algorithm and the PLC data is made available via the SCADA interface. The approach demonstrates how knowledge-based management systems can be engineered by utilizing the readily available SCADA functionality.
Application of Wearable Smart System To Support Physical Activity
Authors: ŚWiątek Paweł  , Klukowski Piotr  , Brzostowski Krzysztof  , Drapała Jarosław
Published: February 2025

Wearable smart systems that measure and process vital sings from human body have become important tool in eHealth, rehabilitation and recreation. We developed a system that aims to support recreational physical activity by delivering communication and computational services. The system architecture allows to implement advanced data processing methods in the form of computational services and easily include additional services according to the user needs.
Using an Ontology for Modeling Decision-Making Knowledge
Authors: Kornyshova Elena  , Deneckère Rébecca
Published: February 2025

Decision-making (DM) knowledge is often represented as a set of basic definitions such as alternatives, criteria, decision matrix, and decision itself. However, this domain is much richer, and many other related notions are useful to make right decisions: preferences, weights, thresholds, and so on. This knowledge must be formalized within a model. In addition, the practical needs for DM as well as the number of researches dealing with decision-making increasingly grow. We suggest using the ontology representation for representing DM knowledge. In this paper, we analyze different ontology fundamentals, we present the DM ontology (DMO) and its application to the selection on an ERP tool, and we show how the ontology fundamentals were used for representing DM knowledge.
Pixel-based background initialization using spatio-temporal restrictions
Authors: Villalba-Espinosa Juan  , González-Linares José M.  , Cózar Julián R.  , Guil Nicolás
Published: February 2025

A precise background detection is required for video surveillance applications in order to detect foreground objects. However, in the presence of cluttered scenes, standard techniques for background segmentation can fail. In this work we present a new technique for foreground detection that is able to detect the correct background in complex scenes. It works grouping neighbour pixels that fulfill some kind of spatial and temporal criteria. Spatial criterion is based on the appearance similarity of neighbour pixels while temporal criterion looks for the best temporal correlation in the whole video sequence. Initially, a set of seed points of the image are selected and both criteria are applied in an alternate way until all the pixels of the image have been visited and their background value has been calculated.
Sem-PubMed: a Semantic Medical Digital Library that Integrates Ontology Learning and Query Reformulation
Authors: El Kafsi Safa  , Ben Mustapha Nesrine  , Baazaoui Zghal Hajer  , Moreno Antonio
Published: February 2025

Ontologies, as the conceptualization of concepts and their interrelations in a specific domain, have proven to be a useful toolin the area of Information Retrieval. However, the relevance of the information they contain depends on their regular updating. Recently, new techniques that automatically build and enrich ontologies have been proposed. A previous work proposed a generic approach that allows any search engine to develop its semantic layer by applying Ontology Learning techniques on Web snippets.This paper applies this idea to PubMed, a well-known medical digital library. The new system (Sem-PubMed) builds automatically new ontology fragments related to the user’s query and then it reformulates queries using the new concepts in order to improve the accuracy of Information Retrieval. The tests of the system have revealed an improvement of the relevance of the search results along with the automatic enrichment of the domain ontology.
Mutual complement networks use a school building based on evaluation by simulation
Authors: Nakamura Toshio  , Toyoda Kyohei  , Oda Shota  , Ozawa Shunsuke  , So Phalla  , Hujiwara Shunya  , Issiki Yuya  , Mizuno Tadanori  , Yamada Kunihiro
Published: February 2025

We propose a mutually complementary wireless and wired network, developed for residences, for a larger university school building. We evaluated the resulting practical, realizable communication system through simulation.
Addressing Social Isolation and Establishing a Scale-Free Network in a Super-Aging Society
Authors: Shiizuka Hisao  , Hashizume Ayako
Published: February 2025

In this paper, we focus on social isolation among elderly people to better understand the reality of their lives, and analyze the relationship between the factors behind changes in the family structures of the elderly and their social isolation, and changes in modern communities. We also investigate new forms of communication aimed at improving the quality of life in a longevity society. Methodologically, we propose and examine a basic means to enhance local community networks, based on a scale-free network as a foundation for local community formation.
Towards Automatic Estimation of the Body Condition Score of Dairy Cattle Using Hand-held Images and Active Shape Models
Authors: Tedín Rafael  , Becerra J.A.  , Duro Richard J.  , Martínez Lede Ismael
Published: February 2025

The Body Condition Score (BCS) is considered a critical value for dairy farms, since its observation can be used to optimize milk production. Usually, the BCS is calculated by human experts after visual inspection in a time-consuming and subjective process. There are already some papers where this process is almost automated using image processing on some kinds of pictures and, in this work, the first steps towards a fully automated method based on pictures taken with common photographic cameras are described. Active Shape Models (ASM) are used to obtain a set of features that describe the back shape of cows and those features feed a classifier that computes the BCS. We show that the BCS can be estimated using only a set of angles from the back view with an error similar to that calculated between scores of two experts. To obtain those angles automatically is the hardest step in this process, but we have already achieved reasonable results on that point too.
Evaluation of interest point detectors for image information extraction
Authors: Barandiaran Iñigo  , Congote John  , Goenetxea Jon  , Ruiz Oscar
Published: February 2025

Interest points extraction and matching is a common task in many computer vision based application, which are used in different domains, such as 3D reconstruction, object recognition, or tracking. We present an evaluation of current state of the art about interest point extraction algorithms to measure several parameters, such as detection quality, invariance to rotation and scale transformation, and computational efficiency.
Bootstrapped Dendritic Classifiers for Alzheimer’s Disease classification on MRI features
Authors: Chyzhyk Darya
Published: February 2025

Bootstrapped Dendritic Classifiers (BDC) is an ensemble of weak Dendritic Classifiers trained combining their output by majority voting to obtain improved classification generalization performance. Weak Dendritic Classifiers are trained on bootstrapped samples of the train data setting a limit on the number of dendrites. There is no additional data preprocessing. This paper explores classification performance and the sensitivity to the number of classifiers and the number of dendrites on the classification of Alzheimer’s Disease (AD) patients, comparing with previous results obtained on the same feature database, which are obtained from T1-weighted MRI data of healthy controls and AD patients.
Regularity Dimension of Medical Images
Authors: Pham Tuan D.
Published: February 2025

It has been hypothesized that by examining the changes of intensity in medical images, we can extract some property that characterizes the pathology of disease process. This paper presents the conceptual framework of the regularity dimension of images which can be utilized in computational neuroscience to obtain objective information to support clinical decision making. As an example model, the proposed approach is applied for studying pattern similarity of white matter hyperintensities of the brain on magnetic resonance imaging.
Incorporating Farthest Neighbours in Instance Space Classification
Authors: Vaccaro-Senna Daniel  , Medhat Gaber Mohamed
Published: February 2025

The nearest neighbour (NN) classifier is often known as a ‘lazy’ approach but it is still widely used particularly in the systems that require pattern matching. Many algorithms have been developed based on NN in an attempt to improve classification accuracy and to reduce the time taken, especially in large data sets. This paper proposes a new classification technique based on k-Nearest Neighbour (k-NN), called k-Nearest & Farthest Neighbours (k-NFN). Farthest neighbours are used to identify classes that an unseen record may not belong to and are considered with the nearest neighbours in the classification decision. Two neighbour voting systems are also proposed to further improve k-NN and k-NFN accuracy. The first uses a ranking system and the second uses a spectrum to consider how near or far a neighbour actually is. The accuracy of our three proposed k-NFN techniques and k-NN are compared using the standard ten cross fold validation experiments on a number of real data sets, evidencing the superiority of our proposed techniques in terms of accuracy.
Reducing the size of ontological representation of services using ontology recomposition
Authors: Rybicki Tomasz
Published: February 2025

Electronic devices provide different kinds of simple services. The services should be able to interoperate with one another. To fully exploit the power of such a service-oriented approach in a pervasive computing environment, a technique of runtime ontology recomposition is proposed. Each service is described with concepts from a common ontology. At runtime, the descriptions present in an environment are merged into a runtime version of the ontology which is then used in resolving service queries. It is shown that by maintaining appropriate level of semantic richness, the recomposition does not impair the quality of service matchmaking and that the required richness level may be easily estimated from the original ontology.
Comparing Clustering Algorithms for Psychomime Classification using Probabilistic Latent Semantic Analysis and Fuzzy c-Means
Authors: Kurosawa Yoshiaki  , Hatamoto Norinobu  , Hamada Shogo  , Takezawa Toshiyuki
Published: February 2025

Our aim was to automatically and appropriately classify Japanese psychomimes such as ‘ukiuki’ and ‘wakuwaku’. Such terms are important because they represent users’ emotions and have multiple meanings. Previous studies on psychomimes have not focused on these characteristics. Therefore, we focused on such characteristics by using two soft-clustering algorithms fuzzy c-means (FCM) and probabilistic latent semantic analysis (pLSA). We conducted experiments on psychomime classification. We report the result comparing both algorithms.
A cognitive model of the web user
Authors: Román Pablo E.  , Velásquez Juan D.
Published: February 2025

The present work describes a novel approach by applying a neurophysiological theory of decision making for describing web user browsing behavior. A dynamic stochastic model of the decision making process is proposed. Such a model is based on the Leaky Competing Accumulator (LCA) model of neurophysiology. It describes the neural activity of different brain regions during the subject resolution of the decision, by means of a stochastic process that evolves until an activity reaches a given threshold that fires the decision. Such a class of stochastic processes applied to decision making has been experimentally studied for nearly forty years. In this context, a web user is confronted with deciding which link to follow according to his/her own purposes, and the process is repeated again for each visited page until leaving the web site. The parameters of the model are adjusted by means of the maximum likelihood method, using the observed sequences of pages and the time spent on each of them. It has been observed that nearly 73% of the real distribution is recovered by this method.
Multilayered Class Discrimination in Large-Scale Taxonomies
Authors: Gomez Juan Carlos  , Moens Marie-Francine
Published: February 2025

In this work we implement and evaluate a methodology to classify multi-labeled web documents into large-scale taxonomies, using their text content. Multi-label hierarchical classification using large-scale taxonomies is a hard task due to problems of scarcity of training data in many nodes of the hierarchy, overlapping of content and complex decision surfaces. We propose a novel feature extraction model called Multilayered Class Discrimination (MCD), which reduces the dimensions of the text-content features of the web documents along the different levels of the hierarchy, helping to discriminate each class from other classes in the same level and reducing the effects of the mentioned problems. The results of categorizing web documents from the DMOZ directory show that our model improves the accuracy of the categorization when compared with the use of word features, and that the results are competitive with the ones presented in the Second LSHTC Challenge.
Computer-aided Diagnosis of Abdominal Aortic Aneurysm after Endovascular Repair Using Texture Analysis
Authors: García G.  , Maiora J.  , Tapia A.  , De Blas M.
Published: February 2025

Endovascular repair is a minimal invasive alternative to open surgical therapy. From a long term perspective, complications such as prostheses displacement or leaks inside the aneurysm sac (endoleaks) could appear influencing the evolution of treatment. The objective of this work is to develop a preliminary Computer-aided diagnosis system (CAD) for an automated classification of EVAR progression from computed tomography angiography CTA images. The system is based on the extraction of texture features from thrombus aneurysm samples and a posterior classification. Regions of interest (ROIs) from patients with different post-EVAR evolution were extracted by experienced radiologists. Three conventional texture-analysis methods such as the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), and the gray level difference method (GLDM), were applied to each ROI to obtain texture features. Classification of the ROI is carried out by three different strategies. In the first one each feature set is fed to a neural network (NN). The second consists of a single neural network fed with a reduced version of texture features after a feature selection process. The third one comprised an ensembles of classifiers (ECs), formed by three NNs, each using as input one of the feature sets. The final decision is based on the application of a voting scheme across the outputs of the individual NNs. Classification results from the three classification strategies are evaluated using a receiver operating-characteristics (ROC) analysis and area under the roc curve (Az) performance. The multiple classification scheme using the three sets of texture features results in a better performance, as compared to the classification performance of the other alternatives.
Wi-Fi Localization via Particle Filtering and Use for M-commerce
Authors: Garofalakis John  , Varvaras Dimitris
Published: February 2025

In this paper we propose a system for localization in a Wi-Fi environment where the exported information is used in m-commerce applications [2–4] via techniques of data mining [29,31]. The system uses the map of the field of interest and by preprocessing it, extracts a graph of the positions a user can be, along with the possible transitions between these positions. This map is used for the localization process along with the particle filtering [11] Monte Carlo technique, which attempts to extract the noise from the measurements collected during the process. In addition to this we apply novel inside-the-particle-filtering-process techniques and propose a computational system that listens to the wireless environment in order to augment the overall system’s performance. Having the extracted positioning information, m-commerce targeted messages are sent to the users. Additionally, the spatio-temporal data are processed by data mining techniques in order to export the correlations between the users’ locations.
Decomposing the Global Financial Crisis: A Self-Organizing Time Map
Authors: Sarlin Peter
Published: February 2025

A key starting point for financial stability surveillance is understanding past, current and possible future risks and vulnerabilities. Through temporal data and dimensionality reduction, or visual dynamic clustering, this paper aims to presents a holistic view of cross-sectional patterns over time with no explicit dependence on historical data. We propose using in financial stability surveillance the Self-Organizing Time Map (SOTM), a recent adaptation of the Self-Organizing Map for exploratory temporal structure analysis, which disentangles cross-sectional data structures over time. This paper uses the SOTM for decomposing and identifying temporal structural changes in macro-financial data before, during and after the global financial crisis of 2007–2009.
An Evaluation Method for Segmental Accommodation Reviews with Text Mining
Authors: Tsujii Koichi  , Ikoma Takashi  , Tsuda Kazuhiko
Published: February 2025

Online accommodation reservations are growing in number, recently. Users of reservation usually refer to the accommodations reviews before making their reservations. However, most of the reviews do not classified for users’ purpose so far and make it hard to understand accommodation reviewing promptly. From those backgrounds, this paper provides analyzing accommodation reviews to find out the characteristics of expressions according to the purpose and examined some methods to present beneficial information at the time of reservation. Moreover, we extracted the differences of accommodation reviews by areas from text mining. From the results of the analysis, we found differences the expressions and the evaluations by area.
A Dialogue Game for Coalition Structure Generation with Self-Interested Agents
Authors: Riley Luke  , Atkinson Katie  , Payne Terry R.
Published: February 2025

Since the seminal work of Dung, Argumentation Frameworks have been shown to find solutions to n-person cooperative games. In multi-agent systems, decentralised methods for multi-agent system coalition structure generation have been proposed. This paper offers the first dialogue game that utilises argumentation frameworks to find a coalition structure and a payoff vector in a decentralised manner. The payoff vector found is in the core set of stable solutions if the core is non-empty. This dialogue game also puts restrictions on the payoff vectors that can be proposed so that the most unfair ones are discarded. Lastly an algorithm is described that allows the agents to find out if the core is empty.
Default Reasoning about Actions via Abstract Argumentation
Authors: Baumann Ringo  , Strass Hannes
Published: February 2025

Reasoning about actions is a subfield of artificial intelligence that is concerned with representing and reasoning about dynamic domains. We propose to employ abstract argumentation for this purpose. Specifically, we present a translation of action domains from a specification language into Dung-style argumentation frameworks (AFs). As the key advantage of our approach, we use existing semantics for argumentation to make predictions about the domain in various manners and utilise existing results about argumentation to show that the approach can be efficiently implemented. This demonstrates the practical value not only of its theoretical results, but also abstract argumentation itself.
The Long and Winding Road: Forty Years of Argumentation
Authors: Bench-Capon Trevor
Published: February 2025

In this paper I review my engagement with argumentation over the past forty years. I describe the perspective I brought from philosophy and the Civil Service, and consider a number of aspects of computational argumentation: knowledge based systems, explanation, context, audiences, schemes and models. A key feature of argumentation is that it is an activity which has to be actively engaged with, whereas a proof is an object to be understood and admired.
Towards Argumentation with Symbolic Dempster-Shafer Evidence
Authors: Tang Yuqing  , Hang Chung-Wei  , Parsons Simon  , Singh Munindar
Published: February 2025

This paper is concerned with the combination of argumentation with the Dempster-Shafer theory of evidence. In particular, we show how logical elements of evidence, associated with numerical degrees of belief, can be combined into arguments.
Conditional Acceptance Functions
Authors: Booth Richard  , Kaci Souhila  , Rienstra Tjitze  , Van Der Torre Leendert
Published: February 2025

Dung-style abstract argumentation theory centers on argumentation frameworks and acceptance functions. The latter take as input a framework and return sets of labelings. This methodology assumes full awareness of the arguments relevant to the evaluation. There are two reasons why this is not satisfactory. Firstly, full awareness is, in general, not a realistic assumption. Second, frameworks have explanatory power, which allows us to reason abductively or counterfactually, but this is lost under the usual semantics. To recover this aspect, we generalize conventional acceptance, and we present the concept of a conditional acceptance function.
TOAST: Online ASPIC
Authors: Snaith Mark  , Reed Chris
Published: February 2025

In this paper, we present TOAST, a system that implements the ASPIC+framework. TOAST accepts a knowledge base and rule set with associated preference and contrariness information, and returns both textual and visual commentaries on the acceptability of arguments in the derived abstract framework.
Argumentation-Based Reinforcement Learning for RoboCup Keepaway
Authors: Gao Yang  , Toni Francesca  , Craven Robert
Published: February 2025

Reinforcement Learning (RL) suffers from several difficulties when applied to domains with no obvious goal-state defined; this leads to inefficiency in RL algorithms. We consider a solution within the context of a widely-used testbed for RL: RoboCup Keepaway. We introduce Argumentation-Based RL (ABRL), using methods from argumentation theory to integrate domain knowledge, represented by arguments, into the SMDP algorithm for RL by using potential-based reward shaping. Empirical results show that ABRL outperforms the original SMDP algorithm, for this game, by improving convergence speed and optimality.
Knowledge based engineering system for structural optical design
Authors: Mouromtsev Dmitry  , Livshits Irina  , Kolchin Maxim
Published: February 2025

This paper describes a knowledge based engineering web application to the problem of starting point selection in optical design. The system architecture is shortly discussed. A formalization of the optical system representation based on formal syntax is given. Examples of rules in natural language and Drools Rule Language syntax illustrate the work of the solution. Development of a knowledge base using decision table is shown.