bluuebunny/crossref_metadata_2025 huggingface dataset:
AGM-Style Revision of Beliefs and Intentions
Authors: van Zee Marc  , Doder Dragan
Published: February 2025

We introduce a logic for temporal beliefs and intentions based on Shoham's database perspective and we formalize his coherence conditions on beliefs and intentions. In order to do this we separate strong beliefs from weak beliefs. Strong beliefs are independent from intentions, while weak beliefs are obtained by adding intentions to strong beliefs and everything that follows from that. We provide AGM-style postulates for the revision of strong beliefs and intentions: strong belief revision may trigger intention revision, but intention revision may only trigger revision of weak beliefs. After revision, the strong beliefs are coherent with the intentions. We show in a representation theorem that a revision operator satisfying our postulates can be represented by a pre-order on interpretations of the beliefs, together with a selection function for the intentions.
Iterative Judgment Aggregation
Authors: Slavkovik Marija  , Jamroga Wojciech
Published: February 2025

Judgment aggregation problems form a class of collective decision-making problems represented in an abstract way, subsuming some well known problems such as voting. A collective decision can be reached in many ways, but a direct one-step aggregation of individual decisions is arguably most studied. Another way to reach collective decisions is by iterative consensus building – allowing each decision-maker to change their individual decision in response to the choices of the other agents until a consensus is reached. Iterative consensus building has so far only been studied for voting problems. Here we propose an iterative judgment aggregation algorithm, based on movements in an undirected graph, and we study for which instances it terminates with a consensus. We also compare the computational complexity of our itterative procedure with that of related judgment aggregation operators.
Towards SPARQL-Based Induction for Large-Scale RDF Data Sets
Authors: Bin Simon  , Bühmann Lorenz  , Lehmann Jens  , Ngonga Ngomo Axel-Cyrille
Published: February 2025

We show how to convert OWL Class Expressions to SPARQL queries where the instances of that concept are with a specific ABox equal to the SPARQL query result. Furthermore, we implement and integrate our converter into the CELOE algorithm (Class Expression Learning for Ontology Engineering), where it replaces the position of a traditional OWL reasoner. This will foster the application of structured machine learning to the Semantic Web, since most data is readily available in triple stores. We provide experimental evidence for the usefulness of the bridge. In particular, we show that we can improve the run time of machine learning approaches by several orders of magnitude.
Burg Matrix Divergence Based Multi-Metric Learning
Authors: Wang Yan  , Li Han-Xiong
Published: February 2025

The basic idea of most distance metric learning methods is to find a space that can optimally classify data points belong to different categories. However, current methods only learn one Mahalanobis distance for each data set, which actually fails to perfectly classify different categories in most real world applications. To improve the classification accuracy of k-nearest-neighbour algorithm, a multi-metric learning method is proposed in this paper to completely classify different categories by sequentially learning sub-metrics. The proposed algorithm is based on minimizing the Burg matrix divergence between metrics. The experiments on five UCI data sets demonstrate the improved performance of Multi-Metric learning when comparing with the state-of-the-art methods.
Multilevel Agent-Based Modelling for Assignment or Matching Problems
Authors: Nongaillard Antoine  , Picault Sébastien
Published: February 2025

Assignment or matching problems have been addressed by various multi-agent methods, focused on enhancing privacy and distribution. Nevertheless, they little rely on the organisational structure provided by Multi-Agent Systems (MAS). We rather start from the intrinsic ability of multilevel MAS to represent intermediate points of view between the individual and the collective levels, to express matching or assignment problems in a homogeneous formalism. This model allows to define relevant metrics to assess the satisfaction of agent groups and allow them to build solutions that improve the overall well-being without disclosing all their individual information.
A General Characterization of Model-Based Diagnosis
Authors: Provan Gregory
Published: February 2025

The Model-Based Diagnosis (MBD) framework developed by Reiter has been a strong theoretical foundation for MBD, yet is limited to models that are described in terms of logical sentences. We propose a more general framework that covers a wide range of modelling languages, ranging from AI-based languages (e.g., logic and Bayesian networks) to FDI-based languages (e.g., linear Gaussian models). We show that a graph-theoretic basis for decomposable system models can be augmented with several languages and corresponding inference algorithms based on valuation algebras.
Increasing Coalition Stability in Large-Scale Coalition Formation with Self-Interested Agents
Authors: Janovsky Pavel  , DeLoach Scott A.
Published: February 2025

In coalition formation with self-interested agents both social welfare of the multi-agent system and stability of individual coalitions must be taken into account. However, in large-scale systems with thousands of agents, finding an optimal solution with respect to both metrics is infeasible.
On Stochastic Primal-Dual Hybrid Gradient Approach for Compositely Regularized Minimization
Authors: Qiao Linbo  , Lin Tianyi  , Jiang Yu-Gang  , Yang Fan  , Liu Wei  , Lu Xicheng
Published: February 2025

We consider a wide spectrum of regularized stochastic minimization problems, where the regularization term is composite with a linear function. Examples of this formulation include graph-guided regularized minimization, generalized Lasso and a class of ℓ1 regularized problems. The computational challenge is that the closed-form solution of the proximal mapping associated with the regularization term is not available due to the imposed linear composition. Fortunately, the structure of the regularization term allows us to reformulate it as a new convex-concave saddle point problem which can be solved using the Primal-Dual Hybrid Gradient (PDHG) approach. However, this approach may be inefficient in realistic applications as computing the full gradient of the expected objective function could be very expensive when the number of input data samples is considerably large. To address this issue, we propose a Stochastic PDHG (SPDHG) algorithm with either uniformly or non-uniformly averaged iterates. Through uniformly averaged iterates, the SPDHG algorithm converges in expectation withrate for general convex objectives and O(log (t)/t) rate for strongly convex objectives, respectively. While with non-uniformly averaged iterates, the SPDHG algorithm is expected to converge with O(1/t) rate for strongly convex objectives. Numerical experiments on different genres of datasets demonstrate that our proposed algorithm outperforms other competing algorithms.
Link Prediction by Incidence Matrix Factorization
Authors: Yokoi Sho  , Kajino Hiroshi  , Kashima Hisashi
Published: February 2025

Link prediction suffers from the data sparsity problem. This paper presents and validates our hypothesis that, for sparse networks, incidence matrix factorization (IMF) could perform better than adjacency matrix factorization (AMF), which has been used in many previous studies. A key observation supporting the hypothesis is that IMF models a partially-observed graph more accurately than AMF. A technical challenge for validating our hypothesis is that, unlike AMF approach, there does not exist an obvious method to make predictions using a factorized incidence matrix. To this end, we newly develop an optimization-based link prediction method adopting IMF. We have conducted thorough experiments using synthetic and real-world datasets to investigate the relationship between the sparsity of a network and the performance of the aforementioned two methods. The experimental results show that IMF performs better than AMF as networks become sparser, which strongly validates our hypothesis.
PA*: Optimal Path Planning for Perception Tasks
Authors: Pereira Tiago  , Veloso Manuela  , Moreira António
Published: February 2025

In this paper we introduce the problem of planning for perception of a target position. Given a sensing target, the robot has to move to a goal position from where the target can be perceived. Our algorithm minimizes the overall path cost as a function of both motion and perception costs, given an initial robot position and a sensing target. We contribute a heuristic search method, PA*, that efficiently searches for an optimal path. We prove the proposed heuristic is admissible, and introduce a new goal state stopping condition.
Planning Search and Rescue Missions for UAV Teams
Authors: Baker Chris A.B.  , Ramchurn Sarvapali  , Teacy W.T. Luke  , Jennings Nicholas R.
Published: February 2025

The coordination of multiple Unmanned Aerial Vehicles (UAVs) to carry out aerial surveys is a major challenge for emergency responders. In particular, UAVs have to fly over kilometre-scale areas while trying to discover casualties as quickly as possible. To aid in this process, it is desirable to exploit the increasing availability of data about a disaster from sources such as crowd reports, satellite remote sensing, or manned reconnaissance. In particular, such information can be a valuable resource to drive the planning of UAV flight paths over a space in order to discover people who are in danger. However challenges of computational tractability remain when planning over the very large action spaces that result. To overcome these, we introduce the survivor discovery problem and present as our solution, the first example of a continuous factored coordinated Monte Carlo tree search algorithm. Our evaluation against state of the art benchmarks show that our algorithm, Co-CMCTS, is able to localise more casualties faster than standard approaches by 7% or more on simulations with real-world data.
An Intelligent System for Aggression De-Escalation Training
Authors: Bosse Tibor  , Gerritsen Charlotte  , de Man Jeroen
Published: February 2025

Artificial Intelligence techniques are increasingly being used to develop smart training applications for professionals in various domains. This paper presents an intelligent training system that enables professionals in the public domain to practice their aggression de-escalation skills. The system is one of the main products of the STRESS project, an interdisciplinary research project involving partners from academia, industry and society. The system makes use of a variety of AI-related techniques, including simulation, virtual agents, sensor fusion, model-based analysis and adaptive support. A preliminary evaluation of the system has been conducted with two groups of potential end users, namely tram conductors and police academy students.
Complexity of Control by Partitioning Veto and Maximin Elections and of Control by Adding Candidates to Plurality Elections
Authors: Maushagen Cynthia  , Rothe Jörg
Published: February 2025

Control by partition refers to situations where an election chair seeks to influence the outcome of an election by partitioning either the candidates or the voters into two groups, thus creating two first-round subelections that determine who will take part in a final round. The model of partition-of-voters control attacks is remotely related to “gerrymandering” (maliciously resizing election districts). While the complexity of control by partition (and other control actions) has been studied thoroughly for many voting systems, there are no such results known for the important veto and maximin voting systems. We settle the complexity of control by partition for veto in a broad variety of models and for maximin with respect to destructive control by partition of candidates. We also observe that a reduction from the literature [8] showing the parameterized complexity of control by adding candidates to plurality elections, parameterized by the number of voters, is technically flawed by giving a counterexample, and we show how this reduction can be fixed.
Strategical Argumentative Agent for Human Persuasion
Authors: Rosenfeld Ariel  , Kraus Sarit
Published: February 2025

Automated agents should be able to persuade people in the same way people persuade each other - via dialogs. Today, automated persuasion modeling and research use unnatural assumptions regarding persuasive interaction, which creates doubt regarding their applicability for real-world deployment with people. In this work we present a novel methodology for persuading people through argumentative dialogs. Our methodology combines theoretical argumentation modeling, machine learning and Markovian optimization techniques that together result in an innovative agent named SPA. Two extensive field experiments, with more than 100 human subjects, show that SPA is able to persuade people significantly more often than a baseline agent and no worse than people are able to persuade each other.
Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis
Authors: Bravo-Marquez Felipe  , Frank Eibe  , Pfahringer Bernhard
Published: February 2025

The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. A drawback of these approaches is the high cost involved in data annotation. Two freely available resources that can be exploited to solve the problem are: 1) large amounts of unlabelled tweets obtained from the Twitter API and 2) prior lexical knowledge in the form of opinion lexicons. In this paper, we propose Annotate-Sample-Average (ASA), a distant supervision method that uses these two resources to generate synthetic training data for Twitter polarity classification. Positive and negative training instances are generated by sampling and averaging unlabelled tweets containing words with the corresponding polarity. Polarity of words is determined from a given polarity lexicon. Our experimental results show that the training data generated by ASA (after tuning its parameters) produces a classifier that performs significantly better than a classifier trained from tweets annotated with emoticons and a classifier trained, without any sampling and averaging, from tweets annotated according to the polarity of their words.
Online Prediction of Exponential Decay Time Series with Human-Agent Application
Authors: Rosenfeld Ariel  , Keshet Joseph  , Goldman Claudia V.  , Kraus Sarit
Published: February 2025

Exponential decay time series are prominent in many fields. In some applications, the time series behavior can change over time due to a change in the user's preferences or a change of environment. In this paper we present an innovative online learning algorithm, which we name Exponentron, for the prediction of exponential decay time series. We state a regret bound for our setting, which theoretically compares the performance of our online algorithm relative to the performance of the best batch prediction mechanism, which can be chosen in hindsight from a class of hypotheses after observing the entire time series. In experiments with synthetic and real-world data sets, we found that the proposed algorithm compares favorably with the classic time series prediction methods by providing up to 41% improvement in prediction accuracy. Furthermore, we used the proposed algorithm for the design of a novel automated agent for the improvement of the communication process between a driver and its automotive climate control system. Throughout extensive human study with 24 drivers we show that our agent improves the communication process and increases drivers' satisfaction, exemplifying the Exponentron's applicative benefit.
Reconsidering AGM-Style Belief Revision in the Context of Logic Programs
Authors: Zhuang Zhiqiang  , Delgrande James  , Nayak Abhaya  , Sattar Abdul
Published: February 2025

Belief revision has been studied mainly with respect to background logics that are monotonic in character. In this paper we study belief revision when the underlying logic is non-monotonic instead—an inherently interesting problem that is under explored. In particular, we will focus on the revision of a body of beliefs that is represented as a logic program under the answer set semantics, while the new information is also similarly represented as a logic program. Our approach is driven by the observation that unlike in a monotonic setting where, when necessary, consistency in a revised body of beliefs is maintained by jettisoning some old beliefs, in a non-monotonic setting consistency can be restored by adding new beliefs as well. We will define two revision functions through syntactic and model-theoretic methods respectively and subsequently provide representation theorems for characterising them.
Upper and Lower Time and Space Bounds for Planning
Authors: Bäckström Christer  , Jonsson Peter
Published: February 2025

There is an extensive literature on the complexity of planning, but explicit bounds on time and space complexity are very rare. On the other hand, problems like the constraint satisfaction problem have been thoroughly analysed in this respect. We provide a number of upper and lower bound results for both plan satisfiability (PSAT) and length-optimal planning (LOP), with an emphasis on monotone planning (where actions have only positive effects) which is used in, for instance, h+and similar heuristics. Let v and a be the number of variables and actions, respectively. We consider both restrictions on the number and polarity of preconditions and effects of actions and the PUBS restrictions in SAS+. For all such classes, we show that PSAT and LOP is either tractable or cannot be solved in subexponential time 2o(v)or time 2o(a), unless the so-called Exponential Time Hypothesis (ETH) is false. There is also a sharp transition: monotone LOP can be solved in time 2o(v)ifbut not if a∈Ω(v). We also study upper bounds and discuss the trade-off between time and space, providing a polynomial-space algorithm for monotone LOP that beats depth-first search in most cases. This raises the important question how lower bounds are affected by polynomial space restrictions.
Fixed-Domain Reasoning for Description Logics
Authors: Gaggl Sarah  , Rudolph Sebastian  , Schweizer Lukas
Published: February 2025

After decades of fruitful research, description logics (DLs) have evolved into a de facto standard in logic-based knowledge representation. In particular, they serve as the formal basis of the standardized and very popular web ontology language (OWL), which also comes with the advantage of readily available user-friendly modeling tools and optimized reasoning engines. In the course of the wide-spread adoption of OWL and DLs, situations have been observed where logically less skilled practitioners are (ab)using these formalisms as constraint languages adopting a closed-world assumption, contrary to the open-world semantics imposed by the classical definitions and the standards. To provide a clear theoretical basis and inferencing support for this often practically reasonable “off-label use” we propose an alternative formal semantics reflecting the intuitive understanding of such scenarios. To that end, we introduce the fixed-domain semantics and argue that this semantics gives rise to an interesting new inferencing task: model enumeration. We describe how the new semantics can be axiomatized in very expressive DLs. We thoroughly investigate the complexities for standard reasoning as well as query answering under the fixed-domain semantics for a wide range of DLs. Further, we present an implementation of a fixed-domain DL reasoner based on a translation into answer set programming (ASP) which is competitive with alternative approaches for standard reasoning tasks and provides the added functionality of model enumeration.
On Redundancy in Simple Temporal Networks
Authors: Lee Jae Hee  , Li Sanjiang  , Long Zhiguo  , Sioutis Michael
Published: February 2025

The Simple Temporal Problem (STP) has been widely used in various applications to schedule tasks. For dynamical systems, scheduling needs to be efficient and flexible to handle uncertainty and perturbation. To this end, modern approaches usually encode the temporal information as an STP instance. This representation contains redundant information, which can not only take a significant amount of storage space, but also make scheduling inefficient due to the non-concise representation. In this paper, we investigate the problem of simplifying an STP instance by removing redundant information. We show that such a simplification can result in a unique minimal representation without loss of temporal information, and present an efficient algorithm to achieve this task. Evaluation on a large benchmark dataset of STP exhibits a significant reduction in redundant information for the involved instances.
Adaptive Symbiotic Collaboration for Targeted Complex Manipulation Tasks
Authors: Silva Rui  , Melo Francisco S.  , Veloso Manuela
Published: February 2025

This paper addresses the problem of human-robot collaboration in the context of manipulation tasks. In particular, we focus on tasks where a robot must perform some complex manipulation that is successfully completed only upon reaching some target pose provided by a human user. We propose an approach in which the robot explicitly reasons about its ability to complete the task and proactively requests the assistance of the human teammate when necessary. Our approach effectively trades-off the benefits arising from the human assistance with the cost of disturbing the user. We also propose an adaptation mechanism that enables the robot to adjust its behavior to the particular manner by which the human user responds to the requests made by the robot. We test our approach in a simple illustrative scenario and in two real interaction scenarios involving the Baxter robot.
Solving Set Optimization Problems by Cardinality Optimization with an Application to Argumentation
Authors: Faber Wolfgang  , Vallati Mauro  , Cerutti Federico  , Giacomin Massimiliano
Published: February 2025

Optimization—minimization or maximization—in the lattice of subsets is a frequent operation in Artificial Intelligence tasks. Examples are subset-minimal model-based diagnosis, nonmonotonic reasoning by means of circumscription, or preferred extensions in abstract argumentation. Finding the optimum among many admissible solutions is often harder than finding admissible solutions with respect to both computational complexity and methodology. This paper addresses the former issue by means of an effective method for finding subset-optimal solutions. It is based on the relationship between cardinality-optimal and subset-optimal solutions, and the fact that many logic-based declarative programming systems provide constructs for finding cardinality-optimal solutions, for example maximum satisfiability (MaxSAT) or weak constraints in Answer Set Programming (ASP). Clearly each cardinality-optimal solution is also a subset-optimal one, and if the language also allows for the addition of particular restricting constructs (both MaxSAT and ASP do) then all subset-optimal solutions can be found by an iterative computation of cardinality-optimal solutions. As a showcase, the computation of preferred extensions of abstract argumentation frameworks using the proposed method is studied.
Web Services Orchestration's Monitor as a Web Service
Authors: Hadded Imene  , Langar Mahjoub  , Robbana Riadh
Published: February 2025

The Web services orchestration is a useful and complicated approach to integrate heterogeneous systems. To verify the conformance of the orchestrated Web service against the intended behavior, we need to analyse the produced system. To this end, in this paper, we define a security framework namely MoWS (Monitoring as a Web service) for monitoring orchestrated web services by inlining, statically, monitor's web service calls, at some critical points of the program, that checks whether the program adheres to some constraints dictated by a security policy. The rewritten program behaves like the original one except that it is aborted when an action is about to violate a security policy. The described approach is supported by a Java plug-in for eclipse.
Aspect-Oriented Visualization of the Health Status: An Example in Treatment of Cervical Spine Defect
Authors: Deng Yihan  , Denecke Kerstin
Published: February 2025

Clinical data is often captured in unstructured texts and scattered in different health information systems. This complicates the aggregation of information in the process of clinical decision making. However, having a quick overview and an efficient representation of relevant aspects of a patient's health status are crucial for this process. While accessing patient data and perusing clinical documents, relevant details need to be discovered quickly. In this paper, we introduce an approach to visualize relevant information from clinical documents by tag clouds. The conventional tag clouds visualize the content of a document using the terms they are containing shown in different sizes with the size calculated based on the term frequency. Important facts and diagnostic results with low occurrence in a text may be ignored by this naïve method. In this paper, we therefore adapt the conventional tag clouds by information extraction and a guidelines-based classification schema, so that the clinical concerns can be visualized more correctly. The aspects are extracted according to a classification schema developed by clinical experts. We evaluate the approach on a set of radiology reports for cervical spine treatment.
A National Medical Information System for Senegal: Architecture and Services
Authors: Camara Gaoussou  , Diallo Al Hassim  , Lo Moussa  , Tendeng Jacques-Noël  , Lo Seynabou
Published: February 2025

In Senegal, great amounts of data are daily generated by medical activities such as consultation, hospitalization, blood test, x-ray, birth, death, etc. These data are still recorded in register, printed images, audios and movies which are manually processed. However, some medical organizations have their own software for non-standardized patient record management, appointment, wages, etc. without any possibility of sharing these data or communicating with other medical structures. This leads to lots of limitations in reusing or sharing these data because of their possible structural and semantic heterogeneity. To overcome these problems we have proposed a National Medical Information System for Senegal (SIMENS). As an integrated platform, SIMENS provides an EHR system that supports healthcare activities, a mobile version and a web portal. The SIMENS architecture proposes also a data and application integration services for supporting interoperability and decision making.
Thesaurus-Based Hierarchical Semantic Grouping of Medical Terms in Information Extraction
Authors: Lassoued Yassine  , Deleris Léa
Published: February 2025

In this paper we describe a semantic approach for grouping medical terms into a hierarchy of concepts based on the UMLS meta-thesaurus. The context of this work is Medical Recap, a Web system that automatically extracts risk information from PubMed abstracts, and then aggregates this knowledge into dependence graphs or Bayesian networks.
A Risk Based Neural Network Approach for Predictive Modeling of Blood Glucose Dynamics
Authors: Frandes Mirela  , Timar Bogdan  , Lungeanu Diana
Published: February 2025

For type 1 diabetes patients, maintaining the blood glucose (BG) at normal values is a challenging task due to e.g. variable insulin reactions, diets, lifestyles, emotional conditions, etc. Hyperglycemic and hypoglycemic events can generate various complications (e.g. diabetic ketoacidosis, retinopathy, neuropathy, etc.), so predicting BG values in time is of great importance for diabetes self-management. Herein, we propose a non-linear autoregressive neural network approach, based on the minimal dataset available from a continuous glucose monitoring (CGM) sensor, with an integrated measure of intra-patient BG variability. The method kept the balance between accuracy and complexity, allowing a fast response with no additional effort or discomfort for the patient.
Towards a Selection Mechanism of Relevant Features for Automatic Epileptic Seizures Detection
Authors: Mera-Gaona Maritza  , Vargas-Canas Rubiel  , Lopez Diego M.
Published: February 2025

Background: Epilepsy diagnosis is frequently confirmed using electroencephalogram (EEG) along with clinical data. The main difficulty in the diagnosis is associated with the large amount of data generated by EEG, which must be analyzed by neurologists for identifying abnormalities. One of the main research challenges in this area is the identification of relevant EEG features that allow automatic detection of epileptic seizures, especially when a large number of EEG features are analyzed.
Cost of Stability and Least Core in Path-Disruption Games
Authors: Persien Victor  , Rey Anja  , Rothe Jörg
Published: February 2025

Path-disruption games are a class of cooperative games in which agents try to block any path from an intruder's source vertex to a target vertex. We study algorithmic properties and characterizations of the least core and the cost of stability for these games.
Testing Universal Design of a Public Media Website with Diverse Users
Authors: Chen Weiqin  , Kessel Siri  , Sanderson Norun C.  , Tatara Naoe
Published: February 2025

Testing with users can identify more issues than other testing methods. Many researchers have argued for the importance of user testing in Universal Design. However, testing Universal Design with diverse users poses many challenges. In this paper we will share our experience with testing the Universal Design of a public media website with real users. We discuss the challenges faced and lessons learned in the process.
Three Case Studies on Methods of Working with Older People on the Design of New Technologies
Authors: Sani Zaidatol Haslinda Abdullah  , Petrie Helen  , Swallow David  , Lewis Andrew
Published: February 2025

The proportion of older adults in the population is rapidly increasing and the proportion of younger adults to care for them is decreasing. Part of the solution to support older adults in living independently is to provide them with appropriate assistive technologies. To develop technologies that are effective for older adults we need methodologies that are appropriate for working with this user group. Yet there is little systematic research on how to work with older adults and how to adapt methods already used with younger adults. This paper reports on three case studies which investigated the use focus groups, expert evaluations and user evaluations with older adults. In the case of focus groups, the size of the focus group was investigated; for expert evaluations, an existing set of heuristics for evaluating apps for older adults was investigated; for user evaluations, a low-fidelity prototype design was evaluated using think-aloud protocols.
The Importance of Process-Oriented Accessibility Guidelines for Web Developers
Authors: Steen-Hansen Linn  , Fagernes Siri
Published: February 2025

Current accessibility research shows that in the web development, the process itself may lead to inaccessible web sites and applications. Common practices typically do not allow sufficient testing. The focus is mainly on complying with minimum standards, and treating accessibility compliance as a sort of bug-fixing process, missing the user perspective. In addition, there is an alarming lack of knowledge and experience with accessibility issues. It has also been argued that bringing accessibility into the development process at all stages is the only way to achieve the highest possible level of accessibility. The work presented in this paper is based on a previous project focusing on guidelines for developing accessible rich Internet applications. The guidelines were classified as either process-oriented or technology-oriented. In this paper, we examine the process-oriented guidelines and give a practical perspective on how these guidelines will make the development process more accessibility-friendly.
Universally Designed Text on the Web: Towards Readability Criteria Based on Anti-Patterns
Authors: Eika Evelyn
Published: February 2025

The readability of web texts affects accessibility. The Web Content Accessibility guidelines (WCAG) state that the recommended reading level should match that of someone who has completed basic schooling. However, WCAG does not give advice on what constitutes an appropriate reading level. Web authors need tools to help composing WCAG compliant texts, and specific criteria are needed. Classic readability metrics are generally based on lengths of words and sentences and have been criticized for being over-simplistic. Automatic measures and classifications of texts' reading levels employing more advanced constructs remain an unresolved problem. If such measures were feasible, what should these be? This work examines three language constructs not captured by current readability indices but believed to significantly affect actual readability, namely, relative clauses, garden path sentences, and left-branching structures. The goal is to see whether quantifications of these stylistic features reflect readability and how they correspond to common readability measures. Manual assessments of a set of authentic web texts for such uses were conducted. The results reveal that texts related to narratives such as children's stories, which are given the highest readability value, do not contain these constructs. The structures in question occur more frequently in expository texts that aim at educating or disseminating information such as strategy and journal articles. The results suggest that language anti-patterns hold potential for establishing a set of deeper readability criteria.
Abstract Dialectical Argumentation Among Close Relatives
Authors: Bochman Alexander
Published: February 2025

We establish a uniform modular translation of Abstract Dialectical Frameworks into the formalism of the causal calculus, and discuss the correspondences this translation creates between a number of semantics suggested for ADFs and their causal counterparts.
Formalizing Balancing Arguments
Authors: Gordon Thomas F.  , Walton Douglas
Published: February 2025

Dung intended his abstract argument frameworks to be used for modeling a particular form of human argumentation, where arguments attack each other and are evaluated following the principle summarized by “The one who has the last word laughs best.” However this form does not fit a wide class of arguments, which is arguably more prototypical and common in human argumentation, namely arguments where pros and cons are balanced to choose among alternative options. Here we present a formal model of structured argument which generalizes Dung abstract argumentation frameworks to also handle balancing. Unlike most other models of structured argument, this model does not map structured arguments to abstract arguments. Rather it generalizes abstract argumentation frameworks, allowing them to be simulated using structured arguments. The model can handle cumulative arguments (“accrual”) without causing an exponential blowup in the number of arguments and has been fully implemented in Version 4 of the Carneades Argumentation System.
Verifiability of Argumentation Semantics
Authors: Baumann Ringo  , Linsbichler Thomas  , Woltran Stefan
Published: February 2025

Dung's abstract argumentation theory is a widely used formalism to model conflicting information and to draw conclusions in such situations. Hereby, the knowledge is represented by argumentation frameworks (AFs) and the reasoning is done via semantics extracting acceptable sets. All reasonable semantics are based on the notion of conflict-freeness which means that arguments are only jointly acceptable when they are not linked within the AF. In this paper, we study the question which information on top of conflict-free sets is needed to compute extensions of a semantics at hand. We introduce a hierarchy of verification classes specifying the required amount of information and show that well-known semantics are exactly verifiable through a certain such class. This also gives a means to study semantics lying between known semantics, thus contributing to a more abstract understanding of the different features argumentation semantics offer.
Basic Desires to Decide Your Active Goals. Motivational Theories in Cognitive Agents
Authors: Sabater-Mir Jordi  , Palma-Zurita Carlos  , Cuadros-Oller Joan
Published: February 2025

There is an important corpus of research in cognitive architectures but they tend to overlook aspects like agent motivations and social behaviour. In this article we present the first step towards a cognitive architecture with advanced social capabilities. We propose a mechanism that allows the agent to activate goals according to its basic needs following a well known motivational theory.
Study of the Anchoring Problem in Generalist Robots Based on ROSPlan
Authors: Escudero-Rodrigo Diego  , Alquezar René
Published: February 2025

In real life environments where robots must deal with complex situations and humans, generalist robots that adapt to novel situations are needed. They are composed by two sub-systems: perception/actuation and knowledge representation, and they need that symbols in the high-level area are coupled to objects and actions of the low-level area. This is the so-called Anchoring Problem. In this paper we present the system we are using to study this problem. It is based on ROSPlan, a framework that provides a generic method for task planning in a ROS system. The high-level area is composed by a planner that uses PDDL files and a knowledge representation system, while the low-level area is defined as a set of robot services exported using ROS actions, services and topics. We plan to contribute to this problem by applying human-robot interaction and learning techniques, and our main objectives are: (1) link an existing symbol with a learned action by interaction, and (2) automated code generation of ad-hoc ROS nodes that connect symbols to specific perceptions/actions.
Automatic Speech Recognition System for Lithuanian Broadcast Audio
Authors: Alumäe Tanel  , Tilk Ottokar
Published: February 2025

This paper describes the development of an automatic broadcast data transcription system for Lithuanian. The system performs fully automatic transcription of broadcast media recordings, including speech/non-speech detection, speaker diarization, speech-to-text conversion and automatic punctuation restoration. The system was developed in collaboration with the Baltic Media Monitoring Group (BMMG). The system is currently used in production for performing various broadcast speech monitoring tasks.
Designing an Annotated Longitudinal Latvian Children's Speech Corpus
Authors: Auziņa Ilze  , Levāne-Petrova Kristīne  , Rābante-Buša Guna  , Darǵis Roberts  , Fábregas Antonio
Published: February 2025

The paper provides an overview of the ongoing development of the Annotated Longitudinal Latvian Children's Speech Corpus. The authors outline the design of this corpus and the layers of annotation (both orthographic and part-off-speech tagging) with which the speech signal is enriched.
Automating Transformations in Data Vault Data Warehouse Loads
Authors: Puonti Mikko  , Raitalaakso Timo  , Aho Timo  , Mikkonen Tommi
Published: February 2025

Data warehousing is a process of integrating multiple data sources into one for, e.g., reporting purposes. An emerging modeling technique for this is the data vault method. The use of data vault creates many structurally similar data processing modifications in the transform phase of ETL work. Is it possible to automate the creation of transformations? Based on our study, the answer is mostly affirmative. Data vault modeling creates certain constraints to data warehouse entities. These model constraints and data vault table populating principles can be used to generate transformation code. Based on the original relational database model and data flow metadata we can gather populating principles. These can then be used to create general templates for each entity. Nevertheless, we need to note that the use of data flow metadata can be only partially automated and includes the only manual work phases in the process. In the end we can generate the actual transformation code automatically. In this paper, we carefully describe the creation of automation procedure and analyze the practical problems based on our experiences on PL/SQL proof of concept implementation. To the best of our knowledge, similar has not yet been described in the scientific literature.
An Ontology-Driven Approach for Expert Knowledge Acquisition in the Medical Field
Authors: Otmani Nassim Abdeldjallal  , Comparot Catherine  , Si Mohammed Malik  , Charrel Pierre-Jean
Published: February 2025

Each discipline, to some extent, has its own concise and precise vocabulary used to describe unambiguously the special concepts within the domain and the relationships bounding them. In the medical science for instance, doctors use specialized vocabulary and knowledge for an effective and efficient way of (1) communication, like filling in an EHR (Electronic Health Record), and (2) for problem solving, like the diagnosis process. For those who are unfamiliar with that vocabulary, it is hard for them to express relevant information like describing symptoms to a doctor in order to get diagnosed. In our work, we aim at bringing the common sense knowledge and the basic vocabulary closer to the expertise knowledge for an effective communication between what we call a layperson and an expert illustrated with a case of patient/doctor communication. In this paper, we define a communication process, pointing out the beneficial use of the expertise knowledge and the choice for the Ontology-Driven modelling that will enhance the notion of progressivity in the knowledge acquisition process. We also define our cyclic acquisition process step by step starting from the first and foremost step of processing the messages to the information extraction and the reasoning process until the last but not least step of outputting the ontological representation.
A GOP-Level Bitrate Clustering Recognition Algorithm for Wireless Video Transmission
Authors: Shi Wen-Juan  , Li Song  , Sun Yan-Jing  , Cao Qi  , Zuo Hai-Wei
Published: February 2025

The wireless video transmission process, due to the complexity and variability of wireless communication channels, requires to adjust the bitrates to match the dynamic wireless channel. An analysis of the video frame quality could be used as an important basis to adjust the wireless video bitrates. This paper aims at detecting and recognizing transmission bitrates based on video frame quality for wireless networks. According to the distribution characteristics of video frame quality, this paper proposes a GOP-level bitrate clustering recognition algorithm (GLBCR) by video coding GOP structural feature and temporal continuity of video frames to recognize different bitrates for wireless video. GLBCR uses PSNR between each pair of original and terminal decoding frame as the feature to quantify the degradation of video frame quality. The algorithm extracts the PSNR values of all I-frames by the peak detector function, then uses PSNR similarity measure to recursively split the frame interval into subintervals. Finally, the different video bitrates can be recognized by GLBCR. The proposed algorithm is evaluated by using the LIVE mobile video quality assessment (VQA) database. The results show that the proposed algorithm can recognize the change of video bitrates by analyzing video frame quality, it is well consistent with the real bitrate changes in wireless video transmission with small amount of calculation.
A Method for Woodcut Rendering from Images
Authors: Zhang Hong-Qiang  , Wang Shu-Wen  , Ma Cong  , Pi Bing-Kun
Published: February 2025

This paper demonstrates a method for obtaining the woodcut rendering results of images. The first four steps to produce woodcut rendering simulation image include: boundary extraction, histogram matching, image edge enhancement and binarization processing. First, we used the Roberts operator to extract the image boundary. Second, we used the histogram matching to adjust the color distribution of the gray image. Third, through image fusion we fused the image boundary and the adjusted image. Finally, we used image binarization by setting the threshold to get woodcut rendering results. Experimental results showed that the algorithm has low computational complexity, and the real-time characteristic of it is very good. Using the method in this paper, we can get the woodcut rendering results with excellent, artistic effects.
Network Evolution via Preference and Coordination Game
Authors: Dong En-Ming  , Li Jian-Ping  , Xie Zheng
Published: February 2025

As an efficient technique to research complex system, networks can reveal the interactions in many systems. The interactions in many real-life networks are complex, for example, the cooperation and competition in social networks. The different strategies in game theory can be used to describe the complex interactions. A network evolution model is proposed with the consideration of both preference and coordination game. Theoretical analysis and experiment results show that the model can well describe many statistical properties in real-life complex networks, like scale-free and high clustering coefficient.
A Study of Filtering Method for Accurate Indoor Positioning System Using Bluetooth Low Energy Beacons
Authors: Jin Young Hyun  , Jang Wonseob  , Li Bin  , Kwon Soo Jeong  , Lim Sung Hoon  , Yoon Andy Kyung-yong
Published: February 2025

Fingerprinting technique is an essential element in the indoor positioning system (IPS). Common methods utilize Wi-Fi signals. However, most of the Wi-Fi, because it is pre-installed for other purposes, is difficult to fully used for fingerprinting. Bluetooth beacon fingerprinting (BBF) can be used to overcome the drawbacks of the Wi-Fi fingerprinting. With BBF cost reduction is possible, and it also has advantages that can flexibly correspond to the environment variation. This study determines whether it is possible to achieve IPS with implementation of BLE beacon-based fingerprinting. This study presents an optimized fingerprinting method for building a fingerprinting data. In order to increase the accuracy, we applied filtering and probabilistic smoothing technique of fluctuation correction to necessary part of the signal. Result of the study proposes that BLE beacon fingerprinting can be a replacement for Wi-Fi based fingerprinting.
A Fuzzy MEBN Ontology Language Based on OWL2
Authors: Zheng Zhi-Yun  , Liu Zhuo-Yun  , Li Lun  , Li Dun  , Wang Zhen-Fei
Published: February 2025

With the rapid development of the Semantic Web research, the demand for representation and reasoning with uncertain information increases. Despite ontology is capable of modeling the semantic and knowledge in knowledge-based system, classical ontology languages are not appropriate to deal with uncertainty in knowledge, which is inherent in most of the real world application domains. In this paper, we address this issue by extending the power of expression in current ontology language, that is, proposing a Fuzzy Multi Entity Bayesian Networks ontology language which extends the PR-OWL and based on combination of Fuzzy MEBN and ontology, defining and studying its syntax and semantics, and showing representation of domain knowledge by RDF graphs. The proposed language Fuzzy PR-OWL will move beyond the current limitation of modeling the knowledge with fuzzy semantic or fuzzy relation in PR-OWL. By providing a principled means of uncertainty representation and reasoning, Fuzzy PR-OWL can serve for many applications with fuzzy and probability knowledge.