bluuebunny/crossref_metadata_2025 huggingface dataset:
Re-evaluation of Bjurei's Equation of Height Correction for Severe Adolescent Idiopathic Scoliosis
Authors: Ma Wei-Wei  , Mao Sai-hu  , Qiu Yong  , Qian Bang-ping  , Sun Xu  , Jiang Jun  , Shao Xiang
Published: February 2025

Introduction: Bjurei's equation has been used for height correction in scoliosis patients for decades. A significant controversy on the accuracy of height correction, however, was found in patients with severe scoliosis.
Measurement of the Positional Variability of Surface Anatomical Landmark Over Time
Authors: Shannon T.M.L.
Published: February 2025

For many patients, the motivation in seeking treatment is the improvement of their appearance rather than to correct an underlying skeletal deformity, so cosmetic concerns and the psychosocial impacts of scoliosis are important factors in the clinical decision-making process. There is new emphasis on quantifying the components of back surface asymmetry with the objective of producing an agreed scoring to be used in developing treatment plans and assessing results, but to date most techniques continue to rely on either a visual assessment or a single record of the relative positions of anatomical landmarks.
Surgical Treatment of Scoliosis Associated with Rare Diseases: Scoliosis in Arthrogryposis
Authors: Greggi T.  , Di Silvestre M.  , Martikos K.  , Bakaloudis G.  , Lolli F.  , Vommaro F.  , Spina M.
Published: February 2025

Introduction: The reported incidence of scoliosis in arthrogryposis varies from 30% to 67% and, in most cases, the curves progresses rapidly and becomes stiff from early age.
A Functional Model of Social Media and its Application to Business Intelligence
Authors: Meredith Rob  , O'Donnell Peter
Published: February 2025

The marketing departments of the major business intelligence (BI) software vendors have been quick to associate their products with the popular term ‘Web 2.0,’ branding the new releases of their product suites ‘BI 2.0.’ This paper argues that beyond its value as a device to enhance sales and marketing, the functions typically found in Web 2.0 web sites can be usefully applied to BI applications. It explores the application and role of Web 2.0 concepts within BI applications. The paper develops a simple framework to help understand the collaboration that is afforded by Web 2.0 applications. It classifies the functions that are provided in social media platforms to foster user collaboration and contribution. The framework is then used to examine how these forms of collaboration can be used to create more effective and ‘active’ BI applications.
The Involvement of Group Decision and Communication Support In Building a SME Cluster in China
Authors: Zhang Junxiang  , Humphreys Patrick
Published: February 2025

An international consensus has emerged on the need for strong and early action on sustainable development, involving creative initiatives aimed at improving the quality and diversity of life in new ecologically and creatively informed developments. But there has been relatively little research completed on how the collaboration and internationalization of SMEs (Small and Medium Enterprises) from emerging markets like China can power this process. In particular, SMEs are more vulnerable to the threats of globalization than larger companies despite their important role in the local economy. Hence this paper will take a bottom-up approach, focusing on the needs in this respect of Chinese “stigmatized” cities, which have benefited economically from industrial development over the past decade, but with severe consequences for the inhabitants, ranging from air pollution, transportation nightmares, ant-social housing, lack of diversity of indigenous services and facilities, and community destruction. The first part of this paper will describes the current problems/developments of the creative industries context in China and how this impacts on SMEs. Then it identifies deficits and problems on existing theories and practice regarding to SME cluster building in the face of globalization, taking a critical social psychological perspective. After addressing shortcomings of the traditional, mainly top-down, approaches and how they may be overcome by taking a bottom-up creative approach to building collaboration between SMEs, the paper confronts the question: how can GDACS (Group decision authoring and communication support) provide a suitable facilitating infrastructure for SMEs' cooperation in a creative industries cluster integrating both top-down and bottom-up perspectives and activities? It considers in particular the case of the building of a multinational SME cluster by the International Creative Industries Alliance (ICIA) of Beijing, following the ICIA's aim to “engage in real-world implementation cases, achieving results and producing outputs in ways that give new social, community, ecological and economic benefits”.
Assessing experts' contribution in a qualitative DSS: an example from organic food sector scenarios
Authors: Gambelli Danilo  , Vairo Daniela  , Zanoli Raffaele
Published: February 2025

Scenario analysis is a qualitative tool for policy analysis and business planning support that enables stakeholders and policymakers to consider decisions with enhanced awareness, due its cognitive implications for planned behavior. In this study, the scenario analysis is used to discuss the future development of the market of organic food products in Europe. A deductive-qualitative approach to scenario has been used, based on experts' assessments. Results of the scenario analysis may provide a basis for decision making under different scenarios outlined, and are integrated by an innovative attempt to evaluate scenario plausibility through cognitive process evaluation.
Improving Decision Quality Through Preference Relaxation
Authors: Dabrowski Maciej  , Acton Thomas
Published: February 2025

In online shopping scenarios, it can be difficult for consumers to process the vast amounts of information available and to make satisfactory buying decisions. Interactive decision aids are a potential solution to this problem. However, decision aids that filter a very large set of alternatives based on initial preferences may eliminate potentially valuable alternatives early in the decision process and possibly negatively impact decision quality. To address this issue we introduce a new kind of decision aid that enables consumers to consider high-quality alternatives they initially eliminated. We develop a model of such a decision aid and evaluate it on a set of 2650 car advertisements gathered from popular used car advertiser website. We discuss the potential impact of our decision aid on decision quality and consideration sets parameters, and give an overview of implications of our study for practitioners and researchers.
Ubiquitous Computing Infrastructure as a Basis for Creativity Support
Authors: Stanek Stanislaw
Published: February 2025

The paperA preliminary version of this paper was published in polish language in Chmielarz, W., Turyna, J. (eds..): Komputerowe systemy zarządzania, Wydawnictwo Naukowe Uniwersytetu Warszawskiego, 2009
The Future Builds on Aluminium: Architecture Case Studies
Authors: Stacey Michael
Published: February 2025

Aluminium has a key role in sustaining human ecology. This can be readily demonstrated by exemplary case study architecture from throughout the globe. The advantages of aluminium as a component of architecture include durability, elegance, and economy and yet it is infinitely recyclable. To articulate why architects and specifiers should consider aluminium to be a sustainable material, the International Aluminium Institute [IAI] launched The Future Builds on Aluminium website in 2009. This paper reviews key case studies from this website and why they were selected by the author.
The DebugIT Core Ontology: semantic integration of antibiotics resistance patterns
Authors: Schober Daniel  , Boeker Martin  , Bullenkamp Jessica  , Huszka Csaba  , Depraetere Kristof  , Teodoro Douglas  , Nadah Nadia  , Choquet Remy  , Daniel Christel  , Schulz Stefan
Published: February 2025

Antibiotics resistance development poses a significant problem in today's hospital care. Massive amounts of clinical data are being collected and stored in proprietary and unconnected systems in heterogeneous format. The DebugIT EU project promises to make this data geographically and semantically interoperable for case-based knowledge analysis approaches aiming at the discovery of patterns that help to align antibiotics treatment schemes. The semantic glue for this endeavor is DCO, an application ontology that enables data miners to query distributed clinical information systems in a semantically rich and content driven manner. DCO will hence serve as the core component of the interoperability platform for the DebugIT project. Here we present DCO and an approach thet uses the semantic web query language SPARQL to bind and ontologically query hospital database content using DCO and information model mediators. We provide a query example that indicates that ontological querying over heterogeneous information models is feasible via SPARQL construct- and resource mapping queries.
A Mobile Phone Based Telemonitoring Concept for the Simultanous Acquisition of Biosignals Physiological Parameters
Authors: Kumpusch Hannes  , Hayn Dieter  , Kreiner Karl  , Falgenhauer Markus  , Mor Jürgen  , Schreier Günter
Published: February 2025

Congestive Heart Failure (CHF) is a common chronic heart disease with high socioeconomic impact. Conventional treatment of CHF is often ineffective and inefficient, since self-management is complex and patients are insufficiently involved in therapy management. With telemedical concepts, continuous monitoring of the health status can be ensured, and consequently therapy management can be adapted to the individual requirements of every individual patient. Therefore, a mobile phone based patient terminal for the concurrent acquisition of biosignals (e.g. ECG) and bioparameters (e.g. blood pressure) for patients with CHF has been developed and prototypically implemented. Usability and interoperability aspects were especially considered by using Bluetooth and Near Field Communication (NFC) technology for data acquisition and standardized data formats for transmission of the data to a central monitoring centre. Results indicated that even complicated measurements like the acquisition of ECG signals could be accomplished autonomously by the patients in an intuitive and easy-to-use way. Through the usage of IHE conform HL7 messages, self-measured data could easily be integrated into a higher-ranking eHealth infrastructure.
Design and Assessment of a Common, Multi-National Public Health Informatics Infrastructure to Enable H1N1 Influenza Surveillance
Authors: Advani Aneel  , Turuvekere Aarti M.  , Liu Conan  , Rubin Ken  , Lamer Chris  , Cullen Theresa
Published: February 2025

Public health organizations in different nations face similar needs for gathering and analyzing population health data to detect and manage infectious disease outbreaks, including outbreaks of the 2009 Novel H1N1 Influenza A virus or “swine flu.” This paper presents our progress to date on the design and assessment of a multi-national public health informatics infrastructure for data collection and disease surveillance. This initial work, under the aegis of an open health tools collaborative, lays the foundation for best practices in patient care and public health preparedness in the national health IT sector. This multinational collaboration is the first to identify essential electronic health record (EHR) data sets as well as standard public health informatics indicators to electronically monitor a notifiable public health condition internationally.
Spatiotemporal Antibiotic Resistance Pattern Monitoring using Geographical Information System based Hierarchical Cluster Analysis
Authors: Hewapathirana Roshan  , Wijayarathna Gamini
Published: February 2025

Bacterial antimicrobial resistance in both the medical and agricultural fields has become a serious problem worldwide. Antibiotic resistant strains of bacteria are an increasing threat to human health, with resistance mechanisms having been described to all known antimicrobials currently available for clinical use. Monitoring the geotemporal variations of antibiotic resistance pattern is crucial factor in planning a successful therapeutic guidelines preventing further emergence of antibiotic resistance. This study is based on the retrospective spatiotemporal analysis of laboratory results of Antibiotic Sensitivity Tests, time stamped with the date and time of the microbiological specimen dispatched to the laboratory. Geographic location of the isolated bacterial colony is specified with the latitude and the longitude of the patient's location. Agglomerative Hierarchical Clustering was performed on antimicrobial resistance findings based on the geographic locations generating series of Heatmaps to visualize the extent of the resistance pattern. Sequential Hierarchical cluster analysis was proven to be effective in visualization of antibiotic resistance using Heatmaps demonstrating the temporal variations of the antibiotic resistance patterns.
ABA: Argumentation Based Agents
Authors: Kakas A.  , Amgoud L.  , Kern-Isberner G.  , Maudet N.  , Moraitis P.
Published: February 2025

Many works have identified the potential benefits of using argumentation to address a large variety of multiagent problems. In this paper we take this idea one step further and develop the concept of a fully integrated argumentation-based agent architecture that allows us to develop agents that are coherently designed on an underlying argumentation based foundation. Under this architecture, an agent is composed of a collection of modules each of which is equipped with a local argumentation theory. Similarly, the intra-agent control of the agent is governed by local argumentation theories that are sensitive to the current situation of the agent through dynamically enabled feasibility arguments.
A Knapsack-Based Approach to Bidding in Ad Auctions
Authors: Berg Jordan  , Greenwald Amy  , Naroditskiy Victor  , Sodomka Eric
Published: February 2025

We model the problem of bidding in ad auctions as a penalized multiple choice knapsack problem (PMCKP), a combination of the multiple choice knapsack problem (MCKP) and the penalized knapsack problem (PKP) [1]. We present two versions of PMCKPGlobalPMCKP and LocalPMCKP, together with a greedy algorithm that solves the linear relaxation of a GlobalPMCKP optimally. We also develop a greedy heuristic for solving LocalPMCKP. Although our heuristic is not optimal, we show that it performs well in TAC AA games.
Preferential vs Rational Description Logics: which one for Reasoning About Typicality?
Authors: Giordano Laura  , Gliozzi Valentina  , Olivetti Nicola  , Pozzato Gian Luca
Published: February 2025

Extensions of Description Logics (DLs) to reason about typicality and defeasible inheritance have been largely investigated. In this paper, we consider two such extensions, namely (i) the extension of DLs with a typicality operator T, having the properties of Preferential nonmonotonic entailment P, and (ii) its variant with a typicality operator having the properties of the stronger Rational entailment R. The first one has been proposed in [6]. Here, we investigate the second one and we show, by a representation theorem, that it is equivalent to the approach to preferential subsumption proposed in [3]. We compare the two extensions, preferential and rational, and argue that the first one is more suitable than the second one to reason about typicality, as the latter leads to very unintuitive inferences.
A Logical Model of Intention and Plan Dynamics
Authors: Lorini Emiliano  , van Ditmarsch Hans  , de Lima Tiago
Published: February 2025

We propose a formal semantics of intention and plan dynamics based on the notion of local assignment. The function of a local assignment is to change the truth value of a given proposition at a specific time point along a history. We combine a static modal logic including a temporal modality and modal operators for mental attitudes belief and choice, with three kinds of dynamic modalities and corresponding three kinds of local assignments operating on agent's beliefs, on agent's choices and on the physical world. An agent's intention is defined in our approach as the agent's choice to perform a given action at a certain time point in the future and two operations called intention generation and intention reconsideration are defined as specific kinds of local assignments on choices. In Section 1 we introduce a static logic of time, action, and mental attitudes. In Section 2 we add the dynamic notion of local assignment to the logic of Section 1. In Section 3, we focus on two specific kinds of local assignment on choice which allow to model the processes of intention and plan generation and reconsideration.
Restarts and Nogood Recording in Qualitative Constraint-based Reasoning
Authors: Westphal Matthias  , Wölfl Stefan  , Li Jason Jingshi
Published: February 2025

This paper introduces restart and nogood recording techniques in the domain of qualitative spatial and temporal reasoning. Nogoods and restarts can be applied orthogonally to usual methods for solving qualitative constraint satisfaction problems. In particular, we propose a more general definition of nogoods that allows for exploiting information about nogoods and tractable subclasses during backtracking search. First evaluations of the proposed techniques show promising results.
Diagnosing Process Trajectories Under Partially Known Behavior
Authors: Friedrich Gerhard  , Mayer Wolfgang  , Stumptner Markus
Published: February 2025

Diagnosis of process executions is an important task in many application domains, especially in the area of workflow management systems and orchestrated Web Services. If executions fail because activities of the process do not behave as intended, recovery procedures re-execute some activities to recover from the failure. We present a diagnosis method for identifying incorrect activities in process executions. Our method is novel both in that it does not require exact behavioral models for the activities and that its accuracy improves upon dependency-based methods. Observations obtained from partial executions and re-executions of a process are exploited. We formally characterize the diagnosis problem and develop a symbolic encoding that can be solved using CLP(FD) solvers. Our evaluation demonstrates that the framework yields superior accuracy to dependency-based methods on realistically-sized examples.
Generating time series reference models based on event analysis
Authors: Lara Juan A.  , Perez Aurora  , Valente Juan P.  , Lopez-Illescas Africa
Published: February 2025

Creating a reference model that represents a given set of time series is a relevant problem as it can be applied to a wide range of tasks like diagnosis, decision support, fraud detection, etc. In some domains, like seismography or medicine, the relevant information contained in the time series is concentrated in short periods of time called events. In this paper, we propose a technique for generating time series reference models based on the analysis of the events they contain. The proposed technique has been applied to time series from two medical domains: Electroencephalography, a neurological procedure to record the electrical activity produced by the brain and Stabilometry, a branch of medicine studying balance-related functions in human beings.
Automatic Creation of a Conceptual Base for Portuguese using Clustering Techniques
Authors: Gonçalo Oliveira Hugo  , Gomes Paulo
Published: February 2025

When a semantic network is based on triples relating terms, ambiguity arises as a problem, so we have used these triples to identify clusters, which can be seen as synsets. We report the results of this approach on a synonymy network extracted from a dictionary and additional tests involving manually created thesaurus. Part of the resulting synsets were also evaluated by human subjects.
An Argumentation-based Approach to Database Repair
Authors: Santos Emanuel  , Martins João Pavão  , Galhardas Helena
Published: February 2025

The access to high-quality data is essential to make accurate decisions. Consequently, when a database becomes inconsistent is crucial to restore its consistency. The main approach for database consistency restoration is based on the notion of repair. In this work, we use Argumentation techniques to provide a better understand of the reasoning process behind database reparation. We introduced an extended argumentation framework that provides a comprehensive and alternative way to identify, represent and resolve the conflicts between tuples in an inconsistent database. We studied the complexity of this framework and show that it can be used to check the optimality of a repair based on the extended notions of locally, semi-globally and globally optimal repair with respect to Denial Constraints and Tuple-Generating Dependencies classes.
A Common Computational Framework for Semiring-based Argumentation Systems
Authors: Bistarelli Stefano  , Santini Francesco
Published: February 2025

We suggest semirings as a mean to parametrically represent “weighted” Argumentation frameworks (AF): different kinds of preference levels related to arguments, e.g. a score representing a “fuzziness”, a “cost” or a probability level of each argument, can be represented by choosing different semirings. The novel idea is to provide a common computational and quantitative framework where attacks (and/or supports) have an associated weight and, consequently, also the computation of the classical Dung's semantics has an associated weight representing how much inconsistency we tolerate in the solution. The proposed semiring-based AF is then casted into a Soft Constraint Satisfaction Problem. Adding suitable sets of constraints permits to characterize the solution of the soft CSP as the desired Dung semantics. This allows for the application of the several solution techniques developed for Soft CSPs to AFs.
Using Crowdsourcing and Active Learning to Track Sentiment in Online Media
Authors: Brew Anthony  , Greene Derek  , Cunningham Pádraig
Published: February 2025

Tracking sentiment in the popular media has long been of interest to media analysts and pundits. With the availability of news content via online syndicated feeds, it is now possible to automate some aspects of this process. There is also great potential to crowdsourceCrowdsourcing is a term, sometimes associated with Web 2.0 technologies, that describes outsourcing of tasks to a large often anonymous community.
Multiscale Adaptive Agent-Based Management of Storage-Enabled Photovoltaic Facilities
Authors: Li Rongxin  , Wang Peter  , James Geoff
Published: February 2025

We propose a multiscale algorithm for autonomous agents to adaptively manage the operation of storage-enabled photovoltaic (PV) facilities based upon sequential decisions in a partially observable environment. The stochastic environment is learned and modeled by an approach called an ϵ-Machine, which operates on a set of a priori determined temporal scales, to give the agent an additional degree of freedom when optimizing its control decisions. We compare the performance of the proposed scheme with those of (1) control decisions based on a heuristic environment model rather than systematic learning, and (2) decisions made on a pre-determined scale. We argue that the systematic environment learning on multiple temporal scales makes the agent highly adaptable and, as a result, able to demonstrate a superior capability in managing the PV-storage facility according to a predetermined objective. The particular application focused upon in the paper is in managing distributed PV facilities as potential replacement of conventional peaking power plants.
Stream-Based Reasoning Support for Autonomous Systems
Authors: Heintz Fredrik  , Kvarnström Jonas  , Doherty Patrick
Published: February 2025

For autonomous systems such as unmanned aerial vehicles to successfully perform complex missions, a great deal of embedded reasoning is required at varying levels of abstraction. To support the integration and use of diverse reasoning modules we have developed DyKnow, a stream-based knowledge processing middleware framework. By using streams, DyKnow captures the incremental nature of sensor data and supports the continuous reasoning necessary to react to rapid changes in the environment.
Top-Down Algorithms for Constructing Structured DNNF: Theoretical and Practical Implications
Authors: Pipatsrisawat Knot  , Darwiche Adnan
Published: February 2025

We introduce a top-down compilation algorithm for constructing structured DNNF for any Boolean function. With appropriate restrictions, the algorithm can produce various subsets of DNNF such as deterministic DNNF and OBDD. We derive a size upper bound for structured DNNF based on this algorithm and use the result to generalize similar upper bounds known for several Boolean functions in the case of OBDD. We then discuss two realizations of the algorithm that work on CNF formulas. We show that these algorithms have time and space complexities that are exponential in the treewidth and the dual treewidth of the input.
Strengthening Landmark Heuristics via Hitting Sets
Authors: Bonet Blai  , Helmert Malte
Published: February 2025

The landmark cut heuristic is perhaps the strongest known polytime admissible approximation of the optimal delete relaxation heuristic h+. Equipped with this heuristic, a best-first search was able to optimally solve 40% more benchmark problems than the winners of the sequential optimization track of IPC 2008. We show that this heuristic can be understood as a simple relaxation of a hitting set problem, and that stronger heuristics can be obtained by considering stronger relaxations. Based on these findings, we propose a simple polytime method for obtaining heuristics stronger than landmark cut, and evaluate them over benchmark problems. We also show that hitting sets can be used to characterize h+and thus provide a fresh and novel insight for better comprehension of the delete relaxation.
Iterative Bounding LAO*
Authors: Warnquist Håkan  , Kvarnström Jonas  , Doherty Patrick
Published: February 2025

Iterative Bounding LAO* is a new algorithm for ϵ-optimal probabilistic planning problems where an absorbing goal state should be reached at a minimum expected cost from a given ini tial state. The algorithm is based on the LAO* algorithm for finding optimal solutions in cyclic AND/OR graphs. The new algorithm uses two heuristics, one upper bound and one lower bound of the optimal cost. The search is guided by the lower bound as in LAO*, while the upper bound is used to prune search branches. The algorithm has a new mechanism for expanding search nodes, and while maintaining the error bounds, it may use weighted heuristics to reduce the size of the explored search space. In empirical tests on benchmark problems, Iterative Bounding LAO* expands fewer search nodes compared to state of the art RTDP variants that also use two-sided bounds.
Using Bayesian Networks in an Industrial Setting: Making Printing Systems Adaptive
Authors: Hommersom Arjen  , Lucas Peter J.F.
Published: February 2025

Control engineering is a field of major industrial importance as it offers principles for engineering controllable physical devices, such as cell phones, television sets, and printing systems. Control engineering techniques assume that a physical system's dynamic behaviour can be completely described by means of a set of equations. However, as modern systems are often of high complexity, drafting such equations has become more and more difficult. Moreover, to dynamically adapt the system's behaviour to a changing environment, observations obtained from sensors at runtime need to be taken into account. However, such observations give an incomplete picture of the system's behaviour; when combined with the incompletely understood complexity of the device, control engineering solutions increasingly fall short. Probabilistic reasoning would allow one to deal with these sources of incompleteness, yet in the area of control engineering such AI solutions are rare. When using a Bayesian network in this context the required model can be learnt, and tuned, from data, uncertainty can be handled, and the model can be subsequently used for stochastic control of the system's behaviour. In this paper we discuss industrial research in which Bayesian networks were successfully used to control complex printing systems.
Integrating Bipolar Fuzzy Mathematical Morphology in Description Logics for Spatial Reasoning
Authors: Hudelot Céline  , Atif Jamal  , Bloch Isabelle
Published: February 2025

Bipolarity is an important feature of spatial information, involved in the expression of preferences and constraints about spatial positioning or in pairs of opposite spatial relations such as left and right. Another important feature is imprecision which has to be taken into account to model vagueness, inherent to many spatial relations (as for instance vague expressions such as close to, to the right of), and to gain in robustness in the representations. In previous works, we have shown that fuzzy sets and fuzzy mathematical morphology are appropriate frameworks, on the one hand, to represent bipolarity and imprecision of spatial relations and, on the other hand, to combine qualitative and quantitative reasoning in description logics extended with fuzzy concrete domains. The purpose of this paper is to integrate the bipolarity feature in the latter logical framework based on bipolar and fuzzy mathematical morphology and description logics with fuzzy concrete domains. Two important issues are addressed in this paper: the modeling of the bipolarity of spatial relations at the terminological level and the integration of bipolar notions in fuzzy description logics. At last, we illustrate the potential of the proposed formalism for spatial reasoning on a simple example in brain imaging.
Computing the Data Semantics of WSDL Specifications via Gradient Boosting
Authors: Valarakos Alexandros G.  , Vouros George A.
Published: February 2025

This paper proposes a method for the semi-automatic semantic annotation of WSDL specifications, given ontologies related to the domain of services. The proposed method uses a synthesis of mapping methods to map input/output messages' parameters to ontology classes. Exploiting validated results provided by humans, the method learns via the gradient boosting learning algorithm to combine the individual mapping methods towards improving its accuracy. The aim is to mitigate difficulties concerning mappings and address limitations of other approaches, even in challenging cases, so as to assist human annotators to perform their work. The paper presents experimental results of the proposed methods.
Verifying Properties of Infinite Sequences of Description Logic Actions
Authors: Baader Franz  , Liu Hongkai  , ul Mehdi Anees
Published: February 2025

The verification problem for action logic programs with non-terminating behaviour is in general undecidable. In this paper, we consider a restricted setting in which the problem becomes decidable. On the one hand, we abstract from the actual execution sequences of a non-terminating program by considering infinite sequences of actions defined by a Büchi automaton. On the other hand, we assume that the logic underlying our action formalism is a decidable description logic rather than full first-order predicate logic.
Optimal Tableaux for Conditional Logics with Cautious Monotonicity
Authors: Schröder Lutz  , Pattinson Dirk  , Hausmann Daniel
Published: February 2025

Conditional logics capture default entailment in a modal framework in which non-monotonic implication is a first-class citizen, and in particular can be negated and nested. There is a wide range of axiomatizations of conditionals in the literature, from weak systems such as the basic conditional logic CK, which allows only for equivalent exchange of conditional antecedents, to strong systems such as Burgess' system 𝒮, which imposes the full Kraus-Lehmann-Magidor properties of preferential logic. While tableaux systems implementing the actual complexity of the logic at hand have recently been developed for several weak systems, strong systems including in particular disjunction elimination or cautious monotonicity have so far eluded such efforts; previous results for strong systems are limited to semantics-based decision procedures and completeness proofs for Hilbert-style axiomatizations. Here, we present tableaux systems of optimal complexity PSPACE for several strong axiom systems in conditional logic, including system 𝒮; the arising decision procedure for system 𝒮 is implemented in the generic reasoning tool CoLoSS.
Classifier Ensemble using Multiobjective Optimization for Named Entity Recognition
Authors: Ekbal Asif  , Saha Sriparna
Published: February 2025

In this paper, we report a multiobjective optimization (MOO) based classifier ensemble technique to solve the problem of Named Entity Recognition (NER). Our underlying assumption is that rather than searching for the best feature set for a particular classifier, ensembling of several classifiers which are trained using different feature representations could be a more fruitful approach. But, it is very crucial to select the appropriate classifiers that can participate in final ensembling. Here, we propose a new technique for classifier ensembling based on MOO that can simultaneously optimize several different classification measures. Maximum Entropy (ME) framework is used to generate a number of classifiers by considering the various combinations of the available features. The proposed technique is evaluated for two resource constrained languages, namely Bengali and Hindi. Evaluation results yield the recall, precision and F-measure values of 72.34%, 84.94% and 78.13%, respectively for Bengali, and 64.93%, 83.29% and 72.97%, respectively for Hindi. Experiments also show that the classifier ensemble identified by the proposed multiobjective based approach outperforms all the individual classifiers, two different baseline ensembles and a classifier ensemble identified by the single objective genetic algorithm (GA) based approach.
Soft Nearest Convex Hull Classifier
Authors: Nalbantov Georgi  , Smirnov Evgueni
Published: February 2025

Consider the classification task of assigning a test instance to one of two or more possible classes. An intuitive way to proceed is to assign the instance to that class, to which the distance is minimal. If one considers the distance to the convex hull of a class as a distance measure, then the resulting classification method is the Nearest Convex Hull (NCH) classifier. There are two key issues with this method per se that severely restrict its applicability, which we solve in this paper: first, how to handle class overlap, and second, how to provide (nonlinear) solutions with better generalization ability. The first problem is handled via using so-called kernel functions and slack variables. The second problem is dealt with using a penalization term that suppresses too complex solutions. We call the resulting method the soft-NCH classifier. In spirit and computationally the method is close to the popular Support Vector Machine (SVM) classifier and can be viewed as an instance-based large-margin classification technique. Advantages of the soft-NCH classifier include its robustness to outliers, good generalization ability and naturally easy handling of multi-class problems. We compare the performance of soft-NCH against state-of-art techniques and report promising results.
On Testing Answer-Set Programs
Authors: Janhunen Tomi  , Niemelä Ilkka  , Oetsch Johannes  , Pührer Jörg  , Tompits Hans
Published: February 2025

Answer-set programming (ASP) is a well-acknowledged paradigm for declarative problem solving, yet comparably little effort has been spent on the investigation of methods to support the development of answer-set programs. In particular, systematic testing of programs, constituting an integral part of conventional software development, has not been discussed for ASP thus far. In this paper, we fill this gap and develop notions enabling the structural testing of answer-set programs, i.e., we address testing based on test cases that are chosen with respect to the internal structure of a given answer-set program. More specifically, we introduce different notions of coverage that measure to what extent a collection of test inputs covers certain important structural components of the program. In particular, we introduce metrics corresponding to path and branch coverage from conventional testing. We also discuss complexity aspects of the considered notions and give strategies how test inputs that yield increasing (up to total) coverage can be automatically generated.
Towards Learning to Rank in Description Logics
Authors: Fanizzi Nicola  , d'Amato Claudia  , Esposito Floriana
Published: February 2025

In the context of knowledge bases expressed in Description Logics, a method for learning functions that can predict the ranking of resources encoding some preference criteria implicitly encoded through examples of rated individuals. The method relies on a kernelized version of the PERCEPTRON RANKING algorithm which is suitable for batch but also online problem settings.
A contextual reading of conditional commitments
Authors: Serrano Juan Manuel  , Saugar Sergio  , Laurendi Rosario  , Buccafurri Francesco
Published: February 2025

This paper puts forward a view on conditional commitments as causal rules, using action language K as the specification framework. The proposal builds upon an operational notion of social context, in such a way that conditional commitments are represented as rules in context. This approach enables the manipulation of conditional commitments in terms of the manipulation of the social interactions which provide their social contexts. Moreover, it allows the programmer to exploit the ASP metatheory underlying action language K to analyze, simplify and transform commitment-based protocols.
Study and implementation of urban rail transit construction engineering security and risk management information system
Authors: Liao Yi-qi  , Jin Huai  , Lv Pei-yin  , Li Jun-wei
Published: February 2025

The large-scale construction period of metro engineering is coming in China. Fifty-five metro lines are planned in twenty cities. The whole length reaches to fifty kilometers and the investments exceed five-hundred billion RMB. But there are many problems existed in management now, including bad information transmission, poor data sharing, incomplete early-warning system, weak interoperability ability and serious leak of precious data source. And this makes the subway construction in high-risk state leading to frequent accidents. In order to reduce the subway construction risk, a risk evaluation and management program is developed in Guangzhou Metro between 2006 and 2010, which is the leading problem in the field. This paper introduces the present situation of the risk management program named “three evaluations and one management”, which developed in Guangzhou Metro construction, and the relevant information system. Meanwhile, the system architecture, core function and implementing effect of information system in Guangzhou Metro line 6 are described.
Support in Abstract Argumentation
Authors: Boella G.  , Gabbay D.M.  , van der Torre L.  , Villata S.
Published: February 2025

In this paper, we consider two drawbacks of Cayrol and Lagasque-Schiex's meta-argumentation theory to model bipolar argumentation frameworks. We consider first the “lost of admissibility” in Dung's sense and second, the definition of notions of attack in the context of a support relation. We show how to prevent these drawbacks by introducing support meta-arguments. Like the model of Cayrol and Lagasque-Schiex, our formalization confirms the use of meta-argumentation to reuse Dung's properties. We do not take a stance towards the usefulness of a support relation among arguments, though we show that if one would like to introduce them, it can be done without extending Dung's theory. Finally, we show how to use meta-argumentation to instantiate an argumentation framework to represent defeasible support. In this model of support, the support relation itself can be attacked.
Types of Dialogue and Burdens of Proof
Authors: Walton Douglas
Published: February 2025

Burden of proof has recently come to be a topic of interest in argumentation systems for artificial intelligence (Prakken and Sartor, 2006, 2007, 2009; Gordon and Walton, 2007, 2009), but so far the main work on the subject seems to be in that type of dialogue which has most intensively been investigated generally, namely persuasion dialogue. The most significant exception is probably deliberation dialogue, where some recent work has begun to tentatively investigate burden of proof in that setting. In this paper, I survey work on burden of proof in the artificial intelligence literature on argumentation, and offer some thoughts on how this work might be extended to the other types of dialogue recognized by Walton and Krabbe (1995) that so far do not appear to have been much investigated in this regard.
Base Logics in Argumentation
Authors: Hunter Anthony
Published: February 2025

There are a number of frameworks for modelling argumentation in logic. They incorporate a formal representation of individual arguments and techniques for comparing conflicting arguments. A common assumption for logic-based argumentation is that an argument is a pair ⟨Φ,α⟩ where Φ is a minimal subset of the knowledgebase such that Φ is consistent and Φ entails the claim α. We call the logic used for consistency and entailment, the base logic. Different base logics provide different definitions for consistency and entailment and hence give us different options for argumentation. This paper discusses some of the commonly used base logics in logic-based argumentation, and considers various criteria that can be used to identify commonalities and differences between them.
A Dynamic Argumentation Framework
Authors: Rotstein Nicolás D.  , Moguillansky Martín O.  , García Alejandro J.  , Simari Guillermo R.
Published: February 2025

This article introduces the notion of dynamics for abstract argumentation frameworks. We consider evidence as the basis from which arguments may be considered valid. The proposed Dynamic Argumentation Framework (DAF) is a refinement of Dung's abstract framework (AF), enriched with additional features. Since an instance of a DAF is equivalent to an AF, the former could be viewed as a template to build different AFs, applying the same knowledge to different situations. This equivalence is important, as we can take advantage of the vast amount of study on argumentation semantics for AFs to apply it to our approach. The DAF's aim is to provide a well-structured knowledge representation tool that allows for the definition of dynamics-aware argumentation-based systems.
Magic Wand Approach to Representation of Personal Technologies
Authors: Malyshkin Victor
Published: February 2025

This paper considers and analysis the idea of magic wand as approach to representation new technologies, their implementability, application and use in different object domain. Magic wand approach in its currently implementable form is suggested to be the standard for any new technology representation. How to describe a new technology, how to accumulate and control the active knowledge base constitute the main subject of the paper.
Systemic Approaches for Software Engineering
Authors: Gruhn Volker  , Schäfer Clemens
Published: February 2025

In this paper we tackle the question how early phases of software projects can be improved by applying methodologies provided by psychological science. We show an approach for developing business information systems, called No-Frills Software Engineering, which benefits from systemic approaches. These approaches allow to resolve communication and interaction issues still persistent in early phases of software projects, which are normally not addressed by conventional software process models.
Spatial Data Mining in Practice: Principles and Case Studies
Authors: Körner Christine  , Hecker Dirk  , Krause-Traudes Maike  , May Michael  , Scheider Simon  , Schulz Daniel  , Stange Hendrik  , Wrobel Stefan
Published: February 2025

Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.
Customer Validation of Commercial Predictive Models
Authors: Bruckhaus Tilmann  , Guthrie William E.
Published: February 2025

A central need in the emerging business of model-based prediction is to enable customers to validate the accuracy of a predictive product. This paper discusses how analysts can evaluate data mining models and their inferences from the customer viewpoint, where the customer is not particularly knowledgeable in data mining. To date, academia has focused primarily on the validation of algorithms through mathematical metrics and benchmarking studies. This type of validation is not sufficient in the business context, where organizations must validate specific models in terms that customers can understand quickly and effortlessly. We describe our predictive business and our customer validation needs. To that end, we discuss examples of customer needs, review issues associated with model validation, and point out how academic research may help to address these business needs.
A Framework for Asynchronous Dialogue Systems
Authors: Treumuth Margus
Published: February 2025

This paper presents a framework for asynchronous dialogue systems. It is used in developing text-based dialogue systems. The framework features web-based asynchronous turn management and AI-assisted live agent chat. Some other features are also briefly covered in this paper (a language independent solution for spell checking the user input, a language independent solution for the word-order problem, semantic resolution and a web-based interface for WOZ data collection). The exploitation of the asynchronous communication pattern has improved the communication style of the user which has resulted in decreased number or single word utterances. Higher word count per utterance is important when performing shallow language analysis without complete semantic understanding. The framework is currently tailored for Estonian language, yet most of its features and modules are language independent.
National Programme for Estonian Language Technology: a Pre-final Summary
Authors: Meister Einar  , Vilo Jaak  , Kahusk Neeme
Published: February 2025

The National Programme for Estonian Language Technology (2006–2010) was launched in 2006 and is approaching to the end in 2010. This paper gives an overview of the programme and projects covering different areas of language technology; also the future prospects will be discussed.
Cultural Algorithms: Less is More
Authors: Almirall Esteve  , Cortés Ulises
Published: February 2025

Cultural Algorithms, by imitating the social behavior of human communities are able to attain good results while exhibiting a remarkable frugality in resources. This paper explores their needs in terms of sample size and how these needs are affected by the complexity of the information being addressed, with a somewhat surprising result: less is more, smaller sample sizes conduce to better results at any level of complexity.