bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Pruning Rules for Heuristic Search Planning
Authors: Krajňanský Michal  , Hoffmann Jörg  , Buffet Olivier  , Fern Alan
Published: February 2025

When it comes to learning control knowledge for planning, most works focus on “how to do it” knowledge which is then used to make decisions regarding which actions should be applied in which state. We pursue the opposite approach of learning “how to not do it” knowledge, used to make decisions regarding which actions should not be applied in which state. Our intuition is that “bad actions” are often easier to characterize than “good” ones. An obvious application, which has not been considered by the few prior works on learning bad actions, is to use such learned knowledge as action pruning rules in heuristic search planning. Fixing a canonical rule language and an off-the-shelf learning tool, we explore a novel method for generating training data, and implement rule evaluators in state-of-the-art planners. The experiments show that the learned rules can yield dramatic savings, even when the native pruning rules of these planners, i.e., preferred operators, are already switched on.
Common Spatial-Spectral Boosting Pattern for Brain-Computer Interface
Authors: Liu Ye  , Zhang Hao  , Zhao Qibin  , Zhang Liqing
Published: February 2025

Classification of multichannel electroencephalogram (EEG) recordings during motor imagination has been exploited successfully for brain-computer interfaces (BCI). Frequency bands and channels configuration that relate to brain activities associated with BCI tasks are often pre-decided as default in EEG analysis without deliberations. However, a steady configuration usually loses effects due to individual variability across different subjects in practical applications. In this paper, we propose an adaptive boosting algorithm in a unifying theoretical framework to model the usually predetermined spatial-spectral configurations into variable preconditions, and further introduce a novel heuristic of stochastic gradient boost for training base learners under these preconditions. We evaluate the effectiveness and robustness of our proposed algorithm based on two data sets recorded from diverse populations including the healthy people and stroke patients. The results demonstrate its superior performance.
Probabilistic Preference Logic Networks
Authors: Lukasiewicz Thomas  , Martinez Maria Vanina  , Simari Gerardo I.
Published: February 2025

Reasoning about an entity's preferences (be it a user of an application, an individual targeted for marketing, or a group of people whose choices are of interest) has a long history in different areas of study. In this paper, we adopt the point of view that grows out of the intersection of databases and knowledge representation, where preferences are usually represented as strict partial orders over the set of tuples in a database or the consequences of a knowledge base. We introduce probabilistic preference logic networks (PPLNs), which flexibly combine such preferences with probabilistic uncertainty. Their applications are clear in domains such as the Social Semantic Web, where users often express preferences in an incomplete manner and through different means, many times in contradiction with each other. We show that the basic problems associated with reasoning with PPLNs (computing the probability of a world or a given query) are #P-hard, and then explore ways to make these computations tractable by: (i) leveraging results from order theory to obtain a polynomial-time randomized approximation scheme (FPRAS) under fixed-parameter assumptions; and (ii) studying a fragment of the language of PPLNs for which exact computations can be performed in fixed-parameter polynomial time.
An Efficient Bayesian Network Structure Learning Algorithm in the Presence of Deterministic Relations
Authors: Mabrouk Ahmed  , Gonzales Christophe  , Jabet-Chevalier Karine  , Chojnacki Eric
Published: February 2025

Faithfulness is one of the main hypotheses on which rely most Bayesian network (BN) structure learning algorithms. When some random variables are deterministically determined by others, faithfulness is ruled out and classical learning algorithms fail to discover many dependences between variables, hence producing incorrect BNs. Even state-of-the-art algorithms dedicated to learning with deterministic variables prove to be inefficient to discover many dependences/independences. For critical applications, e.g., in nuclear safety, such failure is a serious issue. This paper introduces a new hybrid algorithm, combining a constraint-based approach with a greedy search, that includes specific rules dedicated to deterministic nodes that significantly reduce the incorrect learning. Experiments show that our method significantly outperforms state-of-the-art algorithms.
How Hard is Control in Single-Crossing Elections?
Authors: Magiera Krzysztof  , Faliszewski Piotr
Published: February 2025

Election control problems model situations where some entity (traditionally called the election chair) wants to ensure some agent's victory by either adding or deleting candidates or voters. The complexity of deciding if such control actions can be successful is well-studied for many typical voting rules and, usually, such control problems are NP-complete. However, Faliszewski et al. [16] have shown that many control problems become polynomial-time solvable when we consider single-peaked elections. In this paper we show that a similar phenomenon applies to the case of single-crossing elections. Specifically, we consider the complexity of control by adding/deleting candidates/voters under Plurality and Condorcet voting. For each of these control types and each of the rules, we show that if the control type is NP-complete in general, it becomes polynomial-time solvable for single-crossing elections.
Comparing Defeasible Logics
Authors: Maher Michael J.
Published: February 2025

In this paper we seek to formally establish the similarities and differences between two formalizations of defeasible reasoning: the defeasible logics of Nute and Maier, and defeasible logics in the framework of Antoniou et al. Both families of logics have developed from earlier logics of Nute, but their development has followed different paths and they are formulated very differently. We examine these logics from the standpoint of relative inference strength – how much the logics can infer from a given theory – and relative expressiveness – how well one logic can simulate another. We identify similarities between logics in the two families and pinpoint aspects that distinguish them.
Context-free and Context-sensitive Kernels: Update and Deletion Equivalence in abstract Argumentation
Authors: Baumann Ringo
Published: February 2025

Notions of equivalence which guarantee intersubstitutability w.r.t. further modifications have received considerable interest in nonmonotonic reasoning. This paper is within the context of abstract argumentation and we focus on the most general form of a dynamic scenarios, so-called updates as well as certain sub-classes, namely local, normal and arbitrary deletions. We provide characterization theorems for the corresponding equivalence notions and draw the relations to the recently proposed kinds of expansion equivalence [15, 3]. Many of the results rely on abstract concepts like context-free kernels or semantics satisfying isolate-inclusion. Therefore, the results may apply to future semantics as well as further equivalence notions.
A Shapley Value-based Approach to Determine Gatekeepers in Social Networks with Applications
Authors: Narayanam Ramasuri  , Skibski Oskar  , Lamba Hemank  , Michalak Tomasz
Published: February 2025

Inspired by emerging applications of social networks, we introduce in this paper a new centrality measure termed gate-keeper centrality. The new centrality is based on the well-known game-theoretic concept of Shapley value and, as we demonstrate, possesses unique qualities compared to the existing metrics. Furthermore, we present a dedicated approximate algorithm, based on the Monte Carlo sampling method, to compute the gatekeeper centrality. We also consider two well known applications in social network analysis, namely community detection and limiting the spread of mis-information; and show the merit of using the proposed framework to solve these two problems in comparison with the respective benchmark algorithms.
Integrating BDI Agents into a MATSim Simulation
Authors: Padgham Lin  , Nagel Kai  , Singh Dhirendra  , Chen Qingyu
Published: February 2025

MATSim is a mature and powerful traffic simulator, used for large scale traffic simulations, primarily to assess likely results of various infrastructure or road network changes. More recently there has been work to extend MATSim to allow its use in applications requiring what has been referred to as “within day replanning”. In the work described here we have coupled MATSim with a BDI (Belief Desire Intention) system to allow both more extensive modelling of the agent's decision making, as well as reactivity to environmental situations. The approach used allows for all agents to be “intelligent” or for some to be “intelligent”/reactive, while others operate according to plans that are static within a single day. The former is appropriate for simulations such as a bushfire evacuation, where all agents will be reacting to the changing environment. The latter is suited to introducing agents such as taxis into a standard MATSim simulation, as they cannot realistically have a predetermined plan, but must constantly respond to the current situation. We have prototype applications for both bushfire evacuation and taxis. By extending the capabilities of MATSim to allow agents to respond intelligently to changes in the environment, we facilitate the use of MATSim in a wide range of simulation applications. The work also opens the way for MATSim to be used alongside other simulation components, in a simulation integrating multiple components.
Syntactic Possibilistic Goal Generation
Authors: da Costa Pereira Célia  , Tettamanzi Andrea G.B.
Published: February 2025

We propose syntactic deliberation and goal election algorithms for possibilistic agents which are able to deal with incomplete and imprecise information in a dynamic world. We show that the proposed algorithms are equivalent to their semantic counterparts already presented in the literature. We show that they lead to an efficient implementation of a possibilistic BDI model of agency which integrates goal generation.
On support relations in abstract argumentation as abstractions of inferential relations
Authors: Prakken Henry
Published: February 2025

Arguably the significance of an abstract model of argumentation depends on the range of realistic instantiations it allows. This paper therefore investigates for three frameworks for abstract argumentation with support relations whether they can be instantiated with the ASPIC+framework for structured argumentation. Both evidential argumentation systems and a simple extension of Dung's abstract frameworks with support relations proposed by Dung & Thang (2014) are shown to allow such an instantiation. However, for bipolar argumentation frameworks a positive result is only obtained for variants with only direct and secondary attacks; counterexamples are provided for variants with supported attacks, even for the special case of deductive support.
Scoring Rules for the Allocation of Indivisible Goods
Authors: Baumeister Dorothea  , Bouveret Sylvain  , Lang Jérôme  , Nguyen Nhan-Tam  , Nguyen Trung Thanh  , Rothe Jörg
Published: February 2025

We define a family of rules for dividing m indivisible goods among agents, parameterized by a scoring vector and a social welfare aggregation function. We assume that agents' preferences over sets of goods are additive, but that the input is ordinal: each agent simply ranks single goods. Similarly to (positional) scoring rules in voting, a scoring vector s = (s1,...,sm) consists of m nonincreasing nonnegative weights, where siis the score of a good assigned to an agent who ranks it in position i. The global score of an allocation for an agent is the sum of the scores of the goods assigned to her. The social welfare of an allocation is the aggregation of the scores of all agents, for some aggregation function ★ such as, typically, + or min. The rule associated with s and ★ maps a profile to (one of) the allocation(s) maximizing social welfare. After defining this family of rules, and focusing on some key examples, we investigate some of the social-choice-theoretic properties of this family of rules, such as various kinds of monotonicity, separability, envy-freeness, and Pareto efficiency.
Optimal Planning in the Presence of Conditional Effects: Extending LM-Cut with Context Splitting
Authors: Röger Gabriele  , Pommerening Florian  , Helmert Malte
Published: February 2025

The LM-Cut heuristic is currently the most successful heuristic in optimal STRIPS planning but it cannot be applied in the presence of conditional effects. Keyder, Hoffmann and Haslum recently showed that the obvious extensions to such effects ruin the nice theoretical properties of LM-Cut. We propose a new method based on context splitting that preserves these properties.
Knowledge-based Specification of Robot Motions
Authors: Tenorth Moritz  , Bartels Georg  , Beetz Michael
Published: February 2025

In many cases, the success of a manipulation action performed by a robot is determined by how it is executed and by how the robot moves during the action. Examples are tasks such as unscrewing a bolt, pouring liquids and flipping a pancake. This aspect is often abstracted away in AI planning and action languages that assume that an action is successful as long as all preconditions are fulfilled. In this paper we investigate how constraint-based motion representations used in robot control can be combined with a semantic knowledge base in order to let a robot reason about movements and to automatically generate executable motion descriptions that can be adapted to different robots, objects and tools.
Modelling the Dynamics of Emotional Awareness
Authors: Thilakarathne Dilhan J.  , Treur Jan
Published: February 2025

In this paper, based on literature from Cognitive and Affective Neuroscience, a computational agent model is introduced incorporating the role of emotional awareness states in the dynamics of action generation. More specifically, it covers both automatic, unconscious (bottom-up) and more cognitive and conscious (top-down) emotion generation processes, and their mutual interaction. The model was formalised in a dynamical system format. In different scenarios the model shows simulation results that are in line with patterns reported in literature.
Using Ensemble Techniques and Multi-Objectivization to Solve Reinforcement Learning Problems
Authors: Brys Tim  , Taylor Matthew E.  , Nowé Ann
Published: February 2025

Recent work on multi-objectivization has shown how a single-objective reinforcement learning problem can be turned into a multi-objective problem with correlated objectives, by providing multiple reward shaping functions. The information contained in these correlated objectives can be exploited to solve the base, single-objective problem faster and better, given techniques specifically aimed at handling such correlated objectives. In this paper, we identify ensemble techniques as a set of methods that is suitable to solve multi-objectivized reinforcement learning problems. We empirically demonstrate their use on the Pursuit domain.
On the Extension of Learning for Max-SAT
Authors: Abramé André  , Habet Djamal
Published: February 2025

One of the most critical components of Branch & Bound (BnB) solvers for Max-SAT is the estimation of the lower bound. At each node of the search tree, they detect inconsistent subsets (IS) of the formula by unit propagation based methods and apply a treatment to them. The currently best performing Max-SAT BnB solvers perform a very little amount of memorization, thus the same IS may be detected and treated several times during the exploration of the search tree. We address in this paper the problem of increasing the learning performed by BnB solvers. We present new sets of clause patterns which produce unit resolvent clauses when they are transformed by max-resolution. We study experimentally the impact of these transformation' memorization in our solver AHMAXSAT and we discuss their effects on the solver behavior.
Effective and Efficient Identification of Persistent-state Hidden (semi-) Markov Models
Authors: Liu Tingting  , Lemeire Jan
Published: February 2025

The predominant learning strategy for H(S)MMs is local search heuristics, of which the Baum-Welch/ expectation maximization (EM) algorithm is mostly used. It is an iterative learning procedure starting with a predefined topology and randomly-chosen initial parameters. However, state-of-the-art approaches based on arbitrarily defined state numbers and parameters can cause the risk of falling into a local optima and a low convergence speed with enormous number of iterations in learning which is computationally expensive. For models with persistent states, i.e. states with high self-transition probabilities, we propose a segmentation-based identification approach used as a pre-identification step to approximately estimate parameters based on segmentation and clustering techniques. The identified parameters serve as input of the Baum-Welch algorithm. Moreover, the proposed approach identifies automatically the state numbers. Experimental results conducted on both synthetic and real data show that the segmentation-based identification approach can identify H(S)MMs more accurately and faster than the current Baum-Welch algorithm.
Extension–Based Semantics of Abstract Dialectical Frameworks
Authors: Polberg Sylwia
Published: February 2025

One of the most prominent tools for abstract argumentation is the Dung's framework, AF for short. Although powerful, AFs have their shortcomings, which led to development of numerous enrichments. Among the most general ones are the abstract dialectical frameworks, also known as the ADFs. They make use of the so-called acceptance conditions to represent arbitrary relations. This level of abstraction brings not only new challenges, but also requires addressing existing problems in the field. One of the most controversial issues, recognized not only in argumentation, concerns the support or positive dependency cycles. In this paper we introduce a new method to ensure acyclicity of arguments and present a family of extension-based semantics built on it, along with their classification w.r.t. cycles. Finally, we provide ADF versions of the properties known from the Dung setting.
On Evaluating Interestingness Measures for Closed Itemsets
Authors: Buzmakov Aleksey  , Kuznetsov Sergei  , Napoli Amedeo
Published: February 2025

There are a lot of measures for selecting interesting itemsets. But which one is better? In this paper we introduce a methodology for evaluating interestingness measures. This methodology relies on supervised classification. It allows us to avoid experts and artificial datasets in the evaluation process. We apply our methodology to evaluate promising measures for itemset selection, such as leverage and stability. We show that although there is no evident winner between them, stability has a slightly better performance.
Epidemiological Indexes in Healthcare based on Social Cost Factors
Authors: Zimeras Stelios  , Diomidous Marianna  , Kefaliakos Antonis  , Zogas Spiros  , Al-Fantel Konstantina  , Mechili Aggelos
Published: February 2025

Hospital healthcare the last dictate becomes an important subject for analysis which includes factors such as epidemiological indexes and many others like Surgical, Economic, Organizational, Structural issues necessary for the overall evaluation of the general hospital performance. The social cost includes the private cost, the public expenditures and the external cost affecting the society at large from an incoming event, a current action and / or a policy change. For that reason, the calculation of these social costs over time (t) could be defined as a social measure to dictate the variability of social indicators on society. In this study epidemiological factor based on the social cost maybe introduced with the main goal being the evaluation of the hospital healthcare performance.
Improving Real-Time Efficiency of Case-Based Reasoning for Medical Diagnosis
Authors: Park Yoon-Joo
Published: February 2025

Conventional case-based reasoning (CBR) does not perform efficiently for high volume dataset because of case-retrieval time. Some previous researches overcome this problem by clustering a case-base into several small groups, and retrieve neighbors within a corresponding group to a target case. However, this approach generally produces less accurate predictive performances than the conventional CBR. This paper suggests a new case-based reasoning method called the Clustering-Merging CBR (CM-CBR) which produces similar level of predictive performances than the conventional CBR with spending significantly less computational cost.
The Influence of Similarity between Concepts in Evolving Biomedical Ontologies for Mapping Adaptation
Authors: Dos Reis Julio Cesar  , Dinh Duy  , Pruski Cédric  , Da Silveira Marcos  , Reynaud-Delaître Chantal
Published: February 2025

Biomedical ontologies continuously evolve which demands maintain associated mappings up-to-date. This article studies whether similarity calculated between values of concept attributes issued from successive ontology versions plays a role in deciding mapping adaptation actions. We empirically analyse the evolution of official mappings established between large biomedical ontologies. The results point out the relevance of this factor for mapping adaptation.
What's in a Class? Lessons Learnt from the ICD – SNOMED CT Harmonisation
Authors: Schulz Stefan  , Rodrigues Jean-M.  , Rector Alan  , Spackman Kent  , Campbell James  , Üstün Bedirhan  , Chute Christopher G.  , Solbrig Harold  , Della Mea Vincenzo  , Millar Jane  , Brand Persson Kristina
Published: February 2025

The upcoming ICD-11 will be harmonized with SNOMED CT via a common ontological layer (CO). We provide evidence for our hypothesis that this cannot be appropriately done by simple ontology alignment, due to diverging ontological commitment between the two terminology systems. Whereas the common ontology describes clinical situations, ICD-11 linearization codes are best to be interpreted as diagnostic statements. For the binding between ICD codes and classes from the ontological layer, a query-based approach is favoured.
Globalising Health Informatics: The Role of GIScience
Authors: Robertson Hamish  , Nicholas Nick  , Georgiou Andrew  , Johnson Julie  , Travaglia Joanne
Published: February 2025

Health systems globally are undergoing significant changes. New systems are emerging in developing countries where there were previously limited healthcare options, existing systems in emerging and developed economies are under significant resource pressures and population dynamics are creating significant pressures for change. As health systems expand and intensify, information quality and timeliness will be central to their sustainability and continuity. Information collection and transfer across diverse systems and international borders already presents a significant challenge for health system operations and logistics. Geographic information science (giscience) has the potential to support and enhance health informatics in the coming decades as health information transfers become increasingly important. In this article we propose a spatially enabled approach to support and increasingly globalised health informatics environment. In a world where populations are ageing and urbanising and health systems are linked to economic and social policy shifts, knowing where patients, diseases, health care workers and facilities are located becomes central to those systems operational capacities. In this globalising environment, health informatics needs to be spatially enabled informatics.
Trust-based information system architecture for personal wellness
Authors: Ruotsalainen Pekka  , Nykänen Pirkko  , Seppälä Antto  , Blobel Bernd
Published: February 2025

Modern eHealth, ubiquitous health and personal wellness systems take place in an unsecure and ubiquitous information space where no predefined trust occurs. This paper presents novel information model and an architecture for trust based privacy management of personal health and wellness information in ubiquitous environment. The architecture enables a person to calculate a dynamic and context-aware trust value for each service provider, and using it to design personal privacy policies for trustworthy use of health and wellness services. For trust calculation a novel set of measurable context-aware and health information-sensitive attributes is developed. The architecture enables a person to manage his or her privacy in ubiquitous environment by formulating context-aware and service provider specific policies. Focus groups and information modelling was used for developing a wellness information model. System analysis method based on sequential steps that enable to combine results of analysis of privacy and trust concerns and the selection of trust and privacy services was used for development of the information system architecture. Its services (e.g. trust calculation, decision support, policy management and policy binding services) and developed attributes enable a person to define situation-aware policies that regulate the way his or her wellness and health information is processed.
A Reference Data Model of a Metadata Registry Preserving Semantics and Representations of Data Elements
Authors: Löpprich Martin  , Jones Jennifer  , Meinecke Marie-Claire  , Goldschmidt Hartmut  , Knaup Petra
Published: February 2025

Integration and analysis of clinical data collected in multiple data sources over a long period of time is a major challenge even when data warehouses and metadata registries are used. Since most metadata registries focus on describing data elements to establish domain consistent data definition and providing item libraries, hierarchical and temporal dependencies cannot be mapped. Therefore we developed and validated a reference data model, based on ISO/IEC 11179, which allows revision and branching control of conceptually similar data elements with heterogeneous definitions and representations.
Separation of Personal Data in a Biobank Information System
Authors: Müller Thomas H.  , Thasler Reinhard
Published: February 2025

Separation of different types of personal data has been introduced as an effective measure to improve data protection in the context of medical research. In particular, research associated with human biomaterials requires not only secure technologies but also trustworthy processing of personal data on a need-to-know basis. Web-based information systems make use of a technological infrastructure that is well suited to distributed data repositories and remote processing systems. This approach was successfully applied to develop an information system supporting acquisition, processing and storage of remnant biomaterial from surgical treatment, as well as its allocation to research projects. In order to enhance data protection, the contents of the originally unified database were divided into identification data and medical data. A web application was created for each part and appropriate functionality to maintain and access corresponding data was developed. It is concluded that a distribution of biobanking data across separate databases can be achieved if workflows and staff roles are redesigned accordingly.
HTML5 Microdata as a semantic container for Medical Information Exchange
Authors: Kimura Eizen  , Kobayashi Shinji  , Ishihara Ken
Published: February 2025

Achieving interoperability between clinical electronic medical records (EMR) systems and cloud computing systems is challenging because of the lack of a universal reference method as a standard for information exchange with a secure connection. Here we describe an information exchange scheme using HTML5 microdata, where the standard semantic container is an HTML document. We embed HL7 messages describing laboratory test results in the microdata. We also annotate items in the clinical research report with the microdata. We mapped the laboratory test result data into the clinical research report using an HL7 selector specified in the microdata. This scheme can provide secure cooperation between the cloud-based service and the EMR system.
Adaptive Healthcare Processes for Personalized Emergency Clinical Pathways
Authors: Poulymenopoulou M.  , Papakonstantinou D.  , Malamateniou F.  , Vassilacopoulos G.
Published: February 2025

Pre-hospital and in-hospital emergency healthcare delivery involves a variety of activities and people that should be coordinated in order effectively to create an emergency care plan. Emergency care provided by emergency healthcare professionals can be improved by personalized emergency clinical pathways that are instances of relevant emergency clinical guidelines based on emergency case needs as well as on ambulance and hospital resource availability, while also enabling better resource use. Business Process Management Systems (BPMSs) in conjunction with semantic technologies can be used to support personalized emergency clinical pathways by incorporating clinical guidelines logic into the emergency healthcare processes at run-time according to emergency care context information (current emergency case and resource information). On these grounds, a framework is proposed that uses ontology to model knowledge on emergency case medical history, on healthcare resource availability, on relevant clinical guidelines and on process logic; this is inferred to result in the most suitable process model for the case, in line with relevant clinical guidelines.
Examination of Centrality in a Health Social Network
Authors: Alaşan Semiha N.  , Sayin Kamran E.  , Aydin Mehmet N.
Published: February 2025

Growing importance of health information platforms are acknowledges in recent studies. Such platforms are subject to discussion about an extent to which social network characteristics are realized. The platform under examination indeed demonstrates social network peculiarities. In this work, we explore the nature of centrality in one of the leading health information networks in Europe. Among other findings, we identify two nodes (representing patient and physician) are as the most important people in the network in terms of structural analysis Egregiously, these nodes are connected with the other types only and exhibit worth noticing connection patterns. These connections have been discussed along with a medication advice seeking behavior.
Assistive System for People with Apraxia Using A Markov Decision Process
Authors: Jean-Baptiste Emilie M.D.  , Russell Martin  , Rothstein Pia
Published: February 2025

CogWatch is an assistive system to re-train stroke survivors suffering from Apraxia or Action Disorganization Syndrome (AADS) to complete activities of daily living (ADLs). This paper describes the approach to real-time planning based on a Markov Decision Process (MDP), and demonstrates its ability to improve task's performance via user simulation. The paper concludes with a discussion of the remaining challenges and future enhancements.
The use of DRG for Identifying Clinical Trials Centers with High Recruitment Potential: a Feasability Study
Authors: Aegerter Philippe  , Bendersky Noelle  , Tran Thi-Chien  , Ropers Jacques  , Taright Namik  , Chatellier Gilles
Published: February 2025

Recruitment of large samples of patients is crucial for evidence level and efficacy of clinical trials (CT). Clinical Trial Recruitment Support Systems (CTRSS) used to estimate patient recruitment are generally specific to Hospital Information Systems and few were evaluated on a large number of trials. Our aim was to assess, on a large number of CT, the usefulness of commonly available data as Diagnosis Related Groups (DRG) databases in order to estimate potential recruitment. We used the DRG database of a large French multicenter medical institution (1.2 million inpatient stays and 400 new trials each year). Eligibility criteria of protocols were broken down into in atomic entities (diagnosis, procedures, treatments…) then translated into codes and operators recorded in a standardized form. A program parsed the forms and generated requests on the DRG database. A large majority of selection criteria could be coded and final estimations of number of eligible patients were close to observed ones (median difference = 25). Such a system could be part of the feasability evaluation and center selection process before the start of the clinical trial.
Participatory Heuristic Evaluation of a Tablet Computer System for Clinical Microbiology
Authors: Samson Lasse Lefevre  , Pape-Haugaard Louise Bilenberg  , Søgaard Mette  , Schønheyder Henrik Carl  , Hejlesen Ole K.
Published: February 2025

MultiplexBCT is a molecular-based diagnostic test for rapid identification of microorganisms in positive blood cultures. A MultiplexBCT Android tablet computer application is being developed as an accessory to the diagnostic test. The aim of the application is to facility the end user's workflow by supporting data entry and communication of the MultiplexBCT test results in a hospital environment. This paper reports the results and benefit of a participatory heuristic evaluation conducted on the MultiplexBCT application. The design is an extension of heuristic evaluation that includes end users as work-domain experts to complement usability experts for inspection of a user interface. The user interface assessment identified 86 heuristic violations, which consisted of both product- and domain-related issues. These results will be used to guide further development of the MultiplexBCT application to ensure that the system will fully support the workflow of the end users when using the MultiplexBCT diagnostic test.
EHR and Usability of Health Data to benefit Patient and Public Health
Authors: Brighi Raffaella  , Virone Maria Gabriella
Published: February 2025

Digitalized information in health sector can improve primary care quality and facilitate public health researches to benefit society. The state of the art encourages the development of ICT solutions for a greater usability of de-identified health data. Several governments are implementing effective strategies such as Open Government Data and Linked Open Data in order to access public data and promote new economic activities. Legal and technical analyses emphasize the urgent need for a deeper focus on harmonization such that health data can actually empower patient and scientific research. The mentioned goal is possible if three conditions are achieved: harmonization of law, harmonization of legal and technical rules and harmonization of Electronic Health Record system design.
A Knowledge-Based Decision Making Model to Support the Analysis Phase in Software Maintainance
Authors: Kamaludeen Rahma A.  , Cheah Yu.-N.  , Sulaiman Shahida
Published: February 2025

Many software maintenance decisions are made during the lifetime of a software application. These decisions are important in order to keep the software application relevant to the user's needs. However, the expert maintenance knowledge inherent in a software expert needs to be documented and updated all the time to be used in case the software expert is absence and also as future reference. The uniqueness of this study relies on the ability of a knowledge engineering method to capture the software maintenance knowledge of a software application that is inherent in the software expert's mind. This is crucial especially to support software maintenance decision making in the analysis phase of software maintenance. This paper proposes a knowledge management decision making model that can preserve the maintenance knowledge of a software application to support software maintenance decision making.
Hybrid Genetic Algorithm of Interferometric Synthetic Aperture Radar For Three-Dimensional Coastal Deformation
Authors: Marghany Maged
Published: February 2025

The paper has demonstrated InSAR phase unwrapping using Hybrid Genetic Algorithm (GHA). The three dimensional phase unwrapping is performed using three-dimensional best-path avoiding singularity loops (3DBPASL) algorithm. Then phase matching is implemented with 3DBPASL using Hybrid Genetic Algorithm (GHA). Further, the combination between GHA and 3DBPASL is used to eliminate the phase decorrelation impact from the interferograms. The study shows that InSAR produces discontinues interferogram pattern because of the high decorelation. On the contrary, the three-dimensional sorting reliabilities algorithm generated 3-D coastline deformation with bias of −0.06 m, lower than ground measurements and the InSAR method. Therefore,3DBPASL algorithm has a standard error of mean of ± 0.03 m, lower than ground measurements and the InSAR method. Consequently, the 3D-SRA is used to eliminate the phase decorrelation impact from the interferograms. The study also shows the performance of InSAR method using the combination of HGA and 3DBPASL is better than InSAR procedure which is validated by a lower range of error (0.04±0.22 m) with 90% confidence intervals. In conclusion, HGA algorithm can be used to solve the problem of decorrelation and produced accurate 3-D coastline deformation using ENVISAT ASAR data.
Improving Consistency in Fuzzy Preference Relations with an Allocation of Information Granularity
Authors: Cabrerizo Francisco Javier  , Pedrycz Witold  , Chiclana Francisco  , Herrera-Viedma Enrique
Published: February 2025

An important issue to bear in mind in Group Decision Making situations is that of consistency. However, the expression of consistent preferences is often a very difficult task for the decision makers, specially in decision problems with a high number of alternatives and when decision makers use fuzzy preference relations to provide their opinions. It leads to situations where a decision maker may not be able to express all his/her preferences properly and without contradiction. To overcome this problem, we propose the concept of the information granularity being regarded as an important and useful asset supporting the goal to reach consistent fuzzy preference relations. To do so, we develop a concept of granular fuzzy preference relation where each pairwise comparison is formed as a certain information granule instead of a single numeric value. As being more abstract, the granular format of the preference model offers the required flexibility to increase the level of consistency.
Adapted Indoor Positioning Model Based on Dynamic WLAN Fingerprinting RadioMap
Authors: Halshami Iyad  , Ahmad Noor Azurati  , Sahibuddin Shamsul
Published: February 2025

The mobile phone market increased dramatically in the last few years. As result there is a sharp growth in demand for indoor environment computing especially for Location Based Services (LBS). The basic concept of LBS is to determine the mobile users' location, which is important for services such as tracking or navigation in Civil defense; Healthcare; Marketing and Management. Currently, there are many techniques used to find the location of a mobile user in indoor environment. WLAN technique is considered as one of the best choices for indoor positioning due to its low cost, simple configuration and high accuracy. Although the WLAN Received Signal Strength Indicator (RSSI) fingerprinting method is the most accurate positioning method, it has a serious drawback because its Radio Map (RM) become outdated when environmental change occurs. In addition, recalibrating the RM is a time consuming process. This paper presents a novel adapted indoor positioning model which uses the path loss propagation model of the wireless signal to overcome the outdated RM. The experimental results demonstrate that the proposed adapted model is highly efficient in solving the problems mentioned especially in a dynamically changing environment.
Intelligent Synchronization Tool Using ANFIS for Multi Robot Manipulators
Authors: Esmaili Parvaneh  , Haron Habibollah
Published: February 2025

A new intelligent synchronization tool is developed on multi robot manipulators to handle an object in the desired trajectory. The intelligent synchronization tool is based on the ANFIS structure, which tries to compensate synchronization error between robot manipulators. To overcome lumped uncertainty of the robot manipulator a neural network based prediction mode and modified sliding mode control are used. The examples as illustrated can guarantee the robustness, accuracy and decentralized feature for multi robot manipulator system.
Application Engines in VMVC-based Tsunami Modeling Environment
Authors: Hayashi Kensaku  , Vazhenin Alexander  , Marchuk Andrey
Published: February 2025

Currently, Service-Oriented Architecture (SOA) may be considered as a state of the art approach for the complex software design and implementation because of high-level of operability and reusability of system components. Accordingly, we are applying the SOA-patterns to the creating the Tsunami Wave Propagation Modeling Environment because of complexity and versatility of tsunami modeling methods and tools. Our approach is followed an original Virtual Model-View-Controller pattern (VMVC) that is an adaptation of the traditional MVC to SOA. It is demarcating a Functional (View) and an Implementation (Model) task by inducing an Integrator (Controller) that encapsulates non-functional activities such as security, reliability, scalability, and routing. The presented paper is devoted to developing the Tsunami Modeling System components named Application Engines that are functioning at the Model layer. Accordingly, we are distinguishing the following main application engines: Tsunami Wave Propagation/Inundation, and Impaction Engines each of which reflects a corresponding stage of modeling process (tsunami wave propagation, inundation and impaction). We are also focusing on the developing the Tsunami Visualizing Engine (TVE) allowing to transform the digital results of modeling to the human-centered data representation.
AVTAC : A Framework for Automatic Auditing of Access Control in Windows and Linux Systems
Authors: Arrachid Khadija  , Mejri Mohamed  , Sadio Etienne Theodore
Published: February 2025

Access control validation (also called conformity or compliance) consists in verifying at a regular basis whether the implemented access control rules are consistent and complete with respect to a given security policy or not. The compliance of some critical accesses needs to be monthly confirmed while other less critical assesses might me yearly analyzed. The result of the analysis is a detailed report that could be shown to an inspector. All important security referentials such as ISO 27001, COBIT, NIST 800-53 and HIPA recommend that conformity analysis should be done at regular intervals. For a small company, it would be possible to manually validate the access control policy. However for companies with thousands of employees, we need the help of automatic tools to accomplish this task.
SCLP Software for Thermal Process Control of Nanowire Fabrication and Multilayer Nanochip System
Authors: Alias Norma  , Satam Noriza  , Othman Mohd Shahizan  , Che Teh Che Rahim  , Mustaffa Maizatul Nadirah  , Saipol Hafizah Farhah Saipan
Published: February 2025

This paper presents the high performance software to enables scientific researchers for predicting, visualizing and observing the temperature behavior and some parameter characteristics of nanoscale multilayer full chip system and nanowire fabrication. The analysis of scientific algorithms and high performance computing are searchable through a user friendly web-based system. This software will involve some mathematical modeling, numerical simulations and high performance computing technology to improve the accuracy of prediction, visualization quality and parallel performance indicators. The parameters characterization of multilayer full chip system and nanowire fabrication will focus on temperature behavior, electromagnetic wave motion, density distribution, heat spreader and heat sink. The implementations of parallel algorithms for solving the Partial Differential Equation for heat transfer and wave motion problems are based on large sparse parabolic and elliptic types. The discretization technique to obtain a large sparse linear system of equations is based on Finite Difference Method. The SCLP software will support the high performance computing of numerical simulations and its repository using distributed memory architecture. The performance indicators will investigate the parallel programs using Parallel Virtual Machine (PVM) and C language on Linux operating system in terms of run time, accuracy, convergence, errors, speedup, efficiency and effectiveness. As a conclusion, high performance software will be an alternative software system to support huge computational complexity of multilayer nanochip system and nanowire fabrication model.
Towards Domain Ontology Interoperability Measurement
Authors: Sseggujja Hussein  , Selamat Ali
Published: February 2025

The nature of ontologies supports their usage as interoperability support components between information systems. However the need to make ontologies themselves interoperable remains high. To address this situation, there is need to study the semantic heterogenity between the ontologies. This paper specifically looks at domain ontologies and how to measure the interoperability degree between them to establish the extent to which they can replace each other. Different interoperability operations between ontologies are discussed together with the measures that define semantic distance and lexical similarity between the ontologies. A method based on model management theory that enables use of algebraic operations such as match on the ontology models is proposed to measure lexical and structural dimensions of domain ontologies to give a value for their degree of interoperabilty. An example of how to compute the degree of interoperability between two domain ontologies using the proposed approach is given with an explanation of how the identified gaps can be addressed.
Query Failure Explanation in Inconsistent Knowledge Bases Using Argumentation
Authors: Arioua Abdallah  , Tamani Nouredine  , Croitoru Madalina  , Buche Patrice
Published: February 2025

We address the problem of explaining Boolean Conjunctive Query (BCQ) failure in the presence of inconsistency within the Ontology-Based Data Access (OBDA) setting, where inconsistency is handled by the intersection of closed repairs semantics (ICR) and the ontology is represented by Datalog+/− rules. Our proposal relies on an interactive and argumentative approach where the processes of explanation takes the form of a dialogue between the User and the Reasoner. We exploit the equivalence between argumentation and ICR-semantics to prove that the Reasoner can always provide an answer for user's questions.
Generalising argument dialogue with the Dialogue Game Execution Platform
Authors: Bex Floris  , Lawrence John  , Reed Chris
Published: February 2025

In this paper, we present the Dialogue Game Execution Platform (DGEP), which is able to process and execute any dialogue game specified in a general description language and build arguments and dialogue histories in the language of the AIF ontology. Thus, DGEP allows us to generalise techniques for generation and investigation of dialogues and, through a set of web services, connect a wide variety of multi-agent systems and human-computer interfaces for dialogue.