bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of a Critical Care Clinical Information System on Interruption Rates During Intensive Care Nurse and Physician Documentation Tasks
Authors: Ballermann Mark A.  , Shaw Nicola T.  , Arbeau Kelly J.  , Mayes Damon C.  , Noel Gibney R.T.
Published: February 2025

Computerized documentation methods in Intensive Care Units (ICUs) may assist Health Care Providers (HCP) with their documentation workload, but evaluating impacts remains problematic. A Critical Care clinical Information System (CCIS) is an electronic charting tool designed for ICUs that may fit seamlessly into HCP work. Observers followed ICU nurses and physicians in two ICUs in Edmonton, Canada, in which a CCIS had recently been introduced. Observers recorded amounts of time HCPs spent on documentation related tasks, interruptions encountered by HCPs, and contextual information in field notes. Interruption rates varied depending on the charting medium used, with physicians being interrupted less frequently when performing documentation tasks using the CCIS, than when performing documentation tasks using other methods. In contrast, nurses were interrupted more frequently when charting using the CCIS than when using other methods. Interruption rates coupled with qualitative observations suggest that physicians utilize strategies to avoid interruptions if interfaces for entering textual notes are not well adapted to interruption-rich environments such as ICUs. Potential improvements are discussed such that systems like the CCIS may better integrate into ICU work.
Conceptualization of an Electronic System for Documentation of Nursing Diagnosis, Outcomes, and Intervention
Authors: Peres Heloisa Helena Ciqueto  , da Cruz Diná de Almeida Lopes Monteiro  , Lima Antônio Fernandes Costa  , Gaidzinski Raquel Rapone  , Ortiz Diley Cardoso Franco  , Mendes e Trindade Michelle  , Tsukamoto Rosangela  , Batista de Oliveira Neurilene
Published: February 2025

Electronic nursing documentation constitutes technical, scientific, legal, and ethical documents. The objective of this study was to develop an electronic nursing documentation system. The system was developed in four phases (conceptualization, detailing, prototype building, implementation), and the knowledge base was based on domains and classes according to the NANDA-I, NIC, and NOC unified framework. The result is an electronic system (PROCEnf – USP – Nursing Process Electronic Documentation System of the University of São Paulo) which allows documenting nursing process generating reports of nursing process, besides supporting decisions on nursing diagnosis, expected outcomes, and interventions. Integration of different fields of knowledge, as well as the institutional feature of valuing continuous theoretical and practical improvement of nursing process were factors of success of this technological project.
The Evolution and Uptake of a Drug Information System: the Case of a Small Canadian Province
Authors: Mensink Naomi  , Paterson Grace
Published: February 2025

In 2008 the province of PEI, Canada implemented a province-wide, web-based drug information system for the purpose of improving patient safety. An evaluation study using grounded theory examined the human and workflow impact. Results indicated a need for great attention to the details of change management during implementation, including: ensuring application quality of all informational and technical elements, just-in-time training and technical support, on-site preparation for changed workflow processes, and collaboration among all stakeholders throughout.
Standardizing Implementation of a Surgical Information System in Danish Hospitals – A Comparative Study
Authors: Lawton Kitta  , Holdt Marianne  , Kopke Pia  , Sigurðardóttir Hrönn
Published: February 2025

Implementation of IT-systems in modern healthcare organizations is associated with large, complex, and expensive projects. Purchase of the system is costly, but resources used to implement the organizational changes that follow, can be extensive. In an attempt to reduce costs, and at the same time to provide a thorough basis for local implementation, Corporate IT in The Capital Region of Denmark developed a standardized system-specific implementation concept for use by the hospitals' local implementations of a Surgical Information system. The system has been implemented in five hospitals within the Capital Region. Through document analysis and interviews with the local project managers, we investigated the use and effectiveness of the standardized implementation concept across five hospitals involved. The study shows that total resource requirements and duration of projects are difficult to compare due to different constructions of the project organizations. We conclude that the implementation concept supports local IT-implementations, but parts of the concept are difficult to translate into practice, while other parts are directly operational.
Experience Implementing Electronic Health Records in Three East African Countries
Authors: Tierney William M.  , Achieng Marion  , Baker Elaine  , Bell April  , Biondich Paul  , Braitstein Paula  , Kayiwa Daniel  , Kimaiyo Sylvester  , Mamlin Burke  , McKown Brian  , Musinguzi Nicholas  , Nyandiko Winstone  , Rotich Joseph  , Sidle John  , Siika Abraham  , Were Martin  , Wolfe Ben  , Wools-Kaloustian Kara  , Yeung Ada  , Yiannoutsos Constantin  , the Tanzania-Uganda OpenMRS Consortium
Published: February 2025

Introduction: Efficient use of health care resources in low-income countries by providers and local and national managers requires timely access to patient data. Objective: To implement electronic health records (EHRs) in HIV clinics in Kenya, Tanzania, and Uganda. Results: We initially developed and implemented an EHR in Kenya through a mature academic partnership. The EHR was then implemented in six HIV clinics in Tanzania and Uganda in collaboration with their National AIDS Control Programmes. All implementations were successful, but the system's use and sustainability varied depending on who controlled clinic funding. Conclusions: Successful EHR use and sustainability were enhanced by local control of funds, academic partnerships (mainly by leveraging research funds), and in-country technology support.
Traffic Accidents in Crete (1996-2006): the Role of the Emergency Coordination Center
Authors: Vourvahakis Dimitris  , Chronaki Catherine E.  , Kontoyiannis Vasilis  , Panagopoulos Demosthenis  , Stergiopoulos Spyros
Published: February 2025

The general decline in traffic accidents throughout Europe is not the case for Crete, a favorite holiday destination. The extent of problem and reflections on the significant impact of the interplay of organizational, educational, & technological interventions by the Emergency Coordination Center of Crete (ECC-Crete) are presented. 10-year data from 1996-2006 have been analyzed revealing demographic, topological, and qualitative issues of traffic accidents in Grete. Primary source of data is 315000 emergency calls answered by ECC-Crete. Over this 10 year period, ECC-Crete gradually employed advanced medical technologies and electronic protocol-based handling in all phases of an emergency episode contributing to its timely and effective management. GIS/GPS technology and telemetry for biosignals in ambulances, up-to-date triage protocols combined with incidence analysis provide vital information for continuous process improvement. In 2000-2006, process improvement due to technological and organizational changes has led to increased efficiency. The mean reduction was ~75% in dispatch time, ~50% in the time at accident scene for metropolitan areas, and ~75% in time at the emergency ward, mainly due to medical interventions on site.
Investigating health information needs of community radio stations and applying the World Wide Web to disseminate audio products
Authors: Snyders Janus  , van Wyk Elmarie  , van Zyl Hendra
Published: February 2025

The Web and Media Technologies Platform (WMTP) of the South African Medical Research Council (MRC) conducted a pilot project amongst community radio stations in South Africa. Based on previous research done in Africa WMTP investigated the following research question: How reliable is the content of health information broadcast by community radio stations? The main objectives of the project were to determine the 1) intervals of health slots on community radio stations, 2) sources used by community radio stations for health slots, 3) type of audio products needed for health slots, and 4) to develop a user friendly Web site in response to the stations' needs for easy access to audio material on health information.
Access Control in Healthcare: the methodology from legislation to practice
Authors: Ferreira Ana  , Correia Ricardo  , Chadwick David  , Antunes Luis
Published: February 2025

Translating legislation and regulations into access control systems in healthcare is, in practice, not a straightforward task. Excessive regulation can create barriers to appropriate patient treatment. The main objective of this paper is to present a new methodology that can define, from legislation to practice, an access control policy as well as a RBAC model, in order to comprise generic legislation and regulation issues together with the access control needs from the ends users of a healthcare information system. The methodology includes the use of document analysis as well as grounded theory and mixed methods research. This methodology can be easily applied within a healthcare practice or any other domain with similar requirements. It helps to bridge the gap between legislation and end users' needs, while integrating information security into the healthcare processes in a more meaningful way.
Daily activities and fall risk – A follow-up study to identify relevant activities for sensor-based fall risk assessment
Authors: Marschollek Michael  , Rehwald Anja  , Gietzelt Matthias  , Song Bianying  , Wolf Klaus-Hendrik  , Haux Reinhold
Published: February 2025

The demographic change will lead to an increase in the incidence of falls in the elderly. Technological progress allows for unobtrusive physical activity measurement with miniature sensors, e.g. accelerometers. Yet it is unclear which activities or activity patterns are associated with an increased fall risk. The aim of the research for this paper is to identify daily physical activities associated with a high fall risk. A one-year follow-up study was conducted with n=50 geriatric patients who took part in a telephone interview to assess fall events, their consequences and a set of daily physical activities. Descriptive analysis of the data shows that there are marked differences between fallers (n=21) and non-fallers (n=29) in the overall activity level, the amount of shopping activity and associated locomotion, and in the intensity of light household work. The results confirm that there are differences in typical daily activities between fallers and non-fallers that may be used as parameters to enhance fall prediction models.
Measuring the effectiveness of hospital-acquired infection prevention
Authors: Iavindrasana Jimison  , Cohen Gilles  , Depeursinge Adrien  , Müller Henning  , Meyer Rodolphe  , Sax Hugo  , Geissbuhler Antoine
Published: February 2025

This article deals with data on nosocomial infections acquired in the Geneva University Hospitals. Goal of the work is to derive a model from a hospital-acquired infection (HAI) prevalence survey of year Y and apply them to a prevalence survey of years Y+1, Y+2. This analysis permits to evaluate the effectiveness of preventive measures taken after the prevalence survey in year Y. It also analyzes the robustness of the SVM algorithm on time-variable attributes. The model build on the dataset of year Y gives better results than in a previous study. The application of the model on the Y+1 and Y+2 prevalence surveys shows simultaneously improvements and deteriorations of 5 performance measures. This highlights the effectiveness of prevention and reduces the risk of HAI after the prevalence survey of year Y. We introduce a new method to detect redundancy in a dataset with the SVM algorithm.
Evaluating the relevance of disability weights for adjusting disease-cost and comorbidity calculations at the Kigali University Teaching Hospital
Authors: Verbeke Frank  , De Pauw Frank  , Tran Ngoc Candide  , Karara Gustave  , Gasakure Emmanuel  , Nyssen Marc
Published: February 2025

Evaluating the burden of diseases treated in hospitals in terms of (co)morbidity and financial impact is a long standing problem. Proposed solutions often rely on very sophisticated medical registration systems that are less suitable for developing countries. The authors have developed a simple prototype method for calculating financial impact and comorbidity of clinical conditions treated in a Sub-Saharan hospital environment (CALCO method) using disability weights. The developed method has been tested for 4 major clinical entities (tuberculosis, nutritional deficiencies, perinatal complications and malaria) on a dataset of 8.309 electronically registered admissions between Februari 1st2009 and September 1st2009 at the Kigali University Teaching Hospital. Results suggest that the method consists an acceptable instrument for estimating the financial burden of diseases treated in the hospital and that the proposed algorithms provide a useful formal method for quantifying hospital-bound comorbidity. The CALCO method might find its use in future implementations of Performance Based Financing (PBF) programs in Africa.
Impact of Content-Specific Email Reminders on Provider Participation in an Online Intervention: A Denta PBRN Study
Authors: Houston Thomas K.  , Coley Heather L.  , Sadasivam Rajani S.  , Ray Midge N.  , Williams Jessica H.  , Allison Jeroan J.  , Gilbert Gregg H.  , Kiefe Catarina I.  , Kohler Connie
Published: February 2025

Engaging busy healthcare providers in online continuing education interventions is challenging. In an Internet-delivered intervention for dental providers, we tested a series of email-delivered reminders - cues to action. The intervention included case-based education and downloadable practice tools designed to encourage providers to increase delivery of smoking cessation advice to patients. We compared the impact of email reminders focused on 1) general project announcements, 2) intervention related content (smoking cessation), and 3) unrelated content (oral cancer prevention focused content). We found that email reminders dramatically increased participation. The content of the message had little impact on the participation, but day of the week was important – messages sent at the end of the week had less impact, likely due to absence from clinic on the weekend. Email contact, such as day of week an email is sent and notice of new content post-ing, is critical to longitudinal engagement. Further research is needed to understand which messages and how frequently, will maximize participation.
Discovering Collaboration Opportunities in Research-Oriented Networks
Authors: Cucchiarelli Alessandro  , D'Antonio Fulvio
Published: February 2025

In this paper we present a method for discovering collaboration opportunities in research-oriented networks based on opportunity networks, modelled as a directed attributed multigraph, an opportunity exploiter, a set of opportunity patterns and a set of opportunity ranking functions. We show how the discovery of joint-research collaboration opportunities in a research-oriented network can be formulated in terms of the proposed model, and we perform an evaluation on a real-world dataset.
Learning and Meta-Learning for Coordination of Autonomous Unmanned Vehicles
Authors: Tošić Predrag T.  , Vilalta Ricardo
Published: February 2025

We study models of coordination, negotiation and collaboration in multi-agent systems (MAS). More specifically, we investigate scalable models and protocols for various distributed consensus coordination problems in large-scale MAS. Examples of such problems include conflict avoidance, leader election and coalition formation. We are particularly interested in application domains where robotic or unmanned vehicle agents interact with each other in real-time, as they try to jointly complete various tasks in complex dynamic environments, and where decisions often need to be made “on the fly”. Such MAS applications, we argue, necessitate a multi-tiered approach to learning how to coordinate effectively. One such collaborative MAS application domain is ensembles of autonomous micro unmanned aerial vehicles (micro-UAVs). A large ensemble of micro-UAVs on a complex, multi-stage mission comprised of many diverse tasks with varying time and other resource requirements provides an excellent framework for studying multi-tiered learning how to better coordinate. A variety of tasks and their resource demands, complexity and unpredictability of the overall environment, types of coordination problems that the UAVs may encounter in the course of their mission, multiple time scales at which the overall system can use learning and adaptation in order to perform better in the future, and multiple logical and organizational levels at which large ensembles of micro-UAVs can be analyzed and optimized, all suggest the need for a multi-tiered approach to learning. We outline our theoretical and conceptual framework that integrates reinforcement learning and meta-learning, and discuss potential benefits that our framework could provide for enabling autonomous micro-UAVs (and other types of autonomous vehicles) to coordinate more effectively.
Planning with Concurrency under Resources and Time Uncertainty
Authors: Beaudry Éric  , Kabanza Froduald  , Michaud François
Published: February 2025

Planning with actions concurrency under resources and time uncertainty has been recognized as a challenging and interesting problem. Most current approaches rely on a discrete model to represent resources and time, which contributes to the combinatorial explosion of the search space when dealing with both actions concurrency and resources and time uncertainty. A recent alternative approach uses continuous random variables to represent the uncertainty on time, thus avoiding the state-space explosion caused by the discretization of timestamps. We generalize this approach to consider uncertainty on both resources and time. Our planner is based on a forward chaining search in a state-space where the state representation is characterized by a set of object and numeric state variables. Object state variables are associated with random variables tracking the time at which the state variables' current value has been assigned. The search algorithm dynamically generates a Bayesian network that models the dependency between time and numeric random variables. The planning algorithm queries the Bayesian network to estimate the probability that the resources (numerical state variables) remain in a valid state, the probability of success and the expected cost of the generated plans. Experiments were performed on a transport domain in which we introduced uncertainty on the duration of actions and on the fuel consumption of trucks.
Boosting Clustering by Active Constraint Selection
Authors: Vu Viet-Vu  , Labroche Nicolas  , Bouchon-Meunier Bernadette
Published: February 2025

In this paper we address the problem of active query selection for clustering with constraints. The objective is to determine automatically a set of user queries to define a set of must-link or cannot-link constraints. Some works on active constraint learning have already been proposed but they are mainly applied to K-Means like clustering algorithms which are known to be limited to spherical clusters, while we are interested in clusters of arbitrary sizes and shapes. The novelty of our approach relies on the use of a k-nearest neighbor graph to determine candidate constraints coupled with a new constraint utility function. Comparative experiments conducted on real datasets from machine learning repository show that our approach significantly improves the results of constraints based clustering algorithms.
A Very Fast Method for Clustering Big Text Datasets
Authors: Lin Frank  , Cohen William W.
Published: February 2025

Large-scale text datasets have long eluded a family of particularly elegant and effective clustering methods that exploits the power of pair-wise similarities between data points due to the prohibitive cost, time- and space-wise, in operating on a similarity matrix, where the state-of-the-art is at best quadratic in time and in space.
Analyzing Flexible Timeline-based Plans
Authors: Cesta Amedeo  , Finzi Alberto  , Fratini Simone  , Orlandini Andrea  , Tronci Enrico
Published: February 2025

Timeline-based planners have been shown quite successful in addressing real world problems. Nevertheless they are considered as a niche technology in AI P&S research as an application synthesis with such techniques is still considered a sort of “black art”. Authors are currently developing a knowledge engineering tool around a timeline-based problem solving environment; in this framework we aim at integrating verification and validation methods. This work presents a verification process suitable for a timeline-based planner. It shows how a problem of flexible temporal plan verification can be cast as model-checking on timed game automata. Additionally it provides formal properties and checks the effectiveness of the proposed approach with a detailed experimental analysis.
Mental State Ascription Using Dynamic Logic
Authors: Sindlar Michal  , Dastani Mehdi  , Meyer John-Jules
Published: February 2025

In situations where the behavior of a system must be interpreted because its state is not accessible, it is useful to explain observed behavior in mentalistic terms. This paper presents a formalism based on propositional dynamic logic to model ascription of beliefs, goals, or plans on grounds of observed actions. The formalism is used to provide semantics for an existing approach to abducing the mental state of an observed agent; in doing so it is shown how behavior-producing rules can be given different explanatory interpretations.
Majority Merging: from Boolean Spaces to Affine Spaces
Authors: Condotta Jean-François  , Kaci Souhila  , Marquis Pierre  , Schwind Nicolas
Published: February 2025

This paper is centered on the problem of merging (possibly conflicting) information coming from different sources. Though this problem has attracted much attention in propositional settings, propositional languages remain typically not expressive enough for a number of applications, especially when spatial information must be dealt with. In order to fill the gap, we consider a (limited) first-order logical setting, expressive enough for representing and reasoning about information modeled as half-spaces from metric affine spaces. In this setting, we define a family of distance-based majority merging operators which includes the propositional majority operator ΔdH,Σ. We identify a subclass of interpretations of our representation language for which the result of the merging process can be computed and expressed as a formula.
Semantics for the Jason Variant of AgentSpeak (Plan Failure and some Internal Actions)
Authors: Bordini Rafael H.  , Hübner Jomi Fred
Published: February 2025

Jason is a platform for agent-based software development that is characterised both by being based on a programming language with formal semantics as well as having many language and platforms features that are very useful for practical programming, but not fully formalised. In this paper, we make significant progress in the direction of formalising the aspects of the variant of AgentSpeak that is interpreted by Jason that were not included in previous work on giving formal semantics to AgentSpeak. In particular, we give semantics to the plan failure handling mechanism which is unique to Jason, and also for some of the predefined internal actions that can alter an agent's mental state. Such internal actions are essential for some aspects of BDI-based programming, such as checking or dropping current goals or intentions, and therefore need to be formally defined within the operational semantics of the language.
Optimal Task Migration in Service-Oriented Systems: Algorithms and Mechanisms
Authors: Stein Sebastian  , Gerding Enrico  , Jennings Nicholas R.
Published: February 2025

In service-oriented systems, such as grids and clouds, users are able to outsource complex computational tasks by procuring resources on demand from remote service providers. As these providers typically display highly heterogeneous performance characteristics, service procurement can be challenging when the consumer is uncertain about the computational requirements of its task a priori. Given this, we here argue that the key to addessing this problem is task migration, where the consumer can move a partially completed task from one provider to another. We show that doing this optimally is NP-hard, but we also propose two novel algorithms, based on new and established search techniques, that can be used by an intelligent agent to efficiently find the optimal solution in realistic settings. However, these algorithms require full information about the providers' quality of service and costs over time. Critically, as providers are usually self-interested agents, they may lie strategically about these to inflate profits. To address this, we turn to mechanism design and propose a payment scheme that incentivises truthfulness. In empirical experiments, we show that (i) task migration results in an up to 160% improvement in utility, (ii) full information about the providers' costs is necessary to achieve this and (iii) our mechanism requires only a small investment to elicit this information.
Using Background Knowledge to Support Coreference Resolution
Authors: Bryl Volha  , Giuliano Claudio  , Serafini Luciano  , Tymoshenko Kateryna
Published: February 2025

Systems based on statistical and machine learning methods have been shown to be extremely effective and scalable for the analysis of large amount of textual data. However, in the recent years, it becomes evident that one of the most important direction of improvement in natural language processing (NLP) tasks, like word sense disambiguation, coreference resolution, relation extraction, and other tasks related to knowledge extraction, is by exploiting semantics. While in the past, the unavailability of rich and complete semantic descriptions constituted a serious limitation of their applicability, nowadays, the Semantic Web made available a large amount of logically encoded information (e.g. ontologies, RDF(S)-data, linked data, etc.), which constitute a valuable source of semantics. However, web semantics cannot be easily plugged into machine learning systems. Therefore the objective of this paper is to define a reference methodology for combining semantics information available in the web under the form of logical theories, with statistical methods for NLP. The major problems that we have to solve to implement our methodology concern (i) the selection of the correct and minimal knowledge among the large amount available in the web, (ii) the representation of uncertain knowledge, and (iii) the resolution and the encoding of the rules that combine knowledge retrieved from Semantic Web sources with semantics in the text. In order to evaluate the appropriateness of our approach, we present an application of the methodology to the problem of intra-document coreference resolution, and we show by means of some experiments on the ACE 2005 dataset, how the injection of knowledge is correlated to the improvement of the performance of our approach on this tasks.
Feature Selection by Approximating the Markov Blanket in a Kernel-Induced Space
Authors: Lou Qiang  , Obradovic Zoran
Published: February 2025

The proposed feature selection method aims to find a minimum subset of the most informative variables for classification/regression by efficiently approximating the Markov Blanket which is a set of variables that can shield a certain variable from the target. Instead of relying on the conditional independence test or network structure learning, the new method uses Hilbert-Schmidt Independence criterion as a measure of dependence among variables in a kernel-induced space. This allows effective approximation of the Markov Blanket that consists of multiple dependent features rather than being limited to a single feature. In addition, the new method can remove both irrelevant and redundant features at the same time. This method for discovering the Markov Blanket is applicable to both discrete and continuous variables, whereas previous methods cannot be used directly for continuous features and therefore are not applicable to regression problems. Experimental evaluations on synthetic and benchmark classification and regression datasets provide evidence that the new feature selection method can remove useless variables in low and in high dimensional problems more accurately than existing Markov Blanket based alternatives.
Learning When to Use Lazy Learning in Constraint Solving
Authors: Gent Ian P.  , Jefferson Chris  , Kotthoff Lars  , Miguel Ian  , Moore Neil C.A.  , Nightingale Peter  , Petrie Karen
Published: February 2025

Learning in the context of constraint solving is a technique by which previously unknown constraints are uncovered during search and used to speed up subsequent search. Recently, lazy learning, similar to a successful idea from satisfiability modulo theories solvers, has been shown to be an effective means of incorporating constraint learning into a solver. Although a powerful technique to reduce search in some circumstances, lazy learning introduces a substantial overhead, which can outweigh its benefits. Hence, it is desirable to know beforehand whether or not it is expected to be useful. We approach this problem using machine learning (ML). We show that, in the context of a large benchmark set, standard ML approaches can be used to learn a simple, cheap classifier which performs well in identifying instances on which lazy learning should or should not be used. Furthermore, we demonstrate significant performance improvements of a system using our classifier and the lazy learning and standard constraint solvers over a standard solver. Through rigorous cross-validation across the different problem classes in our benchmark set, we show the general applicability of our learned classifier.
Research on Computer-Aided Decision-making Software of Advanced Geological Prediction in Tunnel
Authors: Meng Lubo  , Li Tianbin  , Fang Xing
Published: February 2025

To enhance the speed and accuracy of the tunnel geological prediction based on the engineering geology, rock mechanics and geophysical exploration, non-linear intelligence in science and technology, such as fuzzy comprehensive evaluation, fuzzy neural network, etc. are adopted to solve some key technical issues, such as comprehensive forecast of unfavorable geological conditions. Ultimately, the computer-aided software of tunnel advanced geological prediction is developed. The prediction function of the software is divided into two phases: reconnaissance stage and construction stage. Among the items concerned, reconnaissance stage mainly focuses on inquiring the basic geological conditions about tunnels, and preliminarily predicting rock burst, large deformations and water inflow. Construction stage mainly focuses on prediction of basic geological conditions of tunnel, unfavorable geological conditions like fault, broken rock, cave and rich water as well as construction geological disasters, such as, rock burst, large deformation, collapse, water gushing and gas outburst. Through its application in several long tunnels, the software presents its advantages of timeliness and high prediction accuracy.
Carneades and Abstract Dialectical Frameworks: A Reconstruction
Authors: Brewka Gerhard  , Gordon Thomas F.
Published: February 2025

Carneades is a rather general framework for argumentation. Unlike many other approaches, Carneades captures a number of aspects, like proof burdens, proof standards etc., which are of central importance, in particular in legal argumentation.
Generalizing stable semantics by preferences
Authors: Amgoud Leila  , Vesic Srdjan
Published: February 2025

Different proposals have been made in the literature for refining Dung's argumentation framework by preferences between arguments. The idea is to ignore an attack if the attacked argument is stronger than its attacker. Acceptability semantics are then applied on the remaining attacks. Unfortunately, these proposals may return some unintended results, in particular, when the attack relation is asymmetric.
Argumentation and rules with exceptions
Authors: Verheij Bart
Published: February 2025

Models of argumentation often take a given set of rules or conditionals as a starting point. Arguments to support or attack a position are then built from these rules. In this paper, an attempt is made to develop constraints on rules and their exceptions in such a way that they correspond exactly to arguments that successfully support their conclusions. The constraints take the form of properties of nonmonotonic consequence relations, similar to the ones that have been studied for cumulative inference.
A Framework of Emotional Speech Synthetise Using a Chord and a Scale
Authors: Kurematsu Masaki  , Chiba Hiroki  , Hakura Jun  , Fujita Hamido
Published: February 2025

In this paper, we proposed a method to express emotion in a synthetic speech using musical theories. We define the pitch of each phoneme according to an accent dictionary in the first step. Next, we change the pitch of each phoneme according to a Chord and a Scale which are good for expression emotion in music. In order to evaluate this approach, we did experiments. The experimental results saw the possibility of using Chords and Scales to express emotion in synthetic speech. Future works of our research are as follows. We have to analysis these synthetic speeches and extract the relation between synthetic speeches and emotion using a machine learning techniques. We have to use other musical theories to express emotion appropriately, in synthetic speech. In addition, we have experimented these results with a lot of listeners to evaluate our approach.
Remarks on the Duration of Lithuanian Consonants in a Continuous Speech
Authors: Dereškevičiūtė Sigita  , Kazlauskienė Asta
Published: February 2025

Acoustical (quantitative) properties of consonants of Lithuanian standard language are still not extensively and properly covered by the contemporary research. The objective of this paper is to investigate quantity of consonants in a continuous speech of Standard Lithuanian, to qualify spontaneous duration of the analyzed sounds considering qualitative (articulatory) features and ignoring other factors like the length of the segment or the sound's position in a word. The results show that the most significant and distinctive articulatory feature influencing duration is the manner of articulation and the voicing. The place of articulation and palatalization has no impact on the duration of the analyzed consonants.
Similarity for Attribute-value Representations in Fuzzy Description Logics
Authors: GarcÍa-Cerdaña Àngel  , Armengol Eva  , Dellunde Pilar
Published: February 2025

In this paper we explore the possibility of introducing the equality symbol in the languages of Fuzzy Description Logics (FDLs) interpreted as a similarity relation. The goal is twofold: dealing with attribute-value representations at the domain objects level, and integrating the treatment of similarities inside the description languages and their corresponding knowledge bases.
Comparison of Topologies in Peer-to-Peer Data Sharing Networks
Authors: Campos Jordi  , Piqué Nuria  , Lopez-Sanchez Maite  , Esteva Marc
Published: February 2025

Interactions within Multi-agent systems can be structured in different ways depending on the application scenario and its environmental restrictions. In previous work we have developed a multi-agent system within a Peer-to-Peer (P2P) sharing data network scenario. Its design includes an organisation that structures agents' interactions and an abstract architecture (2-LAMA) that helps to improve its performance. The focus of this paper is to first characterise the environment of this scenario in terms of network topologies and, secondly, to study how it affects system's performance in relation to the proposed architecture. In order to do that, we have set up a series of experiments that consider different network topologies and evaluate their performance. Results show that our architecture effectively helps to improve system's performance in despite of physical network topology.
NeuroNavigator: A Biologically Inspired Universal Cognitive Microcircuit
Authors: Ascoli Giorgio A.  , Samsonovich Alexei V.
Published: February 2025

We present a universal building block for cognitive machines, called NeuroNavigator, inspired by theories of the hippocampus. The module is designed to fit both biological plausibility and constraints of forthcoming neuromorphic hardware. Its functions may range from spatial navigation to episodic memory retrieval. The goal of the present study of NeuroNavigator is to show the scalability of the model. The core of the architecture is based on our previously described model of hippocampal function and includes 3 layers (DG, CA3, CA1) of spiking neurons with noisy STDP synaptic connections among neighboring layers. The model is applied to a spatial navigation paradigm in a hierarchical virtual environment, the metrics of which need to be learned by exploration. The goal in each trial is set arbitrarily as any one of the previously seen objects or features. In order to navigate toward the goal, the agent needs to “imagine” previously performed available moves at the current location and select one of them, using the acquired spatial knowledge. This process controlled by NeuroNavigator is repeated until the goal is reached. Overall, the simulation results show robustness and scalability of the solution based on a biologically-inspired network of spiking neurons and STDP synapses.
GRAVA – Context Programming
Authors: Robertson Paul  , Laddaga Robert
Published: February 2025

Initially developed to support reflective reasoning in computer vision, over the past ten years GRAVA has been applied to a wide range of problems including vision, language, networks, cyber security, and neural modeling. In this paper we provide an overview of the key concepts in GRAVA, how they have evolved over the ten year history, and how they have been applied.
Dopamine and self-directed learning
Authors: Herd Seth  , Mingus Brian  , O'Reilly Randall
Published: February 2025

Humans are intrinsically motivated to learn. Such motivation is necessary to be a human-like learner, and helpful for any learning system designed to achieve general intelligence. We discuss the limited existing computational work in this area, and link them to known and theorized properties of the dopamine system. The relatively well-understood mechanisms by which dopamine release signals unpredicted reward can also serve to signal new learning. Dopamine release leads to maintenance of current representations, which serves to “lock” attention onto topics or tasks in which useful learning is occurring. We thus propose a novel but natural extension of known aspects of dopamine function to perform self-directed learning of arbitrary self-defined tasks. If this hypothesis is correct, detailed experimental evidence on dopamine function can help guide computational research into human-like learning systems.
User-based Collaborative Filtering: Sparsity and Performance
Authors: Redpath Jennifer  , Glass David H.  , McClean Sally  , Chen Luke
Published: February 2025

It is generally assumed that all users in a dataset are equally adversely affected by data sparsity and hence addressing this problem should result in improved performance. However, although all users may be members of a sparse dataset, they do not all suffer equally from the data sparsity problem. This indicates that there is some ambiguity as to which users should be identified as suffering from data sparsity, referred to as sparse users throughout this paper, and targeted with new recommendation improvement strategies. This paper defines sparsity in terms of number of item ratings and average similarity with nearest neighbours and then goes on to look at the impact of sparsity so defined on performance. Counterintuitively, it is found that in top-N recommendations sparse users actually perform better than some other categories of users when a standard approach is used. These results are explained, and empirically verified, in terms of a bias towards users with a low number of ratings. The link between sparsity and performance is also considered in the case of predictions rather than top-N recommendations. This work provides the motivation for targeting improvement approaches towards distinct groups of users as opposed to the entire dataset.
Evaluation of the Archetypes Based Development
Authors: Piho Gunnar  , Tepandi Jaak  , Roost Mart
Published: February 2025

Archetypes based development (ABD) utilizes archetypes and archetype patterns to increase dependability of software, reduces semantic heterogeneity of models and data types, improves maturity of the software development process, leads development of one-off software towards software factories, as well as satisfies the needs of small and medium sized software houses. ABD uses Zachman framework (ZF) for enterprise architecture in combination with business archetypes and archetype patterns as a central idea for the engineering of domains, requirements and software. We explain the ABD and evaluate it from the Bjørner's domain modelling, MDA (Model Driven Architecture), XP (Extreme Programming) and CMMI (Capability Maturity Model Integration) for Development perspectives.
IPRA: Regressing Data with Independent Parameters
Authors: Apolloni Bruno  , Bassis Simone
Published: February 2025

Within the framework of Algorithmic Inference, we recall a linear regression analysis tool based on the identification of the joint probability distribution of the regression coefficients compatible with the sampled data and aimed at finding out the independent components of this distribution. On this distribution we implement specific Independent Component Analysis (ICA) procedures to obtain the parameter independent components giving rise to suitable confidence regions also when the noise term is far from being independent and identically Gaussian.
Ontology Based Standardization of Petri Net Modeling for Signaling Pathways
Authors: Takai-Igarashi Takako
Published: February 2025

Taking account of the great availability of Petri nets in modeling and analyzing large complicated signaling networks, semantics of Petri nets is in need of systematization for the purpose of consistency and reusability of the models. This paper reports on standardization of units of Petri nets on the basis of an ontology that gives an intrinsic definition to the process of signaling in signaling pathways.
Visual Tracking of Laparoscopic Instruments in Standard Training Environments
Authors: Allen Brian F.  , Kasper Florian  , Nataneli Gabriele  , Dutson Erik  , Faloutsos Petros
Published: February 2025

We propose a method for accurately tracking the spatial motion of standard laparoscopic instruments from video. By exploiting the geometric and photometric invariants common to standard FLS training boxes, the method provides robust and accurate tracking of instruments from video. The proposed method requires no modifications to the standard FLS training box, camera or instruments.
Bench Model Surgical Skill Training Improves Novice Ability to Multitask: A Randomized Controlled Study
Authors: Grierson Lawrence  , Melnyk Megan  , Jowlett Nathan  , Backstein David  , Dubrowski Adam
Published: February 2025

Skills training in simulation laboratories is becoming increasingly common. However, the educational benefit of these laboratories remains unclear. This study examined whether such training enables better performance on the simultaneous execution of technical skill and knowledge retention. Twenty-four novice trainees completed the elliptical excision on baseline testing. Following baseline testing twelve of the novices completed a technical practice (simulation training group) session, while the other twelve did not (control group). One week later, all participants returned for dual-task follow up testing in which they performed the excision while listening to a didactic lesson on the staging and treatment of cutaneous melanoma. The dual-tasking during the post test was standardized, whereby excision sutures 3 and 5 were performed alone (single), and sutures 4 and 6 were performed concurrently with the didactic lecture (dual). Seven additional trainees also participated as controls that were randomized to listen to the didactic lesson alone (knowledge retention alone group). Knowledge retention was assessed by a multiple choice questionnaire (MCQ). Technical performance was evaluated with computer and expert-based measures. Time to complete the performance improved among both groups completing the elliptical excision on follow-up testing (p < 0.01). The simulation training group demonstrated superior hand motion performance on simultaneous didactic lesson testing (p < 0.01). Novices from the no-training group performed statistically worse while suturing concurrently with the didactic lesson (p < 0.01). The pretraining of novices in surgical skills laboratories leads to improved technical performance during periods of increased attention demands.
A Generalized Haptic Feedback Approach for Arbitrarily Shaped Objects
Authors: Hu Rui  , Barner Kenneth E.  , Steiner Karl V.
Published: February 2025

In surgery procedures, haptic interaction provides surgeons with indispensable information to accurately locate the surgery target. This is especially critical when visual feedback cannot provide sufficient information and tactile interrogation, such as palpating some region of tissue, is required to locate a specific underlying tumor. However, in most current surgery simulators, the haptic interaction model is usually simplified into a contact sphere or rod model, leaving arbitrarily shaped intersection haptic feedback between target tissue and surgery instrument less unreliable. In this paper, a novel haptic feedback algorithm is introduced for generating the feedback forces in surgery simulations. The proposed algorithm initially employs three Layered Depth Images (LDI) to sample the 3D objects in X, Y and Z directions. A secondary analysis scans through two sampled meshes and detects their penetration volume. Based on the principle that interaction force should minimize the penetration volume, the haptic feedback force is derived directly. Additionally, a post-processing technique is developed to render distinct physical tissue properties across different interaction areas. The proposed approach does not require any pre-processing and is applicable for both rigid and deformable objects.
Simplified Cosserat Rod for Interactive Suture Modeling
Authors: Punak Sukitti  , Kurenov Sergei
Published: February 2025

This paper presents a real-time simulation of a virtual surgical suture, which is a physically-based model adapted from the Cosserat theory of elastic rods. The focus is on achieving a physically plausible simulation of the suture that can be simulated in real time. With simulation parameters adjustment, the virtual surgical suture can be accustomed to exhibit bending and twisting similar to a real suture. It is simple to implement and easy to extend for collision detections and interactions with other virtual objects. Its simulation is similar to a simulation of a composition of two mass-spring chains – for positions and orientations. Test results show that the virtual surgical suture can be used to tie knots in real time.