bluuebunny/crossref_metadata_2025 huggingface dataset:
Numerical implementation of a constitutive model for soil creep
Authors: Puswewala U.G.A.  , Madurapperuma M.A.K.M.
Published: February 2025

A differential form of 1-D creep model, proposed by Bjerrum in 1967, which was later modified for transient loading conditions is extended to multi-dimensional (2-D and 3-D) state of stress and strain by incorporating concepts of visco-plasticity (Vermeer et al., 1998). The devised creep models take into account both volumetric creep strain and deviatoric creep strain, and creep deformation of soil is defined by several material parameters. The model is incorporated as a plane strain element subroutine in a non-linear, time-incrementing finite element program. The model enables the solution of complicated foundation-soil interaction problems involving creep of soil. Numerical analyses are conducted using published experimental data and parametric studies are conducted to evaluate the sensitivity of different input parameters of the model.
Evaluation of Acceptance of Nursing Information System in a German and American Hospital
Authors: Vollmer Anne-Maria  , Prokosch Hans-Ulrich  , Evans Scott  , Kuttler Kathryn
Published: February 2025

Nursing Information Systems (NIS) are not well-adopted and accepted in Germany. The evaluation of a NIS deployment in a German University Hospital supports this assumption. A second side study in the US should point out the differences regarding the technical and organizational differences. We use a questionnaire including standardized instruments like the Unified Theory of Acceptance (UTAUT). Results indicated that nurses in Germany compared to in the US do not use nursing process documentation to the same extent. The main reasons behind the low usage in comparison with the US are deficits in ease-of-use, system performance and the high expenditure of time and paper work for charting nursing plans.
Electronic Nursing Documentation: Patient Care Continuity Using the Clinical Care Classification System (CCC)
Authors: Whittenburg Luann  , Meetim Aunchisa
Published: February 2025

An innovative nursing documentation project conducted at Bumrungrad International Hospital in Bangkok, Thailand demonstrated patient care continuity between nursing patient assessments and nursing Plans of Care using the Clinical Care Classification System (CCC). The project developed a new generation of interactive nursing Plans of Care using the six steps of the American Nurses Association (ANA) Nursing process and the MEDCIN®clinical knowledgebase to present CCC coded concepts as a natural by-product of a nurse's documentation process. The MEDCIN®clinical knowledgebase is a standardized point-of-care terminology intended for use in electronic health record systems. The CCC is an ANA recognized nursing terminology.
Use of Patient Portals in Older Adults: A Comparison of Three Samples
Authors: Nahm Eun-Shim  , Sagherian Knar  , Zhu Shijun
Published: February 2025

Recently, there have been national efforts to use patient portals (PPs) to engage patients in their care. Through PPs, patient can access their health records and communicate with care providers. These functions can be beneficial for older adults who manage multiple chronic conditions. The current nationwide PP implementation, however, lacks strategies for facilitating PP use in older adults. As a first step to fill this gap, this study examined the prevalence of PP use among older adults, the demographic characteristics of older adult PP users, and self-efficacy for using PPs using three samples (two national, one local). Findings suggest that older adult online users are receptive to PPs and can use them like other age groups. Approaches to engage older adults in PP use may differ from those for younger adults, as they have additional support needs. Further research is needed to identify optimal approaches to support older adult populations.
Medication Information Flow in Home Care
Authors: Norri-Sederholm Teija  , Saranto Kaija  , Paakkonen Heikki
Published: February 2025

Critical success factors in medication care involve communication and information sharing. Knowing the information needs of each actor in medication process in home care, is the first step to ensure that the right type of information is available, when needed. The aim of the study was to describe the needed and delivered information in home care in order to perform medication care successfully. A total of 15 nurses from primary home care participated a workshop focusing on medication treatment. The qualitative data was collected by focus group technique. Data was analyzed according to content analysis. Three medication information themes were formulated: Client-related information, medication, and medication error. The critical medication information were generic drug information, validity of the list of medication and client's clinical status. As a conclusion findings, show the diversity of the medication information in home care.
What Features of Smartphone Medication Applications Are Patients with Chronic Diseases and Caregivers Looking for?
Authors: Liu Yisi  , Wang Liuyu  , Chang Polun  , Lamb Karen V.  , Cui Yanyan  , Wu Ying
Published: February 2025

We explored the desired features of medication applications for patients with chronic disease and their caregivers with a questionnaire survey, 50 from patients and 50 from their caregivers. Although the majority of people (75%) are willing to use medication apps, the actual usage rate is quite low (11%). Worrying about privacy of personal information seems to be the main reason of not using applications. The overall score desired for use was 3.29 ± 1.02 (out of 5). Searching medications and diseases and assistance with making doctors' appointments are the most wanted categories. Online shopping for drugs and delivery were the least desired items. The main concerns for people who do not want certain features include: they are not useful, worrying about buying counterfeit drugs and reliability of content. Compared with patients, caregivers seems to be more concerned on nutrition tips for chronic illness, fall detection, and privacy protection (P < 0.05 for all).
An Analysis of the Factor Model on the Workload of Nursing Staff Using a Hospital Management Tool
Authors: Sato Kikue  , Yamashita Koji  , Goshima Mituko  , Kuroda Tomohiro  , Kinosada Yasutomi  , Seiyama Akitoshi
Published: February 2025

We developed a tool that allows a medical facility to offer efficient nursing care with limited human resources by optimizing the distribution of hospital ward nursing tasks. The use of information and communications technology to visualize daily workloads and make use of quantified workload data is important for identifying management elements that allow the efficient allocation of personnel and tasks. The goal of this study was to utilize data from the ward management tool that we developed to consider workflow processes for nursing staff and the relationships between the nursing competence of the nursing staff and the patients' conditions and how these impact on workloads. We found a correlation between workload and staff competence. With respect to the teamwork index and patient condition, structural equation modeling analysis using the intensity of nursing care needs and degree of independent daily living showed that patient condition had a meaningful effect on workload.
Diabetes Applications for Arabic Speakers: A Critical Review of Available Apps for Android and iOS Operated Smartphones
Authors: Alhuwail Dari
Published: February 2025

Today, 415 million adults have diabetes; more than 35 million of diabetic adults live in the Middle East and North Africa region. Smartphone penetration in the region is high and applications, or apps, for diabetics have shown promising results in recent years. This study took place between September and December 2015 and reviewed all currently available smartphone diabetes apps for Arabic speakers in both the Apple App and Google Play stores. There were only few diabetes apps for Arabic speakers; only eighteen apps were discovered and considered for this study. Most apps were informational. Only three apps offered utilities such as glucose reading conversion. The apps had issues related to information quality and adherence to latest evidence-based medical advice. There is a need for more evidence-based Arabic diabetes apps with improved functionality. Future research of Arabic diabetes apps should also focus on the involvement and engagement of the patients in the design of these apps.
Interactive Patient Engagement: System Design to Cross the Continuum of Care
Authors: Burke K.
Published: February 2025

Patient Engagement is a critical and fundamental driver in the transformation of healthcare. Patient involvement through interactive care is a proven approach for improved health outcomes; however, a single strategy to achieve success will not suffice. An interactive patient engagement system design, as a multi-tactic landscape of solutions, is necessary to effectively engage patients.
The Hygiene Games
Authors: Klein Frederic  , Severijns Cassandra  , Albiez Daniela  , Seljutin Eugen  , Jovanović Marko  , Eyvazi Hesar Milad
Published: February 2025

Addressing the correlation of hospital acquired infections and insufficient hand hygiene, we propose a supportive system to enhance the individual hygiene habits of health care workers. By applying gamification to incentivize health care professionals while maintaining a high standard of privacy and usability, the system focuses on technical simplicity by using concepts like bring your own device in a scaleable proof of concept implementation.
Instrumented Shoes for Real-Time Activity Monitoring Applications
Authors: Moufawad el Achkar Christopher  , Lenoble-Hoskovec Constanze  , Major Kristof  , Paraschiv-Ionescu Anisoara  , Büla Christophe  , Aminian Kamiar
Published: February 2025

Activity monitoring in daily life is gaining momentum as a health assessment tool, especially in older adults and at-risk populations. Several research-based and commercial systems have been proposed with varying performances in classification accuracy. Configurations with many sensors are generally accurate but cumbersome, whereas single sensors tend to have lower accuracies. To this end, we propose an instrumented shoes system capable of accurate activity classification and gait analysis that contains sensors located entirely at the level of the shoes. One challenge in daily activity monitoring is providing punctual and subject-tailored feedback to improve mobility. Therefore, the instrumented shoe system was equipped with a Bluetooth® module to transmit data to a smartphone and perform detailed activity profiling of the monitored subjects. The potential applications of such a system are numerous in mobility and fall risk-assessment as well as in fall prevention.
Understanding New Types of Evidence Ready for Translation into Nursing Informatics
Authors: McCormick Kathleen
Published: February 2025

Nurses are the primary deliverers of patient care and observers of patient side effects to medications. The primary objective of this tutorial is to bring the participants up to date in genomic applications for nursing from birth until death. A secondary objective is to define at least 17 pharmacogenomics evidence guidelines ready for implementation into the Electronic Health Record. The target audience are nurses in practice, implementers of EHRs, nursing in leadership and policy-making positions, those focused on defining new areas for nursing research, and educators who are in need of defining criteria for integrating genomics into nursing education.
Documentation and Reporting of Nutrition – Interoperability, Standards, Practice and Procedures
Authors: Rotegård Ann Kristin
Published: February 2025

Interoperability, fragmentation, standardization and data integrity are key challenges in efforts to improve documentation, streamline reporting and ensure quality of care. This workshop aims at demonstrating and discussing health politics and solutions aimed to improve nutritional status in elderly.
Not Lost in Translation: Changing Intervention Delivery Mechanisms in e-Health
Authors: Courtney Karen L.
Published: February 2025

This workshop will explore the challenges in translating existing health interventions to new e-health delivery mechanisms. Challenges to be covered include: identifying and retaining the active ingredients of an intervention; and measurement and validation of newly translated interventions. This session will appeal to health researchers and e-health developers. Participants will have an opportunity to work on cases in small groups to foster in-depth discussion and sharing. Following this session, participants will be able to articulate critical issues to be addressed in translating interventions to a new delivery mechanism and share potential solutions to various translation challenges.
Design of a Recommendation System for Adding Support in the Treatment of Chronic Patients
Authors: Torkar Simon  , Benedik Peter  , Rajkovič Uroš  , Šušteršič Olga  , Rajkovič Vladislav
Published: February 2025

Rapid growth of chronic disease cases around the world is adding pressure on healthcare providers to ensure a structured patent follow-up during chronic disease management process. In response to the increasing demand for better chronic disease management and improved health care efficiency, nursing roles have been specialized or enhanced in the primary health care setting. Nurses become key players in chronic disease management process. Study describes a system to help nurses manage the care process of patient with chronic disease. It supports focusing nurse's attention on those resources/solutions that are likely to be most relevant to their particular situation/problem in nursing domain. System is based on multi-relational property graph representing a flexible modeling construct. Graph allows modeling a nursing ontology and the indices that partition domain into an efficient, searchable space where the solution to a problem is seen as abstractly defined traversals through its vertices and edges.
The Evaluation Imperative
Authors: Nagle Lynn M.
Published: February 2025

With the advent of wide scale adoption and use of clinical information systems and other technologies to support the delivery of healthcare services, the inclusion of iterative evaluation is an often overlooked but important component of project management. In this paper, it is proffered that the evaluation process should be an imperative and initiated at the outset of the design and implementation of clinical information systems including any single component of hardware or software. Ideally, methods of evaluation should be multi-faceted, include formative and summative evaluation approaches and include a range of metrics that will provide organizations with insight to process and technology efficiency and effectiveness as well as human and financial impacts.
User's Satisfaction of Multiple Accounting Record System
Authors: Chen M.C.  , Yu H.C.
Published: February 2025

The study hospital had developed a multiple account recording system that generates the accounting information of the consumed materials based on daily nursing records. A questionnaire survey was delivered to further investigate the impact of the system.
What Facilitates the Use of Telehealth Applications Among Nurses?
Authors: Koivunen Marita  , Saranto Kaija
Published: February 2025

This presentation describes the preliminary results of the facilitators of the use of telehealth applications from nurses' point of view based on a qualitative systematic literature review synthetizing 25 previously published papers. The study brought out two main categories that facilitate the use of telehealth among nurses: 1) Nurses' skills and attitudes, and 3) Changes in nurses' work and operations.
Ontologies of Dynamical Systems and Verifiable Ontology-Based Computation: Towards a Haskell-Based Implementation of Referent Tracking
Authors: Bittner Thomas  , Bona Jonathan  , Ceusters Werner
Published: February 2025

Migrating data from electronic healthcare records (EHR) to data repositories for biomedical research provides an opportunity for the design of extracttransfer-load procedures that not only make the repository a faithful representation of what is stated in the EHR, but also of how what is stated in the EHR (may or may not) correspond to what in reality the statements are about. This includes, for example, annotating which EHR statements are inconsistent with other statements, or which statements cannot possibly be true because of what we know about reality. While one goal of ontologies is to provide background information for determining the reliability of assertions that have been introduced without using an underlying ontology, one goal of Referent Tracking is to make explicit all the implicit assumptions that need to be taken into account to interpret given data correctly. In this paper, using Basic Formal Ontology as an example, we explore the potential of Haskell to implement software that is provably correct with respect to the semantics specified in the ontology.
Towards an Ontology of Space for GFO
Authors: Baumann Ringo  , Loebe Frank  , Herre Heinrich
Published: February 2025

Space and time are basic categories of any top-level ontology. They account for fundamental assumptions of the modes of existence of those individuals that are said to be in space and time. The present paper is devoted to GFO-Space, the ontology of space in the General Formal Ontology (GFO). This ontology is introduced by a set of axioms formalized in first-order logic and further elucidated by consequences of the axiomatization.
What Is Ontology Reuse?
Authors: Katsumi Megan  , Grüninger Michael
Published: February 2025

The reuse of ontologies is critical to their value as a means of knowledge representation. Unfortunately, reuse also still poses a considerable challenge for the ontological community. One reason for this is the lack of a formal definition of reuse. How can we attempt to perform or even assist this sort of ontology design, if we have no clear understanding of what constitutes reuse, and what does not? In this work we aim to remedy this situation by providing a formal definition of the concepts of reuse and reusability. Beyond providing a clear understanding of these concepts, part of the resulting definition is a characterization of the operations of reuse that can be leveraged to determine how a given ontology(s) must be reused to satisfy some specified requirements. This serves not only to provide direction for the task of reuse, but also to assess the implications of reusing an ontology(s), a priori. Collectively, the solutions presented in this paper serve as a major step in improving the current state of reuse.
An Intelligent Data Analysis Framework for Supporting Perception of Geospatial Phenomena
Authors: Roda Fernando  , Zanni-Merk Cecilia
Published: February 2025

Land use and urban development surveys involve the interpretation of a large volume of data coming from satellite images processing as well as from remote sensors networks. In order to facilitate this interpretation, the development of a multipurpose Intelligent Data Analysis (IDA) framework for supporting geographical data perception is proposed here. The framework makes use of semantic technologies and relies on a novel knowledge model composed by a foundational ontology (DOLCE Ultra-Lite, also called DUL), three core reference ontologies (the Temporal Abstraction Ontology or TAO, the Semantic Sensor Network ontology or SSN and the SWRL Temporal Ontology or SWRLTO) and two specific domain ontologies (the Urban Ontology or URO and the Geographic Data ontology or GeoD, developed by our team). They play different and well specific roles in the whole process of perception. The paper shows how to apply SSN to manage measurements of geographical regions provided by satellite images processing software. In a similar way, TAO has been extended to deal with the abstractions resulting from geographical data interpretation. An example shows a SWRL based implementation of a perception process that gradually abstracts geographical features and objects.
Markov Logic Networks with Numerical Constraints
Authors: Chekol Melisachew Wudage  , Huber Jakob  , Meilicke Christian  , Stuckenschmidt Heiner
Published: February 2025

Markov logic networks (MLNs) have proven to be useful tools for reasoning about uncertainty in complex knowledge bases. In this paper, we extend MLNs with numerical constraints and present an efficient implementation in terms of a cutting plane method. This extension is useful for reasoning over uncertain temporal data. To show the applicability of this extension, we enrich log-linear description logics (DLs) with concrete domains (datatypes). Thereby, allowing to reason over weighted DLs with datatypes. Moreover, we use the resulting formalism to reason about temporal assertions in DB-pedia, thus illustrating its practical use.
Unsupervised Activity Recognition Using Latent Semantic Analysis on a Mobile Robot
Authors: Duckworth Paul  , Alomari Muhannad  , Gatsoulis Yiannis  , Hogg David C.  , Cohn Anthony G.
Published: February 2025

We show that by using qualitative spatio-temporal abstraction methods, we can learn common human movements and activities from long term observation by a mobile robot. Our novel framework encodes multiple qualitative abstractions of RGBD video from detected activities performed by a human as encoded by a skeleton pose estimator. Analogously to informational retrieval in text corpora, we use Latent Semantic Analysis (LSA) to uncover latent, semantically meaningful, concepts in an unsupervised manner, where the vocabulary is occurrences of qualitative spatio-temporal features extracted from video clips, and the discovered concepts are regarded as activity classes. The limited field of view of a mobile robot represents a particular challenge, owing to the obscured, partial and noisy human detections and skeleton pose-estimates from its environment. We show that the abstraction into a qualitative space helps the robot to generalise and compare multiple noisy and partial observations in a real world dataset and that a vocabulary of latent activity classes (expressed using qualitative features) can be recovered.
Extending the Description Logic
Authors: Baader Franz  , Fernández Gil Oliver
Published: February 2025

In a previous paper, we have introduced an extension of the lightweight Description Logic EL that allows us to define concepts in an approximate way. For this purpose, we have defined a graded membership function deg, which for each individual and concept yields a number in the interval [0, 1] expressing the degree to which the individual belongs to the concept. Threshold concepts C~tfor ~∈{<,≤,>,≥} then collect all the individuals that belong to C with degree ~t. We have then investigated the complexity of reasoning in the Description Logic, which is obtained fromby adding such threshold concepts. In the present paper, we extend these results, which were obtained for reasoning without TBoxes, to the case of reasoning w.r.t. acyclic TBoxes. Surprisingly, this is not as easy as might have been expected. On the one hand, one must be quite careful to define acyclic TBoxes such that they still just introduce abbreviations for complex concepts, and thus can be unfolded. On the other hand, it turns out that, in contrast to the case of EL, adding acyclic TBoxes toincreases the complexity of reasoning by at least on level of the polynomial hierarchy.
Real-Time Timeline Summarisation for High-Impact Events in Twitter
Authors: Zhou Yiwei  , Kanhabua Nattiya  , Cristea Alexandra I.
Published: February 2025

Twitter has become a valuable source of event-related information, namely, breaking news and local event reports. Due to its capability of transmitting information in real-time, Twitter is further exploited for timeline summarisation of high-impact events, such as protests, accidents, natural disasters or disease outbreaks. Such summaries can serve as important event digests where users urgently need information, especially if they are directly affected by the events. In this paper, we study the problem of timeline summarisation of high-impact events that need to be generated in real-time. Our proposed approach includes four stages: classification of real-world events reporting tweets, online incremental clustering, post-processing and sub-events summarisation. We conduct a comprehensive evaluation of different stages on the “Ebola outbreak” tweet stream, and compare our approach with several baselines, to demonstrate its effectiveness. Our approach can be applied as a replacement of a manually generated timeline and provides early alarms for disaster surveillance.
Towards Better Models of Externalities in Sponsored Search Auctions
Authors: Gatti Nicola  , Rocco Marco  , Serafino Paolo  , Ventre Carmine
Published: February 2025

Sponsored Search Auctions (SSAs) arguably represent the problem at the intersection of computer science and economics with the deepest applications in real life. Within the realm of SSAs, the study of the effects that showing one ad has on the other ads, a.k.a. externalities in economics, is of utmost importance and has so far attracted the attention of much research. However, even the basic question of modeling the problem has so far escaped a definitive answer. The popular cascade model is arguably too idealized to really describe the phenomenon yet it allows a good comprehension of the problem. Other models, instead, describe the setting more adequately but are too complex to permit a satisfactory theoretical analysis. In this work, we attempt to get the best of both approaches: firstly, we define a number of general mathematical formulations for the problem in the attempt to have a rich description of externalities in SSAs and, secondly, prove a host of results drawing a nearly complete picture about the computational complexity of the problem. We complement these approximability results with some considerations about mechanism design in our context.
Skeleton-Based Orienteering for Level Set Estimation
Authors: Bottarelli Lorenzo  , Bicego Manuele  , Blum Jason  , Farinelli Alessandro
Published: February 2025

In recent years, the use of unmanned vehicles for monitoring spatial environmental phenomena has gained increasing attention. Within this context, an interesting problem is level set estimation, where the goal is to identify regions of space where the analyzed phenomena (for example the PH value in a body of water) is above or below a given threshold level. Typically, in the literature this problem is approached with techniques which search for the most interesting sampling locations to collect the desired information (i.e., locations where we can gain the most information by sampling). However, the common assumption underlying this class of approaches is that acquiring a sample is expensive (e.g., in terms of consumed energy and time). In this paper, we take a different perspective on the same problem by considering the case where a mobile vehicle can continuously acquire measurements with a negligible cost, through high rate sampling sensors. In this scenario, it is crucial to reduce the path length that the mobile platform executes to collect the data. To address this issue, we propose a novel algorithm, called Skeleton-Based Orienteering for Level Set Estimation (SBOLSE). Our approach starts from the LSE formulation introduced in [10] and formulates the level set estimation problem as an orienteering problem. This allows one to determine informative locations while considering the length of the path. To combat the complexity associated with the orienteering approach, we propose a heuristic approach based on the concept of topological skeletonization. We evaluate our algorithm by comparing it with the state of the art approaches (i.e., LSE and LSE-batch) both on a real world dataset extracted from mobile platforms and on a synthetic dataset extracted from CO2 maps. Results show that our approach achieves a near optimal classification accuracy while significantly reducing the travel distance (up to 70% w.r.t LSE and 30% w.r.t. LSE-batch).
Propositional Abduction with Implicit Hitting Sets
Authors: Ignatiev Alexey  , Morgado Antonio  , Marques-Silva Joao
Published: February 2025

Logic-based abduction finds important applications in artificial intelligence and related areas. One application example is in finding explanations for observed phenomena. Propositional abduction is a restriction of abduction to the propositional domain, and complexity-wise is in the second level of the polynomial hierarchy. Recent work has shown that exploiting implicit hitting sets and propositional satisfiability (SAT) solvers provides an efficient approach for propositional abduction. This paper investigates this earlier work and proposes a number of algorithmic improvements. These improvements are shown to yield exponential reductions in the number of SAT solver calls. More importantly, the experimental results show significant performance improvements compared to the the best approaches for propositional abduction.
Automatic Bridge Bidding Using Deep Reinforcement Learning
Authors: Yeh Chih-Kuan  , Lin Hsuan-Tien
Published: February 2025

Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which requires cooperative decision making under partial information. Existing artificial intelligence systems for bridge bidding rely on and are thus restricted by human-designed bidding systems or features. In this work, we propose a pioneering bridge bidding system without the aid of human domain knowledge. The system is based on a novel deep reinforcement learning model, which extracts sophisticated features and learns to bid automatically based on raw card data. The model includes an upper-confidence-bound algorithm and additional techniques to achieve a balance between exploration and exploitation. Our experiments validate the promising performance of our proposed model. In particular, the model advances from having no knowledge about bidding to achieving superior performance when compared with a champion-winning computer bridge program that implements a human-designed bidding system.
Decidable Reasoning in a First-Order Logic of Limited Conditional Belief
Authors: Schwering Christoph  , Lakemeyer Gerhard
Published: February 2025

In a series of papers, Liu, Lakemeyer, and Levesque address the problem of decidable reasoning in expressive first-order knowledge bases. Here, we extend their ideas to accommodate conditional beliefs, as in “if she is Australian, then she presumably eats Kangaroo meat.” Perhaps the most prevalent semantics of a conditional belief is to evaluate the consequent in the most-plausible worlds consistent with the premise. In this paper, we devise a technique to approximate this notion of plausibility, and complement it with Liu, Lakemeyer, and Levesque's weak inference. Based on these ideas, we develop a logic of limited conditional belief, and provide soundness, decidability, and (for the propositional case) tractability results.
Strategic Voting in a Social Context: Considerate Equilibria
Authors: Gourvès Laurent  , Lesca Julien  , Wilczynski Anaëlle
Published: February 2025

In a voting system, voters may adopt a strategic behaviour in order to manipulate the outcome of the election. This naturally entails a game theoretic conception of voting. The specificity of our work is that we embed the voting game into a social context where agents and their relations are given by a graph, i.e. a social network. We aim at integrating the information provided by the graph in a refinement of the game-theotical analysis of an election. We consider coalitional equilibria immune to deviations performed by realistic coalitions based on the social network, namely the cliques of the graph. Agents are not fully selfish as they have consideration for their relatives. The corresponding notion of equilibrium was introduced by Hoefer et al. [12] and called considerate equilibrium. We propose to study its existence and the ability of the agents to converge to such an equilibrium in strategic voting games using well-known voting rules: Plurality, Antiplurality, Plurality with runoff, Borda, k-approval, STV, Maximin and Copeland.
Partial Order Temporal Plan Merging for Mobile Robot Tasks
Authors: Mudrova Lenka  , Lacerda Bruno  , Hawes Nick
Published: February 2025

For many mobile service robot applications, planning problems are based on deciding how and when to navigate to certain locations and execute certain tasks. Typically, many of these tasks are independent from one another, and the main objective is to obtain plans that efficiently take into account where these tasks can be executed and when execution is allowed. In this paper, we present an approach, based on merging of partial order plans with durative actions, that can quickly and effectively generate a plan for a set of independent goals. This plan exploits some of the synergies of the plans for each single task, such as common locations where certain actions should be executed. We evaluate our approach in benchmarking domains, comparing it with state-of-the-art planners and showing how it provides a good trade-off between the approach of sequencing the plans for each task (which is fast but produces poor results), and the approach of planning for a conjunction of all the goals (which is slow but produces good results).
Topic-Level Influencers Identification in the Microblog Sphere
Authors: Wang Yakun  , Zhang Zhongbao  , Su Sen  , Chang Cheng  , Zia Muhammad Azam
Published: February 2025

This paper studies the problem of identifying influencers on specific topics in the microblog sphere. Prior works usually use the cumulative number of social links to measure users' topic-level influence, which ignores the dynamics of influence. As a result, they usually find faded influencers. To address the limitations of prior methods, we propose a novel probabilistic generative model to capture the variation of influence over time. Then a influence decay method is proposed to measure users' current topic-level influence.
Strategic Path Planning Allowing on-the-Fly Updates
Authors: Keidar Ofri  , Agmon Noa
Published: February 2025

This work deals with the problem of strategic path planning while avoiding detection by a mobile adversary. In this problem, an evading agent is placed on a graph, where one or more nodes are defined as safehouses. The agent's goal is to find a path from its current location to a safehouse, while minimizing the probability of meeting a mobile adversarial agent at a node along its path (i.e., being captured). We examine several models of this problem, where each one has different assumptions on what the agents know about their opponent, all using a framework for computing node utility. We use several risk attitudes for computing the utility values, whose impact on the actual performance of the path planning algorithms is highlighted by an empirical analysis. Furthermore, we allow the agents to use information gained along their movement, in order to efficiently update their motion strategies on-the-fly. Analytic and empiric analysis show that on-the-fly updates increase the probability that our agent reaches its destination safely.
Using a Deep Understanding of Network Activities for Network Vulnerability Assessment
Authors: Lange Mona  , Kuhr Felix  , Möller Ralf
Published: February 2025

In data-communication networks, network reliability is of great concern to both network operators and customers. Therefore, network operators want to determine what services could be affected by software vulnerabilities being exploited that are present within their data-communication network. To determine what services could be affected by a software vulnerability being exploited, it is fundamentally important to know the ongoing tasks in a network. A particular task may depend on multiple network services, spanning many network devices. Unfortunately, dependency details are often not documented and are difficult to discover by relying on human expert knowledge. In monitored networks huge amounts of data are available and by applying data mining techniques, we are able to extract information of ongoing network activities. From a data mining perspective, we are interested to test the potential of applying data mining techniques to real-life applications.
Employing Hypergraphs for Efficient Coalition Formation with Application to the V2G Problem
Authors: Christianos Filippos  , Chalkiadakis Georgios
Published: February 2025

This paper proposes, for the first time in the literature, the use of hypergraphs for the efficient formation of effective coalitions. We put forward several formation methods that build on existing hypergraph algorithms, and exploit hypergraph structure to identify agents with desirable characteristics. Our approach allows the near-instantaneous formation of high quality coalitions, while adhering to multiple stated requirements regarding coalition quality. Moreover, our methods are shown to scale to dozens of thousands of agents within fractions of a second; with one of them scaling to even millions of agents within seconds. We apply our approach to the problem of forming coalitions to provide (electric) vehicle-to-grid (V2G) services. Ours is the first approach able to deal with large-scale, realtime coalition formation for the V2G problem, while taking multiple criteria into account for creating electric vehicle coalitions.
A Typicality-Based Revision to Handle Exceptions in Description Logics
Authors: Micalizio Roberto  , Pozzato Gian Luca
Published: February 2025

We propose a methodology to revise a Description Logic knowledge base when detecting exceptions. Our approach relies on the methodology for debugging a Description Logic terminology, addressing the problem of diagnosing inconsistent ontologies by identifying a minimal subset of axioms responsible for an inconsistency. In the approach we propose, once the source of the inconsistency has been localized, the identified axioms are revised in order to obtain a consistent knowledge base including the detected exception about an individual x. To this aim, we make use of a nonmonotonic extension of the Description Logic ALC based on the combination of a typicality operator and the well established nonmonotonic mechanism of rational closure, which allows to deal with prototypical properties and defeasible inheritance.
Hiding Actions in Concurrent Games
Authors: Malvone Vadim  , Murano Aniello  , Sorrentino Loredana
Published: February 2025

We study a class of determined two-player reachability games, played by Player0and Player1under imperfect information. Precisely, we consider the case in which Player0wins the game if Player1cannot prevent him from reaching a target state. We show that the problem of deciding such a game is EXPTIME-COMPLETE.
How Good Is Predictive Routing in the Online Version of the Braess Paradox?
Authors: Varga László Z.
Published: February 2025

With the online routing game model we can investigate the online routing problem, where each subsequent agent of the traffic flow may select different route based on real-time data. Recent investigations proved that if the agents of such system use the selfish shortest path search strategy, then in some situations sometimes the multi-agent system may be worse off with real-time data than without real-time data, even if anticipatory techniques are applied to predict the future state of the environment. We investigate the online Braess paradox, where each subsequent agent of the traffic flow may select different route, using anticipatory techniques.
Lexicographic Refinements in Possibilistic Decision Trees
Authors: Ben Amor Nahla  , El Khalfi Zeineb  , Fargier Hélène  , Sabbadin Régis
Published: February 2025

Possibilistic decision theory has been proposed twenty years ago and has had several extensions since then. Because of the lack of decision power of possibilistic decision theory, several refinements have then been proposed. Unfortunately, these refinements do not allow to circumvent the difficulty when the decision problem is sequential. In this article, we propose to extend lexicographic refinements to possibilistic decision trees. We show, in particular, that they still benefit from an Expected Utility (EU) grounding. We also provide qualitative dynamic programming algorithms to compute lexicographic optimal strategies. The paper is completed with an experimental study that shows the feasibility and the interest of the approach.
Validating Cross-Perspective Topic Modeling for Extracting Political Parties' Positions from Parliamentary Proceedings
Authors: van der Zwaan Janneke M.  , Marx Maarten  , Kamps Jaap
Published: February 2025

In the literature, different topic models have been introduced that target the task of viewpoint extraction. Because, generally, these studies do not present thorough validations of the models they introduce, it is not clear in advance which topic modeling technique will work best for our use case of extracting viewpoints of political parties from parliamentary proceedings. We argue that the usefulness of methods like topic modeling depend on whether they yield valid and reliable results on real world data. This means that there is a need for validation studies. In this paper, we present such a study for an existing topic model for viewpoint extraction called cross-perspective topic modeling [11]. The model is applied to Dutch parliamentary proceedings, and the resulting topics and opinions are validated using external data. The results of our validation show that the model yields valid topics (content and criterion validity), and opinions with content validity. We conclude that cross-perspective topic modeling is a promising technique for extracting political parties' positions from parliamentary proceedings. Second, by exploring a number of validation methods, we demonstrate that validating topic models is feasible, even without extensive domain knowledge.
A New Kernelized Associative Memory and Some of Its Applications
Authors: Saltz Matthew  , Belanche Lluís A.
Published: February 2025

The classical Bidirectional Associative Memory (BAM) allows for the storage of pairs of vectors, such that when either member of the pair is presented to the BAM, the other member may be successfully recalled. This work presents a novel BAM, improved with respect to its capacity and noise performance through the use of the kernel trick, a common technique in machine learning for transforming linear methods into nonlinear methods. By kernelizing the BAM's energy function directly and defining new methods for recall, the kernel BAM shows improved performance compared to both the original BAM as well as a previously existing nonlinear BAM. This is demonstrated with thorough experimentation on synthetic datasets, and several practical applications are given on real data.
Belief Contraction Within Fragments of Propositional Logic
Authors: Creignou Nadia  , Ktari Raïda  , Papini Odile
Published: February 2025

Recently, belief change within the framework of fragments of propositional logic has gained attention. In the context of revision it has been proposed to refine existing operators so that they operate within propositional fragments, and that the result of revision remains in the fragment under consideration. In this paper we generalize this notion of refinement to belief change operators. Whereas the notion of refinement allowed one to define concrete rational operators adapted to propositional fragments in the context of revision and update, it has to be specified for contraction. We propose a specific notion of refinement for contraction operators, called reasonable refinement. This allows us to provide refined contraction operators that satisfy the basic postulates for contraction. We study the logical properties of reasonable refinements of two well-known model-based contraction operators. Our approach is not limited to the Horn fragment but applicable to many fragments of propositional logic, like Horn, Krom and affine fragments.
Repetitive Branch-and-Bound Using Constraint Programming for Constrained Minimum Sum-of-Squares Clustering
Authors: Guns Tias  , Dao Thi-Bich-Hanh  , Vrain Christel  , Duong Khanh-Chuong
Published: February 2025

Minimum sum-of-squares clustering (MSSC) is a widely studied task and numerous approximate as well as a number of exact algorithms have been developed for it. Recently the interest of integrating prior knowledge in data mining has been shown, and much attention has gone into incorporating user constraints into clustering algorithms in a generic way.
Is Spearman's Law of Diminishing Returns (SLODR) Meaningful for Artificial Agents?
Authors: Hernández-Orallo José
Published: February 2025

The progress of artificial intelligence is reaching a point that some research questions that were only relevant for human and other animal agents are becoming relevant for artificial agents as well. One of those questions comes from human intelligence research and is known as Spearman's Law of Diminishing Returns (SLODR). Charles Spearman, the father of factor analysis and the g factor (a dominant factor explaining most of the variance in cognitive tests for human populations), observed that when the analysis was restricted to the subpopulation of most able subjects, the relevance of this dominant factor diminished, as if the power of general intelligence were saturated or not fully used by the most able individuals. In about a century, there have been numerous theoretical explanations and experiments to confirm or reject Spearman's hypothesis. However, all of them have been based on human or animal populations. In this paper, we analyse for the first time whether the SLODR makes sense for artificial agents and what its role should be in the analysis of general-purpose AI. We use a synthetic scenario based on modified elementary cellular automata (ECA) where the ECA rules work as tasks and the population of agents is generated with an agent policy language. Different slices of the population by ability and of the tasks by difficulty are analysed, showing that SLODR does not really appear. Indeed, even if very slightly, we find the reverse, i.e., that more correlation takes place for more able subpopulations, what we conjecture as the Universal Law of Augmenting Returns (ULOAR).
Vertical Optimization of Resource Dependent Flight Paths
Authors: Knudsen Anders N.  , Chiarandini Marco  , Larsen Kim S.
Published: February 2025

Flight routes are paths calculated on a network of waypoints representing 3D-coordinates. A common approach is first to calculate a path in a 2D-network, taking into account feasibility constraints, and then to optimize the altitude of the flight.
Interval-Based Relaxation for General Numeric Planning
Authors: Scala Enrico  , Haslum Patrik  , Thiebaux Sylvie  , Ramirez Miquel
Published: February 2025

We generalise the interval-based relaxation to sequential numeric planning problems with non-linear conditions and effects, and cyclic dependencies. This effectively removes all the limitations on the problem placed in previous work on numeric planning heuristics, and even allows us to extend the planning language with a wider set of mathematical functions. Heuristics obtained from the generalised relaxation are pruning-safe. We derive one such heuristic and use it to solve discrete-time control-like planning problems with autonomous processes. Few planners can solve such problems, and search with our new heuristic compares favourably with them.