bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision Making on Strategic Investment: How to Determine the Specification of BTO Products
Authors: Sato Yuji
Published: February 2025

This paper considers how to optimize the specification of new built-to-order technology system which takes into user's preference for the system for a manufacturing company. This paper addresses this issue by combining cost-benefit analysis evaluating potential treatment system, and the AHP quantifying subjective judgment in evaluations. A case study is carried out to demonstrate the applicability of the proposed approach. The results show some evidence that the diagnosis procedure succeeded in quantifying user's preference for alternative systems, and that the specification was optimized successfully.
Decomposition of a Term-Document Matrix Representation for Faithful Customer Analysis
Authors: Yang Jianxiong  , Watada Junzo
Published: February 2025

The recent rapid growth of the services industry has led to an increase in the number of service quality improvement research studies. However, analyzing service quality and determining the factors influencing consumers' perceptions of service quality are a challenging problem. The objective of this paper was to apply a data mining method to the current problems of Customer Relationship Management (CRM), analyze corporate communications systems and then identify possible data mining applications. We apply simple statistical and machine-learning techniques to study the dynamics of occurrence frequencies of events by scrutinizing user comments and corresponding customer satisfaction scores. Our analysis revealed that in the context of customer support centers, the service experience of customers strongly influences the satisfaction and service quality that the customers experienced. As a result of this study, we have identified a method of capturing the hearts of faithful customers.
MobiS - Personalized Mobility Services for energy efficiency and security through advanced Artificial Intelligence techniques
Authors: Costa Ruben  , Figueiras Paulo  , Maló Pedro  , Jermol Mitja  , Kalaboukas Kostas
Published: February 2025

MobiS is a European research project funded under the FP7 work programme. The main goal of MobiS is to create a new concept and solution of a federated, customized and intelligent mobility platform by applying novel Future Internet technologies and Artificial Intelligence methods that will monitor, model and manage the urban mobility complex network of people, objects, natural, social and business environment in real-time. MobiS intelligence is based on the symbiotic relation between these stakeholders, innovative prediction and reasoning methods that are using learned multi-criteria function to provide more efficient, energy-aware and environmental friendly citizen mobility. MobiS will be able to combine novel artificial intelligence services and traditional information platform services coming from the following sources: a) existing transport private or public service providers, b) ambient data, based on sensor infrastructures and c) social networking data. To achieve these challenging objectives, the project will develop prediction/planning/reasoning services, multi-criteria decision functions and, federated mobility-based services that correspond to the above mentioned information sources. Solutions will be tested in three pilots: 1) An inter-city mobility scenario in Sweden (Stockholm-Hudiksvall-Sundsvall), where an existing crowdsourcing application will be used, 2) an intra-city scenario in Greece (Thessaloniki) combining a traffic information system and the crowdsourcing application and, 3) a country-wide (inter-city) mobility scenario in Slovenia with a social media application, and selected traffic information system already operated in various parts of the country and in the main cities.
Outliers Accommodation in Fuzzy Control Systems over WSAN
Authors: Januário F.  , Santos A.  , Lucena C.  , Palma L.  , Cardoso A.  , Gil P.
Published: February 2025

Wireless sensor and actuator networks have become an important research topic in the control systems area. These networks can provide flexibility, low operational and maintenance costs as well as scalability, which are important characteristics in the context of supervision systems. In networked control applications over wireless sensor networks, a particular source of performance degradation stems from outliers in sampled raw data. This demands the implementation of methodologies for their detection and accommodation. In the context of the networked control system over wireless sensor and actuator networks this paper proposes a detection and accommodation architecture based on the multi-agent paradigm, while the control system relies on a PID-fuzzy controller, where the scaling factors are tuned by solving a nonlinear constrained optimization problem. Experimental results collected from a laboratory testbed show the relevance of incorporating an outliers cancellation methodology.
Cartogram-Based Data Visualization Using the Growing Hierarchical SOM
Authors: Martín Àngela  , Vellido Alfredo
Published: February 2025

Model interpretability is a problem of knowledge extraction from the patterns found in data to which data visualization can contribute. Nonlinear dimensionality reduction techniques provide flexible visual insight, but their locally varying representation distortion makes interpretation far from intuitive. In this paper, we apply a cartogram method, based on techniques of geographic representation, to data visualization. It allows reintroducing this distortion, measured as a U-matrix, in the visual maps of the Growing Hierarchical Self Organising Map (GHSOM).
A Comparative Study of Stream Ciphers and Hash Functions for RFID Authentications
Authors: Mikami Shugo  , Watanabe Dai  , Sakiyama Kazuo
Published: February 2025

RFID tags are extensively used in many applications, even though RFID systems suffer from security and privacy risks, such as data forgery and tracking. RFID authentication protocols and lightweight cryptographic algorithms have been developed to overcome these risks. Saarinen et al. have studied some design requirements for the lightweight cryptographic algorithms from the viewpoint of implementation [33]. They have proposed lightweight stream ciphers to generate Tag-IDs. However, the Tag-ID length they evaluated is too short to realize secure RFID authentications and compact implementations of the lightweight hash functions are inappropriate for generating the Tag-IDs because they take a large number of clock cycles. In this paper, we evaluate hardware performance of certain lightweight stream ciphers for generating long Tag-IDs defined in RFID standards, such as the EPC Data Standard and ISO/IEC 15963. We evaluate hardware performance of certain lightweight hash functions with parallel implementation to meet a low area requirement and to achieve high speed performance. We show that as the Tag-IDs become longer, the hash functions take a large number of clock cycles while the stream ciphers take smaller number of clock cycles. Our results reveal that the light weight stream ciphers are suitable for generating the Tag-IDs for RFID applications which require quick responses.
Combining Constraint Types From Public Data in Aerial Image Segmentation
Authors: Jacobsen Thomas Stig  , Jensen Jacob Jon  , Jensen Daniel Rune  , Samuelsen Niels Nørgaard
Published: February 2025

We introduce a method for image segmentation that constraints the clustering with map and point data. The method is showcased by applying the spectral clustering algorithm on aerial images for building detection with constraints built from a height map and address point data. We automatically detect the number of clusters using the elongated K-means algorithm instead of using the standard spectral clustering approach of a predefined number of clusters. The results are refined by filtering out noise with a binary morphological operator. Point data is used for semi-supervised labelling of building clusters. Our evaluation show that the combination of constraints have a positive impact on the clustering quality achieved. Finally we argue how the presented constraint types may be used in other applications.
Formalizing Theatrical Performances Using Multi-Agent Organizations
Authors: Jensen Andreas Schmidt  , Spurkeland Johannes Svante  , Villadsen Jørgen
Published: February 2025

Theatrical performances usually follow strict scripts and actors are not allowed to deviate. A Danish theatrical group, Theater 770° Celsius, has invented a new method called In Real Life, in which only certain events in the storyline are specified and the actors are supposed to improvise to reach these events. The method bears a resemblance to multi-agent systems and we show how it can be formalized using the multi-agent organizational model OperA.
Defining dependencies (and constituents)
Authors: Gerdes Kim  , Kahane Sylvain
Published: February 2025

The paper proposes a mathematical method of defining dependency and constituency provided linguistic criteria to characterize the acceptable fragments of an utterance have been put forward. The method can be used to define syntactic structures of sentences, as well as discourse structures for texts, or even morphematic structures for words. Our methodology leads us to put forward the notion of connection, simpler than dependency, and to propose various representations which are not necessarily trees. We also study criteria to refine and hierarchize a connection structure in order to obtain a tree.
Modeling Collections of French Local Administration Documents
Authors: Mimouni Nada  , Salotti Sylvie  , Paul Ève
Published: February 2025

The French local administration produces a huge amount of heterogeneous and interdependent documents, which creates a content management issue for the administrators and an increasing need of accessibility for citizens. To meet those requirements, the Légilocal project, a French initiative, develops tools to ease the production of local administrative acts, the sharing of information among local administration clerks and the semantic search of documents. This paper presents the underlying Légilocal ontology-based documents model which takes into account not only the semantic annotations of documents, their various types, their logical structures and their different versions but also the document collection viewed as a semantic network of documents.
Swiss Post and Public Administration: From Physical to Digital
Authors: Morel Denis
Published: February 2025

Swiss Post has been innovating for years, honing its expertise as an actor in the digital world and exploiting the unique selling propositions it owns in the physical world. The newest example of its successful entry into the digital realm is the electronic health record, with which Swiss Post ensures the secure exchange of medical data. The development of digital services is related to the role of a trusted intermediary that Swiss Post already plays in the physical world, and one that it aims to also assume in the digital world in the future, in particular for government-related processes. This chapter shows the development of Swiss Post's digital service for the government, as well as providing examples and sharing thoughts on future developments.
Postal Operators as Viable e-Government Partners: A Case Study of Three Major Postal Operators
Authors: Burhan Muqbil
Published: February 2025

The digital economy has transformed several industries, resulting in higher productivity, greater consumer choice and lower prices.Digital platforms of communication are producing massive competition in the postal sector. Traditional mail volume is down, while delivery points are up. The digital world provides opportunities to various public, as well as private, organizations to broaden their services and product range. Most postal operators worldwide have responded positively to this opportunity, and adopted postal e-services to improve the quality of existing services and introduce new value-added services. e-Government, which involves use of information and communication technology to facilitate the development of small and efficient government structures, has found a natural implementation partner in postal services, which have a vast and extensive network of outlets and a broad outreach in the country. This study explores the extent of the various roles played by different postal operators in implementing e-government services. Case studies based on Empresa Brasileira de Correios e Telegrafos, Post Italiane and Saudi Post are taken up to analyze their role as e-government service-provider partners. The study will help to outline the potential role of postal operators in helping governments implement digital services.
The Digital Postal Network at the Heart of Service Innovation
Authors: Donohoe Paul
Published: February 2025

The postal network has been at the heart of communications and commerce for centuries, across vast countries and around the globe. It is a “network of networks” that covers the world. The expansion of the Internet and the development of the Information Society in the past 20 years has challenged the postal industry to evolve and innovate, in order to be relevant to the new needs of the digital citizen. Universal Postal Union (UPU) research on the development of new services has shown that this evolution continues to gather momentum every day. At the heart of this innovation is the emergence of a digital postal network, which is a complementary channel for the availability of postal services. This is again a “network of networks,” which interconnects national digital postal services to provide international services, thereby enabling greater trust in cross-border communication and commerce in the digital space. The UPU is tasked by its members to extend the single postal territory, embrace innovations around digital postal services, and build a technical and legal framework under which these international services can be managed and trusted.
Knowledge Sharing among Users of Scientific Social Networking Platforms
Authors: Corvello Vincenzo  , Genovese Alessia  , Verteramo Saverino
Published: February 2025

This study presents an empirical research, which examines how scientific social networking sites can support researchers' work. In particular, the study investigates knowledge sharing behaviors of users of the scientific platform ResearchGate. A questionnaire was distributed to members of the social network. One hundred and fifteen responses were obtained. The results of this study provide a profile of scientific social networks users. Users consider these platforms mainly as a means to communicate their results and their knowledge. The level of utilization of knowledge obtained from these social networks is still low.
Ephemeral & Persistent Performances – A Framework for Assessing Profile-Centric Social Computing Use
Authors: Sugrue Aonghus  , Feller Joseph  , Carton Fergal
Published: February 2025

Decisions to use technology are often based on an instrumental desire to achieve some immediate result. An enduring principle of effective design is the presentation of clear and comprehensive feedback to the user to facilitate their assessment of whether or not the interaction with a particular object resulted in a desired and expected outcome. However, emerging systems in the realm of social computing appear to often violate this principle as a user is typically provided with limited observable feedback on the full consequences of their ephemeral interactions with the social computing system. The design of such technologies often result in feedback presented to the user which, although sufficiently comprehensive to ensure the users' instrumental need is satisfactorily met, conceals the full spectrum of future consequences. Such limited but ‘sufficient’ feedback may lead to the user continuing to engage with the social computing technology without fully appreciating the entire consequences of same. This limited feedback becomes problematic when considering profile-centric social computing platform use, which is intimately associated with an individuals' identity. Towards addressing this issue, this conceptual paper presents a novel framework, built using a sociomaterial performativity perspective along with an exhibitionist perspective, which seeks to enable current and prospective users to interrogate potential consequences of their intended social computing use. The implications of this a priori interrogation of potential consequences of IT use suggest the importance of questioning the conventional wisdom of “learn by doing” when considering contemporary technologies: indeed “learn before doing” may be more appropriate in certain scenarios.
Steady-State Dynamic Steering
Authors: Marzbani Hormoz  , Ahmad Salahuddin Mohd Harithuddin  , Simic Milan  , Fard M.  , Jazar Reza N.
Published: February 2025

In order to keep the autonomous car on a road, using the autodriver algorithm [1], kinematic condition of steering has been used to set the steer angles such that the kinematic center of rotation is on the road curvature center. The road and tire characteristics, along with the dynamics of the moving vehicle cause the vehicle to turn around another point that is not necessarily at the road curvature center. In other words the position of the dynamic turning center is different from the kinematically calculated one. Steady-state responses have been used, to produce a new method for finding the steer angles which will result in a much closer dynamic response to the actual road. The method and the proof have been explained. It is used for determining the steer angles for a sample road, and validated by comparing the results using the kinematically calculated steering angles.
Bayesian Inferential Reasoning Model for Crime Investigation
Authors: Wang Jing  , Xu Zhijie
Published: February 2025

Forensic inferential reasoning is a “fact-finding” journey for crime investigation and evidence presentation. In complex legal practices involving various forms of evidence, conventional decision making processes based on human intuition and piece-to-piece evidence explanation often fail to reconstruct meaningful and convincing legal hypothesis. It is necessary to develop logical system for evidence management and relationship evaluations. In this paper, a forensic application-oriented inferential reasoning model has been devised base on Bayesian Networks. It provides an effective approach to identify and evaluate possible relationships among different evidence. The model has been developed into an adaptive framework than can be further extended to support information visualisation and interaction. Based on the system experiments, the model has been successfully used in verifying the logical relationships between DNA testing results and confessions acquired from the suspect in a simulated criminal investigation, which provided a firm foundation for the future developments.
Conditioned Belief Propagation Revisited
Authors: Geier Thomas  , Richter Felix  , Biundo Susanne
Published: February 2025

Belief Propagation (BP) applied to cyclic problems is a well known approximate inference scheme for probabilistic graphical models. To improve the accuracy of BP, a divide-and-conquer approach termed Conditioned Belief Propagation (CBP) has been proposed in the literature. It recursively splits a problem by conditioning on variables, applies BP to subproblems, and merges the results to produce an answer to the original problem. In this essay, we propose a reformulated version of CBP that exhibits anytime behavior, and allows for more specific tuning by formalizing a further decision point that decides which subproblem is to be decomposed next. We propose some simple and easy to compute heuristics, and demonstrate their performance using an empirical evaluation on randomly generated problems.
Comparing Data Distribution Using Fading Histograms
Authors: Sebastião Raquel  , Gama João  , Mendonça Teresa
Published: February 2025

The emergence of real temporal applications under non-stationary scenarios has drastically altered the ability to generate and gather information. Nowadays, under dynamic scenarios, potentially unbounded and massive amounts of information are generated at high-speed rate, known as data streams. Dealing with evolving data streams imposes the online monitoring of data in order to detect changes. The contribution of this paper is to present the advantage of using fading histograms to compare data distribution for change detection purposes. In an windowing scheme, data distributions provided by the fading histograms are compared using the Kullback-Leibler divergence. The experimental results support that the detection delay time is smaller when using fading histograms to represent data instead of standard histograms.
Influence of internal values and social networks for achieving sustainable organizations
Authors: Sánchez-Maroño N.  , Alonso-Betanzos A.  , Fontenla-Romero O.  , Brinquis-Núñez C.  , Polhill J.G.  , Craig T.
Published: February 2025

The LOw Carbon At Work (LOCAW) project has studied the everyday behavior of employees in six organizations in order to achieve a more sustainable Europe. Of these six, four organizations were involved in backcasting workshops to obtain future scenarios aimed at significantly improving engagement with pro-environmental behaviors by 2050. From these scenarios policies were extracted from the workshop participants that achieve this aim in their organization. Agent Based Models (ABM) were designed to model the organizations using actual information from the organization; the design also placed special emphasis on the representation of the social network. ABMs were then used to simulate the effects of the different policies derived from the backcasting scenarios. In this paper, the results for two organizations, UDC and Aquatim, are shown. These experimental results demonstrate the influence of different social networks and internal motivations of employees to determine the effectiveness of a given policy.
Bargaining for Coalition Structure Formation
Authors: Fatima Shaheen  , Michalak Tomasz  , Wooldridge Michael
Published: February 2025

Many multiagent settings require a collection of agents to partition themselves into coalitions. In such cases, the agents may have conflicting preferences over the possible coalition structures that may form. We investigate a noncooperative bargaining game to allow the agents to resolve such conflicts and partition themselves into non-overlapping coalitions. The game has a finite horizon and is played over discrete time periods. The bargaining agenda is defined exogenously. An important element of the game is a parameter 0≤δ≤1 that represents the probability that bargaining ends in a given round. Thus, δ is a measure of the degree of democracy (ranging from democracy for δ=0, through increasing levels of authoritarianism as δ approaches 1, to dictatorship for δ=1). For this game, we focus on the question of how a player's position on the agenda affects his power. We also analyse the relation between the distribution of the power of individual players, the level of democracy, and the welfare efficiency of the game. Surprisingly, we find that purely democratic games are welfare inefficient due to an uneven distribution of power among the individual players. Interestingly, introducing a degree of authoritarianism into the game makes the distribution of power more equitable and maximizes welfare.
Controlling Two-Stage Voting Rules
Authors: Guo Jiong  , Raj Shrestha Yash
Published: February 2025

We study the computational complexity of control problems for two-stage voting rules. An example of a two-stage voting rule is the Black's procedure. The first stage of the Black's procedure selects the Condorcet winner if one exists; otherwise, in the second stage the Borda winner is selected. The computational complexity of the manipulation problem of two-stage voting rules has recently been studied by Narodytska and Walsh [20] and Fitzsimmons et al. [14]. Extending their work, we consider the control problems for similar scenarios, focusing on constructive control by adding or deleting votes, denoted as CCAV and CCDV, respectively.
Combining Restarts, Nogoods and Decompositions for Solving CSPs
Authors: Jégou Philippe  , Terrioux Cyril
Published: February 2025

From a theoretical viewpoint, the (tree-)decomposition methods offer a good approach when the (tree)-width of constraint networks (CSPs) is small. In this case, they have often shown their practical interest. However, sometimes, a bad choice for the root cluster (a tree-decomposition is a tree of clusters) may drastically degrade the performance of the solving.
Constrained Latent Dirichlet Allocation for Subgroup Discovery with Topic Rules
Authors: Li Rui  , Ahmadi Zahra  , Kramer Stefan
Published: February 2025

Subgroup discovery is the task of identifying subgroups that show the most unusual statistical (distributional) characteristics with respect to a given target variable, at the intersection of predictive and descriptive induction. Redundancy and lack of rule interpretability constitute the major challenges in subgroup discovery today. We address these two issues by constrained latent Dirichlet allocation (LDA) to identify co-occurring feature values (descriptions) for subgroup rule search, obtaining a less redundant and more diverse rule set. Latent Dirichlet Allocation, as a topic modeling approach, is able to identify diverse topics, from which the rules can be derived. The resulting rules are less redundant and can also be interpreted by the corresponding topic. Experimental results on six benchmark datasets show that the presented approach provides rule sets with better rule redundancy and diversity compared to those of four existing algorithms. One unique and interesting advantage of the proposed method is that it can categorize rules by topics as well as the assignment of a probability to each feature value of a discovered rule, which can be used in the interpretation of the results.
The Logical Difference for ℰℒℋ
Authors: Ludwig Michel  , Walther Dirk
Published: February 2025

We propose a novel approach for detecting semantic differences between ontologies. In this paper we investigate the logical difference for ℰℒ-terminologies extended with role inclusions, and domain & range restrictions of roles. Three types of queries are covered: concept subsumption, instance and conjunctive queries. Using a hypergraph representation of such ontologies, we show that logical differences can be detected by checking for the existence of simulations between the corresponding hypergraphs. A minor adaptation of the simulation notions allows us to capture different types of queries. We also evaluate our hypergraph approach by applying a prototype implementation on large ontologies.
Some Elements for a Prehistory of Artificial Intelligence in the Last Four Centuries
Authors: Marquis Pierre  , Papini Odile  , Prade Henri
Published: February 2025

Artificial intelligence (AI) was not born ex nihilo in the mid-fifties of the XXthcentury. Beyond its immediate roots in cybernetics and in computer science that started about two decades before, its emergence is the result of a long and slow process in the history of humanity. This can be articulated around two main questions: the formalization of reasoning and the design of machines having autonomous capabilities in terms of computation and action. The aim of this paper is to gather some insufficiently known elements about the prehistory of AI in the last 350 years that precede the official birth of AI, a time period where only a few very well-known names, such as Thomas Bayes and Georges Boole, are usually mentioned in relation with AI.
Imprecise Probabilistic Horn Clause Logic
Authors: Michels Steffen  , Hommersom Arjen  , Lucas Peter J.F.  , Velikova Marina
Published: February 2025

Approaches for extending logic to deal with uncertainty immanent to many real-world problems are often on the one side purely qualitative, such as modal logics, or on the other side quantitative, such as probabilistic logics. Research on combinations of qualitative and quantitative extensions to logic which put qualitative constraints on probability distributions, has mainly remained theoretical until now. In this paper, we propose a practically useful logic, which supports qualitative as well as quantitative uncertainty and can be extended with modalities with varying level of quantitative precision. This language has a solid semantic foundation based on imprecise probability theory. While in general imprecise probabilistic inference is much harder than the precise case, this is the first expressive imprecise probabilistic formalism for which probabilistic inference is shown to be as hard as corresponding precise probabilistic problems. A second contribution of this paper is an inference algorithm for this language based on the translation to a weighted model counting (WMC) problem, an approach also taken by state-of-the-art probabilistic inference methods for precise problems.
Statistical Constraints
Authors: Rossi Roberto  , Prestwich Steven  , Tarim S. Armagan
Published: February 2025

We introduce statistical constraints, a declarative modelling tool that links statistics and constraint programming. We discuss two statistical constraints and some associated filtering algorithms. Finally, we illustrate applications to standard problems encountered in statistics and to a novel inspection scheduling problem in which the aim is to find inspection plans with desirable statistical properties.
Hybrid Query Answering Over OWL Ontologies
Authors: Stoilos Giorgos  , Stamou Giorgos
Published: February 2025

Query answering over OWL 2 DL ontologies is an important reasoning task for many modern applications. Unfortunately, due to its high computational complexity, OWL 2 DL systems are still not able to cope with datasets containing billions of data. Consequently, application developers often employ provably scalable systems which only support a fragment of OWL 2 DL and which are, hence, most likely incomplete for the given input. However, this notion of completeness is too coarse since it implies that there exists some query and some dataset for which these systems would miss answers. Nevertheless, there might still be a large number of user queries for which they can compute all the right answers even over OWL 2 DL ontologies. In the current paper, we investigate whether, given a query 𝒬 with only distinguished variables over an OWL 2 DL ontology 𝒯 and a system ans, it is possible to identify in an efficient way if ans is complete for 𝒬, 𝒯 and every dataset. We give sufficient conditions for (in)completeness and present a hybrid query answering algorithm which uses ans when it is complete, otherwise it falls back to a fully-fledged OWL 2 DL reasoner. However, even in the latter case, our algorithm still exploits ans as much as possible in order to reduce the search space of the OWL 2 DL reasoner. Finally, we have implemented our approach using a concrete system ans and OWL 2 DL reasoner obtaining encouraging results.
The PeerRank Method for Peer Assessment
Authors: Walsh Toby
Published: February 2025

We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages. We identify some formal properties of the PeerRank method (for example, it satisfies axioms of unanimity, no dummy, no discrimination and symmetry), discuss some examples, compare with related work and evaluate the performance on some synthetic data. Our results show considerable promise, reducing the error in grade predictions by a factor of 2 or more in many cases over the natural baseline of averaging peer grades.
Preference Inference Based on Lexicographic Models
Authors: Wilson Nic
Published: February 2025

With personalisation becoming more prevalent, it can often be useful to be able to infer additional preferences from input user preferences. Preference inference techniques assume a set of possible user preference models, and derive inferences that hold in all models satisfying the inputs; the more restrictive one makes the set of possible user preference models, the more inferences one gets. Sometimes it can be useful to have an adventurous form of preference inference when the input information is relatively weak, for example, in a conversational recommender system context, to give some justification for showing some options before others. This paper considers an adventurous inference based on assuming that the user preferences are lexicographic, and also an inference based on an even more restrictive preference model. We show how preference inference can be efficiently computed for these cases, based on a relatively general language of preference inputs.
Analysis of interval-based possibilistic networks
Authors: Benferhat Salem  , Lagrue Sylvain  , Tabia Karim
Published: February 2025

This paper proposes interval-based possibilistic networks. This extension allows to compactly encode and reason with epistemic uncertainty and imprecise beliefs as well as with multiple expert knowledge. We propose a natural semantics based on compatible possibilistic networks. The paper shows finally that computing uncertainty bounds of an event can be done in interval-based networks without extra computational cost.
Performance Evaluation of Vector Evaluated Gravitational Search Algorithm II
Authors: Muhammad Badaruddin  , Ibrahim Zuwairie  , Ghazali Kamarul Hawari  , Ghazali Mohd Riduwan  , Saealal Muhammad Salihin  , Lim Kian Sheng  , Nawawi Sophan Wahyudi  , Ab. Aziz Nor Azlina  , Mubin Marizan  , Mokhtar Norrima
Published: February 2025

This paper presents a performance evaluation of a novel Vector Evaluated Gravitational Search Algorithm II (VEGSAII) for multi-objective optimization problems. The VEGSAII algorithm uses a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximization of every objective function is realized by exchanging a variable between populations. The results shows that the VEGSA is outperformed by other multi-objective optimization algorithms and further enhancements are needed before it can be employed in any application.
The DIAMOND System for Computing with Abstract Dialectical Frameworks
Authors: Ellmauthaler Stefan  , Strass Hannes
Published: February 2025

We present DIAMOND, an implementation of Brewka and Woltran's abstract dialectical frameworks (ADFs). The system uses answer set programming encodings to compute interpretations of ADFs according to various semantics. We evaluate the performance of the system using an actual reasoning problem as opposed to using randomly generated frameworks.
ContoExam: an ontology on context-aware examinations
Authors: Brandt Paul  , Basten Twan  , Stuijk Sander
Published: February 2025

Patient observations in health care, subjective surveys in social research or dyke sensor data in water management are all examples of measurements. Several ontologies already exist to express measurements, W3C's SSN ontology being a prominent example. However, these ontologies address quantities and properties as being equal, and ignore the foundation required to establish comparability between sensor data. Moreover, a measure of an observation in itself is almost always inconclusive without the context in which the measure was obtained. ContoExam addresses these aspects, providing for a unifying capability for context-aware expressions of observations about quantities and properties alike, by aligning them to ontological foundations, and by binding observations inextricably with their context.
Multi-Domain Recurrent Neural Network Language Model for Medical Speech Recognition
Authors: Tilk Ottokar  , Alumäe Tanel
Published: February 2025

We evaluate back-off n-gram and recurrent neural network language models for an automatic speech recognition system for medical applications. We also propose an effective and simple multi-domain recurrent neural network architecture which enables training a joint model for all domains. The multi-domain recurrent neural network model outperforms all other compared models.
The Development of Conversational Agent Based Interface
Authors: Vīra Inese  , Vasiļjevs Andrejs
Published: February 2025

In this paper we present two prototypes of 3D based virtual agents: one chatbot which in addition to the ability to hold a conversation can perform translation from English into Spanish, Russian, and French; and another which supplies currency conversion (lats to euro and euro to lats) in the Latvian language. Both chatbots are voice controlled, with natural mimicry and representations of human-like emotions. We describe the motivation, development process, design and architecture of these mobile applications. The evaluation of both applications and their usage in selected scenarios is also presented.
Comparing feature-based and distance-based representations for classification similarity learning
Authors: López-Iñesta Emilia  , Grimaldo Francisco  , Arevalillo-Herráez Miguel
Published: February 2025

The last decades have shown an increasing interest in studying how to automatically capture the likeness or proximity among data objects due to its importance in machine learning and pattern recognition. Under this scope, two major approaches have been followed that use either feature-based or distance-based representations to perform learning and classification tasks. This paper presents the first results of a comparative experimental study between these two approaches for computing similarity scores using a classification-based method. In particular, we use the Support Vector Machine, as a flexible combiner both for a high dimensional feature space and for a family of distance measures, to finally learn similarity scores in a CBIR context. We analyze both the influence of the different input data formats and the training size on the performance of the classifier. Then, we found that a low dimensional multidistance-based representation can be convenient for small to medium-size training sets whereas it is detrimental as the training size grows.
Fear Assessment: Why data center servers should be turned off
Authors: Fernández-Cerero Damián  , Fernández-Montes Alejandro  , González-Abril Luis  , Ortega Juan A.  , Álvarez Juan A.
Published: February 2025

The purpose of this paper is to demonstrate the extra costs incurred by maintaining all the machines of a data center executing continuously for the fear of damaging hardware, degradating service or losing data. In order to demonstrate this, an objective function which minimizes energy consumption depending on the number of times that the machines are switched on/off is provided. Finally we demonstrate that no matter how afraid of damaging hardware an administrator is, it is always a better option to turn off machines in order to reduce costs.
Combinatorial and Multi-Unit Auctions Applied to Digital Preservation of Self-Preserving Objects
Authors: Olvera Jose Antonio  , Carrillo Paulo Nicolás  , De La Rosa Josep Lluis
Published: February 2025

The optimization of the budget of self-preserving digital objects is a new bottom-up approach for the digital preservation, that is in this paper developed through micro negotiations as combinatorial and multi-unit of preservation and curation services for these objects to be preserved, accessible and reproducible for long term despite frequent updates of software and hardware that causes them to digital obsolescence.
A Computational creativity system to support chocolate designers decisions
Authors: Ruiz Francisco J.  , Raya Cristóbal  , Samà Albert  , Agell Núria
Published: February 2025

In this paper, a new formulation of the central ideas of the well-established theory of Boden about creativity is presented. This new formulation redefines some terms and reviews the formal mechanisms of exploratory and transformational creativity. The presented approach is based on the conceptual space proposed by Boden and formalized by other authors in a way that facilitates the implementation of these mechanisms. The presented formulation is applied to a the real case of creative designing in which a new combination of chocolate and fruit is desired. The experimentation has been conducted jointly with a Spanish chocolate chef. Data collected from the chef has been used to validate the proposed system. Experimental results show that the formulation presented is not only useful for understanding how the creative mechanisms of design works, but also facilitates its implementation in real cases to support creativity processes.
RFID Multi-Ownership Transfer Protocol in VMI Environments
Authors: Lo Nai-Wei  , Wang I-Fan  , Chuang Yo-Hsuan
Published: February 2025

Radio frequency identification (RFID) is a contactless automatic identification technology that is used in various fields. Its easy-to-read feature is particularly suitable for applications in logistics supply chain management, and it permits business partners in a supply chain to effectively control their inventory and share information. Vendor managed inventory (VMI) is an inventory comanagement approach used for integrating enterprise resources and inhibiting the bullwhip effect. Moreover, a vendor can manage inventory according to a mutual agreement for sharing inventory costs, which were previously solely borne by the dealer. An RFID multiownership transfer protocol intended for use in VMI environments is presented in this paper. The protocol was developed to solve the difficulties in RFID system implementation in VMI environments and to improve the efficiency of inventory cost allocation. Furthermore, the protocol was designed to handle one object with multiple owners according to the situations encountered; ownership can be assigned to particular users or terminated as required. Possible security concerns and strategies to prevent such attacks are discussed. The proposed protocol is referred to as multiownership transfer protocol with a trusted third party (TTP) because it allows business partners to manage their inventory in the control of the TTP. It also provides a mechanism to divide business into two levels for facilitating information privacy.
Interpretative Argumentation Schemes
Authors: Walton Douglas  , Macagno Fabrizio  , Sartor Giovanni
Published: February 2025

This paper presents a set of argumentation schemes that can be used to identify, analyze and evaluate types of arguments characteristically used in cases of contested statutory interpretation in law. These schemes represent forms of argument already identified in the literature as leading forms of argument used in cases of statutory interpretation where legal disputes about how to interpret a statute have generally arisen.
Analysing Norms with Transition Systems
Authors: Bench-Capon Trevor
Published: February 2025

The design and analysis of norms is a somewhat neglected topic in AI and Law. In recent years powerful techniques to model and analyse norms have been developed in the Multi-Agent Systems community. In this paper I consider these techniques from and AI and Law perspective, and suggest a framework for the exploration of these issues.
Evaluation of A Flipped Classroom & Analysis of Students' Learning Situation in A Computer-Programming Course
Authors: Hayashi Yasuhiro  , Fukamachi Ken-ichi  , Komatsugawa Hiroshi
Published: February 2025

We have put into practice the flipped classroom as a way of utilizing e-learning contents in our computer-programming course since 2013. The main feature of this practice is to use most of the time usually dedicated to lecture for practicing by assigning the students the e-learning materials as preparation before the class. We gave the students homework to learn vocabularies and grammar of the C programming language. This decreased the time a teacher spent lecturing and the students were assigned applied-problems to make practical software in addition to conventional basic problems in training. Our goal is to maintain the students' motivation to learn the computer programming through the sense of accomplishment that each student obtains by finishing practical assignments in the training. We confirmed the effectiveness of this approach by comparing examination scores between last year and this year, and putting questionnaires to the students. Additionally, we analyzed the learning situation of the students who were week in programming. The results are shown in this paper.
Time – A Multidimensional Concept
Authors: Heimbürger Anneli
Published: February 2025

There exist a lot of studies about time, its interpretations, different features and structures from several scientific points of view. In our paper, we propose a multidimensional framework of time. The main idea of the paper is to present a synthesis of different dimensions of time. We discuss some parts of the framework to illustrate and highlight the multidimensional features of time. We also demonstrate an early-stage implementation of the framework as a “Time on Wall” course in the eEducation/Optima environment. By means of the “Time on Wall”, we are able to teach different dimensions of time across disciplines and faculties and to illustrate different time scales.
An Explorative Cultural-Image Analyzer for Detection, Visualization, and Comparison of Historical-Color Trends
Authors: Itabashi Yoshiko  , Sasaki Shiori  , Kiyoki Yasushi
Published: February 2025

This paper presents an explorative cultural-image analyzer and its application to comparative analyses of cultural arts and crafts. The goal of this system is to provide a new image-exploration environment that reflects the diversity of humans' sense of color and the breadth of cultural human knowledge by detecting and visualizing characteristic historical color-trends within cultural-image data sets. The primary components of this system are the two explorative analysis methods with feature estimation and evaluation of culture-dependent colors: (a) image-group exploration and (b) color exploration. The system visualizes the distinct differences among image groups aggregated by the attributes such as author, era, region, etc. and provides notable images for users through the image-group exploration method. In addition, the system visualizes the subtle differences of colors in images and provides a key to analyze cultural art works by the color-exploration method, with a zooming function for color distributions and cultural-color name estimation. By utilizing the existing annotations and attributes that are available for most images, the system analyzes the differences of colors among image-groups defined by statistical analysis and visualizes the representations of each image-group on an overview map. This system enables a user to analyze the characteristics of a collection of cultural art works by browsing the representative images of each image-group, exploring the specified culture-dependent colors with high accuracy, and observing subtle differences of colors among image-groups according to culture-dependent color names at a glance.
Design and Prototypical Implementation of an Integrated Graph-Based Conceptual Data Model
Authors: Sedlmeier Matthias  , Gogolla Martin
Published: February 2025

The paper introduces a new, comprehensive integrated conceptual data model in a precise way. Central language features cover core modeling concepts as classification, membership, inheritance, interfaces, structured types, aliasing, aggregation, composition, constraints, clustering and relationships. Schema states are thought of as being realized as graphs with appropriate navigation options. A prototypical graph database implementation and accompanying examples are discussed.