bluuebunny/crossref_metadata_2025 huggingface dataset:
A Discussion on the Role of Decision Support in a Location Analysis Case Study
Authors: Clímaco João  , Fernandes Sérgio  , Captivo M. Eugénia
Published: February 2025

In this paper, we discuss a case study regarding transfer stations siting in a Solid Waste Management System, building a multi-criteria model potentially adequate to the involvement of multiple actors. A Decision Support System (DSS), named SABILOC ([1]), integrating an Interactive Bi-objective Integer Programming Module and a Multi-attribute Module is used. The last module allows an a posteriori more detailed analysis of the solutions selected during the first module. Furthermore, the DSS includes meaningful graphical tools and integrates a Geographic Information System (GIS).
Managing Health Information Delivery Processes for Better Medical Decision Making
Authors: Lederman Reeva  , Dreyfus Sulette
Published: February 2025

Hospitals often retain inadequate systems because they are concerned about the organisational and financial cost of IT investments that do not clearly lead to increased revenue. Previous work has not carefully examined the health care, organisational and business process issues caused by widespread underinvestment in Health Information Systems, leading to compromised decision making.
Positioning Predictive Analytics – A Design Evaluation
Authors: O'Flaherty Brian  , Heavin Ciara
Published: February 2025

Software as a service, cloud computing and predictive analytics are examples of new phenomena that can have a profound impact on the pervasiveness and accessibility of organizational decision making. The power of propensity scoring based on historic data delivered as a service via cloud computing heralds the availability of capabilities that previously required teams of quantitative specialist to deliver. This paper outlines the development of a predictive analytics cloud service for customer retention and attempts to position the outcomes of this implementation in the context of decision support, knowledge management and work practice evolution.
Multi-Criteria Evaluation of Mobile Triage Decision Systems
Authors: Delir Haghighi Pari  , Burstein Frada  , Churilov Leonid  , Patel Akrati
Published: February 2025

Mobile Clinical Decision Support Systems (CDSS) aim to assist medical practitioners with decision making and provide an easy access to information on the move using mobile devices such as a mobile phone. Despite many benefits of mobile CDSS, these systems need to undergo a comprehensive evaluation to determine the correctness of the actions that decision maker take and validate the system's knowledge and recommendation. In this paper, we discuss the impact of mobile CDSS on decision making process and outcomes, and describe the multi-criteria evaluation of a mobile field triage decision support system in terms of effectiveness, efficiency, satisfaction and use. In this research, data was collected from a group of qualified paramedics using both quantitative and qualitative methods. The results and findings shed new light on the role of mobile decision support systems in improving healthcare delivery and provided better understanding of the barriers to the adoption of these systems and the impact of different stakeholders' views in evaluating the success of the DSS.
A PSO based NN-SVM for Short-Term Load Forecasting
Authors: Xu Zhenyuan  , Watada Junzo  , Xue Jiliang
Published: February 2025

Load forecasting has become one of the core research topics in the power system. As power load has time-variant characteristics and nonlinear characteristics, different computational intelligent techniques, neural networks (NN) in particular, are used in short-term load forecasting (STLF) to make it more effective. This study proposes a Particle Swarm Optimization (PSO) - based neural network with support vector machine (NN-SVM) model to predict the power load in short-term forecasting by using a radial-basis-function neural network (RBFNN), SVM and PSO. There are two stages in the proposed model. The first stage applies the RBFNN to predict monthly variations, and the second stage trains the SVM through hourly data to obtain the final forecast for short-term load forecasting (STLF). In the process of SVM training and NN learning, PSO is used to find the optimal parameters. The results of several experiments show that this new model performs more accurately and stably than some conventional models including RBFNN, RGA-SVM, Karman filter in STLF.
On The Properties of Belief Tracking for Online Contingent Planning using Regression
Authors: Brafman Ronen  , Shani Guy
Published: February 2025

Planning under partial observability typically requires some representation of the agent's belief state – either online to determine which actions are valid, or offline for planning. Due to its potential exponential size, efficient maintenance of a belief state is, thus, a key research challenge in this area. The state-of-the-art factored belief tracking (FBT) method addresses this problem by maintaining multiple smaller projected belief states, each involving only a subset of the variable set. Its complexity is exponential in the size of these subsets, as opposed to the entire variable set, without jeopardizing completeness. In this paper we develop the theory of regression to serve as an alternative tool for belief-state maintenance. Regression is a well known technique enjoying similar, and potentially even better worst-case complexity, as its complexity depends on the actions and observations that actually took place, rather than all actions and potential observations, as in the FBT method. On the other hand, FBT is likely to have better amortized complexity if the number of queries to the belief state is very large. An empirical comparison of regression with FBT-based belief maintenance is carried out, showing that the two perform similarly.
A Profit-Aware Negotiation Mechanism for On-Demand Transport Services
Authors: Egan Malcolm  , Jakob Michal
Published: February 2025

As new markets for transportion arise, on-demand transport services are set to grow as more passengers seek affordable personalized journeys. To reduce passenger prices and increase provider revenue, these journeys will often be shared with other passengers. As such, new negotiation mechanisms between passengers and the service provider are required to plan and price journeys. In this paper, we propose a novel profit-aware negotiation mechanism: a multiagent approach that accounts for both passenger and service provider preferences. Our negotiation mechanism prices each passenger's journey, in addition to providing vehicle routing and scheduling. We prove a stability property of our negotiation mechanism using a connection to hedonic games. This connection yields new insights into the link between vehicle routing and passenger pricing. We also show via simulations the dependence of the service provider profit and passenger prices on the number of passengers as well as passenger demographics. In particular, our key observation is that increasing the number of passengers has the effect of increasing passenger diversity, which in turn increases the service provider's profit.
Progression in Maximum Satisfiability
Authors: Ignatiev A.  , Morgado A.  , Manquinho V.  , Lynce I.  , Marques-Silva J.
Published: February 2025

Maximum Satisfiability (MaxSAT) is a well-known optimization version of Propositional Satisfiability (SAT), that finds a wide range of relevant practical applications. Despite the significant progress made in MaxSAT solving in recent years, many practically relevant problem instances require prohibitively large run times, and many cannot simply be solved with existing algorithms. One approach for solving MaxSAT is based on iterative SAT solving, which may optionally be guided by unsatisfiable cores. A difficulty with this class of algorithms is the possibly large number of times a SAT solver is called, e.g. for instances with very large clause weights. This paper proposes the use of geometric progressions to tackle this issue, thus allowing, for the vast majority of problem instances, to reduce the number of calls to the SAT solver. The new approach is also shown to be applicable to core-guided MaxSAT algorithms. Experimental results, obtained on a large number of problem instances, show gains when compared to state-of-the-art implementations of MaxSAT algorithms.
Conflict Resolution in Partially Ordered OWL DL Ontologies
Authors: Ji Qiu  , Gao Zhiqiang  , Huang Zhisheng
Published: February 2025

Inconsistency handling in OWL DL ontologies is an important problem because an ontology can easily be inconsistent when it is generated or modified. Current approaches to dealing with inconsistent ontologies often assume that there exists a total order over axioms and use such an order to select axioms to remove. However, in some cases, such as ontology merging, a total order may not be available and we only have a partial order over axioms. In this paper, we consider a general notion of logical inconsistency and define the notion of conflict of an inconsistent ontology. We then propose a general approach to resolving inconsistency of a partially ordered ontology. We instantiate this approach by proposing two algorithms to calculate prioritized hitting sets for a set of conflicts. We implement the algorithms and provide evaluation results on the efficiency and effectiveness by considering both artificial and real-life data sets.
Proactive and Reactive Reconfiguration for the Robust Execution of Multi Modality Plans
Authors: Scala Enrico  , Torasso Pietro
Published: February 2025

The paper addresses the problem of executing a plan in a dynamic environment for tasks involving constraints on consumable resources modeled as numeric fluents. In particular, the paper proposes a novel monitoring and adaptation strategy joining reactivity and proactivity in a unified framework. By exploiting the flexibility of a multi modality plan (where each action can be executed in different modalities), reactivity and proactivity are guaranteed by means of a reconfiguration step. The reconfiguration is performed (i) when the plan is no more valid to recovery from the impasse (reactively), or (ii) under the lead of a kernel based strategy to enforce the tolerance to unexpected situations (proactivity). Both mechanisms have been integrated into a continual planning system and experimentally evaluated over three numeric domains, extensions of planning competition domains. Results show that the approach is able to increase the percentage of cases successfully solved while preserving efficiency in most situations.
Unleashing Dec-MDPs in Security Games: Enabling Effective Defender Teamwork
Authors: Shieh Eric  , Jiang Albert Xin  , Yadav Amulya  , Varakantham Pradeep  , Tambe Milind
Published: February 2025

Multiagent teamwork and defender-attacker security games are two areas that are currently receiving significant attention within multiagent systems research. Unfortunately, despite the need for effective teamwork among multiple defenders, little has been done to harness the teamwork research in security games. This paper is the first to remedy this situation by integrating the powerful teamwork mechanisms offered by Dec-MDPs into security games. We offer the following novel contributions in this paper: (i) New models of security games where a defender team's pure strategy is defined as a Dec-MDP policy for addressing coordination under uncertainty; (ii) New algorithms based on column generation that enable efficient generation of mixed strategies given this new model; (iii) Handling global events during defender execution for effective teamwork; (iv) Exploration of the robustness of randomized pure strategies. The paper opens the door to a potentially new area combining computational game theory and multiagent teamwork.
Qualitative Simulation with Answer Set Programming
Authors: Wiley Timothy  , Sammut Claude  , Bratko Ivan
Published: February 2025

Qualitative Simulation (QSIM) reasons about the behaviour of dynamic physical systems as they evolve over time. The system is represented by a coarse qualitative model rather than precise numerical models. However, for large complex domains, such as robotics for Urban Search and Rescue, existing QSIM implementations are inefficient. ASPQSIM is a novel formulation of the QSIM algorithm in Answer Set Programming that takes advantage of the similarities between qualitative simulation and constraint satisfaction problems. ASPQSIM is compared against an existing QSIM implementation on a variety of domains that demonstrate ASPQSIM provides a significant improvement in efficiency especially on complex domains, and producing simulations in domains that are not solvable by the procedural implementation.
Adaptive Active Learning as a Multi-armed Bandit Problem
Authors: Czarnecki Wojciech M.  , Podolak Igor T.
Published: February 2025

In this paper, we present a new active learning strategy whose main focus is to have the ability to adapt to the unknown (or changing) learning scenario. We introduce the learners' ensemble based approach and model it as the multi-armed bandit problem. Presented application of simple exploration-exploitation trade-off algorithms from the UCB and EXP3 families show an improvement over using the classical strategies. Evaluation on data from UCI database compare three different selection algorithms. In our tests, presented method shows promising results.
Temporal Plan Quality Improvement and Repair using Local Search
Authors: Bajada Josef  , Fox Maria  , Long Derek
Published: February 2025

This paper presents an approach to repair or improve the quality of plans which make use of temporal and numeric constructs. While current state-of-the-art temporal planners are biased towards minimising makespan, the focus of this approach is to maximise plan quality. Local search is used to explore the neighbourhood of an input seed plan and find valid plans of a better quality with respect to the specified cost function. Experiments show that this algorithm is effective to improve plans generated by other planners, or to perform plan repair when the problem definition changes during the execution of a plan.
Problematizing the Participatory Subject in Demands Driven Development of Public Sector
Authors: Gidlund Katarina L.  , Sefyrin Johanna
Published: February 2025

This paper concerns the construction of the individuals to whom public e-services are aimed, and who are expected to participate in demands driven development of public sector. The argument is that these individuals are differently positioned in relation to and have different prerequisites to participate in demands driven development processes, and that this has to be taken into account by practitioners who are working demands driven development of public sector. The aim of the paper is thus to address the need to acknowledge differences in individual users' possibilities to participate in the development of public sector through opening up and critically analyze categories indicating participants – e.g. ‘users’, ‘citizens’ or ‘practitioners’. This is done through a discourse theoretical analysis of a text; the Swedish Guidelines for Demands Driven Development. The analysis of the text shows that the dominant category signifying a participatory subject is ‘target group(s)’, which is articulated according to four different themes. However, none of these themes articulates an unpacking of the category ‘target group(s)’, and the term is instead used to signify everyone as if these were alike and had the same prerequisites and possibilities to participate in demands driven development processes – in discourse theoretical terms ‘target group(s) works as an empty signifier. In this way differences between the individuals who are included in the category are hidden, and practitioners are left with no guidelines for how to deal with these.
Stakeholder Involvement in Public e-Service Development – Broadening the Scope of User Involvement
Authors: Lindgren Ida
Published: February 2025

This paper investigates if user involvement (UI) theory and theory on stakeholder theory (ST) can be merged to form a new theoretical entity that can inform whose voice should be heard in public e-service development. The investigation is based on a hermeneutic literature review and analysis. The result is a merger of ideas on who should be involved (extracted from stakeholder theory) with ideas on why this involvement should be organized (extracted from the user involvement literature). The paper presents research in progress, meaning that the merger presented is not particularly advanced. Still, this merger of ideas is substantial and important as it could function as the fundament for a more elaborate understanding of how to determine who should be involved in public e-service development. Involving the ‘right’ actors is believed to lead to higher quality in public e-services; therefore, advancement in our knowledge on how to identify these actors and finding better ways of involving these actors is needed.
Requirements Engineering of Malaysia Radiation and Nuclear Emergency Plan Simulator
Authors: Ab. Hamid Amy Hamijah  , Abd Rozan Mohd Zaidi  , Ibrahim Roliana  , Deris Safaai  , Selamat Ali  , Muhd Yunus Muhd. Noor
Published: February 2025

Disastrous circumstances such as radiation and nuclear meltdown require a large and complex scale of emergency health and social care capacity planning framework. Devastating issues and challenges of incompleteness, inconsistency, and the infeasibility of provided requirements of the suggested planning framework might create unnecessary conflicts during the system development. In this paper, we proposed requirements engineering of an emergency preparedness and response simulation model (EPRM) for Malaysia Radiation and Nuclear (MRN) emergency plan simulator. This simulator is expected to plan and manage emergencies and disasters included the risk involved when the nuclear reactor is malfunctioning due to natural disasters such as floods, earthquake, and cyber-attacks. We identify the social and technical problems of implementing the emergencies and disasters policies and also the critical and safety of software systems use to run the nuclear plants. We simulate the proposed emergency preparedness and response model (EPRM) through the simulation software. Finally, we measure the effectiveness of the proposed model using thematic synthesis and qualitative regression analysis provided outstandingly sociotechnical aspects of the affected junctures under investigation. From the testing of the proposed model, we found that if the organization is not able to define and identify, the disaster coordinator roles and responsibility, resources and equipment may contribute 65.63% of emergency plan disorder and severe calamities towards first responders, operators, workers, patients and community at large. Also, we found that those themes were to bone up the main simulation workflow diagram. This finding supported and correlated independently towards Information Systems Development (ISD) key concepts by 13.30 (T value). Those themes were confirmed and reliable coherently (average 89% degree) rather than by chance. Finally, the proposed emergency preparedness and response model (EPRM) that is the theory building methods integrated technical and analytical procedures in a sociotechnical outlook. These methods established on developing emergency response perquisites rather than intervention principles alone. Most likely, this approach was significantly useful to justify a mixture of tacit and explicit knowledge among the emergency plan expertise. Consequently, a strategized, simplified and prevailing RANEP simulator can be achieved, though it is certainly as complex.
Rubrics to Assess Usability of Embedded Systems
Authors: Ghazali Masitah  , Umar Muhammad Aminu  , Ashraf Mahmood
Published: February 2025

Embedded systems have more usability dimension as compared to general-purpose computers. Hence, the need to have more diverse and effective ways to evaluate such systems is imperative. This paper proposes the use of rubrics in the evaluation of usability on embedded systems. The result is a generalized rubric for usability testing intended for evaluation on embedded systems. The rubric is evaluated by certified testers through a validation experiment on a mobile robot. The majority of the experts felt that the rubric was complete, likely to be effective in providing good testing results, usable by embedded system testers, and appealing for them to consider it themselves.
Enhancing Model Classification Using Particle Initialized Based Noise Eliminating
Authors: Sahadun Nur A.  , Haron Habibollah  , Ali Nor A.  , Kadir Mohammed R.A.
Published: February 2025

Progress in computational intelligence system classifiers has generated new applications about gender classification to enhance the forensic anthropology post-mortem to recognize the biological profile. In current situations, computational intelligence system classifier (CISS) does not simultaneous perform feature subset selection. In realizing the classification accuracy performance; this article will propose a hybridization model of gender classification through feature selection in terms of classification accuracy. In validating the hybridization model, different classification models were compared. Particle Swarm Optimization (PSO) feature selection is a computational intelligence system that is implemented to analyze influential factors of trabecular bone morphology features through global best particle to bring better fitness without complicated mathematic operators. Then, irrelevant factors of features are eliminated. PSO acts as feature selection approach in dataset preparation process of two CISS called Artificial Neural Network (ANN) and Support Vector Machine (SVM). Empirical evaluation shows that the hybridization classifier model with model classification data of a forensic anthropology post-mortem demonstrates the effectiveness of our feature selection approach in improving classification accuracy. Based on the result, PSO-ANN presents more accurate result than ANN, likewise PSO-SVM produced better result than SVM.
Decomposing Abstract Dialectical Frameworks
Authors: Gaggl Sarah Alice  , Strass Hannes
Published: February 2025

We introduce a decomposition scheme for abstract dialectical frameworks (ADFs). The decomposition proceeds along the ADF's strongly connected components. For several semantics, the decomposition-based version coincides with the original semantics. For others, the scheme defines new semantics. These new semantics allow us to deal with pertinent problems such as odd-length negative cycles in a more general setting, that for instance also encompasses logic programs.
An Ontology-based Taxonomic Key for Afrotropical Bees
Authors: Gerber Aurona  , Eardley Connal  , Morar Nishal
Published: February 2025

The goal of this paper is to report on the development of an ontology-based taxonomic key application that is a first deliverable of a larger project that has as goal the development of ontology-driven computing solutions for problems experienced in taxonomy. The ontology-based taxonomic key was developed from a complex taxonomic data set, namely the Catalogue of Afrotropical Bees. The key is used to identify the genera of African bees and for this paper we developed an ontology-based application, that demonstrates that morphological key data can be captured effectively in a standardised format as an ontology, and furthermore, even though the ontology-based key provides the same identification results as the traditional key, this approach allows for several additional advantages that could support taxonomy in the biological sciences. The morphology ontology for Afrotropical bees, as well as the key application form the basis of a suite of tools that we intend to develop to support the taxonomic processes in this domain.
English-Lithuanian Word Alignment with Bilingwis: Evaluation of the Alignment
Authors: Boizou Loïc  , Kovalevskaitė Jolanta  , Rimkutė Erika  , Wechsler Roger
Published: February 2025

This paper presents the first qualitative evaluation of the word-alignment system Bilingwis for the English-Lithuanian language pair. The evaluation was performed by scoring alignments for the most frequent autosemantic words from the English-Lithuanian parallel corpus. The main tendencies revealed by the evaluation are presented and some problematic issues as well as future improvements are discussed.
Approximate Policy Iteration with Bellman Residuals Minimization
Authors: Esposito Gennaro  , Martin Mario
Published: February 2025

Reinforcement Learning (RL) provides a general methodology to solve complex uncertain decision problems, which are very challenging in many real-world applications. RL problem is modeled as a Markov Decision Process (MDP) deeply studied in the literature. We consider Policy Iteration (PI) algorithms for RL which iteratively evaluate and improve control policies. In handling problems with continuous states or in very large state spaces, generalization is mandatory. Generalization property of RL algorithms is an important factor to predict values for unexplored states. Candidates for value function approximation are Support Vector Regression (SVR) known to have good properties over the generalization ability. SVR has been used in batch frameworks in RL but, smart implementations of incremental exact SVR can extend SVR generalization ability to online RL where the expected reward from states change constantly with experience. Hence our online SVR is a novelty method which allows fast and good estimation of value function achieving RL objective very efficiently. Throughout simulation tests, the feasibility and usefulness of the proposed approach is demonstrated.
Bag-of-Tracklets for Person Tracking in Life-Logging Data
Authors: Aghaei Maedeh  , Radeva Petia
Published: February 2025

By increasing popularity of wearable cameras, life-logging data analysis is becoming more and more important and useful to derive significant events out of this substantial collection of images. In this study, we introduce a new tracking method applied to visual life-logging, called bag-of-tracklets, which is based on detecting, localizing and tracking of people. Given the low spatial and temporal resolution of the image data, our model generates and groups tracklets in a unsupervised framework and extracts image sequences of person appearance according to a similarity score of the bag-of-tracklets. The model output is a meaningful sequence of events expressing human appearance and tracking them in life-logging data. The achieved results prove the robustness of our model in terms of efficiency and accuracy despite the low spatial and temporal resolution of the data.
Argumentation Schemes for Statutory Interpretation: A Logical Analysis
Authors: Sartor Giovanni  , Walton Doug  , Macagno Fabrizio  , Rotolo Antonino
Published: February 2025

This paper shows how defeasible argumentation schemes can be used to represent the logical structure of the arguments used in statutory interpretation. In particular we shall address the eleven kinds of argument identified MacCormick and Summers [6] and the thirteen kinds of argument by Tarello [11]. We show that interpretative argumentation has a distinctive structure where the claim that a legal text ought or may be interpreted in a certain way can be supported or attacked by arguments, whose conflicts may have to be assessed according to further arguments.
Legislation as a complex network: Modelling and analysis of European Union legal sources
Authors: Koniaris Marios  , Anagnostopoulos Ioannis  , Vassiliou Yannis
Published: February 2025

Legislators, designers of legal information systems, as well as citizens face often problems due to the interdependence of the laws and the growing number of references needed to interpret them. Quantifying this complexity is not an easy task. In this paper, we introduce the “Legislation Network” as a novel approach to address related problems. We have collected an extensive data set of a more than 60-year old legislation corpus, as published in the Official Journal of the European Union, and we further analysed it as a complex network, thus gaining insight into its topological structure. Results are quite promising, showing that our approach can lead towards an enhanced explanation in respect to the structure and evolution of legislation properties.
Intercultural Collaboration in a Virtual Environment
Authors: Welzer Tatjana  , Jaakkola Hannu  , Hölbl Marko  , Družovec Marjan  , Ward Anthony E.
Published: February 2025

For successful collaboration, global communication systems are of critical importance, especially those which are enabled by information and communication technology as partners in the process. It seems that partners do not need to know each other personally, but it is enough that they have a good technical support in computerized collaborative tools and software. This also means that collaboration (cooperation) does not depend on geographical location and can be spread all around the world. Establishing a virtual environment means establishing surroundings in which partners have to work and communicate. From a technical point of view, such an environment seems to be quite easy to organise and manage. However, difficulties can be expected in the collaboration of people, even when cooperation is based on a common business culture. In spite of belong to the same business culture, participants can belong to different language and culture groups, which can have an important influence on collaboration of all involved parties. While the language differences can be overcome with the use of English as a lingua franca, we do not have a Culture as a culture franca. This means that attention should be paid to intercultural collaboration in a virtual environment. Education in cultural studies, cultural awareness and appropriate tools are all necessary. In this paper, we will point out all the important components for international collaboration in a virtual environment, from definitions of interculture and collaboration up to methodologies, that supports work in a virtual environment and virtual centres itself.
Artificial Moral Agents: Creative, Autonomous, Social. An Approach Based on Evolutionary Computation
Authors: Muntean Ioan  , Howard Don
Published: February 2025

In this paper we propose a model of artificial normative agency that accommodates some social competencies that we expect from artificial moral agents. The artificial moral agent (AMA) discussed here is based on two components: (i) a version of virtue ethics of human agents (VE) adapted to artificial agents, called here “virtual virtue ethics” (VVE); and (ii) an implementation based on evolutionary computation (EC), more concretely genetic algorithms. The reasons to choose VVE and EC are related to two elements that are, we argue, central to any approach to artificial morality: autonomy and creativity. The greater the autonomy an artificial agent has, the more it needs moral standards. In the virtue ethics, each agent builds her own character in time; creativity comes in degrees as the individual becomes morally competent. The model of an autonomous and creative AMA thus implemented is called GAMA= Genetic(-inspired) Autonomous Moral Agent. First, unlike the majority of other implementations of machine ethics, our model is more agent-centered, than action-centered; it emphasizes the developmental and behavioral aspects of the ethical agent. Second, in our model, the AMA does not make decisions exclusively and directly by following rules or by calculating the best outcome of an action. The model incorporates rules as initial data (as the initial population of the genetic algorithms) or as correction factors, but not as the main structure of the algorithm. Third, our computational model is less conventional, or at least it does not fall within the Turing tradition in computation. Genetic algorithms are excellent searching tools that avoid local minima and generate solutions based on previous results. In the GAMA model, only prospective at this stage, the VVE approach to ethics is better implemented by EC. Finally, the GAMA agents can display sociability through competition among the best moral actions and the desire to win the competition. Both VVE and EC are more adequate to a “social approach” to AMA when compared to the standard approaches. The GAMA is more promising a “moral and social artificial agent”.
Human-Robot Interaction and Human Self-Realization: Reflections on the Epistemology of Discrimination
Authors: Nørskov Marco
Published: February 2025

The ethical debate on robots has become a cutting edge issue in many countries. It is, however, most often approached through an us-versus-them perspective—as if we were watching a soccer game and taking one side. Informed by Eastern as well as Western thought, the meta-ethical aim of this paper is to test the basis for this type of discrimination when it comes to human-robot interaction. Furthermore, the paper will take Heidegger's warning concerning technology as a vantage point and explore the possibility of human-robot interaction forming a praxis that might help humans to be with robots beyond the limits of our conceptual framing and even facilitate the self-development of the human involved.
‘Dynamic’ Categorization and Rationalized Ascription: A Study on NAO
Authors: Hannibal Glenda
Published: February 2025

Several studies on human-robot interaction have shown that when children and adults encounter social robots they make use of ‘dynamic’ categorization in the sense that ascription of e.g. mental states, biological properties, sociality and moral status are context dependent. In the context of a minor pilot study, with the aim of comparing results with those of the larger studies that have been undertaken among American children, I tried to explore the claims of ‘dynamic’ categorization among some Danish schoolchildren at Løsning Skole. Based on the findings, which showed a change of reference to the social robot NAO over time and situation, the pilot study suggests that the schoolchildren's categorization of NAO could be ‘dynamic’. Taken that the findings seems to confirm that categorization is context dependent, I will in the discussion consider the philosophical implications of the ‘dynamic’ categorization claims for (i) ontology and our understanding of categories, and (ii) for the epistemology of classificatory descriptions and its relation to concepts of rationality.
Social Meta-Learning: Learning How to Make Use of Others as a Resource for Learning
Authors: Allen Jedediah W.P.
Published: February 2025

While there is general consensus that robust forms of social learning enable the possibility of human cultural evolution, the specific nature, origins, and development of such learning mechanisms remains an open issue. The current paper offers an action-based approach to the study of social learning in general and imitation learning in particular. From this action-based perspective, imitation itself undergoes learning and development and is modeled as an instance of social meta-learning – children learning how to use others as a resource for further learning. This social meta-learning perspective is then applied empirically to an ongoing debate about the reason children imitate causally unnecessary actions while learning about a new artifact (i.e., over-imitate). Results suggest that children over-imitate because it is the nature of learning about social realities in which cultural artifacts are a central aspect.
A Dual-Camera Tracking Scheme with Object Information Hiding
Authors: Chang Sheng-Hao  , Su Po-Chyi  , Chou Yung-Chieh Joseph
Published: February 2025

A new application of information hiding in visual surveillance is presented in this research. The proposed framework is composed of three main components. The first component is a moving object detection mechanism, in which a static camera with a broader view of the scene is responsible for determining the objects to be tracked. The second component utilizes the information of estimated position/size of detected object to control the Pan-Tilt-Zoom (PTZ) camera for capturing sufficiently large regions of interest (ROI) in the targets. The third component is an information hiding scheme, which embeds the ROI acquired from the PTZ camera and the related labels into the video recorded by the static camera. The process of retrieving the ROI can thus be facilitated. Experimental results will demonstrate the feasibility of this interesting framework.
Measuring Strength of Coupling between Java Classes
Authors: Lee Jonathan  , Lee Shin-Jie  , Hung Dong-Sheng  , Hung Pei-Yuan  , Chin Ray  , Wu Chia-Chih
Published: February 2025

The loose coupling design principle is to restrict the interactions among modules and therefore contain the changes local to the modules themselves. It can always be regarded as an indicator of good design quality in terms of software maintenance. In order to maintain source code in software systems, it is crucial and always helpful to identify the coupling relationships among classes. Although efforts have been made on developing metrics of coupling strength between modules, little emphasis has been put on investigating the strength of coupling between Java classes. In this work, we explore the coupling relationships among Java classes based on an extension to a coupling framework proposed by Hitz and Montazeri.
System Analysis and Design of an Online Group-Buying System with Android
Authors: Lin Yan-Fu  , Li Yan-Lin  , Lin Che-Chern
Published: February 2025

This study provides system analysis and design for an android-based online group-buying system. The proposed system is specially designed for small location-related consumer groups. To explain the requirement analysis, business processes, and software structure, three types of UML diagrams are utilized in this study including use case diagrams, activity diagrams and class diagrams. The proposed system is suitable for the consumers living in rural areas with inconvenient living facilities to group-buy their meals.
Prostate Segmentation and Volume Estimation in MRI
Authors: Chang Chuan-Yu  , Chiu Chuan-Huan  , Tsai Yuh-Shyan
Published: February 2025

In order to detect prostate diseases, urologists usually use ultrasound images or magnetic resonance images (MRI) for clinical diagnosis. In clinical practice, region of prostate is manually outlined by urologist. However, outline the prostate boundary manually is highly time-consuming. In this paper, a prostate segmentation and volume estimation in MRI is proposed. Anactive contour model(ACM) is adopted to obtain the initial contour of the prostate. Four textural features extracted from the prostate were used to train the SVM classifier. Non-prostate regions are then excluded by the trained SVM. A quick convex hull is applied to refine the shape of prostate. The volume of the prostate is eventually estimated by the series of segmented prostate regions. The proposed segmentation method achieves high accuracy of 93.7%. Our experimental results show that the proposed prostate segmentation and volume estimation method is highly potential for helping urologists in clinical diagnosis.
An Adaptive Algorithm for Charger Deployment Optimization in Wireless Rechargeable Sensor Networks
Authors: Liao Ji-Hau  , Hong Chi-Ming  , Jiang Jehn-Ruey
Published: February 2025

Wireless chargers are used to refill sensors' power supply in a wireless rechargeable sensor network (WRSN) so that the WRSN can operate sustainably. Since wireless chargers are costly, the problem about how to deploy as few as possible chargers to make a WRSN sustainable is important. This paper proposes a greedy algorithm, named adaptive pair based greedy cone selection (APB-GCS), to consider the Friis propagation model for solving the problem under the assumption that chargers are equipped with directional antennas and can be deployed on grid points at a fixed height and that the sensors are deployed on the floor or object surfaces. According to simulation results, the APB-GCS algorithm outperforms others in terms of the number of deployed chargers with moderate computation complexity.
Locating Wavelet Extrema for Henry-based Fingerprint Core Point Detection
Authors: Wang Jing-Wein  , Le Ngoc Tuyen
Published: February 2025

It is important for a fingerprint classification system to validate the captured core point. The proposed approach aligns the impression region of interest based on Poincare's core points and the local orientation field first. Then, the location of the detected Poincare's core is refined by searching the neighboring ridge curve with the largest curvature based on the Henry system. The presented precise Henry-based core point classifier, which uses a method of locating wavelet extrema for core point detection, consists of skeletonization, 2-D non-separable wavelet transform, curve tracing, and core point localization. This enables it to overcome the shortcoming of the Poincare index and simultaneously address the ambiguity of core sub-region of interest. The innovative framework was tested on the NIST-4, FVC2002 DB1, and FVC2002 DB2 databases. Experimental results compare favorably to state-of-the-art singular point detectors.
An Obstruction-Check Approach to Mining Closed Sequential Patterns in Data Streams
Authors: Chang Ye-In  , Li Chia-En  , Chin Tzu-Lin
Published: February 2025

Online mining sequential patterns over data streams is an important problem in data mining. There are many applications of using sequential patterns in data streams, such as market analysis, network security and web tracking. For solving the problem of data mining in the time-based sliding window, Chang et al. proposed an algorithm called SeqStream. The SeqStream algorithm still scans the sliding window many times when IST(Inverse Closed Sequence Tree) needs to be updated. In this paper, we propose an obstruction-check approach to maintain the result of closed sequential patterns. Our approach is designed based on the lattice structure. Based on the lattice structure, we propose the EULB (Exact Update based on Lattice structure with Bit stream)-Lattice algorithm that is an exact method for mining data streams. We record additional information, instead of scanning the entire sliding window. The simulation results show that the proposed algorithm outperforms the SeqStream algorithm.
News Event Detection Using Random Walk with Restart
Authors: Chen Lun-Chi  , Liao I-En  , Chen Chi-Hao
Published: February 2025

News dataset is one of the most abundant data source for recording any event happening around people. For news event detection, people usually need to collect the related news to explore major events manually. To explore major events in large news datasets is difficult due to the amount of data grows quickly with the rapid development of the Web and also an article of news with unstructured data. How to discover events from unstructured-like articles has become an important problem. In this paper, we propose an event detection algorithm based on five-dimensional named entity feature and random-walk with restart to achieve event detection in news articles with unstructured data. The first part of this algorithm is to categorize news term into five predefined named-entity by exploring the Web page of Wikipedia in order to generate more distinctive features of each news article. The second one is to aggregate the news articles by the similarity between news articles using random-walk with restart clustering algorithm. The experimental results show that the proposed algorithm is indeed effective. Especially it is also demonstrated that this algorithm provides better event detection quality than other approaches in terms of the ability of handling multi-event news articles.
Design of Chinese Language Learning APP in the Context-Aware Learning Environment
Authors: Chen Hong-Ren  , Chiu Hsiao-Han
Published: February 2025

Influenced by the development of wireless networks, mobile learning has gradually entered the lives of everyone. Research related to mobile learning shows that mobile learning can indeed enhance the learning effects for learners, and inspire the active desire for knowledge in the learning process. This study attempts to construct a Chinese learning system using English language interface, combining the Chinese language and context-aware technology, with QR Code and GPS as the main learning mediaproviding for functions of learning Chinese vocabulary pronunciation of Chinese vocabulary Chinese dialogue, in order to develop an innovative mobile learning model for learning Chinese. Learners are interested in the methods of the system, and discussions with them showed that the learning content involved objects with which they frequently come into contact in daily life. Learners are satisfied with the learning content, and feel that it is very helpful.
Online registration system for recreational cycling marathons: Lessons learned
Authors: Fister Dušan  , Ljubič Karin  , Fister Jr. Iztok  , Fister Iztok  , Fong Simon  , Zhuang Yan
Published: February 2025

Recreational cycling marathons are a kind of attractive sport event where a bunch of riders ride a different courses without competing between each other. In the last fifteen years, sport became popular for people of all ages. Therefore, many recreational cyclists has joint to such events in order to test their abilities, have a good time with friends, meet new people, and take care for their health and body. Collecting registrations of cyclists in the events was difficult to manage because of a large time complexity. In the past, each cyclist had to print the registration card that was uploaded on the organizer's webpage, fill it and send to over the classic post. Today, the problem is worldwide solved by using the online registration system. Participants from different countries can register themselves over the Internet. This paper presents the common pitfalls and learned lessons in the past four years, when we began developing such solutions for recreational cycling marathons.
A New Technical Solution for Resource Allocation in Heterogeneous Distributed Platforms
Authors: Nguyen Ha Huy Cuong  , Le Dac-Nhuong  , Le Van Son  , Thuy Nguyen Thanh
Published: February 2025

An allocation of resources to a virtual machine specifies the maximum amount of each individual element of each resource type that will be utilized, as well as the aggregate amount of each resource of each type. In this paper, we present an new approach to improve the deadlock detection algorithm, to schedule the policies of resource supply for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform. We introduce the matrix representation of a deadlock detection problem and some essential features can be mapped into a cloud architecture which can handle multiple requests/grants simultaneously and can detect multiple deadlocks in linear time, hence, significantly improving performance. Our algorithm proposed has a run time complexity of O(min(m,n)), where m is the number of resources and n is the number of processes. The experiments also compare the performance of our algorithm proposed approach with other related works.