bluuebunny/crossref_metadata_2025 huggingface dataset:
Formalising arguments about norms
Authors: Prakken Henry  , Sartor Giovanni
Published: February 2025

In most attempts to model legal systems as formal argumentation systems, legal norms are viewed as an argumentation's system inference rules. Since in formal argumentation systems inference rules are generally assumed to be fixed and independent from the inferences they enable, this approach fails to capture the dialectical connection between norms and arguments, where on the one hand legal arguments are based on norms, and on the other hand the validity of norms depends on arguments. The validity of a new norm can be supported by referring to authoritative sources, such as legislation or precedent, but also through interpretations of such sources, or through analogies or a contrario arguments based on existing authoritative norms. In this contribution arguments about norms are modelled as the application of argument schemes to knowledge bases of facts and norms.
Normal Forms and Normalization for Probabilistic Databases under Sharp Constraints
Authors: Hartmann Sven  , Link Sebastian
Published: February 2025

The data deluge is defined by increasing amounts of large data with increasing degree of uncertainty. In a recent response, probabilistic databases are receiving a great deal of interest from research and industry. One popular approach to probabilistic databases is to extend traditional relational database technology to handle uncertainty. In this approach probabilistic databases are probability distributions over a collection of possible worlds of relational databases. On the one hand, research has seen various efforts to extend query evaluation from relational to probabilistic databases. On the other hand, updates have not received much attention at all. In this paper we show that well-known syntactic normal form conditions capture probabilistic databases with desirable update behavior. Such behavior includes the absence of data redundancy, insertion, deletion, and modification anomalies. We further show that standard normalization procedures can be applied to standard representations of probabilistic databases to obtain database schemata that satisfy the normal form condition, and can thus be updated efficiently.
Linking and Organizing Decision Information Object-oriented Promote Using Different DSS Technologies
Authors: Agahi Faramarz
Published: February 2025

The focus in current decision management systems is on supporting selected decisions and transactional processes, rather than including decisions and information resulting from decisions and action processes over time. Thus, specific information is tailored to use specific DSS technology. The purpose of this research was to understand how decision information is organized in a complex, decision-rich organization. Documentation and archival records are the basis of this case study. An object-oriented framework with five key elements for linking decisions and information is introduced: issue, based-on, decision, agents, and consequence. It is suggested that an overall object-oriented approach would contribute to clarifying the decision context, the relationship to other decisions, and defining the roles and responsibilities of the decision and action team. Thus, the framework as a platfonn facilitates using different DSS technologies.
Bayesian-based Saliency Model for Liver Tumor Enhancement
Authors: Han Xian-Hua  , Chen Yen-Wei  , Xu Gang
Published: February 2025

Automatic tumor enhancement and detection has an essential role for the computer-aided diagnosis of liver tumor in CT volume data. This paper proposes a novel tumor enhancement strategy by extracting a tumor saliency map, which represents the uncommon or tumor tissue compared to the liver and vessel ones in CT volumes. The saliency map can be constructed by exploring the existing probability of tumor in any voxel. However, the tumor prototypes in a test liver volume from a specific patient or common tumor prototypes are extremely difficult to achieve due to requirement of full-searching and large variation of tumor tissues in different liver volumes. Therefore, this paper investigates a tumor-training-data free strategy by only constructing the common healthy liver and vessel prototypes, which can be extracted from any slice of a liver volume, and then applies a nonparametric Bayesian framework for calculating the existing probability of liver or vessel. Finally, the existing probability of tumor can be deduced from that of liver or vessel. The advantages of our proposed strategy mainly include three aspects: (1) it only needs to construct the prototypes of common tissue such as liver or vessel region, which are easily obtained in any liver volume; (2) it proposes an adaptive non-parametric framework for tumor enhancement, which does not need to learn a common classification model for all liver volumes; (3) dispensable to remove the other different structure such as vessel in liver volume as a pre-processing step. Experiments validate that the proposed Bayesian-based saliency model for liver tumor enhancement can perform much better than the conventional approaches such as EM, EM/MPM tumor segmentation methods.
Solving Maximum Weight Clique Using Maximum Satisfiability Reasoning
Authors: Fang Zhiwen  , Li Chu-Min  , Qiao Kan  , Feng Xu  , Xu Ke
Published: February 2025

Satisfiability (SAT) and maximum satisfiability (MaxSAT) techniques are proved to be powerful in solving combinatorial optimization problems. In this paper, we encode the maximum weight clique (MWC) problem into weighted partial MaxSAT and use MaxSAT techniques to solve it. Concretely, we propose a new algorithm based on MaxSAT reasoning called Top-k failed literal detection to improve the upper bound for MWC, and implement an exact branch-and-bound solver for the MWC problem called MaxWClq based on the Top-k failed literal detection algorithm. To our best knowledge, this is the first time that MaxSat techniques are integrated to solve the MWC problem. Experimental evaluations on the broadly used DIMACS benchmark, BHOSLIB benchmark and random graphs show that MaxWClq outperforms state-of-the-art exact algorithms on the vast majority of instances. In particular, our algorithm is surprisingly powerful for dense and hard graphs.
Multilateral Bargaining for Resource Division
Authors: Fatima Shaheen  , Wooldridge Michael
Published: February 2025

We address the problem of how a group of agents can decide to share a resource, represented as a unit-sized pie. We investigate a finite horizon non-cooperative bargaining game, in which the players take it in turns to make proposals on how the resource should be allocated, and the other players vote on whether or not to accept the allocation. Voting is modelled as a Bayesian weighted voting game with uncertainty about the players' weights. The agenda, (i.e., the order in which the players are called to make offers), is defined exogenously. We focus on impatient players with heterogeneous discount factors. In the case of a conflict, (i.e., no agreement by the deadline), all the players get nothing. We provide a Bayesian subgame perfect equilibrium for the bargaining game and conduct an ex-ante analysis of the resulting outcome. We show that, the equilibrium is unique, computable in polynomial time, results in an instant Pareto optimal agreement, and, under certain conditions provides a foundation for the core of the Bayesian voting game. Our analysis also leads to insights on how an individual's bargained share is influenced by his position on the agenda. Finally, we show that, if the conflict point of the bargaining game changes, then the problem of determining a non-cooperative equilibrium becomes NP-hard even under the perfect information assumption.
Bayesian Multiview Dimensionality Reduction for Learning Predictive Subspaces
Authors: Gönen Mehmet  , Gönen Gülefşan Bozkurt  , Gürgen Fikret
Published: February 2025

Multiview learning basically tries to exploit different feature representations to obtain better learners. For example, in video and image recognition problems, there are many possible feature representations such as color- and texture-based features. There are two common ways of exploiting multiple views: forcing similarity (i) in predictions and (ii) in latent subspace. In this paper, we introduce a novel Bayesian multiview dimensionality reduction method coupled with supervised learning to find predictive subspaces and its inference details. Experiments show that our proposed method obtains very good results on image recognition tasks in terms of classification and retrieval performances.
Diagnosis of Hybrid Systems with SMT: Opportunities and Challenges
Authors: Grastien Alban
Published: February 2025

We propose a new approach to diagnosis of hybrid systems. In this approach, questions about the behavior of the system are asked and translated into Satisfiability Modulo Theory (SMT) problems, which are then solved by an SMT solver. We show the reduction to SMT. We also discuss the benefits and the drawbacks of this approach and conclude with a number of research directions that will make this approach applicable to large systems.
Incremental Elicitation of Choquet Capacities for Multicriteria Decision Making
Authors: Benabbou Nawal  , Perny Patrice  , Viappiani Paolo
Published: February 2025

The Choquet integral is one of the most sophisticated and expressive preference models used in decision theory for multicriteria decision making. It performs a weighted aggregation of criterion values using a capacity function assigning a weight to any coalition of criteria, thus enabling positive and/or negative interactions among criteria and covering an important range of possible decision behaviors. However, the specification of the capacity involves many parameters which raises challenging questions, both in terms of elicitation burden and guarantee on the quality of the final recommendation. In this paper, we investigate the incremental elicitation of the capacity through a sequence of preference queries selected one-by-one using a minimax regret strategy so as to progressively reduce the set of possible capacities until a decision can be made. We propose a new approach designed to efficiently compute minimax regret for the Choquet model. Numerical experiments are provided to demonstrate the practical efficiency of our approach.
A Logic of Part and Whole for Buffered Geometries
Authors: Du Heshan  , Alechina Natasha
Published: February 2025

We propose a new qualitative spatial logic for reasoning about part-whole relations between geometries (sets of points) represented in different geospatial datasets, in particular crowd-sourced datasets. Since geometries in crowd-sourced data can be less inaccurate or precise, we buffer geometries by a margin of error or level of tolerance σ, and define part-whole relation for buffered geometries. The relations between geometries considered in the logic are: buffered part of (BPT), Near and Far. We provide a sound and complete axiomatisation of the logic with respect to metric models, and show that its satisfiability problem is NP-complete.
The Effects of Individual Differences on Trust in e-Government Services: An Empirical Evaluation
Authors: Albesher Abdulaziz  , Brooks Laurence
Published: February 2025

Citizen-government interaction has been innovatively improved through the use of e-Government services. Such e-services provide governmental agencies with the opportunity to enhance their reputation and increase citizens' engagement with them. However, accomplishing this transformation will not be achieved without removing any impediments that affect citizens' trust in the provided e-services. This paper aims to understand the way social individual differences (age, gender, education level, and Internet experience) affect citizens' trust in e-Government services in developing countries. A survey, looking at people's perception of trust in e-Government, of citizens drawn from different geographical areas in Saudi Arabia, was conducted. The findings of this research indicate that out of the four factors investigated, only education level has a significant impact on citizen's trust in e-government services. Therefore, it appears that as educational level increases, so does citizen's trust in e-government and so they are more likely to engage with these e-government services.
New Method for Generation of RFLP Structure Elements in PLM Model
Authors: Horváth László  , Rudas Imre J.
Published: February 2025

Engineering modeling for lifecycle management of product information is challenged with the new demand for representation of multidisciplinary product in a single complex model system. Leading developers of product lifecycle management (PLM) systems recognized the need for conceptual modeling of industrial products in higher abstraction level than the classical physical level representation of product and related objects. They turned to PLM models which use representation of product in the requirement, functional, logical, and physical (RFLP) structure. This means introduction of systems engineering (SE) methodology in PLM modeling providing user surface for the definition of R, L, F, and P elements and their connections. It was recognized that definition of elements by engineers is very complex work and needs automation by user defined and integrated procedures. Development of these procedures for the new abstraction levels needs new research results in the PLM modeling methodology. After early recognition of the need for higher level abstraction in product modeling, the Laboratory of Intelligent Engineering Systems (LIES) at the Óbuda University, Budapest, developed five level abstraction model which allowed high level representation of knowledge for decisions at conceptual product development. This knowledge content based abstraction model was utilized at development of the initiative, behavior, context, and action (IBCA) structure. The IBCA structure includes knowledge represented entities for the generation of elements in RFLP structure. This paper introduces the RFLP structure based self adaptive PLM model, its relations to the conventional product modeling, the IBCA structure, and application of the IBCA structure at RFLP element generation. Finally, importance of the proposed method and feasibility of its implementation are discussed.
Framework for Managing of Learning Resources for Specific Knowledge Areas
Authors: Bartuskova Aneta  , Krejcar Ondrej  , Selamat Ali  , Kuca Kamil
Published: February 2025

This paper presents a framework for web-based application, which aspires to maintain learning resources, for purposes of learning courses or organizations. The goal is to create resource-rich hierarchical learning environment, which supports collaborative building of learning resources for specific domain. This paper presents basic model with essential user activities, proposal of information architecture and implementation. Presented version of the learning management system is based on principle of shared hierarchy, user contribution and moderated improvement of learning resources. This includes possibility of entry customization for active students as well as creating new entries and custom groups. Teachers can then evaluate new custom entries, which – if approved – they can add to shared hierarchy, accessible for other learners as well. This continuous process can successfully lead to collaborative building of learning resources and thus enrich communities of teachers and active students, interested in one specific domain.
System Dynamics Analysis: Simulation Case Study on Production
Authors: Guizzi Guido  , Miele Daniela  , Chiocca Daniela  , Santillo Liberatina Carmela  , Romano Elpidio
Published: February 2025

The purpose of this paper is to show the impact of simulation software in the production processes. In particular, the study focused on the simulation techniques to model and to optimize the operations. It was carried out a review of different simulation software. Moreover, in this paper it was developed a case study on olive oil production system. In particular, in the case study, it was developed a simulation model that, starting from a scheduling of arrivals in real time, evaluates the performance of the system in order to optimize the parameters that can be chosen according to the requirements (yields optimization, quantity optimization, quality optimization, revenue maximization, etc.).
Towards an Ontology of Software: a Requirements Engineering Perspective
Authors: Wang Xiaowei  , Guarino Nicola  , Guizzardi Giancarlo  , Mylopoulos John
Published: February 2025

Although software plays an essential role in modern society, its ontological nature is still unclear. For many, software is just code, but this is not illuminating. Several researchers have attempted to understand the core nature of software and programs in terms of concepts such as code, copy, medium and execution. More recently, a proposal was made to consider software as an abstract artifact, distinct from code, just because code may change while the software remains the same. We explore in this paper the implications of such a proposal in the light of software engineering and requirements engineering literature. We make a sharp distinction between different kinds of software artifacts (code, program, software system, and software product), and describe the ways they are inter-connected in the context of a software engineering process.
A First-order Formalization of Event, Object, Process and Role in YAMATO
Authors: Borgo Stefano  , Mizoguchi Riichiro
Published: February 2025

Upper ontologies are sophisticated systems that require an expressive language to be properly formalized and correctly implemented. This paper provides a formal study of one of these ontologies, called YAMATO, by providing an axiomatization in first-order logic of part of the main system. YAMATO, which has been available in OWL for some years and is used in research projects as well as in applications, is quite rich in terms of categories and relations. The system is also interesting from its ontological perspective as it presents a different combination of ontological choices with respects to todays popular upper ontologies. Here we isolate a fairly compact fragment of this system that covers important categories, such as Process and Role, and relations, such as Enacting and CausallyContributing. The axiomatization is a first step towards the full exploitation of YAMATO in information and computational systems.
Tracing Mistakes and Finding Gaps in Automatic Word Alignments for Latvian-English Translation
Authors: Girgzdis Valdis  , Kale Maija  , Vaicekauskis Martins  , Zarina Ieva  , Skadiņa Inguna
Published: February 2025

This paper aims to contribute to an in-depth understanding of computer based word alignment processes in machine translation (MT). The performance of word alignment, based on IBM models and incorporated in GIZA++, has been widely discussed in machine translation literature. The debate has lead towards a general consensus that GIZA++ does not provide sufficiently good results for word alignments. In this paper, we analyse the performance of GIZA++ and Fast Align for the Latvian-English pair against the manually aligned Gold Standard. Experiments showed that Fast Align proved to be approximately 2–3% more accurate and three times faster than GIZA++ in the alignment task. Where it concerns pre-processing, the removal of articles has a small, but positive, influence on alignment quality and machine translation output. We also present a Word Alignment Visualisation tool for analysis and editing of word alignments.
Robot Sociality: Genuine or Simulation?
Authors: Bickhard Mark H.
Published: February 2025

It is clear that people can interact with programs and robots in ways that appear to be, and can seem to participants to be, social. Asking the question of whether or not such interactions could be genuinely social requires examining the nature of sociality and further examining what requirements are involved for the participants in such interactions to co-constitutively engage in genuine social realities — to constitute genuine social agents. I will attempt to address both issues.
An NFC-driven Home Automation Framework: an Integration of WSN, Social Networks and Cloud Computing
Authors: Jiang Zheng-Xun  , Chen Yue-Liang  , Chen Jen-Jee  , Wu Wen-Tai
Published: February 2025

Internet of Things (IoT) emerges as a possible solution to realize a smart life in the modern age. However, how to provide a transparent IoT service to fit users' needs and how to quickly develop smart services with flexibility and scalability are still problems. In this article, we present a novel NFC-driven home automation framework for IoT, which integrates the Wireless Sensor Network (WSN), Social Networks, and the Cloud Computing. Near Field Communication (NFC) technology provides a way for users to exchange information between them and the system by simply contacting. So, we propose to use NFC as the system drive in the framework, such that users can intuitively interact with the system and deliver their intentions. Then, the corresponding service over the framework will control or adjust the “things” at home to fit users' needs. Furthermore, the proposed framework provides a platform for developers to easily and rapidly implement their home automation related services. In the framework, WSN sensing and control, NFC communications and identification, user profile management and preference analysis, and social network integration are all provide as platform services. Application developers are allowed to skip these details. The framework can be applied on Home Automation and Elderly Care. Take Home Automation as an example, we will show how the framework works for home automation, secure house, and social network sharing.
FPGA Implementation for Path and Wall Following Wheeled Robots
Authors: Lin Shiow-Jyu  , He Ju-Xun  , Chen Guan-Wei  , Chen Wang-Wei
Published: February 2025

This paper presents a FPGA implementation for path- and wall- following micro-robots. The distance between the robot and obstacles/adjacent walls will be computed using ultrasonic sensing. Infrared sensing modules installed in front of the proposed system detect the curvature of a marked path. The FPGA controller unit generates a PWM signal and adaptively controls DC motors to achieve wall-following and obstacle avoidance locomotion. Our proposed locomotion algorithm will control robots to simultaneously follow a marked line and avoid obstructions along the path. 15% logic elements and 3% registers are applied in the FPGA controller unit. Since the available reconfigurable hardware resources in FPGA chip are very extensive, multi-functional robotic applications can be implemented by integrating diverse modules for widespread usage.
Mobile Literacy:
Authors: Pegrum Mark
Published: February 2025

Mobile literacy is essential to learning effectively, working efficiently and participating fully in a world increasingly mediated by smart mobile devices. It is a macroliteracy, composed of a range of more particularized literacies: it gives long-established literacies, such as information literacy and multimodal literacy, new inflections, and it gives more newly established literacies, such as network literacy and code literacy, added importance. Depending on the types of mobile learning employed in educational contexts, different constellations of these literacies may be fostered. It is time for educators at all levels to consider how best to support the development of students' mobile literacy, including their critical mobile literacy. Mobile learning which foregrounds mobile literacy opens up the opportunity for educators to engage students in active, collaborative, situated learning, and simultaneously fulfils our obligation to develop students' 21stcentury skills, including their digital literacies. In this way we can prepare them for and support them in their roles as lifelong learners, employees, and citizens in a mobile society.
A Virtual Primary Key for Reversible Watermarking Textual Relational Databases
Authors: Chang Chin-Chen  , Nguyen Thai-Son  , Lin Chia-Chen
Published: February 2025

Watermarking has been applied to protect relational databases from illegal copying and manipulation by attackers. Many watermarking schemes for relational database have been proposed, and these schemes both rely on the actual primary key. Therefore, the watermark cannot be embedded into relational database, if there is not existence of actual primary key. In this paper, we present a new reversible watermarking scheme for textual relational database without depending on the actual primary key. The proposed scheme constructs a virtual primary key from the content of some selected textual attributes. Based on the generated virtual primary key, the watermark is embedded into the textual relational database. Experimental results showed that the proposed scheme achieves strong robustness under a variety of possible attacks, e.g., sorting, deletion, modification, and addition attacks. In addition, in comparisons with three previous schemes, the proposed scheme was more secure and robust, as evidenced by our experimental results and robustness analysis.
Fourth Generation of User-centered Design – Developing for E-government and Cross-sector Collaborations
Authors: Pilemalm Sofie  , Lindgren Ida  , Ramsell Elina
Published: February 2025

This study explores the need for user-centered design (UCD) approaches to adapt to recent societal trends of cross-sector collaborations, civil citizen involvement and e-government initiatives affecting the public sector. This is achieved by studying three cases involving such trends, taking place in the Swedish Emergency Response System. Using results from the cases, information systems development related challenges are identified and related to adaption needs for current UCD approaches. Suggestions of such adaption are provided and a number of inherent challenges for the fourth generation of UCD are discussed, including challenges concerning (a) balancing ideological versus practical needs; (b) resources; (c) lack of know-how; and (d) design techniques and tool challenges.
Enhancing students' skills and capabilities to exploit Open Government Data
Authors: Zotou Maria
Published: February 2025

The current ever-changing world causes economies, trends, technologies and professional domains to constantly shift and transform. This requires that workforce acquire cognitive and transversal skills (e.g. critical thinking, problem solving, ICT usage, capacity to learn) from an early age, i.e. during formal education, so that they will be equipped to properly handle and adapt to these changes and increase their participation in societal or political processes. However, the existing student-centred learning strategies such as Problem Based Learning (PBL) and the utilization of new technologies (e.g. Learning Analytics, Linked Open Data, Semantics etc) often do not show the expected benefits. In the context of this thesis, these issues will be studied and addressed. More specifically, PBL will be extended so as to allow such skills development, inventive tools and methods usage and connection with the web of data. Towards this goal, two main domains are explored in depth: Project Management and learning about Open Government Data (OGD). This paper will focus on the OGD domain.
Sequence learning with informed gaps
Authors: Gay Pablo  , López Beatriz  , Meléndez Joaquim
Published: February 2025

Sequence learning consist of finding sequence patterns following an inclusion relation regarding the ordering of events. This is a process that requires hard computational time, in particular when the lengths of the sequences are large. Therefore most of the research is focused on obtaining fast, more efficient algorithms. However, the succession of events has also some properties, as for example, the temporal distance among two events or gap, that should be taken into account. Such gaps could be the key in complex event processing systems to, for example, firing rules regarding fraud detection. This paper tackles the problem of sequence learning when information about the gap between two consecutive events is taken into account. We follow a constraint programming approach to formulate and solve the problem. The experiments are carried out with data from the Santander Cycle bike hiring system to find pattern behaviours of customer station itineraries, which are used to predict the next station a customer will visit.
Time Domain Features of Multi-channel EMG Applied to Prediction of Physiological Parameters in Fatiguing Bicycling Exercises
Authors: Ražanskas Petras  , Verikas Antanas  , Olsson Charlotte  , Viberg Per-Arne
Published: February 2025

A set of novel time-domain features characterizing multi-channel surface EMG (sEMG) signals of six muscles (rectus femoris, vastus lateralis, and semitendinosus of each leg) is proposed for prediction of physiological parameters considered important in cycling: blood lactate concentration and oxygen uptake. Fifty-one different features, including phase shifts between muscles, active time percentages, sEMG amplitudes, as well as symmetry measures between both legs, were defined from sEMG data and used to train linear and random forest models. The random forests models achieved the coefficient of determination R2=0.962 (lactate) and R2=0.980 (oxygen). The linear models were less accurate. Feature pruning applied enabled creating accurate random forest models (R2>0.9) using as few as 7 (lactate) or 4 (oxygen) time-domain features. sEMG amplitude was important for both types of models. Models to predict lactate also relied on measurements describing interaction between front and back muscles, while models to predict oxygen uptake relied on front muscles only, but also included interactions between the two legs.
Applying an Interactive Machine Learning Approach to Statutory Analysis
Authors: Šavelka Jaromír  , Trivedi Gaurav  , Ashley Kevin D.
Published: February 2025

Statutory analysis is a significant component of research on almost any legal issue and determining if a statutory provision applies is an integral part of the analysis. In this paper we present the initial results from an attempt to support the applicability assessment in situations where the number of statutory provisions to be considered is large. We propose the use of a framework in which a single human expert cooperates with a machine learning text classification algorithm. Our experiments show that an adoption of the approach leads to a better performance during the relevance assessment. In addition, we suggest how to re-use a classification model trained during one statutory analysis for another related analysis. This points to a new way of capturing and re-using knowledge produced in the course of statutory analysis. Our experiments confirm the viability of this approach.
Bridging Representations of Laws, of Implementations and of Behaviours
Authors: Sileno Giovanni  , Boer Alexander  , Van Engers Tom
Published: February 2025

To align representations of law, of implementations of law and of concrete behaviours, we designed a common ground representational model for the three domains, based on the notion of position, building upon Petri nets. This paper reports on work to define subsumption between positional models.
Influence and Responsibility: A Logical Analysis
Authors: Lorini Emiliano  , Sartor Giovanni
Published: February 2025

We present a logical analysis of the relation between social influence and responsibility. In particular, we precisely characterise a notion of influence-based responsibility, namely, a responsibility that depends on the fact that an agent causes a primary violation by another agent. This notion captures the core of the idea of indirect (also called secondary or accomplice) responsibility in legal systems, and can be useful in the governance of multiagent systems. Our analysis uses the STIT logic of action (the logic of seeing to it that). On this basis we shall first formalise a notion of influence between agents, and then the idea of influence-based responsibility.
Adaptive Area-Based Risk Model for Dengue Fever: Algorithm of Dynamic Spreading in Network
Authors: Sesulihatien Wahjoe T.  , Kiyoki Yasushi
Published: February 2025

Dengue fever is the fastest spreading communicable disease in the world. Spreading of virus is driven by increasing number of human moving. In many dengue-endemic countries, problem in dengue spreading is predicting infected area and determine perfect strategy to prevent the disease. Predicting infected area spot relates with pattern of human moving, while strategy to prevent is depend on vulnerability of area. In this paper we proposed an adaptive spreading model of area-disease based on human movement. This method combines an area-based mathematical model with discrete life-cycle of virus. The proposed method includes (1) state-space model of routine movement cycle, (2) algorithm of spreading, (3) prediction of the next infection area by graph relation, and (4) vulnerability value of suspected area. There are two important features in this method: real-time prediction of infected area and flexibility to adapt in the different situation. To perform the simulation we utilize real data of infected people in Surabaya in January 2011.The result shows that this method is suitable for near future prediction and easy to compensate time-varying changing. However, the accuracy needs to be improved.
Consensus Development in Group Decision Making under Uncertainty
Authors: Deng He-Pu  , Zhang Xi-Bao  , Zhao Jing
Published: February 2025

This paper presents a decision support system (DSS) for developing consensus in group decision making. A fuzzy consensus index is proposed to evaluate group consensus in group decision making. This consensus index is incorporated into a DSS so that better consensus decisions can be made through interactive exchanges of information between decision makers and the DSS.
Phenotypic Diversity of Phalaenopsis Based on Statistic Analysis and Data Mining
Authors: Zhang Peng  , Guan Jun-Jiao  , Huang Qing-Mei  , Liu Yan-Fang  , Zhang Jian-Hua
Published: February 2025

With the aid of statistic analysis and data mining, this study investigates the phenotypic diversity of 48 phalaenopsis varieties to provide basis for resource utilization and breeding improvement. Results showed that the order of expression ranges of phalaenopsis characteristics was inflorescence>petals>dorsal sepal>leaf>apical lobe. Two characteristics, inflorescence length and flower number of single inflorescence, had the largest expression ranges. Based on clustering analysis, 48 phalaenopsis varieties can be divided into 7 groups: varieties with an intermediate number of flowers, varieties with a small number of flowers, varieties with a large number of flowers, varieties with small size, varieties with large size, varieties with intermediate size and varieties with conspicuous flowers. Based on genetic distance concluded from cluster analysis, varieties from one group could be hybridized with those from other groups for the purpose of breeding new varieties to meet various demands.
Decision Making and Value Realization in Multi-Actor e-Government Contexts
Authors: Sundberg Leif
Published: February 2025

In the last two decades, governments around the world have been implementing electronic services in order to create a range of public values and meet new demands from a variety of stakeholders. Such activities fall within the scope of the e-Government research field. Developing large-scale information systems has proven to be a challenging task and many initiatives have ended in failure. This paper explores uncertainty in the e-Government context. How is it related to the various actors in the public sector, and how can decision making be adapted here to improve value realization? Using actor-network theory as an analytical lens, the collaboration of five Swedish municipalities in creating common e-services is examined as a case study. The results indicate that factors beyond decision makers' control, such as relations outside of a project, create a high degree of uncertainty. This uncertainty can be reduced by creating durable relations between local and global socio-technical actors. Inscribing values into generic software that has a high grade of interoperability should help to strengthen these networks beyond projects and regional borders.
Can E-Government Give Voice to Citizens? An Empirical Examination of the ‘Jaankari’ Project
Authors: Kumar Mayank  , De Rahul
Published: February 2025

This study examines the use of the ‘Jaankari’ e-government project by marginalized communities. The Jaankari system, implemented in the state of Bihar in India, enables people to call in and make information requests to government departments. Citizens may speak in their own language and from their own location. Results of an analysis of the data of the call records, when regressed against socio-economic parameters, show that people from marginal communities rely on this service. Those from non-dominant castes and women, in particular, use the system in excess of those from more privileged backgrounds. The paper shows implications of these findings for e-governance research and practice.
A Trust-Enhanced Approach to the eParticipation Life Cycle
Authors: Santamaría Alex
Published: February 2025

eParticipation provides a means to involve citizens in eGovernment decisions. The ease of access to eParticipation processes has raised the issue of the trustworthiness of both the institutions promoting processes and the citizens participating in these processes. Our research aims at obtaining a generic eParticipation framework enriched with trust management techniques like the ones used in e-Commerce and social networks. Our work plan includes the following steps: making a systematic review for extract the knowledge base, designing an eParticipation framework definition and incorporating trust techniques, developing support software, implementing several case studies in Spain and Ecuador, and providing results and evaluation.
An Exploration into Practice Intelligence in E-Government: A Way Forward
Authors: Pérez Morote Rosario  , Pontones Rosa Carolina  , Reynolds Martin
Published: February 2025

Practice intelligence (PI) is a new notion that refers to the learned expertise of sense making of problem spaces and the aligned learned expertise relating to appropriate decision/action in a particular problem space. Exploring Practice Intelligence in E-government research involves to specify and codify into academic knowledge the internal cognitive structures of designing and synthesizing the information used by e-government policy makers and practitioners.
Sustainable Linked Open Data Creation: An Experience Report
Authors: Klein Eduard  , Haller Stephan  , Gschwend Adrian  , Jovanovik Milos
Published: February 2025

A flexible platform supporting the linked data life-cycle has been developed and applied in various use cases in the context of the large scale linked open data project Fusepool P3. Besides the description of the aims and achievements, experiences from publishing and reusing linked data in public sector and business are summarized. It is highlighted that without further help it is difficult for domain experts to estimate the time, effort and necessary skills when trying to transfer the platform to other use cases. Applying a new publishing methodology turned out to be useful in these cases.
Leader-Follower MDP Models with Factored State Space and Many Followers – Followers Abstraction, Structured Dynamics and State Aggregation
Authors: Sabbadin Régis  , Viet Anne-France
Published: February 2025

The Leader-Follower Markov Decision Processes (LF-MDP) framework extends both Markov Decision Processes (MDP) and Stochastic Games. It provides a model where an agent (the leader) can influence a set of other agents (the followers) which are playing a stochastic game, by modifying their immediate reward functions, but not their dynamics. It is assumed that all agents act selfishly and try to optimize their own long-term expected reward. Finding equilibrium strategies in a LF-MDP is hard, especially when the joint state space of followers is factored. In this case, it takes exponential time in the number of followers. Our theoretical contribution is threefold. First, we analyze a natural assumption (substitutability of followers), which holds in many applications. Under this assumption, we show that a LF-MDP can be solved exactly in polynomial time, when deterministic equilibria exist for all games encountered in the LF-MDP. Second, we show that an additional assumption of sparsity of the problem dynamics allows us to decrease the exponent of the polynomial. Finally, we present a state-aggregation approximation, which decreases further the exponent and allows us to approximately solve large problems. We empirically validate the LF-MDP approach on a class of realistic animal disease control problems. For problems of this class, we find deterministic equilibria for all games. Using our first two results, we are able to solve the exact LF-MDP problem with 15 followers (compared to 6 or 7 in the original model). Using state-aggregation, problems with up to 50 followers can be solved approximately. The approximation quality is evaluated by comparison with the exact approach on problems with 12 and 15 followers.
Constructing Hierarchical Task Models Using Invariance Analysis
Authors: Lotinac Damir  , Jonsson Anders
Published: February 2025

Hierarchical Task Networks (HTNs) are a common model for encoding knowledge about planning domains in the form of task decompositions. We present a novel algorithm that uses invariant analysis to construct an HTN from the PDDL description of a planning domain and a single representative instance. The algorithm defines two types of composite tasks that interact to achieve the goal of a planning instance. One type of task achieves fluents by traversing invariants in which only one fluent can be true at a time. The other type of task applies a single action, which first involves ensuring that the precondition of the action holds. The resulting HTN can be applied to any instance of the planning domain, and is provably sound. We show that the performance of our algorithm is comparable to algorithms that learn HTNs from examples and use added knowledge.
Hierarchical Strategy Synthesis for Pursuit-Evasion Problems
Authors: Ramaithitima Rattanachai  , Srivastava Siddharth  , Bhattacharya Subhrajit  , Speranzon Alberto  , Kumar Vijay
Published: February 2025

We present a novel approach for solving pursuit-evasion problems where multiple pursuers with limited sensing capabilities are used to detect all possible mobile evaders in a given environment. We make no assumptions about the number, the speed, or the maneuverability of evaders. Our algorithm takes as input a map of the environment and sensor models for the pursuers. We then obtain a graph representation of an environment using a Čech Complex. Even with such a representation, the configuration space grows exponentially with the number of pursuers. In order to address this challenge, we propose an abstraction framework to partition the configuration space into sets of topologically similar configurations that preserve the space of possible evader locations. We validate our approach on several simulated environments with varying topologies and numbers of pursuers.
Minisum and Minimax Committee Election Rules for General Preference Types
Authors: Baumeister Dorothea  , Böhnlein Toni  , Rey Lisa  , Schaudt Oliver  , Selker Ann-Kathrin
Published: February 2025

In committee elections it is often assumed that voters only (dis)approve of each candidate or that they rank all candidates, as it is common for single-winner elections. We suggest an intermediate approach, where the voters rank the candidates into a fixed number of groups. This allows more diverse votes than approval votes, but leaves more freedom than in a linear order. A committee is then elected by applying the minisum or minimax approach to minimize the voters' dissatisfaction. We study the axiomatic properties of these committee election rules as well as the complexity of winner determination and show fixed-parameter tractability for our minimax rules.
Cross-Domain Error Correction in Personality Prediction
Authors: Yakut Kılıç Işıl Do&gbrev  , Pan Shimei
Published: February 2025

In this paper, we analyze domain bias in automated text-based personality prediction, and proposes a novel method to correct domain bias. The proposed approach is very general since it requires neither retraining a personality prediction system using examples from a new domain, nor any knowledge of the original training data used to develop the system. We conduct several experiments to evaluate the effectiveness of the method, and the findings indicate a significant improvement of prediction accuracy.
Distributed Controllers for Norm Enforcement
Authors: Testerink Bas  , Dastani Mehdi  , Bulling Nils
Published: February 2025

This paper focuses on computational mechanisms that control the behavior of autonomous systems at runtime without necessarily restricting their autonomy. We build on existing approaches from runtime verification, control automata, and norm-based systems, and define norm-based controllers that enforce norms by modifying system behavior at runtime to make it norm compliant. For many applications, an autonomous system should comply with a set of norms. We extend our approach to a distributed setting, where a set of norm-based controllers jointly modify the runtime behavior of an autonomous system. The norms that a set of norm-based controllers jointly enforce are investigated and characterized in terms of the norms that are enforced by individual norm-based controllers. We show that a set of norm-based controllers is able to modify the runtime behavior of an autonomous system to make it compliant with all norms that the individual norm-based controllers aim at enforcing.
Behavioral Verification of UML2.0/PoSM Components
Authors: Sakka Rouis Taoufik  , Bhiri Mohamed Tahar  , Kmimech Mourad
Published: February 2025

The component approach aims at reutilization by an easy and efficient component assembly. A coherent component assembly requires that the atomic components be correct. Where, an atomic component is said correct if and only if its partial behaviors associated with the interfaces offered by this component are ensured by the global behavior of the said component. In this paper, our purpose was the verification of the behavioral consistency between the interfaces of a component and the component itself. Our proposed approach combined UML2.0, Port State Machine (PoSM), Wright and CSP to check the behavioral consistency of UML2.0/PoSM components. To achieve this, we proposed to translate each UML2.0/PoSM component to a Wright Component. Using Wr2fdr tool, these Wright components were automatically translated to a CSP specification acceptable by the FDR2 model-checker. Thus, the consistency of a component was checked thanks to FDR2.
A Fuzzy Random Forest Approach for the Detection of Diabetic Retinopathy on Electronic Health Record Data
Authors: Saleh Emran  , Moreno Antonio  , Valls Aida  , Romero-Aroca Pedro  , de La Riva-Fernandez Sofia
Published: February 2025

Diabetic retinopathy is an ophthalmic malady that is the major cause of blindness in diabetic patients. Early detection is important to minimize the risk of vision loss. An screening of the eye fundus can confirm the disease and its severity but this test is costly and time-consuming. In this work, we propose a decision support system that uses fuzzy random forests to analyze the clinical data of each patient in order to detect any sign of developing diabetic retinopathy and to determine the necessity of the screening. The combination of fuzzy sets and a classifier ensemble for the detection of diabetic retinopathy achieves high sensitivity and specificity scores, improving the results given when using a single decision tree.