bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Multipath AODV Routing Protocol for VANET Based on Expected Link Lifetime
Authors: Hu TianLi  , LiWang MingHui  , Huang Lianfen  , Tang YuLiang
Published: February 2025

In Vehicular Ad Hoc Networks (VANETs), the node mobility leads to network topology changed frequently. Due to the vehicle movement, the limited wireless resources and channel fading, the routing protocols in Mobile Ad Hoc Networks (MANETs) are not suitable for VANETs. In this paper we propose a new multipath route protocol based on Ad hoc On-Demand Distance Vector Routing (AODV) for adapting VANETs environment called Enhanced Multipath AODV (EMP-AODV). EMP-AODV use a node-disjoint multipath strategy to set up routing and choose routing to transmission based on a link expected link lifetime algorithm. The simulation results show that our proposed protocol reduces the end-to-end delay and packet loss ratio while improving the network throughput in VANETs.
Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network
Authors: Wu Jian-Zhong  , Hung Chi-Fu  , Tuan Chiu-Ching
Published: February 2025

The people interest will often be changed by the dynamic of environment. We proposed the Dynamic Time Periods Collaborative Filtering Recommendation System based on Contextual Information and Social Network (DTPCS), combining user dynamic similarity and contextual information. Through the Community Network, the friends of similar interests could be found by the check-in information. And give different weight value as the intimacy. We considered the changes of user requirements in different contexts, and explore the impact of contextual factors. The different contextual information will give different weight value to be as a basis for recommendation. Finally, the system sorts the recommend sequence to be a Top-N recommendation list. According to the simulation results, in recommend error, DTPCS is less than the others about 35 %. In recommend calculation time, DTPCS is less than the others about 32 %. In recommend coverage, DTPCS is more than the others about 27 %.
Using a Genetic Model for Asset Allocation in Stock Investment
Authors: Chen Shin-Shou  , Huang Chien-Feng  , Hong Tzung-Pei  , Chang Bao-Rong
Published: February 2025

In this paper, we present a study of asset allocation using genetic algorithms. This method extends a previous version of a stock selection model using Genetic Algorithms (GA) for solving the problem of asset allocation. The GA is used for optimization of model parameters, feature selection as well as the construction of the Pareto front. On top of that, we proposed another GA to search for the optimal allocation of assets. We then present an investigation for this line of research using financial data of various industrial sectors in Taiwan's stock market. Our experimental results show that our proposed method is capable of significantly outperforming the benchmark and the canonical Markowitz method for asset allocation. Based upon these promising results, we expect this GA methodology to advance the current state of research in soft computing for the real-world applications in asset-allocation.
A Design for Multi-Pricing High-Performance Computing System
Authors: Chen Lung-Pin  , Kao Mike  , Wu I-Chen  , Wei Ting-Han
Published: February 2025

In high-performance computing (HPC) systems, resources are allocated to users according to configurable scheduling policies. Unfortunately, these policies often create resource fragments which reduce overall resource utilization. In this paper, we propose an HPC architecture which maximizes the resource utilization by allocating unused resource fragments for jobs which are malleable in the sense that these jobs can be created and terminated at any time without severe penalties. Hence, the whole HPC system can utilize almost 100% of its computing resources by filling its schedule with these malleable jobs. Malleable job computation can then be offered at a lower price for potential customers, allowing for multi-pricing schemes for the HPC system provider.
Scheduling Moldable Jobs with Deadline in HPC-as-a-Service Software System
Authors: Huang Kuo-Chan  , Hung Chun-Hao  , Hsieh Wei
Published: February 2025

As cloud computing emerges, software system has changed from standalone applications into service-based systems based on the Software as a Service (SaaS) model. Recently the concept of HPC as a Service (HPCaaS) was proposed to transform High-Performance Computing (HPC) facilities and applications into a more convenient and accessible service model. To achieve that goal, one inevitable issue of developing such kind of software systems is dealing with parallel job scheduling, taking Quality of Service (QoS) into consideration. This paper presents two new scheduling approaches for online moldable jobs with deadline, which differ in how they backfill the jobs in queue. In addition to traditional average turnaround time, the proposed approaches were evaluated and compared to previous approaches in terms of two other service-oriented performance metrics, including deadline miss rate and total profit, with a series of simulation experiments. The experimental results indicate that the new approaches outperform previous methods significantly in different performance metrics.
Design and Evaluation of Hybrid Wired and Wireless Sensor Networks with Cloud Services for Monitoring of Early-Stage Environmental Corrosion
Authors: Lin Paul I-Hai  , Yen Max  , Chen Dong  , Li MengWei  , Lampo Richard  , McInerney Michael  , Ryan Jeffrey
Published: February 2025

This paper gives a detailed report on the building and management of a hybrid wired and wireless sensor networks with cloud services for monitoring the early-stage environmental corrosion. We attempt to address the design and evaluation of highly reliable communication architectures with redundancies using both wired RS-485-based star network and wireless ZigBee-based mesh networks for low data-rate corrosion monitoring. The lessons learned from problems and issues found after the system deployment and the implemented correction solutions will provide the valuable knowledge and guidelines for future environmental monitoring and sensing applications.
Load Balancing and Multicasting Using the Extended Dijkstra's Algorithm in Software Defined Networking
Authors: Jiang Jehn-Ruey  , Yahya Widhi  , Ananta Mahardeka Tri
Published: February 2025

The extended Dijkstra's algorithm considers not only the edge weights but also the node weights for finding shortest paths from a source node to all other nodes in a given graph. To show the advantage of the extended Dijkstra's algorithm, this paper proposes a load-balancing algorithm and a multicast algorithm in Software Defined Networking (SDN) on the basis of the extended Dijkstra's algorithm for a graph derived from the underlying SDN topology. We use Pyretic to implement the proposed algorithms and compare them with related ones under the Abilene network topology with the Mininet emulation tool. As shown by the comparisons, the proposed algorithms outperform basic algorithms.
The GeoVoCamp Workshop Experience and Ontology Design Pattern Development
Authors: Berg-Cross Gary
Published: February 2025

Geospatial information is of interest to a wide range of scientific analysis and is used to support interoperable solutions as well as the vision of “geo-enabling” the Web. Formalization of geospatial semantics is therefore of interest to a wide set of communities in support of a richer, more semantic Web with support services. The Spatial Ontology Community of Practice (SOCoP) has been engaged in a range of activities and collaborations that support this goal of a geo-enabled Web. Of particular interest is development of small, modular geospatial ontologies that can serve as building blocks for larger, publically available ontologies. These small ontologies are developed in 2–3 day long workshops called VoCamps. Workgroups are formed around defined topics and start a structured process of vocabulary and conceptual model development aimed at producing usable and reusable ontology design patterns (ODPs) that address reoccurring problems. The OPDs are grounded in real, existing data data such as available for interrelated patterns on Motion, Path and Trajectories. Geo-patterns express domain ideas in simple and intuitive ways using a handful of concepts and relations for spatiotemporal properties & geographic knowledge. The overall organizing vision of the geospatial VoCamps is to create a Core of related, modular ODPs over a series of VoCamps and related meetings. The target ontological content of this so called Descartes-Core would be a constellation of related, extensible, aligned group of ODPs which can serve as composable building blocks for a range of purposes. To date 7 workshops with a geospatial focus have been held and a range of ODPs produced such as illustrated by a semantic (annotation) trajectory pattern which is built on prior patterns and demonstrates applicability to a range of data. It, like other patterns, is also extendable and can be made more specific for particular purposes as well as being aligned to a range of external ontologies to support interoperability based on extant semantics is other ontologies.
A Hybrid Heading Estimation Scheme Exploiting Smart-phone Inertial Sensors for PDR based Indoor Navigation
Authors: Kim Nammoon  , Zeng Qingchi  , Kim Youngok
Published: February 2025

In this paper, we propose a hybrid heading estimation scheme exploiting smart-phone inertial sensors, accelerometer, magnetic, gyroscope, for the pedestrian dead reckoning (PDR). Generally the heading estimation of a pedestrian determines the performance of indoor navigation system (INS). Even though various heading estimation schemes are known, most systems are simply based on only one scheme. Based on the performance evaluation of each scheme, in this paper, a hybrid heading estimation scheme is proposed to improve the performance of INS. To verify the effectiveness of our proposed scheme, we performed experiments in indoor environments and compared with various heading estimation schemes with a step detection scheme based on a peak detection scheme. According to the experiment results, the proposed hybrid heading estimation can provide enhanced performance than the conventional schemes.
iOS based Multi-Cloud Manage System
Authors: Wu Jyun-Long  , Chuang Chen-Chia  , Tao C.W.  , Jeng Jin-Tsong  , Huang Ze-Si
Published: February 2025

Recently, some of applications (APP) have been proposed about multi-cloud manage systems for iPhone. In this study, the personal style of multi-cloud manage systems is proposed. Instead of the other multi-cloud manage systems APPs on market which have only the limited-function cloud-drive that offered by companies. In this system, the users of iOS mobile devices not only storage the cloud-drive on the net that offered by companies, but also manage the own cloud-drive created on FTP server. In order to ensure the integrity of the file after be transferred, iOS system use the MD5 algorithm to synchronize files. Comparing the file stored between iOS device and FTP server, while avoiding the occurrence of missing packets.Though this proposed system, we can manage more varied cloud drive on iOS device. With the private cloud-drive, users will not be limited the size of file, storage-size and type, etc.
Gesture Recognition Apply on The Smart TV Assistant
Authors: Lin Ya-Yun  , Lee Pei-Jun
Published: February 2025

Hand tracking is the first task in a gesture recognition system. Skin-color detection and depth information are important clues which can be used to find hand location and to do gesture recognition. In this paper, linear regression approach is used to overcome the problem that color image and depth image are mismatch in the hand location finding. Moreover, some reference points in hand image are found out to recognize the position of fingertips, then the direction of hand area can be found by the proposed algorithm to improve the hand tracking accuracy. According to the information of the reference points in the fingertips, an adapted gesture recognition algorithm is proposed in this paper. Finally, the proposed gesture recognition algorithm is applied to our interactive machine on the smart TV which can support 3D video format. The experimental results show the proposed gesture recognition is satisfactory, and the result that applied on The Smart Assistant that is feasible.
Oblivious Array Access for Secure Multiparty Computation
Authors: Laud Peeter
Published: February 2025

In this chapter, we describe efficient protocols for performing reads and writes in private arrays according to private indices. The protocols are implemented on top of the arithmetic black box (ABB) and can be composed freely to build larger privacy-preserving applications. We present two approaches to speed up private reads and writes — one based on precomputation and the other one on sorting. We show how several different problems become significantly more tractable while preserving the privacy of inputs. In particular, our second approach opens up a large class of parallel algorithms for adoption to run on SMC platforms.
Achieving Optimal Utility for Distributed Differential Privacy Using Secure Multiparty Computation
Authors: Eigner Fabienne  , Kate Aniket  , Maffei Matteo  , Pampaloni Francesca  , Pryvalov Ivan
Published: February 2025

Computing aggregate statistics about user data is of vital importance for a variety of services and systems, but this practice seriously undermines the privacy of users. Recent research efforts have focused on the development of systems for aggregating and computing statistics about user data in a distributed and privacy-preserving manner. Differential privacy has played a pivotal role in this line of research: the fundamental idea is to perturb the result of the statistics before release, which suffices to hide the contribution of individual users.
Characteristics of business intelligence and big data in e-government: Preliminary findings
Authors: Gaardboe Rikke  , Svarre Tanja  , Kanstrup Anne Marie
Published: February 2025

Business intelligence and big data represent two different technologies within decision support systems. The present paper concerns the two concepts within the context of e-government. Thus, the purpose of the paper is to present the preliminary findings regarding publication patterns and topic coverage within the two technologies by conducting a comparative literature review. A total of 281 papers published in the years 2005–2014 were included in the analysis. A rapid increase of papers regarding big data were identified, the majority being journal papers. As regards business intelligence, researchers publish in conference proceedings to a greater extent. Further, big data journal papers are published within a broader range of journal topics compared to business intelligence journal papers. The paper concludes by pointing to further analyses that will be carried out within the 281 selected papers.
Information Systems Evolution Efficiency – Differences between the Public and the Private sector
Authors: Krogstie John  , Veld Tor Kristian
Published: February 2025

This paper provides a comparison between public and private organizations on key figures relative to the information systems support activities. It has been claimed that public sector has a less satisfactory conduct of information system support than private sector. In earlier work we have tried to find support for such claims without getting significant results. Whereas these earlier investigations only had data from around 70 organizations, in this article we present selected data from an extended survey investigations performed among more than 250 Norwegian organizations on how they conduct information systems development and maintenance. This investigation has earlier been compared with similar investigations of this sort. A major finding from the previous comparisons is that even if we witness large changes in the underlying implementation technology and approaches used, a number of aspects such as the overall percentage of time used for maintaining and evolving systems in production compared to time used for development is remarkably stable. When we compare public and private organizations in the last survey on the other hand, we find statistically significant differences supporting the claims of better usage of resources for information systems evolution in private than in public sector.
Investigation and Evaluation of the Usage of Social Media in Saudi Government 2.0
Authors: Alotaibi Reemiah
Published: February 2025

Introducing Government 2.0 which simply translates as employing social media platforms in the government communication system is a worldwide trend. Despite the fact that more than half of the Saudi population are users of social media platforms [1] this wide acceptance of social media networks and tools among Saudi population could be a great opportunity for governmental agencies to communicate with the citizens through these tools. Evidence from the literature [2; 3; 4] showed that Saudi governmental institutions are not fully adopting these platforms to engage the citizens. Little research efforts have been done to evaluate the progress of social media adoption in general. The overall aim of this research is to investigate and evaluate the usage of social media in Saudi government. To conduct this research project, mixed methods were used. The web analysis conducted by the researcher has shown that minority of Saudi Ministries are effectively employing social media platforms. Moreover, the results from the interviews conducted with the Saudi e -government staff used CATWOE Analysis tool to explain the state of Saudi Government 2.0 implementation. The main factor for the gap between usages of social media in Saudi government agencies, which is affecting social media adoption, is that each government agency has its vision and strategic plan for their e-government transformation to fulfil the government goals and initiatives. Future research will be done to explore more factors affecting social media adoption in Saudi government 2.0.
Create, Expand and Exploit Linked Open Statistical Data
Authors: Tambouris Efthimios  , Kalampokis Evangelos  , Tarabanis Konstantinos
Published: February 2025

A major part of Open Data concerns statistics that is structured as multi-dimensional data cubes. Linked Data technologies have the potential to realise the vision of combining and performing analytics on top of previously isolated cubes on the Web. Although a few recently launched tools focus on handling linked data cubes, they fall sort to support the whole lifecycle including the integration of multiple cubes and performing analytics on top of integrated and expanded cubes. In this workshop, we will present the OpenCube Toolkit, a set of open source tools that cover the whole linked data cubes lifecycle in an integrated manner. The participants will be guided to a hands-on tour on using and evaluating some of the tools with real datasets e.g. from Eurostat or Digital Agenda.
Policy modelling and maintenance in social housing: motivating sustainability and managing change
Authors: Cestnik Bojan  , Kern Alenka
Published: February 2025

Public policies are collections of principles that guide state government actions in a given domain. The aim of public policy used by the state Housing Fund for distributing social housing facilities in Slovenia is to ensure transparent and equally fair treatment of all beneficiaries. During the last decade the Fund has established a sustainable structure for policy models that consist of three parts: written requirements for applications, a detailed process description, and a comprehensive decision support model containing rules for ranking the applications according to their priority. In this paper we analyze the main causes of changes in the policy in the last decade and investigate how they are reflected in the underlying conceptual and decision support models. The results of our analysis indicate that the key critical success factor was to maintain a delicate balance between the sustainability of the models and changes in the policy. As a result, the state housing Fund was able to leverage the supply on the Slovenian housing market by efficiently conducting twelve consecutive tenders for offering apartments under favorable terms to citizens.
Comprehensive Framework Embracing the Complexity of Statutory Interpretation
Authors: Araszkiewicz Michał  , Zurek Tomasz
Published: February 2025

The paper discusses the complexity problem in the interpretation of statutory legal norms. The authors propose a comprehensive framework that allows the representation of the interpretation process.
Two Tools for Prototyping Legal CBR
Authors: Bench-Capon Trevor  , Gordon Thomas
Published: February 2025

In this paper we discuss tools for rapid prototyping of legal CBR. We describe how a recent highly quantitative analysis can be realised using a spreadsheet, while a more nuanced approach at a finer level of granularity can be prototyped with the web service Carneades, which displays its results as an argument graph.
Search and Discovery in Legal Document Networks
Authors: Mimouni Nada  , Nazarenko Adeline  , Salotti Sylvie
Published: February 2025

This paper highlights the benefit of semantic information retrieval in legal networks. The needs analysis shows that user queries are often complex and involve both the semantic content and the intertextual links between documents. We propose two methods of search in legal collection graphs, conceptual and semantic, and describe the new functionalities of search and navigation offered by both approaches. The first approach is proposed as a heuristic method based on the formal representation of the collection to allow for approximate answers, when no exact ones are found, and to discover new hidden knowledge.
Finding an Optimal Configuration of the Feed-forward Neural Network
Authors: Štrba Radoslav  , Štolfa Jakub  , Štolfa Svatopluk
Published: February 2025

In this paper we present an algorithm for finding an optimal configuration of the artificial neural network that is used for the classification in our use case based effort estimation tool. This approach is based on feed-forward artificial neural network and is trained using the back-propagation training algorithm. Our goal is to find the optimal number of hidden neurons and the optimal number of training iterations to be able to reach maximal accuracy of neural network during the estimations. We demonstrate the usage of the proposed algorithm and its result on the estimation example that contains training and testing datasets of UseCases obtained from real software project development.
An English Speech Learning and Evaluation System Based on C/S Architecture
Authors: Li Xin-Guang  , Zhang Sheng-Bin  , Xu Ji-You  , Chen Jun-Yu
Published: February 2025

Along with the development of the mobile Internet, the needs of English learners' studying spoken English and the lack of domestic fully-developed spoken English learning software, this paper brings forward a kind of English speech system for learning and evaluation based on Client/Server architecture. The communication between the Client and the Server applies the Socket programming principle, and adopts TCP/IP which provides reliable transmission. The system fully takes advantages of the hardware environment of both ends and sensibly allocates a task to the Client and the Server, which effectively reduces the burden on the Client and adapts to the relatively low configuration and performance of the Client. At the same time, the system returns evaluation and feedback on learners' pronunciation through the multi-parameter evaluation system, so as to instruct users' oral English learning. This paper lastly confirms the effectiveness of the system by experiments.
Land Use and Land Cover Semantics: Distinguishing Occurrents from Continuants
Authors: Camara Gilberto
Published: February 2025

Classifying and describing the Earth's land use and land cover present unique challenges, leading to a lack of harmonization between existing land classification systems. These problems arise partly because current classification systems take a static view of the world. As such, they aim to support unambiguous definitions of land cover and land use classes at one particular moment. Since the Earth's land cover is continuously changing, such static definitions are limited. This talk considers how large Earth Observation data sets can help to define a new generation of spatio-temporal ontologies of land use and land cover. These ontologies will distinguish terms such as “forest” and “savanna”, associated to continuants, from terms such as “deforestation” and “degradation”, related to occurrents. This novel approach is better suited to describe the land dynamics of our ever-changing planet.
Digital Governance Challenges for ICT-Enabled Innovation of Social Protection Systems in the EU
Authors: Misuraca Gianluca  , Viscusi Gianluigi  , Pasi Giulio
Published: February 2025

ICT-Enabled Social Innovation can help making existing service more efficient and effective, but also produce new services as a result of integration among different policy silos. Information and Communication Technologies (ICTs) can also promote conceptual innovation in the sense of changing the base (empirical and analytical) upon which policy are first designed and later evaluated. Building on extensive literature review and analysis of case studies conducted as part of the research on ICT-Enabled Social Innovation (IESI) conducted by the European Commission's Joint Research Centre, Institute for Prospective Technological Studies (JRC-IPTS), this article discusses, the results of an analysis of three selected case studies, exploring the relationship between different typologies of ICT-enabled social innovation implemented and the broader social protection system in which they are embedded in, contributing to determine if a relationship is identified between the nature and type of ICT-enabled social innovation and the modernization of social policies, considering the broader digital governance space in which they are implemented.
The Elderly and the Electronic Government in Brazil
Authors: Moraes Gustavo H.S.M.
Published: February 2025

The use of information technology can increase the quality of life of senior citizens. The elderly tend to be more cautious and seek greater certitude before they act when compared to younger individuals. In Brazil, the elderly are already 13% of the population. The objective of this research was to study what factors influence the elderly in the use of e-government in Brazil. The focus of this study was to investigate an initiative of the Brazilian government to computerize fiscal control mechanisms. We interviewed 137 elderly individuals who have used the program. We used a quantitative methodology for the development of this research, through the multivariate analysis technique of structural equation modeling. The study presented a robust model with a high explanatory power, in which the influencing factors are: Performance Expectancy, Facilitating Conditions and Habit. The research assists in the participation and involvement of the elderly in the current e-government development phase in Brazil, exposing their perceptions.
Digital Business Innovation of Public Services
Authors: Danneels Lieselot
Published: February 2025

The public sector needs to transform itself in order to reap full benefits of new digital technologies. In this regard, a paradigm shift is proposed from New Public Management (NPM) to Digital-Era Governance (DEG). Moving from NPM to DEG entails a full socio-technical change, and it is necessary to investigate what new business models will be needed, and what the impact will be on management, strategy, and governance. This research investigates the implications of moving from NPM to DEG. First, by applying Action Design Research (ADR) in the context of a government lab to examine the implications on strategy in a DEG context. Second, this thesis investigates how the literature is paving the way towards DEG by performing a literature review on open data case studies. In this specific aspect of DEG transformation, focused on ecosystem platforms, it explores how knowledge-based interactions are fostered by open data platforms. Together with the understanding of how to design a DEG strategy, this contributes to a holistic view on how to move towards DEG.
Preference Modeling with Possibilistic Networks and Symbolic Weights: A Theoretical Study
Authors: Ben Amor Nahla  , Dubois Didier  , Gouider Héla  , Prade Henri
Published: February 2025

The use of possibilistic networks for representing conditional preference statements on discrete variables has been proposed only recently. The approach uses non-instantiated possibility weights to define conditional preference tables. Moreover, additional information about the relative strengths of these symbolic weights can be taken into account. The fact that at best we have some information about the relative values of these weights acknowledges the qualitative nature of preference specification. These conditional preference tables give birth to vectors of symbolic weights that reflect the preferences that are satisfied and those that are violated in a considered situation. The comparison of such vectors may rely on different orderings: the ones induced by the product-based, or the minimum-based chain rule underlying the possibilistic network, the discrimin, or leximin refinements of the minimum-based ordering, as well as Pareto ordering, and the symmetric Pareto ordering that refines it. A thorough study of the relations between these orderings in presence of vector components that are symbolic rather numerical is presented. In particular, we establish that the product-based ordering and the symmetric Pareto ordering coincide in presence of constraints comparing pairs of symbolic weights. This ordering agrees in the Boolean case with the inclusion between the sets of preference statements that are violated. The symmetric Pareto ordering may be itself refined by the leximin ordering. The paper highlights the merits of product-based possibilistic networks for representing preferences and provides a comparative discussion with CP-nets and OCF-networks.
A Distributed Asynchronous Solver for Nash Equilibria in Hypergraphical Games
Authors: Wahbi Mohamed  , Brown Kenneth N.
Published: February 2025

Hypergraphical games provides a compact model of a network of self-interested agents, each involved in simultaneous subgames with its neighbors. The overall aim is for the agents in the network to reach a Nash Equilibrium, in which no agent has an incentive to change their response, but without revealing all their private information. Asymmetric Distributed constraint satisfaction (ADisCSP) has been proposed as a solution to this search problem. In this paper, we propose a new model of hypergraphical games as an ADisCSP based on a new global constraint, and a new asynchronous algorithm for solving ADisCSP that is able to find a Nash Equilibrium. We show empirically that we significantly reduce both message passing and computation time, achieving an order of magnitude improvement in messaging and in non-concurrent computation time on dense problems compared to state-of-the art algorithms.
Get Me to My GATE on Time: Efficiently Solving General-Sum Bayesian Threat Screening Games
Authors: Schlenker Aaron  , Brown Matthew  , Sinha Arunesh  , Tambe Milind  , Mehta Ruta
Published: February 2025

Threat Screening Games (TSGs) are used in domains where there is a set of individuals or objects to screen with a limited amount of screening resources available to screen them. TSGs are broadly applicable to domains like airport passenger screening, stadium screening, cargo container screening, etc. Previous work on TSGs focused only on the Bayesian zero-sum case and provided the MGA algorithm to solve these games. In this paper, we solve Bayesian general-sum TSGs which we prove are NP-hard even when exploiting a compact marginal representation. We also present an algorithm based upon a adversary type hierarchical tree decomposition and an efficient branch-and-bound search to solve Bayesian generalsum TSGs. With this we provide four contributions: (1) GATE, the first algorithm for solving Bayesian general-sum TSGs, which uses hierarchical type trees and a novel branch-and-bound search, (2) the Branch-and-Guide approach which combines branch-and-bound search with the MGA algorithm for the first time, (3) heuristics based on properties of TSGs for accelerated computation of GATE, and (4) experimental results showing the scalability of GATE needed for real-world domains.
Parameterized Complexity Results for the Kemeny Rule in Judgment Aggregation
Authors: de Haan Ronald
Published: February 2025

We investigate the parameterized complexity of computing an outcome of the Kemeny rule in judgment aggregation, providing the first parameterized complexity results for this problem for any judgment aggregation procedure. As parameters, we consider (i) the number of issues, (ii) the maximum size of formulas used to represent issues, (iii) the size of the integrity constraint used to restrict the set of feasible opinions, (iv) the number of individuals, and (v) the maximum Hamming distance between any two individual opinions, as well as all possible combinations of these parameters. We provide parameterized complexity results for two judgment aggregation frameworks: formula-based judgment aggregation and constraint-based judgment aggregation. Whereas the classical complexity of computing an outcome of the Kemeny rule in these two frameworks coincides, the parameterized complexity results differ.
Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data
Authors: Spanakis Gerasimos  , Weiss Gerhard  , Roefs Anne
Published: February 2025

Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.) and this particular structure should be taken into account in machine learning algorithms used in EMA like decision trees and its variants. We propose a new algorithm called BBT (standing for Bagged Boosted Trees) that is enhanced by a over/under sampling method and can provide better estimates for the conditional class probability function. Experimental results on a real-world dataset show that BBT can benefit EMA data classification and performance.
Continuous Live Stress Monitoring with a Wristband
Authors: Gjoreski Martin  , Gjoreski Hristijan  , Luštrek Mitja  , Gams Matjaž
Published: February 2025

In this paper we propose a method for continuous stress monitoring using data provided by a commercial wrist device equipped with common physiological sensors and an accelerometer. The method consists of three machine-learning components: a laboratory stress-detector that detects short-term stress every 2 minutes; an activity recognizer that continuously recognizes user's activity and thus provides context information; and a context-based stress detector that first aggregates the predictions of the laboratory detector, and then exploits the user's context in order to provide the final decision in a 20 minute interval. The method was trained on 21 subjects in a laboratory setting and tested on 5 subjects in a real-life setting. The accuracy on 55 days of real-life data was 92%. The method is currently being implemented as a smartphone application, which will be demonstrated at the conference.
A Joint Model for Sentiment-Aware Topic Detection on Social Media
Authors: Xu Kang  , Qi Guilin  , Huang Junheng  , Wu Tianxing
Published: February 2025

Joint sentiment/topic models are widely applied in detecting sentiment-aware topics on the lengthy review data and they are achieved with Latent Dirichlet Allocation (LDA) based model. Nowadays plenty of user-generated posts, e.g., tweets and E-commerce short reviews, are published on the social media and the posts imply the public's sentiments (i.e., positive and negative) towards various topics. However, the existing sentiment/topic models are not applicable to detect sentiment-aware topics on the posts, i.e., short texts, because applying the models to the short texts directly will suffer from the context sparsity problem. In this paper, we propose a Time-User Sentiment/Topic Latent Dirichlet Allocation (TUS-LDA) which aggregates posts in the same timeslice or user as a pseudo-document to alleviate the context sparsity problem. Moreover, we design approaches for parameter inference and incorporating prior knowledge into TUS-LDA. Experiments on the Sentiment140 and tweets of electronic products from Twitter7 show that TUS-LDA outperforms previous models in the tasks of sentiment classification and sentiment-aware topic extraction. Finally, we visualize the sentiment-aware topics discovered by TUS-LDA.
How Hard Is Bribery with Distance Restrictions?
Authors: Yang Yongjie  , Shrestha Yash Raj  , Guo Jiong
Published: February 2025

We study the complexity of the bribery problem with distance restrictions. In particular, in the bribery problem, we are given an election and a distinguished candidate p, and are asked whether we can make p win/not win the election by bribing at most k voters to recast their votes. In the bribery problem with distance restrictions, we require that the votes recast by the bribed voters are close to their original votes. To measure the closeness between two votes, we adopt the prevalent Kendall-Tau distance and the Hamming distance. We achieve a wide range of complexity results for this problem under a variety of voting correspondences, including the Borda, Condorcet, Copelandαfor every 0≤α≤1 and Maximin.
ShapeLearner: Towards Shape-Based Visual Knowledge Harvesting
Authors: Xu Huayong  , Wang Yafang  , Feng Kang  , de Melo Gerard  , Wu Wei  , Sharf Andrei  , Chen Baoquan
Published: February 2025

The deluge of images on the Web has led to a number of efforts to organize images semantically and mine visual knowledge. Despite enormous progress on categorizing entire images or bounding boxes, only few studies have targeted fine-grained image understanding at the level of specific shape contours. For instance, beyond recognizing that an image portrays a cat, we may wish to distinguish its legs, head, tail, and so on. To this end, we present ShapeLearner, a system that acquires such visual knowledge about object shapes and their parts in a semantic taxonomy, and then is able to exploit this hierarchy in order to analyze new kinds of objects that it has not observed before. ShapeLearner jointly learns this knowledge from sets of segmented images. The space of label and segmentation hypotheses is pruned and then evaluated using Integer Linear Programming. Experiments on a variety of shape classes show the accuracy and effectiveness of our method.
Software Engineering Artifact in Software Development Process – Linkage Between Issues and Code Review Processes
Authors: Dalipaj Dorealda  , Gonzalez-Barahona Jesus M.  , Izquierdo-Cortazar Daniel
Published: February 2025

Researchers working with software repositories, often when building performance or quality models, need to recover traceability links between bug reports in issue tracking repositories and reviews in code review systems. However, very often the information stored in bug tracking repositories is not explicitly tagged or linked to the issues reviewing them. Researchers have to adopt various heuristics to tag the data. These includes, for example, identifying if an issue is a bug report or not. In this study we promote a research artifact in software engineering, a reusable unit of research that can be used to support other research endeavors and has acted as a support material that enabled the creation of the results published in a great number of papers until now – linking issues and reviews of the code review software process. We present two state-of-the-practice algorithms on how to link issues and reviews, selecting as our case study the open source cloud computing project of OpenStack. OpenStack enforces strict development guidelines and rules on the quality of the data in its issue tracking repository. We empirically compare the outcome of the two approaches, highlighting the most prominent one.
A Computation Algorithm for the Configuration of BLE Devices Using k-Means Method
Authors: Onishi Kensuke
Published: February 2025

The number of devices which use Bluetooth low-energy (BLE) technology is increasing rapidly. Although the cost of the BLE components is decreasing, the installation costs remain high. When many BLE devices are installed, determination of the locations for installation, referred to as the configuration, becomes complicated and labor intensive.
Architectural Considerations for Decision Analysis Software
Authors: Danielson Mats  , Ekenberg Love
Published: February 2025

In classic decision theory, it is assumed that a decision-maker can assign precise numerical values corresponding to the true value of each consequence, as well as precise numerical probabilities for their occurrences. However, in real-life situations, the ordering of alternatives from most to least preferred is often a delicate matter and an adequate mathematical representation is crucial. In attempting to address real-life problems, where uncertainty about data prevails, some kind of representation of imprecise information is important and several have been proposed. However, general methods have turned out to be insufficient and we demonstrate in this article that there is not one set of coding techniques that result in the best performing software for decision analysis.
Ontology Pattern Languages
Authors: Falbo Ricardo  , Barcellos Monalessa  , Ruy Fabiano  , Guizzardi Giancarlo  , Guizzardi Renata
Published: February 2025

Ontology design patterns are a promising approach for ontology engineering. In this chapter, we introduce the notion of Ontology Pattern Language (OPL) as a way to organize domain-related ontology patterns. This chapter is organized as follows: Section 7.1 presents the motivation for organizing Domain-Related Ontology Patterns (DROPs) as OPLs. Section 7.2 discusses what an OPL is, and how OPLs are represented, showing an example of an OPL for the software process domain, based on ISO Standards (ISP-OPL). Section 7.3 discusses how to build OPLs from core ontologies, taking ISP-OPL as an example. Section 7.4 discusses how an OPL can be used for building a domain ontology. An example applying ISP-OPL for building a domain ontology about the Stakeholder Requirements Definition Process is presented. Section 7.5 presents an overview of existing OPLs. Finally, Section 7.6 presents our final remarks.
Engineering Ontologies with Patterns – The eXtreme Design Methodology
Authors: Blomqvist Eva  , Hammar Karl  , Presutti Valentina
Published: February 2025

When using Ontology Design Patterns (ODPs) for modelling new parts of an ontology, i.e., new ontology modules, or even an entire ontology from scratch, ODPs can be used both as inspiration for different modelling solutions, as well as concrete templates or even “building blocks” reused directly in the new solution. This chapter discusses how ODPs, and in particular Content ODPsIn fact, throughout this chapter when mentioning ODPs, this mainly refers to Content ODPs if not specified further.
A Primer on RDF and OWL
Authors: Maier Frederick
Published: February 2025

This appendix provides a brief introduction to RDF [12,18] and OWL [19,31], the primary modelling formalisms used in Semantic Web applications. The essential structure of RDF graphs is presented, followed by discussions of the concrete RDF syntax Turtle, RDFS, and the semantics of RDF. OWL, its semantics, and tractable OWL profiles are then presented.
Use of Discourse and Syntactic Features for Gender Identification
Authors: Soler-Company Juan
Published: February 2025

Author profiling and Gender Identification have gained relevance in the last few years. The goal of the research in these fields is to extract certain demographic information on the authors of texts by analyzing their writing at several levels. In our work, we address the problem of the identification of the gender (male vs. female) of the authors of opinion pieces published online. Unlike the overwhelming majority of the proposals, we argue that the use of deeper linguistic features (i.e., syntactic and discourse structure), instead of mainly lexical features leads to a higher accuracy of gender identification. Using such features with supervised machine learning, we achieve very competitive results with accuracies over 84%.
A System for Dispute Mediation: The Mediation Dialogue Game
Authors: Janier Mathilde  , Snaith Mark  , Budzynska Katarzyna  , Lawrence John  , Reed Chris
Published: February 2025

We propose a dialogue game for mediation and its formalization in DGDL. This dialectical system is available as software through Arvina for automatic execution. This work expands the literature in dialectical systems, in particular those for more than two players, and shows the practical impact on mediation activity through the opportunity offered to mediators once implemented.
DIAMOND 3.0 – A Native C++ Implementation of DIAMOND
Authors: Ellmauthaler Stefan  , Strass Hannes
Published: February 2025

We present a reimplementation of the DIAMOND system for computing with abstract dialectical frameworks. The original DIAMOND was a script-based tool that called an external ASP solver. This reimplementation uses the clingo library in a native C++ environment and thus avoids communication overhead.
ParliSearch – A System for Large Text Corpus Discourse Analysis
Authors: Darǵis Roberts  , Rābante-Buša Guna  , Auziņa Ilze  , Kruks Sergejs
Published: February 2025

The paper illustrates the ParliSearch – the system that enables easy discourse analysis in large text corpus, providing stem search with additional search criteria. The system contains verbatim reports from debates of plenary sittings of the European Parliament and the Saeima (the Parliament of Latvia).
Experiences from Long-Term Implementation of Social Robots in Danish Educational Institutions
Authors: Kirstein Franziska  , Risager Rikke
Published: February 2025

Introducing social robots in educational institutions comes with many challenges with regards to the involved pedagogues, their available time and lack of technological skills as well as general technical issues such as poor internet connection. In this paper, we describe how Blue Ocean Robotics works with end-users in our Innovation Projects that focus on long-term implementation of social robots in Danish educational institutions.
Dynamic Business Processes and Their Simulation: A Survey
Authors: Vasilecas Olegas  , Rusinaite Toma  , Kalibatiene Diana
Published: February 2025

Run-time adaptability is a key feature of dynamic business environments. Accordingly, the business processes need to be constantly refined and restructured to deal with exceptional situations and changing requirements. Therefore, gaining insight in an existing or proposed future situation needs simulation process. Simulation is a cost-effective way to analyse several alternatives that it will become apparent which parts are critical. However, dynamic business processes (DBP) simulation are not investigated sufficient what prevents their effective use. Therefore, this paper presents a systematic literature review of conference and journal articles on the topic of DBP simulation. The review has been undertaken to define DBP, identify the DBP requirements, analyse proposed solutions for their implementation and dynamic simulation.