bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Block Partition of Geographic Map Based on Road Coverage in Urban Environment
Authors: Wu Guang-Ting  , Sheu Ruey-Kai  , Fan Chih-Tien  , Chang Yue-Shan  , Tai Chih-Hua
Published: February 2025

Smart cities try to deploy new sensors or controller to existing hardware infrastructure to improve the efficiency of city life. How to effectively utilize geographic information to deploy ubiquitous facilities (such as street light control unit) in the urban environment increasingly becomes an important issue. Calculating the minimum block number for facility deployment in a city is similar to the maximum clique problem which belongs to the NP-hard problems. In this paper, we propose an algorithm to find the near minimum block number of geographic map based on road coverage in the urban environment. In the algorithm, we first exploit the Google map as the geographic information to calculate the road coverage of a city and then partitioning the map into numerous partitions according to a given lights (road coverage). We also conduct some experiments to evaluate the performance and accuracy to demonstrate the feasibility of the system. The result shows that the proposed algorithm is effective and efficient.
Scientific Evaluation on a Virtual Machine
Authors: Chou Chau-Yi  , Chen Kuen-Tsann
Published: February 2025

Virtualization technologies provide considerable flexibility in dealing with computational resources. This paper proposes a standard operating procedure (SOP) to evaluate the performance of scientific computation on various platforms in National Center for High-performance Computing (NCHC), including ALPS, the proprietary Formosa 3 PC cluster (F3), and virtual machines (VM). This study adopts three packages of linear system solvers: HPL (High Performance LINPACK), HIPS (Hierarchical Iterative Parallel Solver) and PETSc (Portable, Extensible Toolkit for Scientific Computation) to evaluate the performance of scientific computation as it pertains to industrial applications. HPL is computing dominant for solving large dense linear systems while sparse matrix computations via HIPS and PETSc are memory intensive. These complex, un-symmetric, non-positive definite sparse linear systems arise in industrial applications in the UF collection. We hope to provide a valuable reference for industrial applications.
Optimization of Downlink Bandwidth Allocation for Energy Efficiency over OFDMA Wireless Networks
Authors: Wu Chien-Yu  , Ho Hann-Jang  , Lee Sing-Ling
Published: February 2025

For the multicast channel assignment issue with multi-layer, this paper studies a resource allocation problem for minimizing the energy consumption of subscriber stations in Orthogonal Frequency Division Multiplexing Access (OFDMA) wireless systems. Not only does the requirement have to be guaranteed when multicast users receive video content in layer-based video coding, but also the energy consumption needs to be minimized. In this paper, the energy consumption of each station is optimized by minimizing the number of active symbols. The scheduling process is divided into two phases. First, the number of requested tiles is minimized by proposed scheme Layer Determination Scheme with SVC (LDS) for the issue of optimizing energy consumption. The proposed LDS scheme determines the modulation of the selected layer and assigns available tiles to the selected layer. In the secondary phase, the symbol scheduling problem is minimizing the number of active symbols for each station. The Complementary Matching Algorithm (CMA) is proposed to minimize the total number of active symbols in an OFDMA wireless system. The problems of symbol scheduling are reduced to the subset sum optimization problem to find the complementary subset of a request. In the simulation results for the proposed LDS and CMA algorithms, we found that the solutions generated by our algortihms have better performance in terms of energy consumption and throughput.
A Privacy-Preserving Encoding for Efficient Comparison Queries and Access Control from Predicate Encryption
Authors: Chen Shao-Heng  , Tseng Fu-Kuo  , Chen Rong-Jaye
Published: February 2025

A hidden vector encryption (HVE) scheme, one type of predicate encryption schemes, can support comparison predicates on encrypted keywords through encrypted predicates by pairing transformed keyword vectors with predicate vectors. However, the length of these vectors grows in proportion to the size of the keyword space and so does the system complexity. In this paper, we provide a privacy-preserving encoding for efficient comparison queries, where the length of the vectors and the system complexity is only logarithmically proportional to the size of the keyword space. In addition, our encoding integrates a HVE scheme and a ciphertext-policy attribute based encryption (CPABE) scheme. This integration provides not only the access control based on the searchable keywords but also balanced overheads among encryption, key generation and query for both computation and storage overheads.
Use of Semantic Technology to Create Curated Data Albums
Authors: Ramachandran Rahul  , Kulkarni Ajinkya  , Li Xiang  , Sainju Roshan  , Bakare Rohan  , Basyal Sabin
Published: February 2025

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies.
RPG Game Learning for Language Education with Voice Recognition in Cloud
Authors: Jian Ming-Shen  , Chen Yu-Chih  , Chen Jun-Lin
Published: February 2025

In this paper, we propose a modular role player game-learning system based on voice recognition for language education in cloud. Each designed modular language teaching materials can be embedded into the game platform on the virtual machine in cloud. By connecting to the external voice recognition resource, the pronounce of learners can be recognized. Only when the voice recognition result data (in words) of learners matches the on demand answer, the learners can be identified and enter the further learning unit. By designing different scenarios, different vocabularies or conversations can be added into the RPG game. The information about users in the individual game learning system can be stored in the cloud database. In addition, the whole learning system is established as a virtual machine. System maintainer can configure the learning system easily and quickly. Based on cloud, different remote devices can connect to server for learning.
Business Process Engineering and Secure Multiparty Computation
Authors: Guanciale Roberto  , Gurov Dilian  , Laud Peeter
Published: February 2025

In this chapter we use secure multiparty computation (SMC) to enable privacy-preserving engineering of inter-organizational business processes. Business processes often involve structuring the activities of several organizations, for example when several potentially competitive enterprises share their skills to form a temporary alliance.
Mechanism Design and Strong Truthfulness
Authors: Giannakopoulos Yiannis
Published: February 2025

In this chapter we give a very brief overview of some fundamentals from mechanism design, the branch of game theory dealing with designing protocols to cope with agents' private incentives and selfish behavior. We also present recent results involving a new, extended utilities model that can incorporate externalities, such as malicious and spiteful behavior of the participating players. A new notion of strong truthfulness is proposed and analyzed. It is based on the principle of punishing players that lie. Due to this, strongly truthful mechanisms can serve as subcomponents in bigger mechanism protocols in order to boost truthfulness. The related solution concept equilibria are discussed and the power of the decomposability scheme is demonstrated by an application in the case of the well-known mechanism design problem of scheduling tasks to machines for minimizing the makespan.
Verifiable Computation in Multiparty Protocols with Honest Majority
Authors: Pankova Alisa  , Laud Peeter
Published: February 2025

We present a generic method for turning passively secure protocols into protocols secure against covert attacks. This method adds to the protocol a post-execution verification phase that allows a misbehaving party to escape detection only with negligible probability. The execution phase, after which the computed protocol result is already available to the parties, has only negligible overhead added by our method.
Statistical Analysis Methods Using Secure Multiparty Computation
Authors: Kamm Liina  , Bogdanov Dan  , Pankova Alisa  , Talviste Riivo
Published: February 2025

This chapter gives an overview of privacy-preserving versions of the analysis methods and algorithms that are most commonly used in statistical analysis. We discuss methods for data collection and sharing, and describe privacy-preserving database joins and sorting. From simple statistical measures, we discuss the count and sum of elements, quantiles, the five-number summary, frequency tables, mean, variance, covariance and standard deviation. We look into outlier detection and explore privacy-preserving versions of several different statistical tests, such as Student's t-test, Wilcoxon rank-sum and signed-rank tests and the χ2-test. We discuss how to evaluate the significance of the test statistic in the privacy-preserving environment. We give several options for linear regression and conclude the chapter with a privacy-preserving method for data classification.
Adoption of Common Service Centre with presence of intermediary for delivery of e-Government services: A conceptual framework
Authors: Sharma Rajesh  , Mishra Rajhans
Published: February 2025

Government of India is making large investments for providing e-Government services to citizens under its National e-Governance Plan (NeGP) 2006. Common Service Centres (CSCs) are envisaged as one of the pillars of the delivery mechanism of e-Government services in rural areas where the availability of Internet, literacy levels of citizens and PC penetration is rather low. This channel of delivery is quite different from that of developed countries because citizens do not interact directly with technology. An intermediary called as the Village Level Entrepreneur (VLE) delivers the services to the citizens and technology is manifested to the citizens by results of his interactions at the CSC. The study aims to develop a conceptual framework for adoption of CSCs by the citizens in this unique context. A qualitative study is carried out to identify the factors that can augment the technology adoption constructs. The study reveals that quality of service delivered at CSC may be an important determinant for adoption of CSCs. The proposed conceptual framework is intended to form the basis of future empirical studies in similar context in India and in other developing countries.
Who are the Internet Voters?
Authors: Serdült Uwe  , Germann Micha  , Harris Maja  , Mendez Fernando  , Portenier Alicia
Published: February 2025

Assessing the influence that socio-economic characteristics have on the division between traditional voters and those who choose to vote via the internet is crucial to political debate as well as for the future development of democracies. Does the introduction of internet voting technology simply widen the divide between voters and non-voters, further isolating the part of the electorate already underrepresented in the political process? We address these issues by reviewing the current state of research in 22 empirical studies relating internet voting to socio-economic variables. The results are not homogeneous but suggest that although socio-economic factors do play an important role in explaining the choice of voting channel, they are strongly moderated by the general use of and trust in the internet.
Government Crowdsourcing: the role of trust and community in creating public value
Authors: O'Brien Ann
Published: February 2025

Inspired by the e-Participation framework of Saebo, the activities of online political discourse and decision making are studied in the context of crowdsourcing (platforms which enable the wisdom from the crowd) as an underlying technology; and are examined though the public administration theory of creating public value. Due to the shared locus of power, crowdsourcing is situated at the intersection between government led and citizen led e-Participation. Previous research has shown that a high Sense of Community has positive and significant effects on both internal and external efficacy; translated to government crowdsourcing, it would mean that design features that encouraged a Sense of Community in government crowdsourcing would be best placed to create public value by promoting civic engagement, and enabling the creation of a public that could understand and act in their own interests. As citizens are the arbiters of public value, the second phase of the research looks at the outcomes of government crowdsourcing process from the perspective of the citizen. Proposing that there is a hierarchy of participation; that there are different routes to participation; allowing many different actors with different values to deliberate and learn from the process in different ways according to their needs and time constraints. A key contribution is in the novelty of the subjects being investigated – as these two constructs, crowdsourcing and public value have to date not been researched together. The outcomes of the research will guide interactions and connections in government crowdsourcing communities through the technical, legal and social capabilities offered to community members, similar to the way that architecture guides people in physical spaces.
The Role of Trust in E-Participation: Predictors, Consequences, and Design
Authors: Wimmer Maria A.  , Scherer Sabrina  , Appel Markus
Published: February 2025

E-participation has become subject of considerable research over the past decade. However, trust as a pre-condition and result of e-participation has not yet been extensively investigated in e-participation research. In literature, trust is perceived as a complex construct studied in distinct research disciplines. To identify and implement measures for increasing trust as well as for minimising distrust in e-participation endeavours, a trust model helps to explain the full scope of the trust construct in the context. This contribution introduces a research design that aims on the one hand to analyse predictors and consequences of trust in e-participation based on a trust model for e-participation. On the other hand, a ‘trust-by-design’ approach for designing and implementing e-participation projects is aimed at. The approach combines empirical research to ‘understand’ trust factors with design science research for ‘innovating’ in regards to improving the design of e-participation endeavours by the lessons and insights of the empirical research. Both strands of research also contribute to theory building of trust in e-participation. This paper aims to set the scope of the research, to introduce the research questions and to present the research design with the multidisciplinary setting.
Evaluation of Open Data maintenance models with Public Administrations and SMEs
Authors: Nguyen Quynh Nhu  , Frisiello Antonella  , Lotito Antonio  , Morando Federico
Published: February 2025

The Open Data exploitation is strictly related with the quality of data and by consequence with the maintenance of dataset. The paper presents a qualitative study aimed at investigating open data maintenance process (including sharing and re-use issues). Participatory activities and visual tools, involving public, private and societal stakeholders have been applied. The outcome consists in the design of an Open Data Maintenance Life Cycle and operational proposals raised by groups. The study highlights that innovation by design framework is an effective way to manage complexity and plurality of actors, adoptable by any organizations to improve their innovation capability and gain strategic perspectives.
Guessing Art Styles using Qualitative Colour Descriptors, SVMs and Logics
Authors: Falomir Zoe  , Museros Lledo  , Sanz Ismael  , Gonzalez-Abril Luis
Published: February 2025

An approach for style painting categorization based on their Qualitative Colour Descriptors (QCD) and their similarity (SimQCD) is presented in this paper. For learning the colour features of paintings from Baroque, Impressionism and Post-Impressionism styles, two classifiers are built based on k-Nearest Neighbour (k-NN) and support vector machine (SVM) methods. The results showed accuracies of categorization higher than 75%. Colour logics in art styles are identified from the literature in order to improve and explain the categorization obtained.
Cross-cultural and Environmental Data Analysis in Data Mining Processes for a Global Resilient Society
Authors: Kiyoki Yasushi  , Chen Xing  , Heimbürger Anneli  , Chawakitchareon Petchporn  , Sornlertlamvanich Virach
Published: February 2025

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. In this environmental study, we will use context-dependent differential computation to analyse changes in various factors (temperatures, colours, level of CO2, habitats, sea levels, coral areas, etc.). In this paper, we will discuss a global environmental computing methodology for analysing the diversity of nature and animals, using a large amount of information on global environments.
A Group Decision Method for Internet Public Opinion Emergency with Linguistic Interval Preference
Authors: Yang Fan  , Zhang Qian-Sheng
Published: February 2025

The aim of this paper is to develop a group decision method for Internet Public Opinion Emergency with linguistic interval preference relations. In this paper, the decision experts will express their preferences with linguistic interval terms by comparing any two emergency response plans. For improving the consensus, a constrained nonlinear programming method according to the principal of deviation minimization is proposed to determine the optimal weights of experts. Then, the optimal weights are utilized to adjust the initial decision weights of experts. Finally, by the linguistic interval weighted averaging operator, all linguistic interval preference relations are aggregated into a collective linguistic interval preference relation, and the emergency decision department can select the best emergency plan. An example is examined to demonstrate the model in a practical situation.
Research on the Method of the Weather Cloud Particle Image Cropping
Authors: Liu Meng-Yuan  , Jiao Rui-Li
Published: February 2025

Weather cloud particle image cropping is the preprocessing of cloud microphysical process research by computer image recognition technology. There are two types of cloud particle images: raindrop cloud particle images and ice and snow cloud particle images. According to the characteristics of cloud particle images, this paper uses two methods: calculating connected components and sliding detection window. Cropping methods use sparse matrix and Dulmage-Mendelsohn Decomposition. Cropping time and cropping effect of these methods are compared and analyzed. The results demonstrate that calculating connected components is more suitable for cloud particle image and is beneficial to image feature extraction and shape recognition for cloud particle.
A Novel Long Code Series Acquisition Algorithm for Deep Space Ranging System
Authors: Liu Yi  , Zhang Zheng-Yuan  , Mi Gang
Published: February 2025

Long code acquisition plays an important role in deep space ranging systems. It is one of the key issues in acquiring long codes with high speed and efficiency. In order to get better acquisition performance, a new algorithm based on Fast Fourier Transform (FFT) and a series acquisition algorithm is provided in this paper. The algorithm consists of three parts, namely long code segmentation, Doppler Shift rough estimation, and frequency and phase joint two-dimensional accurate estimation. Long code segmentation divides the long code into several sections. A rough Doppler Shift value is obtained rapidly in the rough estimation part and the precise Doppler Shift value and phase value are obtained based on the rough Doppler Shift value in frequency and phase two-dimensional joint accurate estimation part. Our simulation results show that the new acquisition algorithm is able to acquire the long code accurately with low time-consumption.
Improved Stability Criteria for T-S fuzzy Systems with Time-varying Delay via a New Integral Inequality
Authors: Wang Yong-Hu  , Yang Jun  , Luo Wen-Pin
Published: February 2025

This paper focuses on the further improved stability criteria of uncertain T-S fuzzy systems with time-varying delay by the delay-partitioning approach and a new integral inequality, i.e., the Free-Matrix-Based integral inequality. A modified augmented Lyapunov-Krasovskii functional (LKF) is established by partitioning the delay in all integral terms. Then, new results on tighter bounding inequalities such as Peng-Park's integral inequality (reciprocally convex approach) and the Free-Matrix-based integral inequality (which yields less conservative stability criteria than the Wirtinger-based inequality) are introduced to reduce the enlargement in bounding the derivative of LKF as much as possible, therefore, less conservative results can be expected in terms of esand MIs. Finally, a numerical example is included to show that the proposed conditions are less conservative than existing ones.
Towards Efficient EGovernment: Identifying Important Competencies for EGovernment in European Public Administrations
Authors: Ogonek Nadine  , Gorbacheva Elena  , Räckers Michael  , Becker Jörg  , Krimmer Robert  , Broucker Bruno  , Crompvoets Joep
Published: February 2025

In an attempt to enhance efficiency, public administrations around the world and in particular in Europe are increasingly relying on information technology (IT) to improve their performance and service delivery. This growing use of IT results in a changed set of competencies demanded from civil servants. In order to find out the concrete competencies that are required for successful implementation of eGovernment initiatives and that, consequently, need to be included in the education of future professionals, a European-wide survey was conducted (n=697). This paper reports on the first results of this study, revealing that there is a strong need for professionals with socio-technical, organizational and managerial competencies.
Exploring the Notion of a National Data Infrastructure and the Governance Issues Surrounding It
Authors: Neuroni Alessia C.  , Fraefel Marianne  , Estermann Beat  , Jarchow Thomas  , Golliez André
Published: February 2025

A national data infrastructure (NDI) provides data, data-related services and guidelines for the re-use of data as an easily accessible service to citizens as well as public and private organizations. As such, it allows the efficient sharing of data between providers and consumers, supports new business models, and is thus a key enabler for the digital economy, societal collaboration and political processes. The paper relates to an ongoing project, discusses prevailing concepts on (data) infrastructure development and proposes a classification scheme for conceptualizing national data infrastructures in a given context. The discussion in particular focuses on governance issues related to establishing and maintaining a national data infrastructure that goes beyond the focus on open government data.
A Tool to Generate Public Policies
Authors: Solar Mauricio  , Murua Sergio  , Godoy Pedro  , Yañez Patricio  , Monge Raúl  , Vasquez Álvaro  , Schramm Karen  , Arismendi Tania
Published: February 2025

This article shows the results obtained in the application of a model to assess the digital maturity of a government at a country level. The model, based on maturity model concepts, identifies the relevant variables that need to be improved in the implementation of the digital strategy. This tool shows the weaknesses of the digital strategy guiding the generation of public policies.
Improving Citizen Participation Through Improved Information Access in e-Government: Technology, Organisation and Environment Factors for South Africa
Authors: Piderit Roxanne  , Jojozi Nqabomzi
Published: February 2025

Many governments around the world, including South Africa, are spending millions on improving their e-Government services to allow citizens to interact with government more freely than before. Although the evolution of e-Government started during the early 1990s, it has not achieved the same success in terms of citizen participation as e-commerce. According to the e-Government Readiness Index, South Africa is considered ready for e-Government. However, actual citizen participation in e-Government remains low. Thus, this paper sets out to investigate the technology, organisation and environment factors which impact on information access and citizen participation in e-Government. A model for improving access to information for citizens through e-government is proposed. The elements of this model are derived from an extensive literature review of studies in similar developing economies, while citizen-related factors are confirmed through questionnaire findings. This model can be used to identify areas that need to be addressed in order to ensure that information access and citizen participation in e-Government is enhanced.
Towards Organizational Transformation in Developing Countries: Enterprise Content Management in Rwanda
Authors: Bakunzibake Pierre
Published: February 2025

E-government development is more advanced in developed countries compared to developing countries. Organizational transformation by e-government in developing countries is still at infancy stage. Incremental or radical changes seem to be a subsequent stage in settings where technological implementations are still fresh like in developing countries. In a journey towards organizational transformation, this research work, using design science research, aims 1) to find critical factors influencing implementation of enterprise content management (ECM) in Rwanda as one of the developing countries,2) to carry out an investigation on how these factors are related to literature in order to detect e-government development stage and 3) to eventually propose a next step towards organizational transformation. Preliminarily results show that implementation of ECM in Rwanda has been focusing on deploying a technical tool in government organizations and this implies that work processes re-design and change management are imperative. The overall contribution of this entire study in progress is two-fold: 1) to suggest a practical way in solving some issues related to efficiency in administrative activities for practitioners towards organizational transformation in a developing country and 2) to create new knowledge for e-government researchers on organization matters especially in developing countries.
The Public Value of Sense of Community in eParticipation
Authors: O'Brien Ann  , Scott Murray  , Golden William
Published: February 2025

Concerns over a decline in social capital have been noted resulting from reduced civic and political engagement; recently however, the impact of Web 2.0 has been proposed as a revolutionary force to redress this deficit enabling greater participation by citizens and reinvigorating civic society. eParticipation is an increasingly important area of study to evaluate the promise of social media technologies to engage citizens in the democratic decision making process. This paper responds to the challenge by introducing the public administration paradigm of Public Value to eParticipation research in order to conceptualize and evaluate key issues of value, power, democratic participation and the quality of the decision process. This study introduces Sense of Community (SOC) to the eParticipation research field and highlights the important mediating effects of (SOC) to critical Public Value outcomes. Through the Public Value lens, the quality of the decision making process is reflected in the legitimacy of the public policy mandate; for eParticipation this means looking for ways to improve the quality of the decision making process. The aim of this research is to create a new measure of SOC for eParticipation that is based on Public Value theory.
IT Governance Enabling Long-Term Eletronic Governance Initiatives
Authors: Luciano Edimara  , Macadar Marie Anne  , Wiedenhöft Guilherme
Published: February 2025

IT Governance must be part of e-governance initiatives as a way to promote long-term solutions and increment their effectiveness. We suggest a conceptual model to understand the demands in an integrated way focused on long–term solutions in order to add organicity and transparency throughout the process and to reduce the complexity. The higher the complexity, the higher the transaction costs, which may compromise future investments on new e-government initiatives.
Planning Using Actions with Control Parameters
Authors: Savaş Emre  , Fox Maria  , Long Derek  , Magazzeni Daniele
Published: February 2025

Although PDDL is an expressive modelling language, a significant limitation is imposed on the structure of actions: the parameters of actions are restricted to values from finite (in fact, explicitly enumerated) domains. There is one exception to this, introduced in PDDL2.1, which is that durative actions may have durations that are chosen (possibly subject to explicit constraints in the action models) by the planner. A motivation for this limitation is that it ensures that the set of grounded actions is finite and, ignoring duration, the branching factor of action choices at a state is therefore finite. Although the duration parameter can make this choice infinite, very few planners support this possibility, but restrict themselves to durative actions with fixed durations. In this paper we motivate a proposed extension to PDDL to allow actions with infinite domain parameters, which we call control parameters. We illustrate reasons for using this modelling feature and then describe a planning approach that can handle domains that exploit it, implemented in a new planner, POPCORN (Partial-Order Planning with Constrained Real Numerics). We show that this approach scales to solve interesting problems.
Two Dimensional Uncertainty in Persuadee Modelling in Argumentation
Authors: Hunter Anthony
Published: February 2025

When attempting to persuade an agent to believe (or disbelieve) an argument, it can be advantageous for the persuader to have a model of the persuadee. Models have been proposed for taking account of what arguments the persuadee believes and these can be used in a strategy for persuasion. However, there can be uncertainty as to the accuracy of such models. To address this issue, this paper introduces a two-dimensional model that accounts for the uncertainty of belief by a persuadee and for the confidence in that uncertainty evaluation. This gives a better modeling for using lotteries so that the outcomes involve statements about what the user believes/disbelieves, and the confidence value is the degree to which the user does indeed hold those outcomes (and this is a more refined and more natural modeling than found in [19]). This framework is also extended with a modelling of the risk of disengagement by the persuadee.
A Fuzzy Semantic CEP Model for Situation Identification in Smart Homes
Authors: Jarraya Amina  , Ramoly Nathan  , Bouzeghoub Amel  , Arour Khedija  , Borgi Amel  , Finance Béatrice
Published: February 2025

Uncertainty is an essential issue for smart home applications. Events generated from sensors can be outdated, inaccurate, imprecise or in contradiction with other ones. These unreliable data can lead to dysfunction in smart home applications. To tackle these challenges, we propose a new model named FSCEP (Fuzzy Semantic Complex Event Processing) that integrates fuzzy logic paradigm, semantic features through an ontology and traditional CEP. We confronted FSCEP with other works tackling uncertainty for CEP and experimented it through simulation with early but promising result.
Secure Multi-Agent Planning Algorithms
Authors: Štolba Michal  , Tožička Jan  , Komenda Antonín
Published: February 2025

Multi-agent planning (MAP) is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but is one of the main reasons, why MAP problems cannot be solved centrally.
Robust Real-Time Human Perception with Depth Camera
Authors: Zhang Guyue  , Tian Luchao  , Liu Ye  , Liu Jun  , Liu Xiang An  , Liu Yang  , Chen Yan Qiu
Published: February 2025

Perception of the presence and position of human is crucial for many kinds of Artificial Intelligence (AI) applications. In this paper, we have developed a novel two-staged method for realtime human detection in depth image. The first stage is to quickly scan through the image to detect possible head-top locations in order to ensure all the candidate locations are included. The second stage is to use a novel head-shoulder descriptor (HSD) which jointly encodes the One-hot Depth Difference information and local geometric characteristics of human upper body to filter the detections so as to keep the genuine human locations and discard false positives. The results show that our approach using only depth data is superior to other methods using color and depth images on four datasets. In addition, our method performs well under weak illumination conditions or even total darkness. Moreover, our system is also able to run in real-time on conventional PC without GPU acceleration.
Semi-Supervised Group Sparse Representation: Model, Algorithm and Applications
Authors: Gao Longwen  , Li Yeqing  , Huang Junzhou  , Zhou Shuigeng
Published: February 2025

Group sparse representation (GSR) exploits group structure in data and works well on many problems. However, the group structure must be manually given in advance. In many practical scenarios such as classification, samples are grouped according to their labels. Constructing a consistent group structure in such cases is not easy. The reasons are: 1) samples may be incorrectly labeled; and 2) label assigning in big data is time-consuming and expensive. In this paper, we propose and formulate a new problem, semi-supervised group sparse representation (SS-GSR) to support group sparse representation among both labeled and unlabeled data, while learning a more robust group structure, which can be further exploited to more effectively represent other unlabeled data. We develop a model to tackle the SS-GSR problem, based on the manifold assumption in subspace segmentation that samples in the same group lie close in feature space and span the same subspace. We also propose an alternating algorithm to solve the model. Finally, we validate the model via extensive experiments.
Factors of Collective Intelligence: How Smart Are Agent Collectives?
Authors: Chmait Nader  , Dowe David L.  , Li Yuan-Fang  , Green David G.  , Insa-Cabrera Javier
Published: February 2025

The dynamics and characteristics behind intelligent cognitive systems lie at the heart of understanding, and devising, successful solutions to a variety of multiagent problems. Despite the extant literature on collective intelligence, important questions like “how does the effectiveness of a collective compare to its isolated members?” and “are there some general rules or properties shaping the spread of intelligence across various cognitive systems and environments?” remain somewhat of a mystery. In this paper we develop the idea of collective intelligence by giving some insight into a range of factors hindering and influencing the effectiveness of interactive cognitive systems. We measure the influence of each examined factor on intelligence independently, and empirically show that collective intelligence is a function of them all simultaneously. We further investigate how the organisational structure of equally sized groups shapes their effectiveness. The outcome is fundamental to the understanding and prediction of the collective performance of multiagent systems, and for quantifying the emergence of intelligence over different environmental settings.
Planning Under Uncertainty for Aggregated Electric Vehicle Charging with Renewable Energy Supply
Authors: Walraven Erwin  , Spaan Matthijs T.J.
Published: February 2025

Renewable energy sources introduce uncertainty regarding generated power in smart grids. For instance, power that is generated by wind turbines is time-varying and dependent on the weather. Electric vehicles will become increasingly important in the development of smart grids with a high penetration of renewables, because their flexibility makes it possible to charge their batteries when renewable supply is available. Charging of electric vehicles can be challenging, however, because of uncertainty in renewable supply and the potentially large number of vehicles involved. In this paper we propose a vehicle aggregation framework which uses Markov Decision Processes to control electric vehicles and deals with uncertainty in renewable supply. We present a grouping technique to address the scalability aspects of our framework. In experiments we show that the aggregation framework maximizes the profit of the aggregator, reduces cost of customers and reduces consumption of conventionally-generated power.
Requirements Analysis for Privacy Protection and Third Party Awareness Using Logging Models
Authors: Kaiya Haruhiko  , Yoshioka Nobukazu  , Okubo Takao  , Washizaki Hironori  , Hazeyama Atsuo
Published: February 2025

An information system can store personal information of its primary users such as shopping histories, and some third party wants or happens to know such information. Because the system usually provides its privacy policy and its users have to give their consent to it, they sometimes have to partially give up the protection of their privacy. On the other hand, a chance of a third party to know such information is too limited if the policy is too defensive. We proposed a method to explore trade-offs between protection of such information and access permissions for a third party, and exemplified it. In this method, operation logs of a system are focused. The structure of each log is then modelled for analysing what kinds of information can be accessed by a third party. Access limitations of each third party are explored so as to balance the protection of privacy information against access right of third parties.
Construction and Strength Calculation of Threats
Authors: Morveli-Espinoza Mariela  , Possebom Ayslan T.  , Tacla Cesar A.
Published: February 2025

Threats make part of the set of rhetorical arguments, which are used in negotiation dialogues when a proponent agent tries to persuade his opponent to accept a proposal more readily. When more than one threat is generated, the proponent must evaluate each and select the most adequate. One way of evaluation is calculating the strength of threats, since a strong threat may quickly convince an opponent.
Efficient and Off-The-Shelf Solver: jArgSemSAT
Authors: Cerutti Federico  , Vallati Mauro  , Giacomin Massimiliano
Published: February 2025

jArgSemSAT is a Java re-implementation of ArgSemSAT—a SAT-based solver for abstract argumentation problems—that can be easily integrated in existing argumentation systems (1) as an off-the-shelf, standalone, library; (2) as a Tweety compatible library; and (3) as a fast and robust web service freely available on the Web. Despite being written in Java, jArgSemSAT is very efficient.
SentiRich: Sentiment Analysis of Tweets Based on a Rich Set of Features
Authors: Jabreel Mohammed  , Moreno Antonio
Published: February 2025

This paper proposes the representation of tweets using a novel set of features, which include a bag of negated words and the information provided by seven lexicons. The polarity of tweets is determined by a classifier based on a Support Vector Machine. The system has been evaluated on the standard tweet sets used in the SemEval 2015 competition, obtaining results that, in most cases, outperform those of the state-of-the-art sentiment analysis systems.
Structural Discrimination and Autonomous Vehicles: Immunity Devices, Trump Cards and Crash Optimisation
Authors: Liu Hin-Yan
Published: February 2025

This paper examines the potential for structural discrimination to be woven into the fabric of autonomous vehicle developments, which remain underexplored and undiscussed. The prospect for structural discrimination arises as a result of the coordinated modes of autonomous vehicle behaviour that is prescribed by its code. This leads to the potential for individuated outcomes to be networked and thereby multiplied consistently to any number of vehicles implementing such a code. The aggregated effects of such algorithmic policy preferences will thus cumulate in the reallocation of benefits and burdens to certain categories of persons in a relatively stable manner. The spectre of implicit structural discrimination is therefore raised by the orderly and stable rearrangement of biases that may be expressed by the controlling algorithm. The potential for a much more pernicious form of active structural discrimination looms with the possibility of crash optimisation impulses in which a protective shield is cast over those individuals in which society may have a vested interest in prioritising or safeguarding. A stark dystopian scenario is introduced to sketch the contours whereby personal beacons signal individual identity, and potentially relative worth, to autonomous vehicles engaging in a crash damage calculus. At the risk of introducing these ideas into the development of autonomous vehicles, this paper hopes to spark a debate to foreclose these eventualities.
Arguments for Ethical Systems Design
Authors: Verheij Bart
Published: February 2025

Today's AI applications are so successful that they inspire renewed concerns about AI systems becoming ever more powerful. Addressing these concerns requires AI systems that are designed as ethical systems, in the sense that their choices are context-dependent, value-guided and rule-following. It is shown how techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation in the law can be used for the design of such ethical systems. In this way, AI and Law techniques are extended to the theoretical understanding of intelligent systems guided by embedded values.
Practical and Accurate Insider Trading Liability Calculation
Authors: Chin Andrew
Published: February 2025

Section 16(b) of the Securities Exchange Act of 1934 allows for the recovery of profits realized by certain insiders from trading in a corporation's stock within a period of less than six months. For more than seventy years, U.S. courts and corporate attorneys have calculated this liability following the greedy algorithm described in Smolowe v. Delendo Corp. (2nd Cir. 1943), which the Securities and Exchange Commission proposed as a method of maximizing the recovery in that case. Even though Dantzig's simplex algorithm (1947) subsequently provided a more accurate method for calculating the maximum recovery as the solution to a linear programming problem, the legal community to date has resisted its adoption. This paper provides (1) a brief introduction to Section 16(b) and the Smolowe algorithm; (2) a review of the caselaw that has enshrined the Smolowe algorithm in legal precedent; (3) a proof that the Smolowe algorithm's worst case error is 50%; (4) a description of a new Web-based liability calculator for the legal community's use; and (5) a historically important case where the new calculator yields a larger recovery than the amount actually sought and obtained by the plaintiffs.
Computer Assisted Legal Linguistics (CAL
Authors: Hamann Hanjo  , Vogel Friedemann  , Gauer Isabelle
Published: February 2025

We introduce Computer Assisted Legal Linguistics (CAL2) as a semi-automated method to “make sense” of legal discourse by systematically analyzing large collections of legal texts. Such digital corpora have been increasingly used in computational linguistics in recent years, as part of a quantitative research strategy designed to complement (rather than supplant) the more qualitative methods used hitherto. This use of statistical algorithms to analyze large bodies of text meets with an increasing demand by lawyers for empirical data and the recent turn towards evidence-based jurisprudence. Together, these research strands open exciting avenues for research and for developing useful IT tools to support legal decision-making, as we exemplify using our reference corpus of about 1 billion tokens from the language of German jurisprudence and legal academia.
Low-Dimensional Bigram Analysis for Mobile Data Fragment Classification
Authors: Lee Chien Eao  , Zheng Lilei  , Zhang Ying  , Thing Vrizlynn L.L.
Published: February 2025

File carving is the process which aims to recover files from storage media without the file system meta-data. The ability to perform such recovery is particularly important in this digital era when it involves forensic investigation. Due to the inevitable occurrence of file fragmentation in storage system, fragment classification is an important step in the file recovery process. Following the increase of storage capacity and usage of mobile phones, large amount of personal data tends to be stored on such devices, which is of great interest for forensic analysis during investigations. In this paper, we present an approach in classifying the most commonly found fragment types on mobile phones, which include JPG, MP3, MP4, MOV and SQLite. Departing from the conventional approaches that utilize analysis derived from unigram statistics, we employ bigram statistics in our approach in order to capture the frequency of local byte order which retains meaningful and exploitable pattern in the fragments. While being able to capture more information, the bigram statistics also contain a large amount of redundant data which greatly increases the computational workload. Therefore, we perform dimensionality reduction through Principal Component Analysis (PCA) in order to extract only the most significant dimensions for classification purpose of the targeted file types. Using the resulting features, an average classification accuracy of 96.19% is achieved, comparing to 88.40% while using the unigram statistics alone through Support Vector Machine (SVM).
The Applied Research of Image Dehazing Algorithms in License Plate Location
Authors: Wang Rui  , Wang Guoyu
Published: February 2025

Urban transportation is developing rapidly, which leads to that intelligent traffic is the direction of future traffic. License plate recognition (LPR) technology is one of the key techniques. But in bad weather, images taken by cameras degrade dreadfully, which results in the poorer LPR effect. This paper introduces the propagating deconvolution, dark channel prior and color attenuation prior dehaze algorithms as the pretreatment of LPR, and proposes a license plate location method based on both color and edge information. In addition, this paper compares the influence of various dehaze algorithms on the license plates location.