bluuebunny/crossref_metadata_2025 huggingface dataset:
Evolved: The New Systems of the World
Authors: Williams David
Published: February 2025

The changes resulting from new technology are bringing both possibilities and problems to societies and world posts. Forces in four areas are among those shaping the future: the reprogrammable world, commerce, work, and megacities. World posts will have many opportunities to build new services and ease the disruption to citizens and businesses if they are prepared to adapt their business models.
The Journey of Postal Operators into Digital Services Using the Concept of Business Models
Authors: Kollara Nandkumar
Published: February 2025

Traditional national postal operators are trusted in many countries, having enjoyed stable growth, low costs and societal acceptance for the past 100 years. However, the recent decade has created a threat to their traditional letter delivery services due to advancements in information and communication technology. In response, postal operators have slowly begun to diversify into digital services. This article uses the concept of business models, which have been a recent area of interest in management literature, to provide an innovative perspective on the postal operators' businesses in traditional letter delivery and digital services. The business model concept has four components (partnerships, value proposition, finance and network), and is used in this article to map the traditional letter delivery services business and the ongoing digital services business of postal operators. The key findings are that the business model is standardized for the traditional letter business, such as common products and common revenue generations, and has been consistently used by many postal operators for the last 100 years. However, the business model for the digital services business is not standardized, as this business entails different partnerships and different revenue streams, and has not been consistently used by postal operators since the last decade. The implication is that the business model provides a clear understanding of the current progress of postal operators in the digital-services business area. This understanding will help to create a standardized business model that postal operators can use to diversify into digital services.
Postal Service Innovation: New Value Propositions to Enable International e-Commerce
Authors: Abdallah Farah
Published: February 2025

Shipping is one of the most important barriers for the growth of international e-commerce. Current research focuses primarily on the role of postal operators in supporting domestic e-commerce and analyzes particular services covering a few aspects of the cross-border e-commerce value chain. This book chapter aims at analyzing the role of postal operators in supporting the growth of international e-commerce. It proposes and discusses four key value propositions of postal operators: 1) cost effective shipping services, 2) enhancing trust, 3) customer management, and 4) universal coverage. The chapter also identifies and analyses innovative postal services along the value chain of cross-border e-commerce, namely: access, payment, collection, transport, delivery, customer service, and return services. By the end of this chapter, executives from the e-commerce industry will have an understanding on how to better cooperate with postal operators to provide new value added services for customers. Postal operators' executives will also be invited to develop new global business models based on product innovation and service diversification adapted to the new needs of e-commerce customers.
Helping decision support with regression trees and clustering perturbation
Authors: Parisot Olivier  , Didry Yoann  , Tamisier Thomas
Published: February 2025

Regression trees are helpful tools for knowledge discovery and decision support, due to their simple structure and the easiness to obtain them from data. Nonetheless, when applied to non-trivial datasets, they tend to grow according to the complexity of the data, becoming uneasy to interpret. In this work, we propose a clustering perturbation method to reduce the size of the regression tree obtained from each cluster. A prototype has been developed and tested on several regression datasets.
The process of digitalisation in radiology as a lever for organisational change: the case of the Academic Integrated Hospital o f Verona
Authors: Rossignoli Cecilia  , Zardini Alessandro  , Benetollo Pierpaolo
Published: February 2025

The purpose of this research is to analyse the organisational becoming produced by the implementation of the digitalised system for the Request and Medical Reports of Radiologic Examinations inside the Academic Integrated Hospital of Verona (AOUI) and by the related effects on the actors who interact with it. This system represents a module that will merge into the project of the Electronic Case History, which is currently underdeveloped. The adopted methodology is the qualitative study based on semi-structured interviews. Twelve people have been interviewed among the hospital's representatives, including the directorate (one interview), health management (two interviews), chief of information systems (one interview), radiologists (four interviews), radiology and neurology technicians (two interviews), health physicists (one interview), head nurse of the medicine ward and some nurses. This study highlights positive and negative elements on the organisational level and on the level concerning all people involved in the becoming.
Fisher Laplacian Kernel for Image Analysis
Authors: Sayama Mika  , Tezuka Taro
Published: February 2025

The Fisher kernel is a type of kernels defined on probability distributions. It is the inner product of the Fisher score vectors, which are defined as the gradient vectors of the log likelihoods of the distributions. The main problem that arises when using the Fisher kernel for an arbitrary stochastic model is that the Fisher score vector may become very sparse, making the kernel carrying nearly no information. To cope with it, we combined the Fisher kernel with the Laplacian kernel. In the same vein, we also defined the Fisher L1 exponent kernel, using the exponential function with L1 norm. Three kernels were evaluated for ranking images. We carried out experiments using Caltech image set. The result showed that newly proposed kernels perform better than the original Fisher kernel.
On the Compatibility of Uncertainty Formalisms in Multi-Objective Optimization
Authors: Kalinina Maria  , Larsson Aron  , Sundgren David
Published: February 2025

Multi-objective optimization is a way to manage multiple objectives in analytical decision support systems. However, for real-life problems, different types of uncertainty often become prominent when defining the model. In this paper, we analyze these different types of uncertainties and suggest a suitable typology for a decision process based upon multi-objective optimization models. Uncertainty analysis can be performed based on the proposed typology; therefore, this analysis provides the necessary support for a decision maker in the identification the crucial uncertainty in the decision process.
Using Game Engine Technologies for Increasing Cognitive Stimulation and Perceptive Immersion
Authors: Keenaghan Garrett  , Horváth Imre
Published: February 2025

With the development of more user friendly game engine packages and software, detailed cognitive stimulation of procedural activities has now become easier to develop in virtual reality environments. With packages offering built-in visual editors, the amount of labour hours required for the development of specific applications are reduced. This paper considers the use of computer supported game engine technology to provide location independent comprehension and procedural skills learning for built environment education.
Suggested Legal Framework for Student Data Privacy in the Age of Big Data and Smart Devices
Authors: Weber Alan S.
Published: February 2025

This contribution argues that student data, both official educational records held by education institutions as well as large unofficial student datasets (Big Data) compiled by cloud service providers of free Internet services—such as social media, storage, and productivity applications—need specialized protections no longer afforded by U.S. Federal and state law and the self regulatory frameworks (terms of service and privacy policies) of for-profit data collectors. Smart devices and ambient intelligent systems designed for education will increasingly rely by necessity on large stored datasets with student Personally Identifiable Information (PII) to drive learning feedback algorithms. The ability of students and parents to personally control information about an individual student is diminishing due to the difficulty of de-identifying PII in data re-use, the increasing use of Big Data analytics, and the volume and variety of data now being collected about students that can be aggregated to create identifiable profiles and to re-identify users. In addition, internationally, regulations governing student records are being relaxed to allow more third-party usage of student PII. Loss of privacy control by students presents several potential and serious financial, employment, and social threats. The author proposes and outlines the details of an omnibus Federal data privacy statute that balances privacy, trust and security protections of student data against the benefits from Big Data's research potential to track educational trends and develop lower cost, more efficient, and higher outcomes educational initiatives and learning objects. Due to the transborder transmission of data and the use of U.S. cloud services by foreign institutions and individuals, the issues discussed here will be of concern to international educators.
ADS2 : Anytime Distributed Supervision of Distributed Systems that Face Unreliable or Costly Communication
Authors: Herpson Cédric  , El Fallah Seghrouchni Amal  , Corruble Vincent
Published: February 2025

Nowadays industrial process are mainly distributed, and their supervision systems are still centralized. Consequently, when communications are disrupted, it slows down or stops the supervision process. To allow the anytime supervision of such systems, we propose a distributed approch based on a multi-agent system where each supervision agent autonomously handles both diagnosis and repair on a given location. We demonstrate the advantage of our proposal and evaluates ADS2 using an industrial case-study. Experiments demonstrate the relevance of our approach with an overall reduction of the supervised system down-time of 34%.
Multiobjective Prices of Stability and Anarchy for Multiobjective Games
Authors: Ismaili Anisse
Published: February 2025

We generalize the prices of stability and anarchy to multiobjective games. In the singleobjective case, the loss of overall efficiency induced by selfish behaviors is a deeply studied subject. While the price of anarchy bounds above the loss of efficiency of equilibria, the price of stability bounds it below. In a multiobjective game, each agent individually decides an action, creating an overall alternative which is valued by each agent using his multiobjective valuation function and compared by the Pareto-preference. We issue the study of multiobjective prices of stability and anarchy by discussing several possible definitions.
An Axiomatic Analysis of Structured Argumentation for Prioritized Default Reasoning
Authors: Minh Dung Phan
Published: February 2025

Several systems of argument-based and non-argument-based semantics have been proposed for prioritized default reasoning. As the proposed semantics often sanction contradictory conclusions (even for skeptical reasoners), there is a fundamental need for guidelines for understanding and evaluating them, especially their conceptual foundations and relationships. In this paper, we introduce several natural axioms for structural argumentation with preferences that capture both the consistency and closure postulates. We show that Aspic+semantics do not satisfy key axioms including the consistency postulate and propose a simple one satisfying all axioms. We show that the prescriptive non-argument-based approach to prioritized default reasoning is sound (and complete for a relevant class of knowledge bases) wrt our proposed simple semantics.
Random Forests of Very Fast Decision Trees on GPU for Mining Evolving Big Data Streams
Authors: Marron Diego  , Bifet Albert  , De Francisci Morales Gianmarco
Published: February 2025

Random Forest is a classical ensemble method used to improve the performance of single tree classifiers. It is able to obtain superior performance by increasing the diversity of the single classifiers. However, in the more challenging context of evolving data streams, the classifier has also to be adaptive and work under very strict constraints of space and time. Furthermore, the computational load of using a large number of classifiers can make its application extremely expensive.
The Computational Impact of Partial Votes on Strategic Voting
Authors: Narodytska Nina  , Walsh Toby
Published: February 2025

In many real world elections, agents are not required to rank all candidates. We study three of the most common methods used to modify voting rules to deal with such partial votes. These methods modify scoring rules (like the Borda count), elimination style rules (like single transferable vote) and rules based on the tournament graph (like Copeland) respectively. We argue that with an elimination style voting rule like single transferable vote, partial voting does not change the situations where strategic voting is possible. However, with scoring rules and rules based on the tournament graph, partial voting can increase the situations where strategic voting is possible. As a consequence, the computational complexity of computing a strategic vote can change. For example, with Borda count, the complexity of computing a strategic vote can decrease or stay the same depending on how we score partial votes.
Checking The Correctness of Agent Designs Against Model-Based Requirements
Authors: Abushark Yoosef  , Winikoff Michael  , Miller Tim  , Harland James  , Thangarajah John
Published: February 2025

Agent systems are used for a wide range of applications, and techniques to detect and avoid defects in such systems are valuable. In particular, it is desirable to detect issues as early as possible in the software development lifecycle. We describe a technique for checking the plan structures of a BDI agent design against the requirements models, specified in terms of scenarios and goals. This approach is applicable at design time, not requiring source code. A lightweight evaluation demonstrates that a range of defects can be found using this technique.
Assertional-based Prioritized Removed Sets Revision of DL-Lite
Authors: Benferhat Salem  , Bouraoui Zied  , Papini Odile  , Würbel Eric
Published: February 2025

The paper proposes an extension of “Prioritized Removed Sets Revision” (PRSR) to DL-LiteRstratified knowledge bases. The revision strategy is based on inconsistency minimization and consists in determining smallest subsets of assertions to be dropped from the current DL-LiteRknowledge base, taking the stratification into account, in order to restore consistency and accept the input. We consider different forms of input: membership assertion, positive inclusion axiom or negative inclusion axiom. We show that according to the form of input and under some conditions PRSR can be achieved in polynomial time.
Combining Reasoning on Semantic Web Metadata
Authors: Bozzato Loris  , Serafini Luciano
Published: February 2025

As the amount of available linked data expand and the number of related applications increases, the management of aspects such as provenance and access control of such data begin to become an issue. Current approaches do not provide sufficient support for automatic reasoning over different metadata types and their possible interdependencies. MetaReasons is a framework that supports representation and automated reasoning over metadata in a single logical formalism. Different types of metadata, like data-provenance and accessibility-restrictions, are represented as distinct meta-theories and dependencies between metadata types are represented by rules between different meta-theories. In this paper we present the definition of the MetaReasons framework and two examples meta-theories for provenance and access control. Moreover, we propose a materialization calculus for forward reasoning on the two aspects.
Towards Learning and Classifying Spatio-Temporal Activities in a Stream Processing Framework
Authors: Tiger Mattias  , Heintz Fredrik
Published: February 2025

We propose an unsupervised stream processing framework that learns a Bayesian representation of observed spatio-temporal activities and their causal relations. The dynamics of the activities are modeled using sparse Gaussian processes and their causal relations using a causal Bayesian graph. This allows the model to be efficient through compactness and sparsity in the causal graph, and to provide probabilities at any level of abstraction for activities or chains of activities. Methods and ideas from a wide range of previous work are combined and interact to provide a uniform way to tackle a variety of common problems related to learning, classifying and predicting activities. We discuss how to use this framework to perform prediction of future activities and to generate events.
Towards modeling surprise in economics and finance: a cognitive science perspective
Authors: Baccan Davi  , Macedo Luis  , Sbruzzi Elton
Published: February 2025

In financial markets, market participants need to cope with risk and uncertainty, to forecast possible scenarios, and to constantly analyze and revise their beliefs, expectations, and strategies in the light of the massive amount of economical and financial information they receive. Interestingly, the relevance does not seem to reside in the numbers itself but rather whether they elicit “surprise” for market participants. In this paper we review the presence of the term surprise in economics and finance as well as how it is computed. Then, we present how emotions are defined in cognitive science, provide a formal definition of surprise, and describe the surprise process. Additionally, we present some theories regarding how artificial surprise can be computed. In a case study we compare the two different perspectives on surprise, discussing some similarities and differences. Finally, we present some possible applications of the cognitive science perspective.
Determinants of the Willingness to Contribute to an eConsultation
Authors: Taudes Alfred  , Leo Hannes
Published: February 2025

We report first results of a joint research project of Vienna University of Economics and Business and Cbase GmbH on the design and impact of eConsultations. Using real-world data collected via the cbased platform it is found that one can expect to obtain 11 votes and 1 comment per participant and 1,6 votes per paragraph discussed. In the case of a private eConsultation one can additionally expect 0,6 comments per paragraph. We then discuss the implications of these findings for system design and give an overview over the planned next steps in the project.
Modeling tools of service value networks to support social innovation in a Smart City
Authors: Giangreco Enza  , Marasso Lanfranco  , Chetta Valentina  , Fortunato Laura  , Perlangeli Cosimo
Published: February 2025

The scarcity of public funding collides, today, with a growing demand for social services by the citizens. In this context, the main challenges that governments face in the welfare service sector are the need to improve economic and social sustainability of the service and the creation of public value. This paper presents a new model of sustainability based on the concept of generative welfare. Through the cooperation of different public and private actors, citizens (Public-Private-People Partnership - 4P) and the use of ICTs such as the Idea Management Systems and Gamification techniques, our methodological approach aims to analyze a service value network, model the key processes, identify structural holes and turn them into new public-private business opportunities. This approach, carried out within the Living Lab Puglia Smart Lab, was adopted for the service of collection and redistribution of excess food in the city of Lecce, called “Solidarietà in rete”. The results achieved have allowed to identify significant solutions for the optimization of the value network and to support the development of an IT platform, able to manage this service. This platform enables gaming mechanisms that encourage citizens' participation.
Enterprise architecture in public ICT procurement in Finland
Authors: Lemmetti Juha  , Pekkola Samuli
Published: February 2025

The use of Enterprise Architecture (EA) as a tool to achieve interoperable information systems and efficient public administration processes has advanced in several countries. In Finland the use of EA in public organizations has been made mandatory in recent years. While the outcomes of the Finnish national enterprise architecture (NEA) cannot be studied yet, we analyzed publicly available requests for proposals (RFP) in order to gain insight on the current state of NEA usage. Our aim was to find out, how the EA methodology is present in the procurement documents. By using a conceptual framework from software architectures we were able to show that while the EA methodology can be used in different roles in the course of public procurement, it is still rarely present.
Semi-Automatic Business Process Modeling for E-Government
Authors: Maalouf Eliane  , Sokhn Maria  , Le Calve Anne  , Cretton Fabian
Published: February 2025

Advances in the domain of semantic web technologies have reached the area of business management with the discipline of Semantic Business Process Management. The concepts of Linked Data become interesting to apply on Business Processes to enrich them with information from diverse sources and link them to those sources. The aim of our project is to enhance process modeling of Swiss E-Government public processes by suggesting to the modeler process fragments for auto completion from a semantic repository. Created processes are linked and semantically enriched with ontology concepts related to the domain of cyber administration. This is expected to enhance query results in the repository as well as improve process documentation. We focus on linking the processes to the cyber administration standards.
A Semantic Deliberation Model for e-Participation
Authors: Porwol Lukasz  , Ojo Adegbojega  , Breslin John
Published: February 2025

There have been very few attempts so far to develop a comprehensive and rigorous conceptualization for deliberations in e-participation. Without a rigorous and formal conceptualization of deliberation, consistent content descriptions creation, deliberation records sharing and seamless exploration is difficult. In addition, no e-participation deliberation ontology exists to support citizen-led e-participation particularly when considering contributions made on the social media platforms. This work bridges this gap by providing a rich conceptualization and corresponding formal and executable ontology for deliberation in the context of e-participation. The semantic model covers the core concepts of technology-mediated political discussion and explicitly supports the integrated citizen- and government-led model of e-Participation enabled by social media. Results from the use of the ontology in describing e-Participation deliberation information at Local Government projects are also presented.
Collecting Scholars' Background Knowledge for Profiling
Authors: Amini Bahram  , Ibrahim Roliana  , Othman Mohd Shahizan  , Nematbakhsh Mohammad Ali
Published: February 2025

Collecting precise knowledge from scholars' context for profiling is crucial in recommender systems as profiles provide foundational information for successful recommendation. However, acquiring of scholars' knowledge is often a challenging task since it is associated with difficulties including: what are the appropriate knowledge resources, how knowledge items can be unobtrusively captured, and how heterogeneity among different knowledge sources should be resolved. Despite the availability of various knowledge resources, identification and collecting comprehensive knowledge in an unobtrusive manner is not straightforward. To address these issues, we analyze the scholar academic behaviors and collect various scholars' knowledge diffused over the Web. The result of empirical evaluation shows the efficiency of our approach in terms of completeness and accuracy.
MEReq: A Tool to Capture and Validate Multi-Lingual Requirements
Authors: Kamalrudin Massila  , Sidek Safiah  , Yusop Noorrezam  , Grundy John  , Hosking John
Published: February 2025

Within the era of globalisation that acknowledges differences and diversity, multiple languages have been increasingly used to capture requirements. This practice is particularly prevalent in Malaysia, where both Malay and English languages are used as a media of communication. Nevertheless, capturing requirements in multiple languages is often error-prone due to natural language imprecision being compounded by language differences. Considering that two languages may be used to describe requirements for the same system in different ways, we were motivated to develop MEReq, a tool which uses Essential Use Case (EUC) models to support capturing and checking the inconsistency occurring in English and Malay multi-lingual requirements. MEReq is tablet compatible to minimise time for on-site capture and validation of multi-lingual requirements. This paper describes the MEReq approach and demonstrates its use to capture and validate English and Malay requirements.
Nested Event Model
Authors: Siaw Nyuk Hiong  , Kulathuramaiyer Narayanan  , Ranaivo-Malançon Bali  , Labadin Jane
Published: February 2025

Conventional event extraction using machine learning usually employed pipeline approach to extract event and arguments independently. This approach needs training data which is sometimes difficult to obtain and is domain specific. Nested event structure is common in both open domain and domain specific texts. The underlying structure needs to be taken into consideration in extracting nested events which has been neglected by the pipeline approach. This paper intends to fill these gaps by proposing an event model that is data economical at the same time can extract nested event.
Event Lexical Database: A Semantic Role Labeling Approach
Authors: Siaw Nyuk Hiong  , Kulathuramaiyer Narayanan  , Ranaivo-Malançon Bali  , Labadin Jane
Published: February 2025

Many event models are available for event extraction. These models have event information for agent, time and place. Effort to collect information for similar events to construct a lexical database is yet to be carried out. This research intends to achieve such objective by proposing an automated event extraction approach based on semantic role label (SRL) predicate argument structure (PAS). Events which are indicated by predicates with similar sense are gathered to collect event information for lexical database. This is a generic approach which does not need any event model. This paper has described and illustrated how SRL PAS is mapped to event information to create the lexical database.
Adaptive Learning for Lemmatization in Morphology Analysis
Authors: Ting Mary  , Abdul Kadir Rabiah  , Tengku Sembok Tengku Mohd  , Ahmad Fatimah  , Azman Azreen
Published: February 2025

There are many linguistic morphology tools available in the market for commercial and research purposes. Morphology technique are incorporated into these tools to ensure its ability to study the internal structure of natural language words. This technique plays an important role in reducing the number of vocabularies used, at the same time retains the semantic meaning of the knowledge in NLP system. Among the algorithms implemented, majority of them only has to ability to carry out stemming process instead of a lemmatization process. Even with technology advancement, yet none of the available lemmatization algorithms able to produce 100% accurate result. Inappropriate words produced by the current algorithm might alter the overall meaning it tried to represent, which will directly affect the outcome of NLP system. This paper proposed a new method to handle lemmatization process during the morphological analysis. The method consist three layers of lemmatization process, which incorporate the implementation of a well known Stanford parser API, WordNet database and adaptive learning technique. Stanford parser API is implemented in the first layer of lemmatization process, whereas WordNet database and adaptive learning technique are implemented in the second layer and finally another lemmatization algorithm in the final layer. The lemmatised words yields from the proposed method are much more appropriate compare to the previous algorithms due to user participation in the adaptive learning technique, which will ultimately improve the semantic knowledge represented and stored in the knowledge base.
Algorithm Selection for Preferred Extensions Enumeration
Authors: Cerutti Federico  , Giacomin Massimiliano  , Vallati Mauro
Published: February 2025

Enumerating semantics extensions in abstract argumentation is generally an intractable problem. For preferred semantics four algorithms have been recently proposed, AspartixM, NAD-Alg, PrefSAT and SCC-P, with significant runtime variations. This work is a first comprehensive exploration of the graph features and of their impact on the execution time of state-of-the-art preferred extensions enumeration algorithms. Following other areas of AI, we exploit empirical performance models, predictive models that relate instance features and algorithms performance. The result is an approach able to select the “best” algorithm for any Dung's argumentation framework with an accuracy, on the average, of the 80%. Moreover, we show that an algorithm selection approach based on classification can select the fastest algorithm in about the double of the number of cases where the most efficient algorithm outperforms the other ones (SCC-P), and about three times the number of cases of the second most efficient algorithm (PrefSAT).
Properties of Random VAFs and Implications for Efficient Algorithms
Authors: Dunne Paul E.  , Atkinson Katie
Published: February 2025

By gaining insight into the structure and behaviours of objects drawn at random from a general class, it is often possible to develop algorithms and techniques which ameliorate the computational difficulty of decision questions arising in the general case. In this paper we present a number of approaches for the random generation of value-based argumentation frameworks (VAFs) built on n arguments and using k values. Via an empirical study we consider the behaviour of the associated random VAFs with respect to the issue of how many arguments within them have the property of being “objectively accepted”. Our studies indicate that the property of having no objectively accepted argument exhibits a so-called “phasetransition effect”, similar in nature to those observed in many other well-established AI studies.
Logical Operators for Ontological Modeling
Authors: Borgo Stefano  , Porello Daniele  , Troquard Nicolas
Published: February 2025

We show that logic has more to offer to ontologists than standard first order and modal operators. We first describe some operators of linear logic which we believe are particularly suitable for ontological modeling, and suggest how to interpret them within an ontological framework. After showing how they can coexist with those of classical logic, we analyze three notions of artifact from the literature to conclude that these linear operators allow for reducing the ontological commitment needed for their formalization, and even simplify their logical formulation.
Language Resources and Technology in Latvia (2010–2014)
Authors: Skadiņa Inguna  , Auziņa Ilze  , Bārzdiņš Guntis  , Skadiņš Raivis  , Vasiļjevs Andrejs
Published: February 2025

Although human language technologies have a long history in Latvia, the Latvian language still belongs to under-resourced languages, as there are many gaps in basic language technologies and tools. However, despite difficulties, some of these gaps for both, resources and tools, have been filled in the last five years. The main goal of this paper is to report on recent achievements in language resources and technologies (LRT) for Latvian and to describe the current situation.
Full-duplex Speech-to-text System for Estonian
Authors: Alumäe Tanel
Published: February 2025

The paper describes a distributed online speech-to-text system. The main features of the system are real-time speech recognition and full-duplex user experience, meaning that the partially recognized utterance is progressively displayed to the user during speaking. Other benefits include easy client-server communication protocol and system scalability to many concurrent user sessions. The paper also describes two Estonian speech-to-text applications based on the developed frame-work: a general-domain dictation application with an estimated word error rate of 26.4% and a radiology report dictation system with a word error rate of 13.7%. The system is open-source and based on free software.
Pronunciation and Spelling: the Case of Misspellings in Swedish L2 Written Essays
Authors: Grigonytė Gintarė  , Hammarberg Björn
Published: February 2025

This research presents an investigation performed on the ASU corpus. We analyse to what extent does the pronunciation of intended words reflects in spelling errors done by L2 Swedish learners. We also propose a method that helps to automatically discriminate the misspellings affected by pronunciation from other types of misspellings.
Towards a Remote Associate Test Solver based on Language Data
Authors: Olteţeanu Ana-Maria  , Falomir Zoe
Published: February 2025

The Remote Associate Test (RAT) has been used for measuring creativity in humans. In this paper, the initial parts of a creative cognitive problem-solving framework are implemented to solve the RAT automatically using a knowledge base of language data extracted from the Corpus of Contemporary American English. The results provided by the computational RAT are compared to those obtained in previous RATs carried out on human participants in order to hyphotesize on an associationist creative cognition paradigm.
Learner Group Creation and Utilization in Adaptive E-Learning Systems
Authors: Vagale Vija  , Niedrite Laila
Published: February 2025

The adaptivity provision in the web based e-learning systems nowadays is actual topic. Adaptivity organization can be done by grouping users into previously defined learner groups and by offering these group delegates the appropriate learning scenario. In this article a detailed description of group creation algorithm is presented. A learner group module is made based on the creation algorithm and implemented in Moodle system. It creates course learning groups and classifies learners into already existing groups. Group creation is done by using feature tree. Learner grouping helps to ensure fast system adaptive reaction according to learner characteristics and learning course features. The system offers each learner group their own course learning scenario appropriate to created groups. The created learner group module can be used both in the whole adaptive learning system knowledge area and in individual courses and course categories.
A Learning Design Approach in Constructing an Open and Distance Learning Activity Toolkit
Authors: Fragou Olga  , Kameas Achilles
Published: February 2025

Systematic investigation of e learning technological tools and resources to support tutors' design of educational content is at the core of the ongoing research in Higher Education. Training of ODL Tutors in chunking and restructuring educational content with added pedagogical value is a key factor in ODL strategic planning. As institutions and enterprises are actively collecting data and storing large databases, data mining is one step at the knowledge discovery process, dealing with patterns' and their relationships' extraction from large amounts of data. Basic characteristics and methodology of Design for Pedagogy tool (D4P), as a learning- design- oriented tool developed in Hellenic Open University (HOU) are presented: aim has been to provide support on HOU tutors for designing learning activities and space for storing educational material and activities' structures. Methodology rationale, background design of the ODL toolkit, report on preliminary usability testing are currently presented in a scope of providing scaffold and support to educators for embedding Learning Technology tools into their Open and Distance Learning courses.
Mining Information from Statutory Texts in Multi-Jurisdictional Settings
Authors: Šavelka Jaromír  , Grabmair Matthias  , Ashley Kevin D.
Published: February 2025

In this paper we mine statutory texts for highly-specific functional information using NLP techniques and a supervised ML approach. We focus on regulatory provisions from multiple state jurisdictions (Pennsylvania and Florida), all dealing with the same general topic (i.e., public health system emergency preparedness and response). While the number of annotated provisions from any one jurisdiction is not large, we are investigating whether one can improve classification performance on one jurisdiction's statutory texts by including other jurisdictions' annotated statutory texts dealing with the same general topic. Our experiments suggest that data from one jurisdiction can be used to boost the performance of the classifiers trained for different jurisdictions.
Model of Argument from Social Importance
Authors: Zurek Tomasz
Published: February 2025

The main aim of this work is to formalize one of the mechanisms of resolving conflicts between statutory legal rules. The argument from social importance is based on the distinction between axiological contexts of conflicting norms. One of this norms may be more significant from the point of view of social importance and this norm should prevail over the less significant one.
“Robots Cannot Lie”: Performative Parasites of Robot-Human Theatre
Authors: Borggreen Gunhild
Published: February 2025

The presentation reflects on theories of performative speech acts from the British philosopher of language J.L. Austin's “How To Do Things With Words” (based on a series of lectures in the 1950s). Using Austin's notion of “parasitic” language of an actor on the stage as a starting point, the paper analyses the Japanese Robot-Human Theatre production “Hataraku Watashi” (I, Worker) by Hirata Oriza and Ishiguro Hiroshi from 2008 as a site for investigating the complex layers of intention, effect and cultural conventions in human-robot interaction. Linking the fiction of the stage production to laboratory testing that include robots in simulated real-life situations, the notion of how robots “lie” are discussed in terms of different types of participants: theatre audiences and test persons. The presentation will focus on how the robot acts “parasitic” upon normal circumstances and thus points out the troubled notions of “normal” or “natural” in robot-human interaction, as well as in human-human interaction.
Another Case against Killer Robots
Authors: Kukita Minao
Published: February 2025

An intense dispute is now going on around “killer robots”—lethal autonomous weapons. Typical argument against them are, however, not applicable to other kinds of killer robots, such as euthanasia robots or execution robots. In this article we try to articulate an argument which is more general as to be applicable to any kind of killer robots. We will thereby support the further claim that there are situations in which it is immoral to delegate robots to do morally significant tasks on behalf of human agents.
Evaluation of Crack Flaws in Ultrasonic Imaging
Authors: Liou Ren-Jean  , Kao Kuang-Chian  , Yeh Chin-Yung
Published: February 2025

Cracks are hazardous flaws. They may be caused by material fatigue, mechanical stresses, or corrosion. Ultrasound can penetrate many materials, hence it is widely used for detection of flaws. However, the use of ultrasound is interfered by several phenomena, such as geometric interface, coarse grain, mode conversion, ringing and measuring techniques. It is practically difficult to perform evaluation directly using the originally received ultrasonic signal as false judgment may be obtained. To effectively analyze ultrasonic images, this work first uses the methods of wavelet transform to reject signal noise. The synthetic aperture focusing technique (SAFT) which is similar to sensor fusion, is then applied to reduce the interference and detect the existence of flaws. We emphasis on crack flaws that are located on the edge of materials. The flaws were manually created in our experiments. Extensive analysis are given with respect to various crack length and depth. The results provide special observation of crack flaws.
Fast CU Partition Algorithm Based on Rate-Distortion Variation in HEVC
Authors: Lin Yih-Chuan  , Cheng Hsu-Chun
Published: February 2025

High Efficiency Video Coding standard (HEVC) can achieve higher compression performance than the previous video coding standards. HEVC employs a quad-tree based block partition scheme to handle the potential challenge of compressing the ultra high definition (UHD) videos. The quad-tree based partition segments each input frame into a series of variable-sized blocks, and tries to find the best partition that encodes the frame blocks with the optimal rate-distortion performance. To determine the block size or depth level of partition, a highly computational rate-distortion optimization process is involved to compute the best partition results in terms of minimum rate-distortion cost (RDcost). This paper proposes a novel fast CU partition algorithm based on the feature of rate-distortion variation during HEVC partitioning each block to eliminate the unnecessary partition and RDcost computation. Experimental results show that the proposed algorithm can reduce 17% to 32% execution time of HEVC encoder with negligible loss of performance.
Cloud Storage Aggregator
Authors: Sangla Mahamady Yemrao Chgo  , Chang Che-Pin  , Chao Hsiang  , Ren An-Ni
Published: February 2025

With the emergence of the cloud computing cloud storages services become very popular along with others cloud based services used by both companies and end users. Cloud storage service allow users to store their data in the cloud without any additional software or hardware. Today, users have several cloud storage account for different purposes. They may have multiple accounts taking advantages of the free storage or pricing offer by service providers; others might be concern about quality of services like reliability data security and confidentiality. The goal of managing those multiple accounts becomes a tedious task. Users with multiple accounts cannot keep track of all their files on the cloud each provider requiring login and no transfer option from one cloud to another without additional download and upload steps.
Universal Forgery on Shen et al.'s Linkable and Convertible ID-based Ring Signature Scheme
Authors: Hwang Shin-Jia
Published: February 2025

Recently, Shen et al. proposes their ID-based ring signature scheme. Based on the scheme, they also propose their linkable and convertible ID-based ring signature scheme. However, a universal forgery is found for their ID-based ring signature scheme. Therefore, their linkable and convertible ID-based ring signature scheme is also insecure against universal forgeries.
Dynamic Voltage and Frequency Scaling Optimization for Multicore Architectures
Authors: Chien Ting-Hsuan  , Chang Rong-Guey
Published: February 2025

The power consumption has become a critical issue since the multi-core chip has been used widely in embedded systems. The battery life is determined by the amount of power consumption and there are design challenges for low power and high performance microprocessors. To achieve our objective, we first distribute a workload uniformly and then apply dynamic voltage and frequency scaling (DVFS) to reduce power consumption dynamically. According to workload of the multi-threaded application, we can adjust the number of core to execute before we run the application. Besides, our approach can reassign workload to alive cores if some cores crash. For this case, if a core breaks down at run time, the power management unit will send a trap to OS, then pthread scheduler will dynamically reassign the workload to alive cores. To evaluate the proposed approach, we perform experiments for our own multicore. The result shows that our approach can work well for various cases.
Linked Ocean Data
Authors: Leadbetter Adam M.
Published: February 2025

In the field of oceanographic data management, there is a long history of using controlled vocabularies to define metadata fields and describe data channels in files. These controlled vocabularies were originally published in book form, but are now available as Linked Data resources on the Semantic Web. By ensuring the controlled vocabularies are published in a manner conforming to best practices, their uptake has been wide. This has led to the inception of the Linked Ocean Data concept as a micro-cloud of Linked Data resources. The creation of the Linked Ocean Data cloud and some of the applications of developing this concept are described in this chapter. Also discussed are some emerging methods of using Linked Data techniques to add impact to datasets, allowing data managers and data scientists to tell new stories around their data encouraging new funding opportunities.
An automatic structural detection system for alpha-solenoid repeats
Authors: Cheng Hung-Yi  , Pai Tun-Wen  , Fujita Hamido
Published: February 2025

Tandem repeat structures are widely distributed among all classes of proteins. Various basic structural units of repetitive nature possess functional diversity and reflect important influences on biological responses for different organisms. One of the most common types of protein repeat structure is the α-solenoid tandem repeat class which possesses low sequence similarity between any two repeat units within a structure. Therefore, a successful segmentation system for identifying each repeat unit cannot be achieved mainly based on sequence comparison approaches. For a comprehensive analysis on fundamental repeat unit segmentation, subclass identification, and functional annotation on such repeat structures, we have developed an automatic segmentation system according to geometrical coordinates and physical characteristics. Dihedral angles of Psi and Alpha were applied to define the range of candidate α helix elements, and the included angle between the vectors formulated by previously defined neighboring α helix elements was analyzed for constructing a fundamental repeat unit. To evaluate the performance of our developed prediction system, we employed 923 protein structures collected in the RepeatsDB database and clustered as α-solenoid repeat class. The testing result has shown that our proposed system could achieve a recall rate of 92.39% and a precision rate of 93.52%, respectively.
Effect of relationships in Social Networks on calculating user sentiment profiles
Authors: Petrovic Gajo  , Fujita Hamido
Published: February 2025

In Social Networks there is a trend for people of similar interests to associate with each other. Such a tendency is called homophily, and in this paper we study the effect it has on calculating user sentiment profiles. We collect Twitter data such as tweets and follower relationships, and we use that to calculate initial user profiles (topic sentiments) and user connectivity. The novel approach in this paper is in the way user sentiment is calculated by using the impact of the related users. This impact related users have on one another depends on the connectivity factor between two people. We show that applying such an approach, which utilizes user connectivity when calculating user profiles, leads to better accuracy and we also show that there are indications it might help in tackling the cold start problem.