bluuebunny/crossref_metadata_2025 huggingface dataset:
Complexity Properties of Critical Sets of Arguments
Authors: Booth Richard  , Caminada Martin  , Dunne Paul E.  , Podlaszewski Mikołaj  , Rahwan Iyad
Published: February 2025

In an abstract argumentation framework, there are often multiple plausible ways to evaluate (or label) the status of each argument as accepted, rejected, or undecided. But often there exists a critical set of arguments whose status is sufficient to determine uniquely the status of every other argument. Once an agent has decided its position on a critical set of arguments, then essentially the entire frame-work has been evaluated. Likewise, once a group, e.g. a jury, agrees on the status of a critical set of arguments, all of their different views over all other arguments are resolved. Thus, critical sets of arguments are important both for efficient evaluation by individual agents and for collective agreement by groups of such. To exploit this idea in practice, however, a number of computational questions must be considered. In particular, how much computational effort is needed to verify that a set is, indeed, a critical set or a minimal critical set. In this paper we determine exact bounds on the computational complexity of these and related questions. In addition we provide similar analyses of issues: a concept closely related to critical set and derived in terms of (equivalence) classes of arguments related through “common” labelling behaviours.
Towards Argument Mining from Dialogue
Authors: Budzynska Katarzyna  , Janier Mathilde  , Kang Juyeon  , Reed Chris  , Saint-Dizier Patrick  , Stede Manfred  , Yaskorska Olena
Published: February 2025

Argument mining has started to yield early results in automatic analysis of text to produce representations of reason-conclusion structures. This paper addresses for the first time the question of automatically extracting such structures from dialogical settings of argument. More specifically, we introduce theoretical foundations for dialogical argument mining as well as show the initial implementation in a software for dialogue processing, and the application in corpus analysis. We combine analysis of illocutionary structure with structured argumentation frameworks as our scaffolding, and apply a combination of statistical and grammatically based analytical techniques.
Strong Admissibility Revisited
Authors: Caminada Martin
Published: February 2025

In the current paper, we re-examine the concept of strong admissibility, as was originally introduced by Baroni and Giacomin. We examine the formal properties of strong admissibility, both in its extension-based and in its labelling-based form. Moreover, we show that strong admissibility plays a vital role in discussion-based proof procedures for grounded semantics. In particular it allows one to compare the performance of alternative dialectical proof procedures for grounded semantics, and obtain some remarkable differences between the Standard Grounded Game and the Grounded Persuasion Game.
Multi-Valued and Probabilistic Argumentation Frameworks
Authors: Dondio Pierpaolo
Published: February 2025

In this paper we further progress the analysis of the recently introduced multi-valued argumentation frameworks (MVAFs). MVAFs are an extension of Dung's abstract argumentation, where arguments have a degree of truth associated with them. Here we describe a list of properties of MVAFs considering the major multi-valued logics such as those proposed by Gödel, Zadeh and Łukasiewicz. We then propose a computational framework that joins multi-valued and probabilistic argumentation frameworks to handle situations where arguments affected by vagueness and/or probabilities coexist. The findings are a contribution to the field of non-monotonic approximate reasoning and they also represent a well-grounded proposal towards the introduction of gradualism in argumentation systems.
Story Comprehension through Argumentation
Authors: Diakidoy Irene-Anna  , Kakas Antonis  , Michael Loizos  , Miller Rob
Published: February 2025

This paper presents a novel application of argumentation for automated Story Comprehension (SC). It uses argumentation to develop a computational approach for SC as this is understood and studied in psychology. Argumentation provides uniform solutions to various representational and reasoning problems required for SC such as the frame, ramification, and qualification problems, as well as the problem of contrapositive reasoning with default information. The grounded semantics of argumentation provides a suitable basis for the construction and revision of comprehension models, through the synthesis of the explicit information from the narrative in the text with the implicit (in the reader's mind) common sense world knowledge pertaining to the topic(s) of the story given in the text. We report on the empirical evaluation of the approach through a prototype system and its ability to capture both the majority and the variability of understanding of stories by human readers. This application of argumentation can provide an important test-bed for the more general development of computational argumentation.
A Benchmark Framework for a Computational Argumentation Competition
Authors: Cerutti Federico  , Oren Nir  , Strass Hannes  , Thimm Matthias  , Vallati Mauro
Published: February 2025

We introduce probo, a general benchmark framework for comparing abstract argumentation solvers. probo is intended to act as the core of an argumentation competition intended to run in 2015.
Argdown and the Stacked Masonry Layout: Two User Interfaces for Non-Expert Users
Authors: Voigt Christian
Published: February 2025

In this paper I present two user interface concepts: a) Argdown, a simple markup syntax for incorporating argument semantics into online text messages and b) a Stacked Masonry layout for argument maps. Both aim at making the reconstruction and visualisation of arguments easier and more intuitive for non-expert users. These ideas have been implemented in DIO, a new real-time web application.
Preferential Reasoning Based On Abstract Argumentation Semantics
Authors: Arieli Ofer  , Reinstra Tjitze
Published: February 2025

We introduce a preferential-based setting for reasoning with different types of argumentation-based semantics, including those that are not necessarily conflict-free or admissible. The induced entailments are defined by n-valued labeling and may be computed by answer-set programs.
The Cardiovascular Disease Ontology
Authors: Barton Adrien  , Rosier Arnaud  , Burgun Anita  , Ethier Jean-François
Published: February 2025

This article presents CVDO, an ontology of cardiovascular diseases structured on OBO foundry's principle and based on BFO and FMA. CVDO reorganizes and completes DOID cardiovascular diseases around OGMS tripartite model of disease, and builds its taxonomy of diseases largely by automatic reasoning. It points to the need of OGMS to be supplemented by methodological rules to determine the end point of a disease course, and to locate the material basis of a disease in a causal chain of disorders.
Creating and Working with Corpus of Spoken Lithuanian
Authors: Kamandulytė-Merfeldienė Laura  , Godliauskas Povilas
Published: February 2025

In this article we give a general overview on the development of the Corpus of Spoken Lithuanian. We consider the methodology of the corpus as well as the process of transcribing and coding the collected data. In addition, we provide a brief analysis based on the collected corpus data of spoken adult speech (ADS). The analysis is conducted according to four aspects: distribution of parts of speech, inflectional changes, features of syntax, and usage of loanwords. In the end of the paper we provide conclusions and future expectations.
Compilation of Language Resources and On-Line Dissemination of Knowledge about Endangered Languages and Linguistic Heritage
Authors: Klessa Katarzyna  , Nau Nicole
Published: February 2025

This paper presents selected results of two projects concerning compilation of language resources and dissemination of knowledge about endangered languages and language documentation primarily among non-academic target groups. Both projects include data from one of the Baltic languages, Latgalian. The aim of the paper is to discuss the main features of the resources as well as to show the interest of these projects from a Baltic perspective and their usefulness for further research and development.
Analysis of a collaborative advisory channel for group recommendation
Authors: Pascual Jordi  , Contreras David  , Salamó Maria
Published: February 2025

To date, recommendation systems have focused mainly on recommending products to individual rather than groups of people intending to participate in a group activity. In the last decade, with the growth of interactive activities over the internet such as e-commerce services or social virtual spaces, there has appeared many recommendation scenarios that involve groups of inter-related users. Though there have been attempts to establish group recommendation, most of them focus on off-line environments. In this paper we present a novel web-based environment that supports on-line group recommendation scenarios. Specifically, we propose a Collaborative Advisory CHannel for group recommendation called gCOACH. We introduce the environment with the multiple interaction modalities developed to communicate, coordinate and persuade the group in a case-based group recommender. We demonstrate its usability through a live-user case-study.
Improvement of Mass Detection In Breast X-Ray Images Using Texture Analysis Methods
Authors: Abdel-Nasser Mohamed  , Puig Domenec  , Moreno Antonio
Published: February 2025

In this paper we analyse the performance of various texture analysis methods for the purpose of breast mass detection. We considered well-known methods such as local binary patterns, histogram of oriented gradients, cooccurrence matrix features and Gabor filters. Moreover, we propose the use of local directional number patterns as a new feature extraction method for breast mass detection. For each method, a Support Vector Machine is trained on the extracted features to predict the class (mass/normal) of unknown instances. In order to improve the mass detection capability of each individual method we used classifier majority voting and feature combination techniques. Some experiments were performed on the images obtained from a public breast cancer database, achieving promising levels of sensitivity and specificity.
A Study on User Click Behaviour for WIS User Interface Improvements
Authors: Marenkov Jevgeni  , Robal Tarmo  , Kalja Ahto
Published: February 2025

User behaviour prediction and analysis have become one of the most important approaches in the research of adaptive user interfaces (UI). Web personalisation, automatic UI adoption, recommender engines, flexible and responsive frameworks to user actions and user behaviour prediction provide alternative automatic solutions to enhance sophisticated user interfaces and serve as an alternative to conservative approaches in UI development. Nevertheless, research of unjust and improper user behaviour and its analysis in sophisticated user interfaces are left partly without needed attention. The objective of this paper is to focus on this kind of user behaviour and error analysis. In this paper we show that the rate of user mistakes while exploiting graphical UI depends on whether advanced UI development techniques like prototyping techniques and user tests were applied during UI development or they were disregarded. Moreover, the results demonstrate that graphical user interfaces without additional support for tablets and mobile devices have higher rate of user mistakes. For the latter, user tests were performed in order to analyse the importance and the scale of problem of clicking around UI elements. The paper also delivers solutions to user click misbehaviour problem to significantly decrease the rate of studied user interaction mistakes.
Solving Big Data: Distributing Computation Among Smart Devices
Authors: Preden Jurgo  , Pahtma Raido  , Tomson Timo  , Motus Leo
Published: February 2025

Big data is one of the great challenges for the future Internet of Things applications. There is a vision to network millions of devices into a comprehensive network, however, currently there are no good solutions for doing this, neither are there feasible methods for exploiting the collected data. Collecting data to servers does not offer exploitation scenarios for the huge amounts of data that can be collected using the billions of sensing devices that may be deployed once the Internet of Things vision becomes a reality. Instead of collecting data to servers, the data could be processed by the devices collecting the data, utilizing the processing results right where they are created and used. Processing fewer data items autonomously in a large number of computing nodes results in much less complexity for a single computing node when compared to a scenario where all data is collected for processing to one capable computing node. However, in case of a distributed scenario the complexity is increased in some other areas such as communication and data validation as we can't assume a synchronous system with a fixed architecture and well defined data paths. The current paper presents an architecture based on the proactive middleware ProWare and some application examples that enable the construction of systems where the computation is distributed among individual computing nodes, thus alleviating the big data problem.
Systemic Soundness in Continuous Information Systems Engineering
Authors: Kirikova Marite
Published: February 2025

The necessity to embrace frequent and hard to predict changes in the businesses of enterprises has led to the need for continuous information systems engineering in order to support the enterprises with appropriate information services. Systems engineering prescribes a particular process of systems development from well defined requirements to the verified and validated resulting system. This process, even applied continuously, only partly can be followed in information systems engineering. First, the information system is a socio-technical system, therefore it is impossible to fully engineer it. Second, frequent changes in information system can rapidly cause its deviation from the initial design even up to losing its systemic properties to some extent. The paper claims that systemic properties of information systems should be checked during continuous information systems engineering to support viability of enterprises. The Viable Systems Model and consideration of ecosystemic dependency are proposed as means for assuring systemic soundness of engineered information systems.
Scientia Juris: A Missing Link in the Modelling of Statutory Reasoning
Authors: Araszkiewic Michał
Published: February 2025

This paper extends a semi-formal model of statutory interpretation by introducing doctrinal theories. Doctrinal theorists, also referred to as legal dogmatists, contribute significantly to the understanding of statutory law in continental legal cultures. However, this problem has rarely been addressed in AI and Law research. In this paper the concept of doctrinal theory is reconstructed on the basis of set-theoretically defined interpretive statements and the theory of argumentation schemes.
Extracting Scenarios from a Bayesian Network as Explanations for Legal Evidence
Authors: Vlek Charlotte S.  , Prakken Henry  , Renooij Silja  , Verheij Bart
Published: February 2025

In order to make an informed decision in a criminal trial, conclusions about what may have happened need to be derived from the available evidence. Recently, Bayesian networks have gained popularity as a probabilistic tool for reasoning with evidence. However, in order to make sense of a conclusion drawn from a Bayesian network, a juror needs to understand the context. In this paper, we propose to extract scenarios from a Bayesian network to form the context for the results of computations in that network. We interpret the narrative concepts of scenario schemes, local coherence and global coherence in terms of probabilities. These allow us to present an algorithm that takes the most probable configuration of variables of interest, computed from the Bayesian network, and forms a coherent scenario as a context for these variables. This way, we take advantage of the calculations in a Bayesian network, as well as the global perspective of narratives.
Towards Measures of Complexity: Applying Structural and Linguistic Metrics to German Laws
Authors: Waltl Bernhard  , Matthes Florian
Published: February 2025

The increasing complexity of legal systems has many origins, which are worth a deeper analysis. This paper is an attempt to unveil the complexity in legal texts driven by structural, lexical and syntactical properties. Thereby we transferred established quantitative methods from structural network analysis and linguistics into the domain of legal text analysis. Based on 3 553 German laws, respectively regulations, we calculated several structural and lexical indicators for complexity and determined highly significant correlations (p ≤ 0.01). The papers' contribution is a set of metrics, enabling a structured and objective comparison of legal texts regarding their complexity.
On the Interactional Meaning of Fundamental Legal Concepts
Authors: Sileno Giovanni  , Boer Alexander  , van Engers Tom
Published: February 2025

Rather than as abstract entities, jural relations are analyzed in terms of the bindings they create on the individual behaviour of concurrent social agents. Investigating a simple sale transaction modeled with Petri Nets, we argue that the concepts on the two Hohfeldian squares rely on the implicit reference to a “transcendental” collective entity, to which the two parties believe or are believed to belong. From this perspective, we observe that both liabilities and duties are associated to obligations, respectively of an epistemic or practical nature. The fundamental legal concepts defined by Hohfeld are revisited accordingly, leading to the construction of two Hohfeldian prisms.
Pattern-Based Conceptual Data Modelling
Authors: Albdaiwi Bader  , Noack René  , Thalheim Bernhard
Published: February 2025

Database research and practice has brought up a large body of knowledge and experience. This experience and knowledge is based on solutions for database structures that occur and reoccur in many applications in a similar form. We may distinguish two classes of such solutions: reference models that can be used as a blueprint for a fully fledged schema and stereotypes that are general solutions.
Human Reaction in Thailand based on Social Media Analysis after the East Japan Great Earthquake
Authors: Hashimoto Takako  , Chauksuvanit Teeranoot  , Aramvith Supavadee  , Shirota Yukari
Published: February 2025

After the East Japan Great Earthquake occurred in Japan on 11th March 2011, a large number of messages related to the earthquake were posted to social media web sites not only in Japan but also all over the world. Particularly, in Asian countries, a lot of topics concerned about the earthquake were observed. Exploring topics related to the earthquake on social media gains rich insights into the social contexts. The goal of this research is to analyze Asian people reactions to the East Japan Great Earthquake on social media. As the first target, this paper selected Thai language and analyzed how people reacted to the earthquake by comparing with reactions in Japan. This analysis also presented some characteristics of Thai society and culture.
A Metadata System for Quality Management
Authors: Kramer Frank  , Thalheim Bernhard
Published: February 2025

Data quality becomes an important issue for scientific databases. It is currently managed insufficiently. General solutions for managing the quality data on the object level are represented insufficiently nowadays. These applications need, however, a management system that supports quality treatment over the entire lifespan. We develop an approach to quality management that can be integrated into a metadata management system. This approach is based on a separation of concern through database components, extended views and Information Containers.
An Adaptive Search Path Traverse for Large-scale Video Frame Retrieval
Authors: Nguyen Diep Thi-Ngoc  , Kiyoki Yasushi
Published: February 2025

Multimedia retrieval task is faced with increasingly large datasets and variously changing preferences of users in every query. We realize that the high dimensional representation of physical data which previously challenges search algorithms now brings chances to cope with dynamic contexts. In this paper, we introduce a method of building a large-scale video frame retrieval environment with a fast search algorithm that handles user's dynamic contexts of querying by imagination and controlling response time. The search algorithm quickly finds an initial candidate, which has highest-match possibility, and then iteratively traverses along feature indexes to find other neighbor candidates until the input time bound is elapsed. The experimental studies based on the video frame retrieval system show the feasibility and effectiveness of our proposed search algorithm that can return results in a fraction of a second with a high success rate and small deviation to the expected ones. Moreover, its potential is clear that it can scale to large dataset while preserving its search performance.
Towards Finding Good Twitter Users to Follow Based on User Classification
Authors: Noro Tomoya  , Mizuoka Atsushi  , Tokuda Takehiro
Published: February 2025

Finding good users to follow in social network services such as Twitter is one way to communicate and share information about a topic of interest with other users efficiently. However, it is not easy to find such users due to a massive number of users. In this paper, we classify Twitter users according to tweet frequency, tweet content, communication style, and follow relation, then present a method for finding good users to follow based on the user classification. The method is incorporated with our previous work on Twitter user search, and we show that the search performance is improved by the method.
An Axiomatic Approach to the Relational Concepts
Authors: Palomäki Jari
Published: February 2025

In this paper an axiomatic approach to the relational concept theory, denoted by RKC, is presented. This axiomatic approach is based on the intensional containment relation between the under relations of the given relational concept. Also, it is proposed that an algebraic model for RKC is a complete semilattice, where every relational concept as a principal ideal generated by it defines a Boolean algebra.
Generic Workflows – A Utility to Govern Disastrous Situations
Authors: Tropmann-Frick Marina  , Thalheim Bernhard  , Leber Diethard  , Liehr Clemens  , Czech Gerald
Published: February 2025

Damage caused by natural hazards is increasing all over the world. All countries intensify their efforts to predict and prevent hazard events and to decrease disaster impact. Disaster management is one of the challenging, complex and critical application areas dealing with hyper dynamic situation changes, high velocity, voluminous data and organizational heterogeneity. Successful management of disaster response requires flexible and adaptable solution techniques including very accurate, fast and dynamic activity guidance for supporting of process coordination, decision making and information logistics in real-time.
FOCAPLAS – A platform for cloud application development and running support
Authors: Chen Xing  , Shiohara Keiichi
Published: February 2025

Cloud computing is changing the utilization environments of computers in both enterprise and personal. Application development techniques and methodologies that are suitable to cloud environments are new challenging research topics. As demand for developing business applications is increasing rapidly and commercial profitability is dependent on decreasing the application development costs, it is essentially important to provide methods meeting the requirements of developing applications with low cost and short development time. Therefore, it is required to develop new methods facilitate the cloud application development based on easy-to-accomplish and end-user-composition. In this work, we compiled seven requirements of typical business application development such as data structure, database schema, page transition control, authorization, session management, programming, and input and output interface design. Furthermore we observe that none of current cloud development environments support a majority of these requested features. As a result, we present our own cloud application development platform, called FOCAPLAS that meets all of these requirements. A case study presenting a cloud application developing process is presented demonstrate how to use our FOCAPLAS. We believe that our requirements may serve as a valuable guide for cloud data modeling and our FOCAPLAS will be a useful platform for cloud application development.
Improving detection of apneic events by learning from examples and treatment of missing data
Authors: Hernández-Pereira Elena  , Álvarez-Estévez Diego  , Moret-Bonillo Vicente
Published: February 2025

This paper presents a comparative study over the respiratory pattern classification task involving three missing data imputation techniques, and four different machine learning algorithms. The main goal was to find a classifier that achieves the best accuracy results using a scalable imputation method in comparison to the method used in a previous work of the authors. The results obtained show that the Self-organization maps imputation method allows any classifier to achieve improvements over the rest of the imputation methods, and that the Feedforward neural network classifier offers the best performance regardless the imputation method used.
A proposal of Texture Features for interactive CTA Segmentation by Active Learning
Authors: Maiora J.  , Papakostas G.A.  , Kaburlasos V.G.  , Grana M.
Published: February 2025

Our objective is to create an interactive image segmentation system of the abdominal area for quick volumetric segmentation of the aorta requiring minimal intervention of the human operator. The aforementioned goal is to be achieved by an Active Learning image segmentation system over enhanced image texture features, obtained from the standard Gray Level Co-occurrence Matrix (GLCM) and the Local Binary Patterns (LBP). The process iterates the following steps: first, image segmentation is produced by a Random Forest (RF) classifier trained on a set of image texture features for labeled voxels. The human operator is presented with the most uncertain unlabeled voxels to select some of them for inclusion in the training set, retraining the RF classifier. The approach will be applied to the segmentation of the thrombus in Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients. A priori knowledge on the expected shape of the target structures is used to filter out undesired detections. On going preliminary experiments on datasets containing diverse number of CT slices (between 216 and 560), each one consisting a real human contrast-enhanced sample of the abdominal area, are underway. The segmentation results obtained with simple image features were promising and highlight the capacity of the used texture features to describe the local variation of the AAA thrombus and thus to provide useful information to the classifier.
Computer Aided Diagnosis System for Retinal Analysis: Automatic Assessment of the Vascular Tortuosity
Authors: Sánchez L.  , Barreira N.  , Penedo M.G.  , Coll De Tuero G.
Published: February 2025

The tortuosity of a vessel, that is, how many times a vessel curves, and how these turns are, is an important value for the diagnosis of certain diseases. Clinicians analyze fundus images manually in order to estimate it, but there is many drawbacks as it is a tedious, time-consuming and subjective work. Thus, automatic image processing methods become a necessity, as they make possible the efficient computation of objective parameters. In this paper we will discuss Sirius (System for the Integration of Retinal Images Understanding Service), a web-based application that enables the storage and treatment of various types of diagnostic tests and, more specifically, its tortuosity calculation module.
Analysis of Virus Textures in Transmission Electron Microscopy Images
Authors: Nanni Loris  , Paci Michelangelo  , Caetano Dos Santos Florentino Luciano  , Brahnam Sheryl  , Hyttinen Jari
Published: February 2025

In this paper we propose an ensemble of texture descriptors for analyzing virus textures in transmission electron microscopy images. Specifically, we present several novel multi-quinary (MQ) codings of local binary pattern (LBP) variants: the MQ version of the dense LBP, the MQ version of the rotation invariant co-occurrence among adjacent LBPs, and the MQ version of the LBP histogram Fourier. To reduce computation time as well as to improve performance, a feature selection approach is utilized to select the thresholds used in the MQ approaches. In addition, we propose new variants of descriptors where two histograms, instead of the standard one histogram, are produced for each descriptor. The two histograms (one for edge pixels and the other for non-edge pixels) are calculated for training two different SVMs, whose results are then combined by sum rule. We show that a bag of features approach works well with this problem. Our experiments, using a publicly available dataset of 1500 images with 15 classes and same protocol as in previous works, demonstrate the superiority of our new proposed ensemble of texture descriptors. The MATLAB code of our approach is available at https://www.dei.unipd.it/node/2357.
Can Robots Understand Normative Constraints?
Authors: Esken Frank
Published: February 2025

1. What is meant by “normativity”? Different meanings. 2. How do humans develop an understanding of normative constraints? 3. What does all this mean for robots?
Key Elements for Human-Robot Joint Action
Authors: Clodic Aurélie  , Alami Rachid  , Chatila Raja
Published: February 2025

For more than a decade, the field of human-robot interaction has generated many valuable contributions of interest to the robotics community at large. The field is vast, going all the way from perception to action and decision. In the same time, research on human-human joint action has become a topic of intense research in cognitive psychology and philosophy, bringing elements and even architecture hints to help our understanding of human-human joint action. In this paper, we would like to analyse some findings from these disciplines and connect them to the human-robot joint action case. This work is for us a first step toward the definition of a framework dedicated to human-robot interaction.
Robots and Responsibility: A Reply to Mark Coeckelbergh
Authors: Funk Michael
Published: February 2025

This is a short reply and comment to the approach of Mark Coeckelbergh. In his reflections on “Responsibility, robots, and humans: A preliminary reflection on the phenomenology of self-driving cars”, presented at the conference ROBO-PHILOSOPHY 2014 — SOCIABLE ROBOTS AND THE FUTURE OF SOCIAL RELATIONS (August 20–23, 2014) at Aarhus University, emphasis was on ethical issues of autonomous technologies. His lecture has been embedded into a focal session about robots and responsibility on Friday, August 22. Following discussions have been shaped by a very lively and interested audience, and by a panel discussion with Mark Coeckelbergh, Michael Funk, Minao Kukita, Vincent Mueller, Ezio di Nucci, and Filippo Santoni de Sio. With this little paper I am going to reformulate and catch some of my comments during the discussion.
Integer-based Quality Assessment Measure Using Fixed-point Arithmetic Operations
Authors: Zeng Yi-Chong  , Yang Shih-Jui  , Chang Wen-Tsung
Published: February 2025

Aimed at computation of integer-based image quality assessments, we propose the fixed-point arithmetic operations (FXP) in this paper. Four basic integer-based operations are defined, including, fractional addition, fractional multiplication, fractional division, and fractional square root. Furthermore, four kinds of image quality assessments are measured, including, mean, variance, structure similarity, and peak signal-to-noise ratio. The experiment results demonstrate that the proposed scheme achieves low precision loss, and it needs less computing time than floating-point arithmetic operations (FOP) do in low-level embedded system.
Combining Center-Symmetric Local Ternary Pattern and Local Sign Directional Pattern for Gender Classification
Authors: Lai Chih-Chin  , Huang Yen-Chun
Published: February 2025

Determining the gender of a person in a given image or video is an interesting problem. This is a two-class pattern recognition task which is very useful in many potential real-life applications. As human faces provide important visual information for gender perception, a large number of studies have investigated gender classification from face perception. In this paper, we present a method which uses Center-Symmetric Local Ternary Pattern and Local Sign Directional Pattern for feature extraction to identify the gender from the facial images. The classification is performed by using a support vector machine, which had been shown to be superior to traditional pattern classifiers in gender classification problem. Experimental results on the FERET database are provided to illustrate the proposed approach is an effective method, compared to other similar methods.
Estimation and Evaluation of Body Language in a Presentation Scenario Using RGBD Data
Authors: Wu Yi-Chieh  , Liao Wen-Hung
Published: February 2025

In this research, we capture body movements, facial expressions, and voice data of subjects in the presentation scenario using RGBD-capable Kinect sensor. The acquired videos were accessed by a group of reviewers to indicate their preferences/aversions to the presentation style. We denote the two classes of ruling as Period of Like (POL) and Period of Dislike (POD), respectively. We then employ three types of image features, namely, animation units (AU), skeletal joints, and 3D face vertices to analyze the consistency of the evaluation result, as well as the ability to classify unseen footage based on the training data supplied by 35 evaluators. Finally, we develop a prototype program to help users to identify their strength/weakness during their presentation so that they can improve their skills accordingly.
Gantry-Mounted Laser-Stripe Scanning System for Face Shape Recovery
Authors: Huang Sheng-Wen  , Huang Po-Sen  , Zhang Jia-Hong  , Hong Yu-Mei  , Chen Chia-Yen  , Chang Bao Rong
Published: February 2025

Structured lighting is an approach that has been widely utilized for 3D reconstruction in recent years due to its reliability, simplicity and effectiveness. In this paper, we propose the mounting of a structure lighting system on a gantry to improve its flexibility, efficiency and accuracy. The proposed system is able to obtain more accurate 3D models of objects with reduced time. The paper also proposes calibration methods for the system, which calculate the correspondences between the coordinate systems of the camera, the light source and the rotational axis. Parameters obtained from calibration are used to calculate the 3D data from the acquired 2D images. Experiments have been performed to demonstrate the effectiveness of the proposed method when applied to the 3D reconstruction of human face.
The Time Machine in NoSQL
Authors: Tsai Chia-Ping  , Hsiao Hung-Chang  , Chao Yu-Chang  , Chu Cheng-Lung  , Chung Yeh-Ching
Published: February 2025

Not Only SQL (NoSQL) is a recent technology that is scalable and provides flexible schemas, thereby complementing existing relational database technologies. Although NoSQL is flourishing, present solutions lack the features required by enterprises for critical missions. In this paper, we explore solutions to the data recovery issue in NoSQL. Data recovery for any database table entails restoring the table to a prior state or replaying (insert/update) operations over the table given a time period in the past. Recovery of NoSQL database tables enables applications such as failure recovery, analysis for historical data, debugging, and auditing. In this paper, we firstly present two solutions to address the data recovery problem in NoSQL; each solution has its pros and cons. We implement our proposals based on Apache HBase, a popular NoSQL database in the Hadoop ecosystem. Our implementations are extensively benchmarked with an industrial NoSQL benchmark under real environments.
Design and Implementation of a RESTful Notification Service
Authors: Chang Yao-Wen  , Sheu Ruey-Kai  , Jhu Syuan-Ru  , Chang Yue-Shan
Published: February 2025

Scalability was not an important issue in the history of Internet when there were fewer users, so standalone applications performed well for the most part. With the rapid growth of mobile applications or web services, most users are using the same application or service at the same time which leads to the need to scale applications. To enlarge the capability of handling concurrent requests, the server-side push technologies were then introduced in recent years. There are two famous protocols or frameworks used today, and they are the Pub/Sub protocol, and Ajax Push Model. These two mechanisms are complex to use, and heavy-weight for application developers. To simplify the development and enlarge the ability to accommodate the growth of amount of application users, a RESTful notification service is proposed in this paper. The notification service leverages the event-driven characteristic of JavaScript, and pushes response data asynchronously to different requests. Based on the RESTful software architecture style, the proposed notification service is not only a lightweight system but also has impressive performance.
An Environmental Monitoring System for Air Quality Control
Authors: Yang Chao-Tung  , Liu Jung-Chun  , Wang Yun-Ting  , Wu Chia-Cheng  , Leu Fang-Yie
Published: February 2025

The progress of medical and the promotion of medicine and health, the concept of preventive healthcare is widespread. People begin to concern about the quality of the environment where they stayed. The environmental comfort index include temperature, related humidity, illumination, noise and carbon dioxide. In this paper, we built an environment quality monitoring system, which can adjust the air quality in door and monitor the concentration of harmful gases. The system will alert if the content of harmful gases exceed the standard. We hope that based on these real-time data, the proposed system can help people make right and timely decisions, and act on time to maintain a beneficial environment in the monitored area.
A Big Data Processing Platform for Medical Records in Cloud
Authors: Yang Chao-Tung  , Liu Jung-Chun  , Lu Hsin-Wen  , Yan Yin-Zhen  , Chu Cheng-Chung
Published: February 2025

In this paper, we build a cloud storage platform with HBase for storing and analyzing big data of medical records and improve the performance of importing data into database. The data of medical records are stored in HBase database platform for big data storage. It also can compute through Hadoop MapReduce for HBase database to do distributed computing or cloud computing to process medical records, and to provide functions, including keyword search, data filtering, and basic statistics.
Circuit-Simulation-Based Multi-objective Evolutionary Algorithm with Multi-Level Clock Driving Technique for a-Si:H TFTs Gate Driver Circuit Design Optimization
Authors: Hung Sheng-Chin  , Chen Chieh-Yang  , Chiang Chien-Hsueh  , Li Yiming
Published: February 2025

In this work, dynamic characteristic of a new amorphous silicon gate (ASG) driver circuit is optimized by using multi-objective evolutionary algorithm (MOEA) and hydrogenated amorphous silicon (a-Si:H) TFT circuit simulator on the unified optimization framework (UOF). The ASG driver circuit consisting of 14 a-Si:H TFTs is optimized for the given specifications of the fall time < 3 μs and the ripple voltage < −9 V with simultaneously minimizing the total layout area. More than 50% reductions on the falling time of the ASG driver circuit have been achieved by using the proposed optimization methodology together with a novel 3-level clock driving technique.
RSSI-based Localization for Wireless Sensor Networks with Grid Topologies
Authors: Ho Chen-Yu  , Chen Wei-Mei
Published: February 2025

In Wireless Sensor Networks (WSNs), one of the important processes is localization of sensor nodes. Many applications of WSNs, such as military, medical, dynamic tracking and environment observation, need to localize sensor nodes first for collecting further information. In this paper, we propose a localization scheme in grid topologies for indoor environment. We convert a value of RSSI to its related distance, and use this information to create the corresponding Apollonius circles and calculate their intersection points as estimated positions of sensor nodes. Compared with other common localization algorithms, the algorithm devised in this study was more resilient to noise interferences in the environment.
Research of Clustering Routing Algorithm for Structural Health Monitoring Based on Wireless Sensor Networks
Authors: Ji Sai  , Yang Ping  , Guo Ping  , Xu Xin  , Wang Jin
Published: February 2025

In the field of structural health monitoring based on wireless sensor networks, usually using clustering routing algorithm, the structural damage identification is achieved by two structural features, natural frequencies and mode shapes. This kind of routing algorithm in specific applications needs to meet certain constraints, such as the single-hop-communication between cluster head node and each node in cluster, the overlap between different clusters and so on. To meet with the special constraints for clustering routing algorithm in structural health monitoring, this paper proposed a new method based on minimal connected cover set, which is called Enhanced Greedy Algorithm based D(v) (DEGA) routing algorithm. The DEGA method can achieve the minimum connected cover by node's own degree D(v), and can meet the structural health monitoring routing constraints. The simulation experiments on NS2 show that, DEGA algorithm to get minimum cover set performance is superior to the traditional greedy algorithm. Compared with the classic HEED clustering routing algorithm, DEGA algorithm has better energy resistance, and can be maintain a longer network lifetime.
Predicting Severity of Bug Reports using Implicit Tags
Authors: Kao Wei-Chen  , Lee Chao-Yuan  , Yang Chih-Chuan  , Yang Cheng-Zen
Published: February 2025

For large-scale software projects, bug reports play an important role in software development and maintenance. While many bug reports may be received daily, the severity of the bug reports influences the fixing priority. In this research, we consider the abundant information embedded in tags, and propose a tagging mechanism to assign implicit tags to bug reports by calculating the report similarity. The experimental results show that the proposed tagging mechanism can improve the prediction performance in the most cases.
Weighted Radial Basis Function Kernel-Based Support Vector Machines for Multispectral Magnetic Resonance Brain Image Classification
Authors: Chen Clayton Chi-Chang  , Chen Shih-Yu  , Chen Hsian-Min  , Lin Bor-Hung  , Ouyang Yen-Chieh  , Chai Jyh-Wen  , Yang Ching-Wen  , Lee San-Kan  , Chang Chein-I
Published: February 2025

One commonly used kernel in kernel-based support vector machine (SVM) approaches is radial basis function (RBF). This paper develops a weighted RBF (WRBF)-SVM by extending RBF kernels used by SVM to weighted RBF (WRBF) kernels by introducing a weighting matrix A into RBF kernels in which case there is no need of determining optimal values of parameters used in SVM, a long standing issue encountered in implementing SVM. A key to success in implementing WRBF kernels is to design different appropriate weighting matrices to implement WRBF kernels. Three weighting matrices are of particular interest, covariance matrix, correlation matrix and within-class scatter matrix. Experimental results demonstrate that WRBF kernels significantly improve over un-weighted RBF kernels in SVM-classification. Most importantly, WRBF-SVM offers a significant advantage over kernel-based SVM (KSVM) in that there is no need of determining optimal values of the parameters used by SVM.
A Cloud Based Dynamic Content Delivery Network
Authors: Fan Chih-Tien  , Piboonudompornkul Pawat  , Wu Zong-You
Published: February 2025

In this paper, proposed Cloud based Content delivery network system to serve a dynamic web content delivery service. We used MongoDB as a NoSQL database, deploy inside each HDFS nodes server and connect database together as a replication set of database. This system ensures web content provider will receive a high performance, high availability and fault tolerance from power of NoSQL database technology.
Good characterizations and linear time recognition for 2-probe block graphs
Authors: Le Van Bang  , Peng Sheng-Lung
Published: February 2025

Block graphs are graphs in which every block (biconnected component) is a clique. A graph G = (V, E) is said to be an (unpartitioned) k-probe block graph if there exist k independent sets, 1≤i≤k, such that the graph G′ obtained from G by adding some new edges between certain vertices inside the sets, 1≤i≤k, is a block graph; if the independent setsare given, G is called a partitioned k-probe block graph. In this paper we give good characterizations for 2-probe block graphs, in both unpartitioned and partitioned cases. As an algorithmic implication, partitioned and unpartitioned 2-probe block graphs can be recognized in linear time, improving a recognition algorithm of cubic time complexity previously obtained by Chang et al. (2011) in [3].
NFC Facility Inventory Control and Checking System
Authors: Cheng Rung-Shiang  , Lin Chia-Peng
Published: February 2025

Traditional stocktaking is carried out manually using paper recording method. It takes time and labor, uneasy to be preserved and easy to be damaged. This study combines Android smart device platform with NFC technology, utilizing mobiles to read cards to conduct facility inventory management. Compared to traditional facility inventory control method, cards have advantages of uneasy to be damaged, easy to be preserved, etc., and meanwhile, it can combine with web page to add/edit facility list and management job of the maintainers. Thus, the electronic inventory control system developed by this study can not only be applied in the lab to manage facilities, but also widely applied in daily life and business.
A Task Scheduling Policy for Heterogeneous MapReduce Cluster
Authors: Chiu Chui-Ming  , Huang Sheng-Wei  , Huang Tzu-Chi  , Shieh Ce-Kuen  , Tsai Ming-Fong  , Chen Lien-Wu
Published: February 2025

MapReduce is a programming model and its associated run-time system proposed in 2004, which can process large scale of data in clusters with simple program logic. MapReduce has a potential problem running on a cooperative cluster which is combined with machines having different configurations. The problem will cause unexpected performance degradations and should be avoided. In this paper, a task scheduling policy is proposed to take higher utilization of all computing nodes in heterogeneous clusters.