bluuebunny/crossref_metadata_2025 huggingface dataset:
MNDO: Multivariate Normal Distribution Based Over-Sampling for Binary Classification
Authors: Ambai Kotaro  , Fujita Hamido
Published: February 2025

Datasets that the number of instances of majority classes and minority classes are not equal is called imbalanced datasets. In such a dataset classification, it is difficult to apply learning algorithm. There are many unbalanced datasets in the real world, the imbalance problem is subject to research by researchers in many fields. Sampling is one way to handling the imbalance problem. Sampling technique focuses on balancing instances of majority classes and minority classes. However, with many over-sampling techniques, samples are synthesized using the distance between existing samples without using the correlation of each attribute. In this paper, we propose Multivariate Normal Distribution based Over-sampling (MNDO) considering the correlation in the dataset. MNDO firstly calculate the correlation coefficient of each attribute of the positive class. Next, generate new samples using multivariate normal distribution. Multivariate normal distribution is calculated using two attributes with the strongest correlation. Attributes which correlation is very weak will be over-sampled using the univariate normal distribution. The proposed method uses statistics of positive class, therefore it is possible to recover the missing value that exists in the imbalance dataset. In addition, outliers can be reproduced stochastically, so more realistic samples can be generated. We used 39 imbalance datasets in the experiment. To compare with the existing method, 6 sampling methods (SMOTE, Borderline SMOTE 1, Borderline SMOTE 2, ADASYN, SMOTE-ENN, SMOTE-Tomek), 3 learning methods (SVM, Decision Tree, k-NN) and 2 scaling (Normalize, Standardize) were used. As a result of the experiment, proposed method showed excellent results for some datasets.
Odor Classification of Human Body by Neural Networks
Authors: Omatu Sigeru
Published: February 2025

This paper considers classification of human body odor based on the neural network of learning vector quantization (LVQ). Odors of human body are sweaty odor, middle-aged odor, and age-of-odor, which are called three body odor. The sweaty odor is detected for humans from teenagers to twenties. the middle-aged odor is for humans from thirties to fifties, and the age-of-odor is for humans over fifties. We classify odors into three odors stated above. The sweaty odor is an odor similar to ammonia and isovaleric acid, middle-aged odor is similar to diacetyl, and the age-of-odor is similar to 2-nonenaar. Utilizing a special sampling box, we measure the odors first and classify the chemical products such that such that each of those odors could be classified into true odor using LVQ. After that, we measure the real human body odors for two thousand and one hundered persons. Finally, we develop a hardware (Kunkun body) to classify various odors into each odor among three odors.
Architectural Languages' Connector Support for Modeling Various Component Interactions: A Review
Authors: Ozkaya Mert
Published: February 2025

Software architecture is considered as the high-level decompositions of software systems into components and connectors. Connectors herein represent the component interactions and may be as simple as basic communication links without any functionalities or complex mediators that enforce some quality requirements on the component interactions. In this paper, the existing 124 different architectural languages (ALs) are analysed to understand which type(s) of connectors they support. The connectors considered in this study are procedure call, event, data access, linkage, stream, arbitrator, adaptor, and distributor. The analysis of the ALs for those connector types lead to many interesting results some of which are as follows: (i) the link connector is supported by almost all the ALs, and half of those ALs further support the specifications of high-level connectors (i.e., interaction protocols) for the linked components, (ii) many ALs define the high-level connectors in terms of the formal specification languages and pi-calculus is the their top choice, (iii) most of the ALs consider the procedure call and event connectors basically for method invocation and data transmission respectively, (iv) the data access connectors are mostly used as a simple data flow mechanism, and complex mechanisms (e.g., data translation) are ignored, (v) the stream connectors are supported indirectly via the formal methods, (vi) the arbitrator connectors are mostly considered for dealing with the concurrency issues, and (vii) the distributor connectors are shown the least interest by ALs.
An Ontology of Mechanical Circulatory Assist Devices Aimed at the Realization of a Decision Support System
Authors: Pisanelli Domenico M.  , De Lazzari Claudio  , Genuini Igino
Published: February 2025

In the information system arena ontologies have gained a prominent role. They are universally recognized as a valid tool for building more effective interoperable information systems.
Pattern-Based Methodology for Building the Ontologies of Scientific Subject Domains
Authors: Zagorulko Yury  , Zagorulko Galina  , Borovikova Olesya
Published: February 2025

The development of scientific subject domains ontologies is both challenging and demanding. Ontologies should not only represent a large number of concepts but also ensure their uniform and consistent description. This result is much easier to achieve if you build an ontology using ontology design patterns. These are intended to describe solutions to typical problems arising regularly when developing the ontologies of different subject domains. Ontology design patterns are intended to streamline and facilitate the process of building ontologies and help developers avoid the frequent repetitive errors of ontological modeling. The paper presents a methodology for building the ontologies of scientific subject domains, which offers a representative set of ontology design patterns and a method for their application. The patterns used in this methodology have appeared as a result of solving ontological modeling problems, which the authors of the paper encountered in the process of developing ontologies for various scientific subject domains.
A Proposal for Sentiment Analysis on Twitter for Tourism-Based Applications
Authors: Guzmán de Núñez Xiomarah Maria  , Núñez-Valdez Edward Rolando  , Pascual Espada Jordán  , González Crespo Rubén  , García-Díaz Vicente
Published: February 2025

People rely on other people's opinions to make decisions, especially if they belong to their circle of trust. In addition, there are lots of websites of recognized prestige that provide people opinions about different products and services, which are read by millions of people before making a decision. That is why systems for sentiment analysis are becoming increasingly important to automatically process the information and determine feelings of users. They analyze their written words, usually conditioned by the characteristics of microblogging platforms, in which a large number of messages are published every day, providing a great source of information, impossible to be managed manually. In this work, we show a proposal to analyze the feeling that Twitter users have towards different hotels or hotel chains through a platform that could be easily adapted to other contexts. The goal is to create q a structure based on independent and interchangeable components that will make it possible to conduct studies in a more uniform, open and transparent way.
Video Neuro-Advertising Method for Housing
Authors: Kaklauskas Arturas  , Herrera-Viedma Enrique  , Ubarte Ieva  , Tupenaite Laura  , Raupys Dainius  , Strikaite Agne  , Seniut Mark
Published: February 2025

The objective of this research was the development of the Video Neuroadvertising (hereafter, VINERS) Method for Housing by integrating the multimodal biometric, neuromarketing, affective computing and multiple criteria analysis methods. The goal for the Biometric Method part was to measure and record human biometric, emotional signals (from the face, eyes, heart rate, respiration rate, etc.). The neuromarketing method is also integrated into the study with an aim to examine the affective and cognitive reactions of buyers to marketing incentives and to forecast customer actions by applying biometric technologies. The aim of the affective computing method part was to understand the emotional state of buyers and to tailor its action to the neuromarketing process by providing the most rational reaction to those emotions. The research also incorporated the multiple criteria planning of variants and the multiple criteria analysis methods into one entity. The application of these makes it possible to generate numerous alternative neuromarketing variants in an automatic fashion and to select the most effective ones. The VINERS Method for Housing can additionally assist in determining the Net emotional investment value of a project under deliberation and provide digital recommendations for improving projects. The VINERS Method for Housing was developed during the course of the research. A case study appears at the end of this article in an endeavor to demonstrate the opportunities provided by this method in practice.
A Semantic Document Retrieval System with Semantic Search Technique Based on Knowledge Base and Graph Representation
Authors: Huynh ThanhThuong T.  , Do Nhon V.  , Pham TruongAn N.  , Tran NgocHan T.
Published: February 2025

This paper presents a framework for utilizing domain ontology and graph representation in ad-hoc document retrieval. The main task is to retrieve a ranked list of (text) documents from a fixed corpus in response to free-form keyword queries. In this work, the query and documents are modeled by enhanced graph-based representations. Ranking features are generated by matching the two representations through semantic similarity measures which consider both semantic and statistical information in documents to improve search performance. The suitability of the solution has been demonstrated through applications of document retrieval such as The learning resource repository management system and The Vietnamese online news aggregating system and The job seeking system in the field of Information Technology. The results show that the incorporation of domain ontology with semantic graph structure improves the quality of the retrieval solution compared with documents modeled by bag of words or vector space model only.
Crowdsourcing Challenges in Disaster Management: A Systematic Literature Review
Authors: ul Haq Muhammad Ehsan  , Selamat Ali  , Ghazali Masitah  , Razak Khamarrul Azahari  , Krejcar Ondrej
Published: February 2025

During disaster the communication behavior between society and crisis management authorities significantly changed due to technological evolutions and social media modernizations. The crowdsourcing and its platforms have gained importance for information exchange during and after disaster events. Social media and mobile apps are powerful and effective crowdsourcing platforms for collection of the data from various sources to collaborate and disseminate the processed information during emergency. However, data collection, integration and processing of unstructured data from diverse platforms, reliability and validity of data as well as privacy and security issues are the challenging part of crowdsourcing. The purpose of this study is to identify the various challenges through the systematic literature review during disaster management by using the crowdsourcing. Fifteen challenges have been highlighted and prioritized on the basis of their frequency of occurrence. Crowdsourcing has an important aspect of collecting and sharing of information during the disaster situation and aiming to reduce the disaster effects. It is recommended to address the identified crowdsourcing challenges for providing relief to affected community.
Rewriting Simplified Text into a Controlled Natural Language
Authors: Safwat Hazem  , Zarrouk Manel  , Davis Brian
Published: February 2025

While machine processable Controlled Natural Languages (CNLs) as a natural language interface have proven a popular, unambiguous and user friendly method for non experts to engineer formal knowledge-bases, human-oriented CNLs however remain under-researched despite having found favor within industry over many years. Whether such human orientated CNLs like the machine processable counterparts can be captured automatically as formal knowledge remains an open question. In addition, rewriting all or most of a human-oriented CNL into a machine-oriented CNL could unlock significant silos of general purpose domain knowledge, contained within existing human-oriented CNL content for exploitation by knowledge based systems. This paper explores the feasibility of rewriting a human-orientated CNL represented in Simplified English into a well know machine-oriented CNL represented in ACE CNL and describes preliminary results.
Modelling the “Laboratory of Dilemmas”: Challenges and Suggestions for Argument-Based Decision Making
Authors: Baroni Pietro  , Drivas George  , Giacomin Massimiliano
Published: February 2025

This discussion paper describes the “Laboratory of Dilemmas”, a paradigmatic decision problem presented as a video installation at La Biennale of Venice, and discusses the challenges it poses for argument-based models of decision making. It then sketches and compares two investigation directions to address these challenges and provides some relevant preliminary technical observations.
D-BAS – A Dialog-Based Online Argumentation System
Authors: Krauthoff Tobias  , Meter Christian  , Baurmann Michael  , Betz Gregor  , Mauve Martin
Published: February 2025

In this paper, we present D-BAS, a dialog-based online argumentation system, tailored to support e-participation processes. The main idea of D-BAS is to let users exchange proposals and arguments with each other in the form of a time-shifted dialog where arguments are presented and acted upon one-at-a-time. We highlight the key research challenges that needed to be addressed in order to realize such a system, provide solutions for those challenges, report on a full scale implementation of D-BAS and summarize the findings from a real world e-participation process, where D-BAS provided the infrastructure for online argumentation.
Argumentation Schemes for Data Access Control
Authors: Panisson Alison R.  , Ali Asad  , McBurney Peter  , Bordini Rafael H.
Published: February 2025

One of the main challenges in integrating different smart applications is security. Among the problems related to security, data access control is one of the most important, given that it involves end-users' privacy. In this paper, we propose an approach to the modelling of data access control interfaces using argumentation-based agents. In particular, we introduce argumentation schemes for data access control, which are based on some of the most relevant models for data access control currently available. Our approach considers not only the usual access control policies, describing which category of agents has access to which category of information, but also emergency policies, describing situations where special emergency access control rules apply. Using argumentation-based agents as access control interfaces allows us to deal with the uncertainty about external information, allowing a correct categorisation of agents that request access to information, as well as allowing agents to expose emergency situations in which emergency access control rules may apply.
Ranking Functions over Labelings
Authors: Rienstra Tjitze  , Thimm Matthias
Published: February 2025

We study rankings over labelings as a generalization of traditional labeling-based semantics in abstract argumentation. Our approach is an alternative to recent developments on rankings over arguments. The formal basis is a qualitative abstraction of probability theory called ranking theory. We propose a fundamental property, called SCC stratification, that a ranking-theoretic semantics can be expected to satisfy, present a general scheme to define a ranking-theoretic semantics, and determine conditions under which this scheme satisfies SCC stratification.
AIF-EL – An OWL2-EL-Compliant AIF Ontology
Authors: Cerutti Federico  , Toniolo Alice  , Norman Timothy J.  , Bex Floris  , Rahwan Iyad  , Reed Chris
Published: February 2025

This paper briefly describes AIF-EL, an OWL2-EL compliant ontology for the Argument Interchange Format.
BBC Moral Maze: Test Your Argument
Authors: Lawrence John  , Visser Jacky  , Reed Chris
Published: February 2025

In this paper, we present Test Your Argument, part of a suite of argument technology piloted in conjunction with BBC programming. Test Your Argument offers users the opportunity to interact with real arguments taken from the BBC Radio 4 programme Moral Maze. Users are guided through different aspects of strengthening and critiquing an argument as well as considering both sides of the issue under discussion. Since December 2017, Test Your Argument has had over 10,000 visitors.
The Treatment of Time in Upper Ontologies
Authors: Galton Antony
Published: February 2025

It is generally agreed that as a fundamental feature of the world, time merits treatment within an upper ontology, that is, an ontology that is designed to capture those categories which are sufficiently generic to transcend the specific subject matters of any particular domain ontologies. In this paper I examine how time is handled within three well-known upper ontologies (BFO, DOLCE, and GFO), and follow this with a discussion of three key issues emerging from the survey, namely dimensionality (the treatment of instants and intervals), frame-dependence (as required by the Theory of Relativity), and indexicality (the status of past, present and future). The overall conclusion is that while existing upper ontologies tend to adopt some kind of compromise between a supposedly objective, scientific account of physical time, and a more subjective, cognitive account of time as we experience it, the fundamental philosophical and scientific questions concerning the nature of time have scarcely been addressed by any of them.
Towards a Modern Text-to-Speech System for Latvian
Authors: Darǵis Roberts  , Auziņa Ilze
Published: February 2025

Thanks to the advancements in neural networks there have been many new breakthroughs in human-like speech synthesis over the last couple of years. For Latvian, there have been no new publications about speech synthesis since 2010. The paper describes efforts to apply recent advancements in neural speech synthesis to Latvian using open-source tools.
Language Use in a Multilingual Tweet Corpus
Authors: Milajevs Dmitrijs
Published: February 2025

A trilingual Latvian-Russian-English corpus of tweets is presented with an analysis of users, language and topics. The corpus consists of 1.4 million tweets that cover a period from April 2017 to July 2018. The language analysis reveals that the majority of users mostly use one language. Across topics, there is more Latvian content than in the whole collection. Among many potential use cases, the corpus can be used, for example, to study the public engagement of major Latvian media outlets and public figures, or the factors that determine language choice and content of a tweet.
Clause Branching in MaxSAT and MinSAT
Authors: Argelich Josep  , Li Chu Min  , Manyà Felip  , Soler Joan Ramon
Published: February 2025

We describe a new exact procedure, inspired on recent works on tableaux, for simultaneously solving MaxSAT and MinSAT. The main novelty of our procedure is that it implements a clause branching rule that preserves both the minimum and the maximum number of clauses than can be unsatisfied. Moreover, we prove the correctness of the procedure, and provide a review of the different types of branchings which are available for MaxSAT and MinSAT. We also give some preliminary experimental results that show that the new branching rule, when implemented into an existing branch-and-bound MaxSAT solver, produces substantial gains on industrial instances of the MaxSAT Evaluation.
On the Analysis of Utility and Risk for Masked Data in Big Data: A Small Data Analysis
Authors: Torra Vicenç
Published: February 2025

Data privacy studies methods to ensure that disclosure of sensitive information does not take place. Masking methods are applied to databases prior to their release so that intruders cannot access sensitive information. Masking methods modify the data reducing its quality. Information loss measures have been defined to evaluate in what extent data is still useful for particular analysis. In the case of big data, masking data and evaluating its utility is a complex problem. In this paper we focus on information loss measurement and we explore if we can estimate or give bounds of information loss for large data sets using only random subsets of the whole data set.
N-Channel Convolutional Neural Networks for Irony Detection in Twitter
Authors: Jabreel Mohammed  , Maaroof Najlaa  , Acran Ammar  , Moreno Antonio
Published: February 2025

This paper describes our approach of developing a deep learning system that works on irony detection tasks for English language tweets. The system is composed of multiple channels, where each channel is a convolutional neural network. We used four pre-trained embedding models to design our system, NC-ConvNets. Our system was evaluated on the testing sets of the tasks described in SemEval18 Task3 (Irony Detection in English Tweets) outperforming all the baseline and the state-of-the-art systems.
Classifying and Generalizing Successful Parameter Combinations for Sound Design
Authors: Paz Iván  , Nebot Àngela  , Múgica Francisco  , Romero Enrique
Published: February 2025

Operating parametric systems in the context of sound design imposes cognitive and practical challenges. The present contribution applies rule extraction to analyze and to generalize a set of parameter combinations, which have been preselected by a user since they produce sound results within a desired perceptual category. Then, it is discussed how and under which conditions these generalizations can be used, for example, for the automation of specific tasks.
On Improving Breast Density Segmentation Using Conditional Generative Adversarial Networks
Authors: Saffari Nasibeh  , Rashwan Hatem A.  , Herrera Blas  , Romani Santiago  , Arenas Meritxell  , Puig Domènec
Published: February 2025

Breast density is a crucial factor to follow-up the relapse of breast cancer in mammograms and the risk of local recurrence after conservative surgery and/or radiotherapy. Accurate breast density estimation with visual assessment is still a challenge due to faint contrast and significant variations in background fatty tissues in mammograms. The important key of breast density estimation is to properly detect the dense tissues in a mammographic image. Thus, this paper presents an automatic deep breast density segmentation using conditional Generative Adversarial Networks (cGAN) that consist of two successive deep networks: generator and discriminator. The generator network learns the mapping from the input mammogram to the output binary mask detection the area of the dense tissues. In turn, the discriminator learns a loss function to train this mapping by comparing the ground-truth and the predicted mask under observing the input mammogram as a condition. The performance of the proposed model was evaluated on the public INbreast mammographic datasets. The proposed model can segment the dense regions with overall recall, precision and F-score about 95%, 92%, and 93%, respectively, outperforming state-of-the-art of breast density segmentation. The proposed model can segment more than 40 images with a size of 512×512 per second on a recent GPU.
A Lattice Structure for the Set of Interval-Valued Fuzzy-Qualitative Labels
Authors: Prats Francesc  , Sánchez Mónica  , Agell Núria
Published: February 2025

Order-of-magnitude models in qualitative reasoning are basic theoretical tools to model real-world problems in which only incomplete qualitative knowledge is available. In this paper, we introduce interval-valued fuzzy sets into order-of-magnitude qualitative reasoning, generalizing the order-of-magnitude spaces of both qualitative labels and fuzzy-qualitative labels. We present a new lattice structure of linguistic descriptions in an interval-valued fuzzy framework, and introduce the interval-valued fuzzy-qualitative labels as interval-valued fuzzy sets with certain requirements. We define two operations related to the inclusion in the set of interval-valued fuzzy-qualitative labels. The set of interval-valued fuzzy-qualitative labels is structured as a lattice with these two operations.
Quaternion Phase CNN
Authors: Moya-Sánchez E. Ulises  , Colores Sebastian  , Sánchez Abraham  , Cortés Ulises
Published: February 2025

The main aim of this work is to present a Quaternion Phase Convolutional Neural Network. We encode 3 quaternion phases and its magnitude as an input. Our approach is bio-inspired and is expressed in one mathematical framework. The main result is to obtain a new space feature representation for deep learning which can capture non-trivial equivariant features, related to illumination, and rotation.
Implementing Guideline-Based, Experience-Based, and Case-Based Approaches to Enrich Decision Support for the Management of Breast Cancer Patients in the DESIREE Project
Authors: Seroussi Brigitte  , Lamy Jean-Baptiste  , Muro Naiara  , Larburu Nekane  , Sekar Booma Devi  , Guézennec Gilles  , Bouaud Jacques
Published: February 2025

DESIREE is a European-funded project to improve the management of primary breast cancer. We have developed three decision support systems (DSSs), a guideline-based, an experience-based, and a case-based DSSs, resp. GL-DSS, EXP-DSS, and CB-DSS, that operate simultaneously to offer an enriched multi-modal decision support to clinicians. A breast cancer knowledge model has been built to describe within a common ontology the data model and the termino-ontological knowledge used for representing breast cancer patient cases. It allows for rule-based and subsumption-based reasoning in the GL-DSS to provide best patient-centered reconciled care plans. It also allows for using semantic similarity in the retrieval algorithm implemented in the CB-DSS. Rainbow boxes are used to display patient cases similar to a given query patient. This innovative visualization technique translates the question of deciding the most appropriate treatment into a question of deciding the colour dominance among boxes.
Comparison of Changes in the Number of Included Patients Between Interventional Trials and Observational Studies Published from 1995 to 2014 in Three Leading Journals
Authors: Dezetree Arnaud  , Chazard Emmanuel  , Schlegel Daniel R.  , Sakilay Sylvester  , Elkin Peter L.  , Ficheur Grégoire
Published: February 2025

Introduction: Since the late 1990s, research and administrative institutions have been developing health data warehouses and increasingly reusing claims data. The impact of these changes is not yet completely quantified. Our objective was to compare the change in the number of patients included per study between observational and interventional studies over a 20-year period starting in 1995.
Age-Focused Design – A Pedagogical Approach Integrating Empathy and Embodiment
Authors: Gunawan Sarah  , Jamrozik Julia
Published: February 2025

Architects seldom design for themselves, yet in the course of studying architecture one is rarely presented with the opportunity to design for a real client. The abstract nature of this education model leads to a focus that typically prioritizes formal or technical design exploration and de-emphasizes the role of the user. While Universal Design centers human bodies within design practice, the broad and often vague ambition of universality is difficult for students to engage within an academic context. We argue that approaching Universal Design through the lens of human age emphasizes the physical, sensorial, and cognitive modes of spatial understanding of the young and old and offers a focused perspective through which to address difference and diversity in architectural education. In this paper we outline a pedagogical approach that prioritizes human embodiments, over physical bodies, and integrates empathic understanding as critical to an inclusive, human-centered design methodology. We will discuss how the approach emerged from design seminars and studios taught in the Department of Architecture at the University at Buffalo and was tested through exercises that challenged students to research, empathize with, and ultimately design for the specific needs, abilities, and desires of individuals at the limits of human age.
Investigating and Applying Universal Design: A Partnership Between a University and an Art Museum
Authors: Gallagher Worthley Joanne  , Larrivee Linda S.  , Meyer Susanna E.
Published: February 2025

Community settings allow individuals to connect and interact socially with others, and engage in new learning experiences. One type of community setting, public art museums, provide rich, cultural experiences for individuals that are distinctive and often repeated, due to changing exhibits. Unfortunately, for individuals with disabilities, these settings can have a negative impact if the physical environment or the social interactions with docents impede individuals' abilities to function and benefit from this type of community engagement. The principles of universal design (UD) can transform these negative experiences into positive ones that benefit the individuals and the community settings. One public institution of higher education in the United States, Worcester State University in Worcester, Massachusetts has a unique partnership with a nearby art museum. This partnership allowed two separate projects from two health-related professions to be conducted using the principles of UD. One project from the Occupational Therapy Department examined the physical environment in the context of an undergraduate course, and the other project from the Communication and Sciences Department investigated the delivery of docents' presentations for individuals with communication disorders, such as hearing loss. Although each project examined different aspects of the same museum experience, the recommendations benefit all museum visitors and increase community engagement.
Virtual Reality (VR) Technology: Empowering Managers to Reduce and Eliminate Accessibility Barriers for People with Autism Spectrum Disorders
Authors: O'Sullivan Miriam  , Kearney Gearoid
Published: February 2025

Barriers to accessibility are defined as ‘factors in a person's environment that, through their absence or presence, limit functioning and create disability’ [1]. There are four elements that are incorporated into this term which includes: physical environment, lack of assistive technology, attitudes of others and the lack of or restrictive services, systems and policies [1]. These barriers to accessibility are present for 13% of the Irish population. Many initiatives have been developed and implemented for people with physical disabilities; however, people with intellectual disabilities (ID) remain invisible. This invisible population accounts for 9.7% of our population or 75% of the population of people with disabilities [2]. Thus, it is imperative that we commence to implement Universal Design (UD) approaches that increase accessibility and empower the invisible to become visible. One such invisible group that holds substantial potential to bring immense value to companies is that of people with Autism Spectrum Disorders (ASD) [3]. ASD currently impacts 1 in 68 people worldwide with this figure growing annually at a rate of 10–17% [4]. 80% of people with ASD are either unemployed or underemployed; this can be attributed to barriers to accessibility [5]. Two of the most common barriers to accessibility experienced by those with ASD are environmental and attitudes of others [6,7]. These barriers have the potential to be overcome through the use of Virtual Reality (VR) technology to provide training and education to managers. VR technology is being used to empower managers to reduce these barriers, increase accessibility and develop inclusive environments and cultures. VR technology can be used to empower managers to recognise and reduce the barriers facing those with ASD. VR is a catalyst for managers to be able to identify the environmental barriers facing people with ASD within a work environment. This solution also provides them with the skills necessary to commence making adaptations to the environment to reduce or eliminate these barriers. The use of this technology and paradigm shift brings many benefits for the individual and the company. A mixed method approach has been used for the purposes of data collection. The tools that were utilised were interviews with HR managers and people with ASD; and surveys were circulated to HR managers, senior managers, Chief Executive Officers and people with ASD. The results of these were positive and clearly verified that there is a need to empower managers to increase accessibility within their organisations.
A Combinational Classification for the Customers of Airline Platform Based on Text Mining
Authors: Zhang Zhenhua  , Chen Siyan  , Yuan Shenguo  , Zhang Jing
Published: February 2025

Intelligent customer service platform has the ability of automatic communication and service based on natural language. It can achieve intelligent interaction between human and machine interaction, such as, providing users with higher quality services through instant messaging, Internet, telephone, text messaging, etc. This paper mainly explores the subdivision research of airline customers based on text classification. First of all, we extract the characteristics on text data of airline customer service platform through TF-IDF. Secondly, naive Bayes, SVM, KNN, and logistic regression are used to train the model. Thirdly, a combinational model based on the four algorithms is constructed; Finally, we use 10-fold cross validation to verify the testing results. And experiments show that the combinational algorithms are better than the original methods.
Employing Fuzzy AHP to Determine Relevance of Criteria Constituting the Quality of Social Web Applications
Authors: Orehovački Tihomir
Published: February 2025

Quality is considered to be an essential determinant of the success of every type of software. Special attention should be therefore paid to its evaluation, regardless of the employed software development paradigm. Nevertheless, social Web applications available today are often of poor quality which is due to the lack of suitable quality models, methodologies, and measuring instruments. This paper presents first step towards an evaluation methodology that would generate the quality index as a single score thus enabling analysis and comparison of social Web applications at various levels in the quality model. With an aim to determine relevance of criteria that constitute quality model, an empirical study was carried out in which fuzzy AHP for the purpose of group weighting was applied. Participants in the study were field experts from seven different countries. Study findings revealed that pragmatic criteria are more relevant than hedonic ones when quality evaluation with respect to social Web applications is considered.
A Three-Way Decisions Approach for Computing Approximations in Continuous Data
Authors: Peng Lisha  , Qian Wenbin  , Wang Yinglong  , Shu Wenhao
Published: February 2025

In rough set theory, the computation of approximation is widely used in the field of knowledge discovery and data mining. As continuous data and noisy data exist extensively in practical applications, it's valuable to computing approximations in continuous data which are accompanied by noises. Three-way decisions model is an important vehicle of processing noises, but it can't directly process continuous data. To resolve this problem, in this paper, neighborhood concept is employed to compute approximations of continuous data. Three-way decisions rules with fundamental notion of tri-partition of a universal set are redefined through the approximations. A general theory of three-way decisions for continuous data with noises is built. The experimental results indicate that the proposed approach is effective and feasible.
Research on Extraction Method of Credit Risk Analysis of Corporate Bonds Based on Convolutional Neural Network
Authors: Chen Jun-Hua  , Zheng Ding-Wen  , Hao Yan-Hui  , Wang Tao  , Lei Ming
Published: February 2025

In the credit risk analysis of corporate bonds, multiple risk factors need to be considered comprehensively. These factors often exist in complex nonlinear systems. It is difficult to accurately model these system equations by conventional methods. In view of the complexity and uncertainty in credit analysis, this paper attempts to transform the simulation of complex nonlinear systems into classification and recognition of credit risk factor images, transform risk factors into unstructured data, to propose a credit risk modeling and analysis method based on convolutional neural network (CNN). Experiments show that the accuracy of credit risk identification in corporate bonds in multiple industries is over 80%.
A New RNS Approach for Fast Point Multiplication on Elliptic Curves over F
Authors: Wu Tao
Published: February 2025

Elliptic curve cryptography is an important public-key cryptography following RSA. Here a new RNS (residue number system) approach for fast elliptic curve point multiplication over prime fields is proposed. The approach applies the Montgomery ladder for parallel elliptic curve point doublings (EC-doub) and point additions (EC-add). Meanwhile, residue number system with a wide dynamic range is used to support continuous multiplications, after which only one RNS Montgomery algorithm is enough to bring down the temporary results to valid range. In other words, both tasks can be implemented as many multiplications and a last reduction to improve performance. Also, Barrett modular reductions over small multipliers or modular multiplications over generalized Mersenne numbers can be applied for modular reductions over RNS moduli. A first analysis shows that the computation time for elliptic curve point multiplication over Fp can be reduced to a great extent at the cost of extra multipliers.
Detection on Industrial Nozzle Made of Carbon Fiber Composites Using Infrared Thermography: Case Study
Authors: Li Yin  , Zhang Shu-hui  , Zhang Rui-min  , Yang Zheng-wei  , Zhang Wei
Published: February 2025

In this work, infrared thermography was introduced to explore the capability for detecting defects in the industrial nozzle. To this goal, an industrial nozzle was begun with the detection using infrared thermography, followed by the x-ray computer tomography to validate the results obtained by infrared thermography. Then the corresponding detection results of the nozzle were obtained and the comparative analysis on the detection results was carried out. The obtained results shows that the infrared thermography can be applied to detect the defect in the nozzle effectively and the detection result is obtained quickly using infrared thermography, which paves the way for the industrial application of infrared thermography technology on the large complex aeronautical structures.
A New Model of Assessing the Influence of Terminal-Sensitivity Projectile Steady State Parameters Against Targets
Authors: Li Xingmin  , Zhao Zitian
Published: February 2025

In order to study the influence of steady-state scanning parameters of theTerminal-sensitivity Projectile (TSP) on the target, a new model of assessing the influence of scanning parameters of TSP on the targets was proposed in the paper. The model of influence submunitions against targets was established, which was in the influence of steady-state scanning parameters, and the simulation was carried out by non-linear programming optimization theory provided by MATLAB. The simulation results show that when the scanning angle is 30° and sensor detection range is between 115 m and 145 m, the effect of submunition on targets is optimal when the rotational speed is 5 r/s and fall velocity is 9 m/s. the submunition is against targets at best. Research ideas and methods provide some help of TSP design, and research contents have certain significance for TSP operational effectiveness.
Research on Sensor Node Location Schemes in Wireless Sensor Networks
Authors: Yu Geng  , Ren Wujun  , Zhang Wuxiong  , Yang Liu  , Zhao Yufeng
Published: February 2025

Wireless sensor networks are widely used in various fields of social production and life, where the locations of nodes are crucial. In order to study the location of nodes better, this paper introduces the characteristics of wireless sensor networks. The typical localization algorithms are analyzed in detail. According to the analysis of these algorithms, some problems existing in the present location algorithm and the direction of future work are proposed.
Probabilistic Estimation of the Gas Source Location in Indoor Environments by Combining Gas and Wind Observations
Authors: Sanchez-Garrido C.  , Monroy J.  , Gonzalez-Jimenez J.
Published: February 2025

This work deals with the problem of gas source localization by a mobile robot with gas and wind sensing capabilities. Particularly, we address the problem for the case of indoor environments where the presence of obstacles and the complex structure provoke the chaotic dispersion of the gases. Under these challenging conditions where traditional approaches based on mathematical modeling of the plume cannot be applied, we propose the use of numerical methods to solve the gas dispersion and its exploitation in a probabilistic formulation to estimate the likelihood of the gas source location from a set of sparse observations. We validate our approach with a simulated set of experiments in an office-like environment composed of multiple connected rooms. Two search strategies are compared (active and passive) demonstrating the suitability of our approach to infer the location of the source even when the robot is not actively searching for it.
The Four-Dimensional Instructional Design Approach in the Perspective of Human-Computer Interactions
Authors: Gawlik-Kobylińska Małgorzata
Published: February 2025

The human-computer interaction, as a field of study focusing on the design of computer technology, provides foundations for instructional design of activities performed in cyberspace. Educational context of the instructional designer's work requires adopting a specific approach based on learning theories. The aim of the article to is present a new approach to instructional design which is grounded in a theory of a Danish scientist, Knud Illeris, who distinguished three dimensions of learning (cognitive, emotional, and social). Due to specificity of performance in a virtual space, as well as the development of intelligent systems and virtual reality accessories, the author proposes to add another dimension, a psychomotor one. A new four-dimensional instructional design approach focuses on cognitive, emotional, social and psychomotor aspects of learning. This conceptual framework is universal - it allows for harmonious and well-thought-out planning of e-course components on various applications and devices. The foundations of the approach is based on preliminary research on a small number of responders. The results are promising and constitute an incentive for further research.
Simulated Autonomous Driving on Realistic Road Networks Using Deep Reinforcement Learning
Authors: Klose Patrick  , Mester Rudolf
Published: February 2025

Using Deep Reinforcement Learning (DRL) can be a promising approach to handle various tasks in the field of (simulated) autonomous driving. However, recent publications mainly consider learning in unusual driving environments. This paper presents Driving School for Autonomous Agents (DSA2), a software for validating DRL algorithms in more usual driving environments based on artificial and realistic road networks. We also present the results of applying DSA2to handle the task of driving on a straight road while regulating the velocity of one vehicle according to different speed limits.
Coefficient Thresholding in the Gradient Reconstruction Algorithm for Signals Sparse in the Hermite Transform Basis
Authors: Brajović Miloš  , Vujović Stefan  , Orović Irena  , Stanković Srdjan
Published: February 2025

In this paper, we present a hybrid algorithm for the reconstruction of compressively sensed signals sparse in the Hermite transform (HT) domain. The aim is to combine the advantages of two reconstruction approaches: the gradient algorithm well-known for its wide range applicability, and the recently proposed highly efficient Hermite coefficient tresholding algorithm. The later is based on the theoretically derived signal support detection threshold, which takes into account the specific properties of the observed sparsity domain. Reducing the compressive sensing noise level and increasing the component coefficients values with a partial time-domain reconstruction, the gradient algorithm prepares the signal for the tresholding procedure.
Trimbot Cutting Tools and Manipulator
Authors: Hemming Jochen  , van Tuijl Bart  , Tielen Toon  , Kaljaca Dejan  , IJsselmuiden Joris  , van Henten Eldert  , Mencarelli Angelo  , de Visser Pieter
Published: February 2025

This article describes the tasks and first results of the work package “Manipulator and Control” of the EU project Trimbot2020. This project develops a mobile robot for outdoor hedge, rose and bush trimming. The Kinova Jacohttp://www.trimbot2020.org.
Classifying Forms and Modes of Co-Working in the Ontology of Asymmetric Social Interactions (OASIS)
Authors: Seibt Johanna
Published: February 2025

Since robots cannot work, in the full sense of the term, humans cannot work together with robots. The so-called ‘cooperation’ with robots can take many forms for which we need a new description language. OASIS, the Ontology of Asymmetric Social Interactions, was developed with the goal of providing a simple yet sufficiently precise and discriminative classificatory framework for interactions where one interaction partner fails to possess the capacities required for human normative action and merely simulates such capacities. The framework shall facilitate the interdisciplinary integration of human-robot interaction research and more generally the development of an interdisciplinary theory of sociality. In OASIS five modes of simulations are distinguished and each human action is associated with a matrix listing the possible combinations of modes of simulations of the parts of the action. OASIS uses familiar distinctions in forms of collective intentionality to classify different kinds of coordinated and collaborative social interactions between humans and robots. Unlike extant work in social ontology, OASIS represents any one social interactions as a complex of interactions as viewed from (at least) three perspectives (first, second, and third person). The relationships between these component interactions can be used to formulate, with great precision, specific empirical hypotheses—for example, what is projected as human-robot teamwork in third person perspective, may be experienced in first person perspective as “working alongside” or even “working next to.”
Bad Arguments for Responsibility Sharing
Authors: Mäkelä Pekka  , Hakli Raul
Published: February 2025

We study whether humans and technological artifacts, such as robots, can form hybrid agents that would be fit to be held morally responsible for their actions. We give an argument against such a possibility and criticize several arguments that defend this possibility. We identify three argument forms that have been employed to argue for shared responsibility between humans and machines: (1) Argument from gradual properties, (2) Argument from responsibility gaps, and (3) Argument from extended agency. We analyse these arguments and aim to show that they are invalid.