bluuebunny/crossref_metadata_2025 huggingface dataset:
Key-Linked On-line Databases for Clinical Research
Authors: Müller Thomas H.
Published: February 2025

Separating patient identification data from clinical data and/or information about biomaterial samples is an effective data protection measure, especially in clinical research employing “on-line”, i.e., web-based, data capture. In this paper, we show that this specialised technique can be generalised into a network architecture of interconnected on-line databases potentially serving a variety of purposes. The basic idea of this approach consists of maintaining logical links, i.e., common record keys, between corresponding data structures in pairs of databases while keeping the actual key values hidden from clients. For client systems, simultaneous access to corresponding records is mediated by temporary access tokens. At the relational level, these links are represented by arbitrary unique record keys common to both databases. This architecture allows for integration of related data in different databases without replicating or permanently sharing this data in one place. Each participating on-line database can determine the degree of integration by specifying linkage keys only for those data structures that may be logically connected to other data. Logical links can de designed for specific use cases. In addition, each database controls user access by enforcing its own authorisation scheme. Another advantage is that individual database owners retain considerable leeway in adapting to changing local requirements without compromising the integration into the network. Beyond protecting individual subject identification data, this architecture permits splitting a cooperatively used data pool to achieve many kinds of objectives. Application examples could be clinical registries needing subject contact information for follow-up, biomaterial banks with or without genetic information, and automatic or assisted integration of data from electronic medical records into research data.
A Semantic Model for Multimodal Data Mining in Healthcare Information Systems
Authors: Iakovidis Dimitris  , Smailis Christos
Published: February 2025

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.
Fine-grained Clinical Outcome Extraction and Polarity Classification
Authors: Cai Peng  , Ni Yuan  , Zhu Huijia  , Cao Feng
Published: February 2025

Clinical outcome information is helpful for clinicians to understand the effect of a given intervention. Generally speaking, the outcome generated by an intervention has several aspects, and each aspect has different polarity. In this work we adopt structure learning algorithm to extract fine-grained outcome information and then determine the polarity of each aspect by trained classifier. Word and POS features are integrated by structure learning algorithm. The performance is evaluated on our labeled dataset. Experimental results indicate that although POS information can improve the performance of fine-grained outcome extraction it should be leveraged carefully.
Electronic Medication Ordering with Integrated Drug Database and Clinical Decision Support System
Authors: Čufar Andreja  , Droljc Anže  , Orel Andrej
Published: February 2025

Medication errors have been identified as one of the most important causes of adverse drug events. Computerized physician order-entry (CPOE) systems, coupled with decision support (Medication allergy checking, drug interactions, and dose calculations), are considered to be appropriate solutions for reducing medication errors and standardizing care. It is quite useful if clinical information system (CIS) supports order sets, which help with standardizing care, preventing omission errors, and expediting the ordering process. Order sets are predefined groups of orders pertinent to one or more specific clinical conditions or diagnoses. The article describes how a clinical information system can be used to support medication process (prescribing, ordering, dispensing, administration and monitoring) and offer participating medical teams real time warnings and key information regarding medications and patient status, thus reducing medication errors. Integrated electronic prescribing support system benefits for total parenteral nutrition (TPN) are discussed at the end.
Technical Requirements of a Social Networking Platform for Senior Citizens
Authors: Demski Hans  , Hildebrand Claudia  , López Bolós José  , Tiedge Winfried  , Wengel Stefanie  , Ó Broin Daire  , Palmer Ross
Published: February 2025

Feeling an integrative part of a social community adds to the quality of life. Elderly people who find it more difficult to actively join activities are often threatened by isolation. Social networking can enable communication and sharing activities makes it easier to set up and maintain contacts. This paper describes the development of a social networking platform and activities like gaming and exergaming all of which aim to facilitate social interaction. It reports on the particular challenges that need to be addressed when creating a social networking platform specially designed to meet the needs of the elderly.
Online health information search: what struggles and empowers the users? Results of an online survey
Authors: Pletneva Natalia  , Vargas Alejandro  , Kalogianni Konstantina  , Boyer Célia
Published: February 2025

The most popular mean of searching for online health content is a general search engine for all domains of interest. Being general implies on one hand that the search engine is not tailored to the needs which are particular to the medical and on another hand that health domain and health-specific queries may not always return adequate and adapted results. The aim of our study was to identify difficulties and preferences in online health information search encountered by members of the general public. The survey in four languages was online from the 9th of March until the 27th of April, 2011. 385 answers were collected, representing mostly the opinions of highly educated users, mostly from France and Spain. The most important characteristics of a search engine are relevance and trustworthiness of results. The results currently retrieved do not fulfil these requirements. The ideal representation of the information will be a categorization of the results into different groups. Medical dictionaries/thesauruses, suggested relevant topics, image searches and spelling corrections are regarded as helpful tools. There is a need to work towards better customized solutions which provide users with the trustworthy information of high quality specific to his/her case in a user-friendly environment which would eventually lead to making appropriate health decisions.
Criteria to Assess the Quality of Virtual Patients
Authors: Hege Inga  , Zary Nabil  , Kononowicz Andrzej A.
Published: February 2025

Quality assessment of virtual patients (VP) is important but still insufficiently standardized. We developed categories and criteria to assess the quality of virtual patients. These categories reflect the life cycle of a VP from the planning to the development, implementation and evaluation. Such elaborated criteria will help authors to create VPs and curriculum planers to assess the quality of implemented VPs and choose high quality VPs from repositories.
Fuzzy Evidence Reasoning and Position Fixing
Authors: Filipowicz Włodzimierz
Published: February 2025

In traditional approach to position fixing navigator exploits available uncertain measurements and various systems indications. Thanks to the nautical knowledge position is fixed and evaluated. Final results are rather intuitive than proved by limited data processing [7]. Mathematical apparatus based on probability theory and series of assumptions is not flexible enough to include knowledge and ignorance into position fixing calculation scheme. Limited ability is available regarding fix accuracy evaluation. In the paper Mathematical Theory of Evidence is exploited in order to extend foundations for implementation of new approaches. The theory extended for fuzzy environment creates new opportunities enabling modeling and solving problems with uncertainty [2], [3], [17]. Concept of using the new basement for position fixing was presented by the author in his previous publications. Herein comprehensive introduction to the platform is presented, moreover evidence assignment transformation is depicted in context of solving position fixing problem.
Team of A-Teams for Solving the Resource-Constrained Project Scheduling Problem
Authors: Jedrzejowicz Piotr  , Ratajczak-Ropel Ewa
Published: February 2025

In this paper the Team of A-Teams (TA-Teams) architecture for solving the resource-constrained project scheduling problem (RCPSP) is proposed and experimentally validated. RCPSP belongs to the NP-hard problem class. To solve this problem a parallel cooperating A-Teams consisting of an asynchronous agents implemented using JABAT middleware have been proposed. From one to four A-Teams and four kinds of optimization agent have been used. Computational experiment involves evaluation of the proposed approach in respect of the different parameters settings controlling the working and migration strategies used in the Team of A-Teams approach.
Eye Movement and Time Perception in Combinatorial Thinking
Authors: Miyamoto Katsuo  , Ohsawa Yukio
Published: February 2025

We report a relation with the time which human thought was seeing the object and the time which human was actually looking at. We conducted experiment of the difference of the ranking which subjects thought was seeing the object and the ranking which subjects were looking at, considering a new idea combining two or more products. The results show the tendency that there was a difference of the ranking of which subjects thought was seeing the object and the ranking which subjects were looking at.
Middleware Interoperability in Tactical Defence Environments
Authors: Foster Kate
Published: February 2025

This paper is concerned with integrating mobile technology (such as smartphones and tablets) into tactical defence environments. The motivation for and key technologies involved in this research are discussed in detail. The vehicle for conducting this research is the Defence Science and Technology Organisation’s Net Warrior initiative. The initial work conducted on this research program under Net Warrior has investigated how distributed object and publish/subscribe middleware can be incorporated into a smartphone to achieve information interoperability with tactical platforms and systems.
Using Mutli-Agent Systems to Improve the Level of Autonomy for Operators Controlling Unmanned Vehicles
Authors: Tweedale Jeffrey W.
Published: February 2025

This paper describes preliminary work performed to gain an understanding of how to implement a Multi-Agent System (MAS) that can be used to improve the Level of Automation (LOA) used by operators when controlling Unmanned Air Vehicles (UAVs). The ultimate goal is to build an Autonomous Vehicle Management System (AVMS) with intuitive controls that empower the operator to achieve more with less effort. This concept will be supported by a framework comprised of inter-operable MASs acting as teams. Each team has a modular design that support dynamically linkable capabilities. The design provides the operator with an interface to enabled them to select the mode of operation desired based on trust. Technology can be used to provide automation, but innovation enables humans to make it trusted, robust, efficient and effective. Technology and research will effect the ability to adapt, therefore this paper presents the current issues relating to the existing state of knowledge in this domain. This premise relates to the fact that machines can’t go beyond what they are programmed to achieve.
A Method of Learning Data Selection for Updating Shadow Model with High Accuracy
Authors: Fukui Shinji  , Takeda Yasuchika  , Watanabe Gaku  , Iwahori Yuji  , Woodham Robert J.
Published: February 2025

Detecting shadows is needed for object detection methods because shadows often have a harmful effect on the result. Shadow detection methods based on shadow models are proposed. The shadow model should be updated to detect shadows which are not included in the learning data. When it is updated, outlier should be removed from them. In this paper, a data selection method for removing outlier from the data is proposed. The proposed method selects data by information of object regions and Normalized Distance. Using only the selected data results in obtaining better shadow regions because the better shadow model can be constructed. Results are demonstrated by the experiments using the real videos.
A batch Update Method of Database for Mass Data during Online Entry
Authors: Kudo Tsukasa  , Takeda Yui  , Ishino Masahiko  , Saotome Kenji  , Kataoka Nobuhiro
Published: February 2025

In business systems, databases are usually updated by two methods: the online entry is used for update from many terminals concurrently; the batch update is used to update a great deal of data in a lump. Since online entry updates a small amount of data, concurrency control can be performed by locking the data. On the other hand, the batch update locks a great deal of data for a long while. So, if it’s performed concurrently with online entry, it’s performed as the mini-batch that divides the update to shorten the lock time of each unit. However, because this method cannot treat the entire batch update as a single transaction, various constraints occur on system operations. In this paper, we propose a batch update method utilizing the transaction time database that manages the history of time series of its data. And, we show that the ACID property of the transaction can be maintained by this method as a whole batch update.
FirstOnt: Automatic Construction of Ontologies out of Multiple Ontological Resources
Authors: Bobed Carlos  , Mena Eduardo  , Trillo Raquel
Published: February 2025

Nowadays, a great amount of the contents of the WWW are still mainly only human-oriented. To progressively move into the Semantic Web, the adoption of the use of ontologies is a milestone. However, their elaboration from scratch is quite expensive, which could prevent non-experts from using them in their applications. Due to this reason, different approaches for ontology reusing and engineering have been proposed, but they require domain experts and knowledge engineers. Besides, finding an ontology (if it exists) that fits a specific domain can be a tedious and frustrating experience.
Entropic Dimensionality Reduction in Discriminating Between Alzheimer’s Disease and Vascular Dementia
Authors: Todorov Diman  , Setchi Rossi  , Bayer Antony
Published: February 2025

A set of 479 Mini-Mental State Examinations (MMSE) is analysed with the goal of discriminating between Alzheimer’s Disease and Vascular Dementia. The patient’s gender has been considered as a predictor in addition to answers of MMSE questions. While similar work has been previously reported, fewer patients were studied and methods inappropriate for 0/1 data were used. The study identifies entropic measures as best suited to analysing this type of data. Performance of five such methods at ordering MMSE questions by decreasing order of information contributed to diagnosis is compared. The analysis uses a novel feature selection method based on parallel estimation of conditional mutual information. The newly introduced method performs demonstrably better than classical and state of the art methods. Good predictors are temporal orientation, language recall and abstract thinking however patient gender is a stronger predictor than any of the MMSE questions.
A Framework of Time-Series Analysis Method for ICT Service Diffusion using Textual Data
Authors: Iwashita Motoi
Published: February 2025

In considering service diffusion, how to find the customer perceptions and requirements for the service are essential. Customer perception/requirement for ICT services is always changing depending on its technology development and improvement of operability these days. Therefore, it is necessary to detect customer perceptions and requirements by his/her behavior. In order to solve these issues, this paper proposes a framework of time-series analysis method using textual data. The method analyzes blogs and news events as time-series data. Therefore, we can detect the change of customer perception/requirement through co-occurrence and transition-rate of terms considering network and service structure of ICT service. We apply the method to WiMAX as ICT services at the first trial step.
Meaning Judgment Method for Alphabet Abbreviation Using the Association Mechanism
Authors: Tsuchiya Seiji  , Imono Misako  , Yoshimura Eriko  , Watabe Hirokazu
Published: February 2025

Recently, not only the person and things, but also words are imported by internationalization and informationization, and the scene using the loanword is increasing. However, these expressions are hard to understand for an child and the elderly person. Therefore, when such an expression is used for sentences, it might be hindered to understand entire sentences. Moreover, because an original word is omitted, it is likely to become the same expression as other words. Therefore, the alphabet abbreviation often has the polysemy. In this paper, a method of extracts an alphabet abbreviation from a sentence and judges the meaning of the expression is proposed. This method selects a correct meaning from two or more meanings of the alphabet abbreviation that suit for sentences, judging by using Wikipedia. Moreover, a correct meaning is judged by the association mechanism, using an original knowledge base that defines the concept of the word. The accuracy of the proposed method was 71.5%.
Unsupervised Segmentation of Hyperspectral Images through Evolved Cellular Automata
Authors: Priego Blanca  , Souto Daniel  , Bellas Francisco  , Duro Richard J.
Published: February 2025

The problem of segmenting multidimensional images, in particular hyperspectral images, is still an open subject. The main issue is related to preserving the multidimensional character of the signals throughout the segmentation process avoiding an early projection onto a 2D plane with the consequent loss of the wealth of information these images provide. The approach followed here is based on the use of cellular automata (CA) and their emergent behavior over the hyperspectral cube in order to achieve this objective. Using cellular automata for segmentation in hyperspectral images is not new, but most approaches to this problem involve hand designing the rules for the automata. Additionally, most references found are just extensions of one or three-dimensional methods to multidimensional images, and, as a consequence, average out the spectral information present. The main contributions of this paper is the study of the application of evolutionary methods to produce the CA rule sets that result in the best possible segmentation properties under different circumstances without resorting to any form of projection until the information is presented to the user. The procedure has been tested over synthetic and real hyperspectral images.
Using Differential Evolution to Set Weights to Segments with Different Information Content in the Piecewise Aggregate Approximation
Authors: Muhammad Fuad Muhammad Marwan
Published: February 2025

Time series mining handles several tasks such as classification, clustering and similarity search. These data are high-dimensional in nature so time series representation methods are widely used to reduce the dimensionality of these data so that they can be handled efficiently and effectively. One of the side effects of using representation methods is the loss of information which results from the dimensionality reduction implied in the representation methods. Several representation methods have pointed out that some regions in the times series may contain more information than others so a faithful representation method should be able to reflect the different information contents in different regions of a time series. One of the techniques that can be utilized for this purpose is to set different weights to different regions according to the information they contain, but the challenge is to find an objective scheme to set the weights. Differential evolution is an efficient optimizer that has been successfully used to solve many optimization problems, mainly continuous ones. In this paper we show how differential evolution can be used to set weights to different segments of time series according to their information content. Although our scheme establishes a fully functional time series representation method, with lower bounding distance and a dimensionality reduction technique, we consider this as a by-product of our work and our main aim is to show how the information contents of different time series segments can be reflected using unconventional methods such as the differential evolution. We compare the new scheme with the piecewise aggregate approximation as a method that completely lacks the ability to distinguish regions with high information from others with low information. We show how the new scheme can recover the loss of information caused by dimensionality reduction. We validate our scheme by experiments on different datasets.
Positive Predictive Value based dynamic K-Nearest Neighbor
Authors: Mendialdua Iñigo  , Oses Noelia  , Sierra Basilio  , Lazkano Elena
Published: February 2025

The K Nearest Neighbors classification method assigns to an unclassified observation the class which obtains the best results after a voting criteria is applied among the observation's K nearest, previously classified points. In a validation process the optimal K is selected for each database and all the cases are classified with this K value. However the optimal K for the database does not have to be the optimal K for all the points. In view of that, we propose a new version where the K value is selected dynamically. The new unclassified case is classified with different K values. And looking for each K how many votes has obtained the winning class, we select the class of the most reliable one. To calculate the reliability, we use the Positive Predictive Value (PPV) that we obtain after a validation process. The new algorithm is tested on several datasets and it is compared with the K-Nearest Neighbor rule.
An Analysis of Clustering Approaches to Distributed Learning on Heterogeneously Distributed Datasets
Authors: Peteiro-Barral Diego  , Guijarro-Berdiñas Bertha
Published: February 2025

Advances in communication technologies have contributed to the proliferation of distributed datasets. The most effective approach to distributed learning is to learn locally and then combine the local models. In general, distributed algorithms assume that there is a single model that could be induced from the distributed datasets. Under this view, distribution is treated exclusively as a technical issue. However, real-world distributed datasets frequently present an intrinsic data skewness among their partitions. Despite of its importance, up to the authors’ knowledge, its impact has been barely investigated in the literature. In this paper, the performance of different cluster-based distributed learning methods is analyzed over distinct scenarios by incrementing the differences in the probabilistic distribution of data among partitions. Based on these results the best approach is suggested at every scenario.
Decision table expansion method for software testing
Authors: Uetsuki Keiji  , Matsuodani Tohru  , Takahashi Masakazu  , Tsuda Kazuhiko
Published: February 2025

A function test for software is one of the Black Box testing methods. As a model for a function test, Decision table is useful to describe logical relationship between operating conditions exhaustively for a single function. However, in case that there are relationships between functions, Decision table cannot describe them. As a result, an insufficient or an excessive testing may be performed. In this paper we expand Decision table model to describe the relationships and propose a design method for function combination testing by using the expanded model. Our method makes test cases which achieve exhaustive combination for functions which has relationships each other. On the other hand, it does not make combination for functions which has no relationship. As a result, we can achieve both exhaustive and efficient function testing.
On Preferred Extension Enumeration in Abstract Argumentation
Authors: Nofal Samer  , Dunne Paul  , Atkinson Katie
Published: February 2025

For Dung's theory of abstract argumentation, algorithms have been introduced for enumerating all preferred extensions. Two specific approaches have been set out that are based on labeling arguments as: IN, OUT or UNDEC. The purpose of this paper is to improve the two existing approaches by introducing two enhancements. Firstly, we employ two more informative labels. Secondly, by using these additional labels, we describe a new scheme for how the arguments' labels change in the course of computing the preferred extensions. Supported by empirical evaluation, we argue that these modifications accelerate computations. Moreover, we show how to apply the new algorithm in the context of value-based frameworks for persuasive argument, and hence, it appears that the new algorithm is usable in other formalisms extending Dung's model.
Persuasion Strategies for Argumentation about Plans
Authors: Medellin-Gasque Rolando  , Atkinson Katie  , Bench-Capon Trevor
Published: February 2025

In this paper we offer a proposal to enable agents to discuss the suitability of plans based on an argumentation scheme and associated critical questions. The detail encompassed by the argumentation scheme means that there is a large number of critical questions, and so dialogues may in principle be very lengthy. To improve the efficiency of dialogues we present two strategies for selecting questions. We have implemented the system and here present results showing how both strategies are effective in reducing the number of questions required to reach agreement, although their relative effectiveness is dependent on characteristics of the problem.
Interchanging arguments between Carneades and AIF
Authors: Bex Floris  , Gordon Thomas  , Lawrence John  , Reed Chris
Published: February 2025

We have implemented a translator that translates Carneades Argument Graphs as specified in the LKIF files of the Carneades editor to a database specification of the Argument Interchange Format and vice versa. In this paper the algorithms for this translation are presented.
Grounded Semantics as Persuasion Dialogue
Authors: Caminada Martin  , Podlaszewski Mikołaj
Published: February 2025

In the current work, we provide a formal Mackenzie-style persuasion dialogue for grounded semantics. We show that an argument is in the grounded extension iff the proponent is able to persuade a maximally sceptical opponent in the dialogue.
The Synergy: A Platform for Argumentation-Based Group Decision Making
Authors: Vesic Srdjan  , Ianchuk Mykhailo  , Rubtsov Andrii
Published: February 2025

“The Synergy” is an on-line collaborative argument-based decision making platform. Our goal is to create a system allowing for both user-driven (the users themselves can vote “for” and “against” any particular option) and machine-driven (the system can propose an order of options based on the arguments provided by users) decision making. For the second option, we implemented existing and newly developed decision-making criteria. The basic concepts of our system are an option, a goal and an argument. An argument links an option with a goal. It can be in favour of or against an option and it can be attached a probability measure, which we believe is necessary for representing numerous scenarios in decision making under uncertainty. Our long term goal is to have pre-made answers for some general decisions: like Wikipedia collects data, we will collect PROS and CONS of possible decisions.
Arguing About Firewall Policy
Authors: Applebaum Andy  , Levitt Karl  , Rowe Jeff  , Parsons Simon
Published: February 2025

In this paper, we present a new framework to analyze firewall policy by using argumentation. At the core of this new idea is extending firewall rules with the concept of “reasons” and arguing about the reasons, not the rules. Depending on how the reasons are designed, the resulting framework can be useful in a number of ways: new anomalies in a firewall policy can be identified while, at the same time, stronger recommendations can be given to resolve those anomalies that are detected.
A Value Operations-based methodology for airport concept development for the year 2050
Authors: Smulders F.  , van der Zwan F.M.  , Curran R.
Published: February 2025

One of the core bottlenecks with respect to the continued growth of aviation in the coming 40 years is the airport itself. The 2050+ Airport project investigates the step change innovation needed for the airport to enter the second half of this century. Three concepts are being researched: a time-efficient, cost-effective and ultra-green airport. To support the development of future concepts, the Concept Development Methodology is created. It builds upon the Value Operations Methodology to develop each airport concept and assess the expected benefits for the different users in 2050. The main contribution of this paper is to highlight the development of the Concept Development Methodology and to outline its individual steps. It is shown that the methodology is fit for application in the draft development stage, but further work is required to support detailed concept evaluation. Particularly the Change-Impact sub-method is promising and as such requires more research.
Adaptive Automation Support for Time-Based Operations in ATC
Authors: Uebbing-Rumke M.  , Gürlük H.  , Schulze-Kissing D.
Published: February 2025

Future Air Traffic Control Officers (ATCOs) will have to monitor closely whether aircraft are compliant with given time constraints. This time- based guidance will be a new task compared to current operations where the timely precise adherence to flight plans is not a high priority. This additional requirement will produce new task loads, especially in approach control, where the main task of the ATCOs is to implement conflict-free optimized landing sequences with aircraft of different performance characteristics and wake vortex classes. It is anticipated that from medium traffic load on, the task load will increase to a level that ATCOs couldn't cope without the use of decision support tools (DST). They may even be forced to delegate the time-constraint monitoring completely to the automation. In the DLR project Aviator II a usability study was conducted to evaluate an advanced arrival management system that behaved adaptive to the workload apperception of ATCOs. In three sessions a SME (subject matter expert) worked on a medium traffic scenario with the support of this adaptive DST. After a training and a test run, the SME filled out a usability questionnaire with a focus on the adaptive functioning. The paper describes the concept and implementation of the adaptive DST and summarizes first results regarding the potential benefits of such an innovative feature in a controller working position.
Traveler expectation and airport service improvement: Kaohsiung International Airport, Taiwan
Authors: Chang Yu-Hern  , Wu Kitty  , Lin Ya-Chin
Published: February 2025

This paper aims to explore traveler expectations and service improvements for a regional airport. Using Kaohsiung International Airport (Taiwan) as an example, it is illustrated how a simple integration of three-factor theory and SERVQUAL into QFD can help planners to identify feasible strategies that can increase passenger numbers. Among the 29 identified service attributes, travelers identify four attributes having great potential to increase overall traveler satisfaction if they are improved. These attributes are: design of special category lounge, travel information service, baggage claim wait time, and clarity of airport entry sign. The traveler perception of attribute importance is then translated into designing 20 quality improvement strategies, which are weighted for feasibility by airport managers. The researching findings suggest the following feasible strategies: increase variety of food options, improve wayfinding and airport terminal design, improve airline services, minimize baggage claim wait times, and redevelop and extend departure lounge facilities.
Changing Heartbeat Perception to Induce Anxiety in Virtual Environments
Authors: Chittaro Luca
Published: February 2025

In this paper, we first propose a general technique to induce anxiety in virtual environments (VEs) which exploits auditory heartbeat perception and biofeedback. Then, we consider a VE that reproduces a real-world anxiety-inducing experience (being suddenly surrounded by smoke during a fire evacuation of a building), and we describe an experiment that contrasts 3 conditions: (i) an augmentation of the VE with a bar that indicates when the user's avatar gets hurt, (ii) an augmentation of the VE with the typical audio visual stimuli which are employed in violent videogames when the user's avatar gets hurt, (iii) introduction of the proposed biofeedback technique in the previous condition. We carry out an electrodermal analysis showing that the introduction of the proposed technique produces much higher physiological arousal in terms of skin conductance level (SCL) than the other two conditions. Subjective measures of users' state anxiety are consistent with the recorded physiological reactions.
Virtual Reality for Smoking Cessation: A Case Report
Authors: Pericot-Valverde Irene  , GarcÍa-Rodríguez Olaya  , Ferrer-García Marta  , Secades-Villa Roberto  , Gutiérrez-Maldonado José
Published: February 2025

This study presents a case report describing the use of Virtual Stop Smoking (VSS) program. The VSS includes a multicomponent behavioural approach and a Virtual Reality graded exposure technique. The subject was a 22-year-old female who smoked 20 cigarettes per day. Six weekly 90-minute sessions were conducted once a week over a 6-week period. Measures of efficacy included the number of cigarettes smoked, breath carbon monoxide levels, and self-reported subjective craving. The results obtained supported the efficacy of VSS for smoking cessation.
A Robotic & Virtual Reality Orthopedic Rehabilitation System for the Forearm
Authors: Padilla-Castaneda Miguel A.  , Sotgiu Edoardo  , Frisoli Antonio  , Bergamasco Massimo
Published: February 2025

We describe a robotic and virtual reality system for the rehabilitation of the forearm. It consists of a robotic arm and VR scenarios with a dynamic model of the human upper limb. The system allows to assign specific tasks to perform within the virtual environments. The system simulates the actions of the patient limb and allows exhaustive exercising and motor control, giving visuomotor and haptic feedback and trajectory positioning guidance. The system aids to evaluate the mobility condition of the patient, to personalize the difficult level of the therapy and provides kinematic measures of the patient evolution. The patients recruitment phase has already started for clinical pilot studies.
Psychosocial Implications of Avatar Use in Supporting Therapy for Depression
Authors: Pagliari Claudia  , Burton Christopher  , McKinstry Brian  , Szentatotai Aurora  , David Daniel  , Serrano blanco Antoni  , Ferrini Luis  , Albertini Susanna  , Castro Joan Carlos  , Estevez Soraya  , Wolters Maria
Published: February 2025

Help4Mood is a novel intervention to support the treatment of depressive disorder using an embodied communicative agent (Avatar) to engage the user with therapy tasks. We conducted 10 focus groups with patients and mental health professionals, in the UK, Spain and Romania, in order to explore issues around usability and contextual fit. Emergent themes related to the design and use of Avatars indicated the value of configurability for optimising personalisation and perceived trustworthiness; the importance of supplementing rather than replacing face-to-face interaction, and perceptions of the agent as therapeutic ally or supportive friend. The use of Avatars in psychotherapy is relatively new and its acceptability, value and risks are unknown. These results indicate that users wish to engage with Avatars that meet their personal preferences and fit appropriate role expectations. The perception of Avatar as colleague or friend raises conceptual and ethical issues which merit further research.
Transcription System for Semi-Spontaneous Estonian Speech
Authors: Alumäe Tanel
Published: February 2025

This paper describes a speech-to-text system for semi-spontaneous Estonian speech. The system is trained on about 100 hours of manually transcribed speech and a 300M word text corpus. Compound words are split before building the language model and reconstructed from recognizer output using a hidden event N-gram model. We use a three pass transcription strategy with unsupervised speaker adaptation between individual passes. The system achieves a word error rate of 34.6% on conference speeches and 25.6% on radio talk shows.
Adjusting to User's Style in Dialogue Systems
Authors: Treumuth Margus  , Ojanurme Margus
Published: February 2025

The current research is extending the Asynchronous Dialogue System framework (ADS framework) – a software system that we implemented previously. The ADS framework is a collection of reusable components. This framework can be used in developing text-based natural language dialogue systems. The framework is currently tailored for Estonian language, yet it can be used for English language as most of the components are language independent. The goal of our current research was to explore the adaptability issues in dialogue systems. Mainly, we were looking how to adjust the response from the system to the user's style, in order to provide better interactions with the users. To achieve this goal we needed to implement two additional components for the ADS framework. In text-based human-computer conversations on the internet, the user input is a written request to the dialogue system in a natural language and the output of the system is an answer to the user in the same language. We implemented two additional components for the ADS framework that would analyze the style of the user input and adjust the output of the system accordingly.
Prosodic Patterns of Estonian words: a Corpus-Based Description Using Spontaneous Speech
Authors: Nemoto Rena  , Adda-Decker Martine
Published: February 2025

This paper deals with average prosodic characteristics of Estonian as observed in 25 hours of manually transcribed spontaneous speech. The proposed investigations address the prosodic realisations on a lexical basis, as one of the goals is to make use of these characteristics within the lexical decision process of automatic speech recognition systems. As a first step, the speech corpus was split into word categories according to lexical frequency (most frequent vs. less frequent words), syllabic word length and gender. Average prosodic profiles were computed with respect to fundamental frequency, segment duration and intensity. Statistical analyses confirm a word-initial syllable stress with high average f0 and intensity values, which then progressively decrease towards word-final syllables. Prosodic profiles also reveal that the longer the words, the higher these word-initial stress values are. Whereas f0 and intensity profiles show very similar profiles across word categories, duration profiles reveal less regular patters. For the frequent word category, a rising duration can be observed from the first to final vowels, whilst the other words show longer duration in monosyllabic words and in final vowels of 4-syllabic words. Overall, our results suggest that prosodic cues could contribute to word boundary location in continuous speech.
Perception of Russian Vowels in Singing
Authors: Evgrafova Karina  , Evdokimova Vera
Published: February 2025

There is a significant difficulty in discriminating vowels sung at extremely high fundamental frequencies, especially, when the fundamental frequency (F0) produced is above the region where the first vowel formant (F1) would normally occur. Apart from the difficulties involved in discriminating vowels, aspects of phonology might be expected to contribute to problems of intelligibility. Can such vowels be correctly identified and, if so, does context provide the necessary information or acoustical elements are also operative? The paper studies the perception of sung vowels in order to get insight on the issue of singing intelligibility. A perceptual study on the intelligibility of the Russian vowels was carried out. 49 subjects underwent perceptual tests which were aimed at identifying sung vowels. Classification of the confusions showed that incorrectly identified vowels tend to be confused with [a]. The acoustic analysis of the material was also carried out. The results of perception and acoustic analysis are compared and discussed.
Multicast Session Protection Planner
Authors: Maldonado-Lopez Ferney A.  , Donoso Yezid
Published: February 2025

Several multimedia networking services are based on multicast sessions. Due to high amount of traffic demanded by multicast applications, they need a system or mechanism to protect them from network failures and to keep the service when incidents are present. We propose the Multicast Session Protection Planner (MSPP) as a tool to protect multicast services which uses an elitist multi-objective technique to determine a min-cost multicast tree. MSPP pursues minimizing the cost associated to establish redundant trees on a network with diverse decision variables, maximizing the network availability to satisfy a demand, and considering multiple failures. MSPP is tested over a generated initial topology which includes population demand over a core network. The simulation results shows that the proposed algorithm satisfies recovery resilience requirements of fault-tolerance varying topology. Results demonstrate that MSPP is an useful tool which finds new low-cost high-availability redundant trees, even if the number of failures increases.
A Case-Based Hybrid System for Injection Molding Sensorization
Authors: Pinyol Isaac  , Ventura Raquel  , Cabanillas David
Published: February 2025

Injection molding is a complex process whose fine tuning to ensure desirable qualities depends on several critical interrelated parameters, specially, pressure and temperature inside the molds during injection. Nowadays, it is common to sensorize the internal parts of the molds to monitor and forecast the quality of the final pieces before opening the mold, permitting a fully automation. However, one question arises: which is the best minimal set of sensor types and location in the mold that maximizes the provided information? This is currently done by generic recommendations from human experts or by very time consuming numerical simulations that need to explore the entire surface of the geometries simulating the pressure and temperature curves. In this paper we present an innovative knowledge-based system for injection molding sensorization implemented as a hybrid system (it mixes case-based and rule-based reasoning approaches) that uses the tacit knowledge from human experts to recommend the best location and sensor types. The provided solution is the first step toward a fully automated system for molding sensorization: As a main entrance of numerical simulations, it will bound considerably the exploration surface areas of the molds, leading to a reduction of the computational time of the simulations.
Active Learning of Actions Based on Support Vector Machines
Authors: Ruiz Francisco  , Samà Albert  , Agell Núria
Published: February 2025

Action learning is a methodology based on a machine learning system that makes it possible to select a suitable action or sequence of actions given a state. The main drawback of this methodology is the difficulty of assigning a class to the state-action pair to be included in the training set. This paper proposes an active learning methodology in the learning phase of an action learning process. With the help of an artificial example, the active methodology is compared with a passive methodology consisting of randomly selecting the training set from the pool of unlabelled patterns.
Modelling facial expressions dynamics with Gaussian Process Regression
Authors: Ruiz Adria  , Binefa Xavier
Published: February 2025

It has been shown that the temporal evolution of the face during an expression is important to its correct interpretation. In recent years some approaches have attempt to model facial expressions dynamics by different methods such as HMMs, Dynamic Bayesian Networks or Bayesian temporal manifold model. In this paper, we present a novel approach to model the expression temporal dynamics using Gaussian Process Regression. Specifically, we use it to map the geometric features in any sequence frame into continuous values related with the intensity of the different types of expressions. Using the evolution of these values along the sequence, we are able to determine if the subject has performed any expression. In tests with the Cohn-Kanade database we show that this method can reach a higher performance than other existing methods with a 97.4% of accuracy.
Personal Health Records: Retrieving Contextual Information with Google Custom Search
Authors: Ahsan Mahmud  , Seldon H. Lee  , Sayeed Shohel
Published: February 2025

Ubiquitous personal health records, which can accompany a person everywhere, are a necessary requirement for ubiquitous healthcare. Contextual information related to health events is important for the diagnosis and treatment of disease and for the maintenance of good health, yet it is seldom recorded in a health record. We describe a dual cellphone-and-Web-based personal health record system which can include ‘external’ contextual information. Much contextual information is available on the Internet and we can use ontologies to help identify relevant sites and information. But a search engine is required to retrieve information from the Web and developing a customized search engine is beyond our scope, so we can use Google Custom Search API Web service to get contextual data. In this paper we describe a framework which combines a health-and-environment ‘knowledge base’ or ontology with the Google Custom Search API to retrieve relevant contextual information related to entries in a ubiquitous personal health record.