bluuebunny/crossref_metadata_2025 huggingface dataset:
Expression of Runx2 and type X collagen in human vertebral growth plate of adolescent idiopathic scoliosis
Authors: Wang Shoufeng  , Qiu Yong  , Li Wei-Guo  , Wang Bin  , Ma Weiwei  , Zhu Feng  , Zhu Zezhang  , Yu Yang  , Qian Bangping
Published: February 2025

Runx2 and type X collagen play important roles in vertebral growth, which has been involved in the development and progression of adolescent idiopathic scoliosis (AIS). Seventy-two specimens of vertebral growth plates were collected at the end and apex vertebrae of the scoliotic spine from 12 AIS patients. Expression of Runx2 and type X collagen were examined by immunohistochemistry and in situ hybridization. There were significant difference of the total expression of type X collagen, Runx2 protein and Runx2 mRNA between convex side and concave side of the apex vertebral growth plates. The total expression of type X collagen in the concave side of the lower end vertebral growth plates, and that of Runx2 in the concave side in the upper and lower end vertebral growth plate, were higher than those in the same side of apex (P<0.05). The expression of Runx2 per cell in the concave side at the apex was higher than those in the convex side of the apex and in the concave side of the upper and lower end vertebral growth plates (P<0.05). Expression discrepancy of type X collagen and Runx2 between convex and concave sides of the vertebral growth plate implicate a different chondrocytic kinetics, which may be a possible etiological factor or a secondary change in the progression of adolescent idiopathic scoliosis.
Stress shielding within the scoliotic spine: a progressive risk factor?
Authors: Driscoll M.  , Aubin C.-É.  , Parent S.  , Moreau A.
Published: February 2025

Asymmetrical loading of vertebral endplates is linked to the progression of idiopathic scoliosis based on the Hueter-Volkmann principle. Vertebral loads are induced via body weight and muscle dynamics, however stress distribution over the endplates is governed by regional stiffness. The morphology of a scoliotic spine entails local concave side remodelling: the interverbral disc degenerates, the nucleus migrates, and the trabecular bone mineral density augments. The purpose was to test the hypothesis that the concave-convex biases of the scoliotic spine play a role in the progression of the deformity by altering the stress distribution over the endplates. A right thoracic scoliosis (Cobb 26°) finite element model from T1 to L5 was built and calibrated respecting physiological behaviour. Growth dynamics regulated by mechanical stimuli were modeled according to the Hueter-Volkmann principle. Concave-convex mechanical properties and morphology respected reported in-vivo correlations. Stress analysis on the vertebral endplates and growth simulations were performed with and without presence of concave biases. Inclusion of concave biases increased asymmetrical stress on the apical vertebral endplate by 0.1 MPa and altered lateral stress distribution throughout the model. Concave biases increased vertebral wedging by a mean of 0.8°and induced an additional annual Cobb angle of 3.6° and 5.9° in the thoracic and lumbar sections respectively. Concave-convex biases of the scoliotic spine significantly modify the stress distribution on the vertebral endplates, and thus encourage additional progression.
Risser sign: the value of the lateral spinal radiograph to assess the excursion of the iliac apophysis
Authors: Kotwicki T.
Published: February 2025

The course of the ossification of the iliac apophysis is considered in adolescent patients with idiopathic scoliosis, under the name of the Risser sign, to determine the remaining spinal growth. Although the iliac crest develops in the three-dimensional space as a complex structure, the iliac apophysis ossification has been assessed only on a one plane frontal spinal radiograph. This study points out the usefulness of the lateral radiograph for the visualization of the whole iliac crest, especially the posterior region which otherwise cannot be observed. Two young female pelvis specimen were examined with anatomical measurements and radiography. Lateral spinal radiographs of 201 girls were analyzed for the iliac apophysis excursion.
Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection
Authors: Partalas Ioannis  , Tsoumakas Grigorios  , Vlahavas Ioannis
Published: February 2025

Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. This paper contributes a novel method, based on a new diversity measure that takes into account the strength of the decision of the current ensemble. Experimental comparison of the proposed method, dubbed Focused Ensemble Selection (FES), against state-of-the-art greedy ensemble selection methods shows that it leads to small ensembles with high predictive performance.
Fighting Knowledge Acquisition Bottleneck with Argument Based Machine Learning
Authors: Možina Martin  , Guid Matej  , Krivec Jana  , Sadikov Aleksander  , Bratko Ivan
Published: February 2025

Knowledge elicitation is known to be a difficult task and thus a major bottleneck in building a knowledge base. Machine learning has long ago been proposed as a way to alleviate this problem. Machine learning usually helps the domain expert to uncover some of the more tacit concepts. However, the learned concepts are often hard to understand and hard to extend. A common view is that a combination of a domain expert and machine learning would yield the best results. Recently, argument based machine learning (ABML) has been introduced as a combination of argumentation and machine learning. Through argumentation, ABML enables the expert to articulate his knowledge easily and in a very natural way. ABML was shown to significantly improve the comprehensibility and accuracy of the learned concepts. This makes ABML a most natural tool for constructing a knowledge base. The present paper shows how this is accomplished through a case study of building a knowledge base of an expert system used in a chess tutoring application.
Modeling the Dynamics of Mood and Depression
Authors: Both Fiemke  , Hoogendoorn Mark  , Klein Michel  , Treur Jan
Published: February 2025

Both for developing human-like virtual agents and for developing intelligent systems that make use of knowledge about the emotional state of the user, it is important to model the mood of a person. In this paper, a model for simulating the dynamics of mood is presented, based on psychological theories about a uni-polar clinical depression. The model was analyzed mathematically and by means of simulations, and it was shown that the model exhibits the most important characteristics of the theories. It shows how stress factors under some conditions can lead to a depression, while it will not lead to a depression under other conditions.
Task Driven Coreference Resolution for Relation Extraction
Authors: Xu Feiyu  , Uszkoreit Hans  , Li Hong
Published: February 2025

This paper presents the extension of an existing mimimally supervised rule acquisition method for relation extraction by coreference resolution (CR). To this end, a novel approach to CR was designed and tested. In comparison to state-of-the-art methods for CR, our strategy is driven by the target semantic relation and utilizes domain-specific ontological and lexical knowledge in addition to the learned relation extraction rules. An empirical investigation reveals that newswire texts in our selected domains contain more coreferring noun phrases than prononimal coreferences. This means that existing methods for CR would not suffice and a semantic approach is needed. Our experiments show that the utilization of domain knowledge can boost CR. In our approach, the tasks of relation extraction and CR support each other. On the one hand, reference resolution is needed for the detection of arguments of the target relation. On the other hand, domain modelling for the IE task is used for semantic classification of the referring nouns. Moreover, the application of the learned relation extraction rules often narrows down the number of candidates for CR.
Belief Revision through Forgetting Conditionals in Conditional Probabilistic Logic Programs
Authors: Yue Anbu  , Liu Weiru
Published: February 2025

In this paper, we present a revision strategy of revising a conditional probabilistic logic program (PLP) when new information is received (which is in the form of probabilistic formulae), through the technique of variable forgetting. We first extend the traditional forgetting method to forget a conditional event in PLPs. We then propose two revision operators to revise a PLP based on our forgetting method. By revision through forgetting, the irrelevant knowledge in the original PLP is retained according to the minimal change principle. We prove that our revision operators satisfy most of the postulates for probabilistic belief revision. A main advantage of our revision operators is that a new PLP is explicitly obtained after revision, since our revision operator performs forgetting a conditional event at the syntax level.
Towards Efficient Belief Update for Planning-Based Web Service Composition
Authors: Hoffmann Jörg
Published: February 2025

At the “functional level”, Semantic Web Services (SWS) are described akin to planning operators, with preconditions and effects relative to an ontology; the ontology provides the formal vocabulary and an axiomatisation of the underlying domain. Composing such SWS is similar to planning. A key obstacle in doing so effectively is handling the ontology axioms, which act as state constraints. Computing the outcome of an action involves the frame and ramification problems, and corresponds to belief update. The complexity of such updates motivates the search for tractable classes. Herein we investigate a class that is of practical relevance because it deals with many commonly used ontology axioms, in particular with attribute cardinality upper bounds which are not handled by other known tractable classes. We present an update computation that is exponential only in a comparatively uncritical parameter; we present an approximate update which is polynomial in that parameter as well.
Sequential spatial reasoning in images based on pre-attention mechanisms and fuzzy attribute graphs
Authors: Fouquier Geoffroy  , Atif Jamal  , Bloch Isabelle
Published: February 2025

Spatial relations play a crucial role in model-based image recognition and interpretation due to their stability compared to many other image appearance characteristics, and graphs are well adapted to represent such information. Sequential methods for knowledgebased recognition of structures require to define in which order the structures have to be recognized, which can be expressed as the optimization of a path in the representation graph. We propose to integrate pre-attention mechanisms in the optimization criterion, in the form of a saliency map, by reasoning on the saliency of spatial area defined by spatial relations. Such mechanisms extract knowledge from an image without object recognition in advance and do not require any a priori knowledge on the image. Therefore, pre-attentional mechanisms provide useful knowledge for object segmentation and recognition. The derived algorithms are applied on brain image understanding.
A BDD Approach to the Feature Subscription Problem
Authors: Hadzic T.  , Lesaint D.  , Mehta D.  , O'Sullivan B.  , Quesada L.  , Wilson N.
Published: February 2025

Modern feature-rich telecommunications services offer significant opportunities to human users. To make these services more usable, facilitating personalisation is very important since it enhances the users' experience considerably. However, regardless how service providers organise their catalogues of features, they cannot achieve complete configurability due to the existence of feature interactions. Distributed Feature Composition (DFC) provides a comprehensive methodology, underpinned by a formal architecture model to address this issue. In this paper we present an approach based on using Binary Decision Diagrams (BDD) to find optimal reconfigurations of features when a user's preferences violate the technical constraints defined by a set of DFC rules. In particular, we propose hybridizing constraint programming and standard BDD compilation techniques in order to scale the construction of a BDD for larger size catalogues. Our approach outperforms the standard BDD techniques by reducing the memory requirements by as much as five orders-of-magnitude and compiles the catalogues for which the standard techniques ran out of memory.
An Ensemble of Classifiers for coping with Recurring Contexts in Data Streams
Authors: Katakis Ioannis  , Tsoumakas Grigorios  , Vlahavas Ioannis
Published: February 2025

This paper proposes a general framework for classifying data streams by exploiting incremental clustering in order to dynamically build and update an ensemble of incremental classifiers. To achieve this, a transformation function that maps batches of examples into a new conceptual feature space is proposed. The clustering algorithm is then applied in order to group different concepts and identify recurring contexts. The ensemble is produced by maintaining an classifier for every concept discovered in the streamThe full version of this paper as well as the datasets used for evaluation can be found at: http://mlkd.csd.auth.gr/concept_drift.html
Efficient Data Clustering by Local Density Approximation
Authors: Akodjènou Marc-Ismaël  , Gallinari Patrick
Published: February 2025

The clustering task is a key part of the data mining process. In today's context of massive data, methods with a computational complexity more than linear are unlikely to be applied practically. In this paper, we begin by a simple assumption: local projections of the data should allow to distinguish local cluster structures. From there, we describe how to obtain “pure” local sub-groupings of points, from projections on randomly chosen lines. The clustering of the data is obtained from the clustering of these sub-groupings. Our method has a linear complexity in the dataset size, and requires only one pass on the original dataset. Being local in essence, it can handle twisted geometries typical of many high-dimensional datasets. We describe the steps of our method and report encouraging results.
Not so new: overblown claims for ‘new’ approaches to emotion
Authors: Evans Dylan
Published: February 2025

The non-classical thesis of emotion (NCE) states that the conceptual resources of classical cognitive science cannot adequately account for certain important features of emotion. It also states that these features can be adequately accounted for by employing the conceptual resources of non-classical forms of cognitive science. There is a general problem with all forms of NCE, since they all assume that classical cognitive science is too restrictive, when if anything the reverse is true. In fact, the relationship between classical and non-classical approaches to the study of emotion is much more fuzzy than NCE suggests. Though the two approaches use different terms of art, this does not grant one group privileged access to cognitive resources inaccessible to the other, but merely directs their attention to different features of the phenomena being studied. Thus the real contribution of non-classical models of emotion is to draw our attention to certain key aspects of emotion requiring explanation that had perhaps been somewhat neglected by classical models.
Conflicts between Relevance-Sensitive and Iterated Belief Revision
Authors: Peppas Pavlos  , Fotinopoulos Anastasios Michael  , Seremetaki Stella
Published: February 2025

The original AGM paradigm focuses only on one-step belief revision and leaves open the problem of revising a belief state with whole sequences of evidence. Darwiche and Pearl later addressed this problem by introducing extra (intuitive) postulates as a supplement to the AGM ones. A second shortcoming of the AGM paradigm, seemingly unrelated to iterated revision, is that it is too liberal in its treatment of the notion of relevance. Once again this problem was addressed with the introduction of an extra (also very intuitive) postulate by Parikh. The main result of this paper is that Parikh postulate for relevance-sensitive belief revision is inconsistent with each of the Darwiche and Pearl postulates for iterated belief revision.
BIDFLOW: a New Graph-Based Bidding Language for Combinatorial Auctions
Authors: Croitoru Madalina  , Croitoru Cornelius  , Lewis Paul
Published: February 2025

In this paper we introduce a new graph based bidding language for combinatorial auctions. In our language, each bidder submits to the arbitrator a generalized flow network (netbid) representing her bids. The interpretation of the winner determination problem as an aggregation of individual preferences represented as flowbids allows building an aggregate netbid for its representation. Labelling the nodes with appropriate procedural functions considerably improves upon the expressivity of previous bidding languages.
Unsupervised Web-based Automatic Annotation
Authors: Millan Miquel  , Sánchez David  , Moreno Antonio
Published: February 2025

The success of the Semantic Web depends both on the definition of ontologies used to represent the knowledge as on the annotations performed of the web contents. As manual approaches have low scalability, there is a need of tools capable to generate all this knowledge in an automatic and reliable way. In this paper is presented a complete algorithm to annotate web contents in an automatic and unsupervised manner. It is structured in a three-stepped procedure, based on the usage of several concept similarity measures and linguistic patterns. It is able to detect the entities to annotate, the candidate classes of these entities and, finally, associate them with the classes of an ontology. Some prospective results are presented.
Learning from Data Streams: Synopsis and Change Detection
Authors: Sebastião Raquel  , Gama João  , Mendonça Teresa
Published: February 2025

The aim of this PhD program is the study of algorithms for learning histograms, with the capacity of representing continuous high-speed flows of data and dealing with the current problem of change detection on data streams.
Qualitative modelling to prospect expert's reasoning
Authors: Kansou Kamal  , Della Valle Guy  , Ndiaye Amadou
Published: February 2025

In this paper we present a functional representation of human expert reasoning throughout its statements when assessing the condition of a given object. The human expert statements are represented as conjunctions of elementary propositions. We demonstrate that the conjunctions of elementary propositions can be represented as qualitative additions of qualitative functions in the Q-algebra (Q, ≈, ⌖,). We validate our functional representation by formalizing a representative human expert reasoning: assessment of dough condition in function with the ingredients characteristics.
Dynamic Agent Architectures for Complex Systems
Authors: Tweedale Jeffrey  , Jain Lakhmi
Published: February 2025

Cognitive Science is a field of research attracting significant effort. This research requires the formation of teams of agents in order to dynamically configure the team with the ability to solve the decomposed task of the goal presented. Traditionally all tasks must be completed successfully or the team fails the goal [1,2]. A dynamic architecture would substitute agents within the team with alternative capabilities in order to succeed. It may even compromise and offer a partial solution and offer it to another system to complete. A good communications framework is required to pass messages between separate agent and other systems. Discussion about confined frameworks have recently been extended to enable individual students associated with our Knowledge-Based and Intelligent Information and Engineering Systems (KES) group to fast track the development of their research concepts. A Plug ‘n’ Play concept based on a multi-agent blackboard architecture forms the basis of this research. This paper highlights the core architecture, we believe is required for Multi-Agent System (MAS) developers achieve such flexibility. The research focuses on how agents can be teamed to provide the ability to adapt and dynamically organise the required functionality to automate in a team environment. The model is conceptual and is proposed initially as a blackboard model, where each element represents a block of functionality required to automate a process in order to complete a specific task. Discussion is limited to the formative work within the foundation layers of that framework.
Holistic Electronic & Mobile Government Platform Architecture
Authors: Karantjias Athanasios  , Papastergiou Spyridon  , Polemi Despina
Published: February 2025

The growth of high speed e/m-services imposes requirements including: security, which fosters user trust in mobile services, interoperability, which enables cross-domain mobile service communication and wireless application integration; high administration on the services, organizing embedded logic of applications into separate and easily changed “state machines” to allow new level of processes within governmental services, and scalability and extensibility, which ensure that existing services and modules are not degraded and can be easily extended in new, more advance ones. This paper proposes a secure and interoperable governmental platform architecture for complex e/m services, which integrate peak XML-based technologies.
A Discussion on Domain Modeling in an Example of Motivation-Based Human Resource Management
Authors: Ishibashi Keiichi  , Hashimoto Masaaki  , Katamine Keiichi  , Shiratsuchi Ryoma  , Asaine Keita  , Nakatani Takako  , Ubayashi Naoyasu  , Akiyama Yoshihiro
Published: February 2025

This paper proposes a domain model comprising a static model, dynamic model and scenario generation method for providing a foundation for motivation-based human resource management. One of the main concerns of managers when establishing a management method in an organization, is the individual members' motivation for the method. It is, however, difficult to manage the members' motivation, because the human resource is very complicated. We therefore, propose a domain model by applying Lawler's motivation model. Using this model, we analyze an actual example of successfully establishing CCPM (Critical Chain Project Management) in a company. We discuss primarily the stability of states, motivation, understanding of individuals' roles and their relationship.
Knowledge Acquisition/Transformation to Meet the Quality Needs of the Metallic Constructions Market
Authors: Blesseos Nicholas P.  , Batzias Athanassios F.
Published: February 2025

Small/Medium Enterprizes (SMEs) of the industrial subsector of metallic constructions suffer of lack of means and knowledge to apply quality and process control. This paper deals with the design of a Technical Support Center (TSC), capable to provide the required knowledge to SMEs, by using as a knowledge vehicle the remedial advice offered by the TSC when defects are observed in the products without any evidence about the causing fault in the production line. For this purpose, a methodological framework has been developed under the form of an algorithmic procedure, including 26 activity stages and 8 decision nodes. Fault Tree Analysis (FTA) in its fuzzy version (to deal with uncertainty) was adopted as an inference engine for knowledge extraction and know-how transfer. An implementation referring to the anodization of aluminium is presented, with emphasis on cause-of-fault investigation/identification and communication protocols functioning. The relation of this procedure with (i) a complex/modified Nonaka model and (ii) 2ndorder cybernetics is discussed while a university spin-off is suggested as mostly suitable to play the role of STC, inasmuch as this view is further supported through experience accumulated into the Laboratory of Simulation of Industrial processes at our Department.
A Design Method for the Enterprise Knowledge Management Architecture
Authors: Kanbe Masakazu  , Yamamoto Shuichiro
Published: February 2025

We propose a method for designing an enterprise knowledge management architecture. Our method includes the processes to develop KM assumptions, survey KM Task, evaluate KM assumptions, select KM tasks to be improved and develop KM road map. This method uses the hierarchical knowledge sharing architecture consists of business vision, knowledge process and knowledge management environment to execute knowledge management activities. We illustrate the effectiveness and limitation of our method.
Project Management in Information System Development Education
Authors: Hirota Toyohiko
Published: February 2025

We have taught a laboratory course on system development at the School of Information Science of Kyushu Sangyo University since 2004. Based on our experience, we decided to address various problems of the education in information system development. We categorized these problems from three viewpoints. First, from the viewpoint of system development, the specific project and the method of development are important. Second, from the viewpoint of the project laboratory, it is difficult for students to do their projects effectively. Third, from the viewpoint of project management, it is a problem for both teachers and students to follow the progress of a project and to maintain control of it. In 2007, in order to solve these problems, we made several improvements, including adding Personal Software Process and Earned Value Management.
Image Identification Based on the Pupil Size Analysis During Human-Computer Interaction
Authors: Sobecki Janusz
Published: February 2025

A method for image identification based on the pupil size analysis during human-computer interaction will be presented. The data was gathered during the experiment on 3D interactive representation of fairly large picture collections which facilitates browsing through unstructured sets of icons or pictures. The data was gathered using ASL gaze-tracking device. A group of 18 users took part in the experiment. The obtained gaze tracking results have shown that recorded pupil size patterns may indicate the fact of the presented image identification.
Packet Loss Concealment Algorithm for VoIP Transmission in Unreliable Networks
Authors: Janicki Artur  , Księżak Bartłomiej
Published: February 2025

In this chapter, the authors propose an algorithm for packet loss concealment (PLC) in transmission over IP-based networks with high packet loss rate. The algorithm is a sender-receiver-based extension of ANSI T1.521a Annex B PLC standard for G.711 voice codec. It consists in adding to a transmitted packet redundant parameters describing speech signal in another packet. Efficiency of the proposed algorithm was verified using subjective Absolute Category Rating (ACR) method and objective PESQ algorithm, and compared with original ANSI T1.521a Annex B standard. The intelligibility of speech was assessed using Semantically Unpredictable Sentences (SUS) tests. For high packet loss rates, all assessment methods proved superiority of the proposed algorithm over the original ANSI standard. The ACR tests showed that the proposed method can maintain speech quality above 3 in MOS scale even for packet loss rates of 20%-25%.
Effectiveness of Video Segmentation Techniques for Different Categories of Videos
Authors: Choroś Kazimierz  , Gonet Michał
Published: February 2025

Visual retrieval systems as well as Internet search engines demand efficient techniques of indexing to facilitate fast access to the required video or to the required video sequences in the video databases. Digital video databases are more and more frequently implemented not only in the Internet network but also in local networks and even in local personal computers. Different approaches to digital video indexing are applied: textual approach, extraction and representation of technical or structural features, content-based analysis, and finally segmentation. The segmentation process leads to the partition of a given video into a set of meaningful and individually manageable segments, which then can serve as basic units for indexing. Video has temporal properties such as camera motion, object movements on the scene, sequential composition, and interframe relationships. An effective segmentation technique is able to detect not only abrupt changes but also gradual scene changes, such as fade and dissolve transitions. The nature of movies, mainly the style of video editing has an influence on the effectiveness of temporal segmentation methods. The effectiveness of four methods was analyzed for five different categories of movie: TV talk-show, documentary movie, animal video, action & adventure, and pop music video. The cuts have been recognized as well as cross dissolve effects. The tests have shown that the specific nature of videos has an important influence on the effectiveness of temporal segmentation methods.
Designing and Implementing B2B Applications Using Argumentative Agents
Authors: Bentahar Jamal  , Narenda Nanjagud  , Maamar Zakaria  , Alam Rafiul  , Thiran Philippe
Published: February 2025

This paper presents a framework for modeling and deploying ℬusiness-to-ℬusiness (ℬ2ℬ) applications, with autonomous agents exposing the individual components that implement these applications. This framework consists of three levels identified by strategic, application, and resource, with focus here on the first two levels. The strategic level is about the common vision that independent businesses define as part of their decision of partnership. The application level is about the business processes that get virtually integrated as result of this common vision. Since conflicts are bound to arise among the independent applications/agents, the framework uses a formal model based upon computational argumentation theory through a persuasion protocol to detect and resolve these conflicts. In this protocol, agents can reason about partial information using partial arguments, partial attack and partial acceptability. Agents can then jointly find arguments supporting a new solution for their conflict, which is not known by any of them individually. Termination, soundness, and completeness properties of this protocol are presented. Distributed and centralized coordination strategies are also supported in this framework, which is illustrated with a simple online purchasing case study.
An Emotion Estimation from Human Speech Using Speech Recognition and Speech Synthesize
Authors: Kurematsu Masaki  , Ohashi Marina  , Kinosita Orimi  , Hakura Jun  , Fujita Hamido
Published: February 2025

To enhance estimation of emotion in speech, we propose three new approaches. First approach is that we use more synthetic speeches than our previous work. We define emotion in these speech based on human evaluation and use these speech data to make classifiers. Second approach is that we add some statistics values to our previous approach. Additional statistics values are quartile, range, interquartile range, the upper and lower half of interquartile range and the coefficient of the regression formula. We assume that these values show new viewpoints about speech features. Third approach is that we use phonemic features and syllabic features to estimate emotion in speech. In this paper, phonemic feature is a feature gotten from each phoneme in a speech by frequency analysis. Syllabic feature is a feature gotten from each syllable in a speech by frequency analysis. We use speech recognition to get phonemes and get syllables from a speech based on phonemes. Experimental result shows phonemic features and syllabic features are more useful than using the fundamental frequency and power to estimate anger, disgust fear and sad. The result also says that additional statistics values hardly contribute to estimate emotion. We need to analysis classifiers to evaluate contribution of these statistics. We have some future works. First work is that we use the frequency and power with phonemic features and syllabic features. Second work is that we modify our approach based on the analysis result of our experiment. Third work is that we use our approach in real-time.
Moroccan Modernism Revamped
Authors: Chaouni Aziza
Published: February 2025

Reactivation of Modernist Public Infrastructur; United Nation plaza pedestrian underpass; Commodification of the Modernist Heritage; Villa Suissa/Cafá Chez Paul; What next?; Notes
Legal aspects of E-HEALTH
Authors: Callens Stefaan  , Cierkens Kim
Published: February 2025

Cross-border activities in health care in the European single market are increasing. Many of these cross-border developments are related to e-Health. E-Health describes the application of information and communication technologies across the whole range of functions that affect the health care sector. E-health attracts a growing interest on the European level that highlights the sharp need of appropriate regulatory framework able to ensure its promotion in the European Union. Some Directives constitute a step in this direction. Both the Data Protection Directive, the E-Commerce Directive, the Medical Device Directive and the Directive on Distance Contracting are some of the most important European legal achievements related to e-Health. Although the directives are not adopted especially for e-health applications, they are indirectly very important for e-Health. Firstly, the Data Protection Directive applies to personal data which form part of a filing system and contains several important principles that have to be complied with by e-Health actors processing personal data concerning health. Secondly, the E-commerce Directive applies to services provided at a distance by electronic means. Many e-Health applications fall within this scope. Thirdly, the Medical Devices Directive is of importance for the e-Health sector, especially with regard to e.g. the medical software that is used in many e-health applications. Finally, the Directive on Distance Contracting applies to contracts for goods or services which make use of one or more means of distance communication; E-Health business may involve the conclusion of contracts.
Statistical Monitoring of Injection Moulds
Authors: Berjaga Xavier  , Melendez Joaquim  , Pallares Alvaro
Published: February 2025

In this paper a statistical based methodology to work over the principal component space on injection moulds is presented. The Multiway Principal Component Analysis is applied as a dimensionality reduction step, and fault detection assessment. This methodology allowed to analyse the behaviour of injections with the information directly received from sensors, what ended in a better process understanding. Results concluded that with a low number of variables (some principal components and two control statistics) is enough to detect abnormalities (too short injections, injections with low variability, etc.).
Object-based Place Recognition for Mobile Robots Using Panoramas
Authors: Ribes Arturo  , Ramisa Arnau  , Lopez de Mantaras Ramon  , Toledo Ricardo
Published: February 2025

Object recognition has been widely researched for several decades and in the recent years new methods capable of general object classification have appeared. However very few work has been done to adapt these methods to the challenges raised by mobile robotics. In this article we discuss the data sources (appearence information, temporal context, etc.) that such methods could use and we review several state of the art object recognition methods that build in one or more of these sources. Finally we run an object based robot localization experiment using an state of the art object recognition method and we show that good results are obtained even with a naïve place descriptor.
Bounded Model Checking
Authors: Biere Armin
Published: February 2025

One of the most important industrial applications of SAT is currently Bounded Model Checking (BMC). This technique is typically used for formal hardware verification in the context of Electronic Design Automation. But BMC has successfully been applied to many other domains as well. In practice, BMC is mainly used for falsification, which is concerned with violations of temporal properties. In addition, a considerable part of this chapter discusses complete extensions, including k-induction and interpolation. These extensions also allow to prove properties.
Distribution Transformer Technical Losses Estimation with Aggregated Load Curve Analytical Methodology and Artificial Neural Network Approach Implementation
Authors: Leal Adriano Galindo  , Jardini Jose Antonio  , Ahn Se Un
Published: February 2025

The paper proposes an analytical methodology and three different Artificial Neural Network alternatives using a multi layer perceptron (MLP) in order to estimate technical losses in distribution systems. For each transformer a load curve is set by aggregated customer load curves, and then losses are estimated by transformer's daily load curve stratified, with the proposed methodologies. Theoretically, it can reach a different load curve for each transformer then consequently distinguished losses either. The losses are estimated for distribution transformer, but the methodology can be used on each segment involved in the distribution system (secondary and primary network, distribution transformers, HV/MV transformers). This is done by using the network's data, the consumer's monthly energy consumption data and the typical load curves by class of consumer and type of enterprise developed.
A Heuristic Approach to the Cable Routing Problem in Electrical Panels
Authors: Ittner Alexandre Erwin  , de Sá Claudio Cesar  , Sasse Fernando Deeke
Published: February 2025

In this paper, we present new results concerning the heuristic optimization of cable routing in electrical panels. The problem is modeled and a heuristic solution, using an insertion algorithm and a modified version of the Dijkstra's algorithm, is proposed, analyzed, and compared with human-made solutions. Tests have shown that good results can be obtained from layouts commonly found in the industry.
Definitions in Court Decisions – Automatic Extraction and Ontology Acquisition
Authors: Walter Stephan  , Pinkal Manfred
Published: February 2025

This paper deals with the use of computational linguistic analysis techniques for information access and ontology learning within the legal domain. We present a rule-based approach for extracting and analysing definitions from parsed text and evaluate it on a corpus of about 6000 German court decisions. The results are applied to improve the quality of a text based ontology learning method on this corpus.This paper describes research within the project CORTE funded by the German Science Foundation, DFG PI 154/10-1 (http://www.coli.uni-saarland.de/projects/corte/)
Representing narrative and testimonial knowledge in sense-making software for crime analysis
Authors: van den Braak Susan W.  , van Oostendorp Herre  , Prakken Henry  , Vreeswijk Gerard A.W.
Published: February 2025

In the AVERs sense-making tool for crime analysis different types of information are represented in different ways. More precisely, narrative knowledge is represented in an explanatory direction and testimonial knowledge in an indicative direction. This paper shows that this distinction agrees with the preference of potential users and reduces the number of interpretation errors made by them.
A Description Logic with Concept Instance Ordering and Top-k Restriction
Authors: Vaneková Veronika  , Vojtáš Peter
Published: February 2025

In this paper we introduce a new “scoring” description logic s-EL(D) with concept instance ordering and top-k restriction queries. This enables to create ontologies describing user preferences (ordering of concept instances) and to describe concepts consisting of top-k instances according to this ordering. We construct algorithm for instance problem in s-EL(D). The main application is an extension of web modeling languages to model user preferences and top-k in web services and web search.
A Proposal for a User Oriented Language Based on the Lyee Theory
Authors: Yamada Keizo  , Sasaki Jun  , Tanaka Michiru  , Funyu Yutaka
Published: February 2025

In recent years, a problem has arisen in that users have become estranged from the processes of software development. End user development (EUD) is a solution to this problem. To realize EUD, however, we need to be able to describe the requirements of a user directly as a program.
Opera of Meaning: film and music performance with semantic associative search
Authors: Dubnov Shlomo  , Kiyoki Yasushi
Published: February 2025

Recently artists are exploring ways for incorporating large amounts of information and networking as part of their medium. One of the main challenges in applying information technology to film and opera is in relating different types of media to the meaning of story narrative. Opera of Meaning is a new format for distributed, collaborative and interactive viewing where the association of different media elements is done dynamically by semantic and impression search that is performed by the public during the performance in context of a main story. This opens new research questions in database modeling and semantic technology related to story meaning, media auto-tagging, automatic editing and mixing, user interaction, social networking and more. We plan to offer this format to artists, producers and the public, opening a new venue for social creation and experiencing of impression and meaning in digital media.
Information Modelling and Global Risk Management Systems
Authors: Jaakkola Hannu  , Thalheim Bernhard  , Kidawara Yutaka  , Zettsu Koji  , Chen Xing  , Heimbürger Anneli
Published: February 2025

Utilization of global information resources as a part of risk management is insufficient. The authorities are maintaining information systems mainly for their own purposes, without access to high quality public information sources in Internet and without interoperability between systems of different authorities. Beneficial use of all available information resources would provide an opportunity to create knowledge based on different pieces of information. However, powerful distributed knowledge management, mining of the information items, analysing the quality of them, is needed to create new information to be utilized. The distributed operations needs support of complex network architectures, models supporting mutual understanding over the cultures and language borders, and ability to recognize the context and adapt the results to the new context. This paper opens discussion from different viewpoints to the topic of global risk management. Architectural solutions supporting interoperability, quality of data in wide networks, ubiquity and mobility as well as time dimension of the information space are covered.
Evaluating a Virtual Reality Motor-Skills Simulator
Authors: Ikehara Curtis  , Aschwanden Christoph  , Burgess Lawrence  , Montgomery Kevin  , Mok Darren K.C.
Published: February 2025

Evaluation was performed on a prototype low-cost virtual-reality motor-skills simulator (VRMSS) created at the Telehealth Research Institute, John A. Burns School of Medicine, in conjunction with the National Biocomputation Center, Stanford University. The VRMSS is specifically designed to teach baseline fine-motor skills used in surgery that are based on a matrix of elemental technical skills that comprise the tenets of surgical technique. Fifty-seven participants were randomly assigned to one of three groups (VRMSS, box trainer or no training). After training each group was evaluated using the LapSim from Surgical Sciences. The VRMSS and box trainer were similar in performance, but significantly better than the no training control group. The VRMSS has significant advantages over the box trainer, in that the VRMSS can provide scoring on several parameters of the task without the need of an instructor and the VRMSS is approximately 1/16ththe cost of the Lapsim.
Surgeon-controlled Visualization Techniques for Virtual Reality-guided Cardiac Surgery
Authors: Lo Jennifer  , Moore John  , Wedlake Chris  , Guiraudon Gerard  , Eagleson Roy  , Peters Terry
Published: February 2025

Surgeon-to-computer interaction difficulties in using virtual reality (VR)-guided surgical environments arise from disorientation, insufficient depth information, and delegation of view control. This study focuses on optimizing information delivery for VR-guided beating heart surgery. Initial human factors evaluation and participatory design has provided insight for developing an effective surgeon-controlled interface for VR-guided cardiac interventions. We discuss the motivation for and development of three interface prototypes as well as the methodology used to measure the effect of these novel techniques on user performance and workload.
Neurocognitive and Psychophysiological Analysis of Human Performance within Virtual Reality Environments
Authors: Parsons Thomas D.  , Iyer Arvind  , Cosand Louise  , Courtney Chris  , Rizzo Albert A.
Published: February 2025

Monitoring the neurocognitive and psychophysiological activity of persons operating within a complex environment poses exacting measurement challenges. Three experiments are reported in this paper. In these experiments we made use of VRCPAT to assess persons' neurocognitive and psychophysiological responses to high-fidelity, immersive virtual environments. The first experiment provided continued support for the validity of the VRCPAT as a measure of learning and memory through the use of an increased sample size. In the second experiment we aimed at assessing whether an increase in stimulus complexity would result in a significant decrease in performance on attentional tasks. We also wanted to see whether an increase in stimulus intensity would result in a significant decrease in performance on attentional tasks. The third experiment looked at participants' psychophysiological responses in both low and high immersion virtual environments.
An Efficient Virtual Dissection Tool to Create Generic Models for Anatomical Atlases
Authors: Xiao Mei  , Soh Jung  , Meruvia-Pastor Oscar  , Osborn David  , Lam Nathan  , Hallgrímsson Benedikt  , Sensen Christoph W.
Published: February 2025

We have developed an efficient virtual dissection tool to create generic 3D models for anatomical atlases without the need for artistic drawings. Our custom-developed tool can be used to extract 3D models from 2D medical image stacks, cut the models and align the sub-models. Corresponding 2D medical image portions of the sub-models can then be registered and averaged. In the end, a generic model can be obtained from the averaged 2D images of several subjects. The technique optimizes the functionalities of existing toolkits and the resulting software package will allow biologists to build their atlases more quickly and accurately.