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Introduction: It is estimated that around 50% of cancer patients require Radiotherapy (RT) at some point during their treatment, hence Therapeutic Radiographers/Radiation Therapists (TR/RTTs) have a key role to play in patient management. It is essential for TR/RTTs to keep abreast with new technologies and continuously develop the digital skills necessary for safe RT practice. The RT profession and education is not regulated at European Union level, which leads to heterogeneity in the skills developed and practised among countries. This study aimed to explore the white and grey literature to collate data on the relevant digital skills required for TR/RTTs practice., Methods: An exhaustive systematic search was conducted to identify literature discussing digital skills of TR/RTTs; relevant grey literature was also identified. A thematic analysis was performed to identify and organise these skills into themes and sub-themes., Results: 195 digital skills were identified, organised in 35 sub-themes and grouped into six main themes: (i) Transversal Digital Skills, (ii) RT Planning Image, (iii) RT Treatment Planning, (iv) RT Treatment Administration, (v) Quality, Safety and Risk Management, and (vi) Management, Education and Research., Conclusion: This list can be used as a reference to close current gaps in knowledge or skills of TR/RTTs while anticipating future needs regarding the rapid development of new technologies (such as Artificial Intelligence or Big Data)., Implications for Practice: It is imperative to align education with current and future RT practice to ensure that all RT patients receive the best care. Filling the gaps in TR/RTTs skill sets will improve current practice and provide TR/RTTs with the support needed to develop more advanced skills., Competing Interests: Conflict of interest statement This work was co-funded by the SAFE EUROPE project under the Erasmus+ Sector Skill Alliances programme [grant agreement 2018-2993/001-001]. The European Commission support for the production of this publication does not constitute an endorsement of the contents which reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Evaluation of grazing impacts on land degradation processes is a difficult task due to the heterogeneity and complex interacting factors involved. In this paper, we designed a new methodology based on a predictive index of grazing susceptibility to land degradation index (GSLDI) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery. Seventeen environmental factors (e.g., derivatives of the digital elevation model, small ruminants' stock), and 55 subsequent attributes (e.g., classes/features) were assigned to each sampling point. The impact of SRs stock density on the land degradation process has been evaluated and estimated with two extreme SRs' density scenarios: absence (no stock), and double density (overstocking). We applied the GSLDI methodology to the Curvature Subcarpathians, a region that experiences the highest erosion rates in Romania, and found that SRs grazing is not the major contributor to land degradation, accounting for only 4.6%. This methodology could be replicated in other steep slope grazing areas as a tool to assess and predict susceptible to land degradation, and to establish common strategies for sustainable land-use practices., (© 2022. The Author(s).)

Objective: To evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs., Materials and Methods: In total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations., Results: The system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual., Conclusions: The AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone., Clinical Significance: An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.

This viewpoint presents an overview of the theoretical developments and main research lines in the areas of disinformation, fact-checking and civic responsibility. To that end, it analyses the academic response to this issue in order to consider the primary trends and map out these questions in general terms. Three areas for future research are identified: proposals to achieve territorial, social and economic cohesion to fight this threat; exploratory studies into the new state of information and media literacy; the development of artificial intelligence systems that are free of bias to assist journalists in the early detection of disinformation. [ABSTRACT FROM AUTHOR]

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Ambient Intelligence promises to transform current spaces into electronic environments that are responsive, assistive and sensitive to human presence. Those electronic environments will be fully populated with dozens, hundreds or even thousands of connected devices that share information and thus become intelligent. That massive wave of electronic devices will also invade everyday objects, turning them into smart entities, keeping their native features and characteristics while seamlessly promoting them to a new class of thinking and reasoning everyday objects. Although there are strong expectations that most of the users' needs can be fulfilled without their intervention, there are still situations where interaction is required. This paper presents work being done in the field of human-computer interaction, focusing on smart home environments, while being a part of a larger project called Aging Inside a Smart Home. This initiative arose as a way to deal with a large scourge of our country, where lots of elderly persons live alone in their homes, often with limited or no physical mobility. The project relies on the mobile agent computing paradigm in order to create a Virtual Butler that provides the interface between the elderly and the smart home infrastructure. The Virtual Butler is receptive to user questions, answering them according to the context and knowledge of the AISH. It is also capable of interacting with the user whenever it senses that something has gone wrong, notifying next of kin and/or medical services, etc. The Virtual Butler is aware of the user location and moves to the computing device which is closest to the user, in order to be always present. Its avatar can also run in handheld devices keeping its main functionality in order to track user when s/he goes out. According to the evaluation carried out, the Virtual Butler is assessed as a very interesting and loved digital friend, filling the gap between the user and the smart home. The evaluation also showed that the Virtual Butler concept can be easily ported to other types of possible smart and assistive environments like airports, hospitals, shopping malls, offices, etc.

Objectives: Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence, with many applications in medicine. Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remains underexplored. In this paper we examine a number of approaches to their combination when learning structure and present new techniques for assessing their results., Methods and Materials: Using public-domain medical data, we run an automated causal discovery system, CaMML, which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results. We present an algorithm for generating a single BN from a set of learned BNs that incorporates user preferences regarding complexity vs completeness. These techniques are presented as part of the first detailed workflow for hybrid structure learning within the broader knowledge engineering process., Results: The detailed knowledge engineering workflow is shown to be useful for structuring a complex iterative BN development process. The adjacency matrices make it clear that for our medical case study using the IOWA dataset, the simplest kind of prior information (partially sorting variables into tiers) was more effective in aiding model discovery than either using no prior information or using more sophisticated and detailed expert priors. The method for generating a single BN captures relationships that would be overlooked by other approaches in the literature., Conclusion: Hybrid causal learning of BNs is an important emerging technology. We present methods for incorporating it into the knowledge engineering process, including visualisation and analysis of the learned networks., (Copyright © 2011 Elsevier B.V. All rights reserved.)

During the last years there has been a growing need of developing innovative tools that can help small to medium sized enterprises to predict business failure as well as financial crisis. In this study we present a novel hybrid intelligent system aimed at monitoring the modus operandi of the companies and predicting possible failures. This system is implemented by means of a neural-based multi-agent system that models the different actors of the companies as agents. The core of the multi-agent system is a type of agent that incorporates a case-based reasoning system and automates the business control process and failure prediction. The stages of the case-based reasoning system are implemented by means of web services: the retrieval stage uses an innovative weighted voting summarization of self-organizing maps ensembles-based method and the reuse stage is implemented by means of a radial basis function neural network. An initial prototype was developed and the results obtained related to small and medium enterprises in a real scenario are presented.

We present a new method of multiclass classification based on the combination of one-vs-all method and a modification of one-vs-one method. This combination of one-vs-all and one-vs-one methods proposed enforces the strength of both methods. A study of the behavior of the two methods identifies some of the sources of their failure. The performance of a classifier can be improved if the two methods are combined in one, in such a way that the main sources of their failure are partially avoided.

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This study explores workplace gender bias in images generated by DALL-E 2, an application for synthesising images based on artificial intelligence (AI). To do this, we used a stratified probability sampling method, dividing the sample into segments on the basis of 37 different professions or prompts, replicating the study by Farago, Eggum-Wilkens and Zhang (2020) on gender stereotypes in the workplace. The study involves two coders who manually input different professions into the image generator. DALL-E 2 generated 9 images for each query, and a sample of 666 images was collected, with a confidence level of 99% and a margin of error of 5%. Each image was subsequently evaluated using a 3-point Likert scale: 1, not stereotypical; 2, moderately stereotypical; and 3, strongly stereotypical. Our study found that the images generated replicate gender stereotypes in the workplace. The findings presented indicate that 21.6% of AI-generated images depicting professionals exhibit full stereotypes of women, while 37.8% depict full stereotypes of men. While previous studies conducted with humans found that gender stereotypes in the workplace exist, our research shows that AI not only replicates this stereotyping, but reinforces and increases it. Consequently, while human research on gender bias indicates strong stereotyping in 35% of instances, AI exhibits strong stereotyping in 59.4% of cases. The results of this study emphasise the need for a diverse and inclusive AI development community to serve as the basis for a fairer and less biased AI. [ABSTRACT FROM AUTHOR]

The communicative effects of disintermediation caused by social media promote the expansion of personalist and emotional political discourses that reach the audience directly and evade the traditional journalistic filter. This phenomenon leads to new political communication tactics, but also exposes citizens to potentially fraudulent, contaminated or polarised content. In this context, framed in post-truth, the term 'fake news' gains relevance as a way of referring to disinformation and as a political and performative argument that can be weaponised. This research aims to analyse such use in the discourse of the former president Donald Trump during his presidential term (2017-2021), focussing on Twitter as the main platform in his political communication strategy online. To analyse this, we resort to a methodological triangulation of content, discourse, and sentiment analysis, with the latter combining both lexicon and artificial intelligence (AI) techniques through machine learning on the basis of deep learning and natural language processing, which is applied to his messages published with the term 'fake news' (N = 768). The analysis of the sample, provided here in an open dataset, employs self-developed software that allows each unit of analysis to be filtered and coded around its predominant themes, sentiments, and words. The main results confirm that Trump's attribution of 'fake news' focusses on three main topics: the media (53%), politics (40%) and his cabinet (33%). It also shows how the former president resorts to a personalist agenda, focussed on the defence of his proposals and his team (80%) by delegitimizing his opponents and the press, with a negative tone (72%) loaded with derogatory terms, confirming a weaponised strategy of the term 'fake news' as a political argument of disinformation and disintermediation. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and AI-based chatbots, such as ChatGPT, are transforming the approach to education. In particular, ChatGPT's potential to process large amounts of data and learn from user interactions makes it a beneficial resource for students, albeit with some reluctance from some teachers. This study aimed to explore the acceptance of ChatGPT by university students. The researchers administered an online survey to 400 Spanish university students aged 18-64 (M = 21.80; SD = 6.40). The results of the methodological approach based on the UTAUT2 model for technology adoption showed that: 1) gender was not a determining variable in any construct while the experience of use was a factor conditioning a higher score on all constructs; 2) experience, performance expectancy, hedonic motivation, price value, and habit were influential in behavioral intention to use ChatGPT; 3) facilitating conditions, habit, and behavioral intention were conditioning factors in user behavior. Finally, this report discusses the findings and practical implications of the work and recommends some good uses for ChatGPT. [ABSTRACT FROM AUTHOR]

This article presents the experimental design of ClaimCheck, an artificial intelligence tool for detecting repeated falsehoods in political discourse using a semantic similarity model developed by the fact-checking organization Newtral in collaboration with ABC Australia. The study reviews the state of the art in algorithmic fact-checking and proposes a definition of claim matching. Additionally, it outlines the scheme for annotating similar sentences and presents the results of experiments conducted with the tool. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has been progressively expanding over the last decade, with its transversal application to the journalistic process and the engaging of media and technology companies in developing specific tools and services. This research offers a first catalogue of Spanish technological companies and institutions that develop AI systems applicable to journalism, with services and features grouped into three phases of the journalistic process: 1. Automated gathering and documentation of information; 2. Automated production of content; and 3. Information distribution and audience relations. The research uses a methodology of in-depth interviews with 45 innovation heads of Spanish-based companies and technological centres specialised in the development of AI (N = 25), and is supported by questionnaires to systematise four study categories: company profiles, tools, journalism-specific services and future trends. The results confirm a clear evolution of Spanish technological companies within the AI sector, with services and tools available for the whole journalistic process, mainly in the information gathering and content distribution phases related to monetisation; the automated news production phase is thereby overshadowed. The offering is diversified in terms of formats-textual, audiovisual, sound- and platforms, especially web and social media. The companies consulted testify to the profitability of its implementation and note a growing interest from the media, but warn of an uneven progress that reflects “slowness”, “distrust” and “lack of knowledge” regarding the application of AI. [ABSTRACT FROM AUTHOR]

The boom in artificial intelligence and automated technology in the journalistic profession has given rise to what are called synthetic media (Crusafon, 2022), media outlets that produce and publish texts, audio, videos, and other news content through processes executed solely by algorithms, without any intervention from journalists. This research has several objectives: to identify the first synthetic media outlets already operating, to describe how these newsrooms without journalists work, to better understand the type of content they produce, and to find out whether these are isolated and ephemeral operations or if, on the contrary, they mark the beginning of a trend toward journalism without the direct intervention of journalists. To this end, we have used an exploratory methodology, enabling us to identify four synthetic media outlets, which have been taken as an analysis sample: JX Press Corp (Japan); Reuters News Tracer (United Kingdom), News Republic (France), and Videre AI (Spain). An analysis of the news content on each project’s web pages was combined with in-depth semistructured interviews with the heads of technology and communication of the three European ventures. The Japanese initiative has no human staff, so its chatbot was the only way to obtain information. The purpose was to learn about the initiatives’ news production process, their impact on the journalistic profession, and their viability. This analysis helps demonstrate that the journalistic world’s reliance on artificial intelligence is becoming increasingly evident and that communication agencies are the first companies to invest in developing and distributing synthetic content to benchmark media. These initiatives, although still limited, are the most recent step in the process of gradually integrating artificial intelligence into news production. [ABSTRACT FROM AUTHOR]

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Copyright of El Profesional de la Información is the property of EPI SCP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of El Profesional de la Información is the property of EPI SCP and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Code smells are a popular mechanism for identifying structural design problems in software systems. Several tools have emerged to support the detection of code smells and propose some refactorings. However, existing tools do not guarantee that a smell will be automatically ixed by means of refactorings. This article presents Bandago, an automated approach to ix a speciic type of code smell called Brain Method.A Brain Method centralizes the intelligence of a class and manifests itself as a long and complex method that is diicult to understand and maintain by developers. For each Brain Method, Bandago recommends several refactoring solutions to remove the smell using a search strategy based on simulated annealing. Our approach has been evaluated with several open-source Java applications, and the results show that Bandago can automatically ix more than 60% of Brain Methods. Furthermore, we conducted a survey with 35 industrial developers that showed evidence about the usefulness of the refactorings proposed by Bandago. Also, we compared the performance of the Bandago against that of a third-party refactoring tool. [ABSTRACT FROM AUTHOR]

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Copyright of Informática Educativa Comunicaciones is the property of Asociacion para el Desarrollo de la Informatica Educativa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Students acquire and process information in different ways depending on their learning styles. To be effective, Web-based courses should guarantee that all the students learn despite their different learning styles. To achieve this goal, we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In a previous work, we have presented an approach that uses Bayesian networks to detect a student's learning style in Web-based courses. In this work, we present an enhanced Bayesian model designed after the analysis of the results obtained when evaluating the approach in the context of an Artificial Intelligence course. We evaluated the precision of our Bayesian approach to infer students’ learning styles from the observation of their actions with a Web-based education system during three semesters. We show how the results from one semester enabled us to adjust our initial model and helped teachers improve the content of the course for the following semester, enhancing in this way students’ learning process. We obtained higher precision values when inferring the learning styles with the enhanced model. [ABSTRACT FROM AUTHOR]

Interface agents are computer programs that provide personalized assistance to a user dealing with computer based applications. By understanding the tasks the user performs in a software application an interface agent could be aware of the context that represents the user’s focus of attention at each particular moment. With this purpose, plan recognition aims at identifying the plans or goals of a user from the tasks he (for simplicity, we use “he” to refer to the user, but we do not mean any distinctions about sexes) performs. A prerequisite for the recognition of plans is knowledge of a user’s possible tasks and the combination of these tasks in complex task sequences, which describes typical user behavior. Plan recognition will enable an interface agent to reason about what the user might do next so that it can determine how to assist him. In this work we present the state of the art in Plan Recognition, paying special attention to the features that make it useful to interface agents. These features include the ability to deal with uncertainty, multiple plans, multiple interleaved goals, overloaded tasks, noisy tasks, interruptions and the capability to adapt to a particular user. [ABSTRACT FROM AUTHOR]

Abstract  In this work we propose a new approach for fast visualization and exploration of virtual worlds based on the use of cartographic concepts and techniques. Versions of cartographic maps with different levels of details can be created by using a set of operations named cartographic generalization. Cartographic generalization employs twelve operators and domain-specific knowledge, being the contribution of this work their transposition to 3D virtual worlds. The architecture of a system for 3D generalization is proposed and the system is implemented. Differently from traditional cartographic processes, we use artificial intelligence for both selecting the key objects and applying the operators. As a case study, we present the simplification of the historical quarter of Recife (Brazil). [ABSTRACT FROM AUTHOR]

Traditional synthesis methods usually deal with the Array Factor information or with approaches based on considering ideal radiating elements without interaction between them. Taking into account the coupling between elements increases the complexity of the synthesis tasks, so it is usually disregarded assuming a certain error in the solution. As shown in the paper, this error can be unacceptable in some antenna array designs. A neural network-based solution can exploit the "a priori" knowledge of the radiating system to relate a given radiated field distribution with the voltages that must be applied to each radiating element taking into account mutual coupling effects without increase of complexity from the designer's point of view. A set of techniques based on the use of neural networks for synthesis purposes are presented in this paper. Results of a prototype of antenna array with feeding values designed using the proposed techniques are also presented. [ABSTRACT FROM AUTHOR]

This work presents a novel user profiling technique, named WebProfiler, developed to support incremental learning and adaptation of user profiles for agents assisting users with information-related tasks. This technique aims at acquiring comprehensible user profiles that accurately capture user interests starting from the observation of user behavior on the Web. [ABSTRACT FROM AUTHOR]

People have unique ways of learning, which may greatly affect the learning process and, therefore, its outcome. In order to be effective, e-learning systems should be capable of adapting the content of courses to the individual characteristics of students. In this regard, some educational systems have proposed the use of questionnaires for determining a student learning style; and then adapting their behaviour according to the students' styles. However, the use of questionnaires is shown to be not only a time-consuming investment but also an unreliable method for acquiring learning style characterisations. In this paper, we present an approach to recognize automatically the learning styles of individual students according to the actions that he or she has performed in an e-learning environment. This recognition technique is based upon feed-forward neural networks. [ABSTRACT FROM AUTHOR]

In this letter, the computational power of a class of random access memory (RAM)-based neural networks, called general single-layer sequential weightless neural networks (GSSWNNs), is analyzed. The theoretical results presented, besides helping the understanding of the temporal behavior of these networks, could also provide useful insights for the developing of new learning algorithms. [ABSTRACT FROM AUTHOR]

A hybrid neuro‐symbolic problem solving model is presented in which the aim is to forecast parameters of a complex and dynamic environment in an unsupervised way. In situations in which the rules that determine a system are unknown, the prediction of the parameter values that determine the characteristic behaviour of the system can be a problematic task. The system employs a case‐based reasoning model to wrap a growing cell structures network, a radial basis function network and a set of Sugeno fuzzy models to provide an accurate prediction. Each of these techniques is used in a different stage of the reasoning cycle of the case‐based reasoning system to retrieve, adapt and review the proposed solution to the present problem. This system has been used to predict the red tides that appear in the coastal waters of the north west of the Iberian Peninsula. The results obtained from experiments are presented. [ABSTRACT FROM AUTHOR]

An investigation is described into the application of artificial intelligence to forecasting in the domain of oceanography. A hybrid approach to forecasting the thermal structure of the water ahead of a moving vessel is presented which combines the ability of a case-based reasoning system for identifying previously encountered similar situations and the generalizing ability of an artificial neural network to guide the adaptation stage of the case-based reasoning mechanism. The system has been successfully tested in real time in the Atlantic Ocean; the results obtained are presented and compared with those derived from other forecasting methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an alternative method to predict the Gonadosomatic Index (GSI), based on a technique known as computational neural networks (CNNs), with two main objectives: (1) to develop a quick and reliable method for the prediction of the fish reproductive period under variable environmental conditions, and thus (2) to reduce the field sampling and laboratory efforts. Three different neural architectures (5-6-6-1, 5-8-8-1 and 5-10-10-1), whose 'training' was carried out controlling three threshold determinism coefficients (Rt 2: 0.7, 0.8 and 0.9), were trained to estimate the GSI of an introduced pumpkinseed ( Lepomis gibbosus) population inhabiting a highly fluctuating Mediterranean stream in southern Spain. This GSI estimate was made using several easily measured fish and environmental variables. The correlation (R) between the GSI observed (GSIr) and the GSI predicted by the CNN (GSIe) was very high (>0.8 in all cases). The optimal CNN structure was the 5-6-6-1 with $ R^{2}_{t} = 0.8 $ because it produced the best generalization of the confidence limits of GSIe with respect to GSIr. To compare with traditional multiple regression analysis, we submitted the data to the same process as with CNN. The validation of the regression model produced a much lower correlation (R) than the CNN models. As an example of the predictive capacities of the CNN models, we predict the hypothetical pumpkinseed reproductive cycle of our population but under the environmental conditions found in the Camargue marshes (South France). [ABSTRACT FROM AUTHOR]

ABSTRACT The quality of education hinges on the proficiency and training of educators. Due to the importance of teacher training, the innovative platform Teacher Moments creates simulated classroom scenarios. In this scenario‐based learning, confusion is an important indicator to detect users who struggle with the simulations. Through Teacher Moments, we gathered 7975 audio recording responses from participants who self‐labelled their recordings according to whether they sounded confused. Our dataset stands out for its size, for not including actor‐generated audio, and for measuring confusion, a neglected emotion in artificial intelligence (AI). Our experiments tested unimodal approaches and feature‐level, model‐level and decision‐level fusion. Feature‐level fusion demonstrated superior performance to unimodal methods, achieving a balanced accuracy of 0.6607 on the test set. This outcome highlights the necessity for further investigation in the overlooked area of confusion detection, particularly employing realistic datasets like the one used in this study and exploring new methods. Beyond teacher training, the insights of this research also extend to other directions, such as other professionals making critical decisions, user interface design or adaptive learning systems. [ABSTRACT FROM AUTHOR]