Recursive self-organizing map as a contractive iterative function system

Recently, there has been a considerable research activity in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, the representational capabilities and internal representations of the models are not well understood. We rigorously analyze a generalization of the Self-Organizing Map (SOM) for processing sequential data, Recursive SOM (RecSOM [1]), as a non-autonomous dynamical system consisting off a set of fixed input maps. We show that contractive fixed input maps are likely to produce Markovian organizations of receptive fields o the RecSOM map. We derive bounds on parameter $\beta$ (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed input maps is guaranteed.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Scaling up question-answering to linked data

Linked Data semantic sources, in particular DBpedia, can be used to answer many user queries. PowerAqua is an open multi-ontology Question Answering (QA) system for the Semantic Web (SW). However, the emergence of Linked Data, characterized by its openness, heterogeneity and scale, introduces a new dimension to the Semantic Web scenario, in which exploiting the relevant information to extract answers for Natural Language (NL) user queries is a major challenge. In this paper we discuss the issues and lessons learned from our experience of integrating PowerAqua as a front-end for DBpedia and a subset of Linked Data sources. As such, we go one step beyond the state of the art on end-users interfaces for Linked Data by introducing mapping and fusion techniques needed to translate a user query by means of multiple sources. Our first informal experiments probe whether, in fact, it is feasible to obtain answers to user queries by composing information across semantic sources and Linked Data, even in its current form, where the strength of Linked Data is more a by-product of its size than its quality. We believe our experiences can be extrapolated to a variety of end-user applications that wish to scale, open up, exploit and re-use what possibly is the greatest wealth of data about everything in the history of Artificial Intelligence. © 2010 Springer-Verlag.

Integration of semantically annotated data by the KnoFuss architecture

Most of the existing work on information integration in the Semantic Web concentrates on resolving schema-level problems. Specific issues of data-level integration (instance coreferencing, conflict resolution, handling uncertainty) are usually tackled by applying the same techniques as for ontology schema matching or by reusing the solutions produced in the database domain. However, data structured according to OWL ontologies has its specific features: e.g., the classes are organized into a hierarchy, the properties are inherited, data constraints differ from those defined by database schema. This paper describes how these features are exploited in our architecture KnoFuss, designed to support data-level integration of semantic annotations.

Semantic search components:a blueprint for effective query language interfaces

Formulating complex queries is hard, especially when users cannot understand all the data structures of multiple complex knowledge bases. We see a gap between simplistic but user friendly tools and formal query languages. Building on an example comparison search, we propose an approach in which reusable search components take an intermediary role between the user interface and formal query languages.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Correcting errors in optical data transmission using neural networks

Optical data communication systems are prone to a variety of processes that modify the transmitted signal, and contribute errors in the determination of 1s from 0s. This is a difficult, and commercially important, problem to solve. Errors must be detected and corrected at high speed, and the classifier must be very accurate; ideally it should also be tunable to the characteristics of individual communication links. We show that simple single layer neural networks may be used to address these problems, and examine how different input representations affect the accuracy of bit error correction. Our results lead us to conclude that a system based on these principles can perform at least as well as an existing non-trainable error correction system, whilst being tunable to suit the individual characteristics of different communication links.

A data structure for improved gp analysis via efficient computation and visualisation of population measures

Population measures for genetic programs are defined and analysed in an attempt to better understand the behaviour of genetic programming. Some measures are simple, but do not provide sufficient insight. The more meaningful ones are complex and take extra computation time. Here we present a unified view on the computation of population measures through an information hypertree (iTree). The iTree allows for a unified and efficient calculation of population measures via a basic tree traversal. © Springer-Verlag 2004.

Optimal embedding for watermarking in discrete data spaces

In this paper, we address the problem of robust information embedding in digital data. Such a process is carried out by introducing modifications to the original data that one would like to keep minimal. It assumes that the data, which includes the embedded information, is corrupted before the extraction is carried out. We propose a principled way to tailor an efficient embedding process for given data and noise statistics. © Springer-Verlag Berlin Heidelberg 2005.

Automating Exersises Validation in e-Learning Environments

E-learning is supposing an innovation in teaching, raising from the development of new technologies. It is based in a set of educational resources, including, among others, multimedia or interactive contents accessible through Internet or Intranet networks. A whole spectrum of tools and services support e-learning, some of them include auto-evaluation and automated correction of test-like exercises, however, this sort of exercises are very constrained because of its nature: fixed contents and correct answers suppose a limit in the way teachers may evaluation students. In this paper we propose a new engine that allows validating complex exercises in the area of Data Structures and Algorithms. Correct solutions to exercises do not rely only in how good the execution of the code is, or if the results are same as expected. A set of criteria on algorithm complexity or correctness in the use of the data structures are required. The engine presented in this work covers a wide set of exercises with these characteristics allowing teachers to establish the set of requirements for a solution, and students to obtain a measure on the quality of their solution in the same terms that are later required for exams.

In memoriam: Dimitar Shishkov

Bulgarian and world computer science lost a prominent colleague: Dimitar Petrov Shishkov 22th January 1939, Varna – 8th March 2004, Sofia D. Shishkov graduated mathematics at Sofia University in 1962. In the last year of his studies he started a specialization as a programmer at the Joint Institute of Nuclear Research – Dubna which lasted three years. Then he worked at the Institute of Mathematics for two years. In 1966 D. Shishkov together with a group of experts transferred to the newly created Central Laboratory for Information Technologies. In 1976 he defended his PhD dissertation. He has been an associate professor in computer science at Sofia University since 1985 and a professor in computer science since 2000. His scientific interests and results were in the fields of computer architectures, computational linguistics, artificial intelligence, numerical methods, data structures, etc. He was remarkable with his teaching activities. D. Shishkov was the creator of high-quality software for the first Bulgarian electronic calculator “ELKA” – one of the first calculators in the world as well as for the series of next calculators and for specialized minicomputers. He was the initiator of the international project “Computerization of the natural languages”. He was a member of a range of international scientific organizations. Among his numerous activities was the organization of the I-st Programming competition in 1979. D. Shishkov was the initiator of sport dancing in Bulgaria (1967) and founder of the first sport-dancing high school education in the world. D. Shishkov was a highly accomplished person with a diversity of interests, with a developed social responsibility and accuracy in his work. In 1996 D. Shishkov was awarded with the International Prize ITHEA for outstanding achievements in the field of Information Theories and Applications. We are grateful to D. Shishkov for the chance to work together with him for establishment and development of IJ ITA.