Interactive online learning

This paper describes a system for visual object recognition based on mobile augmented reality gear. The user can train the system to the recognition of objects online using advanced methods of interaction with mobile systems: Hand gestures and speech input control “virtual menus,” which are displayed as overlays within the camera image. Here we focus on the underlying neural recognition system, which implements the key requirement of an online trainable system—fast adaptation to novel object data. The neural three-stage architecture can be adapted in two modes: In a fast training mode (FT), only the last stage is adapted, whereas complete training (CT) rebuilds the system from scratch. Using FT, online acquired views can be added at once to the classifier, the system being operational after a delay of less than a second, though still with reduced classification performance. In parallel, a new classifier is trained (CT) and loaded to the system when ready. The text was submitted by the authors in English. Gunther Heidemann was born in 1966. He studied physics at the Universities of Karlsruhe and Münster and received his PhD (Eng.) from Bielefeld University in 1998. He is currently working within the collaborative research project “Hybrid Knowledge Representation” of the SFB 360 at Bielefeld University. His fields of research are mainly computer vision, robotics, neural networks, data mining, bonification, and hybrid systems. Holger Bekel was born in 1970. He received his BS degree from the University of Bielefeld, Germany, in 1997. In 2002 he received a diploma in Computer Science from the University of Bielefeld. He is currently pursuing a PhD program in Computer Science at the University of Bielefeld, working within the Neuroinformatics Group (AG Neuroinformatik) in the project VAMPIRE (Visual Active Memory Processes and Interactive Retrieval). His fields of research are active vision and data mining. Ingo Bax was born in 1976. He received a diploma in Computer Science from the University of Bielefeld in 2002. He is currently pursuing a PhD program in Computer Science at the Neuroinformatics Group of the University of Bielefeld, working within the VAMPIRE project. His fields of interest are cognitive computer vision and pattern recognition. Helge J. Ritter was born 1958. He studied physics and mathematics at the Universities of Bayreuth, Heidelberg and Munich. After a PhD in physics at Technical University of Munich in 1988, he visited the Laboratory of Computer Science at Helsinki University of Technology and the Beckman Institute for Advanced Science and Technology at the University of Illinois at Urbana-Champaign. Since 1990 he has headed the Neuroinformatics Group at the Faculty of Technology, Bielefeld University. His main interests are principles of neural computation and their application to building intelligent systems. In 1999, she was awarded the SEL Alcatel Research Prize, and in 2001, the Leibniz Prize of the German Research Foundation DFG.     

Adaptive neighborhood selection in peer-to-peer networks based on content similarity and reputation

To address the two most critical issues in P2P file-sharing systems: efficient information discovery and authentic data acquisition, we propose a Gnutella-like file-sharing protocol termed Adaptive Gnutella Protocol (AGP) that not only improves the querying efficiency in a P2P network but also enhances the quality of search results at the same time. The reputation scheme in the proposed AGP evaluates the credibility of peers based on their contributions to P2P services and subsequently clusters nodes together according to their reputation and shared content, essentially transforming the P2P overlay network into a topology with collaborative and reputed nodes as its core. By detecting malicious peers as well as free-riders and eventually pushing them to the edge of the overlay network, our AGP propagates search queries mainly within the core of the topology, accelerating the information discovery process. Furthermore, the clustering of nodes based on authentic and similar content in our AGP also improves the quality of search results. We have implemented the AGP with the PeerSim simulation engine and conducted thorough experiments on diverse network topologies and various mixtures of honest/dishonest nodes to demonstrate improvements in topology transformation, query efficiency, and search quality by our AGP.

A proposal for a set of attributes relevant for Web portal data quality

Data Quality is a critical issue in today’s interconnected society. Advances in technology are making the use of the Internet an ever-growing phenomenon and we are witnessing the creation of a great variety of applications such as Web Portals. These applications are important data sources and/or means of accessing information which many people use to make decisions or to carry out tasks. Quality is a very important factor in any software product and also in data. As quality is a wide concept, quality models are usually used to assess the quality of a software product. From the software point of view there is a widely accepted standard proposed by ISO/IEC (the ISO/IEC 9126) which proposes a quality model for software products. However, until now a similar proposal for data quality has not existed. Although we have found some proposals of data quality models, some of them working as “de facto” standards, none of them focus specifically on web portal data quality and the user’s perspective. In this paper, we propose a set of 33 attributes which are relevant for portal data quality. These have been obtained from a revision of literature and a validation process carried out by means of a survey. Although these attributes do not conform to a usable model, we think that it might be considered as a good starting point for constructing one.

Stereophotogrammetric 3D real-time machine vision

Signal processing algorithms often have to be modified significantly for implementation in hardware. Continuous real-time image processing at high speed is a particularly challenging task. In this paper a hardware-software codesign is applied to a stereophotogrammetric system. To calculate the depth map, an optimized algorithm is implemented as a hierarchical-parallel hardware solution. By subdividing distances to objects and selecting them sequentially, we can apply 3D scanning and ranging over large distances. We designed processor-based object clustering and tracking functions. We can detect objects utilizing density distributions of disparities in the depth map (disparity histogram). Motion parameters of detected objects are stabilized by Kalman filters. The text was submitted by the authors in English. Michael Tornow was born in Magdeburg, Germany, in 1977. He received his diploma engineer degree (Dipl.-Ing.) in electrical engineering at the University of Magdeburg, Germany, in 2002. He is currently working on a PhD thesis focusing on hardware adapted image processing and vision based driver assistance. Robert W. Kuhn received his diploma engineer degree (Dipl.-Ing.) in geodesy at the Technical University of Berlin, Germany, in 2000. His current work on a PhD thesis focuses on calibration and image processing. Jens Kaszubiak was born in Blankenburg, Germany, in 1977. He received his diploma engineer degree (Dipl.-Ing.) in electrical engineering at the University of Magdeburg, Germany, in 2002. His current research work focuses on vision-based driver assistance and hardware-software codesign. Bernd Michaelis was born in Magdeburg, Germany, in 1947. He received a Masters Degree in Electronic Engineering from the Technische Hochschule, Magdeburg, in 1971 and his first PhD in 1974. Between 1974 and 1980 he worked at the Technische Hochschule, Magdeburg, and was granted a second doctoral degree in 1980. In 1993 he became Professor of Technical Computer Science at the Otto-von-Guericke University, Magdeburg. His research work concentrates on the field of image processing, artificial neural networks, pattern recognition, processor architectures, and microcomputers. Professor Michaelis is the author of more than 150 papers. Gerald Krell was born in Magdeburg, Germany, in 1964. He earned his diploma in electrical engineering in 1990 and his doctorate in 1995 at Otto-von-Guericke University of Magdeburg. Since then he has been a research assistant. His primary research interest is focused on digital image processing and compression, electronic hardware development, and artificial neural networks.     

Adaptive component composition and load balancing for distributed stream processing applications

Providing real-time and QoS support to stream processing applications running on top of large-scale overlays is challenging due to the inherent heterogeneity and resource limitations of the nodes and the multiple QoS demands of the applications that must concurrently be met. In this paper we propose an integrated adaptive component composition and load balancing mechanism that (1) allows the composition of distributed stream processing applications on the fly across a large-scale system, while satisfying their QoS demands and distributing the load fairly on the resources, and (2) adapts dynamically to changes in the resource utilization or the QoS requirements of the applications. Our extensive experimental results using both simulations as well as a prototype deployment illustrate the efficiency, performance and scalability of our approach.

Empirical studies to assess the understandability of data warehouse schemas using structural metrics

Data warehouses are powerful tools for making better and faster decisions in organizations where information is an asset of primary importance. Due to the complexity of data warehouses, metrics and procedures are required to continuously assure their quality. This article describes an empirical study and a replication aimed at investigating the use of structural metrics as indicators of the understandability, and by extension, the cognitive complexity of data warehouse schemas. More specifically, a four-step analysis is conducted: (1) check if individually and collectively, the considered metrics can be correlated with schema understandability using classical statistical techniques, (2) evaluate whether understandability can be predicted by case similarity using the case-based reasoning technique, (3) determine, for each level of understandability, the subsets of metrics that are important by means of a classification technique, and assess, by means of a probabilistic technique, the degree of participation of each metric in the understandability prediction. The results obtained show that although a linear model is a good approximation of the relation between structure and understandability, the associated coefficients are not significant enough. Additionally, classification analyses reveal respectively that prediction can be achieved by considering structure similarity, that extracted classification rules can be used to estimate the magnitude of understandability, and that some metrics such as the number of fact tables have more impact than others.

Advances in GML for Geospatial Applications

This paper presents a study of Geography Markup Language (GML), the issues that arise from using GML for spatial applications, including storage, parsing, querying and visualization, as well as the use of GML for mobile devices and web services. GML is a modeling language developed by the Open Geospatial Consortium (OGC) as a medium of uniform geographic data storage and exchange among diverse applications. Many new XML-based languages are being developed as open standards in various areas of application. It would be beneficial to integrate such languages with GML during the developmental stages, taking full advantage of a non-proprietary universal standard. As GML is a relatively new language still in development, data processing techniques need to be refined further in order for GML to become a more efficient medium for geospatial applications.

DocXS distributed operator construction and execution system

In the field of computer vision and pattern recognition, data processing and data analysis tasks are often implemented as a consecutive or parallel application of more-or-less complex operations. In the following we will present DocXS, a computing environment for the design and the distributed and parallel execution of such tasks. Algorithms can be programmed using an Eclipse-based user interface, and the resulting Matlab and Java operators can be visually connected to graphs representing complex data processing workflows. DocXS is platform independent due to its implementation in Java, is freely available for noncommercial research, and can be installed on standard office computers. One advantage of DocXS is that it automatically takes care about the task execution and does not require its users to care about code distribution or parallelization. Experiments with DocXS show that it scales very well with only a small overhead. The text was submitted by the authors in English. Steffen Wachenfeld received B.Sc. and M.Sc. (honors) degrees in Information Systems in 2003 and 2005 from the University of Muenster, Germany, and an M.Sc. (honors) degree in Computer Science in 2003 from the University of Muenster. He is currently a research fellow and PhD student in the Computer Science at the Dept. of Computer Science, University of Muenster. His research interests include low resolution text recognition, computer vision on mobile devices, and systems/system architectures for computer vision and image analysis. He is author or coauthor of more than ten scientific papers and a member of IAPR. Tobias Lohe, M.Sc. degree in Computer Science in 2007 from the University of Muenster, Germany, is currently a research associate and PhD student in Computer Science at the Institute for Robotics and Cognitive Systems, University of Luebeck, Germany. His research interests include medical imaging, signal processing, and robotics for minimally invasive surgery. Michael Fieseler is currently a student of Computer Science at the University of Muenster, Germany. He has participated in research in the field of computer vision and medical imaging. Currently he is working on his Master thesis on depth-based image rendering (DBIR). Xiaoyi Jiang studied Computer Science at Peking University, China, and received his PhD and Venia Docendi (Habilitation) degree in Computer Science from the University of Bern, Switzerland. In 2002 he became an associate professor at the Technical University of Berlin, Germany. Since October 2002 he has been a full professor at the University of Münster, Germany. He has coauthored and coedited two books published by Springer and has served as the co-guest-editor of two special issues in international journals. Currently, he is the Coeditor-in-Chief of the International Journal of Pattern Recognition and Artificial Intelligence. In addition he also serves on the editorial advisory board of the International Journal of Neural Systems and the editorial board of IEEE Transactions on Systems, Man, and Cybernetics—Part B, the International Journal of Image and Graphics, Electronic Letters on Computer Vision and Image Analysis, and Pattern Recognition. His research interests include medical image analysis, vision-based man-machine interface, 3D image analysis, structural pattern recognition, and mobile multimedia. He is a member of IEEE and a Fellow of IAPR.     

Presenting software engineering results using structured abstracts: a randomised experiment

When conducting a systematic literature review, researchers usually determine the relevance of primary studies on the basis of the title and abstract. However, experience indicates that the abstracts for many software engineering papers are of too poor a quality to be used for this purpose. A solution adopted in other domains is to employ structured abstracts to improve the quality of information provided. This study consists of a formal experiment to investigate whether structured abstracts are more complete and easier to understand than non-structured abstracts for papers that describe software engineering experiments. We constructed structured versions of the abstracts for a random selection of 25 papers describing software engineering experiments. The 64 participants were each presented with one abstract in its original unstructured form and one in a structured form, and for each one were asked to assess its clarity (measured on a scale of 1 to 10) and completeness (measured with a questionnaire that used 18 items). Based on a regression analysis that adjusted for participant, abstract, type of abstract seen first, knowledge of structured abstracts, software engineering role, and preference for conventional or structured abstracts, the use of structured abstracts increased the completeness score by 6.65 (SE 0.37, p < 0.001) and the clarity score by 2.98 (SE 0.23, p < 0.001). 57 participants reported their preferences regarding structured abstracts: 13 (23%) had no preference; 40 (70%) preferred structured abstracts; four preferred conventional abstracts. Many conventional software engineering abstracts omit important information. Our study is consistent with studies from other disciplines and confirms that structured abstracts can improve both information content and readability. Although care must be taken to develop appropriate structures for different types of article, we recommend that Software Engineering journals and conferences adopt structured abstracts.

The Efficiency of Critical Slicing in Fault Localization

In software testing, developing effective debugging strategies is important to guarantee the reliability of software under testing. A heuristic technique is to cause failure and therefore expose faults. Based on this approach mutation testing has been found very useful technique in detecting faults. However, it suffers from two problems with successfully testing programs: (1) requires extensive computing resources and (2) puts heavy demand on human resources. Later, empirical observations suggest that critical slicing based on Statement Deletion (Sdl) mutation operator has been found the most effective technique in reducing effort and the required computing resources in locating the program faults. The second problem of mutation testing may be solved by automating the program testing with the help of software tools. Our study focuses on determining the effectiveness of the critical slicing technique with the help of the Mothra Mutation Testing System in detecting program faults. This paper presents the results showing the performance of Mothra Mutation Testing System through conducting critical slicing testing on a selected suite of programs. Zuhoor Abdullah Al-Khanjari is an assistant professor in the Computer Science Department at Sultan Qaboos University, Sultanate of Oman. She received her BSc in mathematics and computing from Sultan Qaboos University, MSc and PhD in Computer Science (Software Engineering) from the University of Liverpool, UK. Her research interests include software testing, database management, e-learning, human-computer interaction, programming languages, intelligent search engines, and web data mining and development. ~Currently, she is the coordinator of the software engineering research group in the Department of Computer Science, College of Science, Sultan Qaboos University. She is also coordinating a program to develop e-learning based undergraduate teaching in the Department of Computer Science. Currently she is holding the position of assistant dean for postgraduate studies and research in the College of Science, Sultan Qaboos University, Sultanate of Oman. Martin Woodward is a Senior Fellow in the Computer Science Department at the University of Liverpool in the UK. After obtaining BSc and Ph.D. degrees in mathematics from the University of Nottingham, he was employed by the University of Oxford as a Research Assistant on secondment to the UK Atomic Energy Authority at the Culham Laboratory. He has been at the University of Liverpool for many years and initially worked on the so-called ‘Testbed’ project, helping to develop automated tools for software testing which are now marketed successfully by a commercial organisation. His research interests include software testing techniques, the relationship between formal methods and testing, and software visualisation. He has served as Editor of the journal ‘Software Testing, Verification and Reliability’ for the past thirteen years. Haider Ramadhan is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his BS and MS in Computer Science from University of North Carolina, and the PhD in Computer Science and AI from Sussex University. His research interests include visualization of software, systems, and process, system engineering, human-computer interaction, intelligent search engines, and Web data mining and development. Currently, he is the chairman of the Computer Science Department, College of Science, Sultan Qaboos University, Sultanate of Oman. Swamy Kutti (N. S. Kutti) is an associate professor in the Computer Science Department at Sultan Qaboos University. He received his B.E. in Electronics Engineering from the University of Madras, M.E. in Communication Engineering from Indian Institute of Science (Bangalore), and the MSc in Computer Science from Monash University (Australia) and PhD in Computer Science from Deakin University (Australia). His research interests include Real-Time Programming, Programming Languages, Program Testing and Verification, eLearning, and Distributed Operating Systems.     

Robust detection and ordering of ellipses on a calibration pattern

The aim of this work is to accurately estimate from an image the parameters of some ellipses and their relative positions with respect to a given pattern. The process is characterized because it is fully automated and is robust against image noise and occlusions. We have built a calibrator pattern with two planes each containing several ordered circles in known 3D positions. Our method is able to estimate the position of every ellipse and to put them into correspondence with the original calibrator circles. The text was submitted by the authors in English. Luis álvarez received an MS in applied mathematics in 1985 and a PhD in mathematics in 1988, both from CompIntense University (Madrid, Spain). Between 1991 and 1992, he worked as a postdoctoral researcher at CEREMADE, Université Paris IX—Dauphine (France). Currently, he is with the Computer Science Department of the University of Las Palmas de Gran Canaria. His research interests are computer vision and partial differential equations. He is the scientific leader of computer vision group of the University of Las Palmas named AMI. Agustín Salgado received an MS in computer science in from the University of Las Palmas de Gran Canaria (Las Palmas, Spain). Currently, he holds a grant from the Computer Science Department of the University of Las Palmas de Gran Canaria, where he is working on his doctoral thesis under the direction of Javier Sánchez. Javier Sánchez received an MS in computer science in 1997 and a PhD in computer science in 2001, both from the University of Las Palmas de Gran Canaria (Las Palmas, Spain). Between 1997 and 1998, he attended some courses of the DBA 127 “Informatique: Systemes Intelligentes” at the Université Paris IX—Dauphine (France). Currently, he is a lecturer at the Computer Science Department of the University of Las Palmas de Gran Canaria. His research interests are computer vision and partial differential equations, specially applied to stereoscopic vision and optical flow estimation.     

Retrieval of Spatial Join Pattern Instances from Sensor Networks

We study the continuous evaluation of spatial join queries and extensions thereof, defined by interesting combinations of sensor readings (events) that co-occur in a spatial neighborhood. An example of such a pattern is “a high temperature reading in the vicinity of at least four high-pressure readings”. We devise protocols for ‘in-network’ evaluation of this class of queries, aiming at the minimization of power consumption. In addition, we develop cost models that suggest the appropriateness of each protocol, based on various factors, including selectivity of query elements, energy requirements for sensing, and network topology. Finally, we experimentally compare the effectiveness of the proposed solutions on an experimental platform that emulates real sensor networks.

Improving the space cost of <Emphasis Type="Italic">k</Emphasis>-NN search in metric spaces by using distance estimators

Similarity searching in metric spaces has a vast number of applications in several fields like multimedia databases, text retrieval, computational biology, and pattern recognition. In this context, one of the most important similarity queries is the k nearest neighbor (k-NN) search. The standard best-first k-NN algorithm uses a lower bound on the distance to prune objects during the search. Although optimal in several aspects, the disadvantage of this method is that its space requirements for the priority queue that stores unprocessed clusters can be linear in the database size. Most of the optimizations used in spatial access methods (for example, pruning using MinMaxDist) cannot be applied in metric spaces, due to the lack of geometric properties. We propose a new k-NN algorithm that uses distance estimators, aiming to reduce the storage requirements of the search algorithm. The method stays optimal, yet it can significantly prune the priority queue without altering the output of the query. Experimental results with synthetic and real datasets confirm the reduction in storage space of our proposed algorithm, showing savings of up to 80% of the original space requirement.

Estimating the number of clusters using a windowing technique

Clustering is the process of partitioning a set of patterns into disjoint and homogeneous meaningful groups (clusters). A fundamental and unresolved issue in cluster analysis is to determine how many clusters are present in a given set of patterns. In this paper, we present the z-windows clustering algorithm, which aims to address this problem using a windowing technique. Extensive empirical tests that illustrate the efficiency and the accuracy of the propsoed method are presented. The text was submitted by the authors in English. Basilis Boutsinas. Received his diploma in Computer Engineering and Informatics in 1991 from the University of Patras, Greece. He also conducted studies in Electronics Engineering at the Technical Education Institute of Piraeus, Greece, and Pedagogics at the Pedagogical Academy of Lamia, Greece. He received his PhD on Knowledge Representation from the University of Patras in 1997. He has been an assistant professor in the Department of Business Administration at the University of Patras since 2001. His primary research interests include data mining, business intelligence, knowledge representation techniques, nonmonotonic reasoning, and parallel AI. Dimitris K. Tasoulis received his diploma in Mathematics from the University of Patras, Greece, in 2000. He attained his MSc degree in 2004 from the postgraduate course “Mathematics of Computers and Decision Making” from which he was awarded a postgraduate fellowship. Currently, he is a PhD candidate in the same course. His research activities focus on data mining, clustering, neural networks, parallel algorithms, and evolutionary computation. He is coauthor of more than ten publications. Michael N. Vrahatis is with the Department of Mathematics at the University of Patras, Greece. He received the diploma and PhD degree in Mathematics from the University of Patras in 1978 and 1982, respectively. He was a visiting research fellow at the Department of Mathematics, Cornell University (1987–1988) and a visiting professor to the INFN (Istituto Nazionale di Fisica Nucleare), Bologna, Italy (1992, 1994, and 1998); the Department of Computer Science, Katholieke Universiteit Leuven, Belgium (1999); the Department of Ocean Engineering, Design Laboratory, MIT, Cambridge, MA, USA (2000); and the Collaborative Research Center “Computational Intelligence” (SFB 531) at the Department of Computer Science, University of Dortmund, Germany (2001). He was a visiting researcher at CERN (European Organization of Nuclear Research), Geneva, Switzerland (1992) and at INRIA (Institut National de Recherche en Informatique et en Automatique), France (1998, 2003, and 2004). He is the author of more than 250 publications (more than 110 of which are published in international journals) in his research areas, including computational mathematics, optimization, neural networks, evolutionary algorithms, and artificial intelligence. His research publications have received more than 600 citations. He has been a principal investigator of several research grants from the European Union, the Hellenic Ministry of Education and Religious Affairs, and the Hellenic Ministry of Industry, Energy, and Technology. He is among the founders of the “University of Patras Artificial Intelligence Research Center” (UPAIRC), established in 1997, where currently he serves as director. He is the founder of the Computational Intelligence Laboratory (CI Lab), established in 2004 at the Department of Mathematics of University of Patras, where currently he serves as director.     

Hiding a halftone secret image in two camouflaged halftone images

In this paper, we shall propose a method to hide a halftone secret image into two other camouflaged halftone images. In our method, we adjust the gray-level image pixel value to fit the pixel values of the secret image and two camouflaged images. Then, we use the halftone technique to transform the secret image into a secret halftone image. After that, we make two camouflaged halftone images at the same time out of the two camouflaged images and the secret halftone image. After overlaying the two camouflaged halftone images, the secret halftone image can be revealed by using our eyes. The experimental results included in this paper show that our method is very practicable. The text was submitted by the authors in English. Wei-Liang Tai received his BS degree in Computer Science in 2002 from Tamkang University, Tamsui, Taiwan, and his MS degree in Computer Science and Information Engineering in 2004 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding, digital watermarking, and image compression. Chi-Shiang Chan received his BS degree in Computer Science in 1999 from National Cheng Chi University, Taipei, Taiwan, and his MS degree in Computer Science and Information Engineering in 2001 from National Chung Cheng University, Chiayi, Taiwan. He is currently a PhD student of Computer Science and Information Engineering at National Chung Cheng University. His research fields are image hiding and image compression. Chin-Chen Chang received his BS degree in Applied Mathematics in 1977 and his MS degree in Computer and Decision Sciences in 1979, both from National Tsing Hua University, Hsinchu, Taiwan. He received his PhD in Computer Engineering in 1982 from National Chiao Tung University, Hsinchu, Taiwan. During the academic years of 1980–1983, he was on the faculty of the Department of Computer Engineering at National Chiao Tung University. From 1983–1989, he was on the faculty of the Institute of Applied Mathematics, National Chung Hsing University, Taichung, Taiwan. From 1989 to 2004, he has worked as a professor in the Institute of Computer Science and Information Engineering at National Chung Cheng University, Chiayi, Taiwan. Since 2005, he has worked as a professor in the Department of Information Engineering and Computer Science at Feng Chia University, Taichung, Taiwan. Dr. Chang is a fellow of the IEEE, a fellow of the IEE, and a member of the Chinese Language Computer Society, the Chinese Institute of Engineers of the Republic of China, and the Phi Tau Phi Society of the Republic of China. His research interests include computer cryptography, data engineering, and image compression.     

Assessing IR-based traceability recovery tools through controlled experiments

We report the results of a controlled experiment and a replication performed with different subjects, in which we assessed the usefulness of an Information Retrieval-based traceability recovery tool during the traceability link identification process. The main result achieved in the two experiments is that the use of a traceability recovery tool significantly reduces the time spent by the software engineer with respect to manual tracing. Replication with different subjects allowed us to investigate if subjects’ experience and ability play any role in the traceability link identification process. In particular, we made some observations concerning the retrieval accuracy achieved by the software engineers with and without the tool support and with different levels of experience and ability.

Partially Observable Markov Decision Process Approximations for Adaptive Sensing

Adaptive sensing involves actively managing sensor resources to achieve a sensing task, such as object detection, classification, and tracking, and represents a promising direction for new applications of discrete event system methods. We describe an approach to adaptive sensing based on approximately solving a partially observable Markov decision process (POMDP) formulation of the problem. Such approximations are necessary because of the very large state space involved in practical adaptive sensing problems, precluding exact computation of optimal solutions. We review the theory of POMDPs and show how the theory applies to adaptive sensing problems. We then describe a variety of approximation methods, with examples to illustrate their application in adaptive sensing. The examples also demonstrate the gains that are possible from nonmyopic methods relative to myopic methods, and highlight some insights into the dependence of such gains on the sensing resources and environment.

Creating ambient music spaces in real and virtual worlds

Sound and, specifically, music is a medium that is used for a wide range of purposes in different situations in very different ways. Ways for music selection and consumption range from completely passive, almost unnoticed perception of background sound environments to the very specific selection of a particular recording of a piece of music with a specific orchestra and conductor at a certain event. Different systems and interfaces exist for the broad range of needs in music consumption. Locating a particular recording is well supported by traditional search interfaces via metadata. Other interfaces support the automatic creation of playlists via artist or album selection, up to more artistic installations of sound environments that users can navigate through. In this paper we present a set of systems that support the creation of as well as the navigation in musical spaces, both in the real world as well as in virtual environments. We show common principles and point out further directions for a more direct coupling of the various spaces and interaction methods, creating ambient sound environments and providing organic interaction with music for different purposes.

Multiple-parameter coupling metrics for layered component-based software

Coupling represents the degree of interdependence between two software components. Understanding software dependency is directly related to improving software understandability, maintainability, and reusability. In this paper, we analyze the difference between component coupling and component dependency, introduce a two-parameter component coupling metric and a three-parameter component dependency metric. An important parameter in both these metrics is coupling distance, which represents the relevance of two coupled components. These metrics are applicable to layered component-based software. These metrics can be used to represent the dependencies induced by all types of software coupling. We show how to determine coupling and dependency of all scales of software components using these metrics. These metrics are then applied to Apache HTTP, an open-source web server. The study shows that coupling distance is related to the number of modifications of a component, which is an important indicator of component fault rate, stability and subsequently, component complexity.