University of Passau

We propose to extend database systems by a Skyline operation. This operation filters out a set of interesting points from a potentially large set of data points. A point is interesting if it is not dominated by any other point. For example, a hotel might be interesting for somebody traveling to Nassau if no other hotel is both cheaper and closer to the beach. We show how SSL can be extended to pose Skyline queries, present and evaluate alternative algorithms to implement the Skyline operation, and show how this operation can be combined with other database operations, e.g., join.

Work on voice sciences over recent decades has led to a proliferation of acoustic parameters that are used quite selectively and are not always extracted in a similar fashion. With many independent teams working in different research areas, shared standards become an essential safeguard to ensure compliance with state-of-the-art methods allowing appropriate comparison of results across studies and potential integration and combination of extraction and recognition systems. In this paper we propose a basic standard acoustic parameter set for various areas of automatic voice analysis, such as paralinguistic or clinical speech analysis. In contrast to a large brute-force parameter set, we present a minimalistic set of voice parameters here. These were selected based on a) their potential to index affective physiological changes in voice production, b) their proven value in former studies as well as their automatic extractability, and c) their theoretical significance. The set is intended to provide a common baseline for evaluation of future research and eliminate differences caused by varying parameter sets or even different implementations of the same parameters. Our implementation is publicly available with the openSMILE toolkit. Comparative evaluations of the proposed feature set and large baseline feature sets of INTERSPEECH challenges show a high performance of the proposed set in relation to its size.

Background: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Methods: Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. Results: In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. Conclusions: For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. Clinical trial number: This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Network virtualization is recognized as an enabling technology for the future Internet. It aims to overcome the resistance of the current Internet to architectural change. Application of this technology relies on algorithms that can instantiate virtualized networks on a substrate infrastructure, optimizing the layout for service-relevant metrics. This class of algorithms is commonly known as "Virtual Network Embedding (VNE)" algorithms. This paper presents a survey of current research in the VNE area. Based upon a novel classification scheme for VNE algorithms a taxonomy of current approaches to the VNE problem is provided and opportunities for further research are discussed.

The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture the emotional content for various styles of speaking, and while on the other, machine learning algorithms need to be insensitive to outliers while being able to model the context. Whereas the latter has been tackled by the use of Long Short-Term Memory (LSTM) networks, the former is still under very active investigations, even though more than a decade of research has provided a large set of acoustic descriptors. In this paper, we propose a solution to the problem of ‘context-aware’ emotional relevant feature extraction, by combining Convolutional Neural Networks (CNNs) with LSTM networks, in order to automatically learn the best representation of the speech signal directly from the raw time representation. In this novel work on the so-called end-to-end speech emotion recognition, we show that the use of the proposed topology significantly outperforms the traditional approaches based on signal processing techniques for the prediction of spontaneous and natural emotions on the RECOLA database.

Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.

Latent Dirichlet allocation (LDA) topic models are increasingly being used in communication research. Yet, questions regarding reliability and validity of the approach have received little attention thus far. In applying LDA to textual data, researchers need to tackle at least four major challenges that affect these criteria: (a) appropriate pre-processing of the text collection; (b) adequate selection of model parameters, including the number of topics to be generated; (c) evaluation of the model’s reliability; and (d) the process of validly interpreting the resulting topics. We review the research literature dealing with these questions and propose a methodology that approaches these challenges. Our overall goal is to make LDA topic modeling more accessible to communication researchers and to ensure compliance with disciplinary standards. Consequently, we develop a brief hands-on user guide for applying LDA topic modeling. We demonstrate the value of our approach with empirical data from an ongoing research project.

We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies). We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.

Journal Article Energy-Efficient Cloud Computing Get access Andreas Berl, Andreas Berl * 1Fakultät für Informatik und Mathematik, University of Passau, Innstr. 43, 94032 Passau, Germany *Corresponding author: berl@uni-passau.de Search for other works by this author on: Oxford Academic Google Scholar Erol Gelenbe, Erol Gelenbe 2Electrical and Electronic Engineering Department, Imperial College London, South Kensington Campus, London SW7 2AZ, UK Search for other works by this author on: Oxford Academic Google Scholar Marco Di Girolamo, Marco Di Girolamo 3HP-European Innovation Centre, HP IIC (Italy Innovation Centre), Italy Search for other works by this author on: Oxford Academic Google Scholar Giovanni Giuliani, Giovanni Giuliani 3HP-European Innovation Centre, HP IIC (Italy Innovation Centre), Italy Search for other works by this author on: Oxford Academic Google Scholar Hermann De Meer, Hermann De Meer 1Fakultät für Informatik und Mathematik, University of Passau, Innstr. 43, 94032 Passau, Germany Search for other works by this author on: Oxford Academic Google Scholar Minh Quan Dang, Minh Quan Dang 4School of Information Technology, International University in Germany, Bruchsal, Germany Search for other works by this author on: Oxford Academic Google Scholar Kostas Pentikousis Kostas Pentikousis 5VTT Technical Research Centre of Finland, Kaitoväylä 1, FI-90571 Oulu, Finland Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 53, Issue 7, September 2010, Pages 1045–1051, https://doi.org/10.1093/comjnl/bxp080 Published: 19 August 2009 Article history Received: 28 July 2009 Revision received: 28 July 2009 Published: 19 August 2009

We present an approach to identifying similar code in programs based on finding similar subgraphs in attributed directed graphs. This approach is used on program dependence graphs and therefore considers not only the syntactic structure of programs but also the data flow within (as an abstraction of the semantics). As a result, there is no tradeoff between precision and recall; our approach is very good in both. An evaluation of our prototype implementation shows that the approach is feasible and gives very good results despite the non polynomial complexity of the problem.

Building software product lines (SPLs) with features is a challenging task. Many SPL implementations support features with coarse granularity - e.g., the ability to add and wrap entire methods. However, fine-grained extensions, like adding a statement in the middle of a method, either require intricate workarounds or obfuscate the base code with annotations. Though many SPLs can and have been implemented with the coarse granularity of existing approaches, fine-grained extensions are essential when extracting features from legacy applications. Furthermore, also some existing SPLs could benefit from fine-grained extensions to reduce code replication or improve readability. In this paper, we analyze the effects of feature granularity in SPLs and present a tool, called Colored IDE (CIDE), that allows features to implement coarse-grained and fine-grained extensions in a concise way. In two case studies, we show how CIDE simplifies SPL development compared to traditional approaches.

Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses.

In order to provide relevant information to mobile users, such as workers engaging in the manual tasks of maintenance and assembly, a wearable computer requires information about the user's specific activities. This work focuses on the recognition of activities that are characterized by a hand motion and an accompanying sound. Suitable activities can be found in assembly and maintenance work. Here, we provide an initial exploration into the problem domain of continuous activity recognition using on-body sensing. We use a mock "wood workshop" assembly task to ground our investigation. We describe a method for the continuous recognition of activities (sawing, hammering, filing, drilling, grinding, sanding, opening a drawer, tightening a vise, and turning a screwdriver) using microphones and three-axis accelerometers mounted at two positions on the user's arms. Potentially "interesting" activities are segmented from continuous streams of data using an analysis of the sound intensity detected at the two different locations. Activity classification is then performed on these detected segments using linear discriminant analysis (LDA) on the sound channel and hidden Markov models (HMMs) on the acceleration data. Four different methods at classifier fusion are compared for improving these classifications. Using user-dependent training, we obtain continuous average recall and precision rates (for positive activities) of 78 percent and 74 percent, respectively. Using user-independent training (leave-one-out across five users), we obtain recall rates of 66 percent and precision rates of 63 percent. In isolation, these activities were recognized with accuracies of 98 percent, 87 percent, and 95 percent for the user-dependent, user-independent, and user-adapted cases, respectively.

XML is rapidly becoming a popular data format. It can be expected that soon large volumes of XML data will exist. XML data is either produced manually (like html documents today), or it is generated by a new generation of software tools for the WWW and/or electronic data interchange (EDI). The purpose of this paper is to present the results of an initial study about storing and querying XML data. As a first step, this study was focussed on the use of relational database systems and on very simplistic schemes to store and query XML data. In other words, we would like to study how the simplest and most obvious approaches perform, before thinking about more sophisticated approaches. In general, numerous different options to store and query XML data exist. In addition to a relational database, XML data can be stored in a file system, an object-oriented database (e.g., Excelon), or a special-purpose (or semi-structured) system such as Lore (Stanford), Lotus Notes, or Tamino (Software AG). It is still unclear which of these options will ultimately find wide-spread acceptance. A file system could be used with very little effort to store XML data, but a file system would not provide any support for querying the XML data. Object-oriented database systems would allow to cluster XML elements and sub-elements; this feature might be useful for certain applications, but the current generation of object-oriented database systems is not mature enough to process complex queries on large databases. It is going to take even longer before special-purpose systems are mature. Even when using an RDBMS, there are many different ways to store XML data. One strategy is to ask the user or a system administrator in order to decide how XML elements are stored in relational tables. Such an approach is supported, e.g., by Oracle 8i. Another option is to infer from the DTDs of the XML documents how the XML elements should be mapped into tables; such an approach has been studied in [4]. Yet another option is to analyze the XML data and the expected query workload; such an approach has been devised, e.g., in [2]. In this work, we will only study very simple ad-hoc schemes; we think that such a study is necessary before adopting a more complex approach. The schemes that we analyze require no input by the user, they work in the absence of DTDs or if DTDs are meaningless, and they do not involve any analysis of the XML data. Due to their simplicity, the approaches we study will not show the best possible performance, but as we will see, some of them will show very good query performance in most situations. Also, there is no guarantee that any of the more sophisticated approaches known so far will perform better than our simple schemes; see [3] for some experimental results in this respect. Furthermore, the results of our study can be used as input for more sophisticated approaches.

Augmented Reality is a technique that enables users to interact with their physical environment through the overlay of digital information. While being researched for decades, more recently, Augmented Reality moved out of the research labs and into the field. While most of the applications are used sporadically and for one particular task only, current and future scenarios will provide a continuous and multi-purpose user experience. Therefore, in this paper, we present the concept of Pervasive Augmented Reality, aiming to provide such an experience by sensing the user's current context and adapting the AR system based on the changing requirements and constraints. We present a taxonomy for Pervasive Augmented Reality and context-aware Augmented Reality, which classifies context sources and context targets relevant for implementing such a context-aware, continuous Augmented Reality experience. We further summarize existing approaches that contribute towards Pervasive Augmented Reality. Based our taxonomy and survey, we identify challenges for future research directions in Pervasive Augmented Reality.

Corruption has been a feature of public institutions for centuries yet only relatively recently has it been made the subject of sustained scientific analysis. Lambsdorff shows how insights from institutional economics can be used to develop a better understanding of why corruption occurs and the best policies to combat it. He argues that rather than being deterred by penalties, corrupt actors are more influenced by other factors such as the opportunism of their criminal counterparts and the danger of acquiring an unreliable reputation. This suggests a novel strategy for fighting corruption similar to the invisible hand that governs competitive markets. This strategy - the 'invisible foot' - shows that the unreliability of corrupt counterparts induces honesty and good governance even in the absence of good intentions. Combining theoretical research with state-of-the-art empirical investigations, this book will be an invaluable resource for researchers and policy-makers concerned with anti-corruption reform

In an industrial scenario, a context-aware wearable computing system could support a production or maintenance worker. The system could recognize the worker's actions and deliver just-in-time information about activities the worker is to perform. This article reports on an ongoing effort to develop and test such real-life industrial activity-tracking systems within the European Union wearIT@work project. Two case studies conducted in cooperation with the European car manufacturer Skoda demonstrate how the system supports the training of assembly workers and quality assurance in the assembly line. The lessons learned from these studies are applicable in other areas. This article is part of a special issue on activity-based computing.

REDLOG is a package that extends the computer algebra system REDUCE to a computer logic system, i.e., a system that provides algorithms for the symbolic manipulation of first-order formulas over some temporarily fixed language and theory. In contrast to theorem provers, the methods applied know about the underlying algebraic theory and make use of it. We illustrate some applications of REDLOG , describe its functionality as it appears to the user, and explain the design issues and implementation techniques. REDLOG is available on the WWW.

In speech emotion recognition, training and test data used for system development usually tend to fit each other perfectly, but further 'similar' data may be available. Transfer learning helps to exploit such similar data for training despite the inherent dissimilarities in order to boost a recogniser's performance. In this context, this paper presents a sparse auto encoder method for feature transfer learning for speech emotion recognition. In our proposed method, a common emotion-specific mapping rule is learnt from a small set of labelled data in a target domain. Then, newly reconstructed data are obtained by applying this rule on the emotion-specific data in a different domain. The experimental results evaluated on six standard databases show that our approach significantly improves the performance relative to learning each source domain independently.