Search

Background Sequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires efficient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an efficient workflow management system. Results We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix) or as a Docker container on DockerHub (https://hub.docker.com/r/mw55/natrix). Conclusion Natrix is a user-friendly and highly extensible workflow for processing Illumina amplicon data.

SummaryThe development of de novo DNA synthesis, polymerase chain reaction (PCR), DNA sequencing and molecular cloning gave researchers unprecedented control over DNA and DNA-mediated processes. To reduce the error probabilities of these techniques, DNA composition has to adhere to method-dependent restrictions. To comply with such restrictions, a synthetic DNA fragment is often adjusted manually or by using custom-made scripts. In this article, we present MESA (Mosla Error Simulator), a web application for the assessment of DNA fragments based on limitations of DNA synthesis, amplification, cloning, sequencing methods and biological restrictions of host organisms. Furthermore, MESA can be used to simulate errors during synthesis, PCR, storage and sequencing processes.Availability and implementationMESA is available at mesa.mosla.de, with the source code available at github.com/umr-ds/mesa_dna_sim.Contactdominik.heider@uni-marburg.deSupplementary informationSupplementary data are available at Bioinformatics online.

An important aspect of Applied Improvisation and Applied Drama is using and perceiving the body. What happens when this room is virtual? Can adaptations be made to enable embodied work onli...

Organ tablature music notation differs considerably in structure and form from the music notation used today. The manual transcription of organ tablature compositions to modern music notation is time-consuming and often prone to errors. In this paper, we present a deep learning approach to automatically recognize organ tablature notation in scanned documents and transcribe it to modern music notation. Our approach is aimed at generating a uniform transcription that remains as close as possible to the original sheet music and therefore does not perform automatic error correction or musical interpretation. The artificial neural network model developed for the recognition of tablature characters is trained using a combination of real annotated tablature staves and tablatures produced by a synthetic data generator. The results of our experiments are evaluated on tablatures taken from two tablature books. We identify several types of error and validate that these are primarily caused by the poor legibility of relevant parts of some tablature scans. Overall, our approach achieves an accuracy of 97.2% and 99.3% correctly recognized bars, depending on whether note pitch and rest characters or note duration and special characters are considered, respectively.

Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.

Network programmability will be required to handle future increased network traffic and constantly changing application needs. However, there is currently no way of using a high-level, easy to use programming language to program Wi-Fi firmware. This impedes rapid prototyping and deployment of novel network services/applications and hinders continuous performance optimization in Wi-Fi networks, since expert knowledge is required for both the used hardware platforms and the Wi-Fi domain. In this paper, we present ReactiFi, a high-level reactive programming language to program Wi-Fi chips on mobile consumer devices. ReactiFi enables programmers to implement extensions of PHY, MAC, and IP layer mechanisms without requiring expert knowledge of Wi-Fi chips, allowing for novel applications and network protocols. ReactiFi programs are executed directly on the Wi-Fi chip, improving performance and power consumption compared to execution on the main CPU. ReactiFi is conceptually similar to functional reactive languages, but is dedicated to the domain-specific needs of Wi-Fi firmware. First, it handles low-level platform-specific details without interfering with the core functionality of Wi-Fi chips. Second, it supports static reasoning about memory usage of applications, which is important for typically memory-constrained Wi-Fi chips. Third, it limits dynamic changes of dependencies between computations to dynamic branching, in order to enable static reasoning about the order of computations. We evaluate ReactiFi empirically in two real-world case studies. Our results show that throughput, latency, and power consumption are significantly improved when executing applications on the Wi-Fi chip rather than in the operating system kernel or in user space. Moreover, we show that the high-level programming abstractions of ReactiFi have no performance overhead compared to manually written C code.

Computer systems used in the field of humanitarian technology are often based on general-purpose single-board computers, such as Raspberry Pis. While these systems offer great flexibility for developers and users, configuration and deployment either introduces overhead by executing scripts on multiple devices or requires deeper technical understanding when building operating system images for such small computers from scratch. In this paper, we present PIMOD, a software tool for configuring operating system images for single-board computer systems. We propose a simple yet comprehensive configuration language. In a configuration profile, called Pifile, a small set of commands is used to describe the configuration of an operating system image. Virtualization techniques are used during the execution of the profile in order to be distribution and platform independent. Commands can be issued in the guest operating system, providing access to the distribution specific tools, e.g., to configure hardware parameters. The implementation of PIMOD is made public under a free and open source license. PIMOD is evaluated in terms of user benefits, performance compared to on-system configuration, and applicability across different hardware platforms and operating systems.

Successful conservation strategies require frequent observations and assessments of the landscape. Although expert surveys provide a great level of detail, the trade-off is the limited spatial coverage and repetition with which they are executed. Remote sensing technology can partially resolve these issues; nevertheless, it still requires experts’ experience to create conservation planning and reaction options. Nature 4.0 seeks to address these shortcomings by developing a prototype of a modular environmental monitoring system for high-resolution observation of species, habitats, and processes. The project combines expert surveys by nature conservationists, remote sensing, and a network of environmental sensors, which are integrated into stationary units as well as attached to unmanned aerial vehicles, rovers, or animals. By utilizing powerful data integration and analysis methods, Nature 4.0 will enable researchers to effectively observe landscapes through a set of diverse lenses. Time series data from the project will also inform the development of early warning indicators. Following the open-source principle, as much of the project as possible will be made publicly available, including, for instance, schematics for sensor units, algorithms for data integration or information on species occurrence. In summary, Nature 4.0 will establish new methods and protocols in the field of comprehensive environmental monitoring by combining traditional sampling, remote sensing, and automated measurement stations. The prototype system is being developed in the Marburg Open Forest, an open research, education, and development platform for environmental monitoring methods. The Marburg Open Forest brings a cross-disciplinary group of scientists together with nature conservation experts from the private sector and the state government, as well as local schools and private citizens to collaborate and bridge the gap between basic and applied environmental research. After one year, we will present the results of the initial phase and share our experience with developing Nature 4.0.

This paper presents a cross-layer reactive monitoring approach for Cloud computing environments. Based on complex event processing (CEP) methodology, our proposal monitors and analyzes performance metrics across Cloud layers to detect and repair performance-related problems. The approach utilizes novel CEP analysis rules and a new action manager framework. The proposed analysis rules are derived from a comprehensive analysis of the interactions between Cloud layers. The results of this study are used to reduce the number of monitored parameters, define the analysis rules and identify the causes of performance-related problems. Our novel action manager framework assigns a set of repair actions to each performance-related problem and checks the success of the applied action. The results of several experiments indicate that the time needed to fix a performance-related problem is reasonably short. They also show that the CPU overhead of using our approach is negligible. Moreover, experimental results demonstrate the merits of our approach in terms of speeding up the repair and reducing the number of triggered alarms compared to baseline methods.

Currently, nearly four billion people live in urban areas. Since this trend is increasing, natural disasters or terrorist attacks in such areas affect an increasing number of people. While information and communication technology is crucial for the operation of urban infrastructures and the well-being of its inhabitants, current technology is quite vulnerable to disruptions of various kinds. In future smart cities, a more resilient urban infrastructure is imperative to handle the increasing number of hazardous situations. We present a novel resilient communication approach based on smart street lights as part of the public infrastructure. It supports people in their everyday life and adapts its functionality to the challenges of emergency situations. Our approach relies on various environmental sensors and in-situ processing for automatic situation assessment, and a range of communication mechanisms (e.g., public WiFi hotspot functionality and mesh networking) for maintaining a communication network. Furthermore, resilience is not only achieved based on infrastructure deployed by a digital city's municipality, but also based on integrating citizens through software that runs on their mobile devices (e.g., smartphones and tablets). Web-based zero-installation and platform-agnostic apps can switch to device-to-device communication to continue benefiting people even during a disaster situation. Our approach, featuring a covert channel for professional responders and the zero-installation app, is evaluated through a prototype implementation based on a commercially available street light., Comment: 2019 IEEE Global Humanitarian Technology Conference (GHTC)

The IoT (Internet of Things) devices continue to penetrate into new areas of our daily lives as well as industry. The evolution of IoT devices comes with the necessity of operation in heterogeneous environments. This evolution brings new challenges in the areas of R&D and education. We identify important features beneficial for R&D engineers as well as for educationalists and students and we propose a novel open platform for rapid development of IoT nodes. This platform is easy to employ in the educational process at the same time.

In personalized medicine for treating cancer, the most challenging task is to identify an adequate treatment for a given patient. The prediction of drug response can highly improve the clinical results and reduce the financial costs associated with chemotherapy treatment. In this paper, we present a new approach to improve the accuracy of predicting the sensitivity of cell lines to anticancer drugs. Our approach relies on selecting relevant features impacting the efficiency of predictive algorithms. We propose a multi-criteria recommendation method to identify the most accurate prediction algorithm according to the available input data. For each iteration, a different algorithm may be selected, and the optimal accuracy may be reached. The experimental results indicate that our method outperforms existing approaches in estimating the sensitivity of cell lines to different drugs.

The purpose of disease registries is to collect and analyze data related to specific diseases in terms of incidence and prevalence. Since the data is typically entered by wearable sensors and/or human caregivers, errors in the data fields are often inevitable. In this paper, we propose a new approach to improve data quality in disease registries based on (a) a semi-random combination of parameters and (b) a learning algorithm for detecting and signaling the loss of quality of the entered data. To implement the approach, we have developed a novel adaptive neuro-fuzzy inference system. It is applied to specific sections of the Tunisian Fanconi Anemia Registry with the aims of reducing false alarms and automatically adjusting the parameters of coefficients of the disease. Our experimental results indicate that both aims can be achieved and effectively lead to improved data quality in disease registries.

This paper presents a novel health analysis approach for heart failure prediction. It is based on the use of complex event processing (CEP) technology, combined with statistical approaches. A CEP engine processes incoming health data by executing threshold-based analysis rules. Instead of having to manually set up thresholds, our novel statistical algorithm automatically computes and updates thresholds according to recorded historical data. Experimental results demonstrate the merits of our approach in terms of speed, precision, and recall.

Text spotting, i.e., the localization and recognition of text occurrences in natural images and videos, is a challenging problem in computer vision. Important applications of text spotting are: video indexing, retrieval and search, the support of blind persons in finding their way in unknown environments and reading street and shop signs for autonomously driving vehicles or car license plate recognition in traffic control systems. Recently, the use of deep convolutional neural networks as well as recognizing whole words instead of single characters led to significant progress in the field of text spotting. Furthermore, there is a tendency towards merging and unifying the single steps of the text spotting pipeline. Despite current progress, it is still difficult to recognize in-scene text in comparison to overlaid text. This work gives an overview of the state-of-the-art in text spotting and highlights current breakthroughs and trends.

In this paper, we present MiniWorld, a novel distributed network emulator. It is based on full virtualization using QEMU/KVM, offers three network backends for emulating both wired and wireless communication, and provides several mobility patterns as well as distance-based link quality models. A snapshot boot mode is offered for accelerated booting of identical environments and repeating emulation runs. To decrease runtimes, MiniWorld supports distributed emulation across multiple computers, based on a resource-aware virtual machine (VM) scheduler. Experimental results demonstrate the performance of MiniWorld with respect to VM boot times, network bandwidth, round trip times, and topology switching times.

Authorization in collaborative systems is defined by a global policy that represents the combination of the collaborators’ access policies. However, the enforcement of such a global policy may create conflicting authorization decisions. In this paper, we categorize two types of conflicts that may occur in such policies. Furthermore, to resolve these conflicts and to reach a unique decision for an access request, we present an approach that uses XACML policy combining algorithms and considers the category of the detected conflicts. The approach is implemented using aspect-oriented finite state machines.

The German Broadcasting Archive (DRA) maintains the cultural heritage of radio and television broadcasts of the former German Democratic Republic (GDR). The uniqueness and importance of the video material stimulates a large scientific interest in the video content. In this paper, we present an automatic video analysis and retrieval system for searching in historical collections of GDR television recordings. It consists of video analysis algorithms for shot boundary detection, concept classification, person recognition, text recognition and similarity search. The performance of the system is evaluated from a technical and an archival perspective on 2,500 hours of GDR television recordings., Comment: TPDL 2016, Hannover, Germany. Final version is available at Springer via DOI

A novel approach to virtual machine (VM) consolidation, based on energy-efficient storage migration and live VM migration, is implemented using Eucalyptus, an open source clone of the Amazon Elastic Compute Cloud. Several experiments demonstrate the potential energy savings.

The MapReduce architectural pattern popularized by Google has successfully been utilized in several scientific applications. Up until now, MapReduce is rarely employed in the field of Systems Biology. We investigate whether a MapReduce approach utilizing on-demand resources from a Cloud is suitable to perform simulation tasks in the area of Metabolic Flux Analysis (MFA). An Amazon ElasticMapReduce Cloud implementation of the parallel, parametric Monte Carlo bootstrap in the context to 13C-MFA is presented. The seamless integration of the application into a service-oriented, BPEL-based scientific workflow framework is shown. A comparison of a straightforward MapReduce implementation using the Hadoop streaming interface on various Amazon ElasticMapReduce instance types and a single CPU core computation approach reveals a speedup of 17 on 64 Amazon cores. I/O operations on many small files within the Reduce step were identified as the limiting step. By exploiting the Hadoop Java API, making use of built-in data types and tuning problem-specific Hadoop parameters, the I/O issues could be resolved. With the revised implementation, a speedup of up to 48 could be achieved on 64 Amazon cores. To investigate the runtimes of a realistic 13C-MFA analysis, 50,000 Monte Carlo samples with a typical metabolic network model have been performed on 20 virtual nodes in 24?h and 23 min with a total cost of $384. Our work demonstrates the possibility to perform scalable Systems Biology applications using Amazon's Cloud MapReduce service. Highlights? Scientific workflows are an attractive option in Metabolic Flux Analysis. ? A service-oriented approach for on-demand Cloud computing with Amazon is presented. ? Compute-intensive, parallel Monte Carlo simulations can be realized using MapReduce. ? A speedup of factor 48 is achieved on 64 cores compared to a single core computation. ? A large-scale simulation is performed on 152 cores in 24?h and $384 total costs.

Cloud computing offers most of its services under multi-tenancy environments. To satisfy security requirements among collaborating tenants, each tenant may define a set of access control policies to secure access to shared data. Several cloud solutions make use of XACML to specify such policies. However, existing implementations of XACML perform a brute force search to compare a request to all existing rules in a given XACML policy. This decreases the decision process (i.e., policy evaluation) performance especially for policies with a large number of rules. In this paper, we propose an automata-based approach for an efficient XACML policy evaluation. We implemented our approach in a cloud policy engine called X2Automata. The engine first converts both XACML policies and access requests to automata. Second, it combines the two automata by a synchronous product. Third, it applies an evaluation procedure to the resulting automaton to decide whether an access request is granted or not. To highlight the efficiency of X2Automata, we compare its performance, based on the OpenStack cloud environment, with the XACML implementation named Balana.

The connectivity of devices, machines and people via Cloud infrastructure can support collaborations among doctors and specialists from different medical organisations. Such collaborations may lead to data sharing and joint tasks and activities. Hence, the collaborating organisations are responsible for managing and protecting data they share. Therefore, they should define a set of access control policies regulating the exchange of data they own. However, existing Cloud services do not offer tools to analyse these policies. In this paper, we propose a Cloud Policy Verification Service (CPVS) for the analysis and the verification of access control policies specified using XACML. The analysis process detects anomalies at two policy levels: a) intra-policy: detects discrepancies between rules within a single security policy (conflicting rules and redundancies), and b) inter-policies: detects anomalies between several security policies such as inconsistency and similarity. The verification process consists in verifying the completeness property which guarantees that each access request is either accepted or denied by the access control policy. In order to demonstrate the efficiency of our method, we also provide the time and space complexities. Finally, we present the implementation of our method and demonstrate how efficiently our approach can detect policy anomalies.

The idea of using image and video data available in the World-Wide Web (WWW) as training data for classifier construction has received some attention in the past few years. In this paper, we present a novel incremental and scalable web-supervised learning system that continuously learns appearance models for image categories with heterogeneous appearances and improves these models periodically. Simply specifying the name of the concept that has to be learned initializes the proposed system, and there is no further supervision afterwards. Textual and visual information on web sites are used to filter out irrelevant and misleading training images. To obtain a robust, flexible, and updatable way of learning, a novel learning framework is presented that relies on clustering in order to identify visual subclasses before using an ensemble of random forests, called random savanna, for subclass learning. Experimental results demonstrate that the proposed web-supervised learning approach outperforms a support vector machine (SVM), while at the same time being simply parallelizable in the training and testing phases.

Reliable video content analysis is an essential prerequisite for effective video search. An important current research question is how to develop robust video content analysis methods that produce satisfactory results for a large variety of video sources, distribution platforms, genres, and content. The work presented in this article exploits the observation that the appearance of objects and events is often related to a particular video sequence, episode, program, or broadcast. This motivates our idea of considering the content analysis task for a single video or episode as a transductive setting: the final classification model must be optimal for the given video only, and not in general, as expected for inductive learning. For this purpose, the unlabeled video test data have to be used in the learning process. In this article, a transductive learning framework for robust video content analysis based on feature selection and ensemble classification is presented. In contrast to related transductive approaches for video analysis (e.g., for concept detection), the framework is designed in a general manner and not only for a single task. The proposed framework is applied to the following video analysis tasks: shot boundary detection, face recognition, semantic video retrieval, and semantic indexing of computer game sequences. Experimental results for diverse video analysis tasks and large test sets demonstrate that the proposed transductive framework improves the robustness of the underlying state-of-the-art approaches, whereas transductive support vector machines do not solve particular tasks in a satisfactory manner.

In this contribution, a medical software system for volumetric analysis of different cerebral pathologies in magnetic resonance imaging (MRI) data is presented. The software system is based on a semi-automatic segmentation algorithm and helps to overcome the time-consuming process of volume determination during monitoring of a patient. After imaging, the parameter settings-including a seed point-are set up in the system and an automatic segmentation is performed by a novel graph-based approach. Manually reviewing the result leads to reseeding, adding seed points or an automatic surface mesh generation. The mesh is saved for monitoring the patient and for comparisons with follow-up scans. Based on the mesh, the system performs a voxelization and volume calculation, which leads to diagnosis and therefore further treatment decisions. The overall system has been tested with different cerebral pathologies-glioblastoma multiforme, pituitary adenomas and cerebral aneurysms- and evaluated against manual expert segmentations using the Dice Similarity Coefficient (DSC). Additionally, intra-physician segmentations have been performed to provide a quality measure for the presented system.

In this paper, an identity-based key agreement protocol for securing mobile telephony in GSM and UMTS networks is presented. The approach allows two mobile phones to perform a session key agreement over an unsecured channel and between different providers using telephone numbers as public keys. Using the created session key, a symmetric encryption of all call data can be performed. Solutions to the problems of multi-domain key generation, key distribution, multi-domain public parameter distribution and inter-domain key agreement are presented. Furthermore, the proposed approach can be speeded up using server-aided cryptography, by outsourcing computationally expensive cryptographic operations to a high-performance backend computing server. The feasibility of the approach is illustrated by presenting experimental results based on a Symbian implementation running on N95-1 and N82-1 Nokia smartphones.

The use of web services in industrial automation, e.g. in fully automated production processes like car manufacturing, promises simplified interaction among the manufacturing devices due to standardized protocols and increased flexibility with respect to process implementation and reengineering. Moreover, the adoption of web services as a seamless communication backbone within the overall industrial enterprise has additional benefits, such as simplified interaction with suppliers and customers (i.e. horizontal integration) and avoidance of a break in the communication paradigm within the enterprise (i.e. vertical integration). The Time-Constrained Services (TiCS) framework is a development and execution environment that empowers automation engineers to develop, deploy, publish, compose, and invoke time-constrained web services. TiCS consists of four functional layers—tool support layer, real-time infrastructural layer, real-time service layer, and hardware layer—which contain several components to meet the demands of a web service based automation infrastructure. This article gives an overview of the TiCS framework. More precisely, the general design considerations and an architectural blueprint of the TiCS framework are presented. Subsequently, selected key components of the TiCS framework are discussed in detail: the SOAP4PLC engine for equipping programmable logic controllers with a web service interface, the SOAP4IPC engine for processing web services in real-time on industrial PCs, the WS-TemporalPolicy language for describing time constraints, and the TiCS Modeler for composing time-constrained web services into a time-constrained BPEL4WS workflow.

In this paper, a novel approach for enabling Grid users to autonomously install and use custom software on demand using an image creation station is presented, while at the same time offering new security mechanisms to protect both software and data from other Grid users and external attackers. An automated dynamic firewalling mechanism enables both virtual organization and user-based network security setups. Furthermore, the Grid environment is partitioned into several zones to protect local cluster resources from compromised Grid middleware. To enable the secure integration of this Grid environment into existing business processes, an extension of BPEL is presented which allows the execution of GSI secured Grid services in combination with existing business web services. The workflow engine transparently handles proxy certificate creation and monitors proxy certificate lifetime. An implementation based on the Globus Toolkit 4, the Sun Grid Engine and the ActiveBPEL Engine is presented. A performance evaluation of the critical components of the new Grid setup is provided.

The use of service-oriented architectures based on web services in the manufacturing layer of industrial enterprises enables vertical integration and promises increased interoperability. Unfortunately, the differing execution patterns—cyclic execution on programmable logic controllers vs. on-demand execution on industrial PCs—complicate the use of web services in the manufacturing layer. This paper introduces sequence-controlled web services that permit to easily export a control module as a web service. An automation engineer can use familiar IEC 61131-3 programming languages to implement the control application and subsequently can export selected modules as web services.

The adoption of service-oriented architectures based on web services in industrial automation promises increased interoperability and exibility. Since the development and deployment of web services requires in-depth programming knowledge, adequate tools to ease the work of automation engineers are required. In particular, the orchestration of existing web services to workflows is a challenging task that is complicated by the fact that manufacturing processes have time constraints, especially real-time constraints. This paper presents the Time-Constrained Services (TiCS) Modeler that supports the assisted orchestration of BPEL4WS workflows with time constraints.

For the evaluation of data from stimulus response experiments dynamic metabolic network models are generated. With an increase of reaction steps and regulatory interdependencies the amount of the simulation data becomes hard to handle. In this paper, we present the application and extension of methods combining visualization and animation of dynamic models to facilitate the analysis of the complex system behaviour. The dynamic changes of metabolite pools and fluxes are simultaneous visualized within the network structure. Depending on the scaling used, different focuses can be set, e.g. to observe local dynamics or global concentration balances. For the visualization of the present inhibition and activation state of certain reaction steps of a metabolic network model a novel quantification method is proposed. The sensitivity analysis of dynamic metabolic network models leads to high-dimensional sensitivity matrices that vary over time. To process the enormous amount of data we use a colour scale transformation and the reorderable matrix method for the visual exploration of the time-varying matrices. The benefits of our methods are illustrated with the help of a metabolic network model of the central carbon metabolism in Escherichia coli.

In this paper, we present an approach based on the K-Nearest Neighbors algorithms for policies clustering that aims to reduce the ABAC policies dimensionality for high scale systems. Since ABAC considers a very large set of attributes for access decisions, it turns out that using such model for large scale systems might be very complicated. To date, researchers have proposed to use data mining techniques to discover roles for RBAC system construction. In this work in progress, we consider the usage of KNN-based techniques for the classification of ABAC policies based on similarity computations of rules in order to enhance the ABAC flexibility and to reduce the number of policy rules.

The German Broadcasting Archive (DRA) maintains the cultural heritage of radio and television broadcasts of the former German Democratic Republic (GDR). The uniqueness and importance of the video material stimulates a large scientific interest in the video content. In this paper, we present an automatic video analysis and retrieval system for searching in historical collections of GDR television recordings. It consists of video analysis algorithms for shot boundary detection, concept classification, person recognition, text recognition and similarity search. The performance of the system is evaluated from a technical and an archival perspective on 2,500 hours of GDR television recordings.

Cloud computing has become a widely used paradigm in many IT domains such as e-health. It offers several advantages to the users, e.g. elasticity, flexibility and the rapid sharing of a huge set of digital data. However, many security and privacy concerns still pose significant challenges. In particular, the most identified problem is how to enforce the user's security policy in the access control of the outsourced data. In fact, cloud environments does not provide facilities to support high level defined security policies. For instance, the swift storage component of openstack supports only fine grained access control to execute a specific action on a specific defined object. In this paper, we designed and implemented a middleware to provide high level security policies while using such swift fine grained primitives. An e-health collaborative application dedicated for remote diagnosis is used to illustrate the suggested approach.

Today cloud security and privacy concerns pose significant challenges. One particular challenge consists in how to take into consideration the user's security policies while accessing the outsourced data. In fact, for a given application, we may have a set of rules, as high level security policy, which needs to hold prior to any query execution. Therefore, the main problem that we tackle in this ongoing project is how to enforce this high level security policy in the cloud storage layer without conflicts. To address this challenges, we propose a middleware denoted curlX which consists of two main processes: the security policy enforcement process and the verification process.

Electronic mail is one of the oldest and widely used services in the Internet. In this paper, an empirical study of the security properties of email server communication within the German IP address space range is presented. Instead of investigating end-user security or end-to-end encryption, we focus on the connections between SMTP servers relying on transport layer security. We analyze the involved ciphers suites, the certificates used and certificate authorities, and the behavior of email providers when communicating with improperly secured email servers. Conclusions drawn from this analysis lead to several recommendations to mitigate the security issues currently present in the email system as it is deployed in the Internet.

The number of security incidents in computer systems is steadily increasing, despite intrusion detection and prevention mechanisms deployed as countermeasures. Many existing intrusion detection and prevention systems struggle to keep up with new threats posed by zero-day attacks and/or have serious performance impacts through extensive monitoring, questioning their effectiveness in most real-life scenarios. In this paper, we present a new approach for reactive security monitoring in a virtualized computer environment based on minimally-intrusive dynamic sensors deployed vertically across virtualization layers and horizontally within a virtual machine instance. The sensor streams are analyzed using a novel federation of complex event processing engines and an optimized query index to maximize the performance of continuous queries, and the results of the analysis are used to trigger appropriate actions on different virtualization layers in response to detected security anomalies. Furthermore, a novel event store that supports fast event logging is utilized for offline analysis of collected historical data. Experiments show that the proposed system can execute tens of thousands of complex, stateful detection rules simultaneously and trigger actions efficiently and with low latency.

In recent years, the number of mobile applications has grown significantly. Not surprisingly, various security and privacy concerns associated with mobile applications have emerged. Several researchers are addressing these problems by analyzing the security properties of mobile application code. Most of the security checks rely on custom scripts and are quite heterogeneous with respect to dependencies, deployment and reporting. In this paper, we present AndroLyze, a distributed framework with unified logging and reporting functionality to perform security checks on large numbers of applications in an efficient manner. AndroLyze provides optimized scheduling algorithms for distributing static code analysis tasks across several machines. Moreover, AndroLyze can handle several versions of a single mobile application to generate a security track record over many versions. To demonstrate the benefits of AndroLyze, we have analyzed the Top Free 500 Android applications of all categories in Google Play collected over three years. The resulting data set consists of almost 40,000 mobile applications and requires about 227 GB of storage space.

Model selection is concerned with the choice of a mathematical model from a set of candidates that best describes a given set of experimental data. Large families of models arise in the context of structured mechanistic modelling in several application fields. In this situation the model selection problem cannot be solved by brute force testing of all possible models because of the high computational costs. However, more information on the different models of a family is available by their interdependencies, given by generalization or simplification relations. Large-scale model selection algorithms should exploit these relations for navigation in the discrete space of all model candidates. This paper presents a general approach for large-scale model selection by specifying the necessary computational primitives for navigating in large model families. As a non-trivial example it is shown how families of biochemical network models arising from the evaluation of stimulus response experiments are mapped to th...

In this paper, an analysis of security threats within service-oriented on-demand Grid computing environments is presented. The analysis is based on identifying three levels of trust relationships and three types of Grid applications in on-demand computing; the trust relationships involve interactions among resource providers, middleware producers, solution producers, and users. The paper presents solutions for addressing the threats inherent to these three increasingly demanding levels. The solutions involve applying sandbox-based approaches using virtual machine technology and jailing mechanisms to ensure trust for the first two levels of on-demand Grid computing, as well as Trusted Computing Platform Alliance (TCPA) technology for the third level of on-demand Grid computing. A brief taxonomy of the presented solutions is introduced.

Improving the energy efficiency of software running in a data center is a challenging task. Several application-specific techniques, such as energy-aware heuristics, controlled approximation and energy-conserving I/O, have been proposed to tackle this problem. In this paper, we introduce data sparsing with artifacts, a novel approach to increase the energy efficiency of applications that are robust to input variations, such as speech and image processing. Data sparsing with artifacts is aimed at reducing the processing times and thus the energy efficiency of such applications while preserving the quality of the results by replacing a random subset of the original data with application-specific artifacts. In contrast to previous work, the proposed approach introduces artifacts at the data layer, without application layer modifications and with general purpose hardware. Data sparsing with artifacts has been integrated into a prototypical file system in userspace (FUSE) and the Hadoop Distributed File System (HDFS). Experiments with MapReduce-based face detection, face recognition and speech recognition algorithms show promising energy savings of up to 10 % with moderate accuracy losses for different data sparsing rates and artifacts.

Many algorithm animation tools have been developed over the last years. The users of such tools can be separated into four roles: the original algorithm programmer, developers of the animation tool, visualizers that generate the animation and end users viewing the animation. Most tools focus on providing features for only one or at most two of these roles. The ANIMAL system is designed to present valuable benefits for the last three roles. The principal research contributions of this work lie in dynamic extensibility, internationalization of GUI components and animation content, reversible animation display and flexible import and export facilities. We also present several core features of ANIMAL including dynamic reconfiguration, internationalization in both GUI and animations, display scaling, export facilities and full video player controls.

This paper presents Mediana, a computer-based, integrated workbench for the management and analysis of media data supporting media researchers. Its main components are the recording and management of multimedia data (i.e. image, video and text data) in a database system, semi-automatical analysis tools for images and videos, and a graphical user interface (GUI) integrating all tools and components applied in media research.

This paper presents a cross-layer self-healing approach for Cloud computing environments, based on the Complex Event Processing method. It analyzes monitored events to detect performance-related problems and performs action to fix them without human intervention. Our proposal makes use of novel analysis rules, derived from a comprehensive study of the relationships between monitored metrics across multiple Cloud layers. The results of our study are used to define and optimize the analysis rules and identify the causes of performance-related problems. The results of several experiments demonstrate the benefits of the proposed approach in terms of speeding up the analysis without affecting the quality of the diagnosis.

Cloud computing is aimed at offering elastic resource allocation on demand in a pay-as-you-go fashion to cloud consumers. To achieve this goal in automatic manner, a resource scaling mechanism is needed that maintains application performance according to Service Level Agreements (SLA) and reduces resource costs at the same time. In this paper, we present a cross-correlation prediction approach based on machine learning that predicts resource demands of multiple resources of virtual machines running in a cloud infrastructure. Based on these predictions, a proactive resource allocation scheme is applied that assigns only the required resources to virtual machines to keep their cost to a minimum. Experimental results with the web serving multi-tier application benchmark of CloudSuite show the effectiveness of our approach compared to a non-cross-correlation prediction technique in achieving better prediction accuracy and better application performance.

In this paper, we present the AnimalScript visualization language. This scripting language uses the flexibility of the Animal system and provides many additional new graphic primitives and animation effects that go beyond the traditional Animal GUI features. AnimalScript can easily be configured by changing the content of a registration file. Users may also have multiple registration files, as AnimalScript will always use the first registration file it finds. AnimalScript can easily be extended with additional features without needing to read, let alone change, any existing code.

Flocculation of yeast cells is an important phenomenon that often occurs in beverage production and applications of white biotechnology. In this paper, a novel model for cell movement and cell-cell interaction to simulate yeast cell flocculation is presented. To simulate this process with acceptable runtimes, a GPU implementation based on OpenCL and Java is described. The implementation allows us to track the cell movement in a detailed manner by a 3D visualization during execution. Experimental results indicate that the GPU implementation is up to factor of 736 faster than a multithreaded C/C++ implementation on a multi-core workstation for simulations with up to 20.000 yeast cells. Moreover, the GPU implementation requires only up to 225 milliseconds to perform simulations with up to 1.000.000 yeast cells.

The goal of the provider with respect to dynamic resource allocation in cloud computing is to maintain application performance according to service level agreements while reducing electrical power costs. To achieve this goal, we present a resource manager that optimizes a utility function expressing the trade-off between the conflicting objectives of maintaining application performance and reducing power costs. It is based on an artificial neural network (ANN) to find the best resource allocation to virtual machines that optimizes the utility function. To provide support for a potentially large number of virtual machines, we present a distributed version of the resource manager consisting of several ANNs in which each ANN is responsible for modeling application performance and power consumption of a single VM while exchanging information with other ANNs to coordinate resource allocation. Simulated and real experiments show the effectiveness of the distributed ANN resource manager over static allocation, a centralized version and a distributed non-coordinated version.

This paper presents a multi-layer monitoring and analysis approach for Cloud computing environments based on the methodology of Complex Event Processing (CEP). Instead of having to manually specify continuous queries on monitored event streams, CEP queries are derived from analyzing the correlations between monitored metrics across multiple Cloud layers. The results of our correlation analysis allow us to reduce the number of monitored parameters and enable us to perform a root cause analysis to identify the causes of performance-related problems. The derived analysis rules are implemented as queries in a CEP engine. The results of several experiments demonstrate the benefits of the proposed approach in terms of precision and recall in comparison with threshold-based methods. They also show the accuracy of our approach in identifying the causes of performance-related problems.