InnoDB database forensics: Enhanced reconstruction of data manipulation queries from redo logs

The InnoDB storage engine is one of the most widely used storage engines for MySQL. This paper discusses possibilities of utilizing the redo logs of InnoDB databases for forensic analysis, as well as the extraction of the information needed from the MySQL definition files, in order to carry out this kind of analysis. Since the redo logs are internal log files of the storage engine and thus cannot easily be changed undetected, this forensic method can be very useful against adversaries with administrator privileges, which could otherwise cover their tracks by manipulating traditional log files intended for audit and control purposes. Based on a prototype implementation, we show methods for recovering Insert, Delete and Update statements issued against a database.     

Interaction of flexible multibody systems with fluids analyzed by means of smoothed particle hydrodynamics

The present work deals with a computational approach to fluid-structure interaction (FSI) problems by coupling of flexible multibody system dynamics and fluid dynamics. Since the methods for the numerical modeling are well known, both for the structural and the fluid part, the focus of this work lies on the coupling formalism. Moreover, the applicability of the presented approach to arbitrary geometries and high structural stiffness is studied, as well as an easy model setup. No restriction should be made on the topology of the structure or the complexity of motion.For the fluid part a meshless method, known as smoothed particle hydrodynamics (SPH) is applied, which fulfills the above requirements. While an explicit time integration scheme in SPH provides a fast simulation of the fluid dynamics, advanced methods from flexible multibody dynamics provide a variety of benefits for the simulation of the solid part. Amongst these are specialized structural finite elements for both small and large deformation bodies, joints, stable implicit time-integration schemes, and model reduction techniques.A rule for the interaction between fluids and structures is derived from imposing a distributed potential over boundary segments of the structures, which the fluid particles respond to. The work is concluded by illustrative examples, demonstrating the successful coupling of flexible multibody systems with fluids.     

Brain–computer interfacing: more than the sum of its parts

The performance of non-invasive electroencephalogram-based (EEG) brain–computer interfaces (BCIs) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio of the EEG, which limit the bandwidth and hence the available applications. Optimization of both individual components of BCIs and the interrelationship between them is crucial to enhance bandwidth. In other words, neuroscientific knowledge and machine learning need to be optimized by considering concepts from human–computer interaction research and usability. In this paper, we present results of ongoing relevant research in our lab that addresses several important issues for BCIs based on the detection of transient changes in oscillatory EEG activity. First, we report on the long-term stability and robustness of detection of oscillatory EEG components modulated by distinct mental tasks, and show that the use of mental task pairs “mental subtraction versus motor imagery” achieves robust and reliable performance (Cohen’s κ > 0.6) in seven out of nine subjects over a period of 4 days. Second, we report on restricted Boltzmann machines (RBMs) as promising tools for the recognition of oscillatory EEG patterns. In an off-line BCI simulation we computed average peak accuracies, averaged over ten subjects, of 80.8 ± 7.2 %. Third, we present the basic framework of the context-aware hybrid Graz-BCI that allows interacting with the massive multiplayer online role playing game World of Warcraft. We show how a more integrated design approach that considers all components of BCIs, their interrelationships, other input signals and contextual information can increase interaction efficacy.     

Graph-based retrieval of building information models for supporting the early design stages

Building information modeling (BIM) principles are transforming today’s communication and working processes in the field of construction, however the early design phases are only rarely supported and information technology is therefore not exploited to its full potential. The early design phases are characterized by an iterative process of searching for plausible solutions. A common approach is to refer to similar examples, which are conventionally found using keyword-based search strategies.To this end we propose a method for indexing spatial configurations along with a sketch-based input method for search strategies that uses so-called semantic fingerprints of buildings. The topology of spatial configurations is extracted from building information models and represented as graphs. For both building information models and the user sketches, the extracted graphs are used as the basis for a subgraph-matching algorithm facilitating an intuitive novel query method for researching similar reference examples. The system is able to present corresponding existing solutions to even rudimentary sketches or fragments of a design idea. In addition to graph matching and sketch-based interaction, more recent BIM-based approaches are also taken into account.     

Design for service compatibility

Service composition is a recent field that has seen a flurry of different approaches proposed towards the goal of flexible distributed heterogeneous interoperation of software systems, usually based on the expectation that such systems must be derived from higher-level models rather than be coded at low level. In practice, achieving service interoperability nonetheless continues to require significant modelling approach at multiple abstraction levels, and existing formal approaches typically require the analysis of the global space of joint executions of interacting services. Based on our earlier work on providing locally checkable consistency rules for guaranteeing the behavioural consistency of inheritance hierarchies, a model-driven approach for creating consistent service orchestrations is proposed. Service execution and interaction is represented with a high-level model in terms of extended Petri net notation; formal criteria are provided for service consistency that can be checked in terms of local model properties, and give a multi-step design approach for developing services that are guaranteed to be interoperable. Finally, it is outlined how the presented results can be carried over and applied to modelling processes using the Business Process Modelling Notation (BPMN).     

A Parallel SPH Implementation on Multi‐Core CPUs

This paper presents a parallel framework for simulating fluids with the Smoothed Particle Hydrodynamics (SPH) method. For low computational costs per simulation step, efficient parallel neighbourhood queries are proposed and compared. To further minimize the computing time for entire simulation sequences, strategies for maximizing the time step and the respective consequences for parallel implementations are investigated. The presented experiments illustrate that the parallel framework can efficiently compute large numbers of time steps for large scenarios. In the context of neighbourhood queries, the paper presents optimizations for two efficient instances of uniform grids, that is, spatial hashing and index sort. For implementations on parallel architectures with shared memory, the paper discusses techniques with improved cache‐hit rate and reduced memory transfer. The performance of the parallel implementations of both optimized data structures is compared. The proposed solutions focus on systems with multiple CPUs. Benefits and challenges of potential GPU implementations are only briefly discussed.     

The neglected user in music information retrieval research

Personalization and context-awareness are highly important topics in research on Intelligent Information Systems. In the fields of Music Information Retrieval (MIR) and Music Recommendation in particular, user-centric algorithms should ideally provide music that perfectly fits each individual listener in each imaginable situation and for each of her information or entertainment needs. Even though preliminary steps towards such systems have recently been presented at the “International Society for Music Information Retrieval Conference” (ISMIR) and at similar venues, this vision is still far away from becoming a reality. In this article, we investigate and discuss literature on the topic of user-centric music retrieval and reflect on why the breakthrough in this field has not been achieved yet. Given the different expertises of the authors, we shed light on why this topic is a particularly challenging one, taking computer science and psychology points of view. Whereas the computer science aspect centers on the problems of user modeling, machine learning, and evaluation, the psychological discussion is mainly concerned with proper experimental design and interpretation of the results of an experiment. We further present our ideas on aspects crucial to consider when elaborating user-aware music retrieval systems.     

Gaussian-weighted Jensen–Shannon divergence as a robust fitness function for multi-model fitting

Model fitting is a fundamental component in computer vision for salient data selection, feature extraction and data parameterization. Conventional approaches such as the RANSAC family show limitations when dealing with data containing multiple models, high percentage of outliers or sample selection bias, commonly encountered in computer vision applications. In this paper, we present a novel model evaluation function based on Gaussian-weighted Jensen–Shannon divergence, and integrate into a particle swarm optimization (PSO) framework using ring topology. We avoid two problems from which most regression algorithms suffer, namely the requirements to specify inlier noise scale and the number of models. The novel evaluation method is generic and does not require any estimation of inlier noise. The continuous and meta-heuristic exploration facilitates estimation of each individual model while delivering the number of models automatically. Tests on datasets comprised of inlier noise and a large percentage of outliers (more than 90 % of the data) demonstrate that the proposed framework can efficiently estimate multiple models without prior information. Superior performance in terms of processing time and robustness to inlier noise is also demonstrated with respect to state of the art methods.     

Classification of small lesions in dynamic breast MRI: eliminating the need for precise lesion segmentation through spatio-temporal analysis of contrast enhancement

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they do not exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal of this work was to improve the classification performance in such small diagnostically challenging lesions while concurrently eliminating the need for precise lesion segmentation. To this end, we introduce a method for topological characterization of lesion enhancement patterns over time. Three Minkowski Functionals were extracted from all five post-contrast images of 60 annotated lesions on dynamic breast MRI exams. For each Minkowski Functional, topological features extracted from each post-contrast image of the lesions were combined into a high-dimensional texture feature vector. These feature vectors were classified in a machine learning task with support vector regression. For comparison, conventional Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used. A new method for extracting thresholded GLCM features was also introduced and investigated here. The best classification performance was observed with Minkowski Functionals area and perimeter, thresholded GLCM features f8 and f9, and conventional GLCM features f4 and f6. However, both Minkowski Functionals and thresholded GLCM achieved such results without lesion segmentation while the performance of GLCM features significantly deteriorated when lesions were not segmented ( $p<0.05$ ). This suggests that such advanced spatio-temporal characterization can improve the classification performance achieved in such small lesions, while simultaneously eliminating the need for precise segmentation.     

mbeddr: instantiating a language workbench in the embedded software domain

Tools can boost software developer productivity, but building custom tools is prohibitively expensive, especially for small organizations. For example, embedded programmers often have to use low-level C with limited IDE support, and integrated into an off-the-shelf tool chain in an ad-hoc way. To address these challenges, we have built mbeddr, an extensible language and IDE for embedded software development based on C. mbeddr is a large-scale instantiation of the Jetbrains MPS language workbench. Exploiting its capabilities for language modularization and composition, projectional editing and multi-stage transformation, mbeddr is an open and modular framework that lets third parties add extensions to C with minimal effort and without invasive changes. End users can combine extensions in programs as needed. To illustrate the approach, in this paper we discuss mbeddr’s support for state machines, components, decision tables, requirements tracing, product line variability and program verification and outline their implementation. We also present our experience with building mbeddr, which shows that relying on language workbenches dramatically reduces the effort of building customized, modular and extensible languages and IDEs to the point where this is affordable by small organizations. Finally, we report on the experience of using mbeddr in a commercial project, which illustrates the benefits to end users.