Query-Sensitive Similarity Measures for Information Retrieval

The application of document clustering to information retrieval has been motivated by the potential effectiveness gains postulated by the cluster hypothesis. The hypothesis states that relevant documents tend to be highly similar to each other and therefore tend to appear in the same clusters. In this paper we propose an axiomatic view of the hypothesis by suggesting that documents relevant to the same query (co-relevant documents) display an inherent similarity to each other that is dictated by the query itself. Because of this inherent similarity, the cluster hypothesis should be valid for any document collection. Our research describes an attempt to devise means by which this similarity can be detected. We propose the use of query-sensitive similarity measures that bias interdocument relationships toward pairs of documents that jointly possess attributes expressed in a query. We experimentally tested three query-sensitive measures against conventional ones that do not take the query into account, and we also examined the comparative effectiveness of the three query-sensitive measures. We calculated interdocument relationships for varying numbers of top-ranked documents for six document collections. Our results show a consistent and significant increase in the number of relevant documents that become nearest neighbors of any given relevant document when query-sensitive measures are used. These results suggest that the effectiveness of a cluster-based information retrieval system has the potential to increase through the use of query-sensitive similarity measures.     

Scheduling strategies for efficient ETL execution

Extract-transform-load (ETL) workflows model the population of enterprise data warehouses with information gathered from a large variety of heterogeneous data sources. ETL workflows are complex design structures that run under strict performance requirements and their optimization is crucial for satisfying business objectives. In this paper, we deal with the problem of scheduling the execution of ETL activities (a.k.a. transformations, tasks, operations), with the goal of minimizing ETL execution time and allocated memory. We investigate the effects of four scheduling policies on different flow structures and configurations and experimentally show that the use of different scheduling policies may improve ETL performance in terms of memory consumption and execution time. First, we examine a simple, fair scheduling policy. Then, we study the pros and cons of two other policies: the first opts for emptying the largest input queue of the flow and the second for activating the operation (a.k.a. activity) with the maximum tuple consumption rate. Finally, we examine a fourth policy that combines the advantages of the latter two in synergy with flow parallelization.     

SaBRe: load-time selective binary rewriting

Binary rewriting consists in disassembling a program to modify its instructions. However, existing solutions suffer from shortcomings in terms of soundness and performance. We present SaBRe, a load-time system for selective binary rewriting. SaBRe rewrites specific constructs—particularly system calls and functions—when the program is loaded into memory, and intercepts them using plugins through a simple API. We also discuss the theoretical underpinnings of disassembling and rewriting. We developed two backends—for x86_64 and RISC-V—which were used to implement three plugins: a fast system call tracer, a multi-version executor, and a fault injector. Our evaluation shows that SaBRe imposes little overhead, typically below 3%.

     

Subclass discriminant Nonnegative Matrix Factorization for facial image analysis

Nonnegative Matrix Factorization (NMF) is among the most popular subspace methods, widely used in a variety of image processing problems. Recently, a discriminant NMF method that incorporates Linear Discriminant Analysis inspired criteria has been proposed, which achieves an efficient decomposition of the provided data to its discriminant parts, thus enhancing classification performance. However, this approach possesses certain limitations, since it assumes that the underlying data distribution is unimodal, which is often unrealistic. To remedy this limitation, we regard that data inside each class have a multimodal distribution, thus forming clusters and use criteria inspired by Clustering based Discriminant Analysis. The proposed method incorporates appropriate discriminant constraints in the NMF decomposition cost function in order to address the problem of finding discriminant projections that enhance class separability in the reduced dimensional projection space, while taking into account subclass information. The developed algorithm has been applied for both facial expression and face recognition on three popular databases. Experimental results verified that it successfully identified discriminant facial parts, thus enhancing recognition performance.     

Visual understanding industrial workflows under uncertainty on distributed service oriented architectures

Service Oriented Architecture (SOA) is an evolution of distributed computing based on the request/reply design paradigm for synchronous and asynchronous applications. In such architectures, application developers or system integrators can build scenarios by composing one or more services without knowing the services’ underlying implementations. In this paper, we adopt a SOA framework for on-line scheduling complex industrial workflows. The proposed SOA consists of three main layers; the input layer, the benchmarking layer, the prediction layer and the scheduler. The main innovation of our SOA architecture is the prediction layer that supports computer vision tools able to visually observe how an industrial workflow is actually executed. This way, we can automatically estimate the actual execution time for a process. In particular a new self-initialized visual tracker algorithm is proposed in this paper to robustly trace workers’ trajectory in a plant via visual observations. Then, part-to-whole curve matching is presented so as to find correspondences among the traced curve and the ideal one and thus improving scheduler efficiency. The input layer interoperably describes industrial operations using the XPDL (an XML-based) format. The benchmarker evaluates much faster than real-time and in an off-line mode how long it takes for an industrial workflow to be executed on a given resource guaranteeing an almost real-time implementation of the video processing algorithms on the plant. The last component of the proposed SOA is the scheduler with the goal to assign the workflows to the available resources. In this paper, a Maximum Benefit First (MBF) scheduler is presented which maximizes the total gain received by the industry when completing execution of all the beneficial operations with minimum violations of their delivery deadlines (that is with the minimum compensations). Experiments have been conducted on a real-world industrial plant of Nissan Iberica automobile construction indicating the efficiency of the proposed system.     

Das datenschutzrechtliche Vollzugsdefizit im Bereich der Telemedien — ein Schreckensbericht

Zusammenfassung  Die Autoren untersuchen das datenschutzrechtliche Vollzugsdefizit im Anwendungsbereich des Telemediengesetzes. Ihr Ergebnis ist, dass das Ausma? des datenschutzrechtlichen Vollzugsdefizits der von au?en zu überprüfenden datenschutzrechtlichen Pflichten der Anbieter „erschreckend“ ist. Nur einem ganz geringen Teil der Anbieter gelingt es, sich rechtskonform zu verhalten. Prof. Dr. Jürgen Kühling Inhaber eines Lehrstuhls für ?ffentliches Recht an der Universit?t Regensburg. Anastasios Sivridis wissenschaftlicher Mitarbeiter und Doktorand am Lehrstuhl Prof. Kühling. Mathis Schwuchow studentische Hilfskraft am Lehrstuhl Prof. Kühling. Thorben Burghardt wissenschaftlicher Mitarbeiter und Doktorand am Lehrstuhl für Systeme der Informationsverwaltung (TH Karlsruhe).