FDDetector: A Tool for Deduplicating Features in Software Product Lines

Duplication is one of the model defects that affect software product lines during their evolution. Many approaches have been proposed to deal with duplication in code level while duplication in features hasn’t received big interest in literature. At the aim of reducing maintenance cost and improving product quality in an early stage of a product line, we have proposed in previous work a tool support based on a conceptual framework. The main objective of this tool called FDDetector is to detect and correct duplication in product line models. In this paper, we recall the motivation behind creating a solution for feature deduplication and we present progress done in the design and implementation of FDDetector

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit

Impact Factor: outdated artefact or stepping-stone to journal certification?

A review of Garfield's journal impact factor and its specific implementation as the Thomson Reuters Impact Factor reveals several weaknesses in this commonly-used indicator of journal standing. Key limitations include the mismatch between citing and cited documents, the deceptive display of three decimals that belies the real precision, and the absence of confidence intervals. These are minor issues that are easily amended and should be corrected, but more substantive improvements are needed. There are indications that the scientific community seeks and needs better certification of journal procedures to improve the quality of published science. Comprehensive certification of editorial and review procedures could help ensure adequate procedures to detect duplicate and fraudulent submissions.Comment: 25 pages, 12 figures, 6 table

Linking Representations with Multimodal Contrastive Learning

Many applications require grouping instances contained in diverse document datasets into classes. Most widely used methods do not employ deep learning and do not exploit the inherently multimodal nature of documents. Notably, record linkage is typically conceptualized as a string-matching problem. This study develops CLIPPINGS, (Contrastively Linking Pooled Pre-trained Embeddings), a multimodal framework for record linkage. CLIPPINGS employs end-to-end training of symmetric vision and language bi-encoders, aligned through contrastive language-image pre-training, to learn a metric space where the pooled image-text representation for a given instance is close to representations in the same class and distant from representations in different classes. At inference time, instances can be linked by retrieving their nearest neighbor from an offline exemplar embedding index or by clustering their representations. The study examines two challenging applications: constructing comprehensive supply chains for mid-20th century Japan through linking firm level financial records - with each firm name represented by its crop in the document image and the corresponding OCR - and detecting which image-caption pairs in a massive corpus of historical U.S. newspapers came from the same underlying photo wire source. CLIPPINGS outperforms widely used string matching methods by a wide margin and also outperforms unimodal methods. Moreover, a purely self-supervised model trained on only image-OCR pairs also outperforms popular string-matching methods without requiring any labels

Data De-Duplication in NoSQL Databases

With the popularity and expansion of Cloud Computing, NoSQL databases (DBs) are becoming the preferred choice of storing data in the Cloud. Because they are highly de-normalized, these DBs tend to store significant amounts of redundant data. Data de-duplication (DD) has an important role in reducing storage consumption to make it affordable to manage in today’s explosive data growth. Numerous DD methodologies like chunking and, delta encoding are available today to optimize the use of storage. These technologies approach DD at file and/or sub-file level but this approach has never been optimal for NoSQL DBs. This research proposes data De-Duplication in NoSQL Databases (DDNSDB) which makes use of a DD approach at a higher level of abstraction, namely at the DB level. It makes use of the structural information about the data (metadata) exploiting its granularity to identify and remove duplicates. The main goals of this research are: to maximally reduce the amount of duplicates in one type of NoSQL DBs, namely the key-value store, to maximally increase the process performance such that the backup window is marginally affected, and to design with horizontal scaling in mind such that it would run on a Cloud Platform competitively. Additionally, this research presents an analysis of the various types of NoSQL DBs (such as key-value, tabular/columnar, and document DBs) to understand their data model required for the design and implementation of DDNSDB. Primary experiments have demonstrated that DDNSDB can further reduce the NoSQL DB storage space compared with current archiving methods (from 17% to near 69% as more structural information is available). Also, by following an optimized adapted MapReduce architecture, DDNSDB proves to have competitive performance advantage in a horizontal scaling cloud environment compared with a vertical scaling environment (from 28.8 milliseconds to 34.9 milliseconds as the number of parallel Virtual Machines grows)

Efficient similarity computations on parallel machines using data shaping

Similarity computation is a fundamental operation in all forms of data. Big Data is, typically, characterized by attributes such as volume, velocity, variety, veracity, etc. In general, Big Data variety appears as structured, semi-structured or unstructured forms. The volume of Big Data in general, and semi-structured data in particular, is increasing at a phenomenal rate. Big Data phenomenon is posing new set of challenges to similarity computation problems occurring in semi-structured data. Technology and processor architecture trends suggest very strongly that future processors shall have ten\u27s of thousands of cores (hardware threads). Another crucial trend is that ratio between on-chip and off-chip memory to core counts is decreasing. State-of-the-art parallel computing platforms such as General Purpose Graphics Processors (GPUs) and MICs are promising for high performance as well high throughput computing. However, processing semi-structured component of Big Data efficiently using parallel computing systems (e.g. GPUs) is challenging. Reason being most of the emerging platforms (e.g. GPUs) are organized as Single Instruction Multiple Thread/Data machines which are highly structured, where several cores (streaming processors) operate in lock-step manner, or they require a high degree of task-level parallelism. We argue that effective and efficient solutions to key similarity computation problems need to operate in a synergistic manner with the underlying computing hardware. Moreover, semi-structured form input data needs to be shaped or reorganized with the goal to exploit the enormous computing power of \textit{state-of-the-art} highly threaded architectures such as GPUs. For example, shaping input data (via encoding) with minimal data-dependence can facilitate flexible and concurrent computations on high throughput accelerators/co-processors such as GPU, MIC, etc. We consider various instances of traditional and futuristic problems occurring in intersection of semi-structured data and data analytics. Preprocessing is an operation common at initial stages of data processing pipelines. Typically, the preprocessing involves operations such as data extraction, data selection, etc. In context of semi-structured data, twig filtering is used in identifying (and extracting) data of interest. Duplicate detection and record linkage operations are useful in preprocessing tasks such as data cleaning, data fusion, and also useful in data mining, etc., in order to find similar tree objects. Likewise, tree edit is a fundamental metric used in context of tree problems; and similarity computation between trees another key problem in context of Big Data. This dissertation makes a case for platform-centric data shaping as a potent mechanism to tackle the data- and architecture-borne issues in context of semi-structured data processing on GPU and GPU-like parallel architecture machines. In this dissertation, we propose several data shaping techniques for tree matching problems occurring in semi-structured data. We experiment with real world datasets. The experimental results obtained reveal that the proposed platform-centric data shaping approach is effective for computing similarities between tree objects using GPGPUs. The techniques proposed result in performance gains up to three orders of magnitude, subject to problem and platform

Review of Indexing Techniques Applied in Information Retrieval

Indexing is one of the important tasks of Information Retrieval that can be applied to any form of data, generated from the web, databases, etc. As the size of corpora increases, indexing becomes too time consuming and labor intensive, therefore, the introduction of computer aided indexer. A review of indexing techniques, both human and automatic indexing has been done in this paper. This paper gives an outline of the use of automatic indexing by discussing various hashing techniques including fuzzy finger printing and locality-sensitive hashing. Two different processes of matching that are used in automatic subject indexing are also reviewed. Accepting the need of automatic indexing in a possible replacement to manual indexing, studies in the development of automatic indexing tools must continu