We present Schism, a novel workload-aware approach for database partitioning and replication designed to improve scalability of sharednothing distributed databases. Because distributed transactions are expensive in OLTP settings (a fact we demonstrate through a series of experiments), our partitioner attempts to minimize the number of distributed transactions, while producing balanced partitions. Schism consists of two phases: i) a workload-driven, graph-based replication/partitioning phase and ii) an explanation and validation phase. The first phase creates a graph with a node per tuple (or group of tuples) and edges between nodes accessed by the same transaction, and then uses a graph partitioner to split the graph into k balanced partitions that minimize the number of cross-partition transactions. The second phase exploits machine learning techniques to find a predicate-based explanation of the partitioning strategy (i.e., a set of range predicates that represent the same replication/partitioning scheme produced by the partitioner). The strengths of Schism are: i) independence from the schema layout, ii) effectiveness on n-to-n relations, typical in social network databases, iii) a unified and fine-grained approach to replication and partitioning. We implemented and tested a prototype of Schism on a wide spectrum of test cases, ranging from classical OLTP workloads (e.g., TPC-C and TPC-E), to more complex scenarios derived from social network websites (e.g., Epinions.com), whose schema contains multiple n-to-n relationships, which are known to be hard to partition. Schism consistently outperforms simple partitioning schemes, and in some cases proves superior to the best known manual partitioning, reducing the cost of distributed transactions up to 30%. 1.

Abstract. The widely known binary relevance method for multi-label classification, which considers each label as an independent binary problem, has been sidelined in the literature due to the perceived inadequacy of its label-independence assumption. Instead, most current methods invest considerable complexity to model interdependencies between labels. This paper shows that binary relevance-based methods have much to offer, especially in terms of scalability to large datasets. We exemplify this with a novel chaining method that can model label correlations while maintaining acceptable computational complexity. Empirical evaluation over a broad range of multi-label datasets with a variety of evaluation metrics demonstrates the competitiveness of our chaining method against related and state-of-the-art methods, both in terms of predictive performance and time complexity. 1

Collaborative models (e.g., wikis) are an increasingly prevalent Web technology. However, the open-access that defines such systems can also be utilized for nefarious purposes. In particular, this paper examines the use of collaborative functionality to add inappropriate hyperlinks to destinations outside the host environment (i.e., link spam). The collaborative encyclopedia, Wikipedia, is the basis for our analysis. Recent research has exposed vulnerabilities in Wikipedia’s link spam mitigation, finding that human editors are latent and dwindling in quantity. To this end, we propose and develop an autonomous classifier for link additions. Such a system presents unique challenges. For example, low barriersto-entry invite a diversity of spam types, not just those with economic motivations. Moreover, issues can arise with how a link is presented (regardless of the destination). In this work, a spam corpus is extracted from over 235,000 link additions to English Wikipedia. From this, 40+ features are codified and analyzed. These indicators are computed using wiki metadata, landing site analysis, and external data sources. The resulting classifier attains 64 % recall at 0.5% false-positives (ROC-AUC = 0.97). Such performance could enable egregious link additions to be blocked automatically with low false-positive rates, while prioritizing the remainder for human inspection. Finally, a live Wikipedia implementation of the technique has been developed. Categories andSubject Descriptors H.5.3 [Group and Organization Interfaces]: collaborative

WEKA is a popular machine learning workbench with a development life of nearly two decades. This article provides an overview of the factors that we believe to be important to its success. Rather than focussing on the software’s functionality, we review aspects of project management and historical development decisions that likely had an impact on the uptake of the project.

We describe a method for identifying the speakers of quoted speech in natural-language textual stories. We have assembled a corpus of more than 3,000 quotations, whose speakers (if any) are manually identified, from a collection of 19th and 20th century literature by six authors. Using rule-based and statistical learning, our method identifies candidate characters, determines their genders, and attributes each quote to the most likely speaker. We divide the quotes into syntactic classes in order to leverage common discourse patterns, which enable rapid attribution for many quotes. We apply learning algorithms to the remainder and achieve an overall accuracy of 83%.

Malicious web pages that host drive-by-download exploits have become a popular means for compromising hosts on the Internet and, subsequently, for creating large-scale botnets. In a drive-bydownload exploit, an attacker embeds a malicious script (typically written in JavaScript) into a web page. When a victim visits this page, the script is executed and attempts to compromise the browser or one of its plugins. To detect drive-by-download exploits, researchers have developed a number of systems that analyze web pages for the presence of malicious code. Most of these systems use dynamic analysis. That is, they run the scripts associated with a web page either directly in a real browser (running in a virtualized environment) or in an emulated browser, and they monitor the scripts ’ executions for malicious activity. While the tools are quite precise, the analysis process is costly, often requiring in the order of

WikiTrust is a reputation system for Wikipedia authors and content. WikiTrust computes three main quantities: edit quality, author reputation, and content reputation. The edit quality measures how well each edit, that is, each change introduced in a revision, is preserved in subsequent revisions. Authors who perform good quality edits gain reputation, and text which is revised by several high-reputation authors gains reputation. Since vandalism on the Wikipedia is usually performed by anonymous or new users (not least because long-time vandals end up banned), and is usually reverted in a reasonably short span of time, edit quality, author reputation, and content reputation are obvious candidates as features to identify vandalism on the Wikipedia. Indeed, using the full set of features computed by WikiTrust, we have been able to construct classifiers that identify vandalism with a recall of 83.5%, a precision of 48.5%, and a false positive rate of 8%, for an area under the ROC curve of 93.4%. If we limit ourselves to the set of features available at the time an edit is made (when the edit quality is still unknown), the classifier achieves a recall of 77.1%, a precision of 36.9%, and a false positive rate of 12.2%, for an area under the ROC curve of 90.4%. Using these classifiers, we have implemented a simple Web API that provides the vandalism estimate for every revision of the English Wikipedia. The API can be used both to identify vandalism that needs to be reverted, and to select highquality, non-vandalized recent revisions of any given Wikipedia article. These recent high-quality revisions can be included in static snapshots of the Wikipedia, or they can be used whenever tolerance to vandalism is low (as in a school setting, or whenever the material is widely disseminated).

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Abstract. We introduce BotSwindler, a bait injection system designed to delude and detect crimeware by forcing it to reveal during the exploitation of monitored information. The implementation of BotSwindler relies upon an out-of-host software agent that drives user-like interactions in a virtual machine, seeking to convince malware residing within the guest OS that it has captured legitimate credentials. To aid in the accuracy and realism of the simulations, we propose a low overhead approach, called virtual machine verification, for verifying whether the guest OS is in one of a predefined set of states. We present results from experiments with real credential-collecting malware that demonstrate the injection of monitored financial bait for detecting compromises. Additionally, using a computational analysis and a user study, we illustrate the believability of the simulations and we demonstrate that they are sufficiently human-like. Finally, we provide results from performance measurements to show our approach does not impose a performance burden. 1

This paper presents a new classification algorithm for realtime inference of emotions from the non-verbal features of speech. It identifies simultaneously occurring emotional states by recognising correlations between emotions and features such as pitch, loudness and energy. Pairwise classifiers are constructed for nine classes from the Mind Reading emotion corpus, yielding an average cross-validation accuracy of 89 % for the pairwise machines and 86 % for the fused machine. The paper also shows a novel application of the classifier for assessing public speaking skills, achieving an average cross-validation accuracy of 81%. Optimisation of support vector machine coefficients is shown to improve the accuracy by up to 25%. The classifier outperforms previous research on the same emotion corpus and achieves real-time performance.