Query: Probabilistic models of ranking novel documents for faceted topic retrieval

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Diversifying search results <=SEP=> We study the problem of answering ambiguous web queries in a setting where there exists a taxonomy of information, and that both queries and documents may belong to more than one category according to this taxonomy. We present a systematic approach to diversifying results that aims to minimize the risk of dissatisfaction of the average user. We propose an algorithm that well approximates this objective in general, and is provably optimal for a natural special case. Furthermore, we generalize several classical IR metrics, including NDCG, MRR, and MAP, to explicitly account for the value of diversification. We demonstrate empirically that our algorithm scores higher in these generalized metrics compared to results produced by commercial search engines.

Latent Dirichlet Allocation <=SEP=> We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

Novelty and redundancy detection in adaptive filtering <=SEP=> This paper addresses the problem of extending an adaptive information filtering system to make decisions about the novelty and redundancy of relevant documents. It argues that relevance and redundance should each be modelled explicitly and separately. A set of five redundancy measures are proposed and evaluated in experiments with and without redundancy thresholds. The experimental results demonstrate that the cosine similarity metric and a redundancy measure based on a mixture of language models are both effective for identifying redundant documents.

Automatic Extraction of Useful Facet Hierarchies from Text Databases <=SEP=> Databases of text and text-annotated data constitute a significant fraction of the information available in electronic form. Searching and browsing are the typical ways that users locate items of interest in such databases. Faceted interfaces represent a new powerful paradigm that proved to be a successful complement to keyword searching. Thus far, the identification of the facets was either a manual procedure, or relied on apriori knowledge of the facets that can potentially appear in the underlying collection. In this paper, we present an unsupervised technique for automatic extraction of facets useful for browsing text databases. In particular, we observe, through a pilot study, that facet terms rarely appear in text documents, showing that we need external resources to identify useful facet terms. For this, we first identify important phrases in each document. Then, we expand each phrase with ";context"; phrases using external resources, such as WordNet and Wikipedia, causing facet terms to appear in the expanded database. Finally, we compare the term distributions in the original database and the expanded database to identify the terms that can be used to construct browsing facets. Our extensive user studies, using the Amazon Mechanical Turk service, show that our techniques produce facets with high precision and recall that are superior to existing approaches and help users locate interesting items faster.

Beyond Independent Relevance: Methods and Evaluation Metrics for Subtopic Retrieval <=SEP=> We present a non-traditional retrieval problem we call subtopic retrieval. The subtopic retrieval problem is concerned with finding documents that cover many different subtopics of a query topic. In such a problem, the utility of a document in a ranking is dependent on other documents in the ranking, violating the assumption of independent relevance which is assumed in most traditional retrieval methods. Subtopic retrieval poses challenges for evaluating performance, as well as for developing effective algorithms. We propose a framework for evaluating subtopic retrieval which generalizes the traditional precision and recall metrics by accounting for intrinsic topic difficulty as well as redundancy in documents. We propose and systematically evaluate several methods for performing subtopic retrieval using statistical language models and a maximal marginal relevance (MMR) ranking strategy. A mixture model combined with query likelihood relevance ranking is shown to modestly outperform a baseline relevance ranking on a data set used in the TREC interactive track.

SWAM: Stuxnet Worm Analysis in Metasploit <=SEP=> Nowadays cyber security is becoming a great challenge. Attacker's community is progressing towards making smart and intelligent malwares (viruses, worms and Root kits). They stealth their existence and also use administrator rights without knowing legal user. Stuxnet worm is an example of a recent malware first detected in July 2010. Its variants were also detected earlier. It is the first type of worm that affects the normal functionality of industrial control systems (ICS) having programmable logic controllers (PLC) through PLC Root kit. Its main goal is to modify ICS behavior by changing the code of PLC and make it to behave in a way that attacker wants. It is a complex piece of malware having different operations and functionalities which are achieved by exploiting zero day vulnerabilities. Stuxnet exploits various vulnerable services in Microsoft Windows. In this paper we will show real time simulation of first three vulnerabilities of these through Metasploit Framework 3.2 and analyze results. A real time scenario is established based on some assumptions. We assumed Proteus design (pressure sensor) as PLC and showed after exploitation that the pressure value drops to an unacceptable level by changing Keil code of this design.

The Quest Data Mining System <=SEP=> The goal of the Quest project at the IBM Almaden Research center is to develop technology to enable a new breed of da&intensive decision-support applications. This paper is a capsule summary of the current functionality and architecture of the Quest data mining System. Our overall approach has been to identify basic data mining operations that cut across applications and develop fast, scalable algorithms for their execution (Agrawal, Imielinski, & Swami 1993a). We wanted our algorithms to:

Hierarchical Active Transfer Learning <=SEP=> We describe a unified active transfer learning framework called Hierarchical Active Transfer Learning (HATL). HATL exploits cluster structure shared between different data domains to perform transfer learning by imputing labels for unlabeled target data and to generate effective label queries during active learning. The resulting framework is flexible enough to perform not only adaptive transfer learning and accelerated active learning but also unsupervised and semi-supervised transfer learning. We derive an intuitive and useful upper bound on HATL’s error when used to infer labels for unlabeled target points. We also present results on synthetic data that confirm both intuition and our analysis. Finally, we demonstrate HATL’s empirical effectiveness on a benchmark data set for sentiment classification.

MuDeL: a language and a system for describing and generating mutants <=SEP=> Mutation Testing is an approach for assessing the quality of a test case suite by analyzing its ability in distinguishing the product under test from a set of alternative products, the so-called mutants. The mutants are generated from the product under test by applying a set of mutant operators, which produce products with slight syntactical differences. The mutant operators are usually based on typical errors that occurs during the software development and can be related to a fault model. In this paper, we propose a language — named MuDeL — for describing mutant operators aiming not only at automating the mutant generation, but also at providing precision and formality to the operator descriptions. The language was designed using concepts that come from transformational and logical programming paradigms, as well as from context-free grammar theory. The language is illustrated with some simple examples. We also describe the mudelgen system, developed to support this language.

An Interactive Web Mapping Visualization of Urban Air Quality Monitoring Data of China <=SEP=> In recent years, main cities in China have been suffering from hazy weather, which is gaining great attention among the public, government managers and researchers in different areas. Many studies have been conducted on the topic of urban air quality to reveal different aspects of the air quality problem in China. This paper focuses on the visualization problem of the big air quality monitoring data of all main cities on a nationwide scale. To achieve the intuitive visualization of this dataset, this study develops two novel visualization tools for multi-granularity time series visualization (timezoom.js) and a dynamic symbol declutter map mashup layer for thematic mapping (symadpative.js). With the two invented tools, we develops an interactive web map visualization application of urban air quality data of all main cities in China. This application shows us significant air pollution findings at the nationwide scale. These results give us clues for further studies on air pollutant characteristics, forecasting and control in China. As the tools are invented for general visualization purposes of geo-referenced time series data, they can be applied to other environmental monitoring data (temperature, precipitation, etc.) through some configurations.

Dynamic Motions by a Quadruped Musculoskeletal Robot with Angle-Dependent Moment Arms <=SEP=> Current robotics have failed to replicate the great dexterity of the animal’s motions. We believe that it is because most of the works have focused on the controller part rather than on its body structure. Indeed, the musculoskeletal system is essential to produce the animal’s motions and several researchers have attempted to exploit it [1][2][3]. However, most of them have used simple mono-articular muscles and torque control which cannot replicate the nonlinearity of the living tissues, muscles and nerves [4]. In contrast, bipedal robots with monoand bi-articular muscles for performing adaptive motions [5][6][7] have been produced. To further investigate the role of the musculoskeletal system for animal motions, we desgned a quadruped robot with monoand bi-articular muscles. We used artificial pneumatic muscles as actuators due to their high mass/power ratio and their high compliance. Although these actuators replicate several characteristics of biological muscles in terms of damping and elasticity, the length-tension relation differs from them [8][9]. In the result, the angle-torque relation of the robot with pneumatic muscles differed substantially from the one of animals. Thus, we tried to bring the angle-torque relation as close as the one of living tissues by devising the angle-moment arm relation.

Foundations of RDF⋆ and SPARQL⋆ (An Alternative Approach to Statement-Level Metadata in RDF) <=SEP=> The standard approach to annotate statements in RDF with metadata has a number of shortcomings including data size blow-up and unnecessarily complicated queries. We propose an alternative approach that is based on nesting of RDF triples and of query patterns. The approach allows for a more compact representation of data and queries, and it is backwards compatible with the standard. In this paper we present the formal foundations of our proposal and of different approaches to implement it. More specifically, we formally capture the necessary extensions of the RDF data model and its query language SPARQL, and we define mappings based on which our extended notions can be converted back to ordinary RDF and SPARQL. Additionally, for such type of mappings we define two desirable properties, information preservation and query result equivalence, and we show that the introduced mappings possess these properties.

Learning Dexterous In-Hand Manipulation <=SEP=> We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object’s appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five [43]. We also include a video of our results: https://youtu. be/jwSbzNHGflM.

First response fire combat: Deep leaning based visible fire detection <=SEP=> Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based approach, based on Google's Inception v3 and evaluate how the use of different optimizers, learning rates and reduction functions impact the convergence time and the results obtained. The experiment used a balanced two-class dataset, composed of images from videos captured through hand-held and drone attached cameras on real fire hazard situations. The results show that using any choice of algorithms, the Inception v3 architecture is able to converge and obtain results that surpass the state-of-the-art fire detection methods. We show that using a proper choice of learning rate, the network is able to achieve a 99% accuracy in less than one hundred interactions.

Accurate Identification of Ontology Alignments at Different Granularity Levels <=SEP=> As more and more ontologies are defined with different terms, ontology matching plays a crucial role in addressing the semantic heterogeneity problem in many different disciplines. Many efforts have been made to discover correspondences among terms in different ontologies. Most studies directly match two ontologies by utilizing terminological and structural methods that rely on ontologies themselves only. However, the decentralized characteristic of ontologies raises the uncertainty in ontology matching. To address this problem, we propose a four-stage ontology matching framework (FOMF) to enhance ontology matching performance. It is built upon the commonly accepted claim that an external comprehensive knowledge base can be used as a semantic bridge between domain ontologies for ontology matching. First, FOMF semantically maps domain ontologies to a knowledge base and then produces different types of alignments, including equivalence, subclass, sameas, and instance alignments. Similarities between two domain ontologies are next employed to enhance the equivalence and sameas alignments discovery. Finally, based on acquired alignments, inferred alignments are deduced to guarantee the completeness of matching results. Our experimental results show the superiority of the proposed method over the existing ones.

Average cost temporal-difference learning <=SEP=> We propose a variant of temporal-di!erence learning that approximates average and di!erential costs of an irreducible aperiodic Markov chain. Approximations are comprised of linear combinations of "xed basis functions whose weights are incrementally updated during a single endless trajectory of the Markov chain. We present a proof of convergence (with probability 1) and a characterization of the limit of convergence. We also provide a bound on the resulting approximation error that exhibits an interesting dependence on the `mixing timea of the Markov chain. The results parallel previous work by the authors, involving approximations of discounted cost-to-go. ! 1999 Elsevier Science Ltd. All rights reserved.

Game Development for Computer Science Education <=SEP=> Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren't typically found in homework: they often situate problems within a compelling alternate reality that unfolds through intriguing narrative, they often draw more upon a player's intrinsic motivations than extrinsic ones, they can facilitate deliberate low intensity practice, and they often emphasize a spirit of play instead of work.
 At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013.
 While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016.
 We extend this working group to a second year---with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals.

ITIL-based IT service support process reengineering <=SEP=> The Information Technology Infrastructure Library (ITIL) supports best practices, reengineering activities and IT service support processes. ITIL framework only provides recommendations, and companies need to utilize this framework to improve their IT service support processes and establish best practices. This study provides a methodology on how to apply the ITIL framework for evaluating the IT service support processes, its reengineering and alignment to best practices, and subsequent integration into a decision support system framework. A case study approach was used to identify a set of Key Performance Indicators (KPI) which were monitored by a decision support system (DSS) for triggering on-going reengineering of IT service support processes. This paper focuses on the implementation of the ITIL guidelines at the operational level, improvement of the service desk, and incident, problem, change, release, and configuration management. It also presents the implementation of the ITIL guidelines at the tactical level for the improvement of the service level, capacity, IT service continuity, service availability, and security management. We conclude by providing recommendations for future research.

Small footprint implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement <=SEP=> For effective speech processing in an automotive environment, speech enhancement is necessary due to significant levels of background noise. In this paper, we present a cost effective small footprint implementation of one particular speech enhancement technique: dual microphone delay-and-sum beamforming. In order to save resources, the implementation utilizes the overlapping frame property used in speech processing systems. The implementation also exhibits a simple interconnection structure leading to even greater resource saving. Experiment results show that the proposed design can produce enhanced output very close to that generated by a theoretical (floating-point) model while only requiring a modest hardware resource usage.

Deep Recurrent Neural Networks for Acoustic Modelling <=SEP=> We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional LongShort Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models

Gaining Access with Social Engineering: An Empirical Study of the Threat <=SEP=> AbstrAct Recently, research on information security has expanded from its purely technological orientation into striving to understand and explain the role of human behavior in security breaches. However, an area that has been lacking theory-grounded empirical study is in social engineering attacks. While there exists an extensive body of anecdotal literature, factors that account for attack success remains largely speculative. To better understand this increasing phenomenon, we developed a theoretical framework and conducted an empirical field study to investigate social engineering attacks, and from these results, we make recommendations for practice and further research.

A support vector machine classifier with automatic confidence and its application to gender classification <=SEP=> In this paper, we propose a support vector machine with automatic confidence (SVMAC) for pattern classification. The main contributions of this work to learning machines are twofold. One is that we develop an algorithm for calculating the label confidence value of each training sample. Thus, the label confidence values of all of the training samples can be considered in training support vector machines. The other one is that we propose a method for incorporating the label confidence value of each training sample into learning and derive the corresponding quadratic programming problems. To demonstrate the e ff ctiveness of the proposed SVMACs, a series of experiments are performed on three benchmarking pattern classification problems and a challenging gender classification problem. Experimental results show that the generalization performance of our SVMACs is superior to that of traditional SVMs.

Mixing Confidential Transactions: Comprehensive Transaction Privacy for Bitcoin <=SEP=> The public nature of the blockchain has been shown to be a severe threat for the privacy of Bitcoin users. Even worse, since funds can be tracked and tainted, no two coins are equal, and fungibility, a fundamental property required in every currency, is at risk. With these threats in mind, several privacyenhancing technologies have been proposed to make Bitcoin more private. However, they either require a deep redesign of the currency, breaking many currently deployed features, or they address only specific privacy issues and consequently provide only very limited guarantees when deployed separately. The goal of this work is to overcome this trade-off. Building on CoinJoin, we design ValueShuffle, the first coin mixing protocol compatible with Confidential Transactions, a proposal to hide payment values in transactions. ValueShuffle ensures a mixing participant’s anonymity and the confidentiality of her payment values not only against an attacker observing the blockchain but also against the other possibly malicious mixing participants and against network attackers. By combining ValueShuffle with the proposal for Confidential Transactions and additionally Stealth Addresses, our solution provides comprehensive privacy (payer anonymity, payee anonymity, and payment value privacy) without breaking with the design or the features of Bitcoin. We demonstrate that the combination of these three privacyenhancing technologies creates synergies that overcome the two major obstacles which so far have prohibited the deployment of coin mixing in practice, namely that users need to mix funds of the same value, and need to do so before they can actually spend the funds. As a result, our approach unleashes the full potential of coin mixing as a privacy solution for Bitcoin.

Approximate Gaussian Elimination <=SEP=> Approximate Gaussian Elimination

Compressed sensing radar <=SEP=> A stylized compressed sensing radar is proposed in which the time- frequency plane is discretized into an N times N grid. Assuming the number of targets K is small (i.e., K Lt N2), then we can transmit a sufficiently "incoherent" pulse and employ the techniques of compressed sensing to reconstruct the target scene. A theoretical upper bound on the sparsity K is presented. Numerical simulations verify that even better performance can be achieved in practice. This novel compressed sensing approach offers great potential for better resolution over classical radar.

ASR-based corrective feedback on pronunciation: does it really work? <=SEP=> We studied a group of immigrants who were following regular, teacher-fronted Dutch classes, and who were assigned to three groups using either a) Dutch CAPT, an ASR-based Computer Assisted Pronunciation Training (CAPT) system that provides feedback on a number of Dutch speech sounds that are problematic for L2 learners b) a CAPT system without feedback c) no CAPT system. Participants were tested before and after the training. The results show that the ASR-based feedback was effective in correcting the errors addressed in the training.

Plant classification system based on leaf features <=SEP=> This paper presents a classification approach based on Random Forests (RF) and Linear Discriminant Analysis (LDA) algorithms for classifying the different types of plants. The proposed approach consists of three phases that are pre-processing, feature extraction, and classification phases. Since most types of plants have unique leaves, so the classification approach presented in this research depends on plants leave. Leaves are different from each other by characteristics such as the shape, color, texture and the margin. The used dataset for this experiments is a database of different plant species with total of only 340 leaf images, was downloaded from UCI- Machine Learning Repository. It was used for both training and testing datasets with 10-fold cross-validation. Experimental results showed that LDA achieved classification accuracy of (92.65%) against the RF that achieved accuracy of (88.82%) with combination of shape, first order texture, Gray Level Co-occurrence Matrix (GLCM), HSV color moments, and vein features.

Interactive Perception: Leveraging Action in Perception and Perception in Action <=SEP=> Recent approaches in robot perception follow the insight that perception is facilitated by interaction with the environment. These approaches are subsumed under the term Interactive Perception (IP). This view of perception provides the following benefits. First, interaction with the environment creates a rich sensory signal that would otherwise not be present. Second, knowledge of the regularity in the combined space of sensory data and action parameters facilitates the prediction and interpretation of the sensory signal. In this survey, we postulate this as a principle for robot perception and collect evidence in its support by analyzing and categorizing existing work in this area. We also provide an overview of the most important applications of IP. We close this survey by discussing remaining open questions. With this survey, we hope to help define the field of Interactive Perception and to provide a valuable resource for future research.

An Unsupervised Approach to Recognizing Discourse Relations <=SEP=> We presentan unsupervisedapproachto recognizingdiscourserelationsof CONTRAST, EXPLANATION-EVIDENCE, CONDITION andELABORATION that hold betweenarbitraryspansof texts. We show that discourserelation classifierstrained on examples that are automaticallyextractedfrom massi ve amountsof text can be usedto distinguishbetweensomeof theserelationswith accuraciesashigh as 93%, even whenthe relationsarenot explicitly markedby cuephrases.

ANCESTRY-CONSTRAINED PHYLOGENETIC ANALYSIS SUPPORTS THE INDO-EUROPEAN STEPPE HYPOTHESIS <=SEP=> Discussion of Indo-European origins and dispersal focuses on two hypotheses. Qualitative evidence from reconstructed vocabulary and correlations with archaeological data suggest that IndoEuropean languages originated in the Pontic-Caspian steppe and spread together with cultural innovations associated with pastoralism, beginning c. 6500–5500 bp. An alternative hypothesis, according to which Indo-European languages spread with the diffusion of farming from Anatolia, beginning c. 9500–8000 bp, is supported by statistical phylogenetic and phylogeographic analyses of lexical traits. The time and place of the Indo-European ancestor language therefore remain disputed. Here we present a phylogenetic analysis in which ancestry constraints permit more accurate inference of rates of change, based on observed changes between ancient or medieval languages and their modern descendants, and we show that the result strongly supports the steppe hypothesis. Positing ancestry constraints also reveals that homoplasy is common in lexical traits, contrary to the assumptions of previous work. We show that lexical traits undergo recurrent evolution due to recurring patterns of semantic and morphological change.*