Query: DOES THIS ANSWER YOUR QUESTION? Towards Dialogue Management for Restricted Domain Question Answering Systems

Relevant passages:
Data-Intensive Question Answering <=SEP=> Microsoft Research Redmond participated for the first time in TREC this year, focusing on the question answering track. There is a separate report in this volume on the Microsoft Research Cambridge submissions for the filtering and Web tracks (Robertson et al., 2002). We have been exploring data-driven techniques for Web question answering, and modified our system somewhat for participation in TREC QA. We submitted two runs for the main QA track (AskMSR and AskMSR2).

Convolution Kernels on Discrete Structures <=SEP=> We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on a innnite set from kernels involving generators of the set. The family of kernels generated generalizes the family of radial basis kernels. It can also be used to deene kernels in the form of joint Gibbs probability distributions. Kernels can be built from hidden Markov random elds, generalized regular expressions, pair-HMMs, or ANOVA de-compositions. Uses of the method lead to open problems involving the theory of innnitely divisible positive deenite functions. Fundamentals of this theory and the theory of reproducing kernel Hilbert spaces are reviewed and applied in establishing the validity of the method.

The Nature of Statistical Learning Theory <=SEP=> 

A stochastic model of human-machine interaction for learning dialog strategies <=SEP=> In this paper, we propose a quantitative model for dialog systems that can be used for learning the dialog strategy. We claim that the problem of dialog design can be formalized as an optimization problem with an objective function reflecting different dialog dimensions relevant for a given application. We also show that any dialog system can be formally described as a sequential decision process in terms of its state space, action set, and strategy. With additional assumptions about the state transition probabilities and cost assignment, a dialog system can be mapped to a stochastic model known as Markov decision process(MDP). A variety of data driven algorithms for finding the optimal strategy (i.e., the one that optimizes the criterion) is available within the MDP framework, based on reinforcement learning. For an effective use of the available training data we propose a combination of supervised and reinforcement learning: the supervised learning is used to estimate a model of the user, i.e., the MDP parameters that quantify the user’s behavior. Then a reinforcement learning algorithm is used to estimate the optimal strategy while the system interacts with the simulated user. This approach is tested for learning the strategy in an air travel information system (ATIS) task. The experimental results we present in this paper show that it is indeed possible to find a simple criterion, a state space representation, and a simulated user parameterization in order to automatically learn a relatively complex dialog behavior, similar to one that was heuristically designed by several research groups.

Exploiting Redundancy in Question Answering <=SEP=> Our goal is to automatically answer brief factual questions of the form ``When was the Battle of Hastings?'' or ``Who wrote The Wind in the Willows?''. Since the answer to nearly any such question can now be found somewhere on the Web, the problem reduces to finding potential answers in large volumes of data and validating their accuracy. We apply a method for arbitrary passage retrieval to the first half of the problem and demonstrate that answer redundancy can be used to address the second half. The success of our approach depends on the idea that the volume of available Web data is large enough to supply the answer to most factual questions multiple times and in multiple contexts. A query is generated from a question and this query is used to select short passages that may contain the answer from a large collection of Web data. These passages are analyzed to identify candidate answers. The frequency of these candidates within the passages is used to ``vote'' for the most likely answer. The approach is experimentally tested on questions taken from the TREC-9 question-answering test collection. As an additional demonstration, the approach is extended to answer multiple choice trivia questions of the form typically asked in trivia quizzes and television game shows.

Compact Microstrip-to-CPS Transition for UWB Application <=SEP=> A novel ultra-wideband (UWB) microstrip-to-CPS (coplanar stripline) transition has been developed. This transition or balun structure has several attractive advantages such as good impedance transformation, compact size and wide bandwidth. After the parallel-coupled line section between the microstrip line and CPS is investigated under varied traversal dimensions, a wide transmitting band is well achieved with the emergence of two transmission poles. Next, such a single transition circuit is optimally designed to cover the whole UWB band (3.1 GHz to 10.6 GHz). To verify the predicted results in experiment, the two back-to-back transitions with the same 50 Omega microstrip feed lines are fabricated and tested. Measured results exhibit the return loss close to 10.0 dB over a band from 3.5 GHz to 10.0 GHz.

A Multilingual Multimedia Indian Sign Language Dictionary Tool <=SEP=> This paper presents a cross platform multilingual multimedia Indian Sign Language (ISL) dictionary building tool. ISL is a linguistically under-investigated language with no source of well documented electronic data. Research on ISL linguistics also gets hindered due to a lack of ISL knowledge and the unavailability of any educational tools. Our system can be used to associate signs corresponding to a given text. The current system also facilitates the phonological annotation of Indian signs in the form of HamNoSys structure. The generated HamNoSys string can be given as input to an avatar module to produce an animated sign representation.

Retrieval of Song Lyrics from Sung Queries <=SEP=> Retrieving the lyrics of a sung recording from a database of text documents is a research topic that has not received much attention so far. Such a retrieval system has many practical applications, e.g. for karaoke applications or for indexing large song databases by their lyric content. We present a new method for lyrics retrieval. An acoustic model trained on singing is used to obtain phoneme probabilities from sung queries, which are then mapped to phoneme sequences. These are compared against lines of textual lyrics in a large corpus in order to retrieve the best-matching song. The approach is tested on three sung datasets. Lyrics are retrieved from a set of 300 possible songs (12,000 lines of lyrics). The results are highly encouraging and could be used further to perform automatic lyrics alignment and keyword spotting for large databases of songs, or for retrieving lyrics from the internet.

IT worker turnover: an empirical examination of intrinsic motivation <=SEP=> This study examined intrinsic motivation's influence on information technology (IT) workers' attitudes and intentions. Drawing on Human Resource management research (Eby & Freeman, 1999), we model Intrinsic motivation as mediating the influence of motivators (i.e., intrinsic job characteristics) and hygiene factors (i.e., pay and supervisory satisfaction) on workplace attitudes (i.e., job satisfaction and affective organizational commitment). In turn, workplace attitudes mediate the influence of intrinsic motivation on turnover intention. The model was tested using data collected from public sector IT workers in the Southeastern United States. Although intrinsic motivation did not fully mediate the influence of motivators and hygiene factors, findings suggest that intrinsic motivation positively influences workplace attitudes and has a mediated influence on turnover intent. Implications for research and practice are offered.

DSCarver: decompose-and-spiral-carve for subtractive manufacturing <=SEP=> We present an automatic algorithm for subtractive manufacturing of freeform 3D objects using high-speed machining (HSM) via CNC. A CNC machine operates a cylindrical cutter to carve off material from a 3D shape stock, following a tool path, to "expose" the target object. Our method decomposes the input object's surface into a small number of patches each of which is fully accessible and machinable by the CNC machine, in continuous fashion, under a fixed cutter-object setup configuration. This is achieved by covering the input surface with a minimum number of accessible regions and then extracting a set of machinable patches from each accessible region. For each patch obtained, we compute a continuous, space-filling, and iso-scallop tool path which conforms to the patch boundary, enabling efficient carving with high-quality surface finishing. The tool path is generated in the form of connected Fermat spirals, which have been generalized from a 2D fill pattern for layered manufacturing to work for curved surfaces. Furthermore, we develop a novel method to control the spacing of Fermat spirals based on directional surface curvature and adapt the heat method to obtain iso-scallop carving. We demonstrate automatic generation of accessible and machinable surface decompositions and iso-scallop Fermat spiral carving paths for freeform 3D objects. Comparisons are made to tool paths generated by commercial software in terms of real machining time and surface quality.

Deep Learning for Video Classification and Captioning <=SEP=> Accelerated by the tremendous increase in Internet bandwidth and storage space, video data has been generated, published and spread explosively, becoming an indispensable part of today's big data. In this paper, we focus on reviewing two lines of research aiming to stimulate the comprehension of videos with deep learning: video classification and video captioning. While video classification concentrates on automatically labeling video clips based on their semantic contents like human actions or complex events, video captioning attempts to generate a complete and natural sentence, enriching the single label as in video classification, to capture the most informative dynamics in videos. In addition, we also provide a review of popular benchmarks and competitions, which are critical for evaluating the technical progress of this vibrant field.

ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models <=SEP=> While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance-and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models.

Efficient and robust computation of an approximated medial axis <=SEP=> The medial axis can be viewed as a compact representation for an arbitrary model; it is an essential geometric structure in many applications. A number of practical algorithms for its computation have been aimed at speeding up its computation and at addressing its instabilities. In this paper we propose a new algorithm to compute the medial axis with arbitrary precision. It exhibits several desirable properties not previously combined in a practical and efficient algorithm. First, it allows for a tradeoff between computation time and accuracy, making it well-suited for applications in which an approximation of the medial axis suffices, but computational efficiency is of particular concern. Second, it is output sensitive: the computation complexity of the algorithm does not depend on the size of the representation of a model, but on the size of the representation of the resulting medial axis. Third, the densities of the approximated medial axis points in different areas are adaptive to local free space volumes, based on the assumption that a coarser approximation in wide open area can still suffice the requirements of the applications. We present theoretical results, bounding the error introduced by the approximation process. The algorithm has been implemented and experimental results are presented that illustrate its computational efficiency and robustness.

Can you hear me now?!: it must be BGP <=SEP=> Industry observers expect VoIP to eventually replace most of the existing land-line telephone connections. Currently however, quality and reliability concerns largely limit VoIP usage to either personal calls on cross-domain services such as Skype and Vonage, or to single-domain services such as trunking, where a core ISP carries long-distance voice as VoIP only within its backbone, to save cost with a unified voice/data infrastructure. This paper investigates the factors that prevent cross-domain VoIP deployments from achieving the quality and reliability of existing land-line telephony (PSTN). We ran over 50,000 VoIP phone calls between 24 locations in US and Europe for a three-week period. Our results indicate that VoIP usability is hindered as much by BGP's slow convergence as network congestion. In fact, about half of the unintelligible VoIP samples in our data occur within 10 minutes of a BGP update

Out-of-bag Estimation <=SEP=> In bagging, predictors are constructed using bootstrap samples from the training set and then aggregated to form a bagged predictor. Each bootstrap sample leaves out about 37% of the examples. These left-out examples can be used to form accurate estimates of important quantities. For instance, they can be used to give much improved estimates of node probabilities and node error rates in decision trees. Using estimated outputs instead of the observed outputs improves accuracy in regression trees. They can also be used to give nearly optimal estimates of generalization errors for bagged predictors. Introduction: We assume that there is a training set T= {(y n ,x n), n=1, ... ,N} and a method for constructing a predictor Q(x,T) using the given training set. The output variable y can either be a class label (classification) or numerical (regression). In bagging (Breiman[1996a]) a sequence of training sets T B,1 , ... , T B,K are generated of the same size as T by bootstrap selection from T. Then K predictors are constructed such that the kth predictor Q(x,T k,B) is based on the kth bootstrap training set. It was shown that if these predictors are aggregated-averaging in regression or voting in classification, then the resultant predictor can be considerably more accurate than the original predictor.

Do citations and readership identify seminal publications? <=SEP=> This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.

Intelligent Human Robot Interaction (iHRI) <=SEP=> 

Near-Field Microwave Imaging Based on Aperture Raster Scanning With TEM Horn Antennas <=SEP=> The design, fabrication, and characterization of an ultrawideband (UWB) antenna for near-field microwave imaging of dielectric objects are presented together with the imaging setup. The focus is on an application in microwave breast tumor detection. The new antenna operates as a sensor with the following properties: 1) direct contact with the imaged body; 2) more than 90% of the microwave power is coupled directly into the tissue; 3) UWB performance; 4) excellent de-coupling from the outside environment; 5) small size; and 6) simple fabrication. The antenna characterization includes return loss, total efficiency, near-field directivity, fidelity, and group velocity. The near-field imaging setup employs planar aperture raster scanning. It consists of two antennas aligned along each other's boresight and moving together to scan two parallel apertures. The imaged object lies between the two apertures. With a blind de-convolution algorithm, the images are de-blurred. Simulation and experimental results confirm the satisfactory performance of the antenna as an UWB sensor for near-field imaging.

Predicting retweet count using visual cues <=SEP=> Social media platforms allow rapid information diffusion, and serve as a source of information to many of the users. Particularly, in Twitter information provided by tweets diffuses over the users through retweets. Hence, being able to predict the retweet count of a given tweet is important for understanding and controlling information diffusion on Twitter. Since the length of a tweet is limited to 140 characters, extracting relevant features to predict the retweet count is a challenging task. However, visual features of images linked in tweets may provide predictive features. In this study, we focus on predicting the expected retweet count of a tweet by using visual cues of an image linked in that tweet in addition to content and structure-based features.

The circulatory system of amphioxus (Branchiostoma floridae) <=SEP=> The endothelial cell of Amphioxus has an endocytotic capability since both ferritin and horseradish peroxidase, when injected into the endostylar artery, became localized within endothelial cell cytoplasmic vesicles. The endothelial cells also contain vesicles within which the lysosomal enzyme acid phosphatase has been localized. Because of the presence of an endocytotic capability and lysosomes, these cells possess both the means and the method for the acquisition and disposal of exogenous proteins.

Algebraic MACs and Keyed-Verification Anonymous Credentials <=SEP=> We consider the problem of constructing anonymous credentials for use in a setting where the issuer of credentials is also the verifier, or more generally where the issuer and verifier have a shared key. In this setting we can use message authentication codes (MACs) instead of public key signatures as the basis for the credential system.
 To this end, we construct two algebraic MACs in prime-order groups, along with efficient protocols for issuing credentials, asserting possession of a credential, and proving statements about hidden attributes (e.g., the age of the credential owner). We prove the security of the first scheme in the generic group model, and prove the security of the second scheme\dash using a dual-system-based approach\dash under decisional Diffie-Hellman (DDH). Our MACs are of independent interest, as they are the only uf-cmva-secure MACs with efficient proofs of knowledge.
 Finally, we compare the efficiency of our new systems to two existing constructions of anonymous credentials: U-Prove and Idemix. We show that the performance of the new schemes is competitive with U-Prove (which does not have multi-show unlinkability), and many times faster than Idemix.

Predicting User Behavior in Display Advertising via Dynamic Collective Matrix Factorization <=SEP=> Conversion prediction and click prediction are two important and intertwined problems in display advertising, but existing approaches usually look at them in isolation. In this paper, we aim to predict the conversion response of users by jointly examining the past purchase behavior and the click response behavior. Additionally, we model the temporal dynamics between the click response and purchase activity into a unified framework. In particular, a novel matrix factorization approach named dynamic collective matrix factorization (DCMF) is proposed to address this problem. Our model considers temporal dynamics of post-click conversions and also takes advantages of the side information of users, advertisements, and items. Experiments on a real-world marketing dataset show that our model achieves significant improvements over several baselines.

Malicious web content detection using machine leaning <=SEP=> Naive users using a browser have no idea about the back-end of the page. The users might be tricked into giving away their credentials or downloading malicious data. Our aim is to create an extension for Chrome which will act as middleware between the users and the malicious websites, and mitigate the risk of users succumbing to such websites. Further, all harmful content cannot be exhaustively collected as even that is bound to continuous development. To counter this we are using machine learning-to train the tool and categorize the new content it sees every time into the particular categories so that corresponding action can be taken.

Accelerating Online CP Decompositions for Higher Order Tensors <=SEP=> Tensors are a natural representation for multidimensional data. In recent years, CANDECOMP/PARAFAC (CP) decomposition, one of the most popular tools for analyzing multi-way data, has been extensively studied and widely applied. However, today's datasets are often dynamically changing over time. Tracking the CP decomposition for such dynamic tensors is a crucial but challenging task, due to the large scale of the tensor and the velocity of new data arriving. Traditional techniques, such as Alternating Least Squares (ALS), cannot be directly applied to this problem because of their poor scalability in terms of time and memory. Additionally, existing online approaches have only partially addressed this problem and can only be deployed on third-order tensors. To fill this gap, we propose an efficient online algorithm that can incrementally track the CP decompositions of dynamic tensors with an arbitrary number of dimensions. In terms of effectiveness, our algorithm demonstrates comparable results with the most accurate algorithm, ALS, whilst being computationally much more efficient. Specifically, on small and moderate datasets, our approach is tens to hundreds of times faster than ALS, while for large-scale datasets, the speedup can be more than 3,000 times. Compared to other state-of-the-art online approaches, our method shows not only significantly better decomposition quality, but also better performance in terms of stability, efficiency and scalability.

Take a Look Around: Using Street View and Satellite Images to Estimate House Prices <=SEP=> When an individual purchases a home, they simultaneously purchase its structural features, its accessibility to work, and the neighborhood amenities. Some amenities, such as air quality, are measurable whilst others, such as the prestige or the visual impression of a neighborhood, are difficult to quantify. Despite the well-known impacts intangible housing features have on house prices, limited attention has been given to systematically quantifying these difficult to measure amenities. Two issues have lead to this neglect. Not only do few quantitative methods exist that can measure the urban environment, but that the collection of such data is both costly and subjective. We show that street image and satellite image data can capture these urban qualities and improve the estimation of house prices. We propose a pipeline that uses a deep neural network model to automatically extract visual features from images to estimate house prices in London, UK. We make use of traditional housing features such as age, size and accessibility as well as visual features from Google Street View images and Bing aerial images in estimating the house price model. We find encouraging results where learning to characterize the urban quality of a neighborhood improves house price prediction, even when generalizing to previously unseen London boroughs. We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract the visual desirability of neighborhoods as proxy variables that are both of interest in their own right, and could be used as inputs to other econometric methods. This is particularly valuable as once the network has been trained with the training data, it can be applied elsewhere, allowing us to generate vivid dense maps of the desirability of London streets.

A reconfigurable low-noise dynamic comparator with offset calibration in 90nm CMOS <=SEP=> This paper presents a reconfigurable, low offset, low noise and high speed dynamic clocked-comparator for medium to high resolution Analog to Digital Converters (ADCs). The proposed comparator reduces the input referred noise by half and shows a better output driving capability when compared with the previous work. The offset, noise and power consumption can be controlled by a clock delay which allows simple reconfiguration. Moreover, the proposed offset calibration technique improves the offset voltage from 11.6mV to 533μV at 1 sigma. A prototype of the comparator is implemented in 90nm 1P8M CMOS with experimental results showing 320μV input referred noise at 1.5GHz with 1.2V supply.

Classification methods for activity recognition <=SEP=> Activity recognition is an important subject in different areas of research, like e-health and ubiquitous computing. In this paper we give an overview of recent work in the field of activity recognition from both body mounted sensors and ambient sensors. We discuss some of the differences among approaches and present guidelines for choosing a suitable approach under various circumstances.

Educational data mining applications and tasks: A survey of the last 10 years <=SEP=> Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning process. In this study we have studied various tasks and applications existing in the field of EDM and categorized them based on their purposes. We have compared our study with other existing surveys about EDM and reported a taxonomy of task.

A CSRR Loaded MIMO Antenna System for ISM Band Operation <=SEP=> A 2 × 2 (four-element) multiple-input multiple-output (MIMO) patch antenna system is designed and fabricated for a 2.45-GHz ISM band operation. It uses complementary split-ring resonator (CSRR) loading on its ground plane for antenna miniaturization. This reduces the single-element antenna size by 76%. The total board size of the proposed MIMO antenna system, including the GND plane is 100 × 50 × 0.8 mm3, while the single-patch antenna element has a size of 14 × 18 mm2. The antenna is fabricated and tested. Measured results are in good agreement with simulations. A minimum measured isolation of 10 dB is obtained given the close interelement spacing of 0.17λ. The maximum measured gain for a single operating element is -0.8 dBi.

Asymptotic Confidence Regions for High-Dimensional Structured Sparsity <=SEP=> In the setting of high-dimensional linear regression models, we propose two frameworks for constructing pointwise and group confidence sets for penalized estimators, which incorporate prior knowledge about the organization of the nonzero coefficients. This is done by desparsifying the estimator by S. van de Geer and B. Stucky and S. van de Geer <italic>et al.</italic>, then using an appropriate estimator for the precision matrix <inline-formula> <tex-math notation="LaTeX">$\Theta$</tex-math></inline-formula>. In order to estimate the precision matrix a corresponding structured matrix norm penalty has to be introduced. After normalization the result is an asymptotic pivot. The asymptotic behavior is studied and simulations are added to study the differences between the two schemes.