High-Frequency Behavior of Graphene-Based Interconnects—Part II: Impedance Analysis and Implications for Inductor Design. This paper provides the first detailed insights into the ultrahigh-frequency behavior of graphene ribbons (GRs) and analyzes their consequences in designing interconnects and low-loss on-chip inductors. In the companion paper (part I), an accurate impedance modeling methodology has been developed based on the Boltzmann equation with the magnetic vector potential Green's function approach incorporating the dependency of current on the nonlocal electric field. Based on the developed methodology, this paper for the first time embarks on the rigorous investigation of the intricate processes occurring at high frequencies in GRs, such as anomalous skin effect (ASE), high-frequency resistance and inductance saturation, intercoupled relation between edge specularity and ASE, and the influence of the linear dimensions on impedance. A comparative study of the high-frequency response of GRs with that of carbon nanotubes (CNTs) and Cu is made to highlight the potential of GR interconnects for high-frequency applications. Subsequently, the high-frequency performance of GR inductors is analyzed, and it is shown that they can achieve 32% and 50% improvements in maximum Q-factor compared to Cu and single-walled CNT inductors with 1/3 metallic fraction, respectively.

Question: What is the high-frequency behavior of graphene?


Taxonomy and Survey of Collaborative Intrusion Detection. The dependency of our society on networked computers has become frightening: In the economy, all-digital networks have turned from facilitators to drivers; as cyber-physical systems are coming of age, computer networks are now becoming the central nervous systems of our physical world—even of highly critical infrastructures such as the power grid. At the same time, the 24/7 availability and correct functioning of networked computers has become much more threatened: The number of sophisticated and highly tailored attacks on IT systems has significantly increased. Intrusion Detection Systems (IDSs) are a key component of the corresponding defense measures; they have been extensively studied and utilized in the past. Since conventional IDSs are not scalable to big company networks and beyond, nor to massively parallel attacks, Collaborative IDSs (CIDSs) have emerged. They consist of several monitoring components that collect and exchange data. Depending on the specific CIDS architecture, central or distributed analysis components mine the gathered data to identify attacks. Resulting alerts are correlated among multiple monitors in order to create a holistic view of the network monitored. This article first determines relevant requirements for CIDSs; it then differentiates distinct building blocks as a basis for introducing a CIDS design space and for discussing it with respect to requirements. Based on this design space, attacks that evade CIDSs and attacks on the availability of the CIDSs themselves are discussed. The entire framework of requirements, building blocks, and attacks as introduced is then used for a comprehensive analysis of the state of the art in collaborative intrusion detection, including a detailed survey and comparison of specific CIDS approaches.

Question: What is the state of the art in collaborative intrusion detection?


Iterative Document Representation Learning Towards Summarization with Polishing. In this paper, we introduce Iterative Text Summarization (ITS), an iteration-based model for supervised extractive text summarization, inspired by the observation that it is often necessary for a human to read an article multiple times in order to fully understand and summarize its contents. Current summarization approaches read through a document only once to generate a document representation, resulting in a sub-optimal representation. To address this issue we introduce a model which iteratively polishes the document representation on many passes through the document. As part of our model, we also introduce a selective reading mechanism that decides more accurately the extent to which each sentence in the model should be updated. Experimental results on the CNN/DailyMail and DUC2002 datasets demonstrate that our model significantly outperforms state-of-the-art extractive systems when evaluated by machines and by humans.

Question: What is the difference between a document representation and a document representation learning?


MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets.. We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.

Question: What is the latest version of the molecular evolutionary genetics analysis software?


Weighted extreme learning machine for imbalance learning. Extreme learning machine (ELM) is a competitive machine learning technique, which is simple in theory and fast in implementation. The network types are ''generalized'' single hidden layer feedforward networks, which are quite diversified in the form of variety in feature mapping functions or kernels. To deal with data with imbalanced class distribution, a weighted ELM is proposed which is able (1) it is simple in theory and convenient in implementation; (2) a wide type of feature mapping functions or kernels are available for the proposed framework; (3) the proposed method can be applied directly into multiclass classification tasks. In addition, after integrating with the weighting scheme, (1) the weighted ELM is able to deal with data with imbalanced class distribution while maintain the good performance on well balanced data as unweighted ELM; (2) by assigning different weights for each example according to users' needs, the weighted ELM can be generalized to cost sensitive learning. & 2012 Elsevier B.V. All rights reserved.

Question: What is the difference between ELM and SVM?


Inferring Query Performance Using Pre-retrieval Predictors. The prediction of query performance is an interesting and important issue in Information Retrieval (IR). Current predictors involve the use of relevance scores, which are time-consuming to compute. Therefore, current predictors are not very suitable for practical applications. In this paper, we study a set of predictors of query performance, which can be generated prior to the retrieval process. The linear and non-parametric correlations of the predictors with query performance are thoroughly assessed on the TREC disk4 and disk5 (minus CR) collections. According to the results, some of the proposed predictors have significant correlation with query performance, showing that these predictors can be useful to infer query performance in practical applications.

Question: What is the correlation of the predictors with query performance?


Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing. Spoken dialogue systems typically use predefined semantic slots to parse users' natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the frame semantics theory to automatically induce and fill the semantic slots in an unsupervised fashion? To do this, we propose the use of a state-of-the-art frame-semantic parser, and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Empirical experiments on a real-world spoken dialogue dataset show that the automatically induced semantic slots are in line with the reference slots created by domain experts: we observe a mean averaged precision of 69.36% using ASR-transcribed data. Our slot filling evaluations also indicate the promising future of this proposed approach.

Question: How can we automatically induce semantic slots?


Comparative Study on Vision Based Rice Seed Varieties Identification. This paper presents a system for automated classification of rice variety for rice seed production using computer vision and image processing techniques. Rice seeds of different varieties are visually very similar in color, shape and texture that make the classification of rice seed varieties at high accuracy challenging. We investigated various feature extraction techniques for efficient rice seed image representation. We analyzed the performance of powerful classifiers on the extracted features for finding the robust one. Images of six different rice seed varieties in northern Vietnam were acquired and analyzed. Our experiments have demonstrated that the average accuracy of our classification system can reach 90.54% using Random Forest method with a simple feature extraction technique. This result can be used for developing a computer-aided machine vision system for automated assessment of rice seeds purity.

Question: What is the average accuracy of the system?


Learning to detect and localize many objects from few examples. The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this work, we propose a new neural model which directly predicts bounding box coordinates. The particularity of our contribution lies in the local computations of predictions with a new form of local parameter sharing which keeps the overall amount of trainable parameters low. Key components of the model are spatial 2D-LSTM recurrent layers which convey contextual information between the regions of the image. We show that this model is more powerful than the state of the art in applications where training data is not as abundant as in the classical configuration of natural images and Imagenet/Pascal VOC tasks. We particularly target the detection of text in document images, but our method is not limited to this setting. The proposed model also facilitates the detection of many objects in a single image and can deal with inputs of variable sizes without resizing.

Question: How to detect many objects in a single image?


Key drivers of internet banking services use. Purpose – The purpose of this paper is to analyse the determinants of internet banking use, paying special attention to the role of product involvement, perceived risk and trust. Design/methodology/approach – The impact of trust, perceived risks, product involvement and TAM beliefs (ease of use and usefulness) on internet banking adoption is tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users and the data are collected through an internet survey. Risk is measured as a formative construct. Findings – Data analysis shows that TAM beliefs and perceived risks (security, privacy, performance and social) have a direct influence on e-banking adoption. Trust appears as a key variable that reduces perceived risk. Involvement plays an important role in increasing perceived ease of use. Practical implications – This research provides banks with knowledge of what aspects to highlight in their communications strategies to increase their internet banking services adoption rates. The research findings show managers that web contents and design are key tools to increase internet banking services adoption. Practical recommendations to increase web usefulness and trust, and guidelines to reduce perceived risk dimensions are also provided. Originality/value – Despite the importance of trust issues and risk perceptions for internet banking adoption, only limited work has been done to identify trust and risk dimensions in an online banking context. We have evaluated the impact of each risk dimension instead of treating risk as a whole. Furthermore, risk has been measured as a formative construct because there is no reason to expect that risk dimensions in online financial services are correlated.

Question: What is the role of trust in internet banking adoption?


Area-Preservation Mapping using Optimal Mass Transport. We present a novel area-preservation mapping/flattening method using the optimal mass transport technique, based on the Monge-Brenier theory. Our optimal transport map approach is rigorous and solid in theory, efficient and parallel in computation, yet general for various applications. By comparison with the conventional Monge-Kantorovich approach, our method reduces the number of variables from O(n2) to O(n), and converts the optimal mass transport problem to a convex optimization problem, which can now be efficiently carried out by Newton's method. Furthermore, our framework includes the area weighting strategy that enables users to completely control and adjust the size of areas everywhere in an accurate and quantitative way. Our method significantly reduces the complexity of the problem, and improves the efficiency, flexibility and scalability during visualization. Our framework, by combining conformal mapping and optimal mass transport mapping, serves as a powerful tool for a broad range of applications in visualization and graphics, especially for medical imaging. We provide a variety of experimental results to demonstrate the efficiency, robustness and efficacy of our novel framework.

Question: What is the area-preservation mapping?


Proof Analysis in Modal Logic. A general method for generating contractionand cut-free sequent calculi for a large family of normal modal logics is presented. The method covers all modal logics characterized by Kripke frames determined by universal or geometric properties and it can be extended to treat also Gödel–Löb provability logic. The calculi provide direct decision methods through terminating proof search. Syntactic proofs of modal undefinability results are obtained in the form of conservativity theorems.

Question: What is a proof analysis in modal logic?


Kqueue - A Generic and Scalable Event Notification Facility. ApplicationsrunningonaUNIX platformneedto benotified whensomeactivity occurson asocketor otherdescriptor, andthis is traditionallydonewith theselect()or poll() systemcalls. However, it hasbeenshown that the performanceof thesecallsdoesnotscalewell with anincreasingnumberof descriptors.Theseinterfacesarealso limited in therespecthatthey areunableto handleother potentiallyinterestingactivitiesthatanapplicationmight be interestedin, thesemight includesignals,file system changes,and AIO completions. This paperpresentsa genericevent delivery mechanism,which allows an application to selectfrom a wide rangeof event sources, andbenotifiedof activity on thesesourcesin a scalable andefficient manner . The mechanismmay be extended to coverfutureeventsourceswithoutchangingtheapplicationinterface.

Question: What is a generic event notification mechanism?


Adaptive Control of KNTU Planar Cable-Driven Parallel Robot with Uncertainties in Dynamic and Kinematic Parameters. This paper addresses the design and implementation of adaptive control on a planar cable-driven parallel robot with uncertainties in dynamic and kinematic parameters. To develop the idea, firstly, adaptation is performed on dynamic parameters and it is shown that the controller is stable despite the kinematic uncertainties. Then, internal force term is linearly separated into a regressor matrix in addition to a kinematic parameter vector that contains estimation error. In the next step to improve the controller performance, adaptation is performed on both the dynamic and kinematic parameters. It is shown that the performance of the proposed controller is improved by correction in the internal forces. The proposed controller not only keeps all cables in tension for the whole workspace of the robot, it is computationally simple and it does not require measurement of the end-effector acceleration as well. Finally, the effectiveness of the proposed control algorithm is examined through some experiments on KNTU planar cable-driven parallel robot and it is shown that the proposed control algorithm is able to provide suitable performance in practice.

Question: What is the difference between a planar cable-driven parallel robot and a planar cable-driven serial robot?


Comparison of automatic shot boundary detection algorithms. Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. A the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic com the shots, only few comparative investigations on early shot boundary detection algorithms have been pu These investigations mainly concentrate on measuring the edit detection performance, however, do not con algorithms' ability to classify the types and to locate the boundaries of the edits correctly. This paper extend comparative investigations. More recent algorithms designed explicitly to detect specific complex editing ope such as fades and dissolves are taken into account, and their ability to classify the types and locate the boun such edits are examined. The algorithms' performance is measured in terms of hit rate, number of false hits, a rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rat solves is usually unacceptably high, ranging from 50% up to over 400%. Moreover, all algorithms seem to fai roughly the same conditions.

Question: What is the difference between a hard cut and a fade?


Self-Adjusting Binary Search Trees. The splay tree, a self-adjusting form of binary search tree, is developed and analyzed. The binary search tree is a data structure for representing tables and lists so that accessing, inserting, and deleting items is easy. On an n-node splay tree, all the standard search tree operations have an amortized time bound of O(log n) per operation, where by "amortized time" is meant the time per operation averaged over a worst-case sequence of operations. Thus splay trees are as efficient as balanced trees when total running time is the measure of interest. In addition, for sufficiently long access sequences, splay trees are as efficient, to within a constant factor, as static optimum search trees. The efficiency of splay trees comes not from an explicit structural constraint, as with balanced trees, but from applying a simple restructuring heuristic, called splaying, whenever the tree is accessed. Extensions of splaying give simplified forms of two other data structures: lexicographic or multidimensional search trees and link/cut trees.

Question: What is a self-adjusting binary search tree?


The return of AdaBoost.MH: multi-class Hamming trees. Within the framework of ADABOOST.MH, we propose to train vector-valued decision trees to optimize the multi-class edge without reducing the multi-class problem toK binary one-againstall classifications. The key element of the method is a vector-valued decision stump, factorized into an input-independent vector of length K and label-independent scalar classifier. At inner tree nodes, the label-dependent vector is discarded and the binary classifier can be used for partitioning the input space into two regions. The algorithm retains the conceptual elegance, power, and computational efficiency of binary ADABOOST. In experiments it is on par with support vector machines and with the best existing multi-class boosting algorithm AOSOLOGITBOOST, and it is significantly better than other known implementations of ADABOOST.MH.

Question: AdaBoost.MH


The Genetic Structure of Marijuana and Hemp. Despite its cultivation as a source of food, fibre and medicine, and its global status as the most used illicit drug, the genus Cannabis has an inconclusive taxonomic organization and evolutionary history. Drug types of Cannabis (marijuana), which contain high amounts of the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC), are used for medical purposes and as a recreational drug. Hemp types are grown for the production of seed and fibre, and contain low amounts of THC. Two species or gene pools (C. sativa and C. indica) are widely used in describing the pedigree or appearance of cultivated Cannabis plants. Using 14,031 single-nucleotide polymorphisms (SNPs) genotyped in 81 marijuana and 43 hemp samples, we show that marijuana and hemp are significantly differentiated at a genome-wide level, demonstrating that the distinction between these populations is not limited to genes underlying THC production. We find a moderate correlation between the genetic structure of marijuana strains and their reported C. sativa and C. indica ancestry and show that marijuana strain names often do not reflect a meaningful genetic identity. We also provide evidence that hemp is genetically more similar to C. indica type marijuana than to C. sativa strains.

Question: What is the difference between marijuana and hemp?


Object-Oriented Design Process Model. ion x x x x Relationship x x x x x

Question: What is the relationship between the object-oriented design process model and the object-oriented design process?


Real time lane detection for autonomous vehicles. An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehiclepsilas heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.

Question: What is the difference between a painted and unpainted road?


Representing Text for Joint Embedding of Text and Knowledge Bases. Models that learn to represent textual and knowledge base relations in the same continuous latent space are able to perform joint inferences among the two kinds of relations and obtain high accuracy on knowledge base completion (Riedel et al., 2013). In this paper we propose a model that captures the compositional structure of textual relations, and jointly optimizes entity, knowledge base, and textual relation representations. The proposed model significantly improves performance over a model that does not share parameters among textual relations with common sub-structure.

Question: What is the compositional structure of textual relations?


Addressing DODAG inconsistency attacks in RPL networks. RPL is a routing protocol for low-power and lossy constrained node networks. A malicious node can manipulate header options used by RPL to track DODAG inconsistencies, thereby causing denial of service attacks, increased control message overhead, and black-holes at the targeted node. RPL counteracts DODAG inconsistencies by using a fixed threshold, upon reaching which all subsequent packets with erroneous header options are ignored. However, the fixed threshold is arbitrary and does not resolve the black-hole issue either. To address this we present a mitigation strategy that allows nodes to dynamically adapt against a DODAG inconsistency attack. We also present the forced black-hole attack problem and a solution that can be used to mitigate it. Results from our experiments show that our proposed approach mitigates these attacks without any significant overhead.

Question: How to mitigate DODAG inconsistency attacks in RPL networks?


Learning Common Representation from RGB and Depth Images. We propose a new deep learning architecture for the tasks of semantic segmentation and depth prediction from RGBD images. We revise the state of art based on the RGB and depth feature fusion, where both modalities are assumed to be available at train and test time. We propose a new architecture where the feature fusion is replaced with a common deep representation. Combined with an encoder-decoder type of the network, the architecture can jointly learn models for semantic segmentation and depth estimation based on their common representation. This representation, inspired by multi-view learning, offers several important advantages, such as using one modality available at test time to reconstruct the missing modality. In the RGB-D case, this enables the cross-modality scenarios, such as using depth data for semantically segmentation and the RGB images for depth estimation. We demonstrate the effectiveness of the proposed network on two publicly available RGB-D datasets. The experimental results show that the proposed method works well in both semantic segmentation and depth estimation tasks.

Question: What is the difference between RGB and depth images?


Parsing C/C++ Code without Pre-processing. It is difficult to develop style-preserving source-to-source transformation engines for C and C++. The main reason is not the complexity of those languages, but the use of the C pre-processor (cpp), especially ifdefs and macros. This has for example hindered the development of refactoring tools for C and C++. In this paper we propose to combine multiple techniques and heuristics to parse C/C++ source files as-is, while still having only a few modifications to the original grammars of C and C++. We rely on the fact that in most C and C++ software, programmers follow a limited number of conventions on the use of cpp which makes it possible to disambiguate different situations by just looking at the context, names, or indentation of cpp constructs. We have implemented a parser, Yacfe, based on these techniques and evaluated it on 16 large open source projects. Yacfe can on average parse 96% of those projects correctly. As a side effect, we also found mistakes in code that was not compiled because it was protected by particular ifdefs, but that was still analyzed by Yacfe. Using Yacfe on new projects may require adapting some of our techniques. We found that as conventions and idioms are shared by many projects, the adaptation time is on average less than 2 hours for a new project.

Question: How to parse c/c++ code without pre-processing?


The scaling laws of human travel. The dynamic spatial redistribution of individuals is a key driving force of various spatiotemporal phenomena on geographical scales. It can synchronize populations of interacting species, stabilize them, and diversify gene pools. Human travel, for example, is responsible for the geographical spread of human infectious disease. In the light of increasing international trade, intensified human mobility and the imminent threat of an influenza A epidemic, the knowledge of dynamical and statistical properties of human travel is of fundamental importance. Despite its crucial role, a quantitative assessment of these properties on geographical scales remains elusive, and the assumption that humans disperse diffusively still prevails in models. Here we report on a solid and quantitative assessment of human travelling statistics by analysing the circulation of bank notes in the United States. Using a comprehensive data set of over a million individual displacements, we find that dispersal is anomalous in two ways. First, the distribution of travelling distances decays as a power law, indicating that trajectories of bank notes are reminiscent of scale-free random walks known as Lévy flights. Second, the probability of remaining in a small, spatially confined region for a time T is dominated by algebraically long tails that attenuate the superdiffusive spread. We show that human travelling behaviour can be described mathematically on many spatiotemporal scales by a two-parameter continuous-time random walk model to a surprising accuracy, and conclude that human travel on geographical scales is an ambivalent and effectively superdiffusive process.

Question: What is the distribution of travelling distances?


Greedy Sampling of Graph Signals. Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression. In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set nontrivial and hard to analyze. Indeed, though conditions for graph signal interpolation from noiseless samples exist, they do not lead to a unique sampling set. The presence of noise makes choosing among these sampling sets a hard combinatorial problem. Although greedy sampling schemes are commonly used in practice, they have no performance guarantee. This work takes a twofold approach to address this issue. First, universal performance bounds are derived for the Bayesian estimation of graph signals from noisy samples. In contrast to currently available bounds, they are not restricted to specific sampling schemes and hold for any sampling sets. Second, this paper provides near-optimal guarantees for greedy sampling by introducing the concept of approximate submodularity and updating the classical greedy bound. It then provides explicit bounds on the approximate supermodularity of the interpolation mean-square error showing that it can be optimized with worst case guarantees using greedy search even though it is not supermodular. Simulations illustrate the derived bound for different graph models and show an application of graph signal sampling to reduce the complexity of kernel principal component analysis.

Question: Greedy Sampling of Graph Signals


Privacy Preserving Data. In this paper we introduce the concept of privacy preserving data mining. In our model, two parties owning conndential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information. This problem has many practical and important applications, such as in medical research with conndential patient records. Data mining algorithms are usually complex, especially as the size of the input is measured in megabytes, if not gigabytes. A generic secure multi-party computation solution, based on evaluation of a circuit computing the algorithm on the entire input, is therefore of no practical use. We focus on the problem of decision tree learning and use ID3, a popular and widely used algorithm for this problem. We present a solution that is considerably more eecient than generic solutions. It demands very few rounds of communication and reasonable bandwidth. In our solution, each party performs by itself a computation of the same order as computing the ID3 algorithm for its own database. The results are then combined using eecient cryptographic protocols, whose overhead is only logarithmic in the number of transactions in the databases. We feel that our result is a substantial contribution, demonstrating that secure multi-party computation can be made practical, even for complex problems and large inputs.

Question: What is the privacy preserving data mining?


Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization. Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics.

Question: What is the first large-scale Canadian peacekeeping mission?


Video Question Answering via Hierarchical Spatio-Temporal Attention Networks. Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the lack of modeling the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoderdecoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the spatio-temporal attentional encoder-decoder learning method with multi-step reasoning process for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.

Question: How to model the temporal dynamics of video contents?


Using Ontology-Based Traffic Models for More Efficient Decision Making of Autonomous Vehicles. The paper describes how a high-level abstract world model can be used to support the decision-making process of an autonomous driving system. The approach uses a hierarchical world model and distinguishes between a low-level model for the trajectory planning and a high-level model for solving the traffic coordination problem. The abstract world model used in the CyberCars-2 project is presented. It is based on a topological lane segmentation and introduces relations to represent the semantic context of the traffic scenario. This makes it much easier to realize a consistent and complete driving control system, and to analyze, evaluate and simulate such a system.

Question: How can ontology-based traffic models be used for more efficient decision making of autonomous vehicles?


Introduction to smart learning analytics: foundations and developments in video-based learning. Smart learning has become a new term to describe technological and social developments (e.g., Big and Open Data, Internet of Things, RFID, and NFC) enable effective, efficient, engaging and personalized learning. Collecting and combining learning analytics coming from different channels can clearly provide valuable information in designing and developing smart learning. Although, the potential of learning analytics to enable smart learning is very promising area, it remains non-investigated and even ill-defined concept. The paper defines the subset of learning analytics that focuses on supporting the features and the processes of smart learning, under the term Smart Learning Analytics. This is followed by a brief discussion on the prospects and drawbacks of Smart Learning Analytics and their recent foundations and developments in the area of Video-Based Learning. Drawing from our experience with the recent international workshops in Smart Environments and Analytics in Video-Based Learning, we present the state-of-the-art developments as well as the four selected contributions. The paper further draws attention to the great potential and need for research in the area of Smart Learning Analytics.

Question: What is the subset of learning analytics that focuses on supporting the features and the processes of smart learning?


An integrated framework for human activity recognition. This poster presents an integrated framework to enable using standard non-sequential machine learning tools for accurate multi-modal activity recognition. Our framework contains simple pre- and post-classification strategies such as class-imbalance correction on the learning data using structure preserving oversampling, leveraging the sequential nature of sensory data using smoothing of the predicted label sequence and classifier fusion, respectively, for improved performance. Through evaluation on recent publicly-available OPPORTUNITY activity datasets comprising of a large amount of multi-dimensional, continuous-valued sensory data, we show that our proposed strategies are effective in improving the performance over common techniques such as One Nearest Neighbor (1NN) and Support Vector Machines (SVM). Our framework also shows better performance over sequential probabilistic models, such as Conditional Random Field (CRF) and Hidden Markov Models (HMM) and when these models are used as meta-learners.

Question: What is the framework for human activity recognition?


Compact Microstrip-to-CPS Transition for UWB Application. 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.

Question: What is a balun?


Path Planning and Steering Control for an Automatic Perpendicular Parking Assist System. This paper considers the perpendicular reverse parking problem of front wheel steering vehicles. Relationships between the widths of the parking aisle and the parking place, as well as the parameters and initial position of the vehicle for planning a collision-free reverse perpendicular parking in one maneuver are first presented. Two types of steering controllers (bang-bang and saturated tanh-type controllers) for straightline tracking are proposed and evaluated. It is demonstrated that the saturated controller, which is continuous, achieves also quick steering avoiding chattering and can be successfully used in solving parking problems. Simulation results and first experimental tests confirm the effectiveness of the proposed control scheme.

Question: How to control a vehicle to park perpendicular?


Understanding of Antecedents to Achieve Customer Trust and Customer Intention to Purchase E-Commerce in Social Media , an Empirical Assessment. Received Dec 9, 2016 Revised Mar 27, 2017 Accepted Apr 11, 2017 This study aims to analyze empirically three factors antecedents of trust they are system quality, information quality, and service quality. Customer trust is used in determining customer intention to purchase of e-commerce in social media (facebook). A number of respondents were 451. The results of this study concluded that three factors antecedents of trust directly had a positive impact to customer trust and indirectly had positive impact on customer intention to purchase in e-commerce transactions on social media. Keyword:

Question: What are the factors that affect customer trust?


Click Through Rate Prediction for Contextual Advertisment Using Linear Regression. This research presents an innovative and unique way of solving the advertisement prediction problem which is considered as a learning problem over the past several years. Online advertising is a multi-billion-dollar industry and is growing every year with a rapid pace. The goal of this research is to enhance click through rate of the contextual advertisements using Linear Regression. In order to address this problem, a new technique propose in this paper to predict the CTR which will increase the overall revenue of the system by serving the advertisements more suitable to the viewers with the help of feature extraction and displaying the advertisements based on context of the publishers. The important steps include the data collection, feature extraction, CTR prediction and advertisement serving. The statistical results obtained from the dynamically used technique show an efficient outcome by fitting the data close to perfection for the LR technique using optimized feature selection. Keywords-Click Through Rate(CTR), Contextual Advertisements, Machine Learning, Web advertisements, Regression Problem.

Question: What is the click through rate prediction for contextual advertisment using linear regression?


Comparison of Three Different CNN Architectures for Age Classification. As one of the powerful tools of machine learning, Convolutional Neural Network (CNN) architectures are used tosolve complex problems like image recognition, video analysisand natural language processing. In this paper, three differentCNN architectures for age classification using face images arecompared. The Morph dataset containing over 55k images isused in experiments and success of a 6-layer CNN and 2 variantsof ResNet with different depths are compared. The images in thedataset are divided into 6 different age classes. While 80% of theimages are used in training of the networks, the rest of the 20% isused for testing. The performance of the networks are comparedaccording to two different criteria namely, the ability to makethe estimation pointing the exact age classes of test images andthe ability to make the estimation pointing the exact age classesor at most neighboring classes of the images. According to theperformance results obtained, with 6-layer network, it is possibleto estimate the exact or neighboring classes of the images withless than 5% error. It is shown that for a 6 class age classificationproblem 6-layer network is more successful than the deeperResNet counterparts since 6-layer network is less susceptible tooverfitting for this problem.

Question: What is the difference between CNN and RNN?


LHCP and RHCP Substrate Integrated Waveguide Antenna Arrays for Millimeter-Wave Applications. Left-hand and right-hand circularly polarized (LHCP and RHCP) substrate integrated waveguide (SIW) antenna arrays are presented at the 28-GHz band for millimeter-wave (mm-wave) applications. Two types of circularly polarized (CP) antenna elements are designed to achieve respective LHCP and RHCP performance. Eight-element LHCP and RHCP antenna arrays have been implemented with feeding networks and measured. Based on the measurement, the LHCP and RHCP antenna arrays have impedance bandwidths of 1.54 and 1.7 GHz within $| S_{\rm{11}}| < - \text{10 dB}$, whereas 3-dB axial-ratio bandwidths are 1.1 and 1.3 GHz, respectively. Each of the fabricated LHCP and RHCP antenna arrays is accomplished with a gain up to 13.09 and 13.52 dBi. Most of the measured results are validated with the simulated ones. The proposed CP antenna arrays can provide low-cost, broadband characteristics, and high-gain radiation performance with CP properties for mm-wave applications.

Question: What is the difference between LHCP and RHCP?


A Card Game Description Language. We present initial research regarding a system capable of generating novel card games. We furthermore propose a method for computationally analysing existing games of the same genre. Ultimately, we present a formalisation of card game rules, and a context-free grammar Gcardgame capable of expressing the rules of a large variety of card games. Example derivations are given for the poker variant Texas hold 'em, Blackjack and UNO. Stochastic simulations are used both to verify the implementation of these well-known games, and to evaluate the results of new game rules derived from the grammar. In future work, this grammar will be used to evolve completely novel card games using a grammarguided genetic program.

Question: What is a card game?


Machinery equipment early fault detection using Artificial Neural Network based Autoencoder. Machinery equipment early fault detection is still in an open challenge. The objective of this paper is to introduce a parametric method Artificial Neural Network based Autoencoder implemented to perform early fault detection of a machinery equipment. The performance of this method is then compared to one of the industry state of the art nonparametric methods called Similarity Based Modeling. The comparison is done by analyzing the implementation result on both artificial and real case dataset. Root Mean Square Error (RMSE) is applied to measure the performance. Based on the result of the research, both of these methods are effective to do pattern recognition and able to identify data anomaly or in this case is fault identification.

Question: What is the difference between parametric and nonparametric methods?


Predictive State Temporal Difference Learning. We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By comparison, subspace identification (SSID) methods are designed to select a feature set which preserves as much information as possible about state. In this paper we connect the two approaches, looking at the problem of reinforcement learning with a large set of features, each of which may only be marginally useful for value function approximation. We introduce a new algorithm for this situation, called Predictive State Temporal Difference (PSTD) learning. As in SSID for predictive state representations, PSTD finds a linear compression operator that projects a large set of features down to a small set that preserves the maximum amount of predictive information. As in RL, PSTD then uses a Bellman recursion to estimate a value function. We discuss the connection between PSTD and prior approaches in RL and SSID. We prove that PSTD is statistically consistent, perform several experiments that illustrate its properties, and demonstrate its potential on a difficult optimal stopping problem.

Question: What is the difference between SSID and RL?


Examining students' online interaction in a live video streaming environment using data mining and text mining. 0747-5632/$ see front matter 2012 Elsevier Ltd. A http://dx.doi.org/10.1016/j.chb.2012.07.020 ⇑ Tel.: +1 757 683 5008; fax: +1 757 683 5639. E-mail address: whe@odu.edu This study analyses the online questions and chat messages automatically recorded by a live video streaming (LVS) system using data mining and text mining techniques. We apply data mining and text mining techniques to analyze two different datasets and then conducted an in-depth correlation analysis for two educational courses with the most online questions and chat messages respectively. The study found the discrepancies as well as similarities in the students' patterns and themes of participation between online questions (student–instructor interaction) and online chat messages (student–students interaction or peer interaction). The results also identify disciplinary differences in students' online participation. A correlation is found between the number of online questions students asked and students' final grades. The data suggests that a combination of using data mining and text mining techniques for a large amount of online learning data can yield considerable insights and reveal valuable patterns in students' learning behaviors. Limitations with data and text mining were also revealed and discussed in the paper. 2012 Elsevier Ltd. All rights reserved.

Question: What is the difference between data mining and text mining?


Minimally supervised question classification on fine-grained taxonomies. This article presents a minimally supervised approach to question classification on fine-grained taxonomies. We have defined an algorithm that automatically obtains lists of weighted terms for each class in the taxonomy, thus identifying which terms are highly related to the classes and are highly discriminative between them. These lists have then been applied to the task of question classification. Our approach is based on the divergence of probability distributions of terms in plain text retrieved from the Web. A corpus of questions with which to train the classifier is not therefore necessary. As the system is based purely on statistical information, it does not require additional linguistic resources or tools. The experiments were performed on English questions and their Spanish translations. The results reveal that our system surpasses current supervised approaches in this task, obtaining a significant improvement in the experiments carried out.

Question: What is the difference between supervised and unsupervised approaches?


An Ultra-Low Power 1.7-2.7 GHz Fractional-N Sub-Sampling Digital Frequency Synthesizer and Modulator for IoT Applications in 40 nm CMOS. This paper introduces an ultra-low power 1.7-2.7-GHz fractional-N sub-sampling digital PLL (SS-DPLL) for Internet-of-Things (IoT) applications targeting compliance with Bluetooth Low Energy (BLE) and IEEE802.15.4 standards. A snapshot time-to-digital converter (TDC) acts as a digital sub-sampler featuring an increased out-of-range gain and without any assistance from the traditional counting of DCO edges, thus further reducing power consumption. With a proposed DCO-divider phase rotation in the feedback path, the impact of the digital-to-time converter's (DTC's) non-linearity on the PLL is reduced and improves fractional spurs by at least 8 dB across BLE channels. Moreover, a "variable-preconditioned LMS" calibration algorithm is introduced to dynamically correct the DTC gain error with fractional frequency control word (FCW) down to 1/16384. Fabricated in 40 nm CMOS, the SS-DPLL achieves phase noise performance of −109 dBc/Hz at 1 MHz offset, while consuming a record-low power of 1.19 mW.

Question: What is a fractional-N sub-sampling digital PLL?


Local Deep Neural Networks for Age and Gender Classification. Local deep neural networks have been recently introduced for gender recognition. Although, they achieve very good performance they are very computationally expensive to train. In this work, we introduce a simplified version of local deep neural networks which significantly reduces the training time. Instead of using hundreds of patches per image, as suggested by the original method, we propose to use 9 overlapping patches per image which cover the entire face region. This results in a much reduced training time, since just 9 patches are extracted per image instead of hundreds, at the expense of a slightly reduced performance. We tested the proposed modified local deep neural networks approach on the LFW and Adience databases for the task of gender and age classification. For both tasks and both databases the performance is up to 1% lower compared to the original version of the algorithm. We have also investigated which patches are more discriminative for age and gender classification. It turns out that the mouth and eyes regions are useful for age classification, whereas just the eye region is useful for gender classification.

Question: What is the performance of local deep neural networks for gender and age classification?


Three Implementations of SquishQL, a Simple RDF Query Language. RDF provides a basic way to represent data for the Semantic Web. We have been experimenting with the query paradigm for working with RDF data in semantic web applications. Query of RDF data provides a declarative access mechanism that is suitable for application usage and remote access. We describe work on a conceptual model for querying RDF data that refines ideas first presented in at the W3C workshop on Query Languages [14] and the design of one possible syntax, derived from [7], that is suitable for application programmers. Further, we present experience gained in three implementations of the query language.

Question: What is the syntax of the query language?


ICP-based pose-graph SLAM. Odometry-like localization solutions can be built upon Light Detection And Ranging (LIDAR) sensors, by sequentially registering the point clouds acquired along a robot trajectory. Yet such solutions inherently drift over time: we propose an approach that adds a graphical model layer on top of such LIDAR odometry layer, using the Iterative Closest Points (ICP) algorithm for registration. Reference frames called keyframes are defined along the robot trajectory, and ICP results are used to build a pose graph, that in turn is used to solve an optimization problem that provides updates for the keyframes upon loop closing, enabling the correction of the path of the robot and of the map of the environment. We present in details the configuration used to register data from the Velodyne High Definition LIDAR (HDL), and a strategy to build local maps upon which current frames are registered, either when discovering new areas or revisiting previously mapped areas. Experiments show that it is possible to build the graph using data from ICP and that the loop closings in the graph level reduces the overall drift of the system.

Question: How to build a pose graph using ICP?


Deep Learning for Imbalanced Multimedia Data Classification. Classification of imbalanced data is an important research problem as lots of real-world data sets have skewed class distributions in which the majority of data instances (examples) belong to one class and far fewer instances belong to others. While in many applications, the minority instances actually represent the concept of interest (e.g., fraud in banking operations, abnormal cell in medical data, etc.), a classifier induced from an imbalanced data set is more likely to be biased towards the majority class and show very poor classification accuracy on the minority class. Despite extensive research efforts, imbalanced data classification remains one of the most challenging problems in data mining and machine learning, especially for multimedia data. To tackle this challenge, in this paper, we propose an extended deep learning approach to achieve promising performance in classifying skewed multimedia data sets. Specifically, we investigate the integration of bootstrapping methods and a state-of-the-art deep learning approach, Convolutional Neural Networks (CNNs), with extensive empirical studies. Considering the fact that deep learning approaches such as CNNs are usually computationally expensive, we propose to feed low-level features to CNNs and prove its feasibility in achieving promising performance while saving a lot of training time. The experimental results show the effectiveness of our framework in classifying severely imbalanced data in the TRECVID data set.

Question: What is the state of the art in imbalanced data classification?


Safe and automatic live update for operating systems. Increasingly many systems have to run all the time with no downtime allowed. Consider, for example, systems controlling electric power plants and e-banking servers. Nevertheless, security patches and a constant stream of new operating system versions need to be deployed without stopping running programs. These factors naturally lead to a pressing demand for live update---upgrading all or parts of the operating system without rebooting. Unfortunately, existing solutions require significant manual intervention and thus work reliably only for small operating system patches.  In this paper, we describe an automated system for live update that can safely and automatically handle major upgrades without rebooting. We have implemented our ideas in Proteos, a new research OS designed with live update in mind. Proteos relies on system support and nonintrusive instrumentation to handle even very complex updates with minimal manual effort. The key novelty is the idea of state quiescence, which allows updates to happen only in safe and predictable system states. A second novelty is the ability to automatically perform transactional live updates at the process level, ensuring a safe and stable update process. Unlike prior solutions, Proteos supports automated state transfer, state checking, and hot rollback. We have evaluated Proteos on 50 real updates and on novel live update scenarios. The results show that our techniques can effectively support both simple and complex updates, while outperforming prior solutions in terms of flexibility, security, reliability, and stability of the update process.

Question: What is a live update?


Estimating the dimension of a linear model.

Question: How to estimate the dimension of a linear model?


Covalsa: Covariance estimation from compressive measurements using alternating minimization. The estimation of covariance matrices from compressive measurements has recently attracted considerable research efforts in various fields of science and engineering. Owing to the small number of observations, the estimation of the covariance matrices is a severely ill-posed problem. This can be overcome by exploiting prior information about the structure of the covariance matrix. This paper presents a class of convex formulations and respective solutions to the high-dimensional covariance matrix estimation problem under compressive measurements, imposing either Toeplitz, sparseness, null-pattern, low rank, or low permuted rank structure on the solution, in addition to positive semi-definiteness. To solve the optimization problems, we introduce the Co-Variance by Augmented Lagrangian Shrinkage Algorithm (CoVALSA), which is an instance of the Split Augmented Lagrangian Shrinkage Algorithm (SALSA). We illustrate the effectiveness of our approach in comparison with state-of-the-art algorithms.

Question: What is the difference between covariance and correlation?


Analysing the use of interactive technology to implement interactive teaching. Recent policy initiatives in England have focused on promoting 'interactive' teaching in schools, with a clear expectation that this will lead to improvements in learning. This expectation is based on the perceived success of such approaches in other parts of the world.At the same time, there has been a large investment in Information and Communication Technology (ICT) resources, and particularly in interactive whiteboard technology. This paper explores the idea of interactive teaching in relation to the interactive technology which might be used to support it. It explains the development of a framework for the detailed analysis of teaching and learning in activity settings which is designed to represent the features and relationships involved in interactivity. When applied to a case study of interactive teaching during a lesson involving a variety of technology-based activities, the framework reveals a confusion of purpose in students' use of an ICT resource that limits the potential for learning when students are working independently. Discussion of relationships between technical and pedagogical interactivity points a way forward concerning greater focus on learning goals during activity in order to enable learners to be more autonomous in exploiting ICT's affordances, and the conclusion identifies the variables and issues which need to be considered in future research which will illuminate this path.

Question: What is the difference between pedagogical and technical interactivity?


Noninvasive in vivo glucose sensing on human subjects using mid-infrared light.. Mid-infrared quantum cascade laser spectroscopy is used to noninvasively predict blood glucose concentrations of three healthy human subjects in vivo. We utilize a hollow-core fiber based optical setup for light delivery and collection along with a broadly tunable quantum cascade laser to obtain spectra from human subjects and use standard chemo-metric techniques (namely partial least squares regression) for prediction analysis. Throughout a glucose concentration range of 80-160 mg/dL, we achieve clinically accurate predictions 84% of the time, on average. This work opens a new path to a noninvasive in vivo glucose sensor that would benefit the lives of hundreds of millions of diabetics worldwide.

Question: What is the average blood glucose concentration of a healthy human?


The immune contexture in human tumours: impact on clinical outcome. Tumours grow within an intricate network of epithelial cells, vascular and lymphatic vessels, cytokines and chemokines, and infiltrating immune cells. Different types of infiltrating immune cells have different effects on tumour progression, which can vary according to cancer type. In this Opinion article we discuss how the context-specific nature of infiltrating immune cells can affect the prognosis of patients.

Question: What is the context-specific nature of infiltrating immune cells?


A Novel Approach for Brain Tumor Detection Using Support Vector Machine , K-Means and PCA Algorithm. The conventional method of detection and classification of brain tumor is by human inspection with the use of medical resonant brain images. But it is impractical when large amounts of data is to be diagnosed and to be reproducible. And also the operator assisted classification leads to false predictions and may also lead to false diagnose. Medical Resonance images contain a noise caused by operator performance which can lead to serious inaccuracies classification. The use Support Vector Machine, Kmean and PCA shown great potential in this field. Principal Component Analysis gives fast and accurate tool for Feature Extraction of the tumors. Kmean is used for Image Segmentation. Support Vector Mahine with Training and Testing of Brain Tumor Images techniques is implemented for automated brain tumor classification.

Question: What is the novel approach for brain tumor detection using support vector machine, k-means and pca algorithm?


Using NLP techniques for file fragment classification. The classification of file fragments is an important problem in digital forensics. The literature does not include comprehensive work on applying machine learning techniques to this problem. In this work, we explore the use of techniques from natural language processing to classify file fragments. We take a supervised learning approach, based on the use of support vector machines combined with the bag-of-words model, where text documents are represented as unordered bags of words. This technique has been repeatedly shown to be effective and robust in classifying text documents (e.g., in distinguishing positive movie reviews from negative ones). In our approach, we represent file fragments as "bags of bytes" with feature vectors consisting of unigram and bigram counts, as well as other statistical measurements (including entropy and others). We made use of the publicly available Garfinkel data corpus to generate file fragments for training and testing. We ran a series of experiments, and found that this approach is effective in this domain as well. a 2012 O. Zhulyn, S. Fitzgerald & G. Mathews. Published by Elsevier Ltd. All rights reserved.

Question: What is the bag-of-words model?


A two-stage short-term load forecasting approach using temperature daily profiles estimation. Electrical load forecasting plays an important role in the regular planning of power systems, in which load is influenced by several factors that must be analysed and identified prior to modelling in order to ensure better and instant load balancing between supply and demand. This paper proposes a two-stage approach for short-term electricity load forecasting. In the first stage, a set of day classes of load profiles are identified using K-means clustering algorithm alongside daily temperature estimation profiles. The proposed estimation method is particularly useful in case of lack of historical regular temperature data. While in the second stage, the stacked denoising autoencoders approach is used to build regression models able to forecast each day type independently. The obtained models are trained and evaluated using hourly electricity power data offered by Algeria's National Electricity and Gas Company. Several models are investigated to substantiate the accuracy and effectiveness of the proposed approach.

Question: What is the denoising autoencoder?


Interactions between location and task affect the spatial and directional firing of hippocampal neurons.. When rats forage for randomly dispersed food in a high walled cylinder the firing of their hippocampal "place" cells exhibits little dependence on the direction faced by the rat. On radial arm mazes and similar tasks, place cells are strongly directionally selective within their fields. These tasks differ in several respects, including the visual environment, configuration of the traversable space, motor behavior (e.g., linear and angular velocities), and behavioral context (e.g., presence of specific, consistent goal locations within the environment). The contributions of these factors to spatial and directional tuning of hippocampal neurons was systematically examined in rats performing several tasks in either an enriched or a sparse visual environment, and on different apparati. Place fields were more spatially and directionally selective on a radial maze than on an open, circular platform, regardless of the visual environment. On the platform, fields were more directional when the rat searched for food at fixed locations, in a stereotypic and directed manner, than when the food was scattered randomly. Thus, it seems that place fields are more directional when the animal is planning or following a route between points of special significance. This might be related to the spatial focus of the rat's attention (e.g., a particular reference point). Changing the behavioral task was also accompanied by a change in firing location in about one-third of the cells. Thus, hippocampal neuronal activity appears to encode a complex interaction between locations, their significance and the behaviors the rat is called upon to execute.

Question: How does the environment affect the spatial and directional firing of hippocampal neurons?


Mining the Web for Bilingual Text. STRAND (Resnik, 1998) is a languageindependent system for automatic discovery of text in parallel translation on the World Wide Web. This paper extends the preliminary STRAND results by adding automatic language identification, scaling up by orders of magnitude, and formally evaluating performance. The most recent end-product is an automatically acquired parallel corpus comprising 2491 English-French document pairs, approximately 1.5 million words per language. 1 I n t r o d u c t i o n Text in parallel translation is a valuable resource in natural language processing. Statistical methods in machine translation (e.g. (Brown et al., 1990)) typically rely on large quantities of bilingual text aligned at the document or sentence level, and a number of approaches in the burgeoning field of crosslanguage information retrieval exploit parallel corpora either in place of or in addition to mappings between languages based on information from bilingual dictionaries (Davis and Dunning, 1995; Landauer and Littman, 1990; Hull and Oard, 1997; Oard, 1997). Despite the utility of such data, however, sources of bilingual text are subject to such limitations as licensing restrictions, usage fees, restricted domains or genres, and dated text (such as 1980's Canadian politics); or such sources simply may not exist for * This work was supported by Department of Defense contract MDA90496C1250, DARPA/ITO Contract N66001-97-C-8540, and a research grant from Sun Microsystems Laboratories. The author gratefully acknowledges the comments of the anonymous reviewers, helpful discussions with Dan Melamed and Doug Oard, and the assistance of Jeff Allen in the French-English experimental evaluation. language pairs of interest. Although the majority of Web content is in English, it also shows great promise as a source of multilingual content. Using figures from the Babel survey of multilinguality on the Web (h tZp : / /www. i s o c . o r g / ) , it is possible to

Question: What is the largest language independent system for automatic discovery of text in parallel translation on the World Wide Web?


Search-Oriented Conversational AI (SCAI). The aim of SCAI@ICTIR2017 is to bring together IR and AI communities to instigate future direction of search-oriented conversational systems. We identified the number of research areas related to conversational AI which is of actual interest to both communities and which have not been fully explored yet. We think it's beneficial to exchange our visions. We solicit the paper submissions and more importantly proposals for panel discussions where researchers can exchange opinions and experiences. We believe that the proposed workshop is relevant to ICTIR since we look for novel contributions to search-oriented conversational systems which are a new and promising area.

Question: What is the aim of the workshop?


Is imitation learning the route to humanoid robots?. This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in the form of movement primitives. It is reviewed here how research on representations of, and functional connections between, action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning are also described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical-learning research. Parallels and differences between biological and computational approaches to imitation are highlighted and an overview of current projects that actually employ imitation learning for humanoid robots is given.

Question: What is imitation learning?


Cryptocash, cryptocurrencies, and cryptocontracts. One of the central challenges for mathematical cryptography is to create a payment system that provides the advantages of cash in a digital world. In this expository article we describe two very different solutions to this problem. The first is an elliptic-curve-based version of a construction of S. Brands, and the second is Bitcoin. We also discuss a generalization of Bitcoin that supports peer-to-peer contracts. AMS subject classifications. 94A60, 68P25, 14G50, 94-02.

Question: What is a peer-to-peer contract?


Naturalizing consciousness: a theoretical framework.. Consciousness has a number of apparently disparate properties, some of which seem to be highly complex and even inaccessible to outside observation. To place these properties within a biological framework requires a theory based on a set of evolutionary and developmental principles. This paper describes such a theory, which aims to provide a unifying account of conscious phenomena.

Question: What is the theory of consciousness?


Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning.. We present an approach for automatic diagnosis of tissue biopsies. Our methodology consists of a quantitative phase imaging tissue scanner and machine learning algorithms to process these data. We illustrate the performance by automatic Gleason grading of prostate specimens. The imaging system operates on the principle of interferometry and, as a result, reports on the nanoscale architecture of the unlabeled specimen. We use these data to train a random forest classifier to learn textural behaviors of prostate samples and classify each pixel in the image into different classes. Automatic diagnosis results were computed from the segmented regions. By combining morphological features with quantitative information from the glands and stroma, logistic regression was used to discriminate regions with Gleason grade 3 versus grade 4 cancer in prostatectomy tissue. The overall accuracy of this classification derived from a receiver operating curve was 82%, which is in the range of human error when interobserver variability is considered. We anticipate that our approach will provide a clinically objective and quantitative metric for Gleason grading, allowing us to corroborate results across instruments and laboratories and feed the computer algorithms for improved accuracy.

Question: What is the Gleason grading system?


High frequency ultrasound imaging of surface and subsurface structures of fingerprints. High frequency ultrasound is suitable for non-invasive evaluation of skin because it can obtain both morphological and biomechanical information. A specially developed acoustic microscope system with the central frequency of 100 MHz was developed. The system was capable of (1) conventional C-mode acoustic microscope imaging of thinly sliced tissue, (2) ultrasound impedance imaging of the surface of in vivo thick tissue and (3) 3D ultrasound imaging of inside of the in vivo tissue. In the present study, ultrasound impedance imaging and 3D ultrasound imaging of in vivo fingerprints were obtained. The impedance image showed pores of the sweat glands in the surface of fingerprint and 3D ultrasound imaging showed glands of the rear surface of fingerprint. Both findings were not visualized by normal optical imaging, thus the system can be applied to pathological diagnosis of skin lesions and assessment of aging of the skin in cosmetic point of view.

Question: What is the difference between high frequency ultrasound and optical imaging?


A Corpus of Proverbs Annotated with Metaphors. Proverbs are commonly metaphoric in nature and the mapping across domains is commonly established in proverbs. The abundance of proverbs in terms of metaphors makes them an extremely valuable linguistic resource since they can be utilized as a gold standard for various metaphor related linguistic tasks such as metaphor identification or interpretation. Besides, a collection of proverbs from various languages annotated with metaphors would also be essential for social scientists to explore the cultural differences between those languages. In this paper, we introduce PROMETHEUS, a dataset consisting of English proverbs and their equivalents in Italian. In addition to the word-level metaphor annotations for each proverb, PROMETHEUS contains other types of information such as the metaphoricity degree of the overall proverb, its meaning, the century that it was first recorded in and a pair of subjective questions responded by the annotators. To the best of our knowledge, this is the first multi-lingual and open-domain corpus of proverbs annotated with word-level metaphors.

Question: What is the first large-scale Canadian peacekeeping mission?


Social Media and Information Overload: Survey Results. A UK-based online questionnaire investigating aspects of usage of usergenerated media (UGM), such as Facebook, LinkedIn and Twitter, attracted 587 participants. Results show a high degree of engagement with social networking media such as Facebook, and a significant engagement with other media such as professional media, microblogs and blogs. Participants who experience information overload are those who engage less frequently with the media, rather than those who have fewer posts to read. Professional users show different behaviours to social users. Microbloggers complain of information overload to the greatest extent. Two thirds of Twitter-users have felt that they receive too many posts, and over half of Twitter-users have felt the need for a tool to filter out the irrelevant posts. Generally speaking, participants express satisfaction with the media, though a significant minority express a range of concerns including information overload and privacy. Keyword list: User-Generated Media, Social Networking, Information Overload, Information Overlook, Facebook, LinkedIn, Twitter

Question: What is the difference between a microblogger and a social media user?


PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python. The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations.

Question: What is a neural circuit simulator?


A Social-Neuroscience Perspective on Empathy. In recent years, abundant evidence from behavioral and cognitive studies and functional-imaging experiments has indicated that individuals come to understand the emotional and affective states expressed by others with the help of the neural architecture that produces such states in themselves. Such a mechanism gives rise to shared representations, which constitutes one important aspect of empathy, although not the sole one. We suggest that other components, including people's ability to monitor and regulate cognitive and emotional processes to prevent confusion between self and other, are equally necessary parts of a functional model of empathy. We discuss data from recent functional-imaging studies in support of such amodel and highlight the role of specific brain regions, notably the insula, the anterior cingulate cortex, and the right temporo-parietal region. Because this model assumes that empathy relies on dissociable informationprocessing mechanisms, it predicts a variety of structural or functional dysfunctions, depending on which mechanism is disrupted. KEYWORDS—empathy; intersubjectivity; affective sharing; perspective taking; emotion regulation Empathy refers to the capacity to understand and respond to the unique affective experiences of another person. At an experiential level of description, this psychological construct denotes a sense of similarity between one's own feelings and those expressed by another person. At a basic level of description, empathy can be conceived of as an interaction between any two individuals, with one experiencing and sharing the feeling of the other. This sharing of feelings does not necessarily imply that one will act or even feel impelled to act in a supportive or sympathetic way. The social and emotional situations eliciting empathy can be quite complex, depending on the feelings experienced by the observed person (target), the relationship of the target to the observer, and t

Question: What is the difference between empathy and sympathy?


Convolutional face finder: a neural architecture for fast and robust face detection. In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.

Question: What is a convolutional neural architecture?


Low-Power 2.4 GHz Wake-Up Radio for Wireless Sensor Networks. Power consumption is a critical issue in many wireless sensor network scenarios where network life expectancy is measured in months or years. Communication protocols typically rely on duty-cycle mechanisms to reduce the power usage at the cost of decreased network responsiveness and increased communication latency. A low-power radio-triggered device can be used to continuously monitor the channel and activate the node for incoming communications, allowing purely asynchronous operations. To be effective, the power consumption of this wake-up receiver must be on the order of tens of microwatts since this device is always active. This paper presents the ongoing efforts to design such a low-power receiver. Initial results indicate an average power consumption below 20 uW.

Question: What is the power consumption of a low-power radio-triggered device?


Energy and Performance Characterization of Mobile Heterogeneous Computing. A modern mobile application processor is a heterogeneous multi-core SoC which integrates CPU and application-specific accelerators such as GPU and DSP. It provides opportunity to accelerate other compute-intensive applications, yet mapping an algorithm to such a heterogeneous platform is not a straightforward task and has many design decisions to make. In this paper, we evaluate the performance and energy benefits of utilizing the integrated GPU and DSP cores to offload or share CPU's compute-intensive tasks. The evaluation is conducted on three representative mobile platforms, TI's OMAP3530, Qualcomn's Snapdragon S2, and Nvidia's Tegra2, using common computation tasks in mobile applications. We identify key factors that should be considered in energy-optimized mobile heterogeneous computing. Our evaluation results show that, by effectively utilizing all the computing cores concurrently, an average of 3.7X performance improvement can be achieved with the cost of 33% more power consumption, in comparison with the case of utilizing CPU only. This stands for 2.8X energy saving.

Question: How to offload compute-intensive tasks to GPU and DSP?


Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. A 3 AIS-Impute (proposed algorithm). Require: partially observed matrix O, parameter λ, decay parameter ν ∈ (0, 1), threshold ; 1: [U0, λ0, V0] = rank-1 SVD(PΩ(O)); 2: c = 1, ̃0 = ‖PΩ(O)‖F , X0 = X1 = λ0U0V > 0 ; 3: for t = 1, 2, . . . do 4: ̃t = ν̃0; θt = (c− 1)/(c+ 2); 5: λt = ν(λ0 − λ) + λ; 6: Yt = Xt + θt(Xt −Xt−1); 7: Z̃t = Yt + PΩ(O − Yt); 8: Vt−1 = Vt−1 − Vt(VtVt−1), remove zero columns; 9: Rt = QR([Vt, Vt−1]); 10: [Ut+1,Σt+1, Vt+1] = approx-SVT(Z̃t, Rt, λt, ̃t); 11: if F (Ut+1Σt+1V > t+1) > F (UtΣtV > t ); c = 1 else c = c+ 1; 12: end for 13: return Xt+1 = Ut+1Σt+1V > t+1.

Question: What is the approximate singular value decomposition?


Microstrip series fed antenna array for millimeter wave automotive radar applications. A 2D tapered microstrip antenna array is analyzed and designed in the paper by adopting the method of Modified Transmission Line Model. The pattern of the E-plane is determined by 20 linear series fed rectangular microstrip patches with tapered width, and 16 linear arrays compose the 2D array fed by four-stage T-junction power divider network. The excitation coefficients in both E and H plane follow the Taylor distribution. The antenna array achieves a pencil beam of 29dB gain at W band, and its half power beamwidths(HPBW) for E plane and H plane are both about 5°. The Side Lobe Level (SLL) of the pattern at the center frequency is lower than -17dB and 19dB at E and H plane respectively.

Question: What is the pencil beam?


Online, interactive learning of gestures for human/robot interfaces. We have developed a gesture recognition system, based on Hidden Markov Models, which can interactively recognize gestures and perform online learning of new gestures. In addition, it is able to update its model of a gesture iteratively with each example it recognizes. This system has demonstrated reliable recognition of 14 di erent gestures after only one or two examples of each. The system is currently interfaced to a Cyberglove for use in recognition of gestures from the sign language alphabet. The system is being implemented as part of an interactive interface for robot teleoperation and programming by example.

Question: What is the sign language alphabet?


Open-Domain Neural Dialogue Systems. Until recently, the goal of developing opendomain dialogue systems that not only emulate human conversation but fulfill complex tasks, such as travel planning, seemed elusive. However, we start to observe promising results in the last few years as the large amount of conversation data is available for training and the breakthroughs in deep learning and reinforcement learning are applied to dialogue. In this tutorial, we start with a brief introduction to the history of dialogue research. Then, we describe in detail the deep learning and reinforcement learning technologies that have been developed for two types of dialogue systems. First is a task-oriented dialogue system that can help users accomplish tasks, ranging from meeting scheduling to vacation planning. Second is a social bot that can converse seamlessly and appropriately with humans. In the final part of the tutorial, we review attempts to developing opendomain neural dialogue systems by combining the strengths of task-oriented dialogue systems and social bots. The tutorial material is available at http://opendialogue.miulab.tw.

Question: What is the history of dialogue research?


Resource Allocation Optimization for Device-to-Device Communication Underlaying Cellular Networks. Device-to-Device (D2D) communication will become an important technology in future networks with the increase of the requirements of local communication services. The interference between cellular communication and D2D communication can be coordinated by proper power control and resource allocation. In this paper, we analyze the resource allocation methods for D2D communication underlaying cellular networks. A novel resource allocation method that D2D can reuse the resources of more than one cellular user is proposed. After that, we discuss the selection of the optimal resource allocation method from the proposed method and the conventional methods. Finally, the performance of different methods is evaluated through numerical simulation. The simulation results show that the proposed method is the optimal resource allocation method when the D2D pair locates at the most part of the cell area in both uplink and downlink. The proposed method can improve the sum throughput of cellular communication and D2D communication significantly.

Question: What is the optimal resource allocation method for D2D communication underlaying cellular networks?


A Critical Review on Outlier Detection Techniques. Outlier Detection is a Data Mining Application. Outlier contains noisy data which is researched in various domains. The various techniques are already being researched that is more generic. We surveyed on various techniques and applications of outlier detection that provides a novel approach that is more useful for the beginners. The proposed approach helps to clean data at university level in less time with great accuracy. This survey includes the existing outlier techniques and applications where the noisy data exists. Our paper defines critical review on various techniques used in different applications of outlier detection that are to be researched further and they gives a particular type of knowledge based data i.e. more useful in research activities. So where the Anomalies is present it will be detected through outlier detection techniques and monitored accordingly especially in educational Data Mining.

Question: What is a critical review?


SDN and Virtualization Solutions for the Internet of Things: A Survey. The imminent arrival of the Internet of Things (IoT), which consists of a vast number of devices with heterogeneous characteristics, means that future networks need a new architecture to accommodate the expected increase in data generation. Software defined networking (SDN) and network virtualization (NV) are two technologies that promise to cost-effectively provide the scale and versatility necessary for IoT services. In this paper, we survey the state of the art on the application of SDN and NV to IoT. To the best of our knowledge, we are the first to provide a comprehensive description of every possible IoT implementation aspect for the two technologies. We start by outlining the ways of combining SDN and NV. Subsequently, we present how the two technologies can be used in the mobile and cellular context, with emphasis on forthcoming 5G networks. Afterward, we move to the study of wireless sensor networks, arguably the current foremost example of an IoT network. Finally, we review some general SDN-NV-enabled IoT architectures, along with real-life deployments and use-cases. We conclude by giving directions for future research on this topic.

Question: What is the state of the art on the application of SDN and NV to IoT?


Software Test Automation practices in agile development environment: An industry experience report. The increased importance of Test Automation in software engineering is very evident considering the number of companies investing in automated testing tools nowadays, with the main aim of preventing defects during the development process. Test Automation is considered an essential activity for agile methodologies being the key to speed up the quality assurance process. This paper presents empirical observations and the challenges of a test team new to agile practices and Test Automation using open source testing tools integrated in software projects that use the Scrum methodology. The results obtained showed some important issues to be discussed and the Test Automation practices collected based on the experiences and lessons learned.

Question: What is the importance of test automation in agile development?


Aggression in the Laboratory : Problems with the Validity of the Modified Taylor Competitive Reaction Time Test as a Measure of Aggression in Media Violence Studies. Many laboratory studies of aggression use a measure known as the modified Taylor Competitive Reaction Time Test (TCRTT), for which validation studies are lacking. Using sound blasts administered by the participant against a fictional human opponent, the TCRTT also allows for multiple methods of measuring aggression. The validity of the TCRTT was tested in 53 college student participants. Participants took a self-report measure of aggressiveness as well as neuropsychological measures of frontal lobe functioning predictive of aggression. Results were not supportive of the TCRTT's validity and indicated concerns regarding the use of the TCRTT as a measure of aggression. Results suggest that labaratory studies of media violence using the TCRTT are of questionable validity.

Question: What is the validity of the TCRTT?


La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs. I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 ~I. ]:~.ECONSTRUCTION PAR LES Cubi termes sp . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 I . La r e c o n s t r u c t i o n p r o p r e m e n t dire, p. 43. 2. Le c o m p o r t e m e n t en fonc t ion du groupe , p. 46. 3. Les condui tes individuel les , p. 49. 4. Les ac t iv i t4s au t r e s que la r econs t ruc t i on , p. 51. I I [ . RECONSTRUCTION PAR Bell icosi termes natalensis . . . . . . . . . . . . . . . . . . . . . 53 I V . ~V[]~CANISME DE LA CORRELATION DES TACIIES INDIVIDUELLES ET DE LA PRETENDUE REGULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 I . L a phase d ' incoordinat ion, p. 57. A. La s i tua t ion s t i m u l a n t e , p. 57 ; B. Les act iv i t~s incoordonn~es , p. 59 ; C. La local isa t ion des d~p6ts de t e r re , p. 60. 2. L a phase de coordinat ion, p. 61. A. La densi t6 cr i t ique des bou le t t e s de te r re et l ' o r i en ta t ion du c o m p o r t e m e n t , p. 61 ; B. La s t i g m e r g i e et les s t i m u l a t i o n s s imul tan6es , p. 62 ; C. N a t u r e des s t imu l i a g i s s a n t s u r les c o n s t r u c t e u r s : les odeurs fo rmes , p. 66 ; D. La t endance l ' un i t4 , p. 67 ; E, R61e de la m~moi re d a n s ia cons t ruc t ion , p. 67 ; F. Essa i de s y n t h ~ s e , p. 68. V. COMPORTEMENT DES TERMITES COMPARE A CELUI D'ANIMAUX SOLITAIRES. 73 VI . CARACTERISTIQUES FONDAMENTALES DU COMPORTEMENT DES INSECTES SOCIAUX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 V I I . AUTEURS CIT#.S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Question: What is the difference between a termite and an ant?


Multi-Modal Fashion Product Retrieval. Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a largescale e-commerce dataset.

Question: How to find a product in the fashion world?


Faceted Search. We live in an information age that requires us, more than ever, to represent, access, and use information. Over the last several decades, we have developed a modern science and technology for information retrieval, relentlessly pursuing the vision of a "memex" that Vannevar Bush proposed in his seminal article, "As We May Think." Faceted search plays a key role in this program. Faceted search addresses weaknesses of conventional search approaches and has emerged as a foundation for interactive information retrieval. User studies demonstrate that faceted search provides more effective information-seeking support to users than best-first search. Indeed, faceted search has become increasingly prevalent in online information access systems, particularly for e-commerce and site search. In this lecture, we explore the history, theory, and practice of faceted search. Although we cannot hope to be exhaustive, our aim is to provide sufficient depth and breadth to offer a useful resource to both researchers and practitioners. Because faceted search is an area of interest to computer scientists, information scientists, interface designers, and usability researchers, we do not assume that the reader is a specialist in any of these fields. Rather, we offer a self-contained treatment of the topic, with an extensive bibliography for those who would like to pursue particular aspects in more depth.

Question: What is faceted search?