OnToKnowledge: Ontology-based Tools for Knowledge Management.. Dieter Fensel, Frank van Harmelen, Michel Klein, Hans Akkermans Free University Amsterdam VUA, Division of Mathematics and Informatics De Boelelaan 1081a, NL-1081 HV Amsterdam, The Netherlands http://www.ontoknowledge.org ; contact: dieter@cs.vu.nl and Jeen Broekstra, Christiaan Fluit, Jos van der Meer, AIdministrator, The Netherlands Hans-Peter Schnurr, Rudi Studer, AIFB, University of Karlsruhe, Germany John Hughes, Uwe Krohn, John Davies, BT, Ipswich, UK Robert Engels, Bernt Bremdal, CognIT, Oslo, Norway Fredrik Ygge, Enersearch AB, Gothenburg, Sweden Thorsten Lau, Bernd Novotny, Ulrich Reimer, Swiss Life, Zürich, Switzerland Ian Horrocks, University of Manchester, UK

Question: Ontology-based Tools for Knowledge Management


Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system. Collaborative filtering (CF) technique is capable of generating personalized recommendations. However, the recommender systems utilizing CF as their key algorithms are vulnerable to shilling attacks which insert malicious user profiles into the systems to push or nuke the reputations of targeted items. There are only a small number of labeled users in most of the practical recommender systems, while a large number of users are unlabeled because it is expensive to obtain their identities. In this paper, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed to take advantage of both types of data. It first trains a naïve Bayes classifier on a small set of labeled users, and then incorporates unlabeled users with EM-λ to improve the initial naïve Bayes classifier. Experiments on MovieLens datasets are implemented to compare the efficiency of Semi-SAD with supervised learning based detector and unsupervised learning based detector. The results indicate that Semi-SAD can better detect various kinds of shilling attacks than others, especially against obfuscated and hybrid shilling attacks.

Question: What is a recommender system?


Emerging Applications of Liquid Metals Featuring Surface Oxides. Gallium and several of its alloys are liquid metals at or near room temperature. Gallium has low toxicity, essentially no vapor pressure, and a low viscosity. Despite these desirable properties, applications calling for liquid metal often use toxic mercury because gallium forms a thin oxide layer on its surface. The oxide interferes with electrochemical measurements, alters the physicochemical properties of the surface, and changes the fluid dynamic behavior of the metal in a way that has, until recently, been considered a nuisance. Here, we show that this solid oxide "skin" enables many new applications for liquid metals including soft electrodes and sensors, functional microcomponents for microfluidic devices, self-healing circuits, shape-reconfigurable conductors, and stretchable antennas, wires, and interconnects.

Question: What are liquid metals?


Comparison of Wavelet and Short Time Fourier Transform Methods in the Analysis of EMG Signals. The electromyographic (EMG) signal observed at the surface of the skin is the sum of thousands of small potentials generated in the muscle fiber. There are many approaches to analyzing EMG signals with spectral techniques. In this study, the short time Fourier Transform (STFT) and wavelet transform (WT) were applied to EMG signals and coefficients were obtained. In these studies, MATLAB 7.01 program was used. According to obtained results, it was determined that WT is more useful than STFT in the fields of eliminating of resolution problem and providing of changeable resolution during analyze.

Question: What is the difference between the short time Fourier transform and wavelet transform?


Extreme Learning Machine for Multilayer Perceptron. Extreme learning machine (ELM) is an emerging learning algorithm for the generalized single hidden layer feedforward neural networks, of which the hidden node parameters are randomly generated and the output weights are analytically computed. However, due to its shallow architecture, feature learning using ELM may not be effective for natural signals (e.g., images/videos), even with a large number of hidden nodes. To address this issue, in this paper, a new ELM-based hierarchical learning framework is proposed for multilayer perceptron. The proposed architecture is divided into two main components: 1) self-taught feature extraction followed by supervised feature classification and 2) they are bridged by random initialized hidden weights. The novelties of this paper are as follows: 1) unsupervised multilayer encoding is conducted for feature extraction, and an ELM-based sparse autoencoder is developed via ℓ1 constraint. By doing so, it achieves more compact and meaningful feature representations than the original ELM; 2) by exploiting the advantages of ELM random feature mapping, the hierarchically encoded outputs are randomly projected before final decision making, which leads to a better generalization with faster learning speed; and 3) unlike the greedy layerwise training of deep learning (DL), the hidden layers of the proposed framework are trained in a forward manner. Once the previous layer is established, the weights of the current layer are fixed without fine-tuning. Therefore, it has much better learning efficiency than the DL. Extensive experiments on various widely used classification data sets show that the proposed algorithm achieves better and faster convergence than the existing state-of-the-art hierarchical learning methods. Furthermore, multiple applications in computer vision further confirm the generality and capability of the proposed learning scheme.

Question: What is the difference between ELM and DL?


Reference scope identification for citances by classification with text similarity measures. This paper targets at the first step towards generating citation summaries - to identify the reference scope (i.e., cited text spans) for citances. We present a novel classification-based method that converts the task into binary classification which distinguishes cited and non-cited pairs of citances and reference sentences. The method models pairs of citances and reference sentences as feature vectors where citation-dependent and citation-independent features based on the semantic similarity between texts and the significance of texts are explored. Such vector representations are utilized to train a binary classifier. For a citance, once the set of reference sentences classified as the cited sentences are collected, a heuristic-based filtering strategy is applied to refine the output. The method is evaluated using the CL-SciSumm 2016 datasets and found to perform well with competitive results.

Question: What is the first step towards generating citation summaries?


Better ϵ-Dependencies for Offline Approximate Nearest Neighbor Search, Euclidean Minimum Spanning Trees, and ϵ-Kernels. Recently, Arya, da Fonseca, and Mount [STOC 2011, SODA 2012] made notable progress in improving the &epsis;-dependencies in the space/query-time tradeoffs for (1 + &epsis;)-factor approximate nearest neighbor search in fixed-dimensional Euclidean spaces. However, &epsis;-dependencies in the preprocessing time were not considered, and so their data structures cannot be used to derive faster algorithms for offline proximity problems. Known algorithms for many such problems, including approximate bichromatic closest pair (BCP) and approximate Euclidean minimum spanning trees (EMST), typically have factors near (1/&epsis;)d/2±O(1) in the running time when the dimension d is a constant.  We describe a technique that breaks the (1/&epsis;)d/2 barrier and yields new results for many well-known proximity problems, including:  • an O((1/&epsis;)d/3+O(1) n)-time randomized algorithm for approximate BCP,  • an O((1/&epsis;)d/3+O(1) n log n)-time algorithm for approximate EMST, and  • an O(n log n + (1/&epsis;)d/3+O(1) n)-time algorithm to answer n approximate nearest neighbor queries on n points.  Using additional bit-packing tricks, we can shave off the log n factor for EMST, and even move most of the &epsis;-factors to a sublinear term.  The improvement arises from a new time bound for exact "discrete Voronoi diagrams", which were previously used in the construction of &epsis;-kernels (or extent-based coresets), a well-known tool for another class of fundamental problems. This connection leads to more results, including:  • a streaming algorithm to maintain an approximate diameter in O((1/&epsis;)d/3+O(1)) time per point using O((1/&epsis;)d/2+O(1)) space, and  • a streaming algorithm to maintain an &epsis;-kernel in O((1/&epsis;)d/4+O(1)) time per point using O((1/&epsis;)d/2+O(1)) space.

Question: What is the approximate diameter of a point set?


Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization. Can one parallelize complex exploration– exploitation tradeoffs? As an example, consider the problem of optimal highthroughput experimental design, where we wish to sequentially design batches of experiments in order to simultaneously learn a surrogate function mapping stimulus to response and identify the maximum of the function. We formalize the task as a multiarmed bandit problem, where the unknown payoff function is sampled from a Gaussian process (GP), and instead of a single arm, in each round we pull a batch of several arms in parallel. We develop GP-BUCB, a principled algorithm for choosing batches, based on the GP-UCB algorithm for sequential GP optimization. We prove a surprising result; as compared to the sequential approach, the cumulative regret of the parallel algorithm only increases by a constant factor independent of the batch size B. Our results provide rigorous theoretical support for exploiting parallelism in Bayesian global optimization. We demonstrate the effectiveness of our approach on two real-world applications.

Question: How to parallelize exploration-exploitation tradeoffs?


Deep, complex, invertible networks for inversion of transmission effects in multimode optical fibres. We use complex-weighted, deep networks to invert the effects of multimode optical fibre distortion of a coherent input image. We generated experimental data based on collections of optical fibre responses to greyscale input images generated with coherent light, by measuring only image amplitude (not amplitude and phase as is typical) at the output of 1m and 10m long, 105 μm diameter multimode fibre. This data is made available as the Optical fibre inverse problem Benchmark collection. The experimental data is used to train complex-weighted models with a range of regularisation approaches. A unitary regularisation approach for complexweighted networks is proposed which performs well in robustly inverting the fibre transmission matrix, which is compatible with the physical theory. A benefit of the unitary constraint is that it allows us to learn a forward unitary model and analytically invert it to solve the inverse problem. We demonstrate this approach, and outline how it has the potential to improve performance by incorporating knowledge of the phase shift induced by the spatial light modulator.

Question: How do you invert a multimode optical fibre?


Bilateral Back-Projection for Single Image Super Resolution. In this paper, a novel algorithm for single image super resolution is proposed. Back-projection [1] can minimize the reconstruction error with an efficient iterative procedure. Although it can produce visually appealing result, this method suffers from the chessboard effect and ringing effect, especially along strong edges. The underlining reason is that there is no edge guidance in the error correction process. Bilateral filtering can achieve edge-preserving image smoothing by adding the extra information from the feature domain. The basic idea is to do the smoothing on the pixels which are nearby both in space domain and in feature domain. The proposed bilateral back-projection algorithm strives to integrate the bilateral filtering into the back-projection method. In our approach, the back-projection process can be guided by the edge information to avoid across-edge smoothing, thus the chessboard effect and ringing effect along image edges are removed. Promising results can be obtained by the proposed bilateral back-projection method efficiently.

Question: What is the bilateral back-projection?


Field assessment of Serious Games for Entrepreneurship in Higher Education. The potential of Serious Games (SGs) in education is widely recognized, and their adoption is significant in particular in children instruction. However, the deployment rate of SGs in higher education (HE) and their proper insertion in meaningful curricula is still quite low. This paper intends to make a first step in the direction of a better characterization of the pedagogical effectiveness of SGs in HE, by providing a qualitative analysis based on our field experience using three games for entrepreneurship, that we have studied in the light of two well established pedagogical paradigms, such as the Revised Bloom's taxonomy and the Kolb's Learning stages. In general, we observe that SGs address several goals of the Bloom's taxonomy, in particular at the lower levels. Moreover, the cyclical nature of the business simulations can be directly mapped to the sequential steps described by Kolb. However, our analysis also shows that SGs have still to significantly evolve in order to become an effective and efficient tool that could be successfully and reliably used in HE. In the light of our experience, we also propose a schema for a proper integration of SGs by supporting different goals in different steps of a formal education process, Our study finally suggests directions for future research in the field.

Question: What is the pedagogical effectiveness of SGs in higher education?


{\Omega}-Net (Omega-Net): Fully Automatic, Multi-View Cardiac MR Detection, Orientation, and Segmentation with Deep Neural Networks. Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): a novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained on a cohort of patients with hypertrophic cardiomyopathy (HCM, N = 42) and healthy control subjects (N = 21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools,

Question: What is the difference between a convolutional neural network and a deep neural network?


The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing. Unbounded, unordered, global-scale datasets are increasingly common in day-to-day business (e.g. Web logs, mobile usage statistics, and sensor networks). At the same time, consumers of these datasets have evolved sophisticated requirements, such as event-time ordering and windowing by features of the data themselves, in addition to an insatiable hunger for faster answers. Meanwhile, practicality dictates that one can never fully optimize along all dimensions of correctness, latency, and cost for these types of input. As a result, data processing practitioners are left with the quandary of how to reconcile the tensions between these seemingly competing propositions, often resulting in disparate implementations and systems. We propose that a fundamental shift of approach is necessary to deal with these evolved requirements in modern data processing. We as a field must stop trying to groom unbounded datasets into finite pools of information that eventually become complete, and instead live and breathe under the assumption that we will never know if or when we have seen all of our data, only that new data will arrive, old data may be retracted, and the only way to make this problem tractable is via principled abstractions that allow the practitioner the choice of appropriate tradeoffs along the axes of interest: correctness, latency, and cost. In this paper, we present one such approach, the Dataflow Model, along with a detailed examination of the semantics it enables, an overview of the core principles that guided its design, and a validation of the model itself via the real-world experiences that led to its development. We use the term "Dataflow Model" to describe the processing model of Google Cloud Dataflow [20], which is based upon technology from FlumeJava [12] and MillWheel [2]. This work is licensed under the Creative Co

Question: What is the dataflow model?


Prescribed performance control of quadruped robot. Due to the complex and high nonlinear structure of the quadruped robots, the dynamic control of quadruped robots has long been a big challenge for the researchers. In this paper, a guaranteed performance adaptive control algorithm is proposed for tracking control of a quadruped robot with parameter uncertainties. The controller is designed based on the sliding mode theory, and incorporates a transformed error, which includes the performance indices. Parameter uncertainties are estimated by a linear network. By choosing a proper Lyapunov function, the prescribed performance with the proposed controller is proven in the scenes of the parameter uncertainties and the environment constraint. Numerical simulations demonstrate the effectiveness and superiority of the proposed controller.

Question: What is a quadruped robot?


Proportional smile design using the recurring esthetic dental (red) proportion.. Dentists have needed an objective way in which to evaluate a smile. A method for determining the ideal size and position of the anterior teeth has been presented here. Use of the FIVE to evaluate the RED proportion and the width-to-height ratio, tempered with sound clinical judgment, gives pleasing and consistent results. With the diversity that exists in nature, rarely does the final result follow all the mathematical rules of proportional smile design. This approach may serve as a foundation on which to base initial smile design, however. When one begins to understand the relationship between beauty, mathematics, and the surrounding world, one begins to appreciate their interdependence.

Question: How to design a smile


Informing the Design of Spoken Conversational Search: Perspective Paper. We conducted a laboratory-based observational study where pairs of people performed search tasks communicating verbally. Examination of the discourse allowed commonly used interactions to be identified for Spoken Conversational Search (SCS). We compared the interactions to existing models of search behaviour. We find that SCS is more complex and interactive than traditional search. This work enhances our understanding of different search behaviours and proposes research opportunities for an audio-only search system. Future work will focus on creating models of search behaviour for SCS and evaluating these against actual SCS systems.

Question: What is the difference between a laboratory-based observational study and a real-world study?


Inventory of Transcribed and Translated Talks. We describe here a Web inventory named WIT3 that offers access to a collection of transcribed and translated talks. The core of WIT3 is the TED Talks corpus, that basically redistributes the original content published by the TED Conference website (http://www.ted.com). Since 2007, the TED Conference, based in California, has been posting all video recordings of its talks together with subtitles in English and their translations in more than 80 languages. Aside from its cultural and social relevance, this content, which is published under the Creative Commons BYNC-ND license, also represents a precious language resource for the machine translation research community, thanks to its size, variety of topics, and covered languages. This effort repurposes the original content in a way which is more convenient for machine translation researchers.

Question: What is the size of the ted talks corpus?


Object Recognition using 3D SIFT in Complex CT Volumes. The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.

Question: How to detect objects in a complex volumetric environment?


Load Disaggregation Based on Aided Linear Integer Programming. Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP)-based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear-programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures and, hence, works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation.

Question: How does load disaggregation based on aided linear integer programming work?


Delphi method: techniques and applications. Cross-impact analysis is a method for revising estimated probabilities of future events in terms of estimated interactions among those events. This Report presents an elementary cross-impact model where the cross-impacts are formulated as relative probabilities. Conditions are derived for the consistency of the matrix of relative probabilities of n events. An extension also provides a necessary condition for the vector of absolute probabilities to be consistent with the relative probability matrix. An averaging technique is formulated for resolving inconsistencies in the matrix, and a nearest-point computation derived for resolving inconsistencies between the set of absolute probabilities and the matrix. Although elementary, the present model clarifies some of the conceptual problems associated with cross-impact analysis, and supplies a relatively sound basis for revising probability estimates in the limited case where interactions can be approximated by relative probabilities.

Question: What is the delphi method?


Formal Design and Verification of Self-Adaptive Systems with Decentralized Control. Feedback control loops that monitor and adapt managed parts of a software system are considered crucial for realizing self-adaptation in software systems. The MAPE-K (Monitor-Analyze-Plan-Execute over a shared Knowledge) autonomic control loop is the most influential reference control model for self-adaptive systems. The design of complex distributed self-adaptive systems having decentralized adaptation control by multiple interacting MAPE components is among the major challenges. In particular, formal methods for designing and assuring the functional correctness of the decentralized adaptation logic are highly demanded.  This article presents a framework for formal modeling and analyzing self-adaptive systems. We contribute with a formalism, called self-adaptive Abstract State Machines, that exploits the concept of multiagent Abstract State Machines to specify distributed and decentralized adaptation control in terms of MAPE-K control loops, also possible instances of MAPE patterns. We support validation and verification techniques for discovering unexpected interfering MAPE-K loops, and for assuring correctness of MAPE components interaction when performing adaptation.

Question: What is a self-adaptive abstract state machine?


A dynamic model for integrating simple web spam classification techniques. Over the last years, Internet spam content has spread enormously inside web sites mainly due to the emergence of new web technologies oriented towards the online sharing of resources and information. In such a situation, both academia and industry have shown their concern to accurately detect and effectively control web spam, resulting in a good number of anti-spam techniques currently available. However, the successful integration of different algorithms for web spam classification is still a challenge. In this context, the present study introduces WSF2, a novel web spam filtering framework specifically designed to take advantage of multiple classification schemes and algorithms. In detail, our approach encodes the life cycle of a case-based reasoning system, being able to use appropriate knowledge and dynamically adjust different parameters to ensure continuous improvement in filtering precision with the passage of time. In order to correctly evaluate the effectiveness of the dynamic model, we designed a set of experiments involving a publicly available corpus, as well as different simple well-known classifiers and ensemble approaches. The results revealed that WSF2 performed well, being able to take advantage of each classifier and to achieve a better performance when compared to other alternatives. WSF2 is an open-source project licensed under the terms of the LGPL publicly available at https://sourceforge.net/

Question: What is a dynamic model for integrating simple web spam classification techniques?


Accelerating Large Graph Algorithms on the GPU Using CUDA. Large graphs involving millions of vertices are common in many practical applications and are challenging to process. Practical-time implementations using high-end computers are reported but are accessible only to a few. Graphics Processing Units (GPUs) of today have high computation power and low price. They have a restrictive programming model and are tricky to use. The G80 line of Nvidia GPUs can be treated as a SIMD processor array using the CUDA programming model. We present a few fundamental algorithms – including breadth first search, single source shortest path, and all-pairs shortest path – using CUDA on large graphs. We can compute the single source shortest path on a 10 million vertex graph in 1.5 seconds using the Nvidia 8800GTX GPU costing $600. In some cases optimal sequential algorithm is not the fastest on the GPU architecture. GPUs have great potential as high-performance co-processors.

Question: How to compute the single source shortest path on a 10 million vertex graph in 1.5 seconds using the Nvidia 8800GTX GPU costing $600?


Major components of the gravity recommendation system. The Netflix Prize is a collaborative filtering problem. This subfield of machine learning became popular in the late 1990s with the spread of online services that used recommendation systems (e.g. Amazon, Yahoo! Music, and of course Netflix). The aim of such a system is to predict what items a user might like based on his/her and other users' previous ratings. The Netflix Prize dataset is much larger than former benchmark datasets, therefore the scalability of the algorithms is a must. This paper describes the major components of our blending based solution, called the Gravity Recommendation System (GRS). In the Netflix Prize contest, it attained RMSE 0.8743 as of November 2007. We now compare the effectiveness of some selected individual and combined approaches on a particular subset of the Prize dataset, and discuss their important features and drawbacks.

Question: What is the gravity recommendation system?


Richard Thaler and the Rise of Behavioral Economics. The emergence of behavioral economics is one of the most prominent conceptual developments in the social sciences in recent decades. The central figure in the field in its early years was Richard Thaler. In this article, I review and discuss his scientific contributions. JEL classification: B2, D9, G1

Question: What is the JEL classification of behavioral economics?


Early Career Award. Clarifying the emotive functions of asymmetrical frontal cortical activity.. Asymmetrical activity over the frontal cortex has been implicated in the experience and expression of emotions and motivations. Explanations of the research have suggested that relatively greater left frontal activity is associated with positive affect and/or approach motivation, and that relatively greater right frontal activity is associated with negative affect and/or withdrawal motivation. In past research, affective valence and motivational direction were confounded, as only positive (negative) affects that were associated with approach (withdrawal) motivation were examined. Consequently, this research is unable to address whether asymmetrical frontal activity is associated with affective valence, motivational direction, or some combination of valence and motivation. In this article, I review research on the emotion of anger, a negative emotion often associated with approach motivation, that suggests that asymmetrical frontal cortical activity is due to motivational direction and not affective valence. Methodological and theoretical implications for the study of the frontal asymmetry specifically, and for emotion and motivation more generally, are discussed.

Question: What is the difference between affective valence and motivational direction?


The coevolution of cultural groups and ingroup favoritism.. Cultural boundaries have often been the basis for discrimination, nationalism, religious wars, and genocide. Little is known, however, about how cultural groups form or the evolutionary forces behind group affiliation and ingroup favoritism. Hence, we examine these forces experimentally and show that arbitrary symbolic markers, though initially meaningless, evolve to play a key role in cultural group formation and ingroup favoritism because they enable a population of heterogeneous individuals to solve important coordination problems. This process requires that individuals differ in some critical but unobservable way and that their markers be freely and flexibly chosen. If these conditions are met, markers become accurate predictors of behavior. The resulting social environment includes strong incentives to bias interactions toward others with the same marker, and subjects accordingly show strong ingroup favoritism. When markers do not acquire meaning as accurate predictors of behavior, players show a markedly reduced taste for ingroup favoritism. Our results support the prominent evolutionary hypothesis that cultural processes can reshape the selective pressures facing individuals and so favor the evolution of behavioral traits not previously advantaged.

Question: How do cultural groups form?


Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review. This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.

Question: What is the difference between active learning and active deep learning?


The Big-Data-Driven Intelligent Wireless Network: Architecture, Use Cases, Solutions, and Future Trends. The concept of using big data (BD) for wireless communication network optimization is no longer new. However, previous work has primarily focused on long-term policies in the network, such as network planning and management. Apart from this, the source of the data collected for analysis/model training is mostly limited to the core network (CN). In this article, we introduce a novel data-driven intelligent radio access network (RAN) architecture that is hierarchical and distributed and operates in real time. We also identify the required data and respective workflows that facilitate intelligent network optimizations. It is our strong belief that the wireless BD (WBD) and machine-learning/artificial-intelligence (AI)-based methodology applies to all layers of the communication system. To demonstrate the superior performance gains of our proposed methodology, two use cases are analyzed with system-level simulations; one is the neural-network-aided optimization for Transmission Control Protocol (TCP), and the other is prediction-based proactive mobility management.

Question: What is the difference between the core network and the radio access network?


Robust analysis of feature spaces: color image segmentation. A general technique for the recovery of signi cant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the signi cant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.

Question: What is the mean shift algorithm?


Adult hippocampal neurogenesis and its role in Alzheimer's disease. The hippocampus, a brain area critical for learning and memory, is especially vulnerable to damage at early stages of Alzheimer's disease (AD). Emerging evidence has indicated that altered neurogenesis in the adult hippocampus represents an early critical event in the course of AD. Although causal links have not been established, a variety of key molecules involved in AD pathogenesis have been shown to impact new neuron generation, either positively or negatively. From a functional point of view, hippocampal neurogenesis plays an important role in structural plasticity and network maintenance. Therefore, dysfunctional neurogenesis resulting from early subtle disease manifestations may in turn exacerbate neuronal vulnerability to AD and contribute to memory impairment, whereas enhanced neurogenesis may be a compensatory response and represent an endogenous brain repair mechanism. Here we review recent findings on alterations of neurogenesis associated with pathogenesis of AD, and we discuss the potential of neurogenesis-based diagnostics and therapeutic strategies for AD.

Question: What is the role of neurogenesis in Alzheimer's disease?


A Brief survey of Data Mining Techniques Applied to Agricultural Data. As with many other sectors the amount of agriculture data based are increasing on a daily basis. However, the application of data mining methods and techniques to discover new insights or knowledge is a relatively a novel research area. In this paper we provide a brief review of a variety of Data Mining techniques that have been applied to model data from or about the agricultural domain. The Data Mining techniques applied on Agricultural data include k-means, bi clustering, k nearest neighbor, Neural Networks (NN) Support Vector Machine (SVM), Naive Bayes Classifier and Fuzzy c-means. As can be seen the appropriateness of data mining techniques is to a certain extent determined by the different types of agricultural data or the problems being addressed. This survey summarize the application of data mining techniques and predictive modeling application in the agriculture field.

Question: What is the difference between data mining and predictive modeling?


Bartender: a fast and accurate clustering algorithm to count barcode reads. Motivation Barcode sequencing (bar-seq) is a high-throughput, and cost effective method to assay large numbers of cell lineages or genotypes in complex cell pools. Because of its advantages, applications for bar-seq are quickly growing-from using neutral random barcodes to study the evolution of microbes or cancer, to using pseudo-barcodes, such as shRNAs or sgRNAs to simultaneously screen large numbers of cell perturbations. However, the computational pipelines for bar-seq clustering are not well developed. Available methods often yield a high frequency of under-clustering artifacts that result in spurious barcodes, or over-clustering artifacts that group distinct barcodes together. Here, we developed Bartender, an accurate clustering algorithm to detect barcodes and their abundances from raw next-generation sequencing data.   Results In contrast with existing methods that cluster based on sequence similarity alone, Bartender uses a modified two-sample proportion test that also considers cluster size. This modification results in higher accuracy and lower rates of under- and over-clustering artifacts. Additionally, Bartender includes unique molecular identifier handling and a 'multiple time point' mode that matches barcode clusters between different clustering runs for seamless handling of time course data. Bartender is a set of simple-to-use command line tools that can be performed on a laptop at comparable run times to existing methods.   Availability and implementation Bartender is available at no charge for non-commercial use at https://github.com/LaoZZZZZ/bartender-1.1.   Contact sasha.levy@stonybrook.edu or song.wu@stonybrook.edu.   Supplementary information Supplementary data are available at Bioinformatics online.

Question: What is the difference between a barcode and a barcode sequence?


Autonomous Inverted Helicopter Flight via Reinforcement Learning. Helicopters have highly stochastic, nonlinear, dynamics, and autonomous helicopter flight is widely regarded to be a challenging control problem. As helicopters are highly unstable at low speeds, it is particularly difficult to design controllers for low speed aerobatic maneuvers. In this paper, we describe a successful application of reinforcement learning to designing a controller for sustained inverted flight on an autonomous helicopter. Using data collected from the helicopter in flight, we began by learning a stochastic, nonlinear model of the helicopter's dynamics. Then, a reinforcement learning algorithm was applied to automatically learn a controller for autonomous inverted hovering. Finally, the resulting controller was successfully tested on our autonomous helicopter platform.

Question: How to design a controller for a helicopter?


Trends and Lessons from Three Years Fighting Malicious Extensions. In this work we expose wide-spread efforts by criminals to abuse the Chrome Web Store as a platform for distributing malicious extensions. A central component of our study is the design and implementation of WebEval, the first system that broadly identifies malicious extensions with a concrete, measurable detection rate of 96.5%. Over the last three years we detected 9,523 malicious extensions: nearly 10% of every extension submitted to the store. Despite a short window of operation—we removed 50% of malware within 25 minutes of creation— a handful of under 100 extensions escaped immediate detection and infected over 50 million Chrome users. Our results highlight that the extension abuse ecosystem is drastically different from malicious binaries: miscreants profit from web traffic and user tracking rather than email spam or banking theft.

Question: What is the first system that broadly identifies malicious extensions with a concrete, measurable detection rate of 96.5%?


Two Bitcoins at the Price of One? Double-Spending Attacks on Fast Payments in Bitcoin. Bitcoin is a decentralized payment system that is based on Proof-of-Work. Bitcoin is currently gaining popularity as a digital currency; several businesses are starting to accept Bitcoin transactions. An example case of the growing use of Bitcoin was recently reported in the media; here, Bitcoins were used as a form of fast payment in a local fast-food restaurant. In this paper, we analyze the security of using Bitcoin for fast payments, where the time between the exchange of currency and goods is short (i.e., in the order of few seconds). We focus on doublespending attacks on fast payments and demonstrate that these attacks can be mounted at low cost on currently deployed versions of Bitcoin. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast transactions are not always effective in resisting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we leverage on our findings and propose a lightweight countermeasure that enables the detection of doublespending attacks in fast transactions.

Question: How can I use Bitcoin to pay for fast food?


Bundle Adjustment in the Large. We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.

Question: How large is the bundle adjustment problem?


QR Code Security: A Survey of Attacks and Challenges for Usable Security. QR (Quick Response) codes are two-dimensional barcodes with the ability to encode different types of information. Because of their high information density and robustness, QR codes have gained popularity in various fields of application. Even though they offer a broad range of advantages, QR codes pose significant security risks. Attackers can encode malicious links that lead e.g. to phishing sites. Such malicious QR codes can be printed on small stickers and replace benign ones on billboard advertisements. Although many real world examples of QR code based attacks have been reported in the media, only little research has been conducted in this field and almost no attention has been paid on the interplay of security and human-computer interaction. In this work, we describe the manifold use cases of QR codes. Furthermore, we analyze the most significant attack scenarios with respect to the specific use cases. Additionally, we systemize the research that has already been conducted and identified usable security and security awareness as the main research challenges. Finally we propose design requirements with respect to the QR code itself, the reader application and usability aspects in order to support further research into to making QR code processing both secure and usable.

Question: What is the purpose of QR codes?


OBJECTIVE SELF-AWARENESS THEORY : RECENT PROGRESS AND ENDURING PROBLEMS By :. Objective self-awareness theory has undergone fundamental changes in the 3 decades since Duval and Wicklund's (1972) original formulation. We review new evidence that bears on the basic tenets of the theory. Many of the assumptions of self-awareness theory require revision, particularly how expectancies influence approach and avoidance of self-standard discrepancies; the nature of standards, especially when they are changed; and the role of causal attribution in directing discrepancy reduction. However, several unresolved conceptual issues remain; future theoretical and empirical directions are discussed. Article: The human dilemma is that which arises out of a man's capacity to experience himself as both subject and object at the same time. Both are necessary--for the science of psychology, for therapy, and for gratifying living. (May, 1967, p. 8) Although psychological perspectives on the self have a long history (e.g., Cooley, 1902; James, 1890; Mead, 1934), experimental research on the self has emerged only within the last 40 years. One of the earliest "self theories" was objective self-awareness (OSA) theory (Duval & Wicklund, 1972). OSA theory was concerned with the self-reflexive quality of the consciousness. Just as people can apprehend the existence of environmental stimuli, they can be aware of their own existence: "When attention is directed inward and the individual's consciousness is focused on himself, he is the object of his own consciousness--hence 'objective' self awareness" (Duval & Wicklund, 1972, p. 2). This is contrasted with "subjective self-awareness" that results when attention is directed away from the self and the person "experiences himself as the source of perception and action" (Duval & Wicklund, 1972, p. 3). By this, Duval and Wicklund (1972,chap. 3) meant consciousness of one's existence on an organismic level, in which such existence is undifferentiated as a

Question: What is the difference between objective and subjective self awareness?


An Architecture for Local Energy Generation, Distribution, and Sharing. The United States electricity grid faces significant problems resulting from fundamental design principles that limit its ability to handle the key energy challenges of the 21st century. We propose an innovative electric power architecture, rooted in lessons learned from the Internet and microgrids, which addresses these problems while interfacing gracefully into the current grid to allow for non-disruptive incremental adoption. Such a system, which we term a "Local" grid, is controlled by intelligent power switches (IPS), and can consist of loads, energy sources, and energy storage. The desired result of the proposed architecture is to produce a grid network designed for distributed renewable energy, prevalent energy storage, and stable autonomous systems. We will describe organizing principles of such a system that ensure well-behaved operation, such as requirements for communication and energy transfer protocols, regulation and control schemes, and market-based rules of operation.

Question: What is the difference between a microgrid and a local grid?


Illuminant Aware Gamut-Based Color Transfer. This paper proposes a new approach for color transfer between two images. Our method is unique in its consideration of the scene illumination and the constraint that the mapped image must be within the color gamut of the target image. Specifically, our approach first performs a white-balance step on both images to remove color casts caused by different illuminations in the source and target image. We then align each image to share the same 'white axis' and perform a gradient preserving histogram matching technique along this axis to match the tone distribution between the two images. We show that this illuminant-aware strategy gives a better result than directly working with the original source and target image's luminance channel as done by many previous methods. Afterwards, our method performs a full gamut-based mapping technique rather than processing each channel separately. This guarantees that the colors of our transferred image lie within the target gamut. Our experimental results show that this combined illuminant-aware and gamut-based strategy produces more compelling results than previous methods. We detail our approach and demonstrate its effectiveness on a number of examples.

Question: How to transfer colors between images?


From Vision to NLP : A Merge. The study of artificial intelligence can be simplified into one goal: trying to mimic/enhance human senses. This paper attempts to combine computer vision and natural language processing to create a question answer system. This system takes a question and an image as input and outputs a response to the answer based on how the RCNN understands the question asked. The system correlates the question with the image by leveraging attention and memory mechanisms. Mentor: Arun Chaganty

Question: What is the goal of the paper?


PEGASUS: A Policy Search Method for Large MDPs and POMDPs. We propose a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP), given a model. Our approach is based on the following observation: Any (PO)MDP can be transformed into an "equivalent" POMDP in which all state transitions (given the current state and action) are deterministic. This reduces the general problem of policy search to one in which we need only consider POMDPs with deterministic transitions. We give a natural way of estimating the value of all policies in these transformed POMDPs. Policy search is then simply performed by searching for a policy with high estimated value. We also establish conditions under which our value estimates will be good, recovering theoretical results similar to those of Kearns, Mansour and Ng [7], but with "sample complexity" bounds that have only a polynomial rather than exponential dependence on the horizon time. Our method applies to arbitrary POMDPs, including ones with infinite state and action spaces. We also present empirical results for our approach on a small discrete problem, and on a complex continuous state/continuous action problem involving learning to ride a bicycle.

Question: What is a policy search method?


Social Entrepreneurship: A Critique and Future Directions. Work on social entrepreneurship constitutes a field of study that intersects a number of domains, including entrepreneurial studies, social innovation, and nonprofit management. Scholars are beginning to contribute to the development of this new discipline through efforts that attempt to trace the emergence of social entrepreneurship as well as by comparing it to other organizational activities such as conventional entrepreneurship. However, as a nascent field, social entrepreneurship scholars are in the midst of a number of debates involving definitional and conceptual clarity, boundaries of the field, and a struggle to arrive at a set of relevant and meaningful research questions. This paper examines the promise of social entrepreneurship as a domain of inquiry and suggests a number of research areas and research questions for future study.

Question: What is social entrepreneurship?


Surgical Management of Hidradenitis Suppurativa: Outcomes of 590 Consecutive Patients.. BACKGROUND Hidradenitis suppurativa is a progressive, recurrent inflammatory disease. Surgical management is potentially curative with limited efficacy data.   OBJECTIVE To evaluate hidradenitis surgical patients.   METHODS Retrospective review of outcomes of 590 consecutive surgically treated patients.   RESULTS Most patients were white (91.0% [435/478]), men (337 [57.1%]), smokers (57.7% [297/515]) with Hurley Stage III disease (476 [80.7%]). Procedure types were excision (405 [68.6%]), unroofing (168 [28.5%]), and drainage (17 [2.9%]) treating disease of perianal/perineum (294 [49.8%]), axilla (124 [21.0%]), gluteal cleft (76 [12.9%]), inframammary (12 [2.0%]), and multiple surgical sites (84 [14.2%]). Postoperative complications occurred in 15 patients (2.5%) and one-fourth (144 [24.4%]) suffered postoperative recurrence, which necessitated reoperation in one-tenth (69 [11.7%]) of patients. Recurrence risk was increased by younger age (hazard ratio [HR], 0.8; 95% confidence interval [CI], 0.7-0.9), multiple surgical sites (HR, 1.6; 95% CI, 1.1-2.5), and drainage-type procedures (HR, 3.5; 95% CI, 1.2-10.7). Operative location, disease severity, gender, and operative extent did not influence recurrence rate.   CONCLUSION Excision and unroofing procedures were effective treatments with infrequent complications and low recurrence rates. Well-planned surgical treatment aiming to remove or unroof the area of intractable hidradenitis suppurativa was highly effective in the management of this challenging disease.

Question: What is the recurrence rate of hidradenitis suppurativa?


An Unsupervised Clustering Tool for Unstructured Data. We present an unsupervised clustering tool, Principal Direction Divisive Partitioning, which is a scal-able and versatile top-down method applicable to any set of data that can be represented as numerical vectors. A description of the basic method, a summary of the main application areas where this has been used, and some recent results on the selection of signiicant words as well as the process of updating clusters as new data arrives is discussed.

Question: What is a unsupervised clustering tool?


First- and second-order optimality conditions for piecewise smooth objective functions. Any piecewise smooth function that is specified by an evaluation procedure involving smooth elemental functions and piecewise linear functions like min and max can be represented in the so-called abs-normal form. By an extension of algorithmic, or automatic, differentiation, one can then compute certain first and second order derivative vectors and matrices that represent a local piecewise linearization and provide additional curvature information. On the basis of these quantities we characterize local optimality by first and second order necessary and sufficient conditions, which generalize the corresponding KKT theory for smooth problems. The key assumption is the Linear Independence Kink Qualification (LIKQ), a generalization of LICQ familiar from nonlinear optimization. It implies that the objective has locally a so-called VU decomposition and renders everything tractable in terms of matrix factorizations and other simple linear algebra operations. By yielding descent directions whenever they are violated the new optimality conditions point the way to a superlinearly convergent generalized QP solver, which is currently under development. We exemplify the theory on two nonsmooth examples of Nesterov.

Question: What is a generalized QP solver?


Clarithromycin and Tetracycline Binding to Soil Humic Acid in the Absence and Presence of Calcium.. Numerous ionizable organic micropollutants contain positively charged moieties at pH values typical of environmental systems. Describing organic cation and zwitterion interaction with dissolved natural organic matter requires explicit consideration of the pH-dependent speciation of both sorbate and sorbent. We studied the pH-, ionic strength-, and concentration-dependent binding of relatively large, organic cations and zwitterions (viz., the antibiotics clarithromycin and tetracycline) to dissolved humic acid in the absence and presence of Ca(2+) and evaluated the ability of the NICA-Donnan model to describe the data. Clarithromycin interaction with dissolved humic acid was well described by the model including the competitive effect of Ca(2+) on clarithromycin binding over a wide range of solution conditions by considering only the binding of the cationic species to low proton-affinity sites in humic acid. Tetracycline possesses multiple ionizable moieties and forms complexes with Ca(2+). An excellent fit to experimental data was achieved by considering tetracycline cation interaction with both low and high proton-affinity sites of humic acid and zwitterion interaction with high proton-affinity sites. In contrast to clarithromycin, tetracycline binding to humic acid increased in the presence of Ca(2+), especially under alkaline conditions. Model calculations indicate that this increase is due to electrostatic interaction of positively charged tetracycline-Ca complexes with humic acid rather than due to the formation of ternary complexes, except at very low TC concentrations.

Question: What is the pH of the soil?


SyncGC: A Synchronized Garbage Collection Technique for Reducing Tail Latency in Cassandra. Data-center applications running on distributed databases often suffer from unexpectedly high response time fluctuation which is caused by long tail latency. In this paper, we find that long tail latency of user writes is mainly created by the interference with garbage collection (GC) tasks running in various system layers. In order to address the tail latency problem, we propose a synchronized garbage collection technique, called SyncGC. By scheduling multiple GC instances to execute in sync with each other in an overlapped manner, SyncGC prevents user requests from being interfered with GC instances, thereby minimizing their negative impacts on tail latency. Our experimental results with Cassandra show that SyncGC reduces the 99.99th-percentile tail latency and the maximum latency by 35% and 37%, on average, respectively.

Question: What is the 99.99th-percentile tail latency?


Pitch Extraction and Fundamental Frequency: History and Current Techniques. Pitch extraction (also called fundamental frequency estimation) has been a popular topic in many fields of research since the age of computers. Yet in the course of some 50 years of study, current techniques are still not to a desired level of accuracy and robustness. When presented with a single clean pitched signal, most techniques do well, but when the signal is noisy, or when there are multiple pitch streams, many current pitch algorithms still fail to perform well. This report presents a discussion of the history of pitch detection techniques, as well as a survey of the current state of the art in pitch detection technology.

Question: What is the history of pitch extraction?


A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Application. This paper discusses some aspects of testing random and pseudorandom number generators. A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications (NIST Special Publication) is presented. This paper also demonstrates the software implementation of the test suite with output protocols and presents experiences from testing some random and pseudorandom generators.

Question: What is a statistical test suite?


SpiderMAV: Perching and stabilizing micro aerial vehicles with bio-inspired tensile anchoring systems. Whilst Micro Aerial Vehicles (MAVs) possess a variety of promising capabilities, their high energy consumption severely limits applications where flight endurance is of high importance. Reducing energy usage is one of the main challenges in advancing aerial robot utility. To address this bottleneck in the development of unmanned aerial vehicle applications, this work proposes an bioinspired mechanical approach and develops an aerial robotic system for greater endurance enabled by low power station-keeping. The aerial robotic system consists of an multirotor MAV and anchoring modules capable of launching multiple tensile anchors to fixed structures in its operating envelope. The resulting tensile perch is capable of providing a mechanically stabilized mode for high accuracy operation in 3D workspace. We explore generalised geometric and static modelling of the stabilisation concept using screw theory. Following the analytical modelling of the integrated robotic system, the tensile anchoring modules employing high pressure gas actuation are designed, prototyped and then integrated to a quadrotor platform. The presented design is validated with experimental tests, demonstrating the stabilization capability even in a windy environment.

Question: How does the bio-inspired tensile anchoring system work?


Random synaptic feedback weights support error backpropagation for deep learning. The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning.

Question: What is the difference between backpropagation and error backpropagation?


A practical approach for computing the diameter of a point set. We present an approximation algorithm for computing the diameter of a point-set in $d$-dimensions. The new algorithm is sensitive to the "hardness" of computing the diameter of the given input, and for most inputs it is able to compute the {\em exact} diameter extremely fast. The new algorithm is simple, robust, has good empirical performance, and can be implemented quickly. As such, it seems to be the algorithm of choice in practice for computing/approximating the diameter.

Question: What is the diameter of a point set?


Multi-document English Text Summarization using Latent Semantic Analysis. In today's busy schedule, everybody expects to get the information in short but meaningful manner. Huge long documents consume more time to read. For this, we need summarization of document. Work has been done on Single-document but need of multiple document summarization is encouraging. Existing methods such as cluster approach, graph-based approach and fuzzy-based approach for multiple document summaries are improving. The statistical approach based on algebraic method is still topic of research. It demands for improvement in the approach by considering the limitations of Latent Semantic Analysis (LSA). Firstly, it reads only input text and does not consider world knowledge, for example women and lady it does not consider synonyms. Secondly, it does not consider word order, for example I will deliver to you tomorrow, deliver I will to you or tomorrow I will deliver to you. These different clauses may wrongly convey same meaning in different parts of document. Experimental results have overcomed the limitation and prove LSA with tf-idf method better in performance than KNN with tf-idf.

Question: How to summarize multiple documents?


utomatic detection of skin defects in citrus fruits using a multivariate image nalysis approach. One of the main problems in the post-harvest processing of citrus is the detection of visual defects in order to classify the fruit depending on their appearance. Species and cultivars of citrus present a high rate of unpredictability in texture and colour that makes it difficult to develop a general, unsupervised method able of perform this task. In this paper we study the use of a general approach that was originally developed for the detection of defects in random colour textures. It is based on a Multivariate Image Analysis strategy and uses Principal Component Analysis to extract a reference eigenspace from a matrix built by unfolding colour and spatial data from samples of defect-free peel. Test images are also unfolded and projected onto the reference eigenspace and the result is a score matrix which is used to compute defective maps based on the T2 statistic. In addition, a multiresolution scheme is introduced nsupervised Methods in the original method to speed up the process. Unlike the techniques commonly used for the detection of defects in fruits, this is an unsupervised method that only needs a few samples to be trained. It is also a simple approach that is suitable for real-time compliance. Experimental work was performed on 120 samples of oranges and mandarins from four different cultivars: Clemenules, Marisol, Fortune, and Valencia. The success ratio for the detection of individual defects was 91.5%, while the classification ratio of damaged/sound samples was 94.2%. These results show that the studied method can be suitable for on. the task of citrus inspecti

Question: What is the name of the method?


The ArchC Architecture Description Language and Tools. This paper presents an architecture description language (ADL) called ArchC, which is an open-source SystemC-based language that is specialized for processor architecture description. Its main goal is to provide enough information, at the right level of abstraction, in order to allow users to explore and verify new architectures, by automatically generating software tools like simulators and co-verification interfaces. ArchC's key features are a storage-based co-verification mechanism that automatically checks the consistency of a refined ArchC model against a reference (functional) description, memory hierarchy modeling capability, the possibility of integration with other SystemC IPs and the automatic generation of high-level SystemC simulators and assemblers. We have used ArchC to synthesize both functional and cycle-based simulators for the MIPS and Intel 8051 processors, as well as functional models of architectures like SPARC V8, TMS320C62x, XScale and PowerPC.

Question: What is the archC architecture description language?


Cloud Service Negotiation: A Research Roadmap. Cloud services are Internet-based XaaS (X as a Service) services, where X can be hardware, software or applications. As Cloud consumers value QoS (Quality of Service), Cloud providers should make certain service level commitments in order to achieve business success. This paper argues for Cloud service negotiation. It outlines a research roadmap, reviews the state of the art, and reports our work on Cloud service negotiation. Three research problems that we formulate are QoS measurement, QoS negotiation, and QoS enforcement. To address QoS measurement, we pioneer a quality model named CLOUDQUAL for Cloud services. To address QoS negotiation, we propose a tradeoff negotiation approach for Cloud services, which can achieve a higher utility. We also give some ideas to solve QoS enforcement, and balance utility and success rate for QoS negotiation.

Question: What is cloud service negotiation?


Participatory Simulations: Building Collaborative Understanding Through Immersive Dynamic Modeling. The Participatory Simulations Project explores how a new kind of collaborative learning environment, which is supported by small, wearable computers, can facilitate collaborative theory-building and lead to a richer understanding of scientific experimentation. In a Participatory Simulation, participants become "agents" in a full-scale simulation. Unlike previous work, Participatory Simulations combines the notion of a microworld with the affordances of real world experience. By involving a large number of people (typically between 15 and 30) in a "life-sized" experience, the project brings a microworld off of the computer screen and into the physical world. In particular, this study explored the use of a series of Participatory Simulations in a high school Biology class. The students became "agents" in a simulation of a disease infecting their community. The results of this project show that Participatory Simulations fully engage participants in the simulation, facilitate collaborative problem solving, provide a substrate for collaboratively designing and running experiments, and support the definition of new vocabulary to discuss the underlying rules of the simulation. Thesis Advisor: Mitchel Resnick Associate Professor of Media Arts and Sciences Fukutake Career Development Professor of Research in Education This research was sponsored by the National Science Foundation (grant # CDA-9616444), the Lego Group, and the Things That Think and Digital Life consortia. Participatory Simulations: Building Collaborative Understanding through Immersive Dynamic Modeling

Question: What is a Participatory Simulation?


Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition. Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk.  In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.

Question: How can I make my face unrecognizable?


A Wireless Headstage for Combined Optogenetics and Multichannel Electrophysiological Recording. This paper presents a wireless headstage with real-time spike detection and data compression for combined optogenetics and multichannel electrophysiological recording. The proposed headstage, which is intended to perform both optical stimulation and electrophysiological recordings simultaneously in freely moving transgenic rodents, is entirely built with commercial off-the-shelf components, and includes 32 recording channels and 32 optical stimulation channels. It can detect, compress and transmit full action potential waveforms over 32 channels in parallel and in real time using an embedded digital signal processor based on a low-power field programmable gate array and a Microblaze microprocessor softcore. Such a processor implements a complete digital spike detector featuring a novel adaptive threshold based on a Sigma-delta control loop, and a wavelet data compression module using a new dynamic coefficient re-quantization technique achieving large compression ratios with higher signal quality. Simultaneous optical stimulation and recording have been performed in-vivo using an optrode featuring 8 microelectrodes and 1 implantable fiber coupled to a 465-nm LED, in the somatosensory cortex and the Hippocampus of a transgenic mouse expressing ChannelRhodospin (Thy1::ChR2-YFP line 4) under anesthetized conditions. Experimental results show that the proposed headstage can trigger neuron activity while collecting, detecting and compressing single cell microvolt amplitude activity from multiple channels in parallel while achieving overall compression ratios above 500. This is the first reported high-channel count wireless optogenetic device providing simultaneous optical stimulation and recording. Measured characteristics show that the proposed headstage can achieve up to 100% of true positive detection rate for signal-to-noise ratio (SNR) down to 15 dB, while achieving up to

Question: What is the difference between a wireless headstage and a wireless headstage?


Video shot boundary detection: Seven years of TRECVid activity. Shot boundary detection (SBD) is the process of automatically detecting the boundaries between shots in video. It is a problem which has attracted much attention since video became available in digital form as it is an essential pre-processing step to almost all video analysis, indexing, summarisation, search, and other contentbased operations. Automatic SBD was one of the tracks of activity within the annual TRECVid benchmarking exercise, each year from 2001 to 2007 inclusive. Over those seven years we have seen 57 different research groups from across the world work to determine the best approaches to SBD while using a common dataset and common scoring metrics. In this paper we present an overview of the TRECVid shot boundary detection task, a high-level overview of the most significant of the approaches taken, and a comparison of performances, focussing on one year (2005) as an example.

Question: What is the best approach to shot boundary detection?


Weighted Task Regularization for Multitask Learning. Multitask Learning has been proven to be more effective than the traditional single task learning on many real-world problems by simultaneously transferring knowledge among different tasks which may suffer from limited labeled data. However, in order to build a reliable multitask learning model, nontrivial effort to construct the relatedness between different tasks is critical. When the number of tasks is not large, the learning outcome may suffer if there exists outlier tasks that inappropriately bias majority. Rather than identifying or discarding such outlier tasks, we present a weighted regularized multitask learning framework based on regularized multitask learning, which uses statistical metrics, such as Kullback-Leibler divergence, to assign weights prior to regularization process that robustly reduces the impact of outlier tasks and results in better learned models for all tasks. We then show that this formulation can be solved using dual form like optimizing a standard support vector machine with varied kernels. We perform experiments using both synthetic dataset and real-world dataset from petroleum industry which shows that our methodology outperforms existing methods.

Question: What is the difference between multitask learning and regularized multitask learning?


Factorization machines with follow-the-regularized-leader for CTR prediction in display advertising. Predicting ad click-through rates is the core problem in display advertising, which has received much attention from the machine learning community in recent years. In this paper, we present an online learning algorithm for click-though rate prediction, namely Follow-The-Regularized-Factorized-Leader (FTRFL), which incorporates the Follow-The-Regularized-Leader (FTRL-Proximal) algorithm with per-coordinate learning rates into Factorization machines. Experiments on a real-world advertising dataset show that the FTRFL method outperforms the baseline with stochastic gradient descent, and has a faster rate of convergence.

Question: How to predict click-through rates?


Text classification and named entities for new event detection. New Event Detection is a challenging task that still offers scope for great improvement after years of effort. In this paper we show how performance on New Event Detection (NED) can be improved by the use of text classification techniques as well as by using named entities in a new way. We explore modifications to the document representation in a vector space-based NED system. We also show that addressing named entities preferentially is useful only in certain situations. A combination of all the above results in a multi-stage NED system that performs much better than baseline single-stage NED systems.

Question: How to improve the performance of a named entity detection system?


Disease recognition in Mango crop using modified rotational kernel transform features. Soft computing in the field of agriculture science is being employed with computer vision techniques in order to detect the diseases in crops to increase the overall yield. A Modified Rotation Kernel Transformation(MRKT) based directional feature extraction scheme is presents to resolve the issues occurring due to shape, color or other deceptive features during plant disease recognition. The MRKT based scheme is used to calculate the directional features and histograms for plant parts like leaf and fruit of Mango crop. These histograms and the directional feature set with use of artificial neural network lead to a better recognition technique of Anthracnose disease which occurs in form of black spots on fruits and leaves of mango plant. The results obtained using proposed MRKT directional feature set have shows the better results with accuracy up to 98%.

Question: What is the disease that occurs in mango crop?


Cracking the Code of Biodiversity Responses to Past Climate Change.. How individual species and entire ecosystems will respond to future climate change are among the most pressing questions facing ecologists. Past biodiversity dynamics recorded in the paleoecological archives show a broad array of responses, yet significant knowledge gaps remain. In particular, the relative roles of evolutionary adaptation, phenotypic plasticity, and dispersal in promoting survival during times of climate change have yet to be clarified. Investigating the paleo-archives offers great opportunities to understand biodiversity responses to future climate change. In this review we discuss the mechanisms by which biodiversity responds to environmental change, and identify gaps of knowledge on the role of range shifts and tolerance. We also outline approaches at the intersection of paleoecology, genomics, experiments, and predictive models that will elucidate the processes by which species have survived past climatic changes and enhance predictions of future changes in biological diversity.

Question: How will biodiversity respond to future climate change?


CRM Strategies for A Small-Sized Online Shopping Mall Based on Association Rules and Sequential Patterns. Data mining has a tremendous contribution to the extraction of knowledge and information which have been hidden in a large volume of data. This study has proposed customer relationship management (CRM) strategies for a small-sized online shopping mall based on association rules and sequential patterns obtained by analyzing the transaction data of the shop. We first defined the VIP customer in terms of recency, frequency and monetary value. Then, we developed a model which classifies customers into VIP or non-VIP, using various techniques such as decision tree, artificial neural network and bagging with each of these as a base classifier. Last, we identified association rules and sequential patterns from the transactions of VIPs, and then these rules and patterns were utilized to propose CRM strategies for the online shopping mall.

Question: What is a customer relationship management strategy?


On the Traveling Salesman Problem with Simple Temporal Constraints. Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for

Question: What is the general framework of the Traveling Salesman Problem with Simple Temporal Constraints?


Expression Cloning of TMEM16A as a Calcium-Activated Chloride Channel Subunit. Calcium-activated chloride channels (CaCCs) are major regulators of sensory transduction, epithelial secretion, and smooth muscle contraction. Other crucial roles of CaCCs include action potential generation in Characean algae and prevention of polyspermia in frog egg membrane. None of the known molecular candidates share properties characteristic of most CaCCs in native cells. Using Axolotl oocytes as an expression system, we have identified TMEM16A as the Xenopus oocyte CaCC. The TMEM16 family of "transmembrane proteins with unknown function" is conserved among eukaryotes, with family members linked to tracheomalacia (mouse TMEM16A), gnathodiaphyseal dysplasia (human TMEM16E), aberrant X segregation (a Drosophila TMEM16 family member), and increased sodium tolerance (yeast TMEM16). Moreover, mouse TMEM16A and TMEM16B yield CaCCs in Axolotl oocytes and mammalian HEK293 cells and recapitulate the broad CaCC expression. The identification of this new family of ion channels may help the development of CaCC modulators for treating diseases including hypertension and cystic fibrosis.

Question: What is the function of TMEM16A?


Adolescent thriving: the role of sparks, relationships, and empowerment.. Although most social science research on adolescence emphasizes risks and challenges, an emergent field of study focuses on adolescent thriving. The current study extends this line of inquiry by examining the additive power of identifying and nurturing young people's "sparks," giving them "voice," and providing the relationships and opportunities that reinforce and nourish thriving. A national sample of 1,817 adolescents, all age 15 (49% female), and including 56% white, 17% Hispanic/Latino, and 17% African-American adolescents, completed an online survey that investigated their deep passions or interests (their "sparks"), the opportunities and relationships they have to support pursuing those sparks, and how empowered they feel to make civic contributions (their "voice"). Results consistently supported the hypothesis that linking one's spark with a sense of voice and supportive opportunities and relationships strengthens concurrent outcomes, particularly those reflecting prosociality, during a key developmental transition period. The three developmental strengths also predicted most outcomes to a greater degree than did demographics. However, less than 10 percent of 15-year-olds reported experiencing high levels of all three strengths. The results demonstrate the value of focusing on thriving in adolescence, both to reframe our understanding of this age group and to highlight the urgency of providing adolescents the opportunities and relationships they need to thrive.

Question: What is the difference between thriving and thriving?


Development of the Conformity to Masculine Norms Inventory. This article describes the construction of the Conformity to Masculine Norms Inventory (CMNI), and 5 studies that examined its psychometric properties. Factor analysis indicated 11 distinct factors: Winning, Emotional Control, Risk-Taking, Violence, Dominance, Playboy, SelfReliance, Primacy of Work, Power Over Women, Disdain for Homosexuals, and Pursuit of Status. Results from Studies 2–5 indicated that the CMNI had strong internal consistency estimates and good differential validity comparing men with women and groups of men on healthrelated questions; all of the CMNI subscales were significantly and positively related to other masculinity-related measures, with several subscales being related significantly and positively to psychological distress, social dominance, aggression, and the desire to be more muscular, and significantly and negatively to attitudes toward psychological help seeking and social desirability; and CMNI scores had high test–retest estimates for a 2–3 week period.

Question: What is the conformity to masculine norms inventory?


Face recognition in unconstrained videos with matched background similarity. Recognizing faces in unconstrained videos is a task of mounting importance. While obviously related to face recognition in still images, it has its own unique characteristics and algorithmic requirements. Over the years several methods have been suggested for this problem, and a few benchmark data sets have been assembled to facilitate its study. However, there is a sizable gap between the actual application needs and the current state of the art. In this paper we make the following contributions. (a) We present a comprehensive database of labeled videos of faces in challenging, uncontrolled conditions (i.e., 'in the wild'), the 'YouTube Faces' database, along with benchmark, pair-matching tests1. (b) We employ our benchmark to survey and compare the performance of a large variety of existing video face recognition techniques. Finally, (c) we describe a novel set-to-set similarity measure, the Matched Background Similarity (MBGS). This similarity is shown to considerably improve performance on the benchmark tests.

Question: What is the difference between a face recognition algorithm and a face recognition system?


Smart cities based on web semantic technologies. A good communication and interaction between citizens and the administration is important and crucial, also can greatly help in improving the quality of urban life of citizen. In this paper, we propose a semantic data model for managing and resolving the problems that exist in cities such as water leak, street faults, broken street lights, and potholes. The main idea is to focus on the best practices of linked open data to describe all issues, and then integrate them in the dataset provided by DBpedia. Hence, our approach is based on the standards of the World Wide Web Consortium.

Question: What is the best practices of linked open data?


A Brief Tour Through Provenance in Scientific Workflows and Databases. Within computer science, the term provenance has multiple meanings, due to different motivations, perspectives, and assumptions prevalent in the respective communities. This chapter provides a high-level "sightseeing tour" of some of those different notions and uses of provenance in scientific workflows and databases.

Question: What is the difference between provenance and lineage?


Fusion of laser and monocular camera data in object grid maps for vehicle environment perception. Occupancy grid maps provide a reliable vehicle environmental model and usually process data from range finding sensors. Object grid maps additionally contain information about the classes of objects, which is crucial for applications like autonomous driving. Unfortunately, they lack the precision of occupancy grid maps, since they mostly process classification results from camera data by projecting the corresponding images onto the ground plane. This paper proposes a modular framework to create precise object grid maps. The presented algorithm creates classical occupancy grid maps and object grid maps. In a combination step, it transforms both maps into the same frame of discernment based on the Dempster-Shafer theory of evidence. This allows fusing the maps to one object grid map, which contains valuable object information and at the same time benefits from the precision of the occupancy grid map.

Question: How to create a precise object grid map?


Machine learning - a probabilistic perspective. "Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real-world case studies. The accompanying software package includes source code for many of the figures, making it both easy and very tempting to dive in and explore these methods for yourself. A must-buy for anyone interested in machine learning or curious about how to extract useful knowledge from big data." John Winn, Microsoft Research

Question: What is machine learning?


Speedy transactions in multicore in-memory databases. Silo is a new in-memory database that achieves excellent performance and scalability on modern multicore machines. Silo was designed from the ground up to use system memory and caches efficiently. For instance, it avoids all centralized contention points, including that of centralized transaction ID assignment. Silo's key contribution is a commit protocol based on optimistic concurrency control that provides serializability while avoiding all shared-memory writes for records that were only read. Though this might seem to complicate the enforcement of a serial order, correct logging and recovery is provided by linking periodically-updated epochs with the commit protocol. Silo provides the same guarantees as any serializable database without unnecessary scalability bottlenecks or much additional latency. Silo achieves almost 700,000 transactions per second on a standard TPC-C workload mix on a 32-core machine, as well as near-linear scalability. Considered per core, this is several times higher than previously reported results.

Question: What is the name of the database?


A Survey on Software Estimation Techniques in Traditional and Agile Development Models. Software projects mostly exceeds budget, delivered late and does not meet with the customer's satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer's to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.

Question: What is the difference between traditional and agile development models?


High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications. This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.

Question: What is a high performance computer?


Active noise control: a tutorial review. Active noise control (ANC) is achieved by introducing a canceling "antinoise" wave through an appropriate array of secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. ANC has application to a wide variety of problems in manufacturing, industrial operations, and consumer products. The emphasis of this paper is on the practical aspects of ANC systems in terms of adaptive signal processing and digital signal processing (DSP) implementation for real-world applications. In this paper, the basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control. This algorithm is then modified for narrow-band feedforward and adaptive feedback control. In turn, these single-channel ANC algorithms are expanded to multiple-channel cases. Various online secondary-path modeling techniques and special adaptive algorithms, such as lattice, frequency-domain, subband, and recursive-least-squares, are also introduced. Applications of these techniques to actual problems are highlighted by several examples.

Question: What is the difference between active noise control and noise cancellation?


A selectional auto-encoder approach for document image binarization. Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of documents analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.

Question: What is a convolutional auto-encoder?


Measuring People-Flow through Doorways Using Easy-to-Install IR Array Sensors. People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.

Question: How many people are in a room?


Millimeter-Wave Microstrip Comb-Line Antenna Using Reflection-Canceling Slit Structure. A microstrip comb-line antenna is developed in the millimeter-wave band. When the element spacing is one guide wavelength for the broadside beam in the traveling-wave excitation, reflections from all the radiating elements are synthesized in phase. Therefore, the return loss increases significantly. Furthermore, re-radiation from elements due to the reflection wave degrades the design accuracy for the required radiation pattern. We propose the way to improve the reflection characteristic of the antenna with arbitrary beam directions including strictly a broadside direction. To suppress the reflection, we propose a reflection-canceling slit structure installed on the feeding line around each radiating element. A 27-element linear array antenna with a broadside beam is developed at 76.5 GHz. To confirm the feasibility of the simple design procedure, the performance is evaluated through the measurement in the millimeter-wave band.

Question: How to suppress the reflection of the antenna?