DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter. The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figurative Language in Twitter. The proposed approach employs syntactical and morphological features, which indicate sentiment polarity in both figurative and non-figurative tweets. These features were combined with others that indicate presence of figurative language in order to predict a fine-grained sentiment score. The method is supervised and makes use of structured knowledge resources, such as SentiWordNet sentiment lexicon for assigning sentiment score to words and WordNet for calculating word similarity. We have experimented with different classification algorithms (Naïve Bayes, Decision trees, and SVM), and the best results were achieved by an SVM classifier with linear kernel.

Question: What is the best classification algorithm for sentiment analysis?


Wavelet filter evaluation for image compression. Choice of filter bank in wavelet compression is a critical issue that affects image quality as well as system design. Although regularity is sometimes used in filter evaluation, its success at predicting compression performance is only partial. A more reliable evaluation can be obtained by considering an L-level synthesis/analysis system as a single-input, single-output, linear shift-variant system with a response that varies according to the input location module (2(L),2(L)). By characterizing a filter bank according to its impulse response and step response in addition to regularity, we obtain reliable and relevant (for image coding) filter evaluation metrics. Using this approach, we have evaluated all possible reasonably short (less than 36 taps in the synthesis/analysis pair) minimum-order biorthogonal wavelet filter banks. Of this group of over 4300 candidate filter banks, we have selected and present here the filters best suited to image compression. While some of these filters have been published previously, others are new and have properties that make them attractive in system design.

Question: What is a wavelet filter bank?


Mixed Initiative in Dialogue: An Investigation into Discourse Segmentation. Conversation between two people is usually of Mixed-Initiative, with Control over the conversation being transferred from one person to another. We apply a set of rules for the transfer of control to 4 sets of dialogues consisting of a total of 1862 turns. The application of the control rules lets us derive domain-independent discourse structures. The derived structures indicate that initiative plays a role in the structuring of discourse. In order to explore the relationship of control and initiative to discourse processes like centering, we analyze the distribution of four different classes of anaphora for two data sets. This distribution indicates that some control segments are hierarchically related to others. The analysis suggests that discourse participants often mutually agree to a change of topic. We also compared initiative in Task Oriented and Advice Giving dialogues and found that both allocation of control and the manner in which control is transferred is radically different for the two dialogue types. These differences can be explained in terms of collaborative planning principles.

Question: What is the difference between task oriented and advice giving dialogues?


Do cheerfulness , exhilaration , and humor production moderate pain tolerance ? A FACS study. Prior studies have shown that watching a funny film leads to an increase in pain tolerance. The present study aimed at separating three factors considered potentially essential (mood, behavior, and cognition related to humor) and examined whether they are responsible for this effect. Furthermore, the study examined whether trait cheerfulness and trait seriousness, as measured by the State-TraitCheerfulness-Inventory (STCI; Ruch et al. 1996), moderate changes in pain tolerance. Fifty-sixty female subjects were assigned randomly to three groups, each having a different task to pursue while watching a funny film: (1) get into a cheerful mood without smiling or laughing ("Cheerfulness"); (2) smile and laugh extensively ("Exhilaration"); and (3) produce a humorous commentary to the film ("Humor production"). Pain tolerance was measured using the cold pressor test before, immediately after, and twenty minutes after the film. Results indicated that pain tolerance increased for participants from before to after watching the funny film and remained high for the twenty minutes. This effect was moderated by facial but not verbal indicators of enjoyment of humor. Participants low in trait seriousness had an overall higher pain tolerance. Subjects with a high score in trait cheerfulness showed an increase in pain tolerance after producing humor while watching the film whereas subjects low in trait cheerfulness showed a similar increase after smiling and laughter during the film. DOI: https://doi.org/10.1515/humr.2004.009 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-77579 Originally published at: Zweyer, K; Velker, B; Ruch, Willibald (2004). Do cheerfulness, exhilaration, and humor production moderate pain tolerance? A FACS study. HUMOR: International Journal of Humor Research, 17(12):85-119. DOI: https://doi.org/10.1515/hum

Question: How does cheerfulness moderate pain tolerance?


Discipline and practices of TDD: (test driven development). This panel brings together practitioners with experience in Agile and XP methodologies to discuss the approaches and benefits of applying Test Driven Development (TDD). The goal of TDD is clean code that works. The mantra of TDD is: write a test; make it run; and make it right. Open questions to be addressed by the panel include: - How are TDD approaches to be applied to databases, GUIs, and distributed systems? What are the quantitative benchmarks that can demonstrate the value of TDD, and what are the best approaches to solve the ubiquitous issue of scalability.

Question: How to apply TDD to databases, GUIs, and distributed systems?


POMDP-based dialogue manager adaptation to extended domains. Existing spoken dialogue systems are typically designed to operate in a static and well-defined domain, and are not well suited to tasks in which the concepts and values change dynamically. To handle dynamically changing domains, techniques will be needed to transfer and reuse existing dialogue policies and rapidly adapt them using a small number of dialogues in the new domain. As a first step in this direction, this paper addresses the problem of automatically extending a dialogue system to include a new previously unseen concept (or slot) which can be then used as a search constraint in an information query. The paper shows that in the context of Gaussian process POMDP optimisation, a domain can be extended through a simple expansion of the kernel and then rapidly adapted. As well as being much quicker, adaptation rather than retraining from scratch is shown to avoid subjecting users to unacceptably poor performance during the learning stage.

Question: How to extend a dialogue system to a new domain?


Three stage 6–18 GHz high gain and high power amplifier based on GaN technology. A monolithic three stage HPA has been developed for wide band applications. This MMIC is fabricated on UMS 0.25 µm GaN technology based on SiC substrate. At 18GHz, the MMIC achieved in CW mode 10W of output power with 20dB linear gain and 20% power added efficiency. The HPA provided 6 to 10W output power over 6 to 18GHz with minimum small signal gain of 18dB. These obtained performances are very promising and very close to the simulations; this will allow a very short term further improvement. This demonstration is the first MMIC on the UMS 0.25µm GaN technology.

Question: What is the first MMIC on the UMS 0.25µm GaN technology?


An overview of reservoir computing: theory, applications and implementations. Training recurrent neural networks is hard. Recently it has however been discovered that it is possible to just construct a random recurrent topology, and only train a single linear readout layer. State-ofthe-art performance can easily be achieved with this setup, called Reservoir Computing. The idea can even be broadened by stating that any high dimensional, driven dynamic system, operated in the correct dynamic regime can be used as a temporal 'kernel' which makes it possible to solve complex tasks using just linear post-processing techniques. This tutorial will give an overview of current research on theory, application and implementations of Reservoir Computing.

Question: What is reservoir computing?


Towards Security-Aware Virtual Environments for Digital Twins. Digital twins open up new possibilities in terms of monitoring, simulating, optimizing and predicting the state of cyber-physical systems (CPSs). Furthermore, we argue that a fully functional, virtual replica of a CPS can also play an important role in securing the system. In this work, we present a framework that allows users to create and execute digital twins, closely matching their physical counterparts. We focus on a novel approach to automatically generate the virtual environment from specification, taking advantage of engineering data exchange formats. From a security perspective, an identical (in terms of the system's specification), simulated environment can be freely explored and tested by security professionals, without risking negative impacts on live systems. Going a step further, security modules on top of the framework support security analysts in monitoring the current state of CPSs. We demonstrate the viability of the framework in a proof of concept, including the automated generation of digital twins and the monitoring of security and safety rules.

Question: How can we create a digital twin of a physical system?


Smile Esthetics – A Literature. Esthetic dental treatment involves artistic and subjective components design to create the illusion of beauty. An organised systematic approach is required to evaluate, diagnose and resolve esthetic problems predictably. Our ultimate goal is to achieve pleasing composition in the smile by creating an arrangement of various esthetic elements. One of the most important tasks in esthetic dentistry is creating harmonious proportions between the widths of maxillary anterior teeth when restoring or replacing these teeth This review article describes application of the Golden Proportion and Red Proportion in dentistry also the smile design for completely edentulous and for full mouth rehabilitation. Also the future scope for designing smile .

Question: What is the golden proportion?


UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes. The 2016 Clinical TempEval challenge addresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events' temporal relations to the document creation times (DocTimeRel), and (6) narrative container relations among events and times. In this article, we present an end-to-end system that addresses all six sub-tasks. Our system achieved the best performance for all six sub-tasks when plain texts were given as input. It also performed best for narrative container relation identification when gold standard event/time annotations were given.

Question: What is the best performance for all six sub-tasks?


An Adaption of BIOASQ Question Answering dataset for Machine Reading systems by Manual Annotations of Answer Spans. BIOASQ Task B Phase B challenge focuses on extracting answers from snippets for a given question. The dataset provided by the organizers contains answers, but not all their variants. Henceforth a manual annotation was performed to extract all forms of correct answers. This article shows the impact of using all occurrences of correct answers for training on the evaluation scores which are improved significantly.

Question: What is the impact of using all occurrences of correct answers for training on the evaluation scores?


Indoor scene segmentation using a structured light sensor. In this paper we explore how a structured light depth sensor, in the form of the Microsoft Kinect, can assist with indoor scene segmentation. We use a CRF-based model to evaluate a range of different representations for depth information and propose a novel prior on 3D location. We introduce a new and challenging indoor scene dataset, complete with accurate depth maps and dense label coverage. Evaluating our model on this dataset reveals that the combination of depth and intensity images gives dramatic performance gains over intensity images alone. Our results clearly demonstrate the utility of structured light sensors for scene understanding.

Question: How can a structured light sensor assist with indoor scene segmentation?


Introduction to Statistical Learning Theory. The goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.

Question: What is statistical learning theory?


A column approximate minimum degree ordering algorithm. Sparse Gaussian elimination with partial pivoting computes the factorization PAQ = LU of a sparse matrix A, where the row ordering P is selected during factorization using standard partial pivoting with row interchanges. The goal is to select a column preordering, Q, based solely on the nonzero pattern of A, that limits the worst-case number of nonzeros in the factorization. The fill-in also depends on P, but Q is selected to reduce an upper bound on the fill-in for any subsequent choice of P. The choice of Q can have a dramatic impact on the number of nonzeros in L and U. One scheme for determining a good column ordering for A is to compute a symmetric ordering that reduces fill-in in the Cholesky factorization of ATA. A conventional minimum degree ordering algorithm would require the sparsity structure of ATA to be computed, which can be expensive both in terms of space and time since ATA may be much denser than A. An alternative is to compute Q directly from the sparsity structure of A; this strategy is used by MATLAB's COLMMD preordering algorithm. A new ordering algorithm, COLAMD, is presented. It is based on the same strategy but uses a better ordering heuristic. COLAMD is faster and computes better orderings, with fewer nonzeros in the factors of the matrix.

Question: What is the minimum degree ordering algorithm?


Architecture, the Backbone of Robotic Systems. Architectures form the backbone of complete robotic systems. The right choice of architecture can go a long way in facilitating the specification, implementation and validation of robotic systems. Conversely, of course, the wrong choice can make one's life miserable. We present some of the needs of robotic systems, describe some general classes of robot architectures, and discuss how different architectural styles can help in addressing those needs. The paper, like the field itself, is somewhat preliminary, yet it is hoped that it will provide guidance for those who use, or develop, robot architectures.

Question: What is the backbone of a robotic system?


Unite the People : Closing the loop between 3 D and 2 D Human Representations Supplementary Material. We have obtained human segmentation labels to integrate shape information into the SMPLify 3D fitting procedure and for the evaluation of methods introduced in the main paper. The labels consist of foreground segmentation for multiple human pose datasets and six body part segmentation for the LSP dataset. Whereas we discuss their use in the context of the UP dataset in the main paper, we discuss the annotation tool that we used for the collection (see Sec. 2.1) as well as the direct use of the human labels for model training (see Sec. 2.2) in this document.

Question: What is the human segmentation labels?


Too Much of a Good Thing? The Relationship Between Number of Friends and Interpersonal Impressions on Facebook. A central feature of the online social networking system, Facebook, is the connection to and links among friends. The sum of the number of one's friends is a feature displayed on users' profiles as a vestige of the friend connections a user has accrued. In contrast to offline social networks, individuals in online network systems frequently accrue friends numbering several hundred. The uncertain meaning of friend status in these systems raises questions about whether and how sociometric popularity conveys attractiveness in non-traditional, non-linear ways. An experiment examined the relationship between the number of friends a Facebook profile featured and observers' ratings of attractiveness and extraversion. A curvilinear effect of sociometric popularity and social attractiveness emerged, as did a quartic relationship between friend count and perceived extraversion. These results suggest that an overabundance of friend connections raises doubts about Facebook users' popularity and desirability. Zu viel des Guten? Zur Beziehung zwischen der Anzahl der Freunde und interpersonalen Eindrücken bei Facebook Eine zentrale Eigenschaft des sozialen Online-Netzwerks Facebook ist die Verbindung von Freunden. Die Gesamtanzahl der Freunde eines Nutzers wird als Merkmal im Benutzerprofil angezeigt und dient als eine Statistik der Freundeverbindungen, die ein Nutzer gesammelt hat. Im Gegensatz zu Offline-Netzwerken, haben Personen in OnlineNetzwerken oft mehrere Hundert Freunde. Die unklare Bedeutung des Freundestatus in diesem System wirft die Frage auf, ob und wie soziometrische Popularität die Attraktivität auf nicht-traditionelle, nichtlineare Weise ausdrückt. In einem Experiment wurde die Beziehung zwischen der Anzahl der Freunde im Facebook-Profil und der Einschätzung von Attraktivität und Extraversion durch den Beobachter untersucht. Es zeigten sich ein kurvilin

Question: How many friends does a person have on facebook?


Probabilistic Semi-Dense Mapping from Highly Accurate Feature-Based Monocular SLAM. In the last years several direct (i.e. featureless) monocular SLAM approaches have appeared showing impressive semi-dense or dense scene reconstructions. These works have questioned the need of features, in which consolidated SLAM techniques of the last decade were based. In this paper we present a novel feature-based monocular SLAM system that is more robust, gives more accurate camera poses, and obtains comparable or better semi-dense reconstructions than the current state of the art. Our semi-dense mapping operates over keyframes, optimized by local bundle adjustment, allowing to obtain accurate triangulations from wide baselines. Our novel method to search correspondences, the measurement fusion and the inter-keyframe depth consistency tests allow to obtain clean reconstructions with very few outliers. Against the current trend in direct SLAM, our experiments show that by decoupling the semi-dense reconstruction from the trajectory computation, the results obtained are better. This opens the discussion on the benefits of features even if a semi-dense reconstruction is desired.

Question: What is semi-dense mapping?


Weakly-supervised text-to-speech alignment confidence measure. This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment.

Question: How to evaluate text-to-speech alignment systems outputs?


Measuring Negative and Positive Thoughts in Children: An Adaptation of the Children's Automatic Thoughts Scale (CATS). The aim of this study is to describe the factor structure and psychometric properties of an extended version of the Children's Automatic Thoughts Scale (CATS), the CATS-Negative/Positive (CATS-N/P). The CATS was originally designed to assess negative self-statements in children and adolescents. However, positive thoughts also play a major role in childhood disorders such as anxiety and depression. Therefore, positive self-statements were added to the CATS. The CATS-N/P was administered to a community sample of 554 children aged 8–18 years. The results of a confirmatory factor analysis revealed that the positive self-statements formed a separate and psychometrically sound factor. Internal and short-term test–retest reliability was good. Boys reported more hostile and positive thoughts than girls; and younger children reported more negative thoughts concerning physical threat, social threat, and failure than older children. In conclusion, the results of the current study support the use of the CATS-N/P for the measurement of positive and negative thoughts in children. The application of the CATS-N/P can facilitate further research on cognitive factors in different childhood disorders.

Question: What is the aim of this study?


Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition. Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.

Question: What is the Big Five personality traits?


Controlling negative and positive power at the ankle with a soft exosuit. The soft exosuit is a new approach for applying assistive forces over the wearer's body through load paths configured by the textile architecture. In this paper, we present a body-worn lower-extremity soft exosuit and a new control approach that can independently control the level of assistance that is provided during negative- and positive-power periods at the ankle. The exosuit was designed to create load paths assisting ankle plantarflexion and hip flexion, and the actuation system transmits forces from the motors to the suit via Bowden cables. A load cell and two gyro sensors per leg are used to measure real-time data, and the controller performs position control of the cable on a step-by-step basis with respect to the power delivered to the wearer's ankle by controlling two force parameters, the pretension and the active force. Human subjects testing results demonstrate that the controller is capable of modulating the amount of power delivered to the ankle joint. Also, significant reductions in metabolic rate (11%-15%) were observed, which indicates the potential of the proposed control approach to provide benefit to the wearer during walking.

Question: How does the soft exosuit work?


Competing Memes Propagation on Networks: A Network Science Perspective. In this paper, we study the intertwined propagation of two competing "memes" (or data, rumors, etc.) in a composite network. Within the constraints of this scenario, we ask two key questions: (a) which meme will prevail? and (b) can one influence the outcome of the propagations? Our model is underpinned by two key concepts, a structural graph model (composite network) and a viral propagation model (SI1I2S). Using this framework, we formulate a non-linear dynamic system and perform an eigenvalue analysis to identify the tipping point of the epidemic behavior. Based on insights gained from this analysis, we demonstrate an effective and accurate prediction method to determine viral dominance, which we call the EigenPredictor. Next, using a combination of synthetic and real composite networks, we evaluate the effectiveness of various viral suppression techniques by either a) concurrently suppressing both memes or b) unilaterally suppressing a single meme while leaving the other relatively unaffected.

Question: What is the tipping point of the epidemic behavior?


Contrast masking in human vision.. Contrast masking was studied psychophysically. A two-alternative forced-choice procedure was used to measure contrast thresholds for 2.0 cpd sine-wave gratings in the presence of masking sine-wave gratings. Thresholds were measured for 11 masker contrasts spanning three log units, and seven masker frequencies ranging +/- one octave from the signal frequency. Corresponding measurements were made for gratings with horizontal widths of 0.75 degrees (narrow fields) and 6.0 degrees (wide fields). For high contrast maskers at all frequencies, signal thresholds were related to masking contrast by power functions with exponents near 0.6. For a range of low masking contrasts, signal thresholds were reduced by the masker. For the wide fields, high contrast masking tuning functions peaked at the signal frequency, were slightly asymmetric, and had approximately invariant half-maximum frequencies that lie 3/4 octave below and 1 octave above the signal frequency. The corresponding low contrast tuning functions exhibited peak threshold reduction at the signal frequency, with half-minimum frequencies at roughly +/- 0.25 octaves. For the narrow fields, the masking tuning functions were much broader at both low and high masking contrasts. A masking model is presented that encompasses contrast detection, discrimination, and masking phenomena. Central constructs of the model include a linear spatial frequency filter, a nonlinear transducer, and a process of spatial pooling that acts at low contrasts only.

Question: What is contrast masking?


Deep learning from temporal coherence in video. This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.

Question: How does deep learning from temporal coherence in video work?


Sequence-to-Sequence RNNs for Text Summarization. In this work, we cast text summarization as a sequence-to-sequence problem and apply the attentional encoder-decoder RNN that has been shown to be successful for Machine Translation (Bahdanau et al. (2014)). Our experiments show that the proposed architecture significantly outperforms the state-of-the art model of Rush et al. (2015) on the Gigaword dataset without any additional tuning. We also propose additional extensions to the standard architecture, which we show contribute to further improvement in performance.

Question: What is the state-of-the-art model for text summarization?


A Microprocessor-based Hypercube Supercomputer. Each node in the NCUBE/ten parallel processor is organized around a custom, VAX-like, 32-bit CPU chip. With 1024 nodes, the NCUBE/ten provides a throughput of 500 MELOPS.

Question: What is a microprocessor-based hypercube supercomputer?


Fuzzy Ontology and LSTM-Based Text Mining: A Transportation Network Monitoring System for Assisting Travel. Intelligent Transportation Systems (ITSs) utilize a sensor network-based system to gather and interpret traffic information. In addition, mobility users utilize mobile applications to collect transport information for safe traveling. However, these types of information are not sufficient to examine all aspects of the transportation networks. Therefore, both ITSs and mobility users need a smart approach and social media data, which can help ITSs examine transport services, support traffic and control management, and help mobility users travel safely. People utilize social networks to share their thoughts and opinions regarding transportation, which are useful for ITSs and travelers. However, user-generated text on social media is short in length, unstructured, and covers a broad range of dynamic topics. The application of recent Machine Learning (ML) approach is inefficient for extracting relevant features from unstructured data, detecting word polarity of features, and classifying the sentiment of features correctly. In addition, ML classifiers consistently miss the semantic feature of the word meaning. A novel fuzzy ontology-based semantic knowledge with Word2vec model is proposed to improve the task of transportation features extraction and text classification using the Bi-directional Long Short-Term Memory (Bi-LSTM) approach. The proposed fuzzy ontology describes semantic knowledge about entities and features and their relation in the transportation domain. Fuzzy ontology and smart methodology are developed in Web Ontology Language and Java, respectively. By utilizing word embedding with fuzzy ontology as a representation of text, Bi-LSTM shows satisfactory improvement in both the extraction of features and the classification of the unstructured text of social media.

Question: What is a transportation network monitoring system?


vDesign: a CAVE-based virtual design environment using hand interactions. The cave automatic virtual environment (CAVE) system is one of the most fully immersive systems for virtual reality environments. By providing users with realistic perception and immersive experience, CAVE systems have been widely used in many fields, including military, education, health care, entertainment, design, and others. In this paper, we focus on the design applications in the CAVE. The design applications involve many interactions between the user and the CAVE. However, the conventional interaction tool, the wand, cannot provide fast and convenient interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using hand interactions. The hand interactions in vDesign are classified into menu navigation and object manipulations. For menu navigation, we define two interactions: activating the main menu and selecting a menu item. For object manipulations, we define three interactions: moving, rotating, and scaling an object. By using the proposed hand interactions, we develop the functions of image segmentation and image composition in vDesign. With the image segmentation function, the designer can select and cut the interested objects from different images. With the image composition function, the designer can manipulate the segmented objects and combine them as a composite image. We implemented the vDesign prototype in CAVE and conducted experiments to evaluate the interaction performance in terms of manipulation time and distortion. The experimental results demonstrated that the proposed hand interactions can provide faster and more accurate interactions compared to the traditional wand interactions.

Question: What is the difference between the CAVE and the virtual reality?


High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence. Given i.i.d. observations of a random vector X ∈ R, we study the problem of estimating both its covariance matrix Σ, and its inverse covariance or concentration matrix Θ = (Σ). When X is multivariate Gaussian, the non-zero structure of Θ is specified by the graph of an associated Gaussian Markov random field; and a popular estimator for such sparse Θ is the l1-regularized Gaussian MLE. This estimator is sensible even for for non-Gaussian X, since it corresponds to minimizing an l1-penalized log-determinant Bregman divergence. We analyze its performance under high-dimensional scaling, in which the number of nodes in the graph p, the number of edges s, and the maximum node degree d, are allowed to grow as a function of the sample size n. In addition to the parameters (p, s, d), our analysis identifies other key quantities that control rates: (a) the l∞-operator norm of the true covariance matrix Σ; and (b) the l∞ operator norm of the sub-matrix Γ∗SS , where S indexes the graph edges, and Γ∗ = (Θ) ⊗ (Θ); and (c) a mutual incoherence or irrepresentability measure on the matrix Γ∗ and (d) the rate of decay 1/f(n, δ) on the probabilities {|Σ̂ij − Σ∗ij | > δ}, where Σ̂ is the sample covariance based on n samples. Our first result establishes consistency of our estimate Θ̂ in the elementwise maximum-norm. This in turn allows us to derive convergence rates in Frobenius and spectral norms, with improvements upon existing results for graphs with maximum node degrees d = o( √ s). In our second result, we show that with probability converging to one, the estimate Θ̂ correctly specifies the zero pattern of the concentration matrix Θ∗. We illustrate our theoretical results via simulations for various graphs and problem parameters, showing good correspondences between the theoretical predictions and behavior in simulations. AMS 2000 subject classifications: Primary 62F12; secondary 6

Question: What is the difference between the l1-regularized Gaussian MLE and the l1-regularized Gaussian MLE?


Toward a Universal Cortical Algorithm: Examining Hierarchical Temporal Memory in Light of Frontal Cortical Function. Representations One aspect of frontal cortical function that has been given less attention than it is due, both in this review and in the literature in general, is the biological basis of the generation and manipulation of abstract frontal representations. Response properties of some PFC cells have been shown to correspond with abstract rules (Wallis et al, 2001), and models such as those of Norman & Shallice (1980, 1986; Shallice, 1982; Shallice & Burgess, 1991, 1996), Grafman (2002) and Fuster (1997) have emphasized the requirement that frontal cortex dynamically generate and manipulate abstract

Question: What is the biological basis of the generation and manipulation of abstract frontal representations?


Islanding detection of active distribution system with parallel inverters. Average absolute frequency deviation value (AFDVavg) based active islanding detection technique is recently introduced for islanding detection of active distribution system with a single active DG. This paper implements, evaluates and analyze the performance of AFDVavg technique for active distribution system with parallel inverter. The main focus is on islanding detection of an ADN with parallel inverters including the worst case i.e. zero power imbalance condition. The case study is done on an ADN which is energized by two parallel inverter based DGs. Worst case of islanding has been simulated successfully by adjusting the equivalent load of ADN to match the total generation. The resultant quality factor of the equivalent load is kept 1 for testing islanding. Computer simulations are done in MATLAB.

Question: How to detect islanding in an active distribution system with parallel inverters?


[Skin signs in child abuse].. Child abuse is far more prevalent today than is generally recognized. Up to 90% of victims suffer physical abuse that can be observed in signs on the skin. Dermatologists are particularly qualified to identify these signs and distinguish them from other conditions that can mimic abuse. This review covers the signs of child abuse that can be observed on the skin. We discuss clues that can help differentiate between lesions caused by abuse and those that are accidental, and we describe the skin conditions that mimic physical abuse.

Question: What are the signs of child abuse?


Gunshot detection and localization using sensor networks. Acoustic sensors or sensor networks can be efficiently used to spot and observe targets of interest and have the ability to conclude the exact location of an object or event within the sensor network's coverage range. For the detection and spot estimation of shooter, the schemes of gunshot localization require one or more sensing technologies. In current situation the importance of counter sniper systems cannot be denied. An efficient counter sniper system that can provide immediate shooter location will provide many benefits to our security personnel. In this paper, the Time Difference Of Arrival (TDOA) of Shock Wave and Muzzle Blast is used to estimate the shooter location. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The result shows that the scheme worked very fine and is capable of locating sniper fires very efficiently, competently and effectively. The designed algorithm worked very well and results were found to be very accurately.

Question: How to detect a sniper?


Online Attendance Management System Using RFID with Object Counter. Educational institutions' administrators in our country and the whole world are concerned about regularity of student attendance. Student overall academic performance is affected by it. The conventional method of taking attendance by calling names or signing on paper is very time consuming, and hence inefficient. Radio Frequency Identification (RFID) based attendance system is one of the solutions to address this problem. A system that can automatically capture student's attendance by flashing their student card at the RFID reader and save all the mentioned troubles was given by T.S.Lim et al. [1].The RFID system using web-based applications such as ASP.NET and IIS server to cater the recording and reporting of the students' attendances was given by Abdul Aziz Mohammed et.al. [2]. Arulogun O. T. et al made an attempt to solve recurrent lecture attendance monitoring problem in developing countries using RFID technology [3]. Rajan Patel et al [4] presents the integration of ubiquitous computing systems into classroom for managing the students' attendance using RFID technology. H.B. Hamid et al [5] gave a system that has been built using the web-based applications such as JSP, MySQL and Apache to cater the recording and reporting of the students' attendances. NetBeans IDE 6.1 is used for developing the overall system. We have proposed the system in this paper using C#. Microsoft Visual Studio is used for the system designing. Also, the issue related to fake /false attendance through the RFID system has been addressed, we eliminate it by using a special object counter for the head count. Ankita Agrawal & Ashish Bansal 132

Question: What is the attendance management system using RFID with object counter?


An exact algorithm for solving the vertex separator problem. Given G = (V,E) a connected undirected graph and a positive integer β(|V |), the vertex sperator problem is to find a partition of V into noempty three classes A, B, C such that there is no edge between A and B, max{|A|, |B|} ≤ β(|V |) and |C| is minimum. In this paper we consider the vertex separator problem from a polyhedral point of view. We introduce new classes of valid inequalities for the associated polyhedron. Using a natural lower bound for the optimal solution, we present successful computational experiments.

Question: What is the vertex separator problem?


Digital Comics Image Indexing Based on Deep Learning. The digital comic book market is growing every year now, mixing digitized and digital-born comics. Digitized comics suffer from a limited automatic content understanding which restricts online content search and reading applications. This study shows how to combine state-of-the-art image analysis methods to encode and index images into an XML-like text file. Content description file can then be used to automatically split comic book images into sub-images corresponding to panels easily indexable with relevant information about their respective content. This allows advanced search in keywords said by specific comic characters, action and scene retrieval using natural language processing. We get down to panel, balloon, text, comic character and face detection using traditional approaches and breakthrough deep learning models, and also text recognition using LSTM model. Evaluations on a dataset composed of online library content are presented, and a new public dataset is also proposed.

Question: How to index images into an XML-like text file?


Biomedical Image Processing. medical fields has led to the automated processing ofpictorial data. Here, a device called the cytocomputer searches for genetic mutations. A computer revolution has occurred not only in technical fields but also in medicine, where vast amounts of information must be processed quickly and accurately. Nowhere is the need for image processing techniques more apparent than in clinical diagnosis or mass screening applications where data take the form of digital images. New high-resolution scanning techniques such as computed tomography, nuclear magnetic resonance, po-sitron emission tomography, and digital radiography produce images containing immense amounts of relevant information for medical analysis. But as these scanning techniques become more vital to clinical diagnosis, the work for specialists who must visually examine the resultant images increases. In many cases, quantitative data in the form of measurements and counts are needed to supplement nonimage patient data, and the manual extraction of these data is a time-consuming and costly step in an otherwise automated process. Furthermore, subtle variants of shade and shape can be the earliest clues to a diagnosis, placing the additional burden of complete thoroughness on the examining specialist. For the last five years, the University of Michigan and the Environmental Research Institute of Michigan have conducted a unique series of studies that involve the processing of biomedical imagery on a highly parallel computer specifically designed for image processing. System designers have incorporated the requirements of extracting a verifiable answer from an image in a reasonable time into an integrated approach to hardware and software design. The system includes a parallel pipelined image processor, called a cytocomputer, and a high-level language specifically created for image processing, C-3PL, the cytocomputer parallel picture processing language. These studies have involved a grea

Question: What is a cytocomputer?


Model predictive path-following control of an A.R. drone quadrotor. This paper addresses the design and implementation of the Extended Prediction Self-Adaptive Control (EPSAC) approach to Model Predictive Control (MPC) for path-following. Special attention is paid to the closed-loop performance in order to achieve a fast response without overshoot, which are the necessary conditions to ensure the desired tracking performance in confined or indoor environments. The performance of the proposed MPC strategy is compared to the one achieved using PD controllers. Experimental results using the low-cost quadrotor AR.Drone 2.0 validate the reliability of the proposed strategy for 2D and 3D movements.

Question: What is the difference between a model predictive controller and a PD controller?


A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes. We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods forn-gram language models. Experiments verify that our model gives cross entropy results superior to interpolated Kneser-Ney and comparable to modified Kneser-Ney.

Question: What is a hierarchical Bayesian language model?


Dynamic Identification of Manipulator : Comparison between CAD and Actual Parameters. It is essential to know the dynamic parameters of the robot for its precise control and simulation. Philosophy of identification is based on finding the model using its input-output data. The identification equation of the manipulator is derived from Newton-Euler equations, using manipulator kinematic, i.e., geometric parameters and joint values as input and joint torque data as output. In this paper, the dynamic parameters are identified for the CAD model provided by the robot manufacturer in simulation. And experimentally for the installed seven degrees of freedom (DOF) robot KUKA-iiwaR800. The variation between the joint torques predicted from the estimated base parameters obtained using CAD model and actual robot are presented. The factors responsible for the variation are also highlighted.

Question: How to identify the dynamic parameters of a robot?


Representing Attitude : Euler Angles , Unit Quaternions , and Rotation Vectors. We present the three main mathematical constructs used to represent the attitude of a rigid body in threedimensional space. These are (1) the rotation matrix, (2) a triple of Euler angles, and (3) the unit quaternion. To these we add a fourth, the rotation vector, which has many of the benefits of both Euler angles and quaternions, but neither the singularities of the former, nor the quadratic constraint of the latter. There are several other subsidiary representations, such as Cayley-Klein parameters and the axis-angle representation, whose relations to the three main representations are also described. Our exposition is catered to those who seek a thorough and unified reference on the whole subject; detailed derivations of some results are not presented. Keywords–Euler angles, quaternion, Euler-Rodrigues parameters, Cayley-Klein parameters, rotation matrix, direction cosine matrix, transformation matrix, Cardan angles, Tait-Bryan angles, nautical angles, rotation vector, orientation, attitude, roll, pitch, yaw, bank, heading, spin, nutation, precession, Slerp

Question: What is the difference between Euler angles and quaternions?


The Evolution of Accuracy and Bias in Social Judgment. Humans are an intensely social species and therefore it is essential for our interpersonal judgments to be valid enough to help us to avoid enemies, form useful alliances and find suitable mates; flawed judgments can literally be fatal. An evolutionary perspective implies that humans ought to have developed sufficient skills at solving problems of interpersonal judgment, including gauging the personalities of others, to be useful for the basic tasks of survival and reproduction. Yet, the view to be derived from the large and influential bias-and-error literature of social psychology is decidedly different—the social mind seems riddled with fundamental design flaws. We will argue in this paper that flawed design is probably the least plausible explanation for the existence of so many errors. We present an evolutionarily-based taxonomy of known bias effects that distinguishes between biases that are trivial or even artifactual and lead virtually nowhere, and those that have interesting implications and deserve further study. Finally, we present an evolutionary perspective that suggests that the ubiquity, automaticity, and success of interpersonal judgment, among other considerations, presents the possibility of a universal Personality Judgment Instinct. Archeological evidence and behavioral patterns observed in extant hunter-gatherer groups indicate that the human species has been intensely social for a long time (e.g., Chagnon, 1983, Tooby & Devore, 1987). Human offspring have a remarkably extended period of juvenile dependency, which both requires and provides the skills for surviving in a complex social world (Hrdy, 1999). Humans evolved language and universal emotional expressions which serve the social purpose of discerning and influencing the thoughts of others (e. and humans will infer social intentions on the basis of minimal cues, as Heider and Simmel (1944) demonstrated in their classic experiment involv

Question: What is the evolutionary perspective?


Suicidal strangulation by double ligature: a case report.. This article reports a self-strangulation suicide in which a double complex group of ligatures was used. Although similar cases have been referenced in forensic literature, this case is notable because of the unusual method used by the victim. A 61-year-old man was found in a locked room with a double elaborate ligature comprising six wire clothes hangers completely encircling the neck and a black rubber band in a double loop. Autopsy also documented parallel superficial cut lesions in proximal forearm interpreted as hesitation marks. It stresses the importance of characteristics such as analysis of number of ligatures, position of the knots, number of knots and turns of ligature marks and the absence of any defense and relevant internal injuries inherent to suicide ligature strangulation cases.

Question: What is the difference between a suicide and a homicide?


The Internet of Things: A survey. This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details. 2010 Elsevier B.V. All rights reserved.

Question: What is the Internet of Things?


Learning Background-Aware Correlation Filters for Visual Tracking. Correlation Filters (CFs) have recently demonstrated excellent performance in terms of rapidly tracking objects under challenging photometric and geometric variations. The strength of the approach comes from its ability to efficiently learn - on the fly - how the object is changing over time. A fundamental drawback to CFs, however, is that the background of the target is not modeled over time which can result in suboptimal performance. Recent tracking algorithms have suggested to resolve this drawback by either learning CFs from more discriminative deep features (e.g. DeepSRDCF [9] and CCOT [11]) or learning complex deep trackers (e.g. MDNet [28] and FCNT [33]). While such methods have been shown to work well, they suffer from high complexity: extracting deep features or applying deep tracking frameworks is very computationally expensive. This limits the real-time performance of such methods, even on high-end GPUs. This work proposes a Background-Aware CF based on hand-crafted features (HOG [6]) that can efficiently model how both the foreground and background of the object varies over time. Our approach, like conventional CFs, is extremely computationally efficient- and extensive experiments over multiple tracking benchmarks demonstrate the superior accuracy and real-time performance of our method compared to the state-of-the-art trackers.

Question: What is the background of the target?


Fast Component-Based QR Code Detection in Arbitrarily Acquired Images. Quick Response (QR) codes are a type of 2D barcode that is becoming very popular, with several application possibilities. Since they can encode alphanumeric characters, a rich set of information can be made available through encoded URL addresses. In particular, QR codes could be used to aid visually impaired and blind people to access web based voice information systems and services, and autonomous robots to acquire context-relevant information. However, in order to be decoded, QR codes need to be properly framed, something that robots, visually impaired and blind people will not be able to do easily without guidance. Therefore, any application that aims assisting robots or visually impaired people must have the capability to detect QR codes and guide them to properly frame the code. A fast component-based two-stage approach for detecting QR codes in arbitrarily acquired images is proposed in this work. In the first stage, regular components present at three corners of the code are detected, and in the second stage geometrical restrictions among detected components are verified to confirm the presence of a code. Experimental results show a high detection rate, superior to 90 %, at a fast speed compatible with real-time applications.

Question: How to detect QR codes in arbitrarily acquired images?


Haptic Virtual Fixtures for Robot-Assisted Manipulation. Haptic virtual fixtures are software-generated force and position signals applied to human operators in order to improve the safety, accuracy, and speed of robot-assisted manipulation tasks. Virtual fixtures are effective and intuitive because they capitalize on both the accuracy of robotic systems and the intelligence of human operators. In this paper, we discuss the design, analysis, and implementation of two categories of virtual fixtures: guidance virtual fixtures, which assist the user in moving the manipulator along desired paths or surfaces in the workspace, and forbidden-region virtual fixtures, which prevent the manipulator from entering into forbidden regions of the workspace. Virtual fixtures are analyzed in the context of both cooperative manipulation and telemanipulation systems, considering issues related to stability, passivity, human modeling, and applications.

Question: What is the difference between a virtual fixture and a haptic device?


Analysis of H.264 Bitstream Prioritization for Dual TCP/UDP Streaming of HD video over WLANs. Flexible Dual-TCP/UDP Streaming Protocol (FDSP) is a new method for streaming H.264-encoded HD video over wireless networks. The method takes advantage of the hierarchical structure of H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit less important elements. FDSP was shown to outperform pure-UDP streaming in visual quality and pure-TCP streaming in delay. In this work, FDSP is expanded to include a new parameter called Bitstream Prioritization (BP). The newly modified algorithm, FDSP-BP, is analyzed to measure the impact of BP on the quality of streaming for partially and fully congested networks. Our analysis shows that FDSP-BP is superior to pure-TCP streaming methods with respect to rebuffering instances, while still maintaining high visual quality.

Question: What is the impact of bitstream prioritization on the quality of streaming?


A Comprehensive Study for Software Testing and Test Cases Generation Paradigms. Software testing is accounted to be an essential part in software development life cycle in terms of cost and manpower, where its total cost is considerable high. Consequently, many studies [48] have been conducted to minimize the associated cost and human effort to fix bugs and errors, and to improve the quality of testing process by automatically generating test cases. Test cases can be generated from different phases (requirement phase, design phase and after development phase). Though, test case generation at early stages is more effective rather than that after development, where time and effort used for finding and fixing errors and bugs is less than that after development. At later stage, fixing errors results in enormous code correction, consuming a lot of time and effort. In this paper, we study the different paradigms of testing techniques for generating test cases, where we investigate their coverage and associated capabilities. We finally propose a preliminary model for a generic automated test cases generation.

Question: What is a comprehensive study for software testing and test cases generation paradigms?


Age and Gender Estimation of Unfiltered Faces. This paper concerns the estimation of facial attributes-namely, age and gender-from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research-absence of data-we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.

Question: How old is this person?


The impact of agile practices on communication in software development. Agile software development practices such as eXtreme Programming (XP) and SCRUM have increasingly been adopted to respond to the challenges of volatile business environments, where the markets and technologies evolve rapidly and present the unexpected. In spite of the encouraging results so far, little is known about how agile practices affect communication. This article presents the results from a study which examined the impact of XP and SCRUM practices on communication within software development teams and within the focal organization. The research was carried out as a case study in F-Secure where two agile software development projects were compared from the communication perspective. The goal of the study is to increase the understanding of communication in the context of agile software development: internally among the developers and project leaders and in the interface between the development team and stakeholders (i.e. customers, testers, other development teams). The study shows that agile practices improve both informal and formal communication. However, it further indicates that, in larger development situations involving multiple external stakeholders, a mismatch of adequate communication mechanisms can sometimes even hinder the communication. The study highlights the fact that hurdles and improvements in the communication process can both affect the feature requirements and task subtask dependencies as described in coordination theory. While the use of SCRUM and some XP practices facilitate team and organizational communication of the dependencies between product features and working tasks, the use of agile practices requires that the team and organization use also additional plan-driven practices to ensure the efficiency of external communication between all the actors of software development.

Question: What is the impact of agile practices on communication?


A Sensing Chair Using Pressure Distribution Sensors. One challenge in multimodal interface research is the lack of robust subsystems that support multimodal interactions. By focusing on a chair—an object that is involved in virtually all human–computer interactions, the sensing chair project enables an ordinary office chair to become aware of its occupant's actions and needs. Surface-mounted pressure distribution sensors are placed over the seatpan and backrest of the chair for real time capturing of contact information between the chair and its occupant. Given the similarity between a pressure distribution map and a grayscale image, pattern recognition techniques commonly used in computer and robot vision, such as principal components analysis, have been successfully applied to solving the problem of sitting posture classification. The current static posture classification system operates in real time with an overall classification accuracy of 96% and 79% for familiar (people it had felt before) and unfamiliar users, respectively. Future work is aimed at a dynamic posture tracking system that continuously tracks not only steady-state (static) but transitional (dynamic) sitting postures. Results reported here form important stepping stones toward an intelligent chair that can find applications in many areas including multimodal interfaces, intelligent environment, and safety of automobile operations.

Question: What is a pressure distribution sensor?


Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting. Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theorydriven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.

Question: How do you retrofit a document?


A Review of Wideband Wide-Angle Scanning 2-D Phased Array and Its Applications in Satellite Communication. In this review, research progress on the wideband wide-angle scanning two-dimensional phased arrays is summarized. The importance of the wideband and the wide-angle scanning characteristics for satellite communication is discussed. Issues like grating lobe avoidance, active reflection coefficient suppression and gain fluctuation reduction are emphasized in this review. Besides, techniques to address these issues and methods to realize the wideband wide-angle scanning phased array are reviewed.

Question: What is the importance of wideband wide-angle scanning?


A Dictionary-based Approach to Racism Detection in Dutch Social Media. We present a dictionary-based approach to racism detection in Dutch social media comments, which were retrieved from tw o public Belgian social media sites likely to attract racist reactio ns. These comments were labeled as racist or non-racist by mu ltiple annotators. For our approach, three discourse dictionaries were create d: first, we created a dictionary by retrieving possibly raci st and more neutral terms from the training data, and then augmenting these with more general words to remove some bias. A second dictionary w s created through automatic expansion using a word2vec model trained on a large corpus of general Dutch text. Finall y, third dictionary was created by manually filtering out incorrect expansions. We trained multiple Support Vector Machines, using the dist ribu ion of words over the different categories in the dictionaries as f eatures. The best-performing model used the manually clean ed dictionary and obtained an F-score of 0.46 for the racist class on a test s et consisting of unseen Dutch comments, retrieved from the s ame sites used for the training set. The automated expansion of the dic tionary only slightly boosted the model's performance, and this increase in performance was not statistically significant. The fact t hat the coverage of the expanded dictionaries did increase i ndicates that the words that were automatically added did occur in the corpus, b t were not able to meaningfully impact performance. The di ctionaries, code, and the procedure for requesting the corpus are availa ble at:https://github.com/clips/hades.

Question: What is a dictionary-based approach to racism detection in Dutch social media comments?


Blockchained Post-Quantum Signatures. Inspired by the blockchain architecture and existing Merkle tree based signature schemes, we propose BPQS, an extensible post-quantum (PQ) resistant digital signature scheme best suited to blockchain and distributed ledger technologies (DLTs). One of the unique characteristics of the protocol is that it can take advantage of application-specific chain/graph structures in order to decrease key generation, signing and verification costs as well as signature size. Compared to recent improvements in the field, BPQS outperforms existing hash-based algorithms when a key is reused for reasonable numbers of signatures, while it supports a fallback mechanism to allow for a practically unlimited number of signatures if required. We provide an open source implementation of the scheme and benchmark it.

Question: What is a blockchain?


Semi-supervised deep learning by metric embedding. Deep networks are successfully used as classification models yielding state-ofthe-art results when trained on a large number of labeled samples. These models, however, are usually much less suited for semi-supervised problems because of their tendency to overfit easily when trained on small amounts of data. In this work we will explore a new training objective that is targeting a semi-supervised regime with only a small subset of labeled data. This criterion is based on a deep metric embedding over distance relations within the set of labeled samples, together with constraints over the embeddings of the unlabeled set. The final learned representations are discriminative in euclidean space, and hence can be used with subsequent nearest-neighbor classification using the labeled samples.

Question: What is semi-supervised deep learning?


Modeling and simulation of a PV pumping system under real climatic conditions. This article presents the modeling and simulation of a PV pumping system operating over the sun which has been developed under PSIM environment. The main objective is to optimize the PV pumping system through the introduction of two stages. The first one concerns the insertion of a DC-DC converter working with an MPPT in order to get the maximum power from the PV array. The second concerns the optimization of the DC-AC inverter which is controlled through a PWM control in order to get good voltage and current outputs spectrum with reduced losses and harmonics rate. The improvements brought by the MPPT and the PWM on the PV pumping system are shown through the example treated under real climatic conditions.

Question: How to optimize a PV pumping system?


Reducing restraint use for older adults in acute care.. Although no evidence shows that restraints are effective for maintaining safety, preventing disruption of treatment, or controlling behavior, they're still commonly used in acute care facilities (especially in critical care) in the United States, where the reported prevalence of their use ranges from 7.4% to 17%.[1–3] They may be used to protect patients from falls or to prevent them from inadvertently removing tubes and other devices.[4] Older adults are three times more likely to be restrained during an acute hospital admission than younger patients, even though this practice is associated with poor outcomes.[1] (See Adverse effects of physical restraints.)

Question: What are the adverse effects of physical restraints?


Variational Auto-encoded Deep Gaussian Processes. We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.

Question: What is a variational autoencoder?


Multiple Kernel Fuzzy Clustering. While fuzzy c-means is a popular soft-clustering method, its effectiveness is largely limited to spherical clusters. By applying kernel tricks, the kernel fuzzy c-means algorithm attempts to address this problem by mapping data with nonlinear relationships to appropriate feature spaces. Kernel combination, or selection, is crucial for effective kernel clustering. Unfortunately, for most applications, it is uneasy to find the right combination. We propose a multiple kernel fuzzy c-means (MKFC) algorithm that extends the fuzzy c-means algorithm with a multiple kernel-learning setting. By incorporating multiple kernels and automatically adjusting the kernel weights, MKFC is more immune to ineffective kernels and irrelevant features. This makes the choice of kernels less crucial. In addition, we show multiple kernel k-means to be a special case of MKFC. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed MKFC algorithm.

Question: What is the multiple kernel fuzzy c-means algorithm?


Endurance exercise performance: the physiology of champions.. Efforts to understand human physiology through the study of champion athletes and record performances have been ongoing for about a century. For endurance sports three main factors--maximal oxygen consumption (.VO(2,max)), the so-called 'lactate threshold' and efficiency (i.e. the oxygen cost to generate a given running speed or cycling power output)--appear to play key roles in endurance performance. and lactate threshold interact to determine the 'performance .VO(2)' which is the oxygen consumption that can be sustained for a given period of time. Efficiency interacts with the performance .VO(2) to establish the speed or power that can be generated at this oxygen consumption. This review focuses on what is currently known about how these factors interact, their utility as predictors of elite performance, and areas where there is relatively less information to guide current thinking. In this context, definitive ideas about the physiological determinants of running and cycling efficiency is relatively lacking in comparison with .VO(2,max) and the lactate threshold, and there is surprisingly limited and clear information about the genetic factors that might pre-dispose for elite performance. It should also be cautioned that complex motivational and sociological factors also play important roles in who does or does not become a champion and these factors go far beyond simple physiological explanations. Therefore, the performance of elite athletes is likely to defy the types of easy explanations sought by scientific reductionism and remain an important puzzle for those interested in physiological integration well into the future.

Question: What is the difference between a champion and an ordinary person?


ECONOMICS AND IDENTITY. This paper considers how identity, a person's sense of self, affects economic outcomes. We incorporate the psychology and sociology of identity into an economic model of behavior. In the utility function we propose, identity is associated with different social categories and how people in these categories should behave. We then construct a simple game-theoretic model showing how identity can affect individual interactions. The paper adapts these models to gender discrimination in the workplace, the economics of poverty and social exclusion, and the household division of labor. In each case, the inclusion of identity substantively changes conclusions of previous economic analysis.

Question: How does identity affect economic outcomes?


Language Oriented Programming: The Next Programming Paradigm. Ideally, being a programmer means I can do anything on a computer. I have complete freedom, complete control. But in reality, programmers today have very It is time to begin the next technology revolution in software development, and the shape of this revolution is becoming more and more clear. The next programming paradigm is nearly upon us. It is not yet fully formed—different parts have different names: Intentional programming, MDA, generative programming, etc. I suggest uniting all of these new approaches under one name, 'language-oriented programming', and this article explains the main principles of this new programming paradigm. Today's mainstream approach to programming has some crucial built-in assumptions which hold us back like chains around our necks, though most programmers don't realize this. With all the progress made so far in programming, we are still in the Stone Age. We've got our trusty stone axe (object-oriented programming), which serves us well, but tends to chip and crack when used against the hardest problems. To advance beyond stone, we must tame fire. Only then can we forge new tools and spark a new age of invention and an explosion of new technologies. I'm talking about the limitations of programming which force the programmer to think like the computer rather than having the computer think more like the programmer. These are serious, deeply-ingrained limitations which will take a lot of effort to overcome. I'm not being pretentious when I say that this will be the next big paradigm shift in programming. We will need to completely redefine the way we write programs. In this article, I present my view and my current work toward Language Oriented Programming (LOP). First I will show what is wrong with mainstream programming today, then I'll explain the concept of LOP by using the example of my existing implementation, the Meta Programming System (MPS). This article is intended to

Question: What is wrong with mainstream programming today?


Calculating the maximum execution time of real-time programs. In real-time systems, the timing behavior is an important property of each task. It has to be guaranteed that the execution of a task does not take longer than the specified amount of time. Thus, a knowledge about the maximum execution time of programs is of utmost importance. This paper discusses the problems for the calculation of the maximum execution time (MAXT... MAximum eXecution Time). It shows the preconditions which have to be met before the MAXT of a task can be calculated. Rules for the MAXT calculation are described. Triggered by the observation that in most cases the calculated MAXT far exceeds the actual execution time, new language constructs are introduced. These constructs allow programmers to put into their programs more information about the behavior of the algorithms implemented and help to improve the self checking property of programs. As a consequence, the quality of MAXT calculations is improved significantly. In a realistic example, an improvement fator of 11 has been achieved.

Question: How to calculate the maximum execution time of a program?


Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision. Neural networks have achieved state-ofthe-art performance on several structuredoutput prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural networkbased framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.

Question: What is the name of the neural network?


Inverted index compression and query processing with optimized document ordering. Web search engines use highly optimized compression schemes to decrease inverted index size and improve query throughput, and many index compression techniques have been studied in the literature. One approach taken by several recent studies first performs a renumbering of the document IDs in the collection that groups similar documents together, and then applies standard compression techniques. It is known that this can significantly improve index compression compared to a random document ordering. We study index compression and query processing techniques for such reordered indexes. Previous work has focused on determining the best possible ordering of documents. In contrast, we assume that such an ordering is already given, and focus on how to optimize compression methods and query processing for this case. We perform an extensive study of compression techniques for document IDs and present new optimizations of existing techniques which can achieve significant improvement in both compression and decompression performances. We also propose and evaluate techniques for compressing frequency values for this case. Finally, we study the effect of this approach on query processing performance. Our experiments show very significant improvements in index size and query processing speed on the TREC GOV2 collection of 25.2 million web pages.

Question: What is the best compression technique for inverted index compression?


The orbital angular momentum (OAM) multiplexing controversy: OAM as a subset of MIMO. Recently the orbital angular momentum (OAM) multiplexing scheme has been proposed to increase without limit the channel capacity between a transmitter and a receiver node communicating in line of sight. A controversy arose about the far-field effectiveness of this method and about it being or not a subset of MIMO communication schemes. In this contribution we first illustrate that OAM multiplexing can be completely understood as line of sight MIMO, and hence that the technique cannot bring additional advantages in far field communications. Secondly, we show that it is possible to build similar line of sight near field MIMO systems based on lenses with similar or better performance than their OAM counterparts.

Question: What is the orbital angular momentum multiplexing controversy?


CRISP-DM 1.0: Step-by-step data mining guide. This document describes the CRISP-DM process model, including an introduction to the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide and the CRISP-DM reports, as well as an appendix with additional useful and related information. This document and information herein, are the exclusive property of the partners of the CRISP-DM All trademarks and service marks mentioned in this document are marks of their respective owners and are as such acknowledged by the members of the CRISP-DM consortium. Foreword CRISP-DM was conceived in late 1996 by three " veterans " of the young and immature data mining market. DaimlerChrysler (then Daimler-Benz) was already experienced, ahead of most industrial and commercial organizations, in applying data mining in its business operations. SPSS (then ISL) had been providing services based on data mining since 1990 and had launched the first commercial data mining workbench – Clementine – in 1994. NCR, as part of its aim to deliver added value to its Teradata data warehouse customers, had established teams of data mining consultants and technology specialists to service its clients' requirements. At that time, early market interest in data mining was showing signs of exploding into widespread uptake. This was both exciting and terrifying. All of us had developed our approaches to data mining as we went along. Were we doing it right? Was every new adopter of data mining going to have to learn, as we had initially, by trial and error? And from a supplier's perspective, how could we demonstrate to prospective customers that data mining was sufficiently mature to be adopted as a key part of their business processes? A standard process model, we reasoned, non-proprietary and freely available, would address these issues for us and for all practitioners. A year later we had formed a consortium, invented an acronym (CRoss-Industry Standard Process for Data Mining), obtained f

Question: What is the CRISP-DM process model?


Rapid Object Detection using a Boosted Cascade of Simple Features. This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called the "Integral Image" which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features from a larger set and yields extremely efficient classifiers[6]. The third contribution is a method for combining increasingly more complex classifiers in a "cascade" which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. The cascade can be viewed as an object specific focus-of-attention mechanism which unlike previous approaches provides statistical guarantees that discarded regions are unlikely to contain the object of interest. In the domain of face detection the system yields detection rates comparable to the best previous systems. Used in real-time applications, the detector runs at 15 frames per second without resorting to image differencing or skin color detection.

Question: What is the difference between AdaBoost and gradient boosting?


Emotional intelligence and social and academic adaptation to school.. In a sample of 127 Spanish adolescents, the ability to understand and manage emotions, assessed by a performance measure of emotional intelligence (the MSCEIT), correlated positively with teacher ratings of academic achievement and adaptation for both males and females. Among girls, these emotional abilities also correlated positively with peer friendship nominations. After controlling for IQ and the Big Five personality traits, the ability to understand and manage emotions remained significantly associated with teacher ratings of academic adaptation among boys and peer friendship nominations among girls. Self-perceived emotional intelligence was unrelated to these criteria. These findings provide partial support for hypotheses that emotional abilities are associated with indicators of social and academic adaptation to school.

Question: What is the relationship between emotional intelligence and academic adaptation?


Health hazards and waste management.. Different methods of waste management emit a large number of substances, most in small quantities and at extremely low levels. Raised incidence of low birth weight births has been related to residence near landfill sites, as has the occurrence of various congenital malformations. There is little evidence for an association with reproductive or developmental effects with proximity to incinerators. Studies of cancer incidence and mortality in populations around landfill sites or incinerators have been equivocal, with varying results for different cancer sites. Many of these studies lack good individual exposure information and data on potential confounders, such as socio-economic status. The inherent latency of diseases and migration of populations are often ignored. Waste management workers have been shown to have increased incidence of accidents and musculoskeletal problems. The health impacts of new waste management technologies and the increasing use of recycling and composting will require assessment and monitoring.

Question: What are the health hazards of waste management?


Language Problems and ADHD Symptoms: How Specific Are the Links?. Symptoms of inattention and hyperactivity frequently co-occur with language difficulties in both clinical and community samples. We explore the specificity and strength of these associations in a heterogeneous sample of 254 children aged 5 to 15 years identified by education and health professionals as having problems with attention, learning and/or memory. Parents/carers rated pragmatic and structural communication skills and behaviour, and children completed standardised assessments of reading, spelling, vocabulary, and phonological awareness. A single dimension of behavioural difficulties including both hyperactivity and inattention captured behaviour problems. This was strongly and negatively associated with pragmatic communication skills. There was less evidence for a relationship between behaviour and language structure: behaviour ratings were more weakly associated with the use of structural language in communication, and there were no links with direct measures of literacy. These behaviour problems and pragmatic communication difficulties co-occur in this sample, but impairments in the more formal use of language that impact on literacy and structural communication skills are tied less strongly to behavioural difficulties. One interpretation is that impairments in executive function give rise to both behavioural and social communication problems, and additional or alternative deficits in other cognitive abilities impact on the development of structural language skills.

Question: How specific are the links between language and behaviour?


Map-based navigation in mobile robots:: II. A review of map-learning and path-planning strategies. This article reviews map-learning and path-planning strategies within the context of mapbased navigation in mobile robots. Concerning map-learning, it distinguishes metric maps from topological maps and describes procedures that help maintain the coherency of these maps. Concerning path-planning, it distinguishes continuous from discretized spaces and describes procedures applicable when the execution of a plan fails. It insists on the need for an integrated conception of such procedures, that must be tightly tailored to the specific robot that is used notably to the capacities and limitations of its sensory-motor equipment and to the specific environment that is experienced. A hierarchy of navigation strategies is outlined in the discussion, together with the sort of adaptive capacities each affords to cope with unexpected obstacles or dangers encountered on an animat or robot's way to its goal.

Question: What is a map-learning strategy?


Paraphrase Detection Using Recursive Autoencoder. In this paper, we tackle the paraphrase detection task. We present a novel recursive autoencoder architecture that learns representations of phrases in an unsupervised way. Using these representations, we are able to extract features for classification algorithms that allow us to outperform many results from previous works.

Question: What is the paraphrase detection task?


Ultra wideband antennas: past and present. Since the release by the Federal Communications Commission (FCC) of a bandwidth of 7.5GHz (from 3.1GHz to 10.6GHz) for ultra wideband (UWB) wireless communications, UWB is rapidly advancing as a high data rate wireless communication technology. As is the case in conventional wireless communication systems, an antenna also plays a very crucial role in UWB systems. However, there are more challenges in designing a UWB antenna than a narrow band one. A suitable UWB antenna should be capable of operating over an ultra wide bandwidth as allocated by the FCC. At the same time, satisfactory radiation properties over the entire frequency range are also necessary. This paper focuses on UWB planar printed circuit board (PCB) antenna design and analysis. Studies have been undertaken covering the areas of UWB fundamentals and antenna theory. Extensive investigations were also carried out on the development of UWB antennas from the past to present. First, the planar PCB antenna designs for UWB system is introduced and described. Next, the special design considerations for UWB antennas are summarized. State-of-the-art UWB antennas are also reviewed. Finally, a new concept for the design of a UWB antenna with a bandwidth ranging from 2GHz-11.3GHz is introduced, which satisfies the system requirements for S-DMB, WiBro, WLAN, CMMB and the entire UWB . KeywordsUltra wideband, planar PCB Antenna and bandwidth.

Question: What is the bandwidth of a UWB antenna?


A Video Representation Using Temporal Superpixels. We develop a generative probabilistic model for temporally consistent super pixels in video sequences. In contrast to supermodel methods, object parts in different frames are tracked by the same temporal super pixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate super pixels in an online fashion. We consider four novel metrics to quantify performance of a temporal super pixel representation and demonstrate superior performance when compared to supermodel methods.

Question: What is a superpixel?


Geometry and Meaning. Geometry and Meaning is an interesting book about a relationship between geometry and logic defined on certain types of abstract spaces and how that intimate relationship might be exploited when applied in computational linguistics. It is also about an approach to information retrieval, because the analysis of natural language, especially negation, is applied to problems in IR, and indeed illustrated throughout the book by simple examples using search engines. It is refreshing to see IR issues tackled from a different point of view than the standard vector space (Salton, 1968). It is an enjoyable read, as intended by the author, and succeeds as a sort of tourist guide to the subject in hand. The early part of the book concentrates on the introduction of a number of elementary concepts from mathematics: graph theory, linear algebra (especially vector spaces), lattice theory, and logic. These concepts are well motivated and illustrated with good examples, mostly of a classificatory or taxonomic kind. One of the major goals of the book is to argue that non-classical logic, in the form of a quantum logic, is a candidate for analyzing language and its underlying logic, with a promise that such an approach could lead to improved search engines. The argument for this is aided by copious references to early philosophers, scientists, and mathematicians, creating the impression that when Aristotle, Descartes, Boole, and Grassmann were laying the foundations for taxonomy, analytical geometry, logic, and vector spaces, they had a more flexible and broader view of these subjects than is current. This is especially true of logic. Thus the historical approach taken to introducing quantum logic (chapter 7) is to show that this particular kind of logic and its interpretation in vector space were inherent in some of the ideas of these earlier thinkers. Widdows claims that Aristotle was never respected for his mathematics and that Grassmann's Ausdehnungslehre was

Question: What is the difference between a vector space and a lattice?


VOCALOID - commercial singing synthesizer based on sample concatenation. The song submitted here to the "Synthesis of Singing Challenge" is synthesized by the latest version of the singing synthesizer "Vocaloid", which is commercially available now. In this paper, we would like to present the overview of Vocaloid, its product lineups, description of each component, and the synthesis technique used in Vocaloid.

Question: What is Vocaloid?


Object Detection Featuring 3D Audio Localization for Microsoft HoloLens - A Deep Learning based Sensor Substitution Approach for the Blind. Finding basic objects on a daily basis is a difficult but common task for blind people. This paper demonstrates the implementation of a wearable, deep learning backed, object detection approach in the context of visual impairment or blindness. The prototype aims to substitute the impaired eye of the user and replace it with technical sensors. By scanning its surroundings, the prototype provides a situational overview of objects around the device. Object detection has been implemented using a near real-time, deep learning model named YOLOv2. The model supports the detection of 9000 objects. The prototype can display and read out the name of augmented objects which can be selected by voice commands and used as directional guides for the user, using 3D audio feedback. A distance announcement of a selected object is derived from the HoloLens's spatial model. The wearable solution offers the opportunity to efficiently locate objects to support orientation without extensive training of the user. Preliminary evaluation covered the detection rate of speech recognition and the response times of the server.

Question: What is the name of the paper?


Feature selection for high-dimensional data. This paper offers a comprehensive approach to feature selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection in the context of high-dimensional data. First, we focus on the basis of feature selection, providing a review of its history and basic concepts. Then, we address different topics in which feature selection plays a crucial role, such as microarray data, intrusion detection, or medical applications. Finally, we delve into the open challenges that researchers in the field have to deal with if they are interested to confront the advent of "Big Data" and, more specifically, the "Big Dimensionality".

Question: What is the basis of feature selection?


An introduction to hidden Markov models. The basic theory of Markov chains has been known to mathematicians and engineers for close to 80 years, but it is only in the past decade that it has been applied explicitly to problems in speech processing. One of the major reasons why speech models, based on Markov chains, have not been developed until recently was the lack of a method for optimizing the parameters of the Markov model to match observed signal patterns. Such a method was proposed in the late 1960's and was immediately applied to speech processing in several research institutions. Continued refinements in the theory and implementation of Markov modelling techniques have greatly enhanced the method, leading to a wide range of applications of these models. It is the purpose of this tutorial paper to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.

Question: What is a Markov model?


A Flooding Warning System based on RFID Tag Array for Energy Facility. Passive radio-frequency identification (RFID) tags are widely used due to its economic cost and satisfactory performance. So far, passive RFID tags are mostly applied to identify certain objects, such as underground pipe identification under buried conditions. However, there is lack of study on the application of buried tags for further application. In this paper, the performances of buried RFID tags are studied to develop a flooding warning system based on RFID tag array for energy facility such as power stations. In this study, the corresponding signal strength received by the RFID reader is evaluated when the RFID tags are buried by seven materials respectively. The results show that flood warning detector can be constructed using passive RFID tag array and reader.

Question: What is the purpose of this paper?


Neural Extractive Text Summarization with Syntactic Compression. Recent neural network approaches to summarization are largely either sentence-extractive, choosing a set of sentences as the summary, or abstractive, generating the summary from a seq2seq model. In this work, we present a neural model for single-document summarization based on joint extraction and compression. Following recent successful extractive models, we frame the summarization problem as a series of local decisions. Our model chooses sentences from the document and then decides which of a set of compression options to apply to each selected sentence. We compute this set of options using discrete compression rules based on syntactic constituency parses; however, our approach is modular and can flexibly use any available source of compressions. For learning, we construct oracle extractive-compressive summaries that reflect uncertainty over our model's decision sequence, then learn both of our components jointly with this supervision. Experimental results on the CNN/Daily Mail and New York Times datasets show that our model achieves the state-of-the-art performance on content selection evaluated by ROUGE. Moreover, human and manual evaluation show that our model's output generally remains grammatical.

Question: What is the state of the art in neural summarization?