An Empirical Study of Algorithms for Point-Feature Label Placement. A major factor affecting the clarity of graphical displays that include text labels is the degree to which labels obscure display features (including other labels) as a result of spatial overlap. Point-feature label placement (PFLP) is the problem of placing text labels adjacent to point features on a map or diagram so as to maximize legibility. This problem occurs frequently in the production of many types of informational graphics, though it arises most often in automated cartography. In this paper we present a comprehensive treatment of the PFLP problem, viewed as a type of combinatorial optimization problem. Complexity analysis reveals that the basic PFLP problem and most interesting variants of it are NP-hard. These negative results help inform a survey of previously reported algorithms for PFLP; not surprisingly, all such algorithms either have exponential time complexity or are incomplete. To solve the PFLP problem in practice, then, we must rely on good heuristic methods. We propose two new methods, one based on a discrete form of gradient descent, the other on simulated annealing, and report on a series of empirical tests comparing these and the other known algorithms for the problem. Based on this study, the first to be conducted, we identify the best approaches as a function of available computation time.

Question: What is the best algorithm for point-feature label placement?


Ethical Artificial Intelligence. First Edition Please send typo and error reports, and any other comments, to hibbard@wisc.edu.

Question: What is the first edition of?


DISCO: A Multilingual Database of Distributionally Similar Words. This paper1 presents DISCO, a tool for retrieving the distributional similarity between two given words, and for retrieving the distributionally most similar words for a given word. Pre-computed word spaces are freely available for a number of languages including English, German, French and Italian, so DISCO can be used off the shelf. The tool is implemented in Java, provides a Java API, and can also be called from the command line. The performance of DISCO is evaluated by measuring the correlation with WordNet-based semantic similarities and with human relatedness judgements. The evaluations show that DISCO has a higher correlation with semantic similarities derived from WordNet than latent semantic analysis (LSA) and the web-based PMI-IR.

Question: What is the distributional similarity between "discipline" and "disciple"?


Quadcopter control system. The paper presents two different types of approach for mathematical modeling of quadcopter kinematics and dynamics. The first one is based on the equations of classical mechanics and the other one is derived from Denavit-Hartenberg formalism and Lagrangian mechanics. The obtained models were used to design the control of the quadcopter motion on one rotation axis. The paper also offers some details of the physical and software implementation of the system on which tests have been made.

Question: What is the difference between classical mechanics and Lagrangian mechanics?


Efficacy of botulinum toxin A in upper limb function of hemiplegic patients. Botulinum toxin A has been reported to reduce spasticity and increase the comfort of hemiplegic patients. The aim of this study was to assess the efficacy of the treatment on disability, especially in manual activities, and to attempt to identify predictive factors of improvement. Twenty patients (mean age: 54.4 years; M: 14; right hemiplegia: 12) were included, with a delay of at least three months after unilateral hemispheric stroke. Botulinum toxin A (BOTOX®) was injected into the arm adductors (8 cases), forearm flexors (17 cases), pronators, wrist and finger flexors (20 cases), with a total dose of 200 to 300 U. Examination (day 1 and 15, month 2 and 5) consisted of spasticity assessment (modified Ashworth scale), muscle strength, passive range of motion (goniometry), and pain, followed by functional tests, especially the Rivermead Motor Assessment (RMA) and Nine-hole Peg Test (NHPT). Performance in daily living was assessed with the Functional Independence Measure (FIM), and an original analysis of hand grasp, grip and pinches used in domestic activities (9 items), and of comfort of patients and caregivers. Significant reduction in spasticity was observed on the elbow flexors, pronators, wrist and fingers flexors, especially at day 15 (mean 0.90 to 1 point), with wide variations in effect. Muscle strength was increased in wrist and fingers extensors, with concomitant increase in the opening of the thumb to index finger space. There was no effect on the NHPT requiring distal manipulation, but the RMA, which especially concerned picking up and releasing a tennis ball, showed significant improvement. Furthermore, use of the upper limb in daily living increased, particularly for internal grasping of objects, and for grasping by the top, transporting and releasing of objects. Patients and caregivers reported facilitation in dressing, and in proximal and distal care of the upper limb. The g

Question: What is the efficacy of botulinum toxin A in upper limb function of hemiplegic patients?


Layering concepts in anterior composite restorations.. SUMMARY With increasing patient demands for esthetic dentition, composite resin restorations enjoy great popularity due to excellent esthetics, acceptable longevity, and relatively low costs. Shading concepts used by manufacturers, however, may be confusing to many clinicians.   PURPOSE To review and describe the current main shading concepts, evaluate their potential for creating natural esthetics, and provide guidelines for application.

Question: What is the difference between layering and shading?


Detection of hiding in the least significant bit. In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF) method that has certain asymptotic optimality properties and methods that are based on knowledge or estimation of the host PMF and, hence, an appropriate likelihood ratio (LR). b) For a known host PMF, it is shown that the composite hypothesis testing problem corresponding to an unknown hiding rate reduces to a worst-case simple hypothesis testing problem. c) Using the results for a known host PMF, practical tests based on the estimation of the host PMF are obtained. These are shown to be superior to the state of the art in terms of receiver operating characteristics as well as self-calibration across different host images. Estimators for the hiding rate are also developed.

Question: What is the least significant bit?


Geometric Scene Parsing with Hierarchical LSTM. This paper addresses the problem of geometric scene parsing, i.e. simultaneously labeling geometric surfaces (e.g. sky, ground and vertical plane) and determining the interaction relations (e.g. layering, supporting, siding and affinity) between main regions. This problem is more challenging than the traditional semantic scene labeling, as recovering geometric structures necessarily requires the rich and diverse contextual information. To achieve these goals, we propose a novel recurrent neural network model, named Hierarchical Long Short-Term Memory (H-LSTM). It contains two coupled sub-networks: the Pixel LSTM (P-LSTM) and the Multi-scale Super-pixel LSTM (MS-LSTM) for handling the surface labeling and relation prediction, respectively. The two sub-networks provide complementary information to each other to exploit hierarchical scene contexts, and they are jointly optimized for boosting the performance. Our extensive experiments show that our model is capable of parsing scene geometric structures and outperforming several state-of-theart methods by large margins. In addition, we show promising 3D reconstruction results from the still images based on the geometric parsing.

Question: What is the name of the paper?


Reading Between the Lines: Content-Agnostic Detection of Spear-Phishing Emails. Spear-phishing is an effective attack vector for infiltrating companies and organisations. Based on the multitude of personal information available online, an attacker can craft seemingly legit emails and trick his victims into opening malicious attachments and links. Although anti-spoofing techniques exist, their adoption is still limited and alternative protection approaches are needed. In this paper, we show that a sender leaves content-agnostic traits in the structure of an email. Based on these traits, we develop a method capable of learning profiles for a large set of senders and identifying spoofed emails as deviations thereof. We evaluate our approach on over 700,000 emails from 16,000 senders and demonstrate that it can discriminate thousands of senders, identifying spoofed emails with 90% detection rate and less than 1 false positive in 10,000 emails. Moreover, we show that individual traits are hard to guess and spoofing only succeeds if entire emails of the sender are available to the attacker.

Question: How to detect spear-phishing emails?


An incremental strategy for tractor-trailer vehicle global trajectory optimization in the presence of obstacles. Trajectory planning is a critical aspect in driving an autonomous tractor-trailer vehicle. This study considers trajectory planning as a minimal-time optimal control problem that incorporates the kinematics, mechanical/physical constraints, environmental requirements and an optimization criterion. This formulation benefits in the ability to directly handle time-dependent requirements. A gradient-based numerical solver is adopted to tackle the formulated optimal control problem. An incremental strategy is proposed to enhance the global optimization ability of that solver and to help find satisfactory solutions in challenging cases. Specifically, when an optimal solution is found, we doubt whether that is merely a local optimum and then still expect to make evolution; when optimal solutions are not easy to obtain, we expect at least one feasible solution first, which is taken as a preliminary guess to assist the subsequent optimization process. Both procedures proceed alternatively until no progress can be made any further. Some intricate simulation results are well beyond manual operation ability. Moreover, our overall proposal, as a unified and open framework, can deal with a wide variety of user-specified requirements.

Question: How to solve a problem in a tractor-trailer vehicle?


A Context-aware Time Model for Web Search. In web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.

Question: What is the context-aware time model?


The power of naive query segmentation. We address the problem of query segmentation: given a keyword query submitted to a search engine, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve good segmentation performance on a gold standard but are fairly intricate. Our method is easy to implement and comes with a comparable accuracy.

Question: How to segment a query?


Combinatory Categorial Grammar. Combinatory Categorial Grammar (CCG) is a radically lexicalized theory of grammar in which all language-specific information, including the linear order of heads, arguments, and adjuncts, is specified in the lexicon, from which it is projected onto sentences by language-independent universal typedependent combinatory rules of low "slightly non-context-free" expressive power, applying to strictly adjacent phonologically-realised categories. Syntactic and phonological derivation are isomorphic, and are synchronously coupled with semantic composition in a purely type-dependent rule-to-rule relation.

Question: What is combinatory categorial grammar?


A journey to highly dynamic, self-adaptive service-based applications. Future software systems will operate in a highly dynamic world. Systems will need to operate correctly despite of unespected changes in factors such as environmental conditions, user requirements, technology, legal regulations, and market opportunities. They will have to operate in a constantly evolving environment that includes people, content, electronic devices, and legacy systems. They will thus need the ability to continuously adapt themselves in an automated manner to react to those changes. To realize dynamic, self-adaptive systems, the service concept has emerged as a suitable abstraction mechanism. Together with the concept of the service-oriented architecture (SOA), this led to the development of technologies, standards, and methods to build service-based applications by flexibly aggregating individual services. This article discusses how those concepts came to be by taking two complementary viewpoints. On the one hand, it evaluates the progress in software technologies and methodologies that led to the service concept and SOA. On the other hand, it discusses how the evolution of the requirements, and in particular business goals, influenced the progress towards highly dynamic self-adaptive systems. Finally, based on a discussion of the current state of the art, this article points out the possible future evolution of the field.

Question: What is the future of the service concept?


SALMA: Standard Arabic Language Morphological Analysis. Morphological analyzers are preprocessors for text analysis. Many Text Analytics applications need them to perform their tasks. This paper reviews the SALMA-Tools (Standard Arabic Language Morphological Analysis) [1]. The SALMA-Tools is a collection of open-source standards, tools and resources that widen the scope of Arabic word structure analysis - particularly morphological analysis, to process Arabic text corpora of different domains, formats and genres, of both vowelized and non-vowelized text. Tag-assignment is significantly more complex for Arabic than for many languages. The morphological analyzer should add the appropriate linguistic information to each part or morpheme of the word (proclitic, prefix, stem, suffix and enclitic); in effect, instead of a tag for a word, we need a subtag for each part. Very fine-grained distinctions may cause problems for automatic morphosyntactic analysis - particularly probabilistic taggers which require training data, if some words can change grammatical tag depending on function and context; on the other hand, fine-grained distinctions may actually help to disambiguate other words in the local context. The SALMA - Tagger is a fine grained morphological analyzer which is mainly depends on linguistic information extracted from traditional Arabic grammar books and prior-knowledge broad-coverage lexical resources; the SALMA - ABCLexicon. More fine-grained tag sets may be more appropriate for some tasks. The SALMA - Tag Set is a standard tag set for encoding, which captures long-established traditional fine-grained morphological features of Arabic, in a notation format intended to be compact yet transparent.

Question: What is the standard tag set for encoding?


Preserving differential privacy in convolutional deep belief networks. The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing $$\epsilon $$ ϵ -differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions.

Question: How to preserve differential privacy in convolutional deep belief networks?


The 8 requirements of real-time stream processing. Applications that require real-time processing of high-volume data steams are pushing the limits of traditional data processing infrastructures. These stream-based applications include market feed processing and electronic trading on Wall Street, network and infrastructure monitoring, fraud detection, and command and control in military environments. Furthermore, as the "sea change" caused by cheap micro-sensor technology takes hold, we expect to see everything of material significance on the planet get "sensor-tagged" and report its state or location in real time. This sensorization of the real world will lead to a "green field" of novel monitoring and control applications with high-volume and low-latency processing requirements.Recently, several technologies have emerged---including off-the-shelf stream processing engines---specifically to address the challenges of processing high-volume, real-time data without requiring the use of custom code. At the same time, some existing software technologies, such as main memory DBMSs and rule engines, are also being "repurposed" by marketing departments to address these applications.In this paper, we outline eight requirements that a system software should meet to excel at a variety of real-time stream processing applications. Our goal is to provide high-level guidance to information technologists so that they will know what to look for when evaluation alternative stream processing solutions. As such, this paper serves a purpose comparable to the requirements papers in relational DBMSs and on-line analytical processing. We also briefly review alternative system software technologies in the context of our requirements.The paper attempts to be vendor neutral, so no specific commercial products are mentioned.

Question: What are the 8 requirements of real-time stream processing?


A Subthreshold to Above-Threshold Level Shifter Comprising a Wilson Current Mirror. In this brief, we propose a novel level shifter circuit that is capable of converting subthreshold to above-threshold signal levels. In contrast to other existing implementations, it does not require a static current flow and can therefore offer considerable static power savings. The circuit has been optimized and simulated in a 90-nm process technology. It operates correctly across process corners for supply voltages from 100 mV to 1 V on the low-voltage side. At the target design voltage of 200 mV, the level shifter has a propagation delay of 18.4 ns and a static power dissipation of 6.6 nW. For a 1-MHz input signal, the total energy per transition is 93.9 fJ. Simulation results are compared to an existing subthreshold to above-threshold level shifter implementation from the paper of Chen et al.

Question: What is a level shifter?


Recovering free space of indoor scenes from a single image. In this paper we consider the problem of recovering the free space of an indoor scene from its single image. We show that exploiting the box like geometric structure of furniture and constraints provided by the scene, allows us to recover the extent of major furniture objects in 3D. Our "boxy" detector localizes box shaped objects oriented parallel to the scene across different scales and object types, and thus blocks out the occupied space in the scene. To localize the objects more accurately in 3D we introduce a set of specially designed features that capture the floor contact points of the objects. Image based metrics are not very indicative of performance in 3D. We make the first attempt to evaluate single view based occupancy estimates for 3D errors and propose several task driven performance measures towards it. On our dataset of 592 indoor images marked with full 3D geometry of the scene, we show that: (a) our detector works well using image based metrics; (b) our refinement method produces significant improvements in localization in 3D; and (c) if one evaluates using 3D metrics, our method offers major improvements over other single view based scene geometry estimation methods.

Question: How to recover free space of indoor scenes from a single image?


U-Air: when urban air quality inference meets big data. Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.

Question: What is the semi-supervised learning approach based on a co-training framework?


Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch. Giving robots the ability to classify surface textures requires appropriate sensors and algorithms. Inspired by the biology of human tactile perception, we implement a neurorobotic texture classifier with a recurrent spiking neural network, using a novel semisupervised approach for classifying dynamic stimuli. Input to the network is supplied by accelerometers mounted on a robotic arm. The sensor data are encoded by a heterogeneous population of neurons, modeled to match the spiking activity of mechanoreceptor cells. This activity is convolved by a hidden layer using bandpass filters to extract nonlinear frequency information from the spike trains. The resulting high-dimensional feature representation is then continuously classified using a neurally implemented support vector machine. We demonstrate that our system classifies 18 metal surface textures scanned in two opposite directions at a constant velocity. We also demonstrate that our approach significantly improves upon a baseline model that does not use the described feature extraction. This method can be performed in real-time using neuromorphic hardware, and can be extended to other applications that process dynamic stimuli online.

Question: How does this work?


Robust Appointment Scheduling. We consider the problem of appointment scheduling in a robus t optimization framework. The appointment scheduling problem arises in many service operations, for e xample health care. For each job, we are given its minimum and maximum possible execution times. The objective is to find an appointment schedule for which the cost in the worst case scenario of the realization of the processi ng times of the jobs is minimized. We present a global balancing heuristic, which gives an easy to compute closed f orm optimal schedule when the underage costs of the jobs are non-decreasing. In addition, for the case where we h av the flexibility of changing the order of execution of the jobs, we give simple heuristics to find a near-optimal seq uence of the jobs. Working paper, Operations Research Center, MIT. Last updat ed March 3, 2012. Amazon.com, 333 Boren Avenue N, Seattle WA 98101, USA. Email : mshashi@alum.mit.edu. TU Berlin, Matheon, Berlin, Germany. Email: sebastian.stiller@tu-berlin.de.

Question: What is the minimum number of jobs that can be scheduled in a robust manner?


Simulating cartoon style animation. Traditional hand animation is in many cases superior to simulated motion for conveying information about character and events. Much of this superiority comes from an animator's ability to abstract motion and play to human perceptual effects. However, experienced animators are difficult to come by and the resulting motion is typically not interactive. On the other hand, procedural models for generating motion, such as physical simulation, can create motion on the fly but are poor at stylizing movement. We start to bridge this gap with a technique that creates cartoon style deformations automatically while preserving desirable qualities of the object's appearance and motion. Our method is focused on squash-and-stretch deformations based on the velocity and collision parameters of the object, making it suitable for procedural animation systems. The user has direct control of the object's motion through a set of simple parameters that drive specific features of the motion, such as the degree of squash and stretch. We demonstrate our approach with examples from our prototype system.

Question: How to simulate cartoon style animation?


An integrated fuzzy MCDM approach for supplier evaluation and selection. A fuzzy multi-criteria group decision making approach that makes use of quality function deployment (QFD), fusion of fuzzy information and 2-tuple linguistic representation model is developed for supplier selection. The proposed methodology seeks to establish the relevant supplier assessment criteria while also considering the impacts of inner dependence among them. Two interrelated house of quality matrices are constructed, and fusion of fuzzy information and 2-tuple linguistic representation model are employed to compute the weights of supplier selection criteria and subsequently the ratings of suppliers. The proposed method is apt to manage non-homogeneous information in a decision setting with multiple information sources. The decision framework presented in this paper employs ordered weighted averaging (OWA) operator, and the aggregation process is based on combining information by means of fuzzy sets on a basic linguistic term set. The proposed framework is illustrated through a case study conducted in a private hospital in Istanbul.

Question: How to evaluate suppliers?


Interactive play objects and the effects of open-ended play on social interaction and fun. This paper describes a study that examines the influence of openended play in interactive play objects on social interaction and fun experience of children. We developed a prototype to examine whether children enjoy playing with simple intelligent objects. Children between 7 and 11 years old were asked to play with the objects in a free-play and pre-set game session. The study shows that children create a wide variety of games and practice many social skills when negotiating the rules of various games. Overall, children felt playing with the objects in the free-play sessions was more fun than in the pre-set sessions. The insights will be used to design the next version of our play concept.

Question: What is the influence of open-ended play on social interaction and fun experience of children?


Agents of Things (AoT): An intelligent operational concept of the Internet of Things (IoT). In this conceptual paper, we review the definitions, characteristics, and architecture of the Internet of Things (IoT) concept. We then identify the deficiencies of the IoT concept, analyze them, and discover the issue of lack of reasoning and intelligence in the IoT concept. We propose a solution to augment the IoT with intelligent software agents resulting in a new concept called the Agents of Things (AoT). The paper presents the AoT architecture and a scenario for one application of the proposed concept. Finally, it discusses the benefits of implementing AoT concept to solve real world issues and the future work.

Question: What is the Internet of Things?


Offline handwritten signature verification — Literature review. The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. The objective of signature verification systems is to discriminate if a given signature is genuine (produced by the claimed individual), or a forgery (produced by an impostor). This has demonstrated to be a challenging task, in particular in the offline (static) scenario, that uses images of scanned signatures, where the dynamic information about the signing process is not available. Many advancements have been proposed in the literature in the last 5–10 years, most notably the application of Deep Learning methods to learn feature representations from signature images. In this paper, we present how the problem has been handled in the past few decades, analyze the recent advancements in the field, and the potential directions for future research.

Question: What is the area of Handwritten Signature Verification?


Deep convolutional neural network for latent fingerprint enhancement. In this work, we propose a novel latent fingerprint enhancement method based on FingerNet inspired by recent development of Convolutional Neural Network (CNN). Although CNN is achieving superior performance in many computer vision tasks from low-level image processing to high-level semantic understanding, limited attention has been paid in fingerprint community. The proposed FingerNet has three major parts: one common convolution part shared by two different deconvolution parts, which are the enhancement branch and the orientation branch. The convolution part is to extract fingerprint features particularly for enhancement purpose. The enhancement deconvolution branch is employed to remove structured noise and enhance fingerprints as its task. The orientation deconvolution branch performs the task of guiding enhancement through a multi-task learning strategy. The network is trained in the manner of pixels-to-pixels and end-to-end learning, that can directly enhance latent fingerprint as the output. We also study some implementation details such as single-task learning, multi-task learning, and the residual learning. Experimental results of the FingerNet system on latent fingerprint dataset NIST SD27 demonstrate effectiveness and robustness of the proposed method.

Question: What is the fingerprint enhancement method?


Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism. With the rapid development of electronic commerce, the number of transactions by credit cards are increasing rapidly. As online shopping becomes the most popular transaction mode, cases of transaction fraud are also increasing. In this paper, we propose a novel fraud detection method that composes of four stages. To enrich a cardholder's behavioral patterns, we first utilize the cardholders' historical transaction data to divide all cardholders into different groups such that the transaction behaviors of the members in the same group are similar. We thus propose a window-sliding strategy to aggregate the transactions in each group. Next, we extract a collection of specific behavioral patterns for each cardholder based on the aggregated transactions and the cardholder's historical transactions. Then we train a set of classifiers for each group on the base of all behavioral patterns. Finally, we use the classifier set to detect fraud online and if a new transaction is fraudulent, a feedback mechanism is taken in the detection process in order to solve the problem of concept drift. The results of our experiments show that our approach is better than others.

Question: What is the difference between a credit card and a debit card?


Sentiment Adaptive End-to-End Dialog Systems. v This work focuses on incorporating sentiment information into task-oriented dialogue systems. v Current end-to-end approaches only consider user semantic inputs in learning and under-utilize other user information. v But the ultimate evaluator of dialog systems is the end-users and their sentiment is a direct reflection of user satisfaction and should be taken into consideration. v Therefore, we propose to include user sentiment obtained through multimodal information (acoustic, dialogic and textual), in the end-to-end learning framework to make systems more user-adaptive and effective. v We incorporated user sentiment information in both supervised and reinforcement learning settings. v In both settings, adding sentiment information reduced the dialog length and improved the task success rate on a bus information search task. Multimodal Sentiment Detector v We manually annotated 50 dialogs with 517 conversation turns to train this sentiment detector. The annotated set is open to public. v Prediction made by the detector will be used in the supervised learning and reinforcement learning. v Three sets of features: 1) Acoustic features; 2) Dialogic features; 3) Textual features. v Dialogic features include: 1) Interruption; 2) Button usage; 3) Repetitions; 4) Start over. These four categories of dialog features are chosen based on the previous literature and the observed statistics in the dataset.

Question: What is the canadian military?


Project Success Prediction in Crowdfunding Environments. Crowdfunding has gained widespread attention in recent years. Despite the huge success of crowdfunding platforms, the percentage of projects that succeed in achieving their desired goal amount is only around 40%. Moreover, many of these crowdfunding platforms follow "all-or-nothing" policy which means the pledged amount is collected only if the goal is reached within a certain predefined time duration. Hence, estimating the probability of success for a project is one of the most important research challenges in the crowdfunding domain. To predict the project success, there is a need for new prediction models that can potentially combine the power of both classification (which incorporate both successful and failed projects) and regression (for estimating the time for success). In this paper, we formulate the project success prediction as a survival analysis problem and apply the censored regression approach where one can perform regression in the presence of partial information. We rigorously study the project success time distribution of crowdfunding data and show that the logistic and log-logistic distributions are a natural choice for learning from such data. We investigate various censored regression models using comprehensive data of 18K Kickstarter (a popular crowdfunding platform) projects and 116K corresponding tweets collected from Twitter. We show that the models that take complete advantage of both the successful and failed projects during the training phase will perform significantly better at predicting the success of future projects compared to the ones that only use the successful projects. We provide a rigorous evaluation on many sets of relevant features and show that adding few temporal features that are obtained at the project's early stages can dramatically improve the performance.

Question: What is the censored regression approach?


Understanding packet delivery performance in dense wireless sensor networks. Wireless sensor networks promise fine-grain monitoring in a wide variety of environments. Many of these environments (e.g., indoor environments or habitats) can be harsh for wireless communication. From a networking perspective, the most basic aspect of wireless communication is the packet delivery performance: the spatio-temporal characteristics of packet loss, and its environmental dependence. These factors will deeply impact the performance of data acquisition from these networks.In this paper, we report on a systematic medium-scale (up to sixty nodes) measurement of packet delivery in three different environments: an indoor office building, a habitat with moderate foliage, and an open parking lot. Our findings have interesting implications for the design and evaluation of routing and medium-access protocols for sensor networks.

Question: What is the packet delivery performance in dense wireless sensor networks?


Primal-Dual Group Convolutions for Deep Neural Networks. In this paper, we present a simple and modularized neural network architecture, named primal-dual group convolutional neural networks (PDGCNets). The main point lies in a novel building block, a pair of two successive group convolutions: primal group convolution and dual group convolution. The two group convolutions are complementary: (i) the convolution on each primal partition in primal group convolution is a spatial convolution, while on each dual partition in dual group convolution, the convolution is a point-wise convolution; (ii) the channels in the same dual partition come from different primal partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion (as used in ResNeXt), and the Xception block are special cases of primal-dual group convolutions. Empirical results over standard benchmarks, CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.

Question: What is a primal-dual group convolution?


Huffman Coding-Based Adaptive Spatial Modulation. Antenna switch enables multiple antennas to share a common RF chain. It also offers an additional spatial dimension, i.e., antenna index, that can be utilized for data transmission via both signal space and spatial dimension. In this paper, we propose a Huffman coding-based adaptive spatial modulation that generalizes both conventional spatial modulation and transmit antenna selection. Through the Huffman coding, i.e., designing variable length prefix codes, the transmit antennas can be activated with different probabilities. When the input signal is Gaussian distributed, the optimal antenna activation probability is derived through optimizing channel capacity. To make the optimization tractable, closed form upper bound and lower bound are derived as the effective approximations of channel capacity. When the input is discrete QAM signal, the optimal antenna activation probability is derived through minimizing symbol error rate. Numerical results show that the proposed adaptive transmission offers considerable performance improvement over the conventional spatial modulation and transmit antenna selection.

Question: Huffman coding-based adaptive spatial modulation


FlowFence: Practical Data Protection for Emerging IoT Application Frameworks. Emerging IoT programming frameworks enable building apps that compute on sensitive data produced by smart homes and wearables. However, these frameworks only support permission-based access control on sensitive data, which is ineffective at controlling how apps use data once they gain access. To address this limitation, we present FlowFence, a system that requires consumers of sensitive data to declare their intended data flow patterns, which it enforces with low overhead, while blocking all other undeclared flows. FlowFence achieves this by explicitly embedding data flows and the related control flows within app structure. Developers use FlowFence support to split their apps into two components: (1) A set of Quarantined Modules that operate on sensitive data in sandboxes, and (2) Code that does not operate on sensitive data but orchestrates execution by chaining Quarantined Modules together via taint-tracked opaque handles—references to data that can only be dereferenced inside sandboxes. We studied three existing IoT frameworks to derive key functionality goals for FlowFence, and we then ported three existing IoT apps. Securing these apps using FlowFence resulted in an average increase in size from 232 lines to 332 lines of source code. Performance results on ported apps indicate that FlowFence is practical: A face-recognition based doorcontroller app incurred a 4.9% latency overhead to recognize a face and unlock a door.

Question: What is the size of the canadian military?


A vision for personalized service level agreements in the cloud. Public Clouds today provide a variety of services for data analysis such as Amazon Elastic MapReduce and Google BigQuery. Each service comes with a pricing model and service level agreement (SLA). Today's pricing models and SLAs are described at the level of compute resources (instance-hours or gigabytes processed). They are also different from one service to the next. Both conditions make it difficult for users to select a service, pick a configuration, and predict the actual analysis cost. To address this challenge, we propose a new abstraction, called a Personalized Service Level Agreement, where users are presented with what they can do with their data in terms of query capabilities, guaranteed query performance and fixed hourly prices.

Question: What is a personalized service level agreement?


Design, Fabrication, and Characterization of a 3-D CMOS Fluxgate Magnetometer. A dual-core 3-D microfluxgate magnetometer fabricated by a simple and inexpensive fabrication process is described in this paper. The microfluxgate is able to operate along a nearly linear V-B relationship at the second harmonic frequency and features good characteristics of high sensitivity and low noise response. These characteristic results indicate a field-to-voltage transfer coefficient of 11 V/T measured at the second harmonic frequency, power consumption of 67.3 mW, and a field noise response less than 12 nT/√ Hz at 1 Hz. In brief, our proposed device not only enhances responsivity capability and linear V-B characteristics, but also is CMOS process compatible, which is considered both function-efficient and cost-effective.

Question: What is a microfluxgate magnetometer?


The Cyclic Towers of Hanoi. The famous Towers of Hanoi puzzle consists of 3 pegs (A, B, C) on one of which (A) are stacked n rings of different sizes, each ring resting on a larger ring. The objective is to move the n rings one by one until they are all stacked on another peg (B) in such a way that no ring is ever placed on a smaller ring; the other peg (C) can be used as workspace. The problem has tong been a favourite iir programming courses as one which admits a concise recursive solution. This solution hinges on the observation that, when the largest ring is moved from A to B, the n 1 remaining rings must all be on peg C. This immediately leads to the recursive procedure

Question: What is the name of the puzzle?


Accurate, Robust, and Flexible Real-time Hand Tracking. We present a new real-time hand tracking system based on a single depth camera. The system can accurately reconstruct complex hand poses across a variety of subjects. It also allows for robust tracking, rapidly recovering from any temporary failures. Most uniquely, our tracker is highly flexible, dramatically improving upon previous approaches which have focused on front-facing close-range scenarios. This flexibility opens up new possibilities for human-computer interaction with examples including tracking at distances from tens of centimeters through to several meters (for controlling the TV at a distance), supporting tracking using a moving depth camera (for mobile scenarios), and arbitrary camera placements (for VR headsets). These features are achieved through a new pipeline that combines a multi-layered discriminative reinitialization strategy for per-frame pose estimation, followed by a generative model-fitting stage. We provide extensive technical details and a detailed qualitative and quantitative analysis.

Question: How accurate is the hand tracking system?


Neural evidence for the interplay between language, gesture, and action: A review. Co-speech gestures embody a form of manual action that is tightly coupled to the language system. As such, the co-occurrence of speech and co-speech gestures is an excellent example of the interplay between language and action. There are, however, other ways in which language and action can be thought of as closely related. In this paper we will give an overview of studies in cognitive neuroscience that examine the neural underpinnings of links between language and action. Topics include neurocognitive studies of motor representations of speech sounds, action-related language, sign language and co-speech gestures. It will be concluded that there is strong evidence on the interaction between speech and gestures in the brain. This interaction however shares general properties with other domains in which there is interplay between language and action.

Question: What is the neural underpinnings of the interplay between language and action?


Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models. Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy ("torques") from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for longterm predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.

Question: What is the data-efficient reinforcement learning?


Event detection from social media: 5W1H analysis on big data. Increasing number of social media content shared across the globe in real time creates a fascinating area of research. For most events, social media users act as collective sensors by sharing important information about events of any scale. Due to its real time nature, social media is much quicker to respond to such events relative to conventional news media. This paper proposes an event detection system which provides 5W1H (What, Where, When, Why, Who, How) analysis for each detected event. We make use of a myriad of techniques such as anomaly detection, named entity recognition, automatic summary generation, user link analysis. Our experimental results for the system indicate a faster event detection performance compared to conventional news sources. Event analysis results are also in line with the corresponding news articles about detected events.

Question: What is the event detection system?


Design and Analysis of Energy-Efficient Reconfigurable Pre-Emphasis Voltage-Mode Transmitters. This paper analyzes the signaling and digital power overhead of pre-emphasis voltage-mode transmitters. Utilizing a shunt branch in parallel with the differential channel to implement pre-emphasis is shown to have the best overall energy-efficiency. Leveraging this technique, an efficient pre-emphasis voltage mode transmitter architecture with output amplitude control, pre-emphasis coefficient control, and online impedance calibration is proposed and demonstrated. A 65 nm LP CMOS implementation of this architecture dissipates ~ 10 mW from a 1.2 V supply when transmitting 10 Gb/s 200 mV differential amplitude data with 2-tap pre-emphasis, achieving 1 pJ/bit energy efficiency.

Question: What is the energy efficiency of a pre-emphasis voltage mode transmitter?


ReticularSpaces: activity-based computing support for physically distributed and collaborative smart spaces. Smart spaces research focuses on technology for multiple displays and devices for collocated participants. In most approaches, however, users have to cope with heterogeneous interfaces and information organization, as well as a lack of support for collaboration with mobile and remote users outside the smart space. In this paper, we present ReticularSpaces; a multi-display smart space system built on the principles of activity-based computing. The focus of ReticularSpaces is to support: (i) unified interaction with applications and documents through ReticularUI, a novel distributed user interfaces design; (ii) management of the complexity of tasks between users and displays; (iii) mobile users in a local, remote or 'nomadic' settings; and (iv) collaboration among local and remote users. We describe the motivation, design, and architecture of ReticularSpaces, and report from a preliminary feasibility study. The study shows that participants found ReticularSpaces useful and effective, but at the same time reveals new areas for research on smart environments.

Question: What is activity-based computing?


On the Reuse of Past Optimal Queries. Vijay V. Raghavan Hayri Sever The Center for Advanced Computer Studies The Department of Computer Science University of Southwestern Louisiana University of Southwestern Louisiana Lafayette, LA 70504, USA Lafayette, LA 70504, USA e-mail: raghavan@cacs.usl. edu Information Retrieval (IR) systems exploit user feedback by generating an optimal query with respect to a particular information need. Since obtaining an optimal query is an expensive process, the need for mechanisms to save and reuse past optimal queries for future queries is obvions. In this article, we propose the use of a query base, a set of persistent past optimal queries, and investigate similarity measures between queries. The query base can be used either to answer user queries or to formulate optimal queries. We justify the former case analytically and the latter case by experiment.

Question: What is the optimal query for the following information need?


Microwave antenna applications of metasurfaces. This paper reviews the past and current research progress in microwave antenna applications using 2D planar metamaterials or metasurfaces. Firstly, we revisit the definitions of metamaterial and metasurface, and the applications of the ultra-thin metasurfaces for low-profile printed antennas in microwave frequency regime, for the purposes of size reduction and performance enhancement. Then we summarize the different types of metasurfaces and their applications. Finally, we provide the future lookout into microwave antenna and EM related applications using metasurfaces.

Question: What is a metasurface?


Standards for smart education – towards a development framework. Smart learning environments (SLEs) utilize a range of digital technologies in supporting learning, education and training; they also provide a prominent signpost for how future learning environments might be shaped. Thus, while innovation proceeds, SLEs are receiving growing attention from the research community, outputs from which are discussed in this paper. Likewise, this broad application of educational digital technologies is also the remit of standardization in an ISO committee, also discussed in this paper. These two communities share a common interest in, conceptualizing this emerging domain with the aim to identifying direction to further development. In doing so, terminology issues arise along with key questions such as, 'how is smart learning different from traditional learning?' Presenting a bigger challenge is the question, 'how can standardization work be best scoped in today's innovation-rich, networked, cloud-based and data-driven learning environments?' In responding, this conceptual paper seeks to identify candidate constructs and approaches that might lead to stable, coherent and exhaustive understanding of smart learning environments, thereby providing standards development for learning, education and training a needed direction. Based on reviews of pioneering work within smart learning, smart education and smart learning environments we highlight two models, a cognitive smart learning model and a smartness level model. These models are evaluated against current standardization challenges in the field of learning, education and training to form the basis for a development platform for new standards in this area.

Question: What is a smart learning environment?


Facial recognition with PCA and machine learning methods. Facial recognition is a challenging problem in image processing and machine learning areas. Since widespread applications of facial recognition make it a valuable research topic, this work tries to develop some new facial recognition systems that have both high recognition accuracy and fast running speed. Efforts are made to design facial recognition systems by combining different algorithms. Comparisons and evaluations of recognition accuracy and running speed show that PCA + SVM achieves the best recognition result, which is over 95% for certain training data and eigenface sizes. Also, PCA + KNN achieves the balance between recognition accuracy and running speed.

Question: What is facial recognition?


Cellular and synaptic mechanisms of nicotine addiction.. The tragic health effects of nicotine addiction highlight the importance of investigating the cellular mechanisms of this complex behavioral phenomenon. The chain of cause and effect of nicotine addiction starts with the interaction of this tobacco alkaloid with nicotinic acetylcholine receptors (nAChRs). This interaction leads to activation of reward centers in the CNS, including the mesoaccumbens DA system, which ultimately leads to behavioral reinforcement and addiction. Recent findings from a number of laboratories have provided new insights into the biologic processes that contribute to nicotine self-administration. Examination of the nAChR subtypes expressed within the reward centers has identified potential roles for these receptors in normal physiology, as well as the effects of nicotine exposure. The high nicotine sensitivity of some nAChR subtypes leads to rapid activation followed in many cases by rapid desensitization. Assessing the relative importance of these molecular phenomena in the behavioral effects of nicotine presents an exciting challenge for future research efforts.

Question: What is the role of nAChRs in the reward centers?


Approximate analysis of the resonant LCL DC-DC converter. An approximate analysis of LCL converters operating above and below resonance is presented. The study, based on replacing the rectifier and load by an equivalent parallel resistance, applies the fundamental harmonics method and the assumption that the current through the diodes of the output rectifier has a sinusoidal waveform. Equations obtained for the conduction angle of these diodes (less than /spl pi/ in discontinuous and equal to /spl pi/ in continuous conduction modes) are used for developing the output-to-input voltage ratio as a function of three independent parameters. The theoretical and experimental results are in good agreement.

Question: What is the approximate analysis of the resonant LCL DC-DC converter?


An Empirical Exploration of Curriculum Learning for Neural Machine Translation. Machine translation systems based on deep neural networks are expensive to train. Curriculum learning aims to address this issue by choosing the order in which samples are presented during training to help train better models faster. We adopt a probabilistic view of curriculum learning, which lets us flexibly evaluate the impact of curricula design, and perform an extensive exploration on a German-English translation task. Results show that it is possible to improve convergence time at no loss in translation quality. However, results are highly sensitive to the choice of sample difficulty criteria, curriculum schedule and other hyperparameters.

Question: How to choose the order of samples?


Enhancing DataQuality in DataWarehouse Environments. D ecisions by senior management lay the groundwork for lower corporate levels to develop policies and procedures for various corporate activities. However, the potential business contribution of these activities depends on the quality of the decisions and in turn on the quality of the data used to make them. Some inputs are judgmental, others are from transactional systems, and still others are from external sources, but all must have a level of quality appropriate for the decisions they will be part of. Although concern about the quality of one's data is not new, what is fairly recent is using the same data for multiple purposes, which can be quite different from their original purposes. Users working with a particular data set come to know and internalize its deficiencies and idiosyncrasies. This knowledge is lost when data is made available to other parties, like when data needed for decision making is collected in repositories called data warehouses. Here, we offer a conceptual framework for enhancing data quality in data warehouse environments. We explore the factors that should be considered, such as the current level of data quality, the levels of quality needed by the relevant decision processes, and the potential benefits of projects designed to enhance data quality. Those responsible for data quality have to understand the importance of such factors, as well as the interaction among them. This understanding is mandatory in data warehousing environments characterized by multiple users with differing needs for data quality. For warehouses supporting a limited number of decision processes, awareness of these issues coupled with good judgment should suffice. For more complex situations, however, the number and diversity of trade-offs make reliance on judgment alone problematic. For such situations, we offer a methodology that Nothing is more likely to undermine the performance and business value of a data wa

Question: What is the difference between a data warehouse and a data mart?


Lazy Decision Trees. Lazy learning algorithms, exemplified by nearestneighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single "best" decision tree during the training phase, and this tree is then used to classify test instances. The tests at the nodes of the constructed tree are good on average, but there may be better tests for classifying a specific instance. We propose a lazy decision tree algorithm-LAzuDT-that conceptually constructs the "best" decision tree for each test instance. In practice, only a path needs to be constructed, and a caching scheme makes the algorithm fast. The algorithm is robust with respect to missing values without resorting to the complicated methods usually seen in induction of decision trees. Experiments on real and artificial problems are presented.

Question: What is a lazy decision tree?


A User's Guide to Compressed Sensing for Communications Systems. This survey provides a brief introduction to compressed sensing as well as several major algorithms to solve it and its various applications to communications systems. We firstly review linear simultaneous equations as ill-posed inverse problems, since the idea of compressed sensing could be best understood in the context of the linear equations. Then, we consider the problem of compressed sensing as an underdetermined linear system with a prior information that the true solution is sparse, and explain the sparse signal recovery based on 1 optimization, which plays the central role in compressed sensing, with some intuitive explanations on the optimization problem. Moreover, we introduce some important properties of the sensing matrix in order to establish the guarantee of the exact recovery of sparse signals from the underdetermined system. After summarizing several major algorithms to obtain a sparse solution focusing on the 1 optimization and the greedy approaches, we introduce applications of compressed sensing to communications systems, such as wireless channel estimation, wireless sensor network, network tomography, cognitive radio, array signal processing, multiple access scheme, and networked control. key words: compressed sensing, sparse signal, compressible signal, 1norm, underdetermined system

Question: What is the difference between the 1 norm and the 2 norm?


SimRT: an automated framework to support regression testing for data races. Concurrent programs are prone to various classes of difficult-to-detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SimRT, an automated regression testing framework for use in detecting races introduced by code modifications. SimRT employs a regression test selection technique, focused on sets of program elements related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritization technique to improve the rate at which such races are detected. Our empirical study of SimRT reveals that it is more efficient and effective for revealing races than other approaches, and that its constituent test selection and prioritization components each contribute to its performance.

Question: What is a data race?


A Light Rule-based Approach to English Subject-Verb Agreement Errors on the Third Person Singular Forms. Verb errors are one of the most common grammar errors made by non-native writers of English. This work especially focus on an important type of verb usage errors, subject-verb agreement for the third person singular forms, which has a high proportion in errors made by non-native English learners. Existing work has not given a satisfied solution for this task, in which those using supervised learning method usually fail to output good enough performance, and rule-based methods depend on advanced linguistic resources such as syntactic parsers. In this paper, we propose a rule-based method to detect and correct the concerned errors. The proposed method relies on a series of rules to automatically locate subject and predicate in four types of sentences. The evaluation shows that the proposed method gives state-of-the-art performance with quite limited linguistic resources. ∗ Correspondence author † This work was partially supported by the National Natural Science Foundation of China (No. 61170114, and No. 61272248), the National Basic Research Program of China (No. 2013CB329401), the Science and Technology Commission of Shanghai Municipality (No. 13511500200), the European Union Seventh Framework Program (No. 247619), the Cai Yuanpei Program (CSC fund 201304490199 and 201304490171), and the art and science interdiscipline funds of Shanghai Jiao Tong University, No. 14X190040031, and the Key Project of National Society Science Foundation of China, No. 15-ZDA041.

Question: What is the subject of the sentence?


Horse Racing Prediction Using Artificial Neural Networks. Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, QuasiNewton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data collected from AQUEDUCT Race Track in NY, include 100 actual races from 1 January to 29 January 2010. The experimental results demonstrate that NNs are appropriate methods in horse racing prediction context. Results show that BP algorithm performs slightly better than other algorithms but it needs a longer training time and more parameter selection. Furthermore, LM algorithm is the fastest. Key-Words: Artificial Neural Networks, Time Series Analysis, Horse Racing Prediction, Learning Algorithms, BackPropagation

Question: What is the best learning algorithm for horse racing prediction?


Co-Clustering with Side Information for Text mining. Many of the text mining applications contain a huge amount of information from document in the form of text. This text can be very helpful for Text Clustering. This text also includes various kind of other information known as Side Information or Metadata. Examples of this side information include links to other web pages, title of the document, author name or date of Publication which are present in the text document. Such metadata may possess a lot of information for the clustering purposes. But this Side information may be sometimes noisy. Using such Side Information for producing clusters without filtering it, can result to bad quality of Clusters. So we use an efficient Feature Selection method to perform the mining process to select that Side Information which is useful for Clustering so as to maximize the advantages from using it. The proposed technique, CCSI (Co-Clustering with Side Information) system makes use of the process of Co-Clustering or Two-mode clustering which is a data mining technique that allows concurrently clustering of the rows and columns of a matrix.

Question: What is co-clustering?


X-Band GaN SPDT MMIC with over 25 Watt Linear Power Handling. Single pole double throw (SPDT) switches are becoming more and more key components in phased-array radar transmit/receive modules. An SPDT switch must be able to handle the output power of a high power amplifier and must provide enough isolation to protect the low noise amplifier in the receive chain when the T/R module is transmitting. Therefore gallium nitride technology seems to become a key technology for high power SPDT switch design. The technology shows good performance on microwave frequencies and is able to handle high power. An X-band SPDT switch, with a linear power handling of over 25 W, has been designed, measured and evaluated. The circuit is designed in the coplanar waveguide AlGaN/GaN technology established at QinetiQ.

Question: What is the X-band?


Folded dipole antenna near metal plate. The paper presents the effects on antenna parameters when an antenna is placed horizontally near a metal plate. The plate has finite size and rectangular shape. A folded dipole antenna is used and it is placed symmetrically above the plate. The FEM (finite element method) is used to simulate the dependency of antenna parameters on the size of the plate and the distance between the plate and the antenna. The presence of the metal plate, even a small one if it is at the right distance, causes very big changes in the behaviour of the antenna. The bigger the plate, especially in width, the sharper and narrower are the lobes of the radiation pattern. The antenna height defines how many lobes the radiation pattern has. A number of the antenna parameters, including impedance, directivity and front-to-back ratio, change periodically as the antenna height is increased. The resonant frequency of the antenna also changes under the influence of the metal plate.

Question: What is the effect of a metal plate on an antenna?


Trust management of services in cloud environments: Obstacles and solutions. Trust management is one of the most challenging issues in the emerging cloud computing area. Over the past few years, many studies have proposed different techniques to address trust management issues. However, despite these past efforts, several trust management issues such as identification, privacy, personalization, integration, security, and scalability have been mostly neglected and need to be addressed before cloud computing can be fully embraced. In this article, we present an overview of the cloud service models and we survey the main techniques and research prototypes that efficiently support trust management of services in cloud environments. We present a generic analytical framework that assesses existing trust management research prototypes in cloud computing and relevant areas using a set of assessment criteria. Open research issues for trust management in cloud environments are also discussed.

Question: What is the cloud computing model?


Optimal crawling strategies for web search engines. Web Search Engines employ multiple so-called crawlers to maintain local copies of web pages. But these web pages are frequently updated by their owners, and therefore the crawlers must regularly revisit the web pages to maintain the freshness of their local copies. In this paper, we propose a two-part scheme to optimize this crawling process. One goal might be the minimization of the average level of staleness over all web pages, and the scheme we propose can solve this problem. Alternatively, the same basic scheme could be used to minimize a possibly more important search engine embarrassment level metric: The frequency with which a client makes a search engine query and then clicks on a returned url only to find that the result is incorrect. The first part our scheme determines the (nearly) optimal crawling frequencies, as well as the theoretically optimal times to crawl each web page. It does so within an extremely general stochastic framework, one which supports a wide range of complex update patterns found in practice. It uses techniques from probability theory and the theory of resource allocation problems which are highly computationally efficient -- crucial for practicality because the size of the problem in the web environment is immense. The second part employs these crawling frequencies and ideal crawl times as input, and creates an optimal achievable schedule for the crawlers. Our solution, based on network flow theory, is exact as well as highly efficient. An analysis of the update patterns from a highly accessed and highly dynamic web site is used to gain some insights into the properties of page updates in practice. Then, based on this analysis, we perform a set of detailed simulation experiments to demonstrate the quality and speed of our approach.

Question: What is the optimal crawling strategy for web search engines?


Predicting The Next App That You Are Going To Use. Given the large number of installed apps and the limited screen size of mobile devices, it is often tedious for users to search for the app they want to use. Although some mobile OSs provide categorization schemes that enhance the visibility of useful apps among those installed, the emerging category of homescreen apps aims to take one step further by automatically organizing the installed apps in a more intelligent and personalized way. In this paper, we study how to improve homescreen apps' usage experience through a prediction mechanism that allows to show to users which app she is going to use in the immediate future. The prediction technique is based on a set of features representing the real-time spatiotemporal contexts sensed by the homescreen app. We model the prediction of the next app as a classification problem and propose an effective personalized method to solve it that takes full advantage of human-engineered features and automatically derived features. Furthermore, we study how to solve the two naturally associated cold-start problems: app cold-start and user cold-start. We conduct large-scale experiments on log data obtained from Yahoo Aviate, showing that our approach can accurately predict the next app that a person is going to use.

Question: How to predict the next app that a user is going to use?


Privacy as a fuzzy concept: A new conceptualization of privacy for practitioners. • Users may freely distribute the URL that is used to identify this publication. • Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. • User may use extracts from the document in line with the concept of 'fair dealing' under the Copyright, Designs and Patents Act 1988 (?) • Users may not further distribute the material nor use it for the purposes of commercial gain.

Question: What is the concept of privacy?


User Requirements Analysis: A Review of Supporting Methods. Understanding user requirements is an integral part of information systems design and is critical to the success of interactive systems. However specifying these requirements is not so simple to achieve. This paper describes general methods to support user requirements analysis that can be adapted to a range of situations. Some brief case studies are described to illustrate how these methods have been applied in practice.

Question: What is a user requirements analysis?


Taking Empowerment to the Next Level : A Multiple-Level Model of Empowerment , Performance , and Satisfaction. Most research to date has approached employee empowerment as an individual-level phenomenon. In this study we proposed a work-unit-level construct, empowerment climate, and tested a multiple-level model integrating macro and micro approaches to empowerment. Empowerment climate was shown to be empirically distinct from psychological empowerment and positively related to manager ratings of work-unit performance. A cross-level mediation analysis using hierarchical linear modeling showed that psychological empowerment mediated the relationships between empowerment climate and individual performance and job satisfaction.

Question: What is the multiple-level model of empowerment?


A Broadband U-Slot Coupled Microstrip-to-Waveguide Transition. A novel planar broadband microstrip-to-waveguide transition is proposed in this paper. The referred waveguide can be either rectangular waveguide or ridged waveguide. The transition consists of an open-circuited microstrip quarter-wavelength resonator and a resonant U-shaped slot on the upper broadside wall of a short-circuited waveguide. A physics-based equivalent-circuit model is also developed for interpreting the working mechanism and providing a coarse model for engineering design. The broadband transition can be regarded as a stacked two-pole resonator filter. Each coupling circuit can be approximately designed separately using the group-delay information at the center frequency. In addition to its broadband attribute, the transition is compact in size, vialess, and is highly compatible with planar circuits. These good features make the new transition very attractive for the system architecture where waveguide devices need to be surface mounted on a multilayered planer circuit. Two design examples are given to demonstrate the usefulness of the transition: one is a broadband ridged-waveguide bandpass filter and the other is a surface-mountable broadband low-temperature co-fired ceramic laminated waveguide cavity filter. Both filters are with the proposed transition for interfacing with microstrip lines, showing promising potentials in practical applications.

Question: What is a broadband microstrip-to-waveguide transition?


Natural language processing in mental health applications using non-clinical texts. Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

Question: What is the difference between a taxonomy and a scoping review?


Review on Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning. Big Data Analytics and Deep Learning are two high-center of information science. Big Data has become important because of companies i.e. both public and private have been gathering huge measures of domain-specific data, which can contain helpful data about issues, for example, national intelligence, cyber security, fraud detection, marketing, and medical informatics. Deep Learning algorithms extract high-level, complex reflections as information representations through a progressive learning process. A advantage of Deep Learning is the study and learning of huge size of unsupervised information, making it a significant instrument for Big Data Analytics where crude information is to a great extent unlabeled and un-sorted. The present survey gives an idea of the previous work done by several researchers on the Big Data Analytics and Deep Learning applications.

Question: What is the difference between Big Data Analytics and Deep Learning?


Exploiting Structure in Policy Construction. Markov decision processes (MDPs) have recently been appbed to the problem of modeling decisiontheoretic planning While traditional methods for solving MDPs are often practical for small states spaces, their effectiveness for large AI planning problems is questionable We present an algorithm, called structured policy Iteration (SPI), that con structs optimal policies without explicit enumera tion of the state space The algorithm retains the fundamental computational steps of the commonly used modified policy iteration algorithm, but ex ploits the variable and prepositional independencies reflected in a temporal Bayesian network represen tation of MDPs The principles behind SPI can be applied to any structured representation of stochas tic actions, pobcies and value functions, and the algorithm itself can be used in conjunction with re cent approximation methods

Question: How do you exploit structure in policy construction?


C-ICAMA, a centralized intelligent channel assigned multiple access for multi-layer ad-hoc wireless networks with UAVs. Multi-layer ad hoc wireless networks with UAVs is an ideal infrastructure to establish a rapidly deployable wireless communication system any time any where in the world for military applications. In this tactical environment, information traflc is quite asynimetric. Ground jighting units are information consumers and receive jar more data than they transmit. The up-link is used for sending requests for information and some networking configuration overhead with a f a 0 kilobits, while the down-link is used to return the data requested with megabits size (e.g. multimedia file of images and charts). Centralized Intelligent Channel Assigned Multiple Access(C-ICAMA) is a MAC layer protocol proposed for ground backbone nodes to access UAV (Unmanned Aerie1 Vehicle) to solve the highly asymmetric data trafic in this tactical environment. With it's intelligent scheduling algorithm, it can dynamically allocate bandwidth for up-link and downlink to jit the instantaneous status of symmetric trafJic. The results of C-ICAMA is very promising, due to the dynamic bandwidth allocation of asymmetric i.ip-link and down-link, the access delay is tremendously reduced,

Question: What is the difference between a MAC layer protocol and a routing layer protocol?


Language within our grasp. In monkeys, the rostral part of ventral premotor cortex (area F5) contains neurons that discharge, both when the monkey grasps or manipulates objects and when it observes the experimenter making similar actions. These neurons (mirror neurons) appear to represent a system that matches observed events to similar, internally generated actions, and in this way forms a link between the observer and the actor. Transcranial magnetic stimulation and positron emission tomography (PET) experiments suggest that a mirror system for gesture recognition also exists in humans and includes Broca's area. We propose here that such an observation/execution matching system provides a necessary bridge from'doing' to'communicating',as the link between actor and observer becomes a link between the sender and the receiver of each message.

Question: What is the function of mirror neurons?


Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique. D EEP learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 [1]. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [2]. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications. Promising results are emerging. In medical imaging, the accurate diagnosis and/or assessment of a disease depends on both image acquisition and image interpretation. Image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment an

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


Bioconductor: open software development for computational biology and bioinformatics. The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.

Question: What is the goal of the bioconductor project?


My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013. Deep Learning has attracted significant attention in recent years. Here I present a brief overview of my first Deep Learner of 1991, and its historic context, with a timeline of Deep Learning highlights. Note: As a machine learning researcher I am obsessed with proper credit assignment. This draft is the result of an experiment in rapid massive open online peer review. Since 20 September 2013, subsequent revisions published under www.deeplearning.me have absorbed many suggestions for improvements by experts. The abbreviation "TL" is used to refer to subsections of the timeline section. Figure 1: My first Deep Learning system of 1991 used a deep stack of recurrent neural networks (a Neural Hierarchical Temporal Memory) pre-trained in unsupervised fashion to accelerate subsequent supervised learning [79, 81, 82]. 1 ar X iv :1 31 2. 55 48 v1 [ cs .N E ] 1 9 D ec 2 01 3 In 2009, our Deep Learning Artificial Neural Networks became the first Deep Learners to win official international pattern recognition competitions [40, 83] (with secret test sets known only to the organisers); by 2012 they had won eight of them (TL 1.13), including the first contests on object detection in large images (ICPR 2012) [2, 16] and image segmentation (ISBI 2012) [3, 15]. In 2011, they achieved the world's first superhuman visual pattern recognition results [20, 19]. Others have implemented very similar techniques, e.g., [51], and won additional contests or set benchmark records since 2012, e.g., (TL 1.13, TL 1.14). The field of Deep Learning research is far older though—compare the timeline (TL) further down. My first Deep Learner dates back to 1991 [79, 81, 82] (TL 1.7). It can perform credit assignment across hundreds of nonlinear operators or neural layers, by using unsupervised pre-training for a stack of recurrent neural networks (RNN) (deep by nature) as in Figure 1. Such RNN are general computers more powerful th

Question: What is deep learning?


Trapped in the net: will internet addiction become a 21st-century epidemic?.

Question: What is the net?


Question: What is motor learning?


Survey on 3D Hand Gesture Recognition. Three-dimensional hand gesture recognition has attracted increasing research interests in computer vision, pattern recognition, and human-computer interaction. The emerging depth sensors greatly inspired various hand gesture recognition approaches and applications, which were severely limited in the 2D domain with conventional cameras. This paper presents a survey of some recent works on hand gesture recognition using 3D depth sensors. We first review the commercial depth sensors and public data sets that are widely used in this field. Then, we review the state-of-the-art research for 3D hand gesture recognition in four aspects: 1) 3D hand modeling; 2) static hand gesture recognition; 3) hand trajectory gesture recognition; and 4) continuous hand gesture recognition. While the emphasis is on 3D hand gesture recognition approaches, the related applications and typical systems are also briefly summarized for practitioners.

Question: What is the state of the art in 3D hand gesture recognition?


Stochastic Pooling for Regularization of Deep Convolutional Neural Networks. We introduce a simple and effective method for regularizing large convolutional neural networks. We replace the conventional deterministic pooling operations with a stochastic procedure, randomly picking the activation within each pooling region according to a multinomial distribution, given by the activities within the pooling region. The approach is hyper-parameter free and can be combined with other regularization approaches, such as dropout and data augmentation. We achieve state-of-the-art performance on four image datasets, relative to other approaches that do not utilize data augmentation.

Question: How to regularize a deep convolutional neural network?


An Efficient End-to-End Neural Model for Handwritten Text Recognition. Offline handwritten text recognition from images is an important problem for enterprises attempting to digitize large volumes of handmarked scanned documents/reports. Deep recurrent models such as Multi-dimensional LSTMs [10] have been shown to yield superior performance over traditional Hidden Markov Model based approaches that suffer from the Markov assumption and therefore lack the representational power of RNNs. In this paper we introduce a novel approach that combines a deep convolutional network with a recurrent Encoder-Decoder network to map an image to a sequence of characters corresponding to the text present in the image. The entire model is trained end-to-end using Focal Loss [18], an improvement over the standard Cross-Entropy loss that addresses the class imbalance problem, inherent to text recognition. To enhance the decoding capacity of the model, Beam Search algorithm is employed which searches for the best sequence out of a set of hypotheses based on a joint distribution of individual characters. Our model takes as input a downsampled version of the original image thereby making it both computationally and memory efficient. The experimental results were benchmarked against two publicly available datasets, IAM and RIMES. We surpass the state-of-the-art word level accuracy on the evaluation set of both datasets by 3.5% & 1.1%, respectively.

Question: What is the state-of-the-art word level accuracy on the evaluation set of both datasets?


Learning Deployable Navigation Policies at Kilometer Scale from a Single Traversal. Model-free reinforcement learning has recently been shown to be effective at learning navigation policies from complex image input. However, these algorithms tend to require large amounts of interaction with the environment, which can be prohibitively costly to obtain on robots in the real world. We present an approach for efficiently learning goal-directed navigation policies on a mobile robot, from only a single coverage traversal of recorded data. The navigation agent learns an effective policy over a diverse action space in a large heterogeneous environment consisting of more than 2km of travel, through buildings and outdoor regions that collectively exhibit large variations in visual appearance, self-similarity, and connectivity. We compare pretrained visual encoders that enable precomputation of visual embeddings to achieve a throughput of tens of thousands of transitions per second at training time on a commodity desktop computer, allowing agents to learn from millions of trajectories of experience in a matter of hours. We propose multiple forms of computationally efficient stochastic augmentation to enable the learned policy to generalise beyond these precomputed embeddings, and demonstrate successful deployment of the learned policy on the real robot without fine tuning, despite environmental appearance differences at test time. The dataset and code required to reproduce these results and apply the technique to other datasets and robots is made publicly available at rl-navigation.github.io/deployable.

Question: How large is the canadian military?


Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems. Automatically solving algebra word problems has raised considerable interest recently. Existing state-of-the-art approaches mainly rely on learning from human annotated equations. In this paper, we demonstrate that it is possible to efficiently mine algebra problems and their numerical solutions with little to no manual effort. To leverage the mined dataset, we propose a novel structured-output learning algorithm that aims to learn from both explicit (e.g., equations) and implicit (e.g., solutions) supervision signals jointly. Enabled by this new algorithm, our model gains 4.6% absolute improvement in accuracy on the ALG514 benchmark compared to the one without using implicit supervision. The final model also outperforms the current state-of-the-art approach by 3%.

Question: What is the current state-of-the-art approach?


A Systematic Review of Service Level Management in the Cloud. Cloud computing make it possible to flexibly procure, scale, and release computational resources on demand in response to workload changes. Stakeholders in business and academia are increasingly exploring cloud deployment options for their critical applications. One open problem is that service level agreements (SLAs) in the cloud ecosystem are yet to mature to a state where critical applications can be reliably deployed in clouds. This article systematically surveys the landscape of SLA-based cloud research to understand the state of the art and identify open problems. The survey is particularly aimed at the resource allocation phase of the SLA life cycle while highlighting implications on other phases. Results indicate that (i) minimal number of SLA parameters are accounted for in most studies; (ii) heuristics, policies, and optimisation are the most commonly used techniques for resource allocation; and (iii) the monitor-analysis-plan-execute (MAPE) architecture style is predominant in autonomic cloud systems. The results contribute to the fundamentals of engineering cloud SLA and their autonomic management, motivating further research and industrial-oriented solutions.

Question: What is the state of the art in service level management in the cloud?


MADMAC: Multiple Attribute Decision Methodology for Adoption of Clouds. Cloud Adoption decisions tend to involve multiple, conflicting criteria (attributes) with incommensurable units of measurements, which must be compared among multiple alternatives using imprecise and incomplete available information. Multi-attribute Decision Making (MADM) has been shown to provide a rational basis to aid decision making in such scenarios. We present a MADMAC framework for cloud adoption, consisting of 3 Decision Areas (DA) referred to as the Cloud Switch, Cloud Type and Vendor Choice. It requires the definition of Attributes, Alternatives and Attribute Weights, to construct a Decision Matrix and arrive at a relative ranking to identify the optimal alternative. We also present a taxonomy organized in a two level hierarchy: Server-centric clouds, Client-centric clouds and Mobile-centric clouds, which further map to detailed, specific applications or workloads. DSS presented showing algorithms derived from MADMAC can compute and optimize CA decisions separately for the three stages, where the attributes differently influence CA decisions. A modified Wide-band Delphi method is proposed for assessing the relative weights for each attribute, by workload. Relative ranks are calculated using these weights, and the Simple Additive Weighting (SAW) method is used to generate value functions for all the alternatives, and rank the alternatives by their value to finally choose the best alternative. Results from application of the method to four different types of workloads show that the method converges on reasonable cloud adoption decisions. MADMAC's key advantage is its fully quantitative and iterative convergence approach based on proven multi-attribute decision methods, which enables decision makers to comparatively assess the relative robustness of alternative cloud adoption decisions in a defensible manner. Being amenable to automation, it can respond well to even complex arrays of deci

Question: What is the difference between a cloud switch and a cloud type?