Double-spending fast payments in bitcoin. Bitcoin is a decentralized payment system that relies on Proof-of-Work (PoW) to verify payments. Nowadays, Bitcoin is increasingly used in a number of fast payment scenarios, where the time between the exchange of currency and goods is short (in the order of few seconds). While the Bitcoin payment verification scheme is designed to prevent double-spending, our results show that the system requires tens of minutes to verify a transaction and is therefore inappropriate for fast payments. An example of this use of Bitcoin was recently reported in the media: Bitcoins were used as a form of \emph{fast} payment in a local fast-food restaurant. Until now, the security of fast Bitcoin payments has not been studied. In this paper, we analyze the security of using Bitcoin for fast payments. We show that, unless appropriate detection techniques are integrated in the current Bitcoin implementation, double-spending attacks on fast payments succeed with overwhelming probability and can be mounted at low cost. We further show that the measures recommended by Bitcoin developers for the use of Bitcoin in fast payments are not always effective in detecting double-spending; we show that if those recommendations are integrated in future Bitcoin implementations, double-spending attacks on Bitcoin will still be possible. Finally, we propose and implement a modification to the existing Bitcoin implementation that ensures the detection of double-spending attacks against fast payments.

Question: How does bitcoin work?


Text Mining of Tweet for Sentiment Classification and Association with Stock Prices. In present days, the social media and networking act as one of the key platforms for sharing information and opinions. Many people share ideas, express their view points and opinions on various topic of their interest. Social media text has rich information about the companies, their products and various services offered by them. In this research we focus exploring the association of sentiments of social media text and stock prices of a company. The tweets of several company has been extracted and performed sentiment classification using Naïve Bayes classifier and SVM classifier. To perform the classification, N-gram based feature vectors are constructed using important words of tweets. Further, the pattern of association between number of tweets which are positive or negative and stock prices has been explored. Motivated by such an association, the features related to tweets such as number of positive, negative, neutral tweets and total number of tweets are used to predict the stock market status using Support Vector Machine classifier.

Question: How to extract tweets from social media?


Mundanely miraculous: the robot in healthcare. As both hero and villain, robots have played prominent roles in media such as films and books. Now, robots are no longer hidden away from the public conscious in fictive worlds or real-life factories. Robots are becoming a real part of our everyday encounters in environments such as healthcare settings. In this paper, we describe a discourse analysis of 60 YouTube videos that showcase robots in healthcare activities. Our narrative weaves three discourses that construct visions of the healthcare robot: (1) the miraculous robot as the robot that enhances patient care; (2) the mundane robot as the innocuous robot that integrates into the workflow seamlessly; and (3) the preternatural robot as the robot that is miraculous but never mundane. We propose several contrary visions to this dominant narrative of healthcare robots as a framework for future fieldwork that, we argue, should investigate the institutions of robotics.

Question: What is a discourse analysis?


Linear model predictive control of the locomotion of Pepper, a humanoid robot with omnidirectional wheels. The goal of this paper is to present a new real-time controller based on linear model predictive control for an omnidirectionnal wheeled humanoid robot. It is able to control both the mobile base of the robot and its body, while taking into account dynamical constraints. It makes it possible to have high velocity and acceleration motions by predicting the dynamic behavior of the robot in the future. Experimental results are proposed on the robot Pepper made by Aldebaran, showing good performance in terms of robustness and tracking control, efficiently managing kinematic and dynamical constraints.

Question: What is a humanoid robot?


Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization. We address the problem of learning classifiers when observat ions have multiple views, some of which may not be observed for all examples. We a ssume the existence of view generating functions which may complete t he missing views in an approximate way. This situation corresponds for examp le to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems m ay be used to translate each document in the missing languages. We derive a gene ralization error bound for classifiers learned on examples with multiple arti ficially created views. Our result uncovers a trade-off between the size of the train ing set, the number of views, and the quality of the view generating functions. A s a consequence, we identify situations where it is more interesting to use mu ltiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semisupervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 co llecti ns support our findings by showing that additional views obtained from M T may significantly improve the classification performance in the cases identifi d by our trade-off.

Question: What is the trade-off between the size of the training set, the number of views, and the quality of the view generating functions?


A sparse-response deep belief network based on rate distortion theory. Deep belief networks (DBNs) are currently the dominant technique for modeling the architectural depth of brain, and can be trained efficiently in a greedy layer-wise unsupervised learning manner. However, DBNs without a narrow hidden bottleneck typically produce redundant, continuous-valued codes and unstructured weight patterns. Taking inspiration from rate distortion (RD) theory, which encodes original data using as few bits as possible, we introduce in this paper a variant of DBN, referred to as sparse-response DBN (SR-DBN). In this approach, Kullback–Leibler divergence between the distribution of data and the equilibrium distribution defined by the building block of DBN is considered as a distortion function, and the sparse response regularization induced by L1-norm of codes is used to achieve a small code rate. Several experiments by extracting features from different scale image datasets show that our approach SR-DBN learns codes with small rate, extracts features at multiple levels of abstraction mimicking computations in the cortical hierarchy, and obtains more discriminative representation than PCA and several basic algorithms of DBNs. & 2014 Elsevier Ltd. All rights reserved.

Question: What is rate distortion theory?


Multi sensor system for automatic fall detection. To reduce elderly falling risk in the area where video surveillance system is unavailable due to privacy reason and so on, we need a monitor system which does no recognize what they are doing. This paper presents an elderly-falling detection system using ultrasonic sensors. The ultrasonic technology-based multi sensors are couple of receiver and transmitter together, which are connected to Arduino microcontroller in order to send the elderly person's fall related signal using WiFi to the processing unit. The sensors are positioned as an array on the roof and wall in the room. The signal is analyzed to recognize human by sensing distance, and detect the action such as standing, sitting, and falling by pattern matching with the standard templates of top and side signal. In experiments, the proposed system can recognize accuracy of falling detection is 93% approximately.

Question: How to detect elderly falling?


Review of Portable and Low-Cost Sensors for the Ambient Air Monitoring of Benzene and Other Volatile Organic Compounds. This article presents a literature review of sensors for the monitoring of benzene in ambient air and other volatile organic compounds. Combined with information provided by stakeholders, manufacturers and literature, the review considers commercially available sensors, including PID-based sensors, semiconductor (resistive gas sensors) and portable on-line measuring devices as for example sensor arrays. The bibliographic collection includes the following topics: sensor description, field of application at fixed sites, indoor and ambient air monitoring, range of concentration levels and limit of detection in air, model descriptions of the phenomena involved in the sensor detection process, gaseous interference selectivity of sensors in complex VOC matrix, validation data in lab experiments and under field conditions.

Question: What is the range of concentration levels of benzene in ambient air?


A Direct AC–AC Converter for Inductive Power-Transfer Systems. This paper proposes a direct ac-ac converter to generate a high-frequency current for inductive power-transfer (IPT) systems. Unlike traditional dc-ac converters for IPT systems, the proposed converter can achieve a high-frequency current generation directly from an ac power source without a dc link. The power converter can maintain high-frequency resonance by natural circuit oscillation and discrete energy injection control. A variable-frequency control strategy is developed to vary the switching frequency to keep the high-frequency current operating with zero-current switching. Thus, the switching stress, power losses, and electromagnetic interference of the converter are reduced. Theoretical analysis, simulation, and experimental results show that the output track current is fully controllable with good waveforms for contactless power-transfer applications.

Question: How to design a direct ac-ac converter for inductive power-transfer systems?


Estimating the Customer-Level Demand for Electricity Under Real-Time Market Prices. This paper presents estimates of the customer-level demand for electricity by industrial and commercial customers purchasing electricity according to the half-hourly energy prices from the England and Wales (E&W) electricity market. These customers also face the possibility of a demand charge on their electricity consumption during the three half-hour periods that are coincident with E&W system peaks. Although energy charges are largely known by 4:00 P.M. the day prior to consumption, a fraction of the energy charge and the identity of the half-hour periods when demand charges occur are only known with certainty ex post of consumption. Four years of data from a Regional Electricity Company (REC) in the United Kingdom is used to quantify the half-hourly customer-level demands under this real-time pricing program. The econometric model developed and estimated here quantifies the extent of intertemporal substitution in electricity consumption across pricing periods within the day due to changes in all of the components of the day-ahead E&W electricity prices, the level of the demand charge and the probability that a demand charge will be imposed. The results of this modeling framework can be used by retail companies supplying consumers purchasing electricity according to real-time market prices to construct demand-side bids into an electricity market open to competition. The paper closes with several examples of how this might be done.

Question: How much electricity does a customer use?


Artificial intelligence theory (Basic concepts). Taking the bionic approach as a basis, the article discusses the main concepts of the theory of artificial intelligence as a field of knowledge, which studies the principles of creation and functioning of intelligent systems based on multidimensional neural-like growing networks. The general theory of artificial intelligence includes the study of neural-like elements and multidimensional neural-like growing networks, temporary and long term memory, study of the functional organization of the "brain" of the artificial intelligent systems, of the sensor system, modulating system, motor system, conditioned and unconditioned reflexes, reflexes arc (ring), motivation, purposeful behavior, of "thinking", "consciousness", "subconscious and artificial personality developed as a result of training and education".

Question: What is the bionic approach?


A Unified Approach to the Change of Resolution: Space and Gray-Level. Multiple resolution analysis of images is a current trend in computer vision. In most cases, only spatial resolution has been considered. However, image resolution has an additional aspect: gray level, or color, resolution. Color resolution has traditionally been considered in the area of computer graphics. By defining a suitable measure for the comparison of images, changes in resolution can be treated with the same tools as changes in color resolution. A gray tone image, for example, can be compared to a halftone image having only two colors (black and white), but of higher spatial resolution. An important application can be in pyramids, one of the most commonly used multiple (spatial) resolution schemes, where this approach provides a tool to change the color resolution as well. Increasing color resolution while reducing spatial resolution to retain more image details and prevent aliasing is an example of the possibility to find optimal combinations of resolution reduction, spatial and color, to best fit an application.

Question: How to compare images?


Segmentation using Eigenvectors: A Unifying View. Automatic grouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated good performance on this task using methods that are based on eigenvectors of the a nity matrix. These approaches are extremely attractive in that they are based on simple eigendecomposition algorithms whose stability is well understood. Nevertheless, the use of eigendecompositions in the context of segmentation is far from well understood. In this paper we give a unied treatment of these algorithms, and show the close connections between them while highlighting their distinguishing features. We then prove results on eigenvectors of block matrices that allow us to analyze the performance of these algorithms in simple grouping settings. Finally, we use our analysis to motivate a variation on the existing methods that combines aspects from di erent eigenvector segmentation algorithms. We illustrate our analysis with results on real and synthetic images. Human perceiving a scene can often easily segment it into coherent segments or groups. There has been a tremendous amount of e ort devoted to achieving the same level of performance in computer vision. In many cases, this is done by associating with each pixel a feature vector (e.g. color, motion, texture, position) and using a clustering or grouping algorithm on these feature vectors. Perhaps the cleanest approach to segmenting points in feature space is based on mixture models in which one assumes the data were generated by multiple processes and estimates the parameters of the processes and the number of components in the mixture. The assignment of points to clusters can then be easily performed by calculating the posterior probability of a point belonging to a cluster. Despite the elegance of this approach, the estimation process leads to a notoriously di cult optimization. The frequently used EM algorithm [3] often converge

Question: What is the difference between a clustering and a segmentation?


Knowledge warehouse: an architectural integration of knowledge management, decision support, artificial intelligence and data warehousing. Decision support systems (DSS) are becoming increasingly more critical to the daily operation of organizations. Data warehousing, an integral part of this, provides an infrastructure that enables businesses to extract, cleanse, and store vast amounts of data. The basic purpose of a data warehouse is to empower the knowledge workers with information that allows them to make decisions based on a solid foundation of fact. However, only a fraction of the needed information exists on computers; the vast majority of a firm's intellectual assets exist as knowledge in the minds of its employees. What is needed is a new generation of knowledge-enabled systems that provides the infrastructure needed to capture, cleanse, store, organize, leverage, and disseminate not only data and information but also the knowledge of the firm. The purpose of this paper is to propose, as an extension to the data warehouse model, a knowledge warehouse (KW) architecture that will not only facilitate the capturing and coding of knowledge but also enhance the retrieval and sharing of knowledge across the organization. The knowledge warehouse proposed here suggests a different direction for DSS in the next decade. This new direction is based on an expanded purpose of DSS. That is, the purpose of DSS in knowledge improvement. This expanded purpose of DSS also suggests that the effectiveness of a DS will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s) and thereby how well it improves his/her decision making.

Question: What is a knowledge warehouse?


ICIET ' 14 Image Steganography Method Using Integer Wavelet Transform. Digital Steganography explains the art and science of writing hidden messages in such a way that, apart from the sender and intended recipient, no one suspects the existence of the message, a form of security through the state of being unknown. The main two famous schemes used for image steganography are spatial domain embedding and transform domain embedding. The main aim of Wavelet Transform is used to transform original image (cover image) from spatial domain to frequency domain. But here in our proposed work, Integer Wavelet Transform is performed on a gray level cover image and in turn embeds the message bitstream into the LSB's of the integer wavelet coefficients of a the image . The main purpose of the proposed work is to focus on improving embedding capacity and bring down the distortion occurring to the stego image. The refinement of the algorithm plays an important role for accomplishing higher embedding capacity and low distortion rate. The experimental results prove that the assessment metric such as PSNR is improved in a high manner. The experimental results show that the algorithm has a high capacity and a good invisibility.

Question: What is the difference between spatial domain embedding and transform domain embedding?


Decoding actions at different levels of abstraction.. Brain regions that mediate action understanding must contain representations that are action specific and at the same time tolerate a wide range of perceptual variance. Whereas progress has been made in understanding such generalization mechanisms in the object domain, the neural mechanisms to conceptualize actions remain unknown. In particular, there is ongoing dissent between motor-centric and cognitive accounts whether premotor cortex or brain regions in closer relation to perceptual systems, i.e., lateral occipitotemporal cortex, contain neural populations with such mapping properties. To date, it is unclear to which degree action-specific representations in these brain regions generalize from concrete action instantiations to abstract action concepts. However, such information would be crucial to differentiate between motor and cognitive theories. Using ROI-based and searchlight-based fMRI multivoxel pattern decoding, we sought brain regions in human cortex that manage the balancing act between specificity and generality. We investigated a concrete level that distinguishes actions based on perceptual features (e.g., opening vs closing a specific bottle), an intermediate level that generalizes across movement kinematics and specific objects involved in the action (e.g., opening different bottles with cork or screw cap), and an abstract level that additionally generalizes across object category (e.g., opening bottles or boxes). We demonstrate that the inferior parietal and occipitotemporal cortex code actions at abstract levels whereas the premotor cortex codes actions at the concrete level only. Hence, occipitotemporal, but not premotor, regions fulfill the necessary criteria for action understanding. This result is compatible with cognitive theories but strongly undermines motor theories of action understanding.

Question: What is the difference between motor and cognitive theories of action understanding?


The pursuit of happiness: time, money, and social connection.. Does thinking about time, rather than money, influence how effectively individuals pursue personal happiness? Laboratory and field experiments revealed that implicitly activating the construct of time motivates individuals to spend more time with friends and family and less time working-behaviors that are associated with greater happiness. In contrast, implicitly activating money motivates individuals to work more and socialize less, which (although productive) does not increase happiness. Implications for the relative roles of time versus money in the pursuit of happiness are discussed.

Question: Does thinking about time, rather than money, influence how effectively individuals pursue personal happiness?


Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning. Policy improvement methods seek to optimize the parameters of a policy with respect to a utility function. There are two main approaches to performing this optimization: reinforcement learning (RL) and black-box optimization (BBO). Whereas BBO algorithms are generic optimization methods that, due to there generality, may also be applied to optimizing policy parameters, RL algorithms are specifically tailored to leveraging the structure of policy improvement problems. In recent years, benchmark comparisons between RL and BBO have been made, and there has been several attempts to specify which approach works best for which types of problem classes. In this article, we make several contributions to this line of research: 1) We define four algorithmic properties that further clarify the relationship between RL and BBO: action-perturbation vs. parameter-perturbation, gradient estimation vs. rewardweighted averaging, use of only rewards vs. use of rewards and state information, actor-critic vs. direct policy search. 2) We show how the chronology of the derivation of ever more powerful algorithms displays a trend towards algorithms based on parameter-perturbation and reward-weighted averaging. A striking feature of this trend is that it has moved RL methods closer and closer to BBO. 3) We continue this trend by applying two modifications to the state-of-the-art "Policy Improvement with Path Integrals" (PI), which yields an algorithm we denote PI. We show that PI is a BBO algorithm, and, more specifically, that it is a special case of the "Covariance Matrix Adaptation – Evolutionary Strategy" algorithm. Our empirical evaluation demonstrates that the simpler PI outperforms PI on simple evaluation tasks in terms of convergence speed and final cost. 4) Although our evaluation implies that, for these five tasks, BBO outperforms RL, we do not hold this to be a general statement, and p

Question: What is the difference between reinforcement learning and black-box optimization?


Software architecture: a roadmap. Over the past decade software architecture has received increasing attention as an important subfield of software engineering. During that time there has been considerable progress in developing the technological and methodological base for treating architectural design as an engineering discipline. However, much remains to be done to achieve that goal. Moreover, the changing face of technology raises a number of new challenges for software architecture. This paper examines some of the important trends of software architecture in research and practice, and speculates on the important emerging trends, challenges, and aspirations.

Question: What is the changing face of technology?


Test setup for multi-finger gripper control based on robot operating system (ROS). This paper presents the concept for a test setup to prototype control algorithms for a multi-finger gripper. The human-robot interface has to provide enough degrees of freedom (DOF) to intuitively control advanced gripper and to accomplish this task a simple sensor glove equipped with flex and force sensors has been prepared for this project. The software architecture has to support both real hardware and simulation, as well as flexible communication standards, therefore, ROS architecture was employed in this matter. Paper presents some preliminary results for using sensor glove and simulated model of the three finger gripper.

Question: What is a multi-finger gripper?


The future of parking: A survey on automated valet parking with an outlook on high density parking. In the near future, humans will be relieved from parking. Major improvements in autonomous driving allow the realization of automated valet parking (AVP). It enables the vehicle to drive to a parking spot and park itself. This paper presents a review of the intelligent vehicles literature on AVP. An overview and analysis of the core components of AVP such as the platforms, sensor setups, maps, localization, perception, environment model, and motion planning is provided. Leveraging the potential of AVP, high density parking (HDP) is reviewed as a future research direction with the capability to either reduce the necessary space for parking by up to 50 % or increase the capacity of future parking facilities. Finally, a synthesized view discussing the remaining challenges in automated valet parking and the technological requirements for high density parking is given.

Question: What is the future of parking?


Path planning and collision avoidance for robots. An optimal control problem to find the fastest collision-free trajectory of a robot surrounded by obstacles is presented. The collision avoidance is based on linear programming arguments and expressed as state constraints. The optimal control problem is solved with a sequential programming method. In order to decrease the number of unknowns and constraints a backface culling active set strategy is added to the resolution technique.

Question: How to solve a robot collision avoidance problem?


Cross-Entropy Method for Reinforcement Learning. Reinforcement Learning methods have been succesfully applied to various optimalization problems. Scaling this up to real world sized problems has however been more of a problem. In this research we apply Reinforcement Learning to the game of Tetris which has a very large state space. We not only try to learn policies for Standard Tetris but try to learn parameterized policies for Generalized Tetris, which varies from game to game in field size and the chance of a single block occuring. In comparison to a non-parameterized policy we find that a parameterized policy is able to outperform the non-parameterized policy. The increased complexity of learning such a policy using the Cross-Entropy method however reaches a limit at which the policy is too complex to learn and performance drops below the non-parameterized policy.

Question: What is the cross-entropy method?


A CMOS Transceiver for a Multistandard 13.56-MHz RFID Reader SoC. A CMOS transceiver for a multistandard 13.56-MHz radio-frequency identification reader system-on-a-chip (SoC) is designed and fabricated. The SoC consists of an RF/analog part for modulation/demodulation and a digital part for controlling the transceiver functionality. Prior to designing the integrated circuit, pre-experiments using discrete components and commercial tags are performed. With the results, overall functions and specifications are determined. For supporting multistandard, several blocks are designed with digital controls according to the standards. In the transmitter, a digitally controlled amplitude modulator for various modulation indexes and a power control circuit are adopted. In the receiver, a variable gain amplifier and a level-controllable comparator, which are also controlled digitally according to the standard, are introduced. The full transceiver SoC is implemented in the Chartered 0.18-¿m CMOS technology. The measurement results of the implemented chip indicate that the designed transceiver operates in a multistandard mode.

Question: What is a CMOS transceiver?


The ONE simulator for DTN protocol evaluation. Delay-tolerant Networking (DTN) enables communication in sparse mobile ad-hoc networks and other challenged environments where traditional networking fails and new routing and application protocols are required. Past experience with DTN routing and application protocols has shown that their performance is highly dependent on the underlying mobility and node characteristics. Evaluating DTN protocols across many scenarios requires suitable simulation tools. This paper presents the Opportunistic Networking Environment (ONE) simulator specifically designed for evaluating DTN routing and application protocols. It allows users to create scenarios based upon different synthetic movement models and real-world traces and offers a framework for implementing routing and application protocols (already including six well-known routing protocols). Interactive visualization and post-processing tools support evaluating experiments and an emulation mode allows the ONE simulator to become part of a real-world DTN testbed. We show sample simulations to demonstrate the simulator's flexible support for DTN protocol evaluation.

Question: What is the one simulator for DTN protocol evaluation?


Fake Colorized Image Detection. Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection, and image retouching detection are attracting significant attention from researchers. However, image editing techniques develop over time. An emerging image editing technique is colorization, in which grayscale images are colorized with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared with natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark, and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram-based fake colorized image detection and feature encoding-based fake colorized image detection. Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.

Question: How to detect fake colorized images?


Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup. MOTIVATION The biological literature is a major repository of knowledge. Many biological databases draw much of their content from a careful curation of this literature. However, as the volume of literature increases, the burden of curation increases. Text mining may provide useful tools to assist in the curation process. To date, the lack of standards has made it impossible to determine whether text mining techniques are sufficiently mature to be useful.   RESULTS We report on a Challenge Evaluation task that we created for the Knowledge Discovery and Data Mining (KDD) Challenge Cup. We provided a training corpus of 862 articles consisting of journal articles curated in FlyBase, along with the associated lists of genes and gene products, as well as the relevant data fields from FlyBase. For the test, we provided a corpus of 213 new ('blind') articles; the 18 participating groups provided systems that flagged articles for curation, based on whether the article contained experimental evidence for gene expression products. We report on the evaluation results and describe the techniques used by the top performing groups.

Question: What is the KDD Challenge Cup?


An Introduction to Temporal Graphs: An Algorithmic Perspective. A temporal graph is, informally speaking, a graph that changes with time. When time is discrete and only the relationships between the participating entities may change and not the entities themselves, a temporal graph may be viewed as a sequence G1, G2 . . . , Gl of static graphs over the same (static) set of nodes V . Though static graphs have been extensively studied, for their temporal generalization we are still far from having a concrete set of structural and algorithmic principles. Recent research shows that many graph properties and problems become radically different and usually substantially more difficult when an extra time dimension is added to them. Moreover, there is already a rich and rapidly growing set of modern systems and applications that can be naturally modeled and studied via temporal graphs. This, further motivates the need for the development of a temporal extension of graph theory. We survey here recent results on temporal graphs and temporal graph problems that have appeared in the Computer Science community.

Question: What is a temporal graph?


User Based Aggregation for Biterm Topic Model. Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user's preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several stateof-the-art baselines.

Question: How to model user individualities in topic modeling?


Study of the General Kalman Filter for Echo Cancellation. The Kalman filter is a very interesting signal processing tool, which is widely used in many practical applications. In this paper, we study the Kalman filter in the context of echo cancellation. The contribution of this work is threefold. First, we derive a different form of the Kalman filter by considering, at each iteration, a block of time samples instead of one time sample as it is the case in the conventional approach. Second, we show how this general Kalman filter (GKF) is connected with some of the most popular adaptive filters for echo cancellation, i.e., the normalized least-mean-square (NLMS) algorithm, the affine projection algorithm (APA) and its proportionate version (PAPA). Third, a simplified Kalman filter is developed in order to reduce the computational load of the GKF; this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the proposed algorithms, which can be attractive choices for echo cancellation.

Question: What is the difference between the Kalman filter and the adaptive filter?


Designing for Exploratory Search on Touch Devices. Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.

Question: How to design for exploratory search on touch devices?


Self-Adaptive Anytime Stream Clustering. Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data and within the possibly varying inter-arrival times of the stream. Likewise, memory is limited, making it impossible to store all data. For clustering, we are faced with the challenge of maintaining a current result that can be presented to the user at any given time. In this work, we propose a parameter free algorithm that automatically adapts to the speed of the data stream. It makes best use of the time available under the current constraints to provide a clustering of the objects seen up to that point. Our approach incorporates the age of the objects to reflect the greater importance of more recent data. Moreover, we are capable of detecting concept drift, novelty and outliers in the stream. For efficient and effective handling, we introduce the ClusTree, a compact and self-adaptive index structure for maintaining stream summaries. Our experiments show that our approach is capable of handling a multitude of different stream characteristics for accurate and scalable anytime stream clustering.

Question: How to cluster streaming data?


Optimal Dynamic Hotel Pricing ∗. We analyze a confidential reservation database provided by a luxury hotel, "hotel 0", based in a major US city that enables us to observe individual reservations and cancellations at a daily frequency over a 37 month period. We show how the hotel sets prices for various classes of customers and how its prices vary over time. Hotel pricing is a challenging high-dimensional problem since hotels must not only set prices for each current date, but they must also quote prices for a range of future dates, room types and customer types. Our data reveal the full path of room rates quoted for different types of rooms and customers in advance of the arrival date. We find large within and between week variability in room prices, as well as huge seasonal variations in average daily rates and occupancy rates, not only for the hotel we study but also for its direct competitors. We formulate and estimate a structural model of optimal dynamic hotel pricing using the Method of Simulated Moments (MSM). The estimated model provides accurate predictions of the actual prices set by this firm and resulting paths of bookings and cancellations. Prices quoted for bookings generally decline as the arrival date approaches on nonbusy days, but can increase dramatically in the final days before arrival on busy days when there is a high probability of sell-out. Hotel 0's prices co-move strongly with its competitors' prices and we show that a simple price-following strategy where hotel 0 undercuts its competitors' average price by a fixed percentage provides a good first approximation to its pricing behavior. However we show that simple price-following is suboptimal: when hotel 0 expects to sell out, it is optimal to depart from price-following and increase its price significantly above its competitors. On non-busy days, it is not optimal for hotel 0 to cut its prices in the final days before arrival to try to increase occupancy unless its competitors cut their pr

Question: What is the optimal dynamic hotel pricing?


A systematic survey on automated concurrency bug detection, exposing, avoidance, and fixing techniques. Currently, concurrent programs are becoming increasingly widespread to meet the demands of the rapid development of multi-core hardware. However, it could be quite expensive and challenging to guarantee the correctness and efficiency of concurrent programs. In this paper, we provide a systematic review of the existing research on fighting against concurrency bugs, including automated concurrency bug exposing, detection, avoidance, and fixing. These four categories cover the different aspects of concurrency bug problems and are complementary to each other. For each category, we survey the motivation, key issues, solutions, and the current state of the art. In addition, we summarize the classical benchmarks widely used in previous empirical studies and the contribution of active research groups. Finally, some future research directions on concurrency bugs are recommended. We believe this survey would be useful for concurrency programmers and researchers.

Question: What is a concurrency bug?


On channel estimation and optimal training design for amplify and forward relay networks. In this paper, we provide a complete study on the training based channel estimation issues for relay networks that employ the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and relay to destination suffers from many drawbacks. Then we provide a new estimation scheme that directly estimates the overall channels from the source to the destination. The proposed channel estimation well serves the AF based space time coding (STC) that was recently developed. There exists many differences between the proposed channel estimation and that in the traditional single input single out (SISO) and multiple input single output (MISO) systems. For example, a relay must linearly precode its received training sequence by a sophisticatedly designed matrix in order to minimize the channel estimation error. Besides, each relay node is individually constrained by a different power requirement because of the non-cooperation among all relay nodes. We study both the linear least-square (LS) estimator and the minimum mean-square-error (MMSE) estimator. The corresponding optimal training sequences, as well as the optimal preceding matrices are derived from an efficient convex optimization process.

Question: What is the difference between amplify and forward and amplify and forward relay?


Unsupervised Video Object Segmentation with Motion-Based Bilateral Networks. In this work, we study the unsupervised video object segmentation problem where moving objects are segmented without prior knowledge of these objects. First, we propose a motion-based bilateral network to estimate the background based on the motion pattern of non-object regions. The bilateral network reduces false positive regions by accurately identifying background objects. Then, we integrate the background estimate from the bilateral network with instance embeddings into a graph, which allows multiple frame reasoning with graph edges linking pixels from different frames. We classify graph nodes by defining and minimizing a cost function, and segment the video frames based on the node labels. The proposed method outperforms previous state-of-the-art unsupervised video object segmentation methods against the DAVIS 2016 and the FBMS-59 datasets.

Question: How to segment moving objects in video?


Trait conscientiousness and the personality meta-trait stability are associated with regional white matter microstructure. Establishing the neural bases of individual differences in personality has been an enduring topic of interest. However, while a growing literature has sought to characterize grey matter correlates of personality traits, little attention to date has been focused on regional white matter correlates of personality, especially for the personality traits agreeableness, conscientiousness and openness. To rectify this gap in knowledge we used a large sample (n > 550) of older adults who provided data on both personality (International Personality Item Pool) and white matter tract-specific fractional anisotropy (FA) from diffusion tensor MRI. Results indicated that conscientiousness was associated with greater FA in the left uncinate fasciculus (β = 0.17, P < 0.001). We also examined links between FA and the personality meta-trait 'stability', which is defined as the common variance underlying agreeableness, conscientiousness, and neuroticism/emotional stability. We observed an association between left uncinate fasciculus FA and stability (β = 0.27, P < 0.001), which fully accounted for the link between left uncinate fasciculus FA and conscientiousness. In sum, these results provide novel evidence for links between regional white matter microstructure and key traits of human personality, specifically conscientiousness and the meta-trait, stability. Future research is recommended to replicate and address the causal directions of these associations.

Question: What is the uncinate fasciculus?


Heat-induced post-mortem defect of the skull simulating an exit gunshot wound of the calvarium.. A severely burned body was found in a burnt-out apartment following a house fire. At the death scene, police investigators noted a defect at the back of the head of the deceased. This defect perforated the occipital bone and showed external bevelling. To clarify whether this skull wound corresponded to a gunshot exit wound and for identification purposes, a medico-legal autopsy was ordered. External examination of the body showed intense charring with skin burned away and musculature exposed, fourth degree burns of the remaining skin and heat flexures of the limbs as well as heat-related rupture of both the abdominal cavity and chest wall (Fig. 1). An oval defect of the occipital bone with cratering of the external table of the cranium was found (Fig. 2). The defect measured 2 cm in diameter, was sharp-edged and the excavation of the outer table of the cranium showed no charring in contrast to the surrounding parts of the occipital bone (Fig. 3). Apart from this finding, radiology of the body before autopsy was unremarkable. At autopsy, gross sections of the mucosa of the trachea and main bronchi were covered by a thick layer of soot with soot particles also present within the esophageus and the stomach. The brain showed a reduced volume, flattening of gyri and obliteration of sulci but no signs of trauma. Except for the defect of the occipital bone, no other signs of ante-mortem or post-mortem trauma could be detected. The level of carboxyhemoglobin in heart blood was 65% and the blood alcohol level was 285 mg/dL.

Question: What is the difference between a gunshot wound and a skull defect?


Piranha: a scalable architecture based on single-chip multiprocessing. The microprocessor industry is currently struggling with higher development costs and longer design times that arise from exceedingly complex processors that are pushing the limits of instruction-level parallelism. Meanwhile, such designs are especially ill suited for important commercial applications, such as on-line transaction processing (OLTP), which suffer from large memory stall times and exhibit little instruction-level parallelism. Given that commercial applications constitute by far the most important market for high-performance servers, the above trends emphasize the need to consider alternative processor designs that specifically target such workloads. The abundance of explicit thread-level parallelism in commercial workloads, along with advances in semiconductor integration density, identify chip multiprocessing (CMP) as potentially the most promising approach for designing processors targeted at commercial servers.  This paper describes the Piranha system, a research prototype being developed at Compaq that aggressively exploits chip multi-processing by integrating eight simple Alpha processor cores along with a two-level cache hierarchy onto a single chip. Piranha also integrates further on-chip functionality to allow for scalable multiprocessor configurations to be built in a glueless and modular fashion. The use of simple processor cores combined with an industry-standard ASIC design methodology allow us to complete our prototype within a short time-frame, with a team size and investment that are an order of magnitude smaller than that of a commercial microprocessor. Our detailed simulation results show that while each Piranha processor core is substantially slower than an aggressive next-generation processor, the integration of eight cores onto a single chip allows Piranha to outperform next-generation processors by up to 2.9 times (on a per chip basis) on important workloads suc

Question: What is the difference between a research prototype and a commercial microprocessor?


Classification of Moral Foundations in Microblog Political Discourse. Previous works in computer science, as well as political and social science, have shown correlation in text between political ideologies and the moral foundations expressed within that text. Additional work has shown that policy frames, which are used by politicians to bias the public towards their stance on an issue, are also correlated with political ideology. Based on these associations, this work takes a first step towards modeling both the language and how politicians frame issues on Twitter, in order to predict the moral foundations that are used by politicians to express their stances on issues. The contributions of this work includes a dataset annotated for the moral foundations, annotation guidelines, and probabilistic graphical models which show the usefulness of jointly modeling abstract political slogans, as opposed to the unigrams of previous works, with policy frames for the prediction of the morality underlying political tweets.

Question: What is the classification of moral foundations in microblog political discourse?


Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC. The damped Gauss-Newton (dGN) algorithm for CANDECOMP /PARAFAC (CP) decomposition has been successfully applied for di fficult tensor factorization such as collinearity of factors, different magnitudes of factors. Nevertheless, for factorization of an N-D tensor of sizeI1 × . . . × IN with rank R, the algorithm is computationally demanding due to construction of large app roximate Hessian of size (( R ∑ n In) × (R ∑ n In)) and its inversion. In this paper, we propose a fast implementation o f the dGN algorithm which is based on novel expressions of the inverse approximate Hessian in block form. The new imp le entation has a lower computational complexity, which beside computation of the gradient (this part is commo n to both methods) involves inversion of a matrix of the sizeNR2 × NR2, much smaller than the Hessian, if n In ≫ NR. In addition, the implementation has a lower memory requirements, because neither the Hessian nor its in verse need to be stored entire in one time. A variant of the algorithm working with complex valued data is proposed a s well. Complexity and performance of the proposed algorithm is compared with those of dGN and ALS with line sear ch on examples with di fficult benchmark tensors.

Question: What is the complexity of the algorithm?


RevoMaker: Enabling Multi-directional and Functionally-embedded 3D printing using a Rotational Cuboidal Platform. In recent years, 3D printing has gained significant attention from the maker community, academia, and industry to support low-cost and iterative prototyping of designs. Current unidirectional extrusion systems require printing sacrificial material to support printed features such as overhangs. Furthermore, integrating functions such as sensing and actuation into these parts requires additional steps and processes to create "functional enclosures", since design functionality cannot be easily embedded into prototype printing. All of these factors result in relatively high design iteration times. We present "RevoMaker", a self-contained 3D printer that creates direct out-of-the-printer functional prototypes, using less build material and with substantially less reliance on support structures. By modifying a standard low-cost FDM printer with a revolving cuboidal platform and printing partitioned geometries around cuboidal facets, we achieve a multidirectional additive prototyping process to reduce the print and support material use. Our optimization framework considers various orientations and sizes for the cuboidal base. The mechanical, electronic, and sensory components are preassembled on the flattened laser-cut facets and enclosed inside the cuboid when closed. We demonstrate RevoMaker directly printing a variety of customized and fully-functional product prototypes, such as computer mice and toys, thus illustrating the new affordances of 3D printing for functional product design.

Question: What is a cuboidal platform?


Processing of Reference and the Structure of Language: An Analysis of Complex Noun Phrases. Five experiments used self-paced reading time to examine the ways in which complex noun phrases (both conjoined NPs and possessive NPs) inuence the interpretation of referentially dependent expressions. The experimental conditions contrasted the reading of repeated names and pronouns referring to components of a complex NP and to the entire complex NP. The results indicate that the entity introduced by a major constituent of a sentence is more accessible as a referent than the entities introduced by component noun phrases. This pattern of accessibility departs from the advantage of Žrst mention that has been demonstrated using probe-word recognition tasks. It supports the idea that reduced expressions are interpreted as referring directly to prominent entities in a mental model whereas reference by names to entities that are already represented in a mental model is mediated by additional processes. The same interpretive processes appear to operate on coreference within and between sentences.

Question: What is the difference between a noun phrase and a noun?


Integrated CMOS transmit-receive switch using LC-tuned substrate bias for 2.4-GHz and 5.2-GHz applications. CMOS transmit-receive (T/R) switches have been integrated in a 0.18-/spl mu/m standard CMOS technology for wireless applications at 2.4 and 5.2 GHz. This switch design achieves low loss and high linearity by increasing the substrate impedance of a MOSFET at the frequency of operation using a properly tuned LC tank. The switch design is asymmetric to accommodate the different linearity and isolation requirements in the transmit and receive modes. In the transmit mode, the switch exhibits 1.5-dB insertion loss, 28-dBm power, 1-dB compression point (P/sub 1dB/), and 30-dB isolation, at 2.4 and 5.2 GHz. In the receive mode, the switch achieves 1.6-dB insertion loss, 11.5-dBm P/sub 1dB/, and 15-dB isolation, at 2.4 and 5.2 GHz. The linearity obtained in the transmit mode is the highest reported to date in a standard CMOS process. The switch passes the 4-kV Human Body Model electrostatic discharge test. These results show that the switch design is suitable for narrow-band applications requiring a moderate-high transmitter power level (<1 W).

Question: What is the highest power level that can be transmitted?


A highly efficient power harvester with wide dynamic input power range for 900 MHz wireless power transfer applications. This work demonstrates the design of an adaptive reconfigurable rectifier to address the issue of early breakdown voltage in a conventional rectifier and extends the rectifier's operation for wide dynamic input power range. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for rectifier. This design accomplishes 40% of RF-DC power conversion efficiency over a wide dynamic input power range from -10 dBm to 27 dBm, while exhibiting 78% of peak power efficiency at 22 dBm. The power harvester is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer applications.

Question: What is the power harvester for?


The effect of experimental presentation of thin media images on body satisfaction: a meta-analytic review.. OBJECTIVE The effect of experimental manipulations of the thin beauty ideal, as portrayed in the mass media, on female body image was evaluated using meta-analysis.   METHOD Data from 25 studies (43 effect sizes) were used to examine the main effect of mass media images of the slender ideal, as well as the moderating effects of pre-existing body image problems, the age of the participants, the number of stimulus presentations, and the type of research design.   RESULTS Body image was significantly more negative after viewing thin media images than after viewing images of either average size models, plus size models, or inanimate objects. This effect was stronger for between-subjects designs, participants less than 19 years of age, and for participants who are vulnerable to activation of a thinness schema.   CONCLUSION Results support the sociocultural perspective that mass media promulgate a slender ideal that elicits body dissatisfaction. Implications for prevention and research on social comparison processes are considered.

Question: What is the effect of thin media images on body satisfaction?


Docker Layer Placement for On-Demand Provisioning of Services on Edge Clouds. Driven by the increasing popularity of the microservice architecture, we see an increase in services with unknown demand pattern located in the edge network. Pre-deployed instances of such services would be idle most of the time, which is economically infeasible. Also, the finite storage capacity limits the amount of deployed instances we can offer. Instead, we present an on-demand deployment scheme using the Docker platform. In Docker, service images consist of layers, each layer adding specific functionality. This allows different services to reuse layers, avoiding cluttering the storages with redundant replicas. We propose a layer placement method which allows users to connect to a server, retrieve all necessary layers -possibly from multiple locations- and deploy an instance of the requested service within the desired response time. We search for the best layer placement which maximizes the satisfied demand given the storage and delay constraints. We developed an iterative optimization heuristic which is less exhaustive by dividing the global problem in smaller subproblems. Our simulation results show that our heuristic is able to solve the problem with less system resources. Last, we present interesting use-cases to use this approach in real-life scenarios.

Question: What is the docker layer placement?


COID: A cluster–outlier iterative detection approach to multi-dimensional data analysis. Nowadays, most data mining algorithms focus on clustering methods alone. Also, there are a lot of approaches designed for outlier detection. We observe that, in many situations, clusters and outliers are concepts whose meanings are inseparable to each other, especially for those data sets with noise. Thus, it is necessary to treat both clusters and outliers as concepts of the same importance in data analysis. In this paper, we present our continuous work on the cluster–outlier iterative detection algorithm (Shi in SubCOID: exploring cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace. Auburn, pp. 132–135, 2008; Shi and Zhang in Towards exploring interactive relationship between clusters and outliers in multi-dimensional data analysis. IEEE Computer Society. Tokyo, pp. 518–519, 2005) to detect the clusters and outliers in another perspective for noisy data sets. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields, such as pattern recognition, data clustering, and signal processing. Experimental results demonstrate the advantages of our approach.

Question: What is the difference between cluster and outlier?


Advanced Metamorphic Techniques in Computer Viruses. Nowadays viruses use polymorphic techniques to mutate their code on each replication, thus evading detection by antiviruses. However detection by emulation can defeat simpl e polymorphism: thus metamorphic techniques are used which thoro ughly change the viral code, even after decryption. We briefly deta il this evolution of virus protection techniques against detectio n and then study the METAPHOR virus, today's most advanced metamorphic virus. Keywords—Computer virus, Viral mutation, Polymorphism, Metamorphism, MetaPHOR, Virus history, Obfuscation, Viral gen etic techniques

Question: What is the most advanced metamorphic virus?


QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks. Underwater sensor network (UWSN) has emerged in recent years as a promising networking technique for various aquatic applications. Due to specific characteristics of UWSNs, such as high latency, low bandwidth, and high energy consumption, it is challenging to build networking protocols for UWSNs. In this paper, we focus on addressing the routing issue in UWSNs. We propose an adaptive, energy-efficient, and lifetime-aware routing protocol based on reinforcement learning, QELAR. Our protocol assumes generic MAC protocols and aims at prolonging the lifetime of networks by making residual energy of sensor nodes more evenly distributed. The residual energy of each node as well as the energy distribution among a group of nodes is factored in throughout the routing process to calculate the reward function, which aids in selecting the adequate forwarders for packets. We have performed extensive simulations of the proposed protocol on the Aqua-sim platform and compared with one existing routing protocol (VBF) in terms of packet delivery rate, energy efficiency, latency, and lifetime. The results show that QELAR yields 20 percent longer lifetime on average than VBF.

Question: What is the Aqua-sim platform?


Taming THC: potential cannabis synergy and phytocannabinoid-terpenoid entourage effects.. Tetrahydrocannabinol (THC) has been the primary focus of cannabis research since 1964, when Raphael Mechoulam isolated and synthesized it. More recently, the synergistic contributions of cannabidiol to cannabis pharmacology and analgesia have been scientifically demonstrated. Other phytocannabinoids, including tetrahydrocannabivarin, cannabigerol and cannabichromene, exert additional effects of therapeutic interest. Innovative conventional plant breeding has yielded cannabis chemotypes expressing high titres of each component for future study. This review will explore another echelon of phytotherapeutic agents, the cannabis terpenoids: limonene, myrcene, α-pinene, linalool, β-caryophyllene, caryophyllene oxide, nerolidol and phytol. Terpenoids share a precursor with phytocannabinoids, and are all flavour and fragrance components common to human diets that have been designated Generally Recognized as Safe by the US Food and Drug Administration and other regulatory agencies. Terpenoids are quite potent, and affect animal and even human behaviour when inhaled from ambient air at serum levels in the single digits ng·mL(-1) . They display unique therapeutic effects that may contribute meaningfully to the entourage effects of cannabis-based medicinal extracts. Particular focus will be placed on phytocannabinoid-terpenoid interactions that could produce synergy with respect to treatment of pain, inflammation, depression, anxiety, addiction, epilepsy, cancer, fungal and bacterial infections (including methicillin-resistant Staphylococcus aureus). Scientific evidence is presented for non-cannabinoid plant components as putative antidotes to intoxicating effects of THC that could increase its therapeutic index. Methods for investigating entourage effects in future experiments will be proposed. Phytocannabinoid-terpenoid synergy, if proven, increases the likelihood that an extensive pipel

Question: What is the difference between a phytocannabinoid and a terpenoid?


Simple Bayesian Algorithms for Best Arm Identification. This paper considers the optimal adaptive allocation of measurement effort for identifying the best among a finite set of options or designs. An experimenter sequentially chooses designs to measure and observes noisy signals of their quality with the goal of confidently identifying the best design after a small number of measurements. I propose three simple Bayesian algorithms for adaptively allocating measurement effort. One is Top-Two Probability sampling, which computes the two designs with the highest posterior probability of being optimal, and then randomizes to select among these two. One is a variant a top-two sampling which considers not only the probability a design is optimal, but the expected amount by which its quality exceeds that of other designs. The final algorithm is a modified version of Thompson sampling that is tailored for identifying the best design. I prove that these simple algorithms satisfy a strong optimality property. In a frequestist setting where the true quality of the designs is fixed, one hopes the posterior definitively identifies the optimal design, in the sense that that the posterior probability assigned to the event that some other design is optimal converges to zero as measurements are collected. I show that under the proposed algorithms this convergence occurs at an exponential rate, and the corresponding exponent is the best possible among all allocation rules.

Question: What is the best arm identification algorithm?


Maternal Control of Child Feeding During the Weaning Period: Differences Between Mothers Following a Baby-led or Standard Weaning Approach. A controlling maternal feeding style has been shown to have a negative impact on child eating style and weight in children over the age of 12 months. The current study explores maternal feeding style during the period of 6–12 months when infants are introduced to complementary foods. Specifically it examines differences between mothers who choose to follow a traditional weaning approach using spoon feeding and pureés to mothers following a baby-led approach where infants are allowed to self feed foods in their solid form. Seven hundred and two mothers with an infant aged 6–12 months provided information regarding weaning approach alongside completing the Child Feeding Questionnaire. Information regarding infant weight and perceived size was also collected. Mothers following a baby-led feeding style reported significantly lower levels of restriction, pressure to eat, monitoring and concern over child weight compared to mothers following a standard weaning response. No association was seen between weaning style and infant weight or perceived size. A baby-led weaning style was associated with a maternal feeding style which is low in control. This could potentially have a positive impact upon later child weight and eating style. However due to the cross sectional nature of the study it cannot be ascertained whether baby-led weaning encourages a feeding style which is low in control to develop or whether mothers who are low in control choose to follow a baby-led weaning style.

Question: What is the difference between a baby-led and a standard weaning approach?


BigBench: towards an industry standard benchmark for big data analytics. There is a tremendous interest in big data by academia, industry and a large user base. Several commercial and open source providers unleashed a variety of products to support big data storage and processing. As these products mature, there is a need to evaluate and compare the performance of these systems.  In this paper, we present BigBench, an end-to-end big data benchmark proposal. The underlying business model of BigBench is a product retailer. The proposal covers a data model and synthetic data generator that addresses the variety, velocity and volume aspects of big data systems containing structured, semi-structured and unstructured data. The structured part of the BigBench data model is adopted from the TPC-DS benchmark, which is enriched with semi-structured and unstructured data components. The semi-structured part captures registered and guest user clicks on the retailer's website. The unstructured data captures product reviews submitted online. The data generator designed for BigBench provides scalable volumes of raw data based on a scale factor. The BigBench workload is designed around a set of queries against the data model. From a business prospective, the queries cover the different categories of big data analytics proposed by McKinsey. From a technical prospective, the queries are designed to span three different dimensions based on data sources, query processing types and analytic techniques.  We illustrate the feasibility of BigBench by implementing it on the Teradata Aster Database. The test includes generating and loading a 200 Gigabyte BigBench data set and testing the workload by executing the BigBench queries (written using Teradata Aster SQL-MR) and reporting their response times.

Question: What is the business model of bigbench?


Interrater reliability of a modified Ashworth scale of muscle spasticity.. We undertook this investigation to determine the interrater reliability of manual tests of elbow flexor muscle spasticity graded on a modified Ashworth scale. We each independently graded the elbow flexor muscle spasticity of 30 patients with intracranial lesions. We agreed on 86.7% of our ratings. The Kendall's tau correlation between our grades was .847 (p less than .001). Thus, the relationship between the raters' judgments was significant and the reliability was good. Although the results were limited to the elbow flexor muscle group, we believe them to be positive enough to encourage further trials of the modified Ashworth scale for grading spasticity.

Question: How reliable is the modified Ashworth scale?


Public Goods and Ethnic Divisions. We present a model that links heterogeneity of preferences across ethnic groups in a city to the amount and type of public good the city supplies. We test the implications of the model with three related data sets: U. S. cities, U. S. metropolitan areas, and U. S. urban counties. Results show that the shares of spending on productive public goods -education, roads, sewers and trash pickup -in U. S. cities (metro areas/urban counties) are inversely related to the city's (metro area's/county's) ethnic fragmentation, even after controlling for other socioeconomic and demographic determinants. We conclude that ethnic conflict is an important determinant of local public finances.

Question: How does ethnic fragmentation affect public spending?


Review on cyber-physical systems. Cyber-physical systems U+0028 CPS U+0029 are complex systems with organic integration and in-depth collaboration of computation, communications and control U+0028 3C U+0029 technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS.

Question: What is cyber-physical systems?


The human connectome: Origins and challenges. The human connectome refers to a map of the brain's structural connections, rendered as a connection matrix or network. This article attempts to trace some of the historical origins of the connectome, in the process clarifying its definition and scope, as well as its putative role in illuminating brain function. Current efforts to map the connectome face a number of significant challenges, including the issue of capturing network connectivity across multiple spatial scales, accounting for individual variability and structural plasticity, as well as clarifying the role of the connectome in shaping brain dynamics. Throughout, the article argues that these challenges require the development of new approaches for the statistical analysis and computational modeling of brain network data, and greater collaboration across disciplinary boundaries, especially with researchers in complex systems and network science.

Question: What is the human connectome?


Recurrent Neural Network Embedding for Knowledgebase Completion. Knowledge can often be represented using entities connected by relations. For example, the fact that tennis ball is round can be represented as "TennisBall HasShape Round", where a "TennisBall" is one entity, "HasShape" is a relation and "Round" is another entity. A knowledge base is a way to store such structured information, a knowledge base stores triples of the "an entity-relation-an entity" form, and a real world knowledge base often has millions or billions of such triples. There are several well-known knowledge bases including FreeBase [1], WordNet [2], YAGO [3], etc. They are important in fields like reasoning and question answering; for instance if one asks "what is the shape of a tennis ball", we can search the knowledge base for the triple as "TennisBall HasShape Round" and output "round" as the answer.

Question: What is the shape of a tennis ball?


Improving Diversity in Ranking using Absorbing Random Walks. We introduce a novel ranking algorithm called GRASSHOPPER, which ranks items with an emphasis on diversity. That is, the top items should be different from each other in order to have a broad coverage of the whole item set. Many natural language processing tasks can benefit from such diversity ranking. Our algorithm is based on random walks in an absorbing Markov chain. We turn ranked items into absorbing states, which effectively prevents redundant items from receiving a high rank. We demonstrate GRASSHOPPER's effectiveness on extractive text summarization: our algorithm ranks between the 1st and 2nd systems on DUC 2004 Task 2; and on a social network analysis task that identifies movie stars of the world.

Question: How to improve diversity in ranking?


Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach. It is well known that post-secondary science and engineering programs attract fewer female students. In this paper, we analyze gender differences through text mining of over 30,000 applications to the engineering faculty of a large North American university. We use syntactic and semantic analysis methods to highlight differences in motivation, interests and background. Our analysis leads to three main findings. First, female applicants demonstrate a wider breadth of experience, whereas male applicants put a greater emphasis on technical depth. Second, more female applicants demonstrate a greater desire to serve society. Third, female applicants are more likely to mention personal influences for studying engineering.

Question: What are the gender differences in undergraduate engineering applicants?


Sketching in circuits: designing and building electronics on paper. The field of new methods and techniques for building electronics is quickly growing - from research in new materials for circuit building, to modular toolkits, and more recently to untoolkits, which aim to incorporate more off-the-shelf parts. However, the standard mediums for circuit design and construction remain the breadboard, protoboard, and printed circuit board (PCB). As an alternative, we introduce a method in which circuits are hand-made on ordinary paper substrates, connected with conductive foil tape and off-the-shelf circuit components with the aim of supporting the durability, scalability, and accessibility needs of novice and expert circuit builders alike. We also used electrified notebooks to investigate how the circuit design and build process would be affected by the constraints and affordances of the bound book. Our ideas and techniques were evaluated through a series of workshops, through which we found our methods supported a wide variety of approaches and results - both technical and expressive - to electronics design and construction.

Question: How do you sketch in circuits?


Autonomous Stair Climbing for Tracked Vehicles. In this paper, we present an algorithm for autonomous stair climbing with a tracked vehicle. The proposed method achieves robust performance under real-world conditions, without assuming prior knowledge of the stair geometry, the dynamics of the vehicle's interaction with the stair surface, or lighting conditions. Our approach relies on fast and accurate estimation of the robot's heading and its position relative to the stair boundaries. An extended Kalman filter is used for quaternion-based attitude estimation, fusing rotational velocity measurements from a 3-axial gyroscope, and measurements of the stair edges acquired with an onboard camera. A twotiered controller, comprised of a centeringand a headingcontrol module, utilizes the estimates to guide the robot fast, safely, and accurately upstairs. Both the theoretical analysis and implementation of the algorithm are presented in detail, and extensive experimental results demonstrating the algorithm's performance are described.

Question: How to climb stairs?


Smoking and cervical cancer: pooled analysis of the IARC multi-centric case–control study. Background: Smoking has long been suspected to be a risk factor for cervical cancer. However, not all previous studies have properly controlled for the effect of human papillomavirus (HPV) infection, which has now been established as a virtually necessary cause of cervical cancer. To evaluate the role of smoking as a cofactor of progression from HPV infection to cancer, we performed a pooled analysis of 10 previously published case–control studies. This analysis is part of a series of analyses of cofactors of HPV in the aetiology of cervical cancer. Methods: Data were pooled from eight case–control studies of invasive cervical carcinoma (ICC) and two of carcinoma in situ (CIS) from four continents. All studies used a similar protocol and questionnaires and included a PCR-based evaluation of HPV DNA in cytological smears or biopsy specimens. Only subjects positive for HPV DNA were included in the analysis. A total of 1463 squamous cell ICC cases were analyzed, along with 211 CIS cases, 124 adeno- or adeno-squamous ICC cases and 254 control women. Pooled odds ratios (OR) and 95% confidence intervals (CI) were estimated using logistic regression models controlling for sexual and non-sexual confounding factors. Results: There was an excess risk for ever smoking among HPV positive women (OR 2.17 95%CI 1.46–3.22). When results were analyzed by histological type, an excess risk was observed among cases of squamous cell carcinoma for current smokers (OR 2.30, 95%CI 1.31–4.04) and ex-smokers (OR 1.80, 95%CI 0.95–3.44). No clear pattern of association with risk was detected for adenocarcinomas, although the number of cases with this histologic type was limited. Conclusions: Smoking increases the risk of cervical cancer among HPV positive women. The results of our study are consistent with the few previously conducted studies of smoking and cervical cancer that have adequately controlled

Question: What is the role of smoking in cervical cancer?


An OpenNaaS Based SDN Framework for Dynamic QoS Control. Network Service Providers should offer provisioning services with guaranteed Quality of Service (QoS), specifically adapted to the characteristics of the applications running on their network. In this paper we propose the Network Control Layer (NCL), a software framework solution based on Software Defined Networks (SDN), OpenFlow and Network as a Service (NaaS) paradigms. It addresses a major innovation area in the field of network control and management providing on-demand end-to-end network provisioning services with guaranteed QoS, based on the specific requirements of on-top running interactive applications. The NCL implementation is based on the OpenNaaS framework, and it includes mechanisms for network status monitoring and SDN switches configuration based on the interactive applications' QoS network requirements. We demonstrate NCL's utility in the context of control plane models making use of a practical use case.

Question: What is the difference between a network control layer and a network control plane?


THE EXPOSURE OF YOUTH TO UNWANTED SEXUAL MATERIAL ON THE INTERNET A National Survey of Risk , Impact , and Prevention. This national survey of youth, ages 10 to 17, and their caretakers has several implications for the current debate about young people and Internet pornography. Twentyfive percent of youth had unwanted exposure to sexual pictures on the Internet in the past year, challenging the prevalent assumption that the problem is primarily about young people motivated to actively seek out pornography. Most youth had no negative reactions to their unwanted exposure, but one quarter said they were very or extremely upset, suggesting a priority need for more research on and interventions directed toward such negative effects. The use of filtering and blocking software was associated with a modest reduction in unwanted exposure, suggesting that it may help but is far from foolproof. Various forms of parental supervision were not associated with any reduction in exposure. The authors urge that social scientific research be undertaken to inform this highly contentious public policy controversy.

Question: What is the prevalence of unwanted exposure to sexual material on the internet?


Real-Time Optical Flow Calculations on FPGA and GPU Architectures: A Comparison Study. FPGA devices have often found use as higher-performance alternatives to programmable processors for implementing a variety of computations. Applications successfully implemented on FPGAs have typically contained high levels of parallelism and have often used simple statically-scheduled control and modest arithmetic. Recently introduced computing devices such as coarse grain reconfigurable arrays, multi-core processors, and graphical processing units (GPUs) promise to significantly change the computational landscape for the implementation of high-speed real-time computing tasks. One reason for this is that these architectures take advantage of many of the same application characteristics that fit well on FPGAs. One real-time computing task, optical flow, is difficult to apply in robotic vision applications in practice because of its high computational and data rate requirements, and so is a good candidate for implementation on FPGAs and other custom computing architectures. In this paper, a tensor-based optical flow algorithm is implemented on both an FPGA and a GPU and the two implementations discussed. The two implementations had similar performance, but with the FPGA implementation requiring 12× more development time. Other comparison data for these two technologies is then given for three additional applications taken from a MIMO digital communication system design, providing additional examples of the relative capabilities of these two technologies.

Question: What is the difference between FPGA and GPU?


Amortized Efficiency of List Update and Paging Rules. In this article we study the amortized efficiency of the "move-to-front" and similar rules for dynamically maintaining a linear list. Under the assumption that accessing the ith element from the front of the list takes &thgr;(i) time, we show that move-to-front is within a constant factor of optimum among a wide class of list maintenance rules. Other natural heuristics, such as the transpose and frequency count rules, do not share this property. We generalize our results to show that move-to-front is within a constant factor of optimum as long as the access cost is a convex function. We also study paging, a setting in which the access cost is not convex. The paging rule corresponding to move-to-front is the "least recently used" (LRU) replacement rule. We analyze the amortized complexity of LRU, showing that its efficiency differs from that of the off-line paging rule (Belady's MIN algorithm) by a factor that depends on the size of fast memory. No on-line paging algorithm has better amortized performance.

Question: What is the amortized efficiency of the "least recently used" (LRU) replacement rule?


A real-time grading method of apples based on features extracted from defects. This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73 %. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.

Question: What is a hierarchical grading method?


The Effects of Personal Susceptibility and Social Support on Internet Addiction: an Application of Adler's Theory of Individual Psychology. The aim of the present study was to examine the addictive effects of personal susceptibility and social support from family, friends, and significant other on Internet addiction. In this study, 207 medical students completed the UCLA Loneliness Scale, the Academic Expectations Stress Inventory, the Multidimensional Scale of Perceived Social Support, and the Young's Diagnostic Questionnaire for Internet addiction. Participants were recruited via simple random sampling technique. Personal susceptibility variables such as loneliness, academic stress due to other expectations, and academic stress due to self-expectations were significantly positively correlated to, and all domains of social support were significantly negatively correlated to, Internet addiction. After adjusting for demographic variables, social support emerged as a significant predictor of Internet addiction in the hierarchical regression analysis. In support of Adler's individual psychology theory, the present findings suggest that social support from family is a valuable adjunct to prevention and intervention programs aimed at alleviating Internet addiction in medical students.

Question: How can I prevent internet addiction?


A Universal Denoising Framework With a New Impulse Detector and Nonlocal Means. Impulse noise detection is a critical issue when removing impulse noise and impulse/Gaussian mixed noise. In this paper, we propose a new detection mechanism for universal noise and a universal noise-filtering framework based on the nonlocal means (NL-means). The operation is carried out in two stages, i.e., detection followed by filtering. For detection, first, we propose the robust outlyingness ratio (ROR) for measuring how impulselike each pixel is, and then all the pixels are divided into four clusters according to the ROR values. Second, different decision rules are used to detect the impulse noise based on the absolute deviation to the median in each cluster. In order to make the detection results more accurate and more robust, the from-coarse-to-fine strategy and the iterative framework are used. In addition, the detection procedure consists of two stages, i.e., the coarse and fine detection stages. For filtering, the NL-means are extended to the impulse noise by introducing a reference image. Then, a universal denoising framework is proposed by combining the new detection mechanism with the NL-means (ROR-NLM). Finally, extensive simulation results show that the proposed noise detector is superior to most existing detectors, and the ROR-NLM produces excellent results and outperforms most existing filters for different noise models. Unlike most of the other impulse noise filters, the proposed ROR-NLM also achieves high peak signal-to-noise ratio and great image quality by efficiently removing impulse/Gaussian mixed noise.

Question: What is the universal denoising framework based on the nonlocal means?


WiFi-Nano: reclaiming WiFi efficiency through 800 ns slots. The increase in WiFi physical layer transmission speeds from 1~Mbps to 1 Gbps has reduced transmission times for a 1500 byte packet from 12 ms to 12 us. However, WiFi MAC overheads such as channel access and acks have not seen similar reductions and cumulatively contribute about 150 us on average per packet. Thus, the efficiency of WiFi has deteriorated from over 80% at 1 Mbps to under 10% at 1 Gbps.  In this paper, we propose WiFi-Nano, a system that uses 800 ns slots} to significantly improve WiFi efficiency. Reducing slot time from 9 us to 800 ns makes backoffs efficient, but clear channel assessment can no longer be completed in one slot since preamble detection can now take multiple slots. Instead of waiting for multiple slots for detecting preambles, nodes speculatively transmit preambles as their backoff counters expire, while continuing to detect premables using self-interference cancellation. Upon detection of preambles from other transmitters, nodes simply abort their own preamble transmissions, thereby allowing the earliest transmitter to succeed. Further, receivers speculatively transmit their ack preambles at the end of packet reception, thereby reducing ack overhead. We validate the effectiveness of WiFi-Nano through implementation on an FPGA-based software defined radio platform, and through extensive simulations, demonstrate efficiency gains of up to 100%.

Question: How does WiFi-Nano improve WiFi efficiency?


User modeling with personas. User demographic and behavior data obtained from real user observation provide valuable information for designers. Yet, such information can be misinterpreted if presented as statistic figures. Personas are fictitious user representations created in order to embody behaviors and motivations that a group of real users might express, representing them during the project development process. This article describes the persona as being an effective tool to the users' descriptive model.

Question: What is a persona?


Cooperative flood detection using GSMD via SMS. This paper proposes architecture for an early warning floods system to alert public against flood disasters. An effective early warning system must be developed with linkages between four elements, which are accurate data collection to undertake risk assessments, development of hazard monitoring services, communication on risk related information and existence of community response capabilities. This project focuses on monitoring water level remotely using wireless sensor network. The project also utilizes Global System for Mobile communication (GSM) and short message service (SMS) to relay data from sensors to computers or directly alert the respective victim's through their mobile phone. It is hope that the proposed architecture can be further develop into a functioning system, which would be beneficial to the community and act as a precautionary action to save lives in the case of flood disaster.

Question: What is the purpose of this project?


Dialog Act Modeling for Conversational Speech. We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42`dialog acts' which were hand-labeled in 1155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We developed several models and algorithms to automatically detect dialog acts from transcribed or automatically recognized words and from prosodic properties of the speech signal, and by using a statistical discourse grammar. All of these components were probabilistic in nature and estimated from data, employing a variety of techniques (hidden Markov models, N-gram language models, maximum entropy estimation, decision tree classiiers, and neural networks). In preliminary studies, we achieved a dialog act labeling accuracy of 65% based on recognized words and prosody, and an accuracy of 72% based on word transcripts. Since humans achieve 84% on this task (with chance performance at 35%) we nd these results encouraging.

Question: What is the dialog act modeling for conversational speech?


TagAssist: Automatic Tag Suggestion for Blog Posts. In this paper, we describe a system called TagAssist that provides tag suggestions for new blog posts by utilizing existing tagged posts. The system is able to increase the quality of suggested tags by performing lossless compression over existing tag data. In addition, the system employs a set of metrics to evaluate the quality of a potential tag suggestion. Coupled with the ability for users to manually add tags, TagAssist can ease the burden of tagging and increase the utility of retrieval and browsing systems built on top of tagging data.

Question: What is a tag suggestion?


Biobanks: transnational, European and global networks.. Biobanks contain biological samples and associated information that are essential raw materials for advancement of biotechnology, human health, and research and development in life sciences. Population-based and disease-oriented biobanks are major biobank formats to establish the disease relevance of human genes and provide opportunities to elucidate their interaction with environment and lifestyle. The developments in personalized medicine require molecular definition of new disease subentities and biomarkers for identification of relevant patient subgroups for drug development. These emerging demands can only be met if biobanks cooperate at the transnational or even global scale. Establishment of common standards and strategies to cope with the heterogeneous legal and ethical landscape in different countries are seen as major challenges for biobank networks. The Central Research Infrastructure for Molecular Pathology (CRIP), the concept for a pan-European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI), and the Organization for Economic Co-operation and Development (OECD) global Biological Resources Centres network are examples for transnational, European and global biobank networks that are described in this article.

Question: What is a biobank?


Personal projects, happiness, and meaning: on doing well and being yourself.. Personal Projects Analysis (B. R. Little, 1983) was adapted to examine relations between participants' appraisals of their goal characteristics and orthogonal happiness and meaning factors that emerged from factor analyses of diverse well-being measures. In two studies with 146 and 179 university students, goal efficacy was associated with happiness and goal integrity was associated with meaning. A new technique for classifying participants according to emergent identity themes is introduced. In both studies, identity-compensatory predictors of happiness were apparent. Agentic participants were happiest if their goals were supported by others, communal participants were happiest if their goals were fun, and hedonistic participants were happiest if their goals were being accomplished. The distinction between happiness and meaning is emphasized, and the tension between efficacy and integrity is discussed. Developmental implications are discussed with reference to results from archival data from a sample of senior managers.

Question: What is the difference between happiness and meaning?


Ezetimibe - a new approach in hypercholesterolemia management.. Suchy et al. [10] discuss the benefits of ezetimibe beyond lipid-lowering as well as its effects on atherosclerosis. Some comments may be of interest. Reducing low density lipoprotein cholesterol (LDL-C) is the main indication for ezetimibe [2, 7, 8]. This drug is useful in patients who cannot achieve LDL-C goals on statins or those who are statin intolerant [2, 4, 7, 8]. Meta-analyses suggested that adding ezetimibe decreases LDL-C levels more than statin monotherapy, after doubling the dose of a statin or selecting a more potent statin [2, 7, 8]. It was also suggested that ezetimibe can increase LDL particle size, especially in patients with high triglyceride levels [3, 11]. Ezetimibe may exert other beneficial actions which may or may not be related to its hypolipidemic capacity (e.g., for non-alcoholic fatty liver disease and renoprotection) [6]. Ezetimibe monotherapy reduced the plasma mass and activity of lipoprotein-associated phospholipase A2 (Lp-PLA2), which is considered risk predictor of cardiovascular (CV) events [9]. Furthermore, ezetimibe may lower the levels of oxidative stress markers [5]. In the end, the most important issue is reducing vascular events. In this context, the Study of Heart and Renal Protection (SHARP) [1] reported that the combination of ezetimibe with simvastatin was associated with reduced risk of CV events compared with placebo after 4.9 years in patients (n = 9,270) who are pre-dialysis or on dialysis. The clinical relevance of these points and those addressed by Suchy et al. [10] requires to be established by future studies.

Question: What is the clinical relevance of the points addressed by Suchy et al. [10]?