Mobile application usability testing in quasi-real conditions. The paper presents the setup and results of a mobile application usability test using mobile eye-tracking glasses. Quasi-real conditions of the natural environment were assured for the testing in order to avoid incidental disorders possible in natural conditions and the weaknesses of a fully lab experiment. The experiment methodology was developed and used to carry out a usability test of the mobile part of a Sale Force Automation (SFA) application in terms of its basic functionality. The paper's outlines are the following: the methodology used, arrangement and realisation of the usability testing experiment, test results and answers to the research questions. A list of general conclusions rounds off the report.

Question: What is quasi-real conditions?


An Experimental Comparison of RGB, YIQ, LAB, HSV, and Opponent Color Models. The increasing availability of affordable color raster graphics displays has made it important to develop a better understanding of how color can be used effectively in an interactive environment. Most contemporary graphics displays offer a choice of some 16 million colors; the user's problem is to find the right color. Folklore has it that the RGB color space arising naturally from color display hardware is user-hostile and that other color models such as the HSV scheme are preferable. Until now there has been virtually no experimental evidence addressing this point. We describe a color matching experiment in which subjects used one of two tablet-based input techniques, interfaced through one of five color models, to interactively match target colors displayed on a CRT. The data collected show small but significant differences between models in the ability of subjects to match the five target colors used in this experiment. Subjects using the RGB color model matched quickly but inaccurately compared with those using the other models. The largest speed difference occurred during the early convergence phase of matching. Users of the HSV color model were the slowest in this experiment, both during the convergence phase and in total time to match, but were relatively accurate. There was less variation in performance during the second refinement phase of a match than during the convergence phase. Two-dimensional use of the tablet resulted in faster but less accurate performance than did strictly one-dimensional usage. Significant learning occurred for users of the Opponent, YIQ, LAB, and HSV color models, and not for users of the RGB color model.

Question: What color model is best for color matching?


Quality evaluation of product reviews using an information quality framework. Available online 27 August 2010

Question: What is the quality of product reviews?


Simplifying and improving mobile based data collection. Social programs often require collection of demographic and program related information. Pen-paper surveys have been the most favorable way of collecting such information. However, with proliferation of smartphones, low cost mobile connectivity with good coverage and availability of several data collection applications that can even work around the connectivity concerns, pen-paper surveys are now being replaced by mobile based data collection. In this work, we discuss the enhancement of Open Data Kit (ODK), an existing mobile based data collection application, with features such as pre-filling and validation SMS. The additional features were motivated from the real world requirements of reducing the data collection effort and minimizing the discrepancies in records for a project requiring information dissemination to beneficiaries of National Rural Employment Guarantee Scheme (NREGS), a government of India scheme. Data collection exercise, using our extended tool, is currently ongoing in two districts across two different states in India. Preliminary results from one deployment indicate that even with 8% of pre-filled fields in a form, there was a 16.1% decrease in the errors. Additionally, time per survey reduced with the pre-filling option. Preliminary results from another deployment indicate that 35% of the pre-filled forms had mismatched or changed information when compared with the data available in government records.

Question: How to simplify and improve mobile based data collection?


Design and fabrication of an autonomous fire fighting robot with multisensor fire detection using PID controller. Recently, Multisensor Fire Detection System (MSFDS) is one of the important research issues. Here, a fire fighter robot is fabricated providing extinguishment platform. The base of the robot is made of the wood of `Rashed tree', locally known as `Kerosene wood'. There is about 1 liter water reserving capacity. An arduino based simple algorithm is used for detection of fire and measurement of distance from fire source while the robot is on its way to extinguish fire. When the fire is detected and the robot is at a distance near to fire, a centrifugal pump is used to throw water for extinguishment purpose. A water spreader is used for effective extinguishing. It is seen that velocity of water is greatly reduced due to the use of water spreader. Two sensors: LM35 and Arduino Flame Sensors are used to detect the fire and distances on its way towards fire. Sensitivity of these sensors at different day times and distances is tested through analog reading of the serial monitor.

Question: How to design a fire fighting robot?


Preference for Nature in Urbanized Societies : Stress , Restoration , and the Pursuit of Sustainability. Urbanicity presents a challenge for the pursuit of sustainability. High settlement density may offer some environmental, economic, and social advantages, but it can impose psychological demands that people find excessive. These demands of urban life have stimulated a desire for contact with nature through suburban residence, leading to planning and transportation practices that have profound implications for the pursuit of sustainability. Some might dismiss people's desire for contact with nature as the result of an anti-urban bias in conjunction with a romantic view of nature. However, research in environmental psychology suggests that people's desire for contact with nature serves an important adaptive function, namely, psychological restoration. Based on this insight, we offer a perspective on an underlying practical challenge: designing communities that balance settlement density with satisfactory access to nature experience. We discuss research on four issues: how people tend to believe that nature is restorative; how restoration needs and beliefs shape environmental preferences; how well people actually achieve restoration in urban and natural environments; and how contact with nature can promote health. In closing, we consider urban nature as a design option that promotes urban sustainability.

Question: How can we design cities that promote sustainability?


Automatic Optimization Computational Method for Unconventional S . W . A . T . H . Ships Resistance. The paper illustrates the main theoretical and computational aspects of an automatic computer based procedure for the parametric shape optimization of a particular unconventional hull typology: that for a catamaran S.W.A.T.H. ship. The goal of the integrated computational procedure is to find the best shape of the submerged hulls of a new U.S.V. (Unmanned Surface Vehicle) S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel, in terms of minimum wave pattern resistance. After dealing with the theoretical aspects the papers presents the numerical aspects of the main software module of the automatic procedure, which integrates a parametric generation routine for innovative and unconventional S.W.A.T.H. (Small Waterplane Area Twin Hull) vessel geometry, a multi-objective, globally convergent and constrained, optimization algorithm and a Computational Fluid Dynamic (C.F.D.) solver. The integrated process is able to find the best shape of the submerged hull of the vessel, subject to the total displaced volume constraint. The hydrodynamic computation is carried out by means of a free surface potential flow method and it is addressed to find the value of wave resistance of each hull variant. Results of the application of the described computational procedure are presented for two optimization cases and the obtained best shapes are compared with a conventional one, featuring a typical torpedo-shaped body, proving the effectiveness of the method in reducing the resistance by a considerable extent, in the order of 40 percent. Keywords—S.W.A.T.H., B.E.M., Wave Resistance, Parametric Modeling, Optimization, Genetic Algorithms

Question: What is the best shape of a ship?


A Faceted Approach to Building Ontologies. Rubén Prieto-Díaz Commonwealth Information Security Center James Madison University prietodiaz@cisat.jmu.edu

Question: What is a faceted approach to building ontologies?


Explaining and Harnessing Adversarial Examples. Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed input results in the model outputting an incorrect answer with high confidence. Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. We argue instead that the primary cause of neural networks' vulnerability to adversarial perturbation is their linear nature. This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Moreover, this view yields a simple and fast method of generating adversarial examples. Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset.

Question: What is the most interesting fact about neural networks?


A Survey of Indoor Positioning and Object Locating Systems. This paper investigated various indoor positioning techniques and presented a comprehensive study about their advantages and disadvantages. Infrared, Ultrasonic and RF technologies are used in different indoor positioning systems. RFID positioning systems based on RSSI technology are the most recent developments. Positioning accuracy was greatly improved by using integrated RFID technologies.

Question: What is the most recent development in indoor positioning?


ACCEPTANCE AND COMMITMENT THERAPY ( ACT ) IN PANIC DISORDER WITH AGORAPHOBIA : A CASE STUDY.

Question: What is ACT?


Viewpoint-independent book spine segmentation. We propose a method to precisely segment books on bookshelves in images taken from general viewpoints. The proposed segmentation algorithm overcomes difficulties due to text and texture on book spines, various book orientations under perspective projection, and book proximity. A shape dependent active contour is used as a first step to establish a set of book spine candidates. A subset of these candidates are selected using spatial constraints on the assembly of spine candidates by formulating the selection problem as the maximal weighted independent set (MWIS) of a graph. The segmented book spines may be used by recognition systems (e.g., library automation), or rendered in computer graphics applications. We also propose a novel application that uses the segmented book spines to assist users in bookshelf reorganization or to modify the image to create a bookshelf with a tidier look. Our method was successfully tested on challenging sets of images.

Question: How to segment books on bookshelves in images taken from general viewpoints?


Word Embedding-based Antonym Detection using Thesauri and Distributional Information. This paper proposes a novel approach to train word embeddings to capture antonyms. Word embeddings have shown to capture synonyms and analogies. Such word embeddings, however, cannot capture antonyms since they depend on the distributional hypothesis. Our approach utilizes supervised synonym and antonym information from thesauri, as well as distributional information from large-scale unlabelled text data. The evaluation results on the GRE antonym question task show that our model outperforms the state-of-the-art systems and it can answer the antonym questions in the F-score of 89%.

Question: What is the state-of-the-art system?


Bluetooth Based Home Automation and Security System Using ARM 9. -Today we are living in 21 century where automation is playing important role in human life. Home automation allows us to control household appliances like light, door, fan, AC etc. It also provides home security and emergency system to be activated. Home automation not only refers to reduce human efforts but also energy saving and time efficiency. The main objective of home automation and security is to help handicapped and old aged people who will enable them to control home appliances and alert them in critical situations. This paper put forwards the design of home automation and security system using ARM7 LPC2148 board. The design is based on a standalone embedded system board ARM7 LPC2148 at home. Home appliances are connected to the ARM7 and communication is established between the ARM7 and ARM9 with Bluetooth device. The home appliances are connected to the input / output ports of the embedded system board and their status is passed to the ARM7. We would develop an authentication to the system for authorized person to access home appliances. The device with low cost and scalable to less modification to the core is much important. It presents the design and implementation of automation system that can monitor and control home appliances via ARM9 S3C2440A board.

Question: What is the difference between ARM7 and ARM9?


A support vector approach for cross-modal search of images and texts. Building bilateral semantic associations between images and texts is among the fundamental problems in computer vision. In this paper, we study two complementary cross-modal prediction tasks: (i) predicting text(s) given a query image ("Im2Text"), and (ii) predicting image(s) given a piece of text ("Text2Im"). We make no assumption on the specific form of text; i.e., it could be either a set of labels, phrases, or even captions. We pose both these tasks in a retrieval framework. For Im2Text, given a query image, our goal is to retrieve a ranked list of semantically relevant texts from an independent text-corpus (i.e., texts with no corresponding images). Similarly, for Text2Im, given a query text, we aim to retrieve a ranked list of semantically relevant images from a collection of unannotated images (i.e., images without any associated textual meta-data). We propose a novel Structural SVM based unified framework for these two tasks, and show how it can be efficiently trained and tested. Using a variety of loss functions, extensive experiments are conducted on three popular datasets (two medium-scale datasets containing few thousands of samples, and one web-scale dataset containing one million samples). Experiments demonstrate that our framework gives promising results compared to competing baseline cross-modal search techniques, thus confirming its efficacy.

Question: What is the difference between a support vector and a structural support vector?


Brain MRI Findings of Nitrogen Gas Inhalation for Suicide Attempt: a Case Report. According to the Organization for Economic Cooperation and Development (OECD) report in 2015, South Korea had the highest suicide rate among all countries that belong to the OECD. In contrast to the pattern in most OECD countries, death rates from suicide in Korea have risen significantly in the last decade (1). Recently, several organizations and internet communities in favor of assisted suicide have promoted the use of nitrogen (N2) gas to that end (2). Nitrogen gas has caused accidental deaths in industrial or laboratory explosion, and during scuba diving and anesthesia (2). Although it is reported that industrial nitrogen asphyxiation hazards resulted in 80 deaths during the period 1992 through 2002, there is a paucity of documentation regarding nitrogen gas as a means of committing suicide (2, 3). Nitrogen is a colorless, odorless, nontoxic, and generally inert gas that is a normal component (78.09%) of the atmosphere, at standard temperature and pressure (4). However, nitrogen can be hazardous when it displaces oxygen resulting in hypoxic damage (2, 3). Nitrogen intoxication manifests with various symptoms such as progressive fatigue, loss of coordination, purposeful movement and balance, nausea, a complete inability to move and unconsciousness (2, 4). Here, we describe a case of brain magnetic resonance imaging (MRI) findings associated with nitrogen gas inhalation, which have been rarely reported previously. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Received: September 18, 2017 Revised: October 1, 2017 Accepted: October 12, 2017

Question: What is the difference between nitrogen and oxygen?


Truncated Importance Sampling for Reinforcement Learning with Experience Replay. Reinforcement Learning (RL) is considered here as an adaptation technique of neural controllers of machines. The goal is to make Actor-Critic algorithms require less agent-environment interaction to obtain policies of the same quality, at the cost of additional background computations. We propose to achieve this goal in the spirit of experience replay. An estimation method of improvement direction of a changing policy, based on preceding experience, is essential here. We propose one that uses truncated importance sampling. We derive bounds of bias of that type of estimators and prove that this bias asymptotically vanishes. In the experimental study we apply our approach to the classic ActorCritic and obtain 20-fold increase in speed of learning.

Question: What is the difference between Actor-Critic and Actor-Critic with Experience Replay?


Modeling and Simulating Apache Spark Streaming Applications. Stream processing systems are used to analyze big data streams with low latency. The performance in terms of response time and throughput is crucial to ensure all arriving data are processed in time. This depends on various factors such as the complexity of used algorithms and configurations of such distributed systems and applications. To ensure a desired system behavior, performance evaluations should be conducted to determine the throughput and required resources in advance. In this paper, we present an approach to predict the response time of Apache Spark Streaming applications by modeling and simulating them. In a preliminary controlled experiment, our simulation results suggest accurate prediction values for an upscaling scenario.

Question: How to predict the response time of Apache Spark Streaming applications?


Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network. The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The experimental results are compared against the existing machine learning and deep learning–based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.

Question: Breast cancer classification with deep learning


A Coplanar Reconfigurable Folded Slot Antenna Without Bias Network for WLAN Applications. The design of a reconfigurable single folded slot antenna is shown. Metal strips are used in the slot to manipulate the ground size around the slot, which yields to changing the slot's perimeter and thus changing the resonant frequency of the antenna. The design of a single folded slot antenna is first illustrated. Later, the design is modified to be reconfigurable. The resonant frequencies for the reconfigurable design are chosen to be applicable with the WLAN applications. The antenna design, simulation, and measurements are described. The simulated results both for the return loss and maximum gain agree with the measurements. The antenna has similar radiation patterns in both bands. The advantage of this design is that the length of the bias lines does not affect the antenna performance, thus making its design, feeding, and matching an extremely simple and low-cost procedure for antenna designers.

Question: What is a reconfigurable folded slot antenna?


Signal Characteristics on Sensor Data Compression in IoT -An Investigation. In Internet of Things (IoT), numerous and diverse types of sensors generate a plethora of data that needs to be stored and processed with minimum loss of information. This demands efficient compression mechanisms where loss of information is minimized. Hence data generated by diverse sensors with different signal features require optimum balance between compression gain and information loss. This paper presents a unique analysis of contemporary lossy compression algorithms applied on real field sensor data with different sensor dynamics. The aim of the work is to classify the compression algorithms based on the signal characteristics of sensor data and to map them to different sensor data types to ensure efficient compression. The present work is the stepping stone for a future recommender system to choose the preferred compression techniques for the given type of sensor data.

Question: What is the difference between lossy and lossless compression?


Fast-and-Light Stochastic ADMM. The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.

Question: What is the fast convergence benefits of SAG-ADMM and SDCA-ADMM?


Ensemble Algorithms in Reinforcement Learning. This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and final performance by combining the chosen actions or action probabilities of different RL algorithms. We designed and implemented four different ensemble methods combining the following five different RL algorithms: Q-learning, Sarsa, actor-critic (AC), QV-learning, and AC learning automaton. The intuitively designed ensemble methods, namely, majority voting (MV), rank voting, Boltzmann multiplication (BM), and Boltzmann addition, combine the policies derived from the value functions of the different RL algorithms, in contrast to previous work where ensemble methods have been used in RL for representing and learning a single value function. We show experiments on five maze problems of varying complexity; the first problem is simple, but the other four maze tasks are of a dynamic or partially observable nature. The results indicate that the BM and MV ensembles significantly outperform the single RL algorithms.

Question: What is reinforcement learning?


Semi-Supervised Detection of Extreme Weather Events in Large Climate Datasets. The detection and identification of extreme weather events in large scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, there are many different types of spatially localized climate patterns of interest (including hurricanes, extra-tropical cyclones, weather fronts, blocking events, etc.) found in simulation data for which labeled data is not available at large scale for all simulations of interest. We present a multichannel spatiotemporal encoder-decoder CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. This architecture is designed to fully model multichannel simulation data, temporal dynamics and unlabelled data within a reconstruction and prediction framework so as to improve the detection of a wide range of extreme weather events. Our architecture can be viewed as a 3D convolutional autoencoder with an additional modified one-pass bounding box regression loss. We demonstrate that our approach is able to leverage temporal information and unlabelled data to improve localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data, and facilitate further work in understanding and mitigating the effects of climate change.

Question: What is the canadian military?


Time-of-Flight sensor calibration for accurate range sensing. Over the past years Time-of-Flight (ToF) sensors have become a considerable alternative to conventional distance sensing techniques like laser scanners or image based stereo-vision. Due to the ability to provide full-range distance information at high frame-rates, ToF sensors achieve a significant impact onto current research areas like online object recognition, collision prevention or scene and object reconstruction. Nevertheless, ToF-cameras like the Photonic Mixer Device (PMD) still exhibit a number of error sources that affect the accuracy of measured distance information. For this reason, major error sources for ToFcameras will be discussed, along with a new calibration approach that combines intrinsic, distance as well as a reflectivity related error calibration in an overall, easy to use system and thus significantly reduces the number of necessary reference images. The main contribution, in this context, is a new intensity-based calibration model that requires less input data compared to other models and thus significantly contributes to the reduction of calibration data. 2010 Elsevier Inc. All rights reserved.

Question: What is the difference between a ToF sensor and a laser scanner?


Clique Graphs and Overlapping Communities. It is shown how to construct a clique graph in which properties of cliques of a fixed order in a given graph are represented by vertices in a weighted graph. Various definitions and motivations for these weights are given. The detection of communities or clusters is used to illustrate how a clique graph may be exploited. In particular a benchmark network is shown where clique graphs find the overlapping communities accurately while vertex partition methods fail. PACS numbers: 89.75.Hc, 89.75.Fb, 89.75.-k

Question: What is a clique graph?


Modeling and Optimization of Building Mix and Energy Supply Technology for Urban Districts. Reducing the energy consumption and associated greenhouse gas emissions of urban areas is paramount in research and practice, encompassing strategies to both reduce energy consumption and carbon intensity in both energy supply and demand. Most methods focus on one of these two approaches but few integrate decisions for supply and demand simultaneously. This paper presents a novel model that endogenously simulates energy supply and demand at a district scale on an hourly time scale. Demand is specified for a variety of building uses, and losses and municipal loads are calculated from the number of buildings in the district. Energy supply is modeled using technology-specific classes, allowing easy addition of specific equipment or types of energy generation. Standard interfaces allow expansion of the model to include new types of energy supply and demand. The model can be used for analysis of a single design alternative or optimization over a large design space, allowing exploration of various densities, mixes of uses, and energy supply technologies. An example optimization is provided for a community near San Francisco, California. This example uses 21 building types, 32 combined heat and power engines, and 16 chillers. The results demonstrate the ability to compare performance tradeoffs and optimize for three objectives: life cycle cost, annual carbon dioxide emissions, and overall system

Question: What is the model called?


4D Light Field Superpixel and Segmentation. Superpixel segmentation of 2D image has been widely used in many computer vision tasks. However, limited to the Gaussian imaging principle, there is not a thorough segmentation solution to the ambiguity in defocus and occlusion boundary areas. In this paper, we consider the essential element of image pixel, i.e., rays in the light space and propose light field superpixel (LFSP) segmentation to eliminate the ambiguity. The LFSP is first defined mathematically and then a refocus-invariant metric named LFSP self-similarity is proposed to evaluate the segmentation performance. By building a clique system containing 80 neighbors in light field, a robust refocus-invariant LFSP segmentation algorithm is developed. Experimental results on both synthetic and real light field datasets demonstrate the advantages over the state-of-the-arts in terms of traditional evaluation metrics. Additionally the LFSP self-similarity evaluation under different light field refocus levels shows the refocus-invariance of the proposed algorithm.

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


N-Party Encrypted Diffie-Hellman Key Exchange Using Different Passwords. We consider the problem of password-authenticated group Diffie-Hellman key exchange among N parties, N−1 clients and a singleserver, using different passwords. Most password-authenticated key exchange schemes in the literature have focused on an authenticated key exchange using a shared password between a client and a server. With a rapid change in modern communication environment such as ad-hoc networks and ubiquitous computing, it is necessary to construct a secure end-to-end channel between clients, which is a quite different paradigm from the existing ones. To achieve this end-to-end security, only a few schemes of three-party setting have been presented where two clients exchange a key using their own passwords with the help of a server. However, up until now, no formally treated and round efficient protocols which enable group members to generate a common session key with clients' distinct passwords have been suggested. In this paper we securely and efficiently extend three-party case to Nparty case with a formal proof of security. Two provably secure N-party EKE protocols are suggested; N-party EKE-U in the unicast network and N-party EKE-M in the multicast network. The proposed N-party EKE-M is provable secure and provides forward secrecy. Especially, the scheme is of constant-round, hence scalable and practical.

Question: What is the difference between a unicast and a multicast network?


Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10−7). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.

Question: What is the difference between bipolar disorder and schizophrenia?


Turning telecommunications call details to churn prediction: a data mining approach. As deregulation, stew technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers: A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory.

Question: What is churn prediction?


Word2vec applied to recommendation: hyperparameters matter. Skip-gram with negative sampling, a popular variant of Word2vec originally designed and tuned to create word embeddings for Natural Language Processing, has been used to create item embeddings with successful applications in recommendation. While these fields do not share the same type of data, neither evaluate on the same tasks, recommendation applications tend to use the same already tuned hyperparameters values, even if optimal hyperparameters values are often known to be data and task dependent. We thus investigate the marginal importance of each hyperparameter in a recommendation setting through large hyperparameter grid searches on various datasets. Results reveal that optimizing neglected hyperparameters, namely negative sampling distribution, number of epochs, subsampling parameter and window-size, significantly improves performance on a recommendation task, and can increase it by an order of magnitude. Importantly, we find that optimal hyper-parameters configurations for Natural Language Processing tasks and Recommendation tasks are noticeably different.

Question: What is the difference between recommendation and natural language processing?


A Secure Color Image Steganography In Transform Domain. Steganography is the art and science of covert communication. The secret information can be concealed in content such as image, audio, or video. This paper provides a novel image steganography technique to hide both image and key in color cover image using Discrete Wavelet Transform (DWT) and Integer Wavelet Transform (IWT). There is no visual difference between the stego image and the cover image. The extracted image is also similar to the secret image. This is proved by the high PSNR (Peak Signal to Noise Ratio), value for both stego and extracted secret image. The results are compared with the results of similar techniques and it is found that the proposed technique is simple and gives better PSNR values than others.

Question: What is a secure color image steganography in transform domain?


Low power/low voltage high speed CMOS differential track and latch comparator with rail-to-rail input. A new CMOS differential latched comparator suitable for low voltage, low-power application is presented. The circuit consists of a constant-gm rail-to-rail common-mode operational transconductance amplifier followed by a regenerative latch in a track and latch configuration to achieve a relatively constant delay. The use of a track and latch minimizes the total number of gain stages required for a given resolution. Potential offset from the constant-gm differential input stage, estimated as the main source of offset, can be minimized by proper choice of transistors sizes. Simulation results show that the circuit requires less than 86 μA with a supply voltage of 1.65 V in a standard CMOS 0.18 μm digital process. The average delay is less than 1 ns and is approximately independent of the common-mode input voltage.

Question: What is a CMOS differential latch comparator?


Why Your Encrypted Database Is Not Secure. Encrypted databases, a popular approach to protecting data from compromised database management systems (DBMS's), use abstract threat models that capture neither realistic databases, nor realistic attack scenarios. In particular, the "snapshot attacker" model used to support the security claims for many encrypted databases does not reflect the information about past queries available in any snapshot attack on an actual DBMS.  We demonstrate how this gap between theory and reality causes encrypted databases to fail to achieve their "provable security" guarantees.

Question: What is the difference between a snapshot attack and a real attack?


How Positive Informational Social Influence Affects Consumers' Decision of Internet Shopping?. Given the amount of perceived risk involved in Internet shopping, many potential Internet shoppers tend to wait and observe the experiences of others who have tried it before considering adopting it. This study explores how positive informational social influence affects consumers' decision of Internet shopping using a laboratory experiment. The research results reveal that positive informational social influence reinforces the relationships between perceived ease of use and consumer attitude toward Internet shopping, as well as attitude and their intention to shop. Implications for the current investigation and future research directions are provided.

Question: How positive informational social influence affects consumers' decision of Internet shopping?


A 1.2 V Reactive-Feedback 3.1–10.6 GHz Low-Noise Amplifier in 0.13 $\mu{\hbox {m}}$  CMOS. A 15.1 dB gain, 2.1 dB (min.) noise figure low-noise amplifier (LNA) fabricated in 0.13 mum CMOS operates across the entire 3.1-10.6 GHz ultrawideband (UWB). Noise figure variation over the band is limited to 0.43 dB. Reactive (transformer) feedback reduces the noise figure, stabilizes the gain, and sets the terminal impedances over the desired bandwidth. It also provides a means of separating ESD protection circuitry from the RF input path. Bias current-reuse limits power consumption of the 0.87mm2 IC to 9 mW from a 1.2 V supply. Comparable measured results are presented from both packaged and wafer probed test samples

Question: What is the noise figure of the amplifier?


Evaluation of Objective Quality Measures for Speech Enhancement. In this paper, we evaluate the performance of several objective measures in terms of predicting the quality of noisy speech enhanced by noise suppression algorithms. The objective measures considered a wide range of distortions introduced by four types of real-world noise at two signal-to-noise ratio levels by four classes of speech enhancement algorithms: spectral subtractive, subspace, statistical-model based, and Wiener algorithms. The subjective quality ratings were obtained using the ITU-T P.835 methodology designed to evaluate the quality of enhanced speech along three dimensions: signal distortion, noise distortion, and overall quality. This paper reports on the evaluation of correlations of several objective measures with these three subjective rating scales. Several new composite objective measures are also proposed by combining the individual objective measures using nonparametric and parametric regression analysis techniques.

Question: What is the objective quality measure?


Multimodal Classification for Analysing Social Media. Classification of social media data is an important approach in understanding user behavior on the Web. Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality. Techniques that are able to leverage multiple modalities are often complex and susceptible to the absence of some modalities. In this paper, we present simple models that combine information from different modalities to classify social media content and are able to handle the above problems with existing techniques. Our models combine information from different modalities using a pooling layer and an auxiliary learning task is used to learn a common feature space. We demonstrate the performance of our models and their robustness to the missing of some modalities in the emotion classification domain. Our approaches, although being simple, can not only achieve significantly higher accuracies than traditional fusion approaches but also have comparable results when only one modality is available.

Question: What is the pooling layer?


The Research of PHP Development Framework Based on MVC Pattern. PHP is one of the leading web development languages, however, the development model of existing PHP organizes without a structure, which mixes the code of data access, the processing of business logic , and web presentation layer together, as a relult, it brought about many problems in the web applications , meanwhile, it could not meet the rapid development of web apply any more. In this paper, a implementation of PHP based on MVC design patterns - FDF framework was provided for PHP developers, which can offer a framework for web applications, separate the data, view and control of web applications, afford to achieve loose coupling, thereby enhanced the efficiency, reliability, maintainability and scalability of application development.

Question: What is the research of PHP development framework based on MVC pattern?


The development of the SGI-16: a shortened sensory gating deficit and distractibility questionnaire for adults with ADHD.. The Sensory Gating Inventory (SGI) is a questionnaire composed of 36 items designed to investigate abnormal perception related to the inability to control sensitivity to sensory stimuli frequently reported in adult with ADHD. This questionnaire can be considered too lengthy to be taken by people with ADHD, and a shortened version is needed. One hundred and sixty-three adults with ADHD responded to the SGI-36. An item reduction process took into account both the results of statistical analyses and the expertise of a steering committee. Construct validity, reliability, and external validity were tested for a short version (16 items). The structure of the SGI-16 was confirmed by principal components factor analysis. Cronbach's alpha coefficients ranged from 0.78 to 0.89. The SGI-16 dimension scores were highly correlated with their respective SGI-36 dimension scores. The SGI-16 seems to be both appropriate and useful for use in clinical practice to investigate perceptual abnormalities in adults with ADHD.

Question: What is the shortened version of the SGI?


Innovation and practice of continuous auditing. Article history: Received 27 August 2010 Received in revised form 23 December 2010 Accepted 6 January 2011 The traditional audit paradigm is outdated in the real time economy. Innovation of the traditional audit process is necessary to support real time assurance. Practitioners and academics are exploring continuous auditing as a potential successor to the traditional audit paradigm. Using technology and automation, continuous auditing methodology enhances the efficiency and effectiveness of the audit process to support real time assurance. This paper defines how continuous auditing methodology introduces innovation to practice in seven dimensions and proposes a four-stage paradigm to advance future research. In addition, we formulate a set of methodological propositions concerning the future of assurance for practitioners and academic researchers. © 2011 Elsevier Inc. All rights reserved.

Question: What is continuous auditing?


A Comparative Study of Demographic Attribute Inference in Twitter. Social media platforms have become a major gateway to receive and analyze public opinions. Understanding users can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes, such as ethnicity, gender, age, among others, have been extensively applied to characterize social media users. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles. Previous work has demonstrated the effectiveness of different user signals such as users' posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users' posts and train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals: self-descriptions and posts aggregated from users' friends and followers, users' profile images, and users' names. We provide a comparative study of these signals side-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age. We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training models and evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self-descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation.

Question: What is the difference between a self-description and a profile image?


Acquiring Hyponymy Relations from Web Documents. This paper describes an automatic method for acquiring hyponymy relations from HTML documents on the WWW. Hyponymy relations can play a crucial role in various natural language processing systems. Most existing acquisition methods for hyponymy relations rely on particular linguistic patterns, such as " NP such as NP". Our method, however, does not use such linguistic patterns, and we expect that our procedure can be applied to a wide range of expressions for which existing methods cannot be used. Our acquisition algorithm uses clues such as itemization or listing in HTML documents and statistical measures such as document frequencies and verb-noun co-occurrences.

Question: Acquiring hyponymy relations from web documents


Question: How to detect muscle fatigue?


Particle swarm optimization for hyper-parameter selection in deep neural networks. Deep neural networks (DNNs) have achieved unprecedented success in a wide array of tasks. However, the performance of these systems depends directly on their hyper-parameters which often must be selected by an expert. Optimizing the hyper-parameters remains a substantial obstacle in designing DNNs in practice. In this work, we propose to select them using particle swarm optimization (PSO). Such biologically-inspired approaches have not been extensively exploited for this task. We demonstrate that PSO efficiently explores the solution space, allowing DNNs of a minimal topology to obtain competitive classification performance over the MNIST dataset. We showed that very small DNNs optimized by PSO retrieve promising classification accuracy for CIFAR-10. Also, PSO improves the performance of existing architectures. Extensive experimental study, backed-up with the statistical tests, revealed that PSO is an effective technique for automating hyper-parameter selection and efficiently exploits computational resources.

Question: What is the smallest DNN that can be trained to classify MNIST?


Software transformations to improve malware detection. Malware is code designed for a malicious purpose, such as obtaining root privilege on a host. A malware detector identifies malware and thus prevents it from adversely affecting a host. In order to evade detection, malware writers use various obfuscation techniques to transform their malware. There is strong evidence that commercial malware detectors are susceptible to these evasion tactics. In this paper, we describe the design and implementation of a malware transformer that reverses the obfuscations performed by a malware writer. Our experimental evaluation demonstrates that this malware transformer can drastically improve the detection rates of commercial malware detectors.

Question: How does a malware detector work?


Code Conjurer: Pulling Reusable Software out of Thin Air. For many years, the IT industry has sought to accelerate the software development process by assembling new applications from existing software assets. However, true component-based reuse of the form Douglas Mcllroy envisaged in the 1960s is still the exception rather than the rule, and most of the systematic software reuse practiced today uses heavyweight approaches such as product-line engineering or domain-specific frameworks. By component, we mean any cohesive and compact unit of software functionality with a well-defined interface - from simple programming language classes to more complex artifacts such as Web services and Enterprise JavaBeans.

Question: What is component-based software reuse?


Flexible dry surface-electrodes for ECG long-term monitoring. In this paper a new generation of dry electrodes for ECG-recording purposes is presented. These electrodes are flexible, biocompatible, biostable and perform a dry signal acquisition without the use of any electrolyte gel or adhesive. Thereby no skin preparation is needed. The material properties allow integration into textiles to form intelligent clothes for long-term monitoring duties. The electrodes can be washed together with the carrier textiles and provide a very good wearing comfort resulting in wide acceptance by the patients. The Department Medical Engineering and Neuroprosthetics of the Fraunhofer Institute for Biomedical Engineering developed these electrodes mainly to process the Electrocardiogram, however, in first tests they proved their capability even for other biosignal requirements like surface-EMG. In either case the signal quality of the dry electrodes is comparable to commercial Ag/AgCl gel-electrodes.

Question: What is the difference between a dry electrode and a gel electrode?


An Engineering Model for Color Difference as a Function of Size. This work describes a first step towards the creation of an engineering model for the perception of color difference as a function of size. Our approach is to non-uniformly rescale CIELAB using data from crowdsourced experiments, such as those run on Amazon Mechanical Turk. In such experiments, the inevitable variations in viewing conditions reflect the environment many applications must run in. Our goal is to create a useful model for design applications where it is important to make colors distinct, but for which a small set of highly distinct colors is inadequate.

Question: How to create an engineering model for color difference as a function of size?


Learning How to Combine Internal and External Denoising Methods. Different methods for image denoising have complementary strengths and can be combined to improve image denoising performance, as has been noted by several authors [11, 7]. Mosseri et al. [11] distinguish between internal and external methods depending whether they exploit internal or external statistics [13]. They also propose a rule-based scheme (PatchSNR) to combine these two classes of algorithms. In this paper, we test the underlying assumptions and show that many images might not be easily split into regions where internal methods or external methods are preferable. Instead we propose a learning based approach using a neural network, that automatically combines denoising results from an internal and from an external method. This approach outperforms both other combination methods and state-of-the-art stand-alone image denoising methods, hereby further closing the gap to the theoretically achievable performance limits of denoising [9]. Our denoising results can be replicated with a publicly available toolbox.

Question: How to combine internal and external denoising methods?


AlterEgo: A Personalized Wearable Silent Speech Interface. We present a wearable interface that allows a user to silently converse with a computing device without any voice or any discernible movements - thereby enabling the user to communicate with devices, AI assistants, applications or other people in a silent, concealed and seamless manner. A user's intention to speak and internal speech is characterized by neuromuscular signals in internal speech articulators that are captured by the AlterEgo system to reconstruct this speech. We use this to facilitate a natural language user interface, where users can silently communicate in natural language and receive aural output (e.g - bone conduction headphones), thereby enabling a discreet, bi-directional interface with a computing device, and providing a seamless form of intelligence augmentation. The paper describes the architecture, design, implementation and operation of the entire system. We demonstrate robustness of the system through user studies and report 92% median word accuracy levels.

Question: What is a wearable interface?


A new ART-counterpropagation neural network for solving a forecasting problem. This study presents a novel Adaptive resonance theory-Counterpropagation neural network (ART-CPN) for solving forecasting problems. The network is based on the ART concept and the CPN learning algorithm for constructing the neural network. The vigilance parameter is used to automatically generate the nodes of the cluster layer for the CPN learning process. This process improves the initial weight problem and the adaptive nodes of the cluster layer (Kohonen layer). ART-CPN involves real-time learning and is capable of developing a more stable and plastic prediction model of input patterns by self-organization. The advantages of ART-CPN include the ability to cluster, learn and construct the network model for forecasting problems. The network was applied to solve the real forecasting problems. The learning algorithm revealed better learning efficiency and good prediction performance. q 2004 Elsevier Ltd. All rights reserved.

Question: What is the difference between ART and CPN?


Characterization of evoked tactile sensation in forearm amputees with transcutaneous electrical nerve stimulation.. OBJECTIVE The goal of this study is to characterize the phenomenon of evoked tactile sensation (ETS) on the stump skin of forearm amputees using transcutaneous electrical nerve stimulation (TENS).   APPROACH We identified the projected finger map (PFM) of ETS on the stump skin in 11 forearm amputees, and compared perceptual attributes of the ETS in nine forearm amputees and eight able-bodied subjects using TENS. The profile of perceptual thresholds at the most sensitive points (MSPs) in each finger-projected area was obtained by modulating current amplitude, pulse width, and frequency of the biphasic, rectangular current stimulus. The long-term stability of the PFM and the perceptual threshold of the ETS were monitored in five forearm amputees for a period of 11 months.   MAIN RESULTS Five finger-specific projection areas can be independently identified on the stump skin of forearm amputees with a relatively long residual stump length. The shape of the PFM was progressively similar to that of the hand with more distal amputation. Similar sensory modalities of touch, pressure, buzz, vibration, and numb below pain sensation could be evoked both in the PFM of the stump skin of amputees and in the normal skin of able-bodied subjects. Sensory thresholds in the normal skin of able-bodied subjects were generally lower than those in the stump skin of forearm amputees, however, both were linearly modulated by current amplitude and pulse width. The variation of the MSPs in the PFM was confined to a small elliptical area with 95% confidence. The perceptual thresholds of thumb-projected areas were found to vary less than 0.99 × 10(-2) mA cm(-2).   SIGNIFICANCE The stable PFM and sensory thresholds of ETS are desirable for a non-invasive neural interface that can feed back finger-specific tactile information from the prosthetic hand to forearm amputees.

Question: What is the difference between a prosthetic hand and a prosthetic arm?


Global Learning of Typed Entailment Rules. Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.

Question: What is the largest data set of typed entailment rules?


A Configurable 12–237 kS/s 12.8 mW Sparse-Approximation Engine for Mobile Data Aggregation of Compressively Sampled Physiological Signals. Compressive sensing (CS) is a promising technology for realizing low-power and cost-effective wireless sensor nodes (WSNs) in pervasive health systems for 24/7 health monitoring. Due to the high computational complexity (CC) of the reconstruction algorithms, software solutions cannot fulfill the energy efficiency needs for real-time processing. In this paper, we present a 12-237 kS/s 12.8 mW sparse-approximation (SA) engine chip that enables the energy-efficient data aggregation of compressively sampled physiological signals on mobile platforms. The SA engine chip integrated in 40 nm CMOS can support the simultaneous reconstruction of over 200 channels of physiological signals while consuming (1% of a smartphone's power budget. Such energyefficient reconstruction enables two-to-three times energy saving at the sensor nodes in a CS-based health monitoring system as compared to traditional Nyquist-based systems, while providing timely feedback and bringing signal intelligence closer to the user.

Question: What is the energy efficiency of the sparse approximation engine?


Cognitive load and detection thresholds in car following situations: safety implications for using mobile (cellular) telephones while driving.. This study was aimed at investigating drivers' ability to detect a car ahead decelerating, while doing mobile phone related tasks. Nineteen participants aged between 20 and 29 years, (2000-125000 km driving experience) drove at 80 km/h, 50 m behind a lead car, on a 30 km section of motorway in normal traffic. During each trial the lead car started to decelerate at an average of 0.47 m/s2 while the participant either looked at the car in front (control), continuously dialed series of three random integers on a numeric keypad (divided visual attention), or performed a memory and addition task (non-visual attention). The results indicated that drivers' detection ability was impaired by about 0.5 s in terms of brake reaction time and almost 1 s in terms of time-to-collision, when they were doing the non-visual task whilst driving. This impairment was similar to when the drivers were dividing their visual attention between the road ahead and dialing numbers on the keypad. It was concluded that neither a hands-free option nor a voice controlled interface removes the safety problems associated with the use of mobile phones in a car.

Question: How many people are in the car?


Digital Entrepreneurship of Born Digital and Grown Digital Firms: Comparing the Effectuation Process of Yihaodian and Suning. This paper examines digital entrepreneurship in born digital and grown digital firms. Of late, digital entrepreneurship has garnered renewed attention from national leaders, practitioners, and academics due to the phenomenal success of a number of digital startups and the radical disruptions brought by the innovative use of technologies. Despite the significance and the promises of digital technology, especially amid the gloomy economy, digital entrepreneurship has remained inadequately analyzed for its predominant focus on entrepreneurs (human agency) and newly founded firms (context). This study offers an in-depth analysis of two entrepreneurial firms, Yihaodian and Suning, which are among the largest e-commerce players in China. By adopting effectuation as our theoretical lens, this study aims to generate an understanding of the digital entrepreneurial process in both born digital and grown digital firms.

Question: What is the effectuation process?


Improving Text Classification by Shrinkage in a Hierarchy of Classes. When documents are organized in a large number of topic categories, the categories are often arranged in a hierarchy. The U.S. patent database and Yahoo are two examples. This paper shows that the accuracy of a naive Bayes text classi er can be signi cantly improved by taking advantage of a hierarchy of classes. We adopt an established statistical technique called shrinkage that smoothes parameter estimates of a data-sparse child with its parent in order to obtain more robust parameter estimates. The approach is also employed in deleted interpolation, a technique for smoothing n-grams in language modeling for speech recognition. Our method scales well to large data sets, with numerous categories in large hierarchies. Experimental results on three real-world data sets from UseNet, Yahoo, and corporate web pages show improved performance, with a reduction in error up to 29% over the traditional at classi er.

Question: How to improve text classification by shrinkage in a hierarchy of classes?


Assistive Robotic Exoskeleton for Helping Limb Girdle Muscular Dystrophy. The Limb Girdle Muscular Dystrophy (LGMD) is a heterogeneous group of muscle diseases, this is manifested by a progressive loss of the strength of the muscles of the pelvic and shoulders girdles. Several devices that can compensate for the loss of some motors skills have been developed in support for the activities of patients presenting such dystrophy. Those devices include the pole or treadmill that can be used with an exoskeleton to improve the phase of displacement among other activities. This work shows a new mechanical design for a robotic-exoskeleton with seven movement axis, having as purpose to help people, with certain LGMD dystrophy or diseases characteristics, in generate movements of flexion/extension in the lower extremity joints and realize the gait cycle. All of the design movement's analysis is simulated in Solidworks® and OpenSim® software.

Question: What is the purpose of this project?


Proposed embedded security framework for Internet of Things (IoT). IoT is going to be an established part of life by extending the communication and networking anytime, anywhere. Security requirements for IoT will certainly underline the importance of properly formulated, implemented, and enforced security policies throughout their life-cycle. This paper gives a detailed survey and analysis of embedded security, especially in the area of IoT. Together with the conventional security solutions, the paper highlights the need to provide in-built security in the device itself to provide a flexible infrastructure for dynamic prevention, detection, diagnosis, isolation, and countermeasures against successful breaches. Based on this survey and analysis, the paper defines the security needs taking into account computational time, energy consumption and memory requirements of the devices. Finally, this paper proposes an embedded security framework as a feature of software/hardware co-design methodology.

Question: What is the security needs taking into account computational time, energy consumption and memory requirements of the devices?


Real-time object tracking via CamShift-based robust framework. In recent years, lots of object tracking methods have been presented for better tracking accuracies. However, few of them can be applied to the real-time applications due to high computational cost. Aiming at achieving better realtime tracking performance, we propose an adaptive robust framework for object tracking based on the CamShift approach, which is notable for its simplicity and high processing efficiency. An adaptive local search method is presented to search for the best object candidate to avoid that the CamShift tracker gets confused by the surrounding background and erroneously incorporates it into the object region. A Kalman filter is also incorporated into our framework for prediction of the object's movement, so as to reduce the search effort and possible tracking failure caused by fast object motion. The experimental results demonstrate that the proposed tracking framework is robust and computationally effective.

Question: What is the real-time object tracking via CamShift-based robust framework?


Game Theory and Distributed Control. Game theory has been employed traditionally as a modeling tool for describing and influencing behavior in societal systems. Recently, game theory has emerged as a valuable tool for controlling or prescribing behavior in distributed engineered systems. The rationale for this new perspective stems from the parallels between the underlying decision making architectures in both societal systems and distributed engineered systems. In particular, both settings involve an interconnection of decision making elements whose collective behavior depends on a compilation of local decisions that are based on partial information about each other and the state of the world. Accordingly, there is extensive work in game theory that is relevant to the engineering agenda. Similarities notwithstanding, there remain important differences between the constraints and objectives in societal and engineered systems that require looking at game theoretic methods from a new perspective. This chapter provides an overview of selected recent developments of game theoretic methods in this role as a framework for distributed control in engineered systems.

Question: What is the difference between game theory and distributed control?


Aspect extraction in sentiment analysis: comparative analysis and survey. Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user's sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

Question: What is aspect extraction?


Small footprint implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement. For effective speech processing in an automotive environment, speech enhancement is necessary due to significant levels of background noise. In this paper, we present a cost effective small footprint implementation of one particular speech enhancement technique: dual microphone delay-and-sum beamforming. In order to save resources, the implementation utilizes the overlapping frame property used in speech processing systems. The implementation also exhibits a simple interconnection structure leading to even greater resource saving. Experiment results show that the proposed design can produce enhanced output very close to that generated by a theoretical (floating-point) model while only requiring a modest hardware resource usage.

Question: What is the cost of speech enhancement?


Cyclic codes over the ring 𝔽p[u, v] / 〈uk, v2, uv-vu〉. Article history: Received 4 June 2014 Received in revised form 29 December 2014 Accepted 13 January 2015 Available online xxxx Communicated by W. Cary Huffman

Question: What is the article history of this paper?


Deep Networks with Stochastic Depth. Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highly desirable at test time, training very deep networks comes with its own set of challenges. The gradients can vanish, the forward flow often diminishes, and the training time can be painfully slow. To address these problems, we propose stochastic depth, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time. We start with very deep networks but during training, for each mini-batch, randomly drop a subset of layers and bypass them with the identity function. This simple approach complements the recent success of residual networks. It reduces training time substantially and improves the test error significantly on almost all data sets that we used for evaluation. With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4.91% on CIFAR-10).

Question: How deep can a convolutional network be?


On the bipolarity of positive and negative affect.. Is positive affect (PA) the bipolar opposite of, or is it independent of, negative affect (NA)? Previous analyses of this vexing question have generally labored under the false assumption that bipolarity predicts an invariant latent correlation between PA and NA. The predicted correlation varies with time frame, response format, and items selected to define PA and NA. The observed correlation also varies with errors inherent in measurement. When the actual predictions of a bipolar model are considered and error is taken into account, there is little evidence for independence of what were traditionally thought opposites. Bipolarity provides a parsimonious fit to existing data.

Question: What is the bipolarity of positive and negative affect?


Historical Document Layout Analysis Competition. This paper presents an objective comparative evaluation of layout analysis methods for scanned historical documents. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2011 and the International Workshop on Historical Document Imaging and Processing (HIP2011), presenting the results of the evaluation of four submitted methods. A commercial state-of-the-art system is also evaluated for comparison. Two scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and the other evaluating the whole pipeline of segmentation and region classification (with a text extraction goal). The results indicate that there is a convergence to a certain methodology with some variations in the approach. However, there is still a considerable need to develop robust methods that deal with the idiosyncrasies of historical documents.

Question: What is the objective of the historical document layout analysis competition?


A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. With the advent of Web 2.0, people became more eager to express and share their opinions on web regarding day-to-day activities and global issues as well. Evolution of social media has also contributed immensely to these activities, thereby providing us a transparent platform to share views across the world. These electronic Word of Mouth (eWOM) statements expressed on the web are much prevalent in business and service industry to enable customer to share his/her point of view. In the last one and half decades, research communities, academia, public and service industries are working rigorously on sentiment analysis, also known as, opinion mining, to extract and analyze public mood and views. In this regard, this paper presents a rigorous survey on sentiment analysis, which portrays views presented by over one hundred articles published in the last decade regarding necessary tasks, approaches, and applications of sentiment analysis. Several sub-tasks need to be performed for sentiment analysis which in turn can be accomplished using various approaches and techniques. This survey covering published literature during 2002-2015, is organized on the basis of sub-tasks to be performed, machine learning and natural language processing techniques used and applications of sentiment analysis. The paper also presents open issues and along with a summary table of a hundred and sixty one articles.

Question: What is opinion mining?


Word Sense Induction for Novel Sense Detection. We apply topic modelling to automatically induce word senses of a target word, and demonstrate that our word sense induction method can be used to automatically detect words with emergent novel senses, as well as token occurrences of those senses. We start by exploring the utility of standard topic models for word sense induction (WSI), with a pre-determined number of topics (=senses). We next demonstrate that a non-parametric formulation that learns an appropriate number of senses per word actually performs better at the WSI task. We go on to establish state-of-the-art results over two WSI datasets, and apply the proposed model to a novel sense detection task.

Question: What is the utility of topic models for word sense induction?


Predictive maintenance techniques. This article discusses the importance of PdM for industrial process applications and investigates a number of emerging technologies that enable this approach, including online energy-efficiency evaluation and continuous condition monitoring. The article gives an overview of existing and future technologies that can be used in these areas. Two methods for bearing fault detection and energy-efficiency estimation are discussed. The article concludes with focus on one pilot installation at Weyerhaeuser's Containerboard Packaging Plant in Manitowoc, Wisconsin, USA, monitoring three critical induction motors: a 75-hrho blower motor, a 50-hrho hydraulic pump motor, and a 200-hp compressor motor. Finally, the field experience gained in this plant is presented as two case studies.

Question: What is predictive maintenance?


Sharing Visual Features for Multiclass and Multiview Object Detection. We consider the problem of detecting a large number of different classes of objects in cluttered scenes. Traditional approaches require applying a battery of different classifiers to the image, at multiple locations and scales. This can be slow and can require a lot of training data since each classifier requires the computation of many different image features. In particular, for independently trained detectors, the (runtime) computational complexity and the (training-time) sample complexity scale linearly with the number of classes to be detected. We present a multitask learning procedure, based on boosted decision stumps, that reduces the computational and sample complexity by finding common features that can be shared across the classes (and/or views). The detectors for each class are trained jointly, rather than independently. For a given performance level, the total number of features required and, therefore, the runtime cost of the classifier, is observed to scale approximately logarithmically with the number of classes. The features selected by joint training are generic edge-like features, whereas the features chosen by training each class separately tend to be more object-specific. The generic features generalize better and considerably reduce the computational cost of multiclass object detection

Question: How to detect a large number of classes in a cluttered scene?


Existential meaning's role in the enhancement of hope and prevention of depressive symptoms.. The authors confirmed that existential meaning has a unique relationship with and can prospectively predict levels of hope and depressive symptoms within a population of college students. Baseline measures of explicit meaning (i.e., an individual's self-reported experience of a sense of coherence and purpose in life) and implicit meaning (i.e., an individual's self-reported embodiment of the factors that are normatively viewed as comprising a meaningful life) explained significant amounts of variance in hope and depressive symptoms 2 months later beyond the variance explained by baseline levels of hope/depression, neuroticism, conscientiousness, agreeableness, openness to experience, extraversion, and social desirability. The authors discuss implications of these findings for the field of mental health treatment and suggest ways of influencing individuals' experience of existential meaning.

Question: What is the relationship between existential meaning and hope?


Twitter spam detection based on deep learning. Twitter spam has long been a critical but difficult problem to be addressed. So far, researchers have developed a series of machine learning-based methods and blacklisting techniques to detect spamming activities on Twitter. According to our investigation, current methods and techniques have achieved the accuracy of around 80%. However, due to the problems of spam drift and information fabrication, these machine-learning based methods cannot efficiently detect spam activities in real-life scenarios. Moreover, the blacklisting method cannot catch up with the variations of spamming activities as manually inspecting suspicious URLs is extremely time-consuming. In this paper, we proposed a novel technique based on deep learning techniques to address the above challenges. The syntax of each tweet will be learned through WordVector Training Mode. We then constructed a binary classifier based on the preceding representation dataset. In experiments, we collected and implemented a 10-day real Tweet datasets in order to evaluate our proposed method. We first studied the performance of different classifiers, and then compared our method to other existing text-based methods. We found that our method largely outperformed existing methods. We further compared our method to non-text-based detection techniques. According to the experiment results, our proposed method was more accurate.

Question: How to detect spam on twitter?


OCR and RFID enabled vehicle identification and parking allocation system. The available parking management systems require human efforts for recording entries of coming and leaving vehicles from parking in sheets. For huge parking it is difficult to keep track of the information. Use of Radio Frequency Identification known as RFID technology reduces human efforts and Optical Character Recognition known as OCR, enabled system will provide an automated system for parking management. And also it will provide the control over the access of parking space by the use of boom barriers. For huge parking it will be an effective system. Parking can be a commercial one or can be a Very Important Person (VIP) parking. Depending on the usage system can be used. Vehicle cannot be parked in parking if it is not registered. OCR acts as a solution for vehicles which are not registered. To keep track of the happenings in parking cameras are used. Places where automated systems are required, the proposed system is an answer. System combines the two strong technologies RFID and OCR.

Question: What is the difference between OCR and RFID?


Millimeter-wave antenna in package on low-cost organic substrate for flip chip chip scale (FCCSP) package. This work proposes a broadband microstrip patch antenna array with H-shaped aperture-coupled stacked rectangular patches is implemented to low-cost and low-loss organic substrate for flip chip chip scale (FCCSP) package for millimeter-wave antenna in package (AiP) application. The proposed antenna array is built on a low-cost and low-loss multilayer organic substrate for flip chip chip scale (FCCSP) package. The return loss of < −10 dB bandwidth of the proposed antenna array is from 55 to 69 GHz, which covers the requirement of IEEE 802.11ad standard. The cross-polarization levels in both and planes are better than 30 dB. The front-to-back ratio of the antenna radiation pattern is better than 20 dB. The prospoed microstrip patch antenna can achieve a wide bandwidth, low cross-polarization levels, low backward radiation levels, and easy integration with radio frequency (RF) circuits. This work provides a cost-effective approach for fifth-generation mobile communications (5G), IEEE 802.11ad, such as smart phone, laptops, and wireless router.

Question: What is the difference between a microstrip patch antenna and a patch antenna?


Introduction to Information Retrieval Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schtze (Stanford University, Yahoo! Research, and University of Stuttgart) Cambridge: Cambridge University Press, 2008, xxi+482 pp; hardbound, ISBN 978-0-521-86571-5, $60.00. Introduction to Information Retrieval is the first textbook with a coherent treatment of classical and web information retrieval, including web search and the related areas of text classification and text clustering. Written from a computer science perspective, it gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents and of methods for evaluating systems, along with an introduction to the use of machine learning methods on text collections. Designed as the primary text for a graduate or advanced undergraduate course in information retrieval, the book will also interest researchers and professionals. A complete set of lecture slides and exercises that accompany the book are available on the web.

Question: What is the first textbook on information retrieval?


BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings. This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledgebased Word Sense Disambiguation (WSD) method based on sense embeddings. Punbased jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation.

Question: What is the best way to split the context?


3D printing: Basic concepts mathematics and technologies. 3D printing is the process of being able to print any object layer by layer. But if we question this proposition, can we find any three dimensional objects that can't be printed layer by layer? To banish any disbeliefs we walked together through the mathematics that prove 3d printing is feasible for any real life object. 3d printers create three dimensional objects by building them up layer by layer. The current generation of 3d printers typically requires input from a CAD program in the form of an STL file, which defines a shape by a list of triangle vertices. The vast majority of 3d printers use two techniques, FDM (Fused Deposition Modelling) and PBP (Powder Binder Printing). One advanced form of 3d printing that has been an area of increasing scientific interest the recent years is bioprinting. Cell printers utilizing techniques similar to FDM were developed for bioprinting. These printers give us the ability to place cells in positions that mimic their respective positions in organs. Finally through series of case studies we show that 3d printers in medicine have made a massive breakthrough lately.

Question: What is the difference between 3d printing and bioprinting?


Design of a smart parking system using wireless sensor network. In this article it is included the design of a smart parking system, the base was the current system "Sistema integrado de Estacionamiento (SIMERT)" and the mobility in Loja - Ecuador, with the use of a sensor network and wireless technology based in Zigbee 900 and Digimesh 2.4 GHz. To the feasibility of the project it was considered a technological platform that could be adapted to the city conditions and that be friendly with the environment, furthermore the suggested solution gives us connectivity with a Web application for integrated parking and usability of administrators and final users.

Question: How to design a smart parking system?


Shining the Floodlights on Mobile Web Tracking — A Privacy Survey. We present the first published large-scale study of mobile web tracking. We compare tracking across five physical and emulated mobile devices with one desktop device as a benchmark. Our crawler is based on FourthParty; however, our architecture avoids clearing state which has the benefit of continual observation of (and by) third-parties. We confirm many intuitive predictions and report a few surprises. The lists of top third-party domains across different categories devices are substantially similar; we found surprisingly few mobile-specific ad networks. The use of JavaScript by tracking domains increases gradually as we consider more powerful devices. We also analyze cookie longevity by device. Finally, we analyze a curious phenomenon of cookies that are used to store information about the user's browsing history on the client. Mobile tracking appears to be an under-researched area, and this paper is only a first step. We have made our code and data available at http://webtransparency.org/ for others to build on.

Question: What is the first published large-scale study of mobile web tracking?


Monitoring of gait performance using dynamic time warping on IMU-sensor data. In this paper, a novel method for monitoring the changes in gait joint angle trajectories recorded using the low-cost and wearable Inertial Measurement Units (IMU) is presented. The introduced method is based on Dynamic Time Warping (DTW), an algorithm commonly used for evaluating the similarity of two time series which may vary in time and speed. DTW is employed as the measure of distance between two gait trajectories taken in different time instances, which could be used as an intuitive and effective measure for the evaluation of gait performances. The experimental results presented in the paper demonstrate that the proposed method is applicable for clinically relevant applications and is consequently adaptable to patients with diseases characterized with gait disorders and to different walking scenarios. The proposed method was firstly validated by applying the DTW-based measure on gait trajectories of five healthy subjects recorded while simulating different levels of walking disabilities. Then proposed measure was applied to estimate the distance between the "healthy" gait trajectories and gait trajectories of three patients with Parkinson's disease (PD) while performing single-task and dual-task overground walking. Also, the proposed measure was demonstrated as an effective measure for monitoring the changes in gait patterns of a PD patient before and after medication-based treatment. This result indicates potential use of proposed method for effective pharmacological management of PD.

Question: How to monitor gait performance using dynamic time warping on IMU-sensor data?


Cuckoo Search via Lévy flights. In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.

Question: What is a meta-heuristic algorithm?


On Throughput Maximization of Time Division Multiple Access With Energy Harvesting Users. In this paper, we consider a multiple-access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. To avoid consuming extra energy on competition for the channel, the users are supposed to share the channel via time division multiple access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for real-time information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first study the optimal transmission scheme in the case where each node has an infinite-capacity battery. By optimization theory, we show that, to maximize the average system throughput, all the users should transmit at an identical optimal power, which solely depends on the energy harvesting rate per time slot. We then study the equal-power TDMA scheme in the case where each node is equipped with a battery of finite capacity. The system is formulated as a polling system consisting of multiple energy queues and one server. By the Markov chain modeling method, we derive the performance of equal-power TDMA in this case, in terms of the energy loss ratio and average system throughput. In addition, we develop an algorithm to efficiently compute the optimal transmission power in the finite-capacity battery case. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user, and analyze its system performance. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, whereas equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have fin

Question: How large is the canadian military?


Workplace harassment: double jeopardy for minority women.. To date there have been no studies of how both sex and ethnicity might affect the incidence of both sexual and ethnic harassment at work. This article represents an effort to fill this gap. Data from employees at 5 organizations were used to test whether minority women are subject to double jeopardy at work, experiencing the most harassment because they are both women and members of a minority group. The results supported this prediction. Women experienced more sexual harassment than men, minorities experienced more ethnic harassment than Whites, and minority women experienced more harassment overall than majority men, minority men, and majority women.

Question: How many women are harassed at work?


Radar target detection and Doppler ambiguity resolution. A Continuous Wave (CW) radar with a linear Frequency Modulation (FM) and a very short chirp duration is considered in this paper. A single target range and Doppler frequency measurement consits of a sequence of L chirp signals with the same chirp duration. The maximum unambiguously measurable radial velocity in this case is limited by a minimum chirp duration. Therefore fast moving targets will cause ambiguous measurement results, that have to be resolved. The technique of resolving ambiguities on the basis of several subsequent measurements with different ambiguity interval lengths is well known as Chinese Remainder Theorem (CRT). It has been extensively described in literature [2]. Two different signal processing approaches to apply this technique for the resolution of ambiguities are compared in this paper. These do not differ in terms of the ambiguity resolution itself, because they are based on the same principle. The detection performance however is different.

Question: What is the difference between the two approaches?


A New ADS-B Authentication Framework Based on Efficient Hierarchical Identity-Based Signature with Batch Verification. Automatic dependent surveillance-broadcast (ADS-B) has become a crucial part of next generation air traffic surveillance technology and will be mandatorily deployed for most of the airspaces worldwide by 2020. Each aircraft equipped with an ADS-B device keeps broadcasting plaintext messages to other aircraft and the ground station controllers once or twice per second. The lack of security measures in ADS-B systems makes it susceptible to different attacks. Among the various security issues, we investigate the integrity and authenticity of ADS-B messages. We propose a new framework for providing ADS-B with authentication based on three-level hierarchical identity-based signature (HIBS) with batch verification. Previous signature-based ADS-B authentication protocols focused on how to generate signatures efficiently, while our schemes can also significantly reduce the verification cost, which is critical to ADS-B systems, since at any time an ADS-B receiver may receive lots of signatures. We design two concrete schemes. The basic scheme supports partial batch verification and the extended scheme provides full batch verification. We give a formal security proof for the extended scheme. Experiment results show that our schemes with batch verification are tremendously more efficient in batch verifying <inline-formula><tex-math notation="LaTeX">$n$ </tex-math><alternatives><inline-graphic xlink:href="yang-ieq1-2459709.gif"/></alternatives></inline-formula> signatures than verifying <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives> <inline-graphic xlink:href="yang-ieq2-2459709.gif"/></alternatives></inline-formula> signatures independently. For example, the running time of verifying 100 signatures is 502 and 484 ms for the basic scheme and the extended scheme respectively, while the time is 2500 ms if verifying the signatures independe

Question: What is the difference between the basic scheme and the extended scheme?