Behavioral resource-aware model inference. Software bugs often arise because of differences between what developers think their system does and what the system actually does. These differences frustrate debugging and comprehension efforts. We describe Perfume, an automated approach for inferring behavioral, resource-aware models of software systems from logs of their executions. These finite state machine models ease understanding of system behavior and resource use.  Perfume improves on the state of the art in model inference by differentiating behaviorally similar executions that differ in resource consumption. For example, Perfume separates otherwise identical requests that hit a cache from those that miss it, which can aid understanding how the cache affects system behavior and removing cache-related bugs. A small user study demonstrates that using Perfume is more effective than using logs and another model inference tool for system comprehension. A case study on the TCP protocol demonstrates that Perfume models can help understand non-trivial protocol behavior. Perfume models capture key system properties and improve system comprehension, while being reasonably robust to noise likely to occur in real-world executions.

Question: What is the difference between a finite state machine and a finite automaton?


VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks. Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.

Question: What is the cloud computing model for vehicular ad hoc networks?


Joint shot boundary detection and key frame extraction. Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative.

Question: How to extract key frames from a video?


A Novel Design of $4 \times 4$ Butler Matrix With Relatively Flexible Phase Differences. This letter presents a novel topology of a 4 ×4 Butler matrix, which can realize relatively flexible phase differences at the output ports. The proposed Butler matrix employs couplers with arbitrary phase-differences to replace quadrature couplers in the conventional Butler matrix. By controlling the phase differences of the applied couplers, the progressive phase differences among output ports of the proposed Butler matrix can be relatively flexible. To facilitate the design, closed-form design equations are derived and presented. For verifying the design concept, a planar 4×4 Butler matrix with four unique progressive phase differences ( - 30°, + 150°, - 120°, and + 60°) is designed and fabricated. At the operating frequency, the amplitude imbalance is less than 0.75 dB, and the phase mismatch is within ±6°. The measured return loss is better than 16 dB, and the isolation is better than 18 dB. The bandwidth with 10 dB return loss is about 15%.

Question: What is a Butler matrix?


Using flipped classroom approach to teach computer programming. Flipped classroom approach has been increasingly adopted in higher institutions. Although this approach has many advantages, there are also many challenges that should be considered. In this paper, we discuss the suitability of this approach to teach computer programming, and we report on our pilot experience of using this approach at Qatar University to teach one subject of computer programming course. It is found that students has positive attitude to this approach, it improves their learning. However, the main challenge was how to involve some of the students in online learning activities.

Question: What is the flipped classroom approach?


PFID: Pittsburgh fast-food image dataset. We introduce the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers. This work was motivated by research on fast food recognition for dietary assessment. The data was collected by obtaining three instances of 101 foods from 11 popular fast food chains, and capturing images and videos in both restaurant conditions and a controlled lab setting. We benchmark the dataset using two standard approaches, color histogram and bag of SIFT features in conjunction with a discriminative classifier. Our dataset and the benchmarks are designed to stimulate research in this area and will be released freely to the research community.

Question: What is the first visual dataset of fast foods with a total of 4,545 still images, 606 stereo pairs, 303 360° videos for structure from motion, and 27 privacy-preserving videos of eating events of volunteers?


Understanding the Potential of Interpreter-based Optimizations for Python. The increasing popularity of scripting languages as general purpose programming environments calls for more efficient execution. Most of these languages, such as Python, Ruby, PHP, and JavaScript are interpreted. Interpretation is a natural implementation given the dynamic nature of these languages and interpreter portability has facilitated wide-spread use. In this work, we analyze the performance of CPython, a commonly used Python interpreter, to identify major sources of overhead. Based on our findings, we investigate the efficiency of a number of optimizations and explore the design options and trade-offs involved.

Question: What is the overhead of python?


B2W2: N-Way Concurrent Communication for IoT Devices. The exponentially increasing number of internet of things (IoT) devices and the data generated by these devices introduces the spectrum crisis at the already crowded ISM 2.4 GHz band. To address this issue and enable more flexible and concurrent communications among IoT devices, we propose B2W2, a novel communication framework that enables N-way concurrent communication among WiFi and Bluetooth Low Energy (BLE) devices. Specifically, we demonstrate that it is possible to enable the BLE to WiFi cross-technology communication while supporting the concurrent BLE to BLE and WiFi to WiFi communications. We conducted extensive experiments under different real-world settings and results show that its throughput is more than 85X times higher than the most recently reported cross-technology communication system [22], which only supports one-way communication (i.e., broadcasting) at any specific time.

Question: What is the spectrum crisis?


lncDML: Identification of long non-coding RNAs by Deep Metric Learning. The next-generation sequencing technologies provide a great deal of transcripts for bioinformatics research. Specially, because of the regulation of long non-coding RNAs (lncRNAs) in various cellular processes, the research on IncRNAs is in full swing. And the solution of IncRNAs identification is the basis for the in-depth study of its functions. In this study, we present an approach to identify the IncRNAs from large scale transcripts, named IncDML which is completely different from previous identification methods. In our model, we extract signal to noise ratio (SNR) and k-mer from transcripts sequences as features. Firstly, we just use the SNR to cluster the original dataset to three parts. In this process, we achieve preliminary identification effect to some extent. Then abandoning traditional feature selection, we directly measure the relationship between each pair of samples by deep metric learning for each part of data. Finally, a novel classifier based on complex network is applied to achieve the final identification. The experiment results show that IncDML is a very effective method for identifying IncRNAs.

Question: What is the difference between IncRNAs and lncRNAs?


Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging. This paper is to explore applications of the blockchain technology to the concept of "proof of X" such as proof of identity, proof of property ownership, proof of specific transaction, proof of college degree, proof of medical records, proof of academic achievements, etc. It describes a novel approach of building a decentralized transparent immutable secure personal archive management and service system. Personal archive is defined as a collection of various artifacts that reflect personal portfolio as well as personal unique identifications. Personal portfolio is beyond of a statement of personal achievement. It is an evidentiary document designed to provide qualitative and quantitative chronically documents and examples. Subjects can tag their information with proof, that is, certified by trusted entities or organizations like universities. Such proofs are associated with confidentiality levels exposed in the public domains. Personal identifications include biometrics as well as other multi-factors such as something the subject "has", the subject "knows" or the subject "acts". Stack holders in a consortium oriented blockchain network serve as verifiers and /or miners that provide their trusted services the delegated proof of stake. Such personal archive based system can be exploited to various applications including professional network like Linkedin, instant credit approval like alipay or live human from social bots like internet social media. A prototype simulation shows that such personal portfolio management and service system is feasible and immune to many ID attacks.

Question: What is the difference between a proof of identity and a proof of property ownership?


Smart Cameras as Embedded Systems. I ncreasingly powerful integrated circuits are making an entire range of new applications possible. Complementary metal-oxide semiconductor (CMOS) sensors, for example, have made the digital camera a commonplace consumer item. These light-sensitive chips, positioned where film would normally be, capture images as reusable digital files that users can upload to their computer, manipulate with software, and distribute electronically. Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Fortunately, Moore's law provides an increasing pool of available computing power to apply to real-time analysis. Smart cameras leverage very large-scale integration (VLSI) to provide such analysis in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these systems run a wide range of algorithms to extract meaning from streaming video. Because they push the design space in so many dimensions, smart cameras are a leading-edge application for embedded system research. has developed a first-generation smart camera system that can detect people and analyze their movement in real time. Although there are many approaches to real-time video analysis, we chose to focus initially on human gesture recognition—identifying whether a subject is walking, standing, waving his arms, and so on. Because much work remains to be done on this problem, we sought to design an embedded system that can incorporate future algorithms as well as use those we created exclusively for this application. As F

Question: What is a smart camera?


A Miniaturized Dual-Band Frequency Selective Surface (FSS) With Closed Loop and Its Complementary Pattern. A single-layer substrate frequency selective surface (FSS) made of miniaturized elements is proposed, with two controllable passbands obtained. Each FSS element consists of a loop wire on the top metal layer and its complementary pattern etched at the bottom one, which provides two transmission poles separated by a transmission zero. An equivalent circuit model is given for predicting the characteristics of the designed FSS, and a good agreement between the simulated and measured transmission coefficients is obtained. Furthermore, the cases of oblique wave incidence and cascading FSSs are also measured and examined.

Question: What is a dual-band frequency selective surface?


A Generative Modeling Approach to Limited Channel ECG Classification. Processing temporal sequences is central to a variety of applications in health care, and in particular multichannel Electrocardiogram (ECG) is a highly prevalent diagnostic modality that relies on robust sequence modeling. While Recurrent Neural Networks (RNNs) have led to significant advances in automated diagnosis with time-series data, they perform poorly when models are trained using a limited set of channels. A crucial limitation of existing solutions is that they rely solely on discriminative models, which tend to generalize poorly in such scenarios. In order to combat this limitation, we develop a generative modeling approach to limited channel ECG classification. This approach first uses a Seq2Seq model to implicitly generate the missing channel information, and then uses the latent representation to perform the actual supervisory task. This decoupling enables the use of unsupervised data and also provides highly robust metric spaces for subsequent discriminative learning. Our experiments with the Physionet dataset clearly evidence the effectiveness of our approach over standard RNNs in disease prediction.

Question: How does a generative model work?


Speed Estimation and Abnormality Detection from Surveillance Cameras. Motivated by the increasing industry trends towards autonomous driving, vehicles, and transportation we focus on developing a traffic analysis framework for the automatic exploitation of a large pool of available data relative to traffic applications. We propose a cooperative detection and tracking algorithm for the retrieval of vehicle trajectories in video surveillance footage based on deep CNN features that is ultimately used for two separate traffic analysis modalities: (a) vehicle speed estimation based on a state of the art fully automatic camera calibration algorithm and (b) the detection of possibly abnormal events in the scene using robust optical flow descriptors of the detected vehicles and Fisher vector representations of spatiotemporal visual volumes. Finally we measure the performance of our proposed methods in the NVIDIA AI CITY challenge evaluation dataset.

Question: How to detect abnormal events in a video surveillance footage?


Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean. Suppression of impulse noise in images is an important problem in image processing. In this paper, we propose a novel adaptive iterative fuzzy filter for denoising images corrupted by impulse noise. It operates in two stages-detection of noisy pixels with an adaptive fuzzy detector followed by denoising using a weighted mean filter on the "good" pixels in the filter window. Experimental results demonstrate the algorithm to be superior to state-of-the-art filters. The filter is also shown to be robust to very high levels of noise, retrieving meaningful detail at noise levels as high as 97%.

Question: How to remove high-density salt-and-pepper noise in images?


Assessing forecast model performance in an ERP environment. Purpose – The paper aims to describe and apply a commercially oriented method of forecast performance measurement (cost of forecast error – CFE) and to compare the results with commonly adopted statistical measures of forecast accuracy in an enterprise resource planning (ERP) environment. Design/methodology/approach – The study adopts a quantitative methodology to evaluate the nine forecasting models (two moving average and seven exponential smoothing) of SAP's ERP system. Event management adjustment and fitted smoothing parameters are also assessed. SAP is the largest European software enterprise and the third largest in the world, with headquarters in Walldorf, Germany. Findings – The findings of the study support the adoption of CFE as a more relevant commercial decision-making measure than commonly applied statistical forecast measures. Practical implications – The findings of the study provide forecast model selection guidance to SAP's 12 þ million worldwide users. However, the CFE metric can be adopted in any commercial forecasting situation. Originality/value – This study is the first published cost assessment of SAP's forecasting models.

Question: What is the cost of forecast error?


Analog Imagery in Mental Model Reasoning: Depictive Models. We investigated whether people can use analog imagery to model the behavior of a simple mechanical interaction. Subjects saw a static computer display of two touching gears that had different diameters. Their task was to determine whether marks on each gear would meet if the gears rotated inward. This task added a problem of coordination to the typical analog rotation task in that the gears had a physical interdependency; the angular velocity of one gear depended on the angular velocity of the other gear. In the first experiment, we found the linear relationship between response time and angular disparity that indicates analog imagery. In the second experiment, we found that people can also solve the problem through a non-analog, visual comparison. We also found that people of varying spatial ability could switch between analog and non-analog solutions if instructed to do so. In the third experiment, we examined whether the elicitation of physical knowledge would influence solution strategies. To do so, we manipulated the visual realism of the gear display. Subjects who saw the most realistic gears coordinated their transformations by using the surfaces of the gears, as though they were relying on the friction connecting the surfaces. Subjects who saw more schematic displays relied on analytic strategies, such as comparing the ratios made by the angles and/or diameters of the two gears. To explain the relationship between spatial and physical knowledge found in the experiments, we constructed a computer simulation of what we call depictive modeling. In a depictive model, general spatial knowledge and context-sensitive physical knowledge have the same ontology. This is different from prior simulations in which a non-analog representation would be needed to coordinate the analog behaviors of physical objects. In our simulation, the inference that coordinates the gear motions emerges from the analog rotations

Question: What is depictive modeling?


AUTOMATIC GENERATION OF PRESENTATION SLIDES FOR ACADEMIC PAPERS USING INTEGER LINEAR PROGRAMMING. Presentations are one of the most common and effective ways of communicating the overview of a work to the audience. Given a specialized paper, programmed era of introduction slides diminishes the exertion of the moderator and aides in making an organized synopsis of the paper. In this Project, we propose the structure of another framework that does this assignment. Any paper that has a theoretical and whose segments can be ordered under presentation, related work, model, examinations and conclusions can be given as info. This XML record is parsed and data in it is extricated. An inquiry particular extractive summarizer has been utilized to create slides. In numerous meetings and social occasions, a moderator takes the guide of slides to present his work deliberately (pictorial). The introduction slides for the paper are then made by utilizing the Integer Linear Programming (ILP) demonstrate with complicatedly organized target breaking points and goals to pick and adjust key expressions and sentences. The framework removed theme and non subject parts from the article in light of the basic talk structure. The isolated subject and non-topic parts were determined to the slides by giving fitting indents in perspective of the examination of their syntactic structure. Keywords— Abstracting methods, Integer Linear Programming, Support Vector Regression model, text mining.

Question: How to generate presentation slides for academic papers using integer linear programming?


The Modified Abbreviated Math Anxiety Scale: A Valid and Reliable Instrument for Use with Children. Mathematics anxiety (MA) can be observed in children from primary school age into the teenage years and adulthood, but many MA rating scales are only suitable for use with adults or older adolescents. We have adapted one such rating scale, the Abbreviated Math Anxiety Scale (AMAS), to be used with British children aged 8-13. In this study, we assess the scale's reliability, factor structure, and divergent validity. The modified AMAS (mAMAS) was administered to a very large (n = 1746) cohort of British children and adolescents. This large sample size meant that as well as conducting confirmatory factor analysis on the scale itself, we were also able to split the sample to conduct exploratory and confirmatory factor analysis of items from the mAMAS alongside items from child test anxiety and general anxiety rating scales. Factor analysis of the mAMAS confirmed that it has the same underlying factor structure as the original AMAS, with subscales measuring anxiety about Learning and Evaluation in math. Furthermore, both exploratory and confirmatory factor analysis of the mAMAS alongside scales measuring test anxiety and general anxiety showed that mAMAS items cluster onto one factor (perceived to represent MA). The mAMAS provides a valid and reliable scale for measuring MA in children and adolescents, from a younger age than is possible with the original AMAS. Results from this study also suggest that MA is truly a unique construct, separate from both test anxiety and general anxiety, even in childhood.

Question: What is the modified abbreviated math anxiety scale?


Automated 3-D Segmentation of Lungs With Lung Cancer in CT Data Using a Novel Robust Active Shape Model Approach. Segmentation of lungs with (large) lung cancer regions is a nontrivial problem. We present a new fully automated approach for segmentation of lungs with such high-density pathologies. Our method consists of two main processing steps. First, a novel robust active shape model (RASM) matching method is utilized to roughly segment the outline of the lungs. The initial position of the RASM is found by means of a rib cage detection method. Second, an optimal surface finding approach is utilized to further adapt the initial segmentation result to the lung. Left and right lungs are segmented individually. An evaluation on 30 data sets with 40 abnormal (lung cancer) and 20 normal left/right lungs resulted in an average Dice coefficient of 0.975±0.006 and a mean absolute surface distance error of 0.84±0.23 mm, respectively. Experiments on the same 30 data sets showed that our methods delivered statistically significant better segmentation results, compared to two commercially available lung segmentation approaches. In addition, our RASM approach is generally applicable and suitable for large shape models.

Question: How to segment lungs with lung cancer?


Generic Battery model covering self-discharge and internal resistance variation. Design and analysis of battery powered systems depend on effective battery model. Hardware-in-the-loop testing of Battery Management System (BMS) extensively uses battery models. Simulation of Hybrid Electric Vehicles highly rely on the battery models which provides actual characteristics. This paper proposes a performance enhanced battery model which can be included in electric circuit design and analysis. Combined electrical and thermal model replicates the real world performance of battery. Self-discharge is the characteristic of a battery which reduces the stored energy without external connection. Effect of self-discharge on State of Charge (SOC) of battery is considered in the paper. Internal resistance variation with SOC of a battery included in the model further enhances its exactness. Proposed model is validated for Sony US 18650 Li-ion battery in MATLAB Simulink.

Question: How to model a battery?


Attributed scattering centers for SAR ATR. High-frequency radar measurements of man-made targets are dominated by returns from isolated scattering centers, such as corners and flat plates. Characterizing the features of these scattering centers provides a parsimonious, physically relevant signal representation for use in automatic target recognition (ATR). In this paper, we present a framework for feature extraction predicated on parametric models for the radar returns. The models are motivated by the scattering behaviour predicted by the geometrical theory of diffraction. For each scattering center, statistically robust estimation of model parameters provides high-resolution attributes including location, geometry, and polarization response. We present statistical analysis of the scattering model to describe feature uncertainty, and we provide a least-squares algorithm for feature estimation. We survey existing algorithms for simplified models, and derive bounds for the error incurred in adopting the simplified models. A model order selection algorithm is given, and an M-ary generalized likelihood ratio test is given for classifying polarimetric responses in spherically invariant random clutter.

Question: What is the radar scattering model?


Simple, Accurate, and Robust Nonparametric Blind Super-Resolution. This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality.

Question: How to estimate the blur-kernel?


Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy.. The aim of this work was to evaluate the performance of a novel non-invasive continuous glucose-monitoring system based on impedance spectroscopy (IS) in patients with diabetes. Ten patients with type 1 diabetes (mean+/-S.D., age 28+/-8 years, BMI 24.2+/-3.2 kg/m(2) and HbA(1C) 7.3+/-1.6%) and five with type 2 diabetes (age 61+/-8 years, BMI 27.5+/-3.2 kg/m(2) and HbA(1C) 8.3+/-1.8%) took part in this study, which comprised a glucose clamp experiment followed by a 7-day outpatient evaluation. The measurements obtained by the NI-CGMD and the reference blood glucose-measuring techniques were evaluated using retrospective data evaluation procedures. Under less controlled outpatient conditions a correlation coefficient of r=0.640 and a standard error of prediction (SEP) of 45 mg dl(-1) with a total of 590 paired glucose measurements was found (versus r=0.926 and a SEP of 26 mg dl(-1) under controlled conditions). Clark error grid analyses (EGA) showed 56% of all values in zone A, 37% in B and 7% in C-E. In conclusion, these results indicate that IS in the used technical setting allows retrospective, continuous and truly non-invasive glucose monitoring under defined conditions for patients with diabetes. Technical advances and developments are needed to expand on this concept to bring the results from the outpatient study closer to those in the experimental section of the study. Further studies will not only help to evaluate the performance and limitations of using such a technique for non non-invasive glucose monitoring but also help to verify technical extensions towards a IS-based concept that offers improved performance under real life operating conditions.

Question: What is the difference between a non-invasive glucose monitoring system and a continuous glucose monitoring system?


Intuitionistic fuzzy sets and L-fuzzy sets. This paper proves that the concepts of intuitionistic fuzzy sets and intuitionistic L-fuzzy sets and the concept of L-fuzzy sets are equivalent. c © 2000 Elsevier Science B.V. All rights reserved

Question: What is intuitionistic fuzzy sets?


Realtime Ray Tracing on GPU with BVH-based Packet Traversal. Recent GPU ray tracers can already achieve performance competitive to that of their CPU counterparts. Nevertheless, these systems can not yet fully exploit the capabilities of modern GPUs and can only handle medium-sized, static scenes. In this paper we present a BVH-based GPU ray tracer with a parallel packet traversal algorithm using a shared stack. We also present a fast, CPU-based BVH construction algorithm which very accurately approximates the surface area heuristic using streamed binning while still being one order of magnitude faster than previously published results. Furthermore, using a BVH allows us to push the size limit of supported scenes on the GPU: We can now ray trace the 12.7 million triangle Power Plant at 1024 times 1024 image resolution with 3 fps, including shading and shadows.

Question: How large is the power plant?


Multiple nutrient deficiency detection in paddy leaf images using color and pattern analysis. Paddy being the staple food of India is majorly affected by deficiency of primary nutrient elements like nitrogen, phosphorus and potassium. Leaves can be deficient with multiple nutrient elements at a same time. This can alter natural color of paddy leaves. Such leaves are considered as defective. The proposed work is to automate multiple nutrient element deficiency identification of paddy leaves. Pattern analysis RGB color features are extracted to identify defective paddy leaves. Firstly the database of healthy, nitrogen, phosphorus and potassium defected paddy leaves are created. For any test image effective comparison at different levels are employed such as multiple color comparison, multiple pattern comparison and combination of color and patterns comparison, so that defectiveness is accurately identified for combination of deficiency such as nitrogen-phosphorus(NP), nitrogen-potassium(NK) and phosphorous-potassium (KP).

Question: How to detect multiple nutrient deficiency in paddy leaves?


Machine Learning Algorithms for Characterization of EMG Signals. —In the last decades, the researchers of the human arm prosthesis are using different types of machine learning algorithms. This review article firstly gives a brief explanation about type of machine learning methods. Secondly, some recent applications of myoelectric control of human arm prosthesis by using machine learning algorithms are compared. This study presents two different comparisons based on feature extraction methods which are time series modeling and wavelet transform of EMG signal. Finally, of characterization of EMG for of human arm prosthesis have been and discussed.

Question: What is a machine learning algorithm?


The recognition of rice images by UAV based on capsule network. It is important to recognize the rice image captured by unmanned aerial vehicle (UAV) for monitoring the growth of rice and preventing the diseases and pests. Aiming at the image recognition, we use rice images captured by UAV as our data source, the structure of capsule network (CapsNet) is built to recognize rice images in this paper. The images are preprocessed through histogram equalization method into grayscale images and through superpixel algorithm into the superpixel segmentation results. The both results are output into the CapsNet. The function of CapsNet is to perform the reverse analysis of rice images. The CapsNet consists of five layers: an input layer, a convolution layer, a primary capsules layer, a digital capsules layer and an output layer. The CapsNet trains classification and predicts the output vector based on routing-by-agreement protocol. Therefore, the features of rice image by UAV can be precisely and efficiently extracted. The method is more convenient than the traditional artificial recognition. It provides the scientific support and reference for decision-making process of precision agriculture.

Question: What is the function of the capsule network?


A 3D Coarse-to-Fine Framework for Automatic Pancreas Segmentation. In this paper, we adopt 3D CNNs to segment the pancreas in CT images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D applications due to the limited amount of annotated 3D data and limited computational resources. We propose a novel 3D-based coarseto-fine framework for volumetric pancreas segmentation to tackle these challenges. The proposed 3D-based framework outperforms the 2D counterpart to a large margin since it can leverage the rich spatial information along all three axes. We conduct experiments on two datasets which include healthy and pathological pancreases respectively, and achieve the state-of-the-art in terms of Dice-Sørensen Coefficient (DSC). Moreover, the worst case of DSC on the NIH dataset was improved by 7% to reach almost 70%, which indicates the reliability of our framework in clinical applications.

Question: What is the state of the art in terms of DSC?


A Tutorial on Support Vector Machines for Pattern Recognition. The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.

Question: What is the VC dimension of a homogeneous polynomial kernel?


Hardware design experiences in ZebraNet. The enormous potential for wireless sensor networks to make a positive impact on our society has spawned a great deal of research on the topic, and this research is now producing environment-ready systems. Current technology limits coupled with widely-varying application requirements lead to a diversity of hardware platforms for different portions of the design space. In addition, the unique energy and reliability constraints of a system that must function for months at a time without human intervention mean that demands on sensor network hardware are different from the demands on standard integrated circuits. This paper describes our experiences designing sensor nodes and low level software to control them.  In the ZebraNet system we use GPS technology to record fine-grained position data in order to track long term animal migrations [14]. The ZebraNet hardware is composed of a 16-bit TI microcontroller, 4 Mbits of off-chip flash memory, a 900 MHz radio, and a low-power GPS chip. In this paper, we discuss our techniques for devising efficient power supplies for sensor networks, methods of managing the energy consumption of the nodes, and methods of managing the peripheral devices including the radio, flash, and sensors. We conclude by evaluating the design of the ZebraNet nodes and discussing how it can be improved. Our lessons learned in developing this hardware can be useful both in designing future sensor nodes and in using them in real systems.

Question: What is the difference between a microcontroller and a microprocessor?


Sindhi Language Processing: A survey. In this era of information technology, natural language processing (NLP) has become volatile field because of digital reliance of today's communities. The growth of Internet usage bringing the communities, cultures and languages online. In this regard much of the work has been done of the European and east Asian languages, in the result these languages have reached mature level in terms of computational processing. Despite the great importance of NLP science, still most of the South Asian languages are under developing phase. Sindhi language is one of them, which stands among the most ancient languages in the world. The Sindhi language has a great influence on the large community in Sindh province of Pakistan and some states of India and other countries. But unfortunately, it is at infant level in terms of computational processing, because it has not received such attention of language engineering community, due to its complex morphological structure and scarcity of language resources. Therefore, this study has been carried out in order to summarize the existing work on Sindhi Language Processing (SLP) and to explore future research opportunities, also some potential research problems. This paper will be helpful for the researchers in order to find all the information regarding SLP at one place in a unique way.

Question: What is the sindhi language?


Validity and reliability of the Cohen 10-item Perceived Stress Scale in patients with chronic headache: Persian version.. BACKGROUND The Cohen Perceived Stress Scale is being used widely in various countries. The present study evaluated the validity and reliability of the Cohen 10-item Perceived Stress Scale (PSS-10) in assessing tension headache, migraine, and stress-related diseases in Iran.   METHODS This study is a methodological and cross-sectional descriptive investigation of 100 patients with chronic headache admitted to the pain clinic of Baqiyatallah Educational and Therapeutic Center. Convenience sampling was used for subject selection. PSS psychometric properties were evaluated in two stages. First, the standard scale was translated. Then, the face validity, content, and construct of the translated version were determined.   RESULTS The average age of participants was 38 years with a standard deviation (SD) of 13.2. As for stress levels, 12% were within the normal range, 36% had an intermediate level, and 52% had a high level of stress. The face validity and scale content were remarkable, and the KMO coefficient was 0.82. Bartlett's test yielded 0.327 which was statistically significant (p<0.0001) representing the quality of the sample. In factor analysis of the scale, the two elements of "coping" and "distress" were determined. A Cronbach's Alpha coefficient of 0.72 was obtained. This confirmed the remarkable internal consistency and stability of the scale through repeated measure tests (0.93).   CONCLUSION The Persian PSS-10 has good internal consistency and reliability. The availability of a validated Persian PSS-10 would indicate a link between stress and chronic headache.

Question: What is the validity and reliability of the Cohen 10-item Perceived Stress Scale in patients with chronic headache?


An IOT based human healthcare system using Arduino uno board. Internet of Things (IOT) visualizes a future of anything anywhere by anyone at any time. The Information and communication technologies help in creating a revolution in digital technology. IOT are known for interconnecting various physical devices with the networks. In IOT, various physical devices are embedded with different types of sensors and other devices to exchange data between them. An embedded system is a combination of software and hardware where they are programmed for functioning specific functions. These data can be accessed from any parts of the world by making use of cloud. This can be used for creating a digital world, smart homes, healthcare systems and real life data exchange like smart banking. Though Internet of things has emerged long back, it is now becoming popular and gaining attention lately. In healthcare industry, some of the hospitals started using sensors implemented in the bed to get the status of patient's movement and other activities. This paper contains various IOT applications and the role of IOT in the healthcare system, challenges in the healthcare system using IOT. Also, introduced a secured surveillance monitoring system for reading and storing patient's details using low power for transmitting the data.

Question: What is the role of IOT in the healthcare system?


PPDB 2.0: Better paraphrase ranking, fine-grained entailment relations, word embeddings, and style classification. We present a new release of the Paraphrase Database. PPDB 2.0 includes a discriminatively re-ranked set of paraphrases that achieve a higher correlation with human judgments than PPDB 1.0's heuristic rankings. Each paraphrase pair in the database now also includes finegrained entailment relations, word embedding similarities, and style annotations.

Question: What is the new version of the paraphrase database?


Quadrifilar helical antennas : Wideband and multiband behavior for GPS applications. A novel multi-frequency printed quadrifilar helix antenna based on Multiple arm technique is presented in this paper. Double frequency and satisfactory antenna characteristics are achieved. The antenna has relatively compact size and hemispherical pattern with excellent circularly polarized coverage. The antenna is designed and simulated with the application of HFSS software. The simulation results and analyses are presented.

Question: What is the size of the antenna?


Computational higher-dimensional type theory. Formal constructive type theory has proved to be an effective language for mechanized proof. By avoiding non-constructive principles, such as the law of the excluded middle, type theory admits sharper proofs and broader interpretations of results. From a computer science perspective, interest in type theory arises from its applications to programming languages. Standard constructive type theories used in mechanization admit computational interpretations based on meta-mathematical normalization theorems. These proofs are notoriously brittle; any change to the theory potentially invalidates its computational meaning. As a case in point, Voevodsky's univalence axiom raises questions about the computational meaning of proofs.   We consider the question: Can higher-dimensional type theory be construed as a programming language? We answer this question affirmatively by providing a direct, deterministic operational interpretation for a representative higher-dimensional dependent type theory with higher inductive types and an instance of univalence. Rather than being a formal type theory defined by rules, it is instead a computational type theory in the sense of Martin-Löf's meaning explanations and of the NuPRL semantics. The definition of the type theory starts with programs; types are specifications of program behavior. The main result is a canonicity theorem stating that closed programs of boolean type evaluate to true or false.

Question: What is a computational type theory?


SMC: A Practical Schema for Privacy-Preserved Data Sharing over Distributed Data Streams. Data collection is required to be safe and efficient considering both data privacy and system performance. In this paper, we study a new problem: distributed data sharing with privacy-preserving requirements. Given a data demander requesting data from multiple distributed data providers, the objective is to enable the data demander to access the distributed data without knowing the privacy of any individual provider. The problem is challenged by two questions: how to transmit the data safely and accurately; and how to efficiently handle data streams? As the first study, we propose a practical method, Shadow Coding, to preserve the privacy in data transmission and ensure the recovery in data collection, which achieves privacy preserving computation in a data-recoverable, efficient, and scalable way. We also provide practical techniques to make Shadow Coding efficient and safe in data streams. Extensive experimental study on a large-scale real-life dataset offers insight into the performance of our schema. The proposed schema is also implemented as a pilot system in a city to collect distributed mobile phone data.

Question: How to efficiently handle data streams?


From IT Addiction to Discontinued Use: A Cognitive Dissonance Perspective. One of the main topics discussed within the realm of the dark side of IT is addiction. IT addiction has been found to bring adverse consequences on users' lives. In order to overcome the difficulties associated with IT addiction, interrupting and quitting addiction has become an important research agenda. Recent research findings have shown that IT addicts do not always feel guilty about their usage, and in many cases, they do not even perceive their usage as problematic. In this study, we draw on cognitive dissonance theory to theorize and propose a model showing that the degree of users' cognitive dissonance can make a difference in their willingness to quit their IT addiction. We tested the model using data collected from 226 social network sites users. The analysis provided empirical support for our model and shed light on the mediation and moderation effects of cognitive dissonance in this process. 1

Question: What is the dark side of IT?


Analysis of a Quadrotor with a Two-Degree-of-Freedom Robotic Arm. The classical kinematic approach used in analyzing systems of a small number of interconnected bodies is unsuitable for researching complex kinematics. Moreover, solving nonlinear algebraic equations that involve trigonometric functions is difficult. However, the computational kinematic approach involves using a set of algebraic kinematic equations that describe the joint connectivity between the bodies of a system as well as the specified motion trajectories. This paper presents an analytical approach for a quadrotor with a two-degree-of-freedom (DOF) robotic arm. The approach, which is based on the computational kinematic approach, was adopted to model algebraic equations to obtain the values of position, velocity, acceleration, and the trajectories of motion of the joint and end effector. MATLAB was employed to simulate and solve the kinematic equations. The results showed that the analysis approach is feasible for the quadrotor with a two-DOF robotic arm.

Question: How to solve a quadrotor with a two-DOF robotic arm?


TRUSTED CODE EXECUTION ON UNTRUSTED PLATFORMS USING INTEL SGX. Today, isolated trusted computation and code execution is of paramount importance to protect sensitive information and workfl ows from other malicious privileged or unprivileged software. Intel Software Guard Extensions (SGX) is a set of security architecture extensions fi rst introduced in the Skylake microarchitecture that enables a Trusted Execution Environment (TEE). It provides an 'inverse sandbox', for sensitive programs, and guarantees the integrity and confi dentiality of secure computations, even from the most privileged malicious software (e.g. OS, hypervisor). SGX-capable CPUs only became available in production systems in Q3 2015, and they are not yet fully supported and adopted in systems. Besides the capability in the CPU, the BIOS also needs to provide support for the enclaves, and not many vendors have released the required updates for the system support. This has led to many wrong assumptions being made about the capabilities, features, and ultimately dangers of secure enclaves. By having access to resources and publications such as white papers, patents and the actual SGX-capable hardware and software development environment, we are in a privileged position to be able to investigate and demystify SGX. In this paper, we fi rst review the previous trusted execution technologies, such as ARM Trust Zone and Intel TXT, to better understand and appreciate the new innovations of SGX. Then, we look at the details of SGX technology, cryptographic primitives and the underlying concepts that power it, namely the sealing, attestation, and the Memory Encryption Engine (MEE). We also consider use cases such as trusted and secure code execution on an untrusted cloud platform, and digital rights management (DRM). This is followed by an overview of the software development environment and the available libraries.

Question: What is the difference between ARM Trust Zone and Intel TXT?


The optimum received power levels of uplink non-orthogonal multiple access (NOMA) signals. Non-orthogonal multiple access (NOMA) has been recently considered as a promising multiple access technique for fifth generation (5G) mobile networks as an enabling technology to meet the demands of low latency, high reliability, massive connectivity, and high throughput. The two dominants types of NOMA are: power-domain and code-domain. The key feature of power-domain NOMA is to allow different users to share the same time, frequency, and code, but with different power levels. In code-domain NOMA, different spread-spectrum codes are assigned to different users and are then multiplexed over the same time-frequency resources. This paper concentrates on power-domain NOMA. In power-domain NOMA, Successive Interference Cancellation (SIC) is employed at the receiver. In this paper, the optimum received uplink power levels using a SIC detector is determined analytically for any number of transmitters. The optimum uplink received power levels using the SIC decoder in NOMA strongly resembles the μ-law encoding used in pulse code modulation (PCM) speech companders.

Question: What is the optimum received power levels of uplink NOMA signals?


Advanced Voltage Support and Active Power Flow Control in Grid-Connected Converters Under Unbalanced Conditions. Supporting the grid and improving its reliability have recently become major requirements for large distributed generation units. Under most grid faults, the accuracy of the traditional voltage support schemes (VSSs) is dramatically affected due to the existence of the zero-sequence voltage. Also, the traditional VSSs have been used only in the STATCOM applications, where the active power is zero. This paper proposes an advanced VSS in the converter-interfaced units, called zero-sequence compensated voltage support (ZCVS), to accurately regulate the three-phase voltages of the connection point within the pre-set safety limits. The proposed scheme not only compensates the zero-sequence component but also considers the active power injection. Unlike the traditional methods, the proposed VSS is adapted even in resistive distribution systems. The contribution of this paper is, however, ternate. As the second contribution, the limited active power oscillation (LAPO) is proposed to be augmented to the ZCVS. This feature limits the oscillation to a specified value which provides an adjustable dc-link voltage oscillation setting while simultaneously supporting the ac host grid, even under severe unbalanced faults. Third, the maximum active power delivery (MAPD) to the ac grid is also formulated for the ZCVS. The successful results of the proposed support scheme and complementary strategies are verified using selected simulation and experimental test cases.

Question: How to support the grid and improve its reliability?


Impact of timing and frequency offsets on multicarrier waveform candidates for 5G. This paper presents a study of the candidate waveforms for 5G when they are subject to timing and carrier frequency offset. These waveforms are: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), circular filter bank multicarrier (C-FBMC), and linear filter bank multicarrier (FBMC). We are particularly interested in multiple access interference (MAI) when a number of users transmit their signals to a base station in an asynchronous or a quasi-synchronous manner. We identify the source of MAI in these waveforms and present some numerical analysis that confirm our findings. The goal of this study is to answer the following question, "Which one of the 5G candidate waveforms has more relaxed synchronization requirements?".

Question: Which one of the 5G candidate waveforms has more relaxed synchronization requirements?


One Century of Brain Mapping Using Brodmann Areas*. 100 years after their publication, Brodmann's maps of the cerebral cortex are universally used to locate neuropsychological functions. On the occasion of this jubilee the life and work of Korbinian Brodmann are reported. The core functions of each single Brodmann area are described and Brodmann's views on neuropsychological processes are depicted. 100 Jahre nach ihrer Veröffentlichung wird Brodmanns Kartierung des zerebralen Kortex universell zur Lokalisation neuropsychologischer Funktionen eingesetzt. Anlässlich dieses Jubiläums werden Leben und Werk von Korbinian Brodmann dargestellt. Die wesentlichen Funktionen der einzelnen Brodmann-Areale werden beschrieben und Brodmanns Ansichten über neuropsychologische Prozesse wiedergegeben.

Question: What is the function of each Brodmann area?


The diagnosis and management of Piriformis Syndrome: myths and facts.. Piriformis Syndrome (PS) is an uncommon, controversial neuromuscular disorder that is presumed to be a compression neuropathy of the sciatic nerve at the level of the piriformis muscle (PM). The diagnosis is hampered by a lack of agreed upon clinical criteria and a lack of definitive investigations such as imaging or electrodiagnostic testing. Treatment has focused on stretching, physical therapies, local injections, including botulinum toxin, and surgical management. This article explores the various sources of controversy surrounding piriformis syndrome including diagnosis, investigation and management. We conclude with a proposal for diagnostic criteria which include signs and symptoms, imaging, and response to therapeutic injections.

Question: What is the diagnosis of piriformis syndrome?


Online LCL filter compensation using embedded FRA. LCL filter control design is challenging because of presence of a resonant pole in the LCL filter transfer function. This paper presents a guideline for LCL filter design, which are often used in grid connected applications, with compensation based on complex poles and zeros. A Software Frequency Response Analyzer (SFRA) is integrated in the control software of the power converter. This enables periodical collection of frequency response data, which can be used to monitor movement of resonant poles due to aging, drifts and degradation of components. The information obtained from embedded SFRA can then be used to adjust the compensation. A feedback linearization method is illustrated to account for the grid voltage disturbances, which cannot be compensated by traditional PI controller. A solar micro inverter board is designed to prove the proposed scheme. Discussions about the embedded design challenges in integration of multiple power stage control, for a micro inverter using a single processor, are included and solutions presented.

Question: How to design a LCL filter?


"Exercise to be fit, not skinny": The effect of fitspiration imagery on women's body image.. Fitspiration is an online trend designed to inspire viewers towards a healthier lifestyle by promoting exercise and healthy food. The present study aimed to experimentally investigate the impact of fitspiration images on women's body image. Participants were 130 female undergraduate students who were randomly assigned to view either a set of Instagram fitspiration images or a control set of travel images presented on an iPad. Results showed that acute exposure to fitspiration images led to increased negative mood and body dissatisfaction and decreased state appearance self-esteem relative to travel images. Importantly, regression analyses showed that the effects of image type were mediated by state appearance comparison. Thus it was concluded that fitspiration can have negative unintended consequences for body image. The results offer support to general sociocultural models of media effects on body image, and extend these to "new" media.

Question: What is the effect of fitspiration on women's body image?


Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems. This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.

Question: What is a corpus for adding memory to goal-oriented dialogue systems?


What Do Customers Crave in Mobile 5G?: A survey spotlights four standout factors.. Increasing customer demand coupled with limitations in current fourth-generation (4G) wireless mobile networks and advances toward the next-generation wireless mobile network have all together created a need for us to look at what customers today expect from the upcoming fifth-generation (5G) network. This study was conducted among existing 4G users in the United States, the United Kingdom, Singapore, and Australia. The research was completed in two parts: qualitative and quantitative. The quantitative part was accomplished by performing data collection and selection through surveys and data evaluation by structural equation modeling (SEM) using SMARTPLS.

Question: What do customers crave in mobile 5g?


"Cheap" Rapid Locomotion of a Quadruped Robot: Self-Stabilization of Bounding Gait. The legged animals are capable of rapid, energy efficient, and adaptive locomotion in a complex environment. Toward a comprehensive understanding of the nature of such ecologically balanced legged locomotion, in this paper, we propose a novel method to achieve a form of bounding gait for a quadruped robot by using a minimalistic approach. Although this method uses a simple sinusoidal position control with no global sensory feedback, it is shown that the rapid bounding is possible in a relatively robust manner by properly exploiting the intrinsic dynamics and the interaction with the environment. The behavioral analyses with the robot experiments show that this relatively complicated dynamic locomotion is achieved even with a simple controller mainly because of a self-stabilization mechanism. Moreover, by exploiting this mechanism of self-stabilization, we propose a unique approach to control the forward velocity of the locomotion.

Question: How to control the forward velocity of a quadruped robot?


Opinion mining from noisy text data. The proliferation of Internet has not only generated huge volumes of unstructured information in the form of web documents, but a large amount of text is also generated in the form of emails, blogs, and feedbacks etc. The data generated from online communication acts as potential gold mines for discovering knowledge. Text analytics has matured and is being successfully employed to mine important information from unstructured text documents. Most of these techniques use Natural Language Processing techniques which assume that the underlying text is clean and correct. Statistical techniques, though not as accurate as linguistic mechanisms, are also employed for the purpose to overcome the dependence on clean text. The chief bottleneck for designing statistical mechanisms is however its dependence on appropriately annotated training data. None of these methodologies are suitable for mining information from online communication text data due to the fact that they are often noisy. These texts are informally written. They suffer from spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on opinion extraction from noisy text data. It is aimed at extracting and consolidating opinions of customers from blogs and feedbacks, at multiple levels of granularity. Ours is a hybrid approach, in which we initially employ a semi-supervised method to learn domain knowledge from a training repository which contains both noisy and clean text. Thereafter we employ localized linguistic techniques to extract opinion expressions from noisy text. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions extracted from a repository.

Question: Opinion mining from noisy text data


Social disconnectedness, perceived isolation, and health among older adults.. Previous research has identified a wide range of indicators of social isolation that pose health risks, including living alone, having a small social network, infrequent participation in social activities, and feelings of loneliness. However multiple forms of isolation are rarely studied together making it difficult to determine which aspects of isolation are most deleterious for health. Using population-based data from the National Social Life, Health, and Aging Project, we combine multiple indicators of social isolation into scales assessing social disconnectedness (e.g., small social network, infrequent participation in social activities) and perceived isolation (e.g., loneliness, perceived lack of social support). We examine the extent to which social disconnectedness and perceived isolation have distinct associations with physical and mental health among older adults. Results indicate that social disconnectedness and perceived isolation are independently associated with lower levels of self-rated physical health. However, the association between disconnectedness and mental health may operate through the strong relationship between perceived isolation and mental health. We conclude that health researchers need to consider social disconnectedness and perceived isolation simultaneously.

Question: How do social disconnectedness and perceived isolation affect health?


Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration. Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.

Question: What is reinforcement learning?


Learning Dense Correspondence via 3D-Guided Cycle Consistency. Discriminative deep learning approaches have shown impressive results for problems where human-labeled ground truth is plentiful, but what about tasks where labels are difficult or impossible to obtain? This paper tackles one such problem: establishing dense visual correspondence across different object instances. For this task, although we do not know what the ground-truth is, we know it should be consistent across instances of that category. We exploit this consistency as a supervisory signal to train a convolutional neural network to predict cross-instance correspondences between pairs of images depicting objects of the same category. For each pair of training images we find an appropriate 3D CAD model and render two synthetic views to link in with the pair, establishing a correspondence flow 4-cycle. We use ground-truth synthetic-to-synthetic correspondences, provided by the rendering engine, to train a ConvNet to predict synthetic-to-real, real-to-real and real-to-synthetic correspondences that are cycle-consistent with the ground-truth. At test time, no CAD models are required. We demonstrate that our end-to-end trained ConvNet supervised by cycle-consistency outperforms state-of-the-art pairwise matching methods in correspondence-related tasks.

Question: How do you train a convolutional neural network to predict cross-instance correspondences?


inTouch: A Medium for Haptic Interpersonal Communication. In this paper, we introduce a new approach for applying haptic feedback technology to interpersonal communication. We present the design of our prototype inTouch system which provides a physical link between users separated by distance.

Question: What is the design of the inTouch system?


Word Embeddings and Their Use In Sentence Classification Tasks. This paper has two parts. In the first part we discuss word embeddings. We discuss the need for them, some of the methods to create them, and some of their interesting properties. We also compare them to image embeddings and see how word embedding and image embedding can be combined to perform different tasks. In the second part we implement a convolutional neural network trained on top of pre-trained word vectors. The network is used for several sentence-level classification tasks, and achieves state-of-art (or comparable) results, demonstrating the great power of pre-trainted word embeddings over random ones.

Question: What is a convolutional neural network?


Evaluating Personalization and Persuasion in E-Commerce. The use of personalization and persuasion has been shown to optimize customers' shopping experience in e-commerce. This study aims to identify the personalization methods and persuasive principles that make an ecommerce company successful. Using Amazon as a case study, we evaluated the personalization methods implemented using an existing process framework. We also applied the PSD model to Amazon to evaluate the persuasive principles it uses. Our results show that all the principles of the PSD model were implemented in Amazon. This study can serve as a guide to e-commerce businesses and software developers for building or improving existing e-commerce platforms.

Question: What is the purpose of this study?


Deep Neural Networks for Object Detection. Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.

Question: What is a deep neural network?


Ontologies and Knowledge Bases. Towards a Terminological Clarification. The word "ontology" has recently gained a good popularity within the knowledge engineering community. However, its meaning tends to remain a bit vague, as the term is used in very different ways. Limiting our attention to the various proposals made in the current debate in AI, we isolate a number of interpretations, which in our opinion deserve a suitable clarification. We elucidate the implications of such various interpretations, arguing for the need of clear terminological choices regarding the technical use of terms like "ontology", "conceptualization" and "ontological commitment". After some comments on the use "Ontology" (with the capital "o") as a term which denotes a philosophical discipline, we analyse the possible confusion between an ontology intended as a particular conceptual framework at the knowledge level and an ontology intended as a concrete artifact at the symbol level, to be used for a given purpose. A crucial point in this clarification effort is the careful analysis of Gruber' s definition of an ontology as a specification of a conceptualization.

Question: What is the difference between ontology and conceptualization?


The Generative Lexicon. In this paper, I will discuss four major topics relating to current research in lexical semantics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best approaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is performed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole.

Question: What is the canadian military?


Performance evaluation and model checking join forces. A call for the perfect marriage between classical performance evaluation and state-of-the-art verification techniques.

Question: What is the perfect marriage between classical performance evaluation and state-of-the-art verification techniques?


Broadband antenna design using different 3D printing technologies and metallization processes. The purpose of this paper is to provide a comprehensive evaluation of how plastic materials, different metallization process, and thickness of the metal can influence the performance of 3D printed broadband antenna structures. A set of antennas were manufactured using Fused Deposition Modeling technology and Polyjet technology. Three different plastic materials ABS, PLA and Vero were employed in the tests. The antenna structures were metallized using three common metallization processes: vacuum metallization, electroplating, and conductive paint. In this project, the broadband performances of the metallized plastic antennas are compared to an original metal antenna by measuring VSWR, radiation patterns (H- and E-Planes), and gain. Measurements show that the performances of these plastic structures, regardless of production method, are very similar to the original metal antenna.

Question: How to design a broadband antenna using 3D printing?


Sustainable Food Supply Chains : The Role of Collaboration and Sustainable Relationships. The life cycle approach is widely used in the analysis of sustainability. Its application to supply chains is necessary since the product flows, from processing of raw materials to the final customer, are considered. The role of the organizational aspects, expressed in terms of relationships between the supply chain agents, is little considered in the life cycle analysis approach. The aim of this paper is to extend the scope of the food chain life cycle analysis by adding the organizational dimension to the environmental, economic and social ones. Within this context, Collaboration and Sustainable Relationships concepts have been explored based on a literature survey. A theoretical framework, describing their role in assessing the organizational dimension in the life cycle analysis of the food supply chains, is defined. A hypothesis on their joint influence on the supply chains performances is formulated.

Question: What is the role of collaboration and sustainable relationships in the life cycle analysis of the food supply chains?


An Unequal Wilkinson Power Divider for a Frequency and Its First Harmonic. This letter presents a Wilkinson power divider operating at a frequency and its first harmonic with unequal power divider ratio. To obtain the unequal property, four groups of 1/6 wavelength transmission lines with different characteristic impedances are needed to match all ports. Theoretically, closed-form equations for the design are derived based on transmission line theory. Experimental results have indicated that all the features of this novel power divider can be fulfilled at f 0 and 2f 0 simultaneously.

Question: How to design a power divider with unequal ratio?


Analysis and Design of a Single-Stage Parallel AC-to-DC Converter. In this paper, a single-stage (S2) parallel ac-to-dc converter based on single-switch two-output boost-flyback converter is presented. The converter contains two semistages. One is the boost-flyback semistage, which transfers partial input power transferred to load directly through one power flow path and has excellent self-power factor correction property when operating in discontinuous conduction mode even though the boost output is close to the peak value of the line voltage. The other one is the flyback dc-to-dc (dc/dc) semistage that provides the output regulation on another parallel power flow path. With this design, the power conversion efficiency is improved and the current stress of control switch is reduced. Furthermore, the calculation process of power distribution and bulk capacitor voltage, design equations, and design procedure for key parameters are also presented. By following the procedure, an 80 W prototype converter has been built and tested. The experimental results show that the measured line harmonic current at the worst condition complies with the IEC61000-3-2 class D limits, the maximum bulk capacitor voltage is about 415.4 V, and the maximum efficiency is about 85.8%. Hence, the proposed S2 converter is suitable for universal input usage.

Question: What is the purpose of the paper?


Dynablock: Dynamic 3D Printing for Instant and Reconstructable Shape Formation. This paper introduces Dynamic 3D Printing, a fast and reconstructable shape formation system. Dynamic 3D Printing can assemble an arbitrary three-dimensional shape from a large number of small physical elements. Also, it can disassemble the shape back to elements and reconstruct a new shape. Dynamic 3D Printing combines the capabilities of 3D printers and shape displays: Like conventional 3D printing, it can generate arbitrary and graspable three-dimensional shapes, while allowing shapes to be rapidly formed and reformed as in a shape display. To demonstrate the idea, we describe the design and implementation of Dynablock, a working prototype of a dynamic 3D printer. Dynablock can form a three-dimensional shape in seconds by assembling 3,000 9 mm blocks, leveraging a 24 x 16 pin-based shape display as a parallel assembler. Dynamic 3D printing is a step toward achieving our long-term vision in which 3D printing becomes an interactive medium, rather than the means for fabrication that it is today. In this paper, we explore possibilities for this vision by illustrating application scenarios that are difficult to achieve with conventional 3D printing or shape display systems.

Question: What is the long-term vision of 3D printing?


Hype and Heavy Tails: A Closer Look at Data Breaches. Recent widely publicized data breaches have exposed the personal information of hundreds of millions of people. Some reports point to alarming increases in both the size and frequency of data breaches, spurring institutions around the world to address what appears to be a worsening situation. But, is the problem actually growing worse? In this paper, we study a popular public dataset and develop Bayesian Generalized Linear Models to investigate trends in data breaches. Analysis of the model shows that neither size nor frequency of data breaches has increased over the past decade. We find that the increases that have attracted attention can be explained by the heavy-tailed statistical distributions underlying the dataset. Specifically, we find that data breach size is log-normally distributed and that the daily frequency of breaches is described by a negative binomial distribution. These distributions may provide clues to the generative mechanisms that are responsible for the breaches. Additionally, our model predicts the likelihood of breaches of a particular size in the future. For example, we find that in the next year there is only a 31% chance of a breach of 10 million records or more in the US. Regardless of any trend, data breaches are costly, and we combine the model with two different cost models to project that in the next three years breaches could cost up to $55 billion.

Question: What is the size of the canadian military?


PLANT LEAF RECOGNITION. This paper proposes an automated system for recognizing plant species based on leaf images. Plant leaf images corresponding to three plant types, are analyzed using three different shape modelling techniques, the first two based on the Moments-Invariant (M-I) model and the Centroid-Radii (C-R) model and the third based on a proposed technique of Binary-Superposition (B-S). For the M-I model the first four central normalized moments have been considered. For the C-R model an edge detector has been used to identify the boundary of the leaf shape and 36 radii at 10 degree angular separation have been used to build the shape vector. The proposed approach consists of comparing binary versions of the leaf images through superposition and using the sum of non-zero pixel values of the resultant as the feature vector. The data set for experimentations consists of 180 images divided into training and testing sets and comparison between them is done using Manhattan, Euclidean and intersection norms. Accuracies obtained using the proposed technique is seen to be an improvement over the M-I and C-R based techniques, and comparable to the best figures reported in extant literature.

Question: What is the difference between the M-I and C-R models?


High-performance polyline intersection based spatial join on GPU-accelerated clusters. The rapid growing volumes of spatial data have brought significant challenges on developing high-performance spatial data processing techniques in parallel and distributed computing environments. Spatial joins are important data management techniques in gaining insights from large-scale geospatial data. While several distributed spatial join techniques based on spatial partitions have been implemented on top of existing Big Data systems, they are not capable of natively exploiting massively data parallel computing power provided by modern commodity Graphics Processing Units (GPUs). In this study, as an important component of our research initiative in developing high-performance spatial join techniques on GPUs, we have designed and implemented a polyline intersection based spatial join technique that is capable of exploiting massively data parallel computing power on GPUs. The proposed polyline intersection based spatial join technique is integrated into a customized lightweight distributed execution engine that natively supports spatial partitions. We empirically evaluate the performance of the proposed spatial join technique on both a standalone GPU-equipped workstation and Amazon EC2 GPU-accelerated clusters and demonstrate its high performance when comparing with the state-of-the-art.

Question: What is the difference between a polyline and a line?


Tweet Sarcasm Detection Using Deep Neural Network. Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets. Results show that neural features give improved accuracies for sarcasm detection, with different error distributions compared with discrete manual features.

Question: What is the difference between neural network and discrete manual features?


Efficient virtual memory for big memory servers. Our analysis shows that many "big-memory" server workloads, such as databases, in-memory caches, and graph analytics, pay a high cost for page-based virtual memory. They consume as much as 10% of execution cycles on TLB misses, even using large pages. On the other hand, we find that these workloads use read-write permission on most pages, are provisioned not to swap, and rarely benefit from the full flexibility of page-based virtual memory.  To remove the TLB miss overhead for big-memory workloads, we propose mapping part of a process's linear virtual address space with a direct segment, while page mapping the rest of the virtual address space. Direct segments use minimal hardware---base, limit and offset registers per core---to map contiguous virtual memory regions directly to contiguous physical memory. They eliminate the possibility of TLB misses for key data structures such as database buffer pools and in-memory key-value stores. Memory mapped by a direct segment may be converted back to paging when needed.  We prototype direct-segment software support for x86-64 in Linux and emulate direct-segment hardware. For our workloads, direct segments eliminate almost all TLB misses and reduce the execution time wasted on TLB misses to less than 0.5%.

Question: How to map a process's linear virtual address space with a direct segment?


Text-to-3 D Scene Generation using Semantic Parsing and Spatial Knowledge with Rule Based System. Scene Generation plays an important role in digital media to represent a news or a specific domain to the viewers. It's not easy to produce a scene from a text. Text may not completely express the whole situation in digital media. Most of the people are not conscious about the news until it's not visualized to them. Text to 3D scene generation is a process where people do not need to read a news. The 3D Scene will represent the situation. It will help people to conscious about their life. In this paper, we introduce a rule-based framework where scene generated from text using semantic parsing and spatial knowledge. Semantic parsing has identified the templates, objects, and constraints and spatial knowledge have built the relation between object and template. Our rule based framework has identified the uncountable noun and some spatial relations to generate 3D scenes.

Question: How to generate a 3D scene from a text?


Word Alignment Modeling with Context Dependent Deep Neural Network. In this paper, we explore a novel bilingual word alignment approach based on DNN (Deep Neural Network), which has been proven to be very effective in various machine learning tasks (Collobert et al., 2011). We describe in detail how we adapt and extend the CD-DNNHMM (Dahl et al., 2012) method introduced in speech recognition to the HMMbased word alignment model, in which bilingual word embedding is discriminatively learnt to capture lexical translation information, and surrounding words are leveraged to model context information in bilingual sentences. While being capable to model the rich bilingual correspondence, our method generates a very compact model with much fewer parameters. Experiments on a large scale EnglishChinese word alignment task show that the proposed method outperforms the HMM and IBM model 4 baselines by 2 points in F-score.

Question: What is the difference between the HMM and IBM model 4 baselines?


Improved resolution from subpixel shifted pictures. In this paper we consider the problem of obtaining a picture with improved resolution from an ensemble of K spatially shifted low-resolution pictures. We show that it can be done and provide a procedure for its implementations. The framework and scheme utilized in the generalized multichannel sampling theorem of Papoulis and Brown was found to be an appropriate representation for our problem. The linear channels of this scheme correspond to the processes that produce the low-resolution pictures (by blur and shifts) and to the process of reconstruction. The total process is shown to consist of two distinct degradation-restoration pairs: blurring-deblurring and undersampling-merging. This paper discusses merging. Deblurring (deconvolution) can be done by any desired scheme independent of merging. It is shown that the procedure achieves the claims of super resolution schemes but, unlike many of them, is well posed and robust. This procedure holds for a selected ensemble so that K is not too large and no two of the pictures practially coincide. The number of multiplications per pixel of the improved picture can be reduced to O(K2). o ESJZ

Question: How to improve resolution from low-resolution pictures?


Improving malware detection by applying multi-inducer ensemble. Detection of malicious software (malware) using ma chine learning methods has been explored extensively to enable fas t detection of new released malware. The performance of these classifiers depen ds on the induction algorithms being used. In order to benefit from mul tiple different classifiers, and exploit their strengths we suggest using an ens emble method that will combine the results of the individual classifiers i nto one final result to achieve overall higher detection accuracy. In this paper we evaluate several combining methods using five different base inducers (C4.5 Dec ision Tree, Naïve Bayes, KNN, VFI and OneR) on five malware datasets. The mai n goal is to find the best combining method for the task of detecting mal icious files in terms of accuracy, AUC and execution time.

Question: What is the best combining method for malware detection?


Improving accuracy in E-Textiles as a platform for pervasive sensing. Recently Electronic Textile (E-Textile) technology enables the weaving computation and communication components into the clothes that we wear and objects that we interact with every day. E-Textile enables the design and development of a broad range of pervasive sensing systems such as smart bed sheets for sleep posture monitoring, insoles in medical shoes for monitoring plantar pressure distribution, smart garments and sensing gloves. However the low cost of E-Textiles come at the cost of accuracy. In this work we propose an actuator-based method that increases the accuracy of E-Textiles by means of enabling real-time calibration. Our proposed system increases system accuracy by 38.55% on average (maximum 58.4%).

Question: How does the accuracy of e-textiles increase?


3D Object Representations for Fine-Grained Categorization. While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.

Question: What is the difference between 2D and 3D object representations?


A review and future direction of agile, business intelligence, analytics and data science. Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions.

Question: What is the future of business intelligence?


Online sketching hashing. Recently, hashing based approximate nearest neighbor (ANN) search has attracted much attention. Extensive new algorithms have been developed and successfully applied to different applications. However, two critical problems are rarely mentioned. First, in real-world applications, the data often comes in a streaming fashion but most of existing hashing methods are batch based models. Second, when the dataset becomes huge, it is almost impossible to load all the data into memory to train hashing models. In this paper, we propose a novel approach to handle these two problems simultaneously based on the idea of data sketching. A sketch of one dataset preserves its major characters but with significantly smaller size. With a small size sketch, our method can learn hash functions in an online fashion, while needs rather low computational complexity and storage space. Extensive experiments on two large scale benchmarks and one synthetic dataset demonstrate the efficacy of the proposed method.

Question: What is the difference between hashing and sketching?


Zero quiescent current, delay adjustable, power-on-reset circuit. A power-on-reset (POR) circuit is extremely important in digital and mixed-signal ICs. It is used to initialize critical nodes of digital circuitry inside the chip during power-on. A novel POR circuit having zero quiescent current and an adjustable delay is proposed and demonstrated. The circuit has been designed in 0.5μm CMOS process to work for a supply voltage ranging from 1.8V to 5.5V. The circuit generates a POR signal after a predetermined delay, after the supply voltage crosses a predefined threshold voltage. This delay can be increased or decreased via programmable fuses. The POR threshold does not depend upon the supply ramp rate. The Brown-Out (BO) voltage for the proposed circuit matches the minimum supply voltage required by the digital circuitry inside the IC. The circuit consumes zero steady state current, making it ideal for low power applications.

Question: What is a power-on-reset circuit?


A Hybrid Approach for Arabic Text Summarization Using Domain Knowledge and Genetic Algorithms. Text summarization is the process of producing a shorter version of a specific text. Automatic summarization techniques have been applied to various domains such as medical, political, news, and legal domains proving that adapting domain-relevant features could improve the summarization performance. Despite the existence of plenty of research work in the domain-based summarization in English and other languages, there is a lack of such work in Arabic due to the shortage of existing knowledge bases. In this paper, a hybrid, single-document text summarization approach (abbreviated as (ASDKGA)) is presented. The approach incorporates domain knowledge, statistical features, and genetic algorithms to extract important points of Arabic political documents. The ASDKGA approach is tested on two corpora KALIMAT corpus and Essex Arabic Summaries Corpus (EASC). The Recall-Oriented Understudy for Gisting Evaluation (ROUGE) framework was used to compare the automatically generated summaries by the ASDKGA approach with summaries generated by humans. Also, the approach is compared against three other Arabic text summarization approaches. The (ASDKGA) approach demonstrated promising results when summarizing Arabic political documents with average F-measure of 0.605 at the compression ratio of 40%.

Question: How to summarize Arabic political documents?


SALT STRESS AND PHYTO-BIOCHEMICAL RESPONSES OF PLANTS-A REVIEW.

Question: What is salt stress?


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Question: What is the art of scientific computing?