A Multi-task Deep Network for Person Re-identification. Person re-identification (ReID) focuses on identifying people across different scenes in video surveillance, which is usually formulated as a binary classification task or a ranking task in current person ReID approaches. In this paper, we take both tasks into account and propose a multi-task deep network (MTDnet) that makes use of their own advantages and jointly optimize the two tasks simultaneously for person ReID. To the best of our knowledge, we are the first to integrate both tasks in one network to solve the person ReID. We show that our proposed architecture significantly boosts the performance. Furthermore, deep architecture in general requires a sufficient dataset for training, which is usually not met in person ReID. To cope with this situation, we further extend the MTDnet and propose a cross-domain architecture that is capable of using an auxiliary set to assist training on small target sets. In the experiments, our approach outperforms most of existing person ReID algorithms on representative datasets including CUHK03, CUHK01, VIPeR, iLIDS and PRID2011, which clearly demonstrates the effectiveness of the proposed approach.

Question: What is the first large-scale Canadian peacekeeping mission?


High speed cascode flyback converter using multilayered coreless printed circuit board (PCB) step-down power transformer. In this paper, design and analysis of the high speed isolated cascode flyback converter using multilayered coreless PCB step down power transformer is presented. The converter is tested for the input voltage variation of 60–120V with a nominal DC input voltage of 90V. The designed converter was simulated and tested successfully up to the output power level of 30W within the switching frequency range of 2.6–3.7MHz. The cascode flyback converter is compared with the single switch flyback converter in terms of operating frequency, gate drive power consumption, conduction losses and stresses on MOSFETs. The maximum energy efficiency of the cascode converter is approximately 81% with a significant improvement of about 3–4% compared to single switch flyback converter. The gate drive power consumption which is more dominant compared to conduction losses of the cascode converter using GaN MOSFET is found to be negligible compared to single switch flyback converter.

Question: What is the maximum energy efficiency of a cascode flyback converter?


Associative Embedding: End-to-End Learning for Joint Detection and Grouping. We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping. A number of computer vision problems can be framed in this manner including multi-person pose estimation, instance segmentation, and multi-object tracking. Usually the grouping of detections is achieved with multi-stage pipelines, instead we propose an approach that teaches a network to simultaneously output detections and group assignments. This technique can be easily integrated into any state-of-the-art network architecture that produces pixel-wise predictions. We show how to apply this method to multi-person pose estimation and report state-of-the-art performance on the MPII and MS-COCO datasets.

Question: What is the difference between a multi-stage pipeline and an end-to-end learning approach?


Cone Trees: animated 3D visualizations of hierarchical information. The task of managing and accessing large information spaces is a problem in large scale cognition. Emerging technologies for 3D visualization and interactive animation offer potential solutions to this problem, especially when the structure of the information can be visualized. We describe one of these Information Visualization techniques, called the Cone Tree, which is used for visualizing hierarchical information structures. The hierarchy is presented in 3D to maximize effective use of available screen space and enable visualization of the whole structure. Interactive animation is used to shift some of the user's cognitive load to the human perceptual system.

Question: What is cone tree?


ASPEC: Asian Scientific Paper Excerpt Corpus. In this paper, we describe the details of the ASPEC (Asian Scientific Paper Excerpt Corpus), which is the first large-size parallel corpus of scientific paper domain. ASPEC was constructed in the Japanese-Chinese machine translation project conducted between 2006 and 2010 using the Special Coordination Funds for Promoting Science and Technology. It consists of a Japanese-English scientific paper abstract corpus of approximately 3 million parallel sentences (ASPEC-JE) and a Chinese-Japanese scientific paper excerpt corpus of approximately 0.68 million parallel sentences (ASPEC-JC). ASPEC is used as the official dataset for the machine translation evaluation workshop WAT (Workshop on Asian Translation).

Question: What is the first large-size parallel corpus of scientific paper domain?


Kernel-Based Learning of Hierarchical Multilabel Classification Models. We present a kernel-based algorithm for hierarchical text c lassification where the documents are allowed to belong to more than one category at a time. The clas sification model is a variant of the Maximum Margin Markov Network framework, where the classifi cation hierarchy is represented as a Markov tree equipped with an exponential family defined o n the edges. We present an efficient optimization algorithm based on incremental conditi onal gradient ascent in single-example subspaces spanned by the marginal dual variables. The optim ization is facilitated with a dynamic programming based algorithm that computes best update dire ctions in the feasible set. Experiments show that the algorithm can feasibly optimize t raining sets of thousands of examples and classification hierarchies consisting of hundreds of nodes. Training of the full hierarchical model is as efficient as training independent SVM-light clas sifiers for each node. The algorithm's predictive accuracy was found to be competitive with other r ecently introduced hierarchical multicategory or multilabel classification learning algorithms .

Question: What is a kernel-based algorithm?


Potter's Wheel: An Interactive Data Cleaning System. Cleaning data of errors in structure and content is important for data warehousing and integration. Current solutions for data cleaning involve many iterations of data "auditing" to find errors, and long-running transformations to fix them. Users need to endure long waits, and often write complex transformation scripts. We present Potter's Wheel, an interactive data cleaning system that tightly integrates transformation and discrepancy detection. Users gradually build transformations to clean the data by adding or undoing transforms on a spreadsheet-like interface; the effect of a transform is shown at once on records visible on screen. These transforms are specified either through simple graphical operations, or by showing the desired effects on example data values. In the background, Potter's Wheel automatically infers structures for data values in terms of user-defined domains, and accordingly checks for constraint violations. Thus users can gradually build a transformation as discrepancies are found, and clean the data without writing complex programs or enduring long delays.

Question: What is an interactive data cleaning system?


Wireless Relay Communications with Unmanned Aerial Vehicles: Performance and Optimization. In this paper, we investigate a communication system in which unmanned aerial vehicles (UAVs) are used as relays between ground-based terminals and a network base station. We develop an algorithm for optimizing the performance of the ground-to-relay links through control of the UAV heading angle. To quantify link performance, we define the ergodic normalized transmission rate (ENTR) for the links between the ground nodes and the relay, and derive a closed-form expression for it in terms of the eigenvalues of the channel correlation matrix. We show that the ENTR can be approximated as a sinusoid with an offset that depends on the heading of the UAV. Using this observation, we develop a closed-form expression for the UAV heading that maximizes the uplink network data rate while keeping the rate of each individual link above a certain threshold. When the current UAV relay assignments cannot meet the minimum link requirements, we investigate the deployment and heading control problem for new UAV relays as they are added to the network, and propose a smart handoff algorithm that updates node and relay assignments as the topology of the network evolves.

Question: What is the performance of the wireless relay communications with unmanned aerial vehicles?


Transforming Web Tables to a Relational Database. HTML tables represent a significant fraction of web data. The often complex headers of such tables are determined accurately using their indexing property. Isolated headers are factored to extract category hierarchies. Web tables are then transformed into a canonical form and imported into a relational database. The proposed processing allows for the formulation of arbitrary SQL queries over the collection of induced relational tables. Keywords—table segmentation; Wang categories; header paths; relational table SQL queries

Question: How to transform a web table to a relational database?


Using Program Analysis to Improve Database Applications. Applications that interact with database management systems (DBMSs) are ubiquitous. Such database applications are usually hosted on an application server and perform many small accesses over the network to a DBMS hosted on the database server to retrieve data for processing. For decades, the database and programming systems research communities have worked on optimizing such applications from different perspectives: database researchers have built highly efficient DBMSs, and programming systems researchers have developed specialized compilers and runtime systems for hosting applications. However, there has been relatively little work that optimizes database applications by considering these specialized systems in combination and looking for optimization opportunities that span across them. In this article, we highlight three projects that optimize database applications by looking at both the programming system and the DBMS in a holistic manner. By carefully revisiting the interface between the DBMS and the application, and by applying a mix of declarative database optimization and modern program analysis techniques, we show that a speedup of multiple orders of magnitude is possible in real-world applications.

Question: What is the canadian military?


On the Influence of Emotional Valence Shifts on the Spread of Information in Social Networks. In this paper, we present a study on 4.4 million Twitter messages related to 24 systematically chosen real-world events. For each of the 4.4 million tweets, we first extracted sentiment scores based on the eight basic emotions according to Plutchik's wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the spread of information. We found that in general OSN users tend to conform to the emotional valence of the respective real-world event. However, we also found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted emotions in the corresponding tweets (i.e. positive messages). To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis postulates that positive emotions serve as an antidote during negative events.

Question: How does the spread of information in social networks depend on the emotional valence of the real-world event?


Online-specific fear of missing out and Internet-use expectancies contribute to symptoms of Internet-communication disorder. Some of the most frequently used online applications are Facebook, WhatsApp, and Twitter. These applications allow individuals to communicate with other users, to share information or pictures, and to stay in contact with friends all over the world. However, a growing number of users suffer from negative consequences due to their excessive use of these applications, which can be referred to as Internet-communication disorder. The frequent use and easy access of these applications may also trigger the individual's fear of missing out on content when not accessing these applications. Using a sample of 270 participants, a structural equation model was analyzed to investigate the role of psychopathological symptoms and the fear of missing out on expectancies towards Internet-communication applications in the development of symptoms of an Internet-communication disorder. The results suggest that psychopathological symptoms predict higher fear of missing out on the individual's Internet-communication applications and higher expectancies to use these applications as a helpful tool to escape from negative feelings. These specific cognitions mediate the effect of psychopathological symptoms on Internet-communication disorder. Our results are in line with the theoretical model by Brand et al. (2016) as they show how Internet-related cognitive bias mediates the relationship between a person's core characteristics (e.g., psychopathological symptoms) and Internet-communication disorder. However, further studies should investigate the role of the fear of missing out as a specific predisposition, as well as specific cognition in the online context.

Question: What is the role of fear of missing out in the development of Internet-communication disorder?


Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition. In this paper we introduce a template-based method for recognizing human actions called action MACH. Our approach is based on a maximum average correlation height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television.

Question: What is the maximum average correlation height filter?


Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing Students. We take a qualitative approach to understanding deaf and hard of hearing (DHH) students' experiences with real-time captioning as an access technology in mainstream university classrooms. We consider both existing human-based captioning as well as new machine-based solutions that use automatic speech recognition (ASR). We employed a variety of qualitative research methods to gather data about students' captioning experiences including in-class observations, interviews, diary studies, and usability evaluations. We also conducted a co-design workshop with 8 stakeholders after our initial research findings. Our results show that accuracy and reliability of the technology are still the most important issues across captioning solutions. However, we additionally found that current captioning solutions tend to limit students' autonomy in the classroom and present a variety of user experience shortcomings, such as complex setups, poor feedback and limited control over caption presentation. Based on these findings, we propose design requirements and recommend features for real-time captioning in mainstream classrooms.

Question: What is the difference between a qualitative and a quantitative approach?


Nonlinear Scale Space with Spatially Varying Stopping Time. A general scale space algorithm is presented for denoising signals and images with spatially varying dominant scales. The process is formulated as a partial differential equation with spatially varying time. The proposed adaptivity is semi-local and is in conjunction with the classical gradient-based diffusion coefficient, designed to preserve edges. The new algorithm aims at maximizing a local SNR measure of the denoised image. It is based on a generalization of a global stopping time criterion presented recently by the author and colleagues. Most notably, the method works well also for partially textured images and outperforms any selection of a global stopping time. Given an estimate of the noise variance, the procedure is automatic and can be applied well to most natural images.

Question: How to denoise an image with spatially varying stopping time?


System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries. In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.

Question: How to control the power-flow of a series hybrid electric vehicle?


Capsule Networks for Hyperspectral Image Classification. Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNNbased network architecture still finds obstacles when effectively exploiting the relationships between hyperspectral imaging (HSI) features in the spectral–spatial domain, which is a key factor to deal with the high level of complexity present in remotely sensed HSI data. Despite the fact that deeper architectures try to mitigate these limitations, they also find challenges with the convergence of the network parameters, which eventually limit the classification performance under highly demanding scenarios. In this paper, we propose a new CNN architecture based on spectral–spatial capsule networks in order to achieve a highly accurate classification of HSIs while significantly reducing the network design complexity. Specifically, based on Hinton's capsule networks, we develop a CNN model extension that redefines the concept of capsule units to become spectral– spatial units specialized in classifying remotely sensed HSI data. The proposed model is composed by several building blocks, called spectral–spatial capsules, which are able to learn HSI spectral–spatial features considering their corresponding spatial positions in the scene, their associated spectral signatures, and also their possible transformations. Our experiments, conducted Manuscript received June 9, 2018; revised August 22, 2018; accepted September 19, 2018. This work was supported in part by the Ministerio de Educación (Resolución de 26 de diciembre de 2014 y de 19 de noviembre de 2015, de la Secretaría de Estado de Educación, Formación Profesional y Universidades, por la que se convocan ayudas para la formación de profesorado universitario, de los subprogramas de Formación y de Movilidad incluidos en el Programa Estatal de Promoción del Talento y su Empleabilidad, and en el marco del

Question: What is the difference between a capsule network and a convolutional neural network?


Performance analysis of downlink NOMA networks over Rayleigh fading channels. This paper analyzes the performance of downlink non-orthogonal multiple-access (NOMA) networks over independent but not necessarily identically distributed Rayleigh fading channels for arbitrary number of network users. Specifically, closed-form expressions for the average SNR, average achievable rate, and outage probability are derived, where it has been shown that network users achieve diversity orders equivalent to their ordered channel gains. Numerical evaluations are presented to validate the derived closed-form expressions for the different performance metrics, which are found to be in agreement with the network simulation results.

Question: What is the performance of downlink NOMA networks over Rayleigh fading channels?


A Traceback Attack on Freenet. Freenet is a popular peer to peer anonymous content-sharing network, with the objective to provide the anonymity of both content publishers and retrievers. Despite more than a decade of active development and deployment and the adoption of well-established cryptographic algorithms in Freenet, it remains unanswered how well the anonymity objective of the initial Freenet design has been met. In this paper we develop a traceback attack on Freenet, and show that the originating machine of a content request message in Freenet can be identified; that is, the anonymity of a content retriever can be broken, even if a single request message has been issued by the retriever. We present the design of the traceback attack, and perform both experimental and simulation studies to confirm the feasibility and effectiveness of the attack. For example, with randomly chosen content requesters (and random contents stored in the Freenet testbed), Emulab-based experiments show that, for <inline-formula><tex-math notation="LaTeX">$24$</tex-math> <alternatives><inline-graphic xlink:href="duan-ieq1-2453983.gif"/></alternatives></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$43$</tex-math><alternatives><inline-graphic xlink:href="duan-ieq2-2453983.gif"/> </alternatives></inline-formula> percent of the content request messages, we can identify their originating machines. We also briefly discuss potential solutions to address the developed traceback attack. Despite being developed specifically on Freenet, the basic principles of the traceback attack and solutions have important security implications for similar anonymous content-sharing systems.

Question: How does freenet work?


Multi-View Inpainting for Image-Based Scene Editing and Rendering. We propose a method to remove objects such as people and cars from multi-view urban image datasets, enabling free-viewpoint IBR in the edited scenes. Our method combines information from multi-view 3D reconstruction with image inpainting techniques, by formulating the problem as an optimization of a global patch-based objective function. We use Image-Based Rendering (IBR) techniques to reproject information from neighboring views, and 3D multi-view stereo reconstruction to perform multiview coherent initialization for inpainting of pixels not filled by reprojection. Our algorithm performs multi-view consistent inpainting for color and 3D by blending reprojections with patch-based image inpainting. We run our algorithm on casually captured datasets, and Google StreetViewdata, removing objects cars, people and pillars, showing that our approach produces results of sufficient quality for free-viewpoint IBR on "cleaned up" scenes, as well as IBR scene editing, such as limited motion of real objects.

Question: How to remove objects from images?


A novel objective function for improved phoneme recognition using time delay neural networks. The authors present single- and multispeaker recognition results for the voiced stop consonants /b, d, g/ using time-delay neural networks (TDNN), a new objective function for training these networks, and a simple arbitration scheme for improved classification accuracy. With these enhancements a median 24% reduction in the number of misclassifications made by TDNNs trained with the traditional backpropagation objective function is achieved. This redundant results in /b, d, g/ recognition rates that consistently exceed 98% for TDNNs trained with individual speakers; it yields a 98.1% recognition rate for a TDNN trained with three male speakers.<<ETX>>

Question: What is ETX?


Dynamic switching-based reliable flooding in low-duty-cycle wireless sensor networks. Reliable flooding in wireless sensor networks (WSNs) is desirable for a broad range of applications and network operations. However, it is a challenging problem to ensure 100% flooding coverage efficiently considering the combined effects of low-duty-cycle operation and unreliable wireless transmission. In this work, we propose a novel dynamic switching-based reliable flooding (DSRF) framework, which is designed as an enhancement layer to provide efficient and reliable flooding over a variety of existing flooding tree structures in low-duty-cycle WSNs. Through comprehensive simulations, we demonstrate that DSRF can effectively improve both flooding energy efficiency and latency.

Question: How to design a reliable flooding algorithm for low-duty-cycle wireless sensor networks?


Aligning movies with scripts by exploiting temporal ordering constraints. Scripts provide rich textual annotation of movies, including dialogs, character names, and other situational descriptions. Exploiting such rich annotations requires aligning the sentences in the scripts with the corresponding video frames. Previous work on aligning movies with scripts predominantly relies on time-aligned closed-captions or subtitles, which are not always available. In this paper, we focus on automatically aligning faces in movies with their corresponding character names in scripts without requiring closed-captions/subtitles. We utilize the intuition that faces in a movie generally appear in the same sequential order as their names are mentioned in the script. We first apply standard techniques for face detection and tracking, and cluster similar face tracks together. Next, we apply a generative Hidden Markov Model (HMM) and a discriminative Latent Conditional Random Field (LCRF) to align the clusters of face tracks with the corresponding character names. Our alignment models (especially LCRF) significantly outperform the previous state-of-the-art on two different movie datasets and for a wide range of face clustering algorithms.

Question: How do you align movies with scripts?


Twin gate rectangular recessed channel (TG-RRC) MOSFET for digital-logic applications. In this paper, twin gate rectangular recessed channel (TG-RRC) MOSFET with independent gate control is used to realize its application in digital electronics by using it as two input logic. The input logic is controlled by the independent gates which have different work functions (Φ1 for gate 1 and Φ2 gate 2) which are separated by oxide layer of 2 nm, thus controlling various electrical parameters such as current density, potential etc. across the channel in a swift manner and also increasing the switching characteristics of the device. It can give the full functionality of "AND" and "NAND" gate. We have shown the structure and functioning of the gates with a simulation on Silvaco TCAD. Since it is one of the fundamental gates has much applicability in digital logics starting from basic Flip-flops to sequential circuits in SRAM or DRAM cells.

Question: What is the function of the gates?


Kinetic Energy of Wind-Turbine Generators for System Frequency Support. As wind power penetration increases and fossil plants are retired, it is feared that there will be insufficient kinetic energy (KE) from the plants to support the system frequency. This paper shows the fear is groundless because the high inertias (H cong 4 seconds) of wind turbine-generators (WTGs) can be integrated to provide frequency support during generation outage.

Question: What is the kinetic energy of wind turbine generators?


Miniature Dual-Mode Microstrip Filters. In order to reduce the size of dual-mode microstrip filters by keeping excellent performance, novel filter geometry is proposed. The novel filter includes a dual-mode resonator based on a meander structure. The effect of input/output feed lines located along a straight line on filter response is investigated for the dual-mode filter. The coupling between degenerate modes of the proposed dual-mode microstrip resonator is discussed depending on the perturbation size. The two dual-mode microstrip bandpass filters with real axis and imaginary axis transmission zeros (TZs) are designed, fabricated, and measured to document the validity of the description of the positive and negative coupling coefficient

Question: What is the dual-mode microstrip filter?


Shot boundary detection from videos using entropy and local descriptor. Video shot segmentation is an important step in key frame selection, video copy detection, video summarization, and video indexing for retrieval. Although some types of video data, e.g., live sports coverage, have abrupt shot boundaries that are easy to identify using simple heuristics, it is much more difficult to identify shot boundaries in other types such as cinematic movies. We propose an algorithm for shot boundary detection able to accurately identify not only abrupt shot boundaries, but also the fade-in and fade-out boundaries typical of cinematic movies. The algorithm is based on analysis of changes in the entropy of the gray scale intensity over consecutive frames and analysis of correspondences between SURF features over consecutive frames. In an experimental evaluation on the TRECVID-2007 shot boundary test set, the algorithm achieves substantial improvements over state of the art methods, with a precision of 97.8% and a recall of 99.3%.

Question: How to detect shot boundaries in videos?


Semantically Guided Depth Upsampling. We present a novel method for accurate and efficient upsampling of sparse depth data, guided by high-resolution imagery. Our approach goes beyond the use of intensity cues only and additionally exploits object boundary cues through structured edge detection and semantic scene labeling for guidance. Both cues are combined within a geodesic distance measure that allows for boundary-preserving depth interpolation while utilizing local context. We model the observed scene structure by locally planar elements and formulate the upsampling task as a global energy minimization problem. Our method determines globally consistent solutions and preserves fine details and sharp depth boundaries. In our experiments on several public datasets at different levels of application, we demonstrate superior performance of our approach over the state-of-the-art, even for very sparse measurements.

Question: How to upsample sparse depth data?


Development of a biped walking robot compensating for three-axis moment by trunk motion. The authors have been using the ZMP (@ro Mfoment&oint) as a criterion to distinguish the stability of walking for a biped walking robot which has a trunk. T6 the authors introduce a control method of dynamic biped walking for a biped walking robot to compensate for the three-axis (pitch, roll and yaw-axis) moment on an arbitraryplanned ZMP by trunk motion. The authors developed a biped walking robot and performed a walking experiment with the robot using the control method. The result was a fast dynamic biped walking a t the walking speed of 0.54 slstep with a 0.3 m step on a flat floor. This walking speed is about 50 percent faster than that with the robot which compensates for only the twoaxis (pitch and roll-axis) moment by t runk motion. -z In 1986, the authors developed a biped walking robot WL12 (Waseda Leg No. 12) which has a trunk for the stabilization of walking. At the same time the authors proposed a control method of dynamic biped walking for a biped walking robot to compensate for the two-axis (pitch and roll-axis) moment on an arbitrary planned ZMP by the trunk[l]-[3). In 1990, by using this walking control method, the authors achieved dynamic biped walking at the fastest walking speed of 0.8 s/step with 0.3 m step length on a flat floor[5]. The method was based on the assumption that the contact point between robot foot and the floor would not slide. That is to say, only the pitch-axis and roll-axis moments were compensated for by trunk motion, and the yaw-axis moment was not taken into account in the stabilization of walking. In the walking experiment, however, as the robot walked faster, the yaw-axis moment began to give the robot a spin on the yaw-axis, which became a considerable problem that affected the stability of walking. In contrast, i r was considered that human beings compensate not only for the pitch and roll-axis moment but also for the yaw-axis mome

Question: What is the fastest walking speed of a biped walking robot?


Enterprise Software System Integration : An Architectural Perspective. The present thesis is concerned with enterprise software systems of companies in the Swedish electricity industry, an industry that for the past few years has been exposed to a fairly tumultuous change process as a consequence of legislative reforms. Previously, the business operations of electric utilities – as well as those of most companies in the computerized world – were supported by a number of isolated software systems performing specific tasks. In recent years, these systems have been extended, and more importantly, integrated into a company-wide system in its own right, in this thesis referred to as the enterprise software system. As enterprise software systems have evolved, so has a need for strategies, methods and techniques for their management. Enterprise software system management involves a number of concerns; of these concerns, software integration is one of the most prominent. The discipline of software architecture is concerned with the modeling of large-scale structures of software systems. While the generated models are employed for a number of purposes , their perhaps most significant function is to serve as a base for reasoning about the represented system. Several methods for analysis of architectures have been proposed in recent years, and although software architecture analysis has displayed considerable success for a number of systems, enterprise software systems have to a large extent been ignored. In the empirical context of the Scandinavian electricity industry, this thesis explores the applicability of software architecture analysis to enterprise software system integration. Two conceptually different architectural analysis methods – deduction-and induction-based approaches – are considered, as well as the engineering process in which architectural analysis is performed. As a result of the investigations, the thesis proposes a modified process for architectural analy

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


Design of a Marx Generator for HEMP filter evaluation taking account of parasitic effect of components. This paper introduces the design of a Marx Generator that can generate high attitude pulses up to 25 kV and 2.5 kA. The pulses have a wave shape representative of that used to test High-altitude ElectroMagnetic (HEMP) filters against the pulse current injection (PCI) requirements. The rise time is 20 ns and the pulse width is 230 ns on a 10 Ω resistor. In the design, it was found that parasitic effect of circuit components could significantly affect the performance of the generator. This is mainly because the pulse width is very narrow and its frequency spectrum is very wideband. The high-frequency circuit models of the components used in the generator were obtained by calculation and optimization, and then further verified by experiment. The verified circuit models were then used to design the three-stage generator. The measured performance agrees very well with the simulated one using the proposed circuit models.

Question: How to design a Marx Generator for HEMP filter evaluation?


Image Stylization using Depth Information for Hatching and Engraving effects by. In this thesis inspired by artists such as Gustave Doré and copperplate engravers, we present a new approach for producing a sketch-type hatching effect as well as engraving art seen in the US dollar bills using depth maps as input. The approach uses the acquired depth to generate pen-and-ink type of strokes that exaggerate surface characteristics of faces and objects. The background on this area is presented with an emphasis on line drawing, pen-and-ink illustrations, engraving art and other artistic styles. The patterns generated are composed of parallel lines that change their directionality smoothly while avoiding intersections between them. The style communicates tone and surface characteristics using curve deformation, thickness, density, and spacing while at the same time adding crosshatching effects. This is achieved with our algorithmic approach which uses the data to perform operations that deforms patterns. The components of our methodology and algorithms are detailed, as well as the equations that govern these effects. This work also presents an array of different results by varying parameters or by rendering variations.

Question: How to generate a hatching effect using depth maps?


Towards parameter-free data mining. Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining process.Data mining algorithms should have as few parameters as possible, ideally none. A parameter-free algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics and computational theory hold great promise for a parameter-free data-mining paradigm. The results are motivated by observations in Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen or so lines of code. We will show that this approach is competitive or superior to the state-of-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/video datasets.

Question: What is the difference between a parameter-free data mining algorithm and a parameter-laden data mining algorithm?


Dynamic Conditional Random Fields for Joint Sentence Boundary and Punctuation Prediction. The use of dynamic conditional random fields (DCRF) has been shown to outperform linear-chain conditional random fields (LCRF) for punctuation prediction on conversational speech texts [1]. In this paper, we combine lexical, prosodic, and modified n-gram score features into the DCRF framework for a joint sentence boundary and punctuation prediction task on TDT3 English broadcast news. We show that the joint prediction method outperforms the conventional two-stage method using LCRF or maximum entropy model (MaxEnt). We show the importance of various features using DCRF, LCRF, MaxEnt, and hidden-event n-gram model (HEN) respectively. In addition, we address the practical issue of feature explosion by introducing lexical pruning, which reduces model size and improves the F1-measure. We adopt incremental local training to overcome memory size limitation without incurring significant performance penalty. Our results show that adding prosodic and n-gram score features gives about 20% relative error reduction in all cases. Overall, DCRF gives the best accuracy, followed by LCRF, MaxEnt, and HEN.

Question: How to predict sentence boundaries and punctuation in conversational speech?


Learning structural SVMs with latent variables. We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.

Question: What is the difference between a large margin and a large margin formulation?


SVM Based Effective Malware Detection System. Malware is coined as an instance of malicious code that has the potential to harm a computer or network. Recent years have encountered massive growth in malwares as existing signature based malware detection approaches are becoming ineffective and intractable. Cyber criminals and malware developers have adapted code obfuscation techniques which undermines the effectiveness of malware defense mechanism. Hence we propounded a system which focuses on static analysis in addition with automated behavior analysis in emulated environment generating behavior reports to investigate malwares. The proposed method uses programs as opcode density histograms and reduces the explosion of features. We employed eigen vector subspace analysis to filter and diminish the misclassification and interference of features. Our system uses a hybrid approach for discovering malware based on support vector machine classifier so that potential of malware detection system can be leveraged to combat with diverse forms of malwares while attaining high accuracy and low false alarms. Keywords—Behavior Analysis, Static Analysis, Opcode Extraction, Malware Detection, Support Vector Machine.

Question: What is a malware?


Development of Robotic Gloves for Hand Rehabilitation Post-Stroke. This paper presents the hardware and software developments that have been achieved and implemented in the robotic gloves for hand rehabilitation post-stroke. The practical results are shown that prove the functionalities of the robotic gloves in common operating conditions. This work focused on development of a lightweight and low-cost robotic glove that patients can wear and use to recover hand functionality. We developed some structures for the robotic glove, as exoskeleton and as soft robotic glove. The paper presents a comparison of these structures.

Question: What is the difference between exoskeleton and soft robotic glove?


Complementary approaches built as web service for arabic handwriting OCR systems via amazon elastic mapreduce (EMR) model. Arabic Optical Character Recognition (OCR) as Web Services represents a major challenge for handwritten document recognition. A variety of approaches, methods, algorithms and techniques have been proposed in order to build powerful Arabic OCR web services. Unfortunately, these methods could not succeed in achieving this mission in case of large large quantity Arabic handwritten documents. Intensive experiments and observations revealed that some of the existing approaches and techniques are complementary and can be combined to improve the recognition rate. Designing and implementing these recent sophisticated complementary approaches and techniques as web services are commonly complex; they require strong computing power to reach an acceptable recognition speed especially in case of large quantity documents. One of the possible solutions to overcome this problem is to benefit from distributed computing architectures such as cloud computing. This paper describes the design and implementation of Arabic Handwriting Recognition as a web service (AHRweb service) based on the complementary approach K-Nearest Neighbor (KNN) /Support Vector Machine (SVM) (K-NN/SVM) via Amazon Elastic MapReduce (EMR) model. The experiments were conducted on a cloud computing environment with a real large scale handwriting dataset from the Institute for Communications Technology (IFN)/ Ecole Nationale d'Ingénieur de Tunis (ENIT) IFN/ENIT database. The J-Sim (Java Simulator) was used as a tool to generate and analyze statistical results. Experimental results show that Amazon Elastic MapReduce (EMR) model constitutes a very promising framework for enhancing large AHRweb service performances.

Question: What is the arabic handwriting recognition as a web service?


Architecture for the Internet of Things (IoT): API and Interconnect. The proposed architecture is structured into a secure API, a backbone, and separate device networks with standard interface to the backbone. The API decouples innovation of services and service logic from protocols and network elements. It also enables service portability between systems, i.e. a service may be allocated to end-systems or servers, with possible relocation and replication throughout its lifecycle. Ubiquitous service provisioning depends on interoperability, not only for provisioning of a standard QoS controlled IP bearer, but also for cross domain naming, security, mobility, multicast, location, routing and management, including fair compensation for utility provisioning. The proposed architecture not only includes these critical elements but also caters for multi-homing, mobile networks with dynamic membership, and third party persistent storage based on indirection.

Question: What is the architecture of the internet of things?


SLR-Tool - A Tool for Performing Systematic Literature Reviews. Systematic literature reviews (SLRs) have been gaining a significant amount of attention from Software Engineering researchers since 2004. SLRs are considered to be a new research methodology in Software Engineering, which allow evidence to be gathered with regard to the usefulness or effectiveness of the technology proposed in Software Engineering for the development and maintenance of software products. This is demonstrated by the growing number of publications related to SLRs that have appeared in recent years. While some tools exist that can support some or all of the activities of the SLR processes defined in (Kitchenham & Charters, 2007), these are not free. The objective of this paper is to present the SLR-Tool, which is a free tool and is available on the following website: http://alarcosj.esi.uclm.es/SLRTool/, to be used by researchers from any discipline, and not only Software Engineering. SLR-Tool not only supports the process of performing SLRs proposed in (Kitchenham & Charters, 2007), but also provides additional functionalities such as: refining searches within the documents by applying text mining techniques; defining a classification schema in order to facilitate data synthesis; exporting the results obtained to the format of tables and charts; and exporting the references from the primary studies to the formats used in bibliographic packages such as EndNote, BibTeX or Ris. This tool has, to date, been used by members of the Alarcos Research Group and PhD students, and their perception of it is that it is both highly necessary and useful. Our purpose now is to circulate the use of SLR-Tool throughout the entire research community in order to obtain feedback from other users.

Question: What is the purpose of the tool?


Multimodal Deep Learning for Cervical Dysplasia Diagnosis. To improve the diagnostic accuracy of cervical dysplasia, it is important to fuse multimodal information collected during a patient's screening visit. However, current multimodal frameworks suffer from low sensitivity at high specificity levels, due to their limitations in learning correlations among highly heterogeneous modalities. In this paper, we design a deep learning framework for cervical dysplasia diagnosis by leveraging multimodal information. We first employ the convolutional neural network (CNN) to convert the low-level image data into a feature vector fusible with other non-image modalities. We then jointly learn the non-linear correlations among all modalities in a deep neural network. Our multimodal framework is an end-to-end deep network which can learn better complementary features from the image and non-image modalities. It automatically gives the final diagnosis for cervical dysplasia with 87.83% sensitivity at 90% specificity on a large dataset, which significantly outperforms methods using any single source of information alone and previous multimodal frameworks.

Question: How does the deep learning framework for cervical dysplasia diagnosis work?


Lesion-specific coronary artery calcium quantification better predicts cardiac events. CT-based coronary artery calcium (CAC) scanning has been introduced as a non-invasive, low-radiation imaging technique for the assessment of the overall coronary arterial atherosclerotic burden. A three dimensional CAC volume contains significant clinically relevant information, which is unused by conventional whole-heart CAC quantification methods. In this paper, we have developed a more detailed distance-weighted lesion-specific CAC quantification framework that predicts cardiac events better than the conventional whole-heart CAC measures. This framework consists of (1) a novel lesion-specific CAC quantification tool that measures each calcific lesion's attenuation, morphologic and geometric statistics; (2) a distance-weighted event risk model to estimate the risk probability caused by each lesion, and (3) a Naive Bayesian technique for risk integration. We have tested our lesion-specific event predictor on 30 CAC positive scans (10 with events and 20 without events), and compared it with conventional whole-heart CAC scores. Experiment results showed our novel approach significantly improves the prediction accuracy, including AUC of ROC analysis was improved from 66 ∼ 68% to 75%, and sensitivities was improved by 20 ∼ 30% at the cutpoints of 80% specificity.

Question: What is the difference between a Naive Bayesian and a Bayesian?


The association between psychological distance and construal level: evidence from an implicit association test.. According to construal level theory (N. Liberman, Y. Trope, & E. Stephan, in press; Y. Trope & N. Liberman, 2003), people use a more abstract, high construal level when judging, perceiving, and predicting more psychologically distal targets, and they judge more abstract targets as being more psychologically distal. The present research demonstrated that associations between more distance and higher level of construal also exist on a pure conceptual level. Eight experiments used the Implicit Association Test (IAT; A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998) to demonstrate an association between words related to construal level (low vs. high) and words related to four dimensions of distance (proximal vs. distal): temporal distance, spatial distance, social distance, and hypotheticality. In addition to demonstrating an association between level of construal and psychological distance, these findings also corroborate the assumption that all 4 dimensions of psychological distance are related to level of construal in a similar way and support the notion that they all are forms of psychological distance.

Question: What is the association between psychological distance and construal level?


Grid Cells and Neural Coding in High-End Cortices. An ultimate goal of neuroscience is to understand the mechanisms of mammalian intellectual functions, many of which are thought to depend extensively on the cerebral cortex. While this may have been considered a remote objective when Neuron was launched in 1988, neuroscience has now evolved to a stage where it is possible to decipher neural-circuit mechanisms in the deepest parts of the cortex, far away from sensory receptors and motoneurons. In this review, we show how studies of place cells in the hippocampus and grid cells in the entorhinal cortex may provide some of the first glimpses into these mechanisms. We shall review the events that led up to the discovery of grid cells and a functional circuit in the entorhinal cortex and highlight what we currently see as the big questions in this field--questions that, if resolved, will add to our understanding of cortical computation in a general sense.

Question: What is the entorhinal cortex?


Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay. This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, called human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as ε-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma's Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.

Question: How to play Atari games with deep reinforcement learning?


Exploring Session Context using Distributed Representations of Queries and Reformulations. Search logs contain examples of frequently occurring patterns of user reformulations of queries. Intuitively, the reformulation "San Francisco" -- "San Francisco 49ers" is semantically similar to "Detroit" -- "Detroit Lions". Likewise, "London" -- "things to do in London" and "New York" -- "New York tourist attractions" can also be considered similar transitions in intent. The reformulation "movies" -- "new movies" and "york" -- "New York", however, are clearly different despite the lexical similarities in the two reformulations. In this paper, we study the distributed representation of queries learnt by deep neural network models, such as the Convolutional Latent Semantic Model, and show that they can be used to represent query reformulations as vectors. These reformulation vectors exhibit favourable properties such as mapping semantically and syntactically similar query changes closer in the embedding space. Our work is motivated by the success of continuous space language models in capturing relationships between words and their meanings using offset vectors. We demonstrate a way to extend the same intuition to represent query reformulations. Furthermore, we show that the distributed representations of queries and reformulations are both useful for modelling session context for query prediction tasks, such as for query auto-completion (QAC) ranking. Our empirical study demonstrates that short-term (session) history context features based on these two representations improves the mean reciprocal rank (MRR) for the QAC ranking task by more than 10% over a supervised ranker baseline. Our results also show that by using features based on both these representations together we achieve a better performance, than either of them individually.

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


Abstractive Multi-Document Summarization : An Overview. In recent times, the necessity of generating single document summary has gained popularity among the researchers due to its extensive applicability. The text summarization can be categorized with different approaches like: extractive and abstractive from single document or multi document, goal of text summarization (intent, focus and coverage), characteristic of text summarization (frequency-based, knowledge-based and discoursebased), level of processing (surface level, entities level and discourse level) and kind of information (lexicon, structure information and deep understanding). Recently, the efforts in research are transferred from single document summarization to multi document summarization Multi-document summarization considerably differs from single in issues related to compression, speed, redundancy and passage selection are critical in the formation of useful summaries. In this paper, we review the techniques that have done for multi document summarization. Next, we describe evaluation method In conclusion; we propose our future work for multi document

Question: What is the difference between single and multi document summarization?


Have I really met timing? - validating primetime timing reports with SPICE. At sign-off everybody is wondering about how good the accuracy of the static timing analysis timing reports generated with PrimeTime¯ really is. Errors can be introduced by STA setup, interconnect modeling, library characterization etc. The claims that path timingcalculated by PrimeTime usually is within a few percent of Spice don't help to ease your uncertainty.When the Signal Integrity features were introduced to PrimeTime there was also a feature added that was hardly announced: PrimeTime can write out timing paths for simulation with Spice that can be used to validate the timing numbers calculated by PrimeTime. By comparingthe numbers calculated by PrimeTime to a simulation with Spice for selected paths the designers can verify the timing and build up confidence or identify errors.This paper will describe a validation flow for PrimeTime timing reports that is based on extraction of the Spice paths, starting the Spice simulation, parsing the simulation results, and creating a report comparing PrimeTime and Spice timing. All these steps are done inside the TCL environment of PrimeTime. It will describe this flow, what is needed for the Spice simulation, how it can be set up, what can go wrong, and what kind of problems in the STA can be identified.

Question: How to validate timing reports with SPICE?


How the Blockchain Revolution Will Reshape the Consumer Electronics Industry [Future Directions]. ^ IEEE ConsumEr ElECtronICs magazInE 19 By early 2016, Pabellon achieved another major milestone: a first touchfree control panel using solely the body's electrolytes to control a matrix of magnetic pixels. Unlike conventional passive near-field communication technology that relies on receiver loop antennas for detection and identification, the magnetic pixel needs only our body salts to work. Each pixel represents a discrete sensing element. When arrayed or matrixed, the collection of pixels can be used to position index fingers approaching but not touching the surface. Pabellon's powertransfer emitter turns into a touch-free pad. Unlike motion and other opticalsensing methods, the position of objects like fingers can be resolved by small changes in the magnetic near field. The ability to finely control the vast amount of things we contact on a daily basis may not require contact alone. The magnetic pixel has a very exciting future in the look-but-don't-touch world that Pabellon envisions.

Question: How does the blockchain revolution reshape the consumer electronics industry?


A compact printed monopole antenna with symmetrical i and rectangular shaped slots for bluetooth/WLAN/WIMAX applications. A compact dual band microstrip fed printed monopole antenna is designed and presented in this paper for Bluetooth/WLAN/WiMAX applications. Two I with rectangular shape slot have been etched on a Roger RT/duroid substrate to form desired monopole antenna. The radiator of the antenna is very compact with an area of only 9.1 × 10.2 mm2. The size of the I & rectangular slots of dual radiating elements were adjusted so as to achieve two different resonant modes for dual band operation. The simulation result shows that the proposed antenna has 10-dB impedance bandwidth of 219.6 MHz and 3.20 GHz which cover the Bluetooth, WLAN 2.4/5.8 GHz and the WiMAX 2.5/3.5 GHz bands. The overall proposed antenna size is 15 × 17 × 0.762 mm 3 with the obtained VSWR less than 1.5.

Question: What is the size of the antenna?


A Case for NOW (Networks of Workstations) - Abstract. In this paper, we argue that because of recent technology advances, networks of workstations (NOWs) are poised to become the primary computing infrastructure for science and engineering, from low end interactive computing to demanding sequential and parallel applications. We identify three opportunities for NOWs that will benefit endusers: dramatically improving virtual memory and file system performance by using the aggregate DRAM of a NOW as a giant cache for disk; achieving cheap, highly available, and scalable file storage by using redundant arrays of workstation disks, using the LAN as the I/O backplane; and finally, multiple CPUs for parallel computing. We describe the technical challenges in exploiting these opportunities – namely, efficient communication hardware and software, global coordination of multiple workstation operating systems, and enterprise-scale network file systems. We are currently building a 100-node NOW prototype to demonstrate that practical solutions exist to these technical challenges.

Question: What is the difference between a network of workstations and a network of workstations?


Mapping moods: Geo-mapped sentiment analysis during hurricane sandy. Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users' locations, but also based on the distance from the disaster.

Question: How can we map moods during a disaster?


Digitally Controlled Oscillator ( DCO )-Based Architecture for RF Frequency Synthesis in a Deep-Submicrometer CMOS Process. A novel digitally controlled oscillator (DCO)-based architecture for frequency synthesis in wireless RF applications is proposed and demonstrated. It deliberately avoids any use of an analog tuning voltage control line. Fine frequency resolution is achieved through high-speed dithering. Other imperfections of analog circuits are compensated through digital means. The presented ideas enable the employment of fully-digital frequency synthesizers using sophisticated signal processing algorithms, realized in the most advanced deep-submicrometer digital CMOS processes which allow almost no analog extensions. They also promote costeffective integration with the digital back-end onto a single silicon die. The demonstrator test chip has been fabricated in a digital 0.13m CMOS process together with a DSP, which acts as a digital baseband processor with a large number of digital gates in order to investigate noise coupling. The phase noise is112 dBc/Hz at 500-kHz offset. The close-in spurious tones are below 62 dBc, while the far-out spurs are below 80 dBc. The presented ideas have been incorporated in a commercial Bluetooth transceiver.

Question: How to design a digital baseband processor?


Pattern Recognition and Machine Learning. The first € price and the £ and $ price are net prices, subject to local VAT. Prices indicated with * include VAT for books; the €(D) includes 7% for Germany, the €(A) includes 10% for Austria. Prices indicated with ** include VAT for electronic products; 19% for Germany, 20% for Austria. All prices exclusive of carriage charges. Prices and other details are subject to change without notice. All errors and omissions excepted. C. Bishop Pattern Recognition and Machine Learning

Question: What is the price of a book?


Influence of Rectifiers on High-Speed Permanent Magnet Generator Electromagnetic and Temperature Fields in Distributed Power Generation Systems. A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on-off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.

Question: What is the influence of the rectifier on the generator?


Web-Based Virtual Learning Environments: A Research Framework and a Preliminary Assessment of Effectiveness in Basic IT Skills Training. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: What is the JSTOR archive?


MAV urban localization from Google street view data. We tackle the problem of globally localizing a camera-equipped micro aerial vehicle flying within urban environments for which a Google Street View image database exists. To avoid the caveats of current image-search algorithms in case of severe viewpoint changes between the query and the database images, we propose to generate virtual views of the scene, which exploit the air-ground geometry of the system. To limit the computational complexity of the algorithm, we rely on a histogram-voting scheme to select the best putative image correspondences. The proposed approach is tested on a 2 km image dataset captured with a small quadroctopter flying in the streets of Zurich. The success of our approach shows that our new air-ground matching algorithm can robustly handle extreme changes in viewpoint, illumination, perceptual aliasing, and over-season variations, thus, outperforming conventional visual place-recognition approaches.

Question: How to localize a micro aerial vehicle in urban environments?


perspectives on Bitcoin and second-generation cryptocurrencies. Bitcoin has emerged as the most successful cryptographic currency in history. Within two years of its quiet launch in 2009, Bitcoin grew to comprise billions of dollars of economic value, even while the body of published research and security analysis justifying the system's design was negligible. In the ensuing years, a growing literature has identified hidden-butimportant properties of the system, discovered attacks, proposed promising alternatives, and singled out difficult future challenges. This interest has been complemented by a large and vibrant community of open-source developers who steward the system, while proposing and deploying numerous modifications and extensions. We provide the first systematic exposition of the second generation of cryptocurrencies, including Bitcoin and the many alternatives that have been implemented as alternate protocols or "altcoins." Drawing from a scattered body of knowledge, we put forward three key components of Bitcoin's design that can be decoupled, enabling a more insightful analysis of Bitcoin's properties and its proposed modifications and extensions. We contextualize the literature into five central properties capturing blockchain stability. We map the design space for numerous proposed modification, providing comparative analyses for alternative consensus mechanisms, currency allocation mechanisms, computational puzzles, and key management tools. We focus on anonymity issues in Bitcoin and provide an evaluation framework for analyzing a variety of proposals for enhancing unlinkability. Finally we provide new insights on a what we term disintermediation protocols, which absolve the need for trusted intermediaries in an interesting set of applications. We identify three general disintermediation strategies and provide a detailed comparative cost analysis. I. WHY BITCOIN IS WORTHY OF RESEARCH Consider two opposing viewpoints on Bitcoin in straw-man form. T

Question: What is the difference between Bitcoin and other cryptocurrencies?


High-order entropy-compressed text indexes. We present a novel implementation of compressed suffix arrays exhibiting new tradeoffs between search time and space occupancy for a given text (or sequence) of n symbols over an alphabet σ, where each symbol is encoded by lg|σ| bits. We show that compressed suffix arrays use just nH<inf>h</inf> + σ bits, while retaining full text indexing functionalities, such as searching any pattern sequence of length m in O(m lg |σ| + polylog(n)) time. The term H<inf>h</inf> ≤ lg |σ| denotes the hth-order empirical entropy of the text, which means that our index is nearly optimal in space apart from lower-order terms, achieving asymptotically the empirical entropy of the text (with a multiplicative constant 1). If the text is highly compressible so that H<inf>n</inf> = o(1) and the alphabet size is small, we obtain a text index with o(m) search time that requires only o(n) bits. Further results and tradeoffs are reported in the paper.

Question: What is the hth-order empirical entropy of the text?


Distressed Stocks in Distressed Times. Financially distressed stocks do not underperform healthy stocks when the entire economy is in distress. The asset beta and financial leverage of distressed stocks rise significantly after major market downturns, resulting in a dramatic increase in equity beta. Hence, a long/short healthyminus-distressed trading strategy leads to significant losses when the market rebounds. Managing this risk mitigates the severe losses of financial distress strategies, and significantly improves their Sharpe ratios.

Question: What is the Sharpe ratio?


Question: What is the convergence of Adam and beyond?


A Semantic Similarity Approach to Paraphrase Detection. This paper presents a novel approach to the problem of paraphrase identification. Although paraphrases often make use of synonymous or near synonymous terms, many previous approaches have either ignored or made limited use of information about similarities between word meanings. We present an algorithm for paraphrase identification which makes extensive use of word similarity information derived from WordNet (Fellbaum, 1998). The approach is evaluated using the Microsoft Research Paraphrase Corpus (Dolan et al., 2004), a standard resource for this task, and found to outperform previously published methods.

Question: What is a semantic similarity approach to paraphrase detection?


A note on multi-image denoising. Taking photographs under low light conditions with a hand-held camera is problematic. A long exposure time can cause motion blur due to the camera shaking and a short exposure time gives a noisy image. We consider the new technical possibility offered by cameras that take image bursts. Each image of the burst is sharp but noisy. In this preliminary investigation, we explore a strategy to efficiently denoise multi-images or video. The proposed algorithm is a complex image processing chain involving accurate registration, video equalization, noise estimation and the use of state-of-the-art denoising methods. Yet, we show that this complex chain may become risk free thanks to a key feature: the noise model can be estimated accurately from the image burst. Preliminary tests will be presented. On the technical side, the method can already be used to estimate a non parametric camera noise model from any image burst.

Question: How to denoise a video?


Depression Scale Recognition from Audio, Visual and Text Analysis. Depression is a major mental health disorder that is rapidly affecting lives worldwide. Depression not only impacts emotional but also physical and psychological state of the person. Its symptoms include lack of interest in daily activities, feeling low, anxiety, frustration, loss of weight and even feeling of selfhatred. This report describes work done by us for Audio Visual Emotion Challenge (AVEC) 2017 during our second year BTech summer internship. With the increase in demand to detect depression automatically with the help of machine learning algorithms, we present our multimodal feature extraction and decision level fusion approach for the same. Features are extracted by processing on the provided Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector approach were applied on the visual data; statistical descriptors on gaze, pose; low level audio features and head pose and text features were also extracted. Classification is done on fused as well as independent features using Support Vector Machine (SVM) and neural networks. The results obtained were able to cross the provided baseline on validation data set by 17% on audio features and 24.5% on video features. Keywords—AVEC 2017, SVM, Depression, Neural network, RMSE, MAE, fusion, speech processing.

Question: What is the difference between depression and anxiety?


HanGuard: SDN-driven protection of smart home WiFi devices from malicious mobile apps. A new development of smart-home systems is to use mobile apps to control IoT devices across a Home Area Network (HAN). As verified in our study, those systems tend to rely on the Wi-Fi router to authenticate other devices. This treatment exposes them to the attack from malicious apps, particularly those running on authorized phones, which the router does not have information to control. Mitigating this threat cannot solely rely on IoT manufacturers, which may need to change the hardware on the devices to support encryption, increasing the cost of the device, or software developers who we need to trust to implement security correctly. In this work, we present a new technique to control the communication between the IoT devices and their apps in a unified, backward-compatible way. Our approach, called HanGuard, does not require any changes to the IoT devices themselves, the IoT apps or the OS of the participating phones. HanGuard uses an SDN-like approach to offer fine-grained protection: each phone runs a non-system userspace Monitor app to identify the party that attempts to access the protected IoT device and inform the router through a control plane of its access decision; the router enforces the decision on the data plane after verifying whether the phone should be allowed to talk to the device. We implemented our design over both Android and iOS (> 95% of mobile OS market share) and a popular router. Our study shows that HanGuard is both efficient and effective in practice.

Question: How to protect smart home WiFi devices from malicious mobile apps?


Surface Management System Field Trial Results. NASA Ames Research Center, in cooperation with the FAA, has completed research and development of a proof-ofconcept Surface Management System (SMS). This paper reports on two recent SMS field tests as well as final performance and benefits analyses. Field tests and analysis support the conclusion that substantial portions of SMS technology are ready for transfer to the FAA and deployment throughout the National Airspace System (NAS). Other SMS capabilities were accepted in concept but require additional refinement for inclusion in subsequent development spirals. SMS is a decision support tool that helps operational specialists at Air Traffic Control (ATC) and NAS user facilities to collaboratively manage the movements of aircraft on the surface of busy airports, thereby improving capacity, efficiency, and flexibility. SMS provides accurate predictions of the future demand and how that demand will affect airport resources – information that is not currently available. The resulting shared awareness enables the Air Traffic Control Tower (ATCT), Terminal Radar Approach Control (TRACON), Air Route Traffic Control Center (ARTCC), and air carriers to coordinate traffic management decisions. Furthermore, SMS uses its ability to predict how future demand will play out on the surface to evaluate the effect of various traffic management decisions in advance of implementing them, to plan and advise surface operations. The SMS concept, displays, and algorithms were evaluated through a series of field tests at Memphis International Airport (MEM). An operational trial in September, 2003 evaluated SMS traffic management components, such as runway configuration change planning; shadow testing in January, 2004 tested tactical components (e.g., Approval Request (APREQ) coordination, sequencing for departure, and Expected Departure Clearance Time (EDCT) compliance). Participants in these evaluations rated the SMS concept and many of the t

Question: What is the difference between a surface management system and a surface management system?


A high-PSR LDO using a feedforward supply-noise cancellation technique. A feed-forward noise cancellation (FFNC) technique to improve the power supply noise rejection (PSR) of a low dropout regulator (LDO) is presented. The proposed FFNC operates in conjunction with a conventional LDO and extends the noise rejection bandwidth by nearly an order of magnitude. Fabricated in 0.18µm CMOS, at 10mA load current, the prototype achieves a PSR of −50dB and −25dB at 1MHz and 10MHz supply noise frequencies, respectively. Compared to a conventional LDO, this represents an improvement of at least 30dB at 1MHz and 15dB at 10MHz. The prototype uses only 20pF load capacitance and occupies an active area of 0.04mm2.

Question: What is a high-PSR LDO?


Machine Learning for Intelligent Systems. Recent research in machine learning has focused on supervised induction for simple classification and reinforcement learning for simple reactive behaviors. In the process, the field has become disconnected from AI's original goal of creating complete intelligent agents. In this paper, I review recent work on machine learning for planning, language, vision, and other topics that runs counter to this trend and thus holds interest for the broader AI research community. I also suggest some steps to encourage further research along these lines.

Question: What is the goal of AI?


Efficient Active Learning for Image Classification and Segmentation Using a Sample Selection and Conditional Generative Adversarial Network. Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.

Question: How to train a robust deep learning system for medical image classification or segmentation?


Adam: A Method for Stochastic Optimization. We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Question: What is the best stochastic optimization algorithm?


A more realistic error distance calculation for indoor positioning systems accuracy evaluation. The accuracy of indoor positioning systems is commonly computed as a metric based on the Euclidean distance from estimated locations to actual locations. This paper suggests that positioning error distances should be computed as the lengths of the paths that a person may follow when going from wrongly estimated positions to the real positions. The paper proposes a method that calculates the paths from floor plan and obstacles information using the visibility graphs and offsetting techniques, which are commonly used in robotics and CAD/CAM for navigation and manufacturing, respectively. Demonstration of the proposed method was done using a WiFi fingerprinting method based on kNN for pedestrian navigation. Comparisons between our proposed distance and the simple Euclidean distance have shown that the error distances are underestimated and that the differences between the two distances cannot be accurately represented by a fixed quantity in the context of an Indoor Positioning System (IPS) deployed in a library building. We consider that our proposed positioning error distance is more in line with the subjective error perceived by IPS users.

Question: How accurate is indoor positioning?


Techniques and Programs Used in Bidding and Playing phases of Imperfect Information Game – an Overview. Bridge is an international imperfect information game played with similar rules all over the world and it is played by millions of players. It is an intelligent game; it increases creativity and knowledge of human mind. Many of the researchers analyses the Bridge bidding and playing phases, and they developed many programs for getting better results. The programs were performing well and it is also matter of time before the computer beats most human bridge players. As such, the researchers mainly focused on the techniques and computer programs which were used in bridge bidding

Question: What are the techniques and programs used in bidding and playing phases of imperfect information game?


Human Performance Issues and User Interface Design for Teleoperated Robots. In the future, it will become more common for humans to team up with robotic systems to perform tasks that humans cannot realistically accomplish alone. Even for autonomous and semiautonomous systems, teleoperation will be an important default mode. However, teleoperation can be a challenging task because the operator is remotely located. As a result, the operator's situation awareness of the remote environment can be compromised and the mission effectiveness can suffer. This paper presents a detailed examination of more than 150 papers covering human performance issues and suggested mitigation solutions. The paper summarizes the performance decrements caused by video images bandwidth, time lags, frame rates, lack of proprioception, frame of reference, two-dimensional views, attention switches, and motion effects. Suggested solutions and their limitations include stereoscopic displays, synthetic overlay, multimodal interfaces, and various predicative and decision support systems.

Question: What is the difference between a teleoperated robot and a teleoperated robot?


OmniPres project IST-2001-39237 Deliverable 5 Measuring Presence : A Guide to Current Measurement Approaches. This compendium constitutes a comprehensive overview of presence measures described in the literature so far. It contains both subjective and objective approaches to presence measurement. The theoretical basis of measures is described, along with research in which they have been applied, and other relevant literature. Authors: Joy van Baren and Wijnand IJsselsteijn Project funded by the European Community under the "Information Society Technologies" Programme

Question: What is the purpose of this document?


Fake Review Detection : Classification and Analysis of Real and Pseudo Reviews. In recent years, fake review detection has attracted significant attention from both businesses and the research community. For reviews to reflect genuine user experiences and opinions, detecting fake reviews is an important problem. Supervised learning has been one of the main approaches for solving the problem. However, obtaining labeled fake reviews for training is difficult because it is very hard if not impossible to reliably label fake reviews manually. Existing research has used several types of pseudo fake reviews for training. Perhaps, the most interesting type is the pseudo fake reviews generated using the Amazon Mechanical Turk (AMT) crowdsourcing tool. Using AMT crafted fake reviews, [36] reported an accuracy of 89.6% using only word n-gram features. This high accuracy is quite surprising and very encouraging. However, although fake, the AMT generated reviews are not real fake reviews on a commercial website. The Turkers (AMT authors) are not likely to have the same psychological state of mind while writing such reviews as that of the authors of real fake reviews who have real businesses to promote or to demote. Our experiments attest this hypothesis. Next, it is naturally interesting to compare fake review detection accuracies on pseudo AMT data and real-life data to see whether different states of mind can result in different writings and consequently different classification accuracies. For real review data, we use filtered (fake) and unfiltered (non-fake) reviews from Yelp.com (which are closest to ground truth labels) to perform a comprehensive set of classification experiments also employing only n-gram features. We find that fake review detection on Yelp's real-life data only gives 67.8% accuracy, but this accuracy still indicates that n-gram features are indeed useful. We then propose a novel and principled method to discover the precise difference between the two typ

Question: What is the difference between fake reviews and real reviews?


Dynamic node creation in backpropagation networks. Summary form only given. A novel method called dynamic node creation (DNC) that attacks issues of training large networks and of testing networks with different numbers of hidden layer units is presented. DNC sequentially adds nodes one at a time to the hidden layer(s) of the network until the desired approximation accuracy is achieved. Simulation results for parity, symmetry, binary addition, and the encoder problem are presented. The procedure was capable of finding known minimal topologies in many cases, and was always within three nodes of the minimum. Computational expense for finding the solutions was comparable to training normal backpropagation (BP) networks with the same final topologies. Starting out with fewer nodes than needed to solve the problem actually seems to help find a solution. The method yielded a solution for every problem tried. BP applied to the same large networks with randomized initial weights was unable, after repeated attempts, to replicate some minimum solutions found by DNC.<<ETX>>

Question: What is the difference between dynamic node creation and backpropagation?


Understanding microblog continuance usage intention: an integrated model. Purpose – The purpose of this paper is to explore the factors affecting users' continuous microblog usage intention. In recent years, the number of microblog users has gradually declined. This research can reveal microblog users' needs and provide the improvement direction of microblog services. Design/methodology/approach – By integrating Wixom and Todd's theoretical framework, the Uses and Gratifications Theory and the DeLone and McLean Information System Success Model, a conceptual model is proposed. In this model, gratification is defined as a kind of behavioral attitude, and satisfaction is viewed as an object-based attitude. The survey data were collected online and analyzed using the partial least squares method. Findings – The results suggest that users' continuance intention (behavioral intention) is jointly determined by users' gratification (behavioral-based attitude) and their habitual microblog usage behavior. Likewise, gratification is positively affected by satisfaction (object-based attitude) which is a joint function of system quality and information quality (object-based beliefs). Originality/value – In this research, Wixom and Todd's principle is applied as the basic theoretical framework; gratification is viewed as a behavior attitude and user satisfaction is identified as an object-based attitude. This research model is a new lens for continuance usage research.

Question: What is the difference between gratification and satisfaction?


A deep learning-based method for relative location prediction in CT scan images. Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate the performance of the study's proposed method using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with the state-of-the-art techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm.

Question: What is a deep learning-based method for relative location prediction in CT scan images?


High Voltage Pulsed Power Supply Using IGBT Stacks. High voltage pulsed power supply using IGBT (insulated gate bipolar transistor) stacks and pulse transformer for plasma source ion implantation is proposed. To increase voltage rating, twelve IGBTs are used in series at each IGBT stack and a step-up pulse transformer is utilized. To increase the current rating, the proposed system makes use of synchronized three pulse generator modules composed of diodes, capacitors and IGBT stacks. The proposed pulsed power supply uses semiconductor switches as main switches. Hence, the system is compact, and has semi-infinite lifetime. In addition, it has high flexibility in parameters such as voltage magnitude (10-60 kV), pulse repetition rate (PRR) (10-2000 pps), and pulse width (2-5 muS).

Question: What is the voltage rating of the proposed pulsed power supply?


Interprofessional education: effects on professional practice and health care outcomes.. BACKGROUND As patient care becomes more complex, effective collaboration between health and social care professionals is required. However, evidence suggests that these professionals do not collaborate well together. Interprofessional education (IPE) offers a possible way forward in this area.   OBJECTIVES To assess the usefulness of IPE interventions compared to education in which the same professions were learning separately from one another.   SEARCH STRATEGY We searched the Cochrane Effective Practice and Organisation of Care Group specialised register, MEDLINE (1968 to 1998) and Cinahl (1982 to 1998). We also hand searched the Journal of Interprofessional Care (1992 to 1998), the Centre for the Advancement of Interprofessional Education Bulletin (1987 to 1998), conference proceedings, the 'grey literature' held by relevant organisations, and reference lists of articles.   SELECTION CRITERIA Randomised trials, controlled before and after studies and interrupted time series studies of IPE interventions designed to improve collaborative practice between health/social care practitioners and/or the health/well being of patients/clients. The participants included chiropodists/podiatrists, complementary therapists, dentists, dietitians, doctors/physicians, hygienists, psychologists, psychotherapists, midwives, nurses, pharmacists, physiotherapists, occupational therapists, radiographers, speech therapists and/or social workers. The outcomes included objectively measured or self reported (validated instrument) patient/client outcomes and reliable (objective or validated subjective) health care process measures.   DATA COLLECTION AND ANALYSIS Two reviewers independently assessed the eligibility of potentially relevant studies.   MAIN RESULTS The total yield from the search strategy was 1042, of which 89 were retained for further consideration. However none of these studies met the i

Question: What is the effectiveness of interprofessional education?


Learning Object Affordances: From Sensory--Motor Coordination to Imitation. Affordances encode relationships between actions, objects, and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy, and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We illustrate the benefits of the acquired knowledge in imitation games.

Question: What is a general model for learning object affordances?


A Solution to Forecast Demand Using Long Short-Term Memory Recurrent Neural Networks for Time Series Forecasting. This study focuses on predicting demand based on data collected which spans across many periods. To help our client build a solution to forecast demand effectively, we developed a model using Long Short-Term Memory (LSTM) Networks, a type of Recurrent Neural Network, to estimate demand based on historical patterns. While there may be many available models for dealing with a time series problem, the LSTM model is relatively new and highly sophisticated to its counterpart. By comparing this study which works excellently for sequential learning, to the other existing models and techniques, we are now closer to solving at least one of many complications apparent across industries. The study becomes even more important for supply chain professionals, especially those in the purchase department, as they can now rely on a highly accurate model instead of basing their forecasts purely on intuition and recent customer behavior. Using data from the M3-Competition, which is a competition conducted by the International Institute of Forecasters, we develop a working framework to help our client compare their existing models (feedforward neural network and exponential smoothing model) with our LSTM model.

Question: What is the difference between a feedforward neural network and an LSTM network?


Camera/Laser/GPS Fusion Method for Vehicle Positioning Under Extended NIS-Based Sensor Validation. Vehicle localization and autonomous navigation consist of precisely positioning a vehicle on road by the use of different kinds of sensors. This paper presents a vehicle localization method by integrating a stereoscopic system, a laser range finder (LRF) and a global localization sensor GPS. For more accurate LRF-based vehicle motion estimation, an outlier-rejection invariant closest point method (ICP) is proposed to reduce the matching ambiguities of scan alignment. The fusion approach starts by a sensor selection step that is applied to validate the coherence of the observations from different sensors. Then the information provided by the validated sensors is fused with an unscented information filter. To demonstrate its performance, the proposed multisensor localization method is tested with real data and evaluated by RTK-GPS data as ground truth. The fusion approach also facilitates the incorporation of more sensors if needed.

Question: What is the difference between a camera and a laser range finder?