Biometric Gait Authentication Using Accelerometer Sensor. This paper presents a biometric user authentication based on a person's gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.

Question: How does this work?


KNOWLEDGE ACQUISITION AND CYBER SICKNESS : A COMPARISON OF VR DEVICES IN VIRTUAL TOURS. SCIENCE JOURNAL 2015 | JUNE | SCIENCE JOURNAL | 613

Question: What is the difference between virtual reality and augmented reality?


Deep Memory Networks for Attitude Identification. We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral.  Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.

Question: What is the difference between deep memory networks and deep learning?


RP-DBSCAN: A Superfast Parallel DBSCAN Algorithm Based on Random Partitioning. In most parallel DBSCAN algorithms, neighboring points are assigned to the same data partition for parallel processing to facilitate calculation of the density of the neighbors. This data partitioning scheme causes a few critical problems including load imbalance between data partitions, especially in a skewed data set. To remedy these problems, we propose a cell-based data partitioning scheme, pseudo random partitioning , that randomly distributes small cells rather than the points themselves. It achieves high load balance regardless of data skewness while retaining the data contiguity required for DBSCAN. In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary , to supplement random partitions. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. The algorithm simultaneously finds the local clusters to each data partition and then merges these local clusters to obtain global clustering. To validate the merit of our approach, we implement RP-DBSCAN on Spark and conduct extensive experiments using various real-world data sets on 12 Microsoft Azure machines (48 cores). In RP-DBSCAN, data partitioning and cluster merging are very light, and clustering on each split is not dragged out by a specific worker. Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times.

Question: What is the difference between DBSCAN and RP-DBSCAN?


Predicting Online Extremism, Content Adopters, and Interaction Reciprocity. We present a machine learning framework that leverages a mixture of metadata, network, and temporal features to detect extremist users, and predict content adopters and interaction reciprocity in social media. We exploit a unique dataset containing millions of tweets generated by more than 25 thousand users who have been manually identified, reported, and suspended by Twitter due to their involvement with extremist campaigns. We also leverage millions of tweets generated by a random sample of 25 thousand regular users who were exposed to, or consumed, extremist content. We carry out three forecasting tasks, (i) to detect extremist users, (ii) to estimate whether regular users will adopt extremist content, and finally (iii) to predict whether users will reciprocate contacts initiated by extremists. All forecasting tasks are set up in two scenarios: a post hoc (time independent) prediction task on aggregated data, and a simulated real-time prediction task. The performance of our framework is extremely promising, yielding in the different forecasting scenarios up to 93% AUC for extremist user detection, up to 80% AUC for content adoption prediction, and finally up to 72% AUC for interaction reciprocity forecasting. We conclude by providing a thorough feature analysis that helps determine which are the emerging signals that provide predictive power in different scenarios.

Question: What is the canadian military?


Synthetic aperture ultrasound imaging.. The paper describes the use of synthetic aperture (SA) imaging in medical ultrasound. SA imaging is a radical break with today's commercial systems, where the image is acquired sequentially one image line at a time. This puts a strict limit on the frame rate and the possibility of acquiring a sufficient amount of data for high precision flow estimation. These constrictions can be lifted by employing SA imaging. Here data is acquired simultaneously from all directions over a number of emissions, and the full image can be reconstructed from this data. The paper demonstrates the many benefits of SA imaging. Due to the complete data set, it is possible to have both dynamic transmit and receive focusing to improve contrast and resolution. It is also possible to improve penetration depth by employing codes during ultrasound transmission. Data sets for vector flow imaging can be acquired using short imaging sequences, whereby both the correct velocity magnitude and angle can be estimated. A number of examples of both phantom and in vivo SA images will be presented measured by the experimental ultrasound scanner RASMUS to demonstrate the many benefits of SA imaging.

Question: What is synthetic aperture imaging?


CardBoardiZer: Creatively Customize, Articulate and Fold 3D Mesh Models. Computer-aided design of flat patterns allows designers to prototype foldable 3D objects made of heterogeneous sheets of material. We found origami designs are often characterized by pre-synthesized patterns and automated algorithms. Furthermore, augmenting articulated features to a desired model requires time-consuming synthesis of interconnected joints. This paper presents CardBoardiZer, a rapid cardboard based prototyping platform that allows everyday sculptural 3D models to be easily customized, articulated and folded. We develop a building platform to allow the designer to 1) import a desired 3D shape, 2) customize articulated partitions into planar or volumetric foldable patterns, and 3) define rotational movements between partitions. The system unfolds the model into 2D crease-cut-slot patterns ready for die-cutting and folding. In this paper, we developed interactive algorithms and validated the usability of CardBoardiZer using various 3D models. Furthermore, comparisons between CardBoardiZer and methods of Autodesk® 123D Make, demonstrated significantly shorter time-to-prototype and ease of fabrication.

Question: How to make a 3D model?


A Hybrid Model to Detect Phishing-Sites Using Supervised Learning Algorithms. Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.

Question: What is phishing?


The Ontological Foundation of REA Enterprise Information Systems. Philosophers have studied ontologies for centuries in their search for a systematic explanation of existence: "What kind of things exist?" Recently, ontologies have emerged as a major research topic in the fields of artificial intelligence and knowledge management where they address the content issue: "What kind of things should we represent?" The answer to that question differs with the scope of the ontology. Ontologies that are subject-independent are called upper-level ontologies, and they attempt to define concepts that are shared by all domains, such as time and space. Domain ontologies, on the other hand, attempt to define the things that are relevant to a specific application domain. Both types of ontologies are becoming increasingly important in the era of the Internet where consistent and machine-readable semantic definitions of economic phenomena become the language of e-commerce. In this paper, we propose the conceptual accounting framework of the Resource-Event-Agent (REA) model of McCarthy (1982) as an enterprise domain ontology, and we build upon the initial ontology work of Geerts and McCarthy (2000) which explored REA with respect to the ontological categorizations of John Sowa (1999). Because of its conceptual modeling heritage, REA already resembles an established ontology in many declarative (categories) and procedural (axioms) respects, and we also propose here to extend formally that framework both (1) vertically in terms of entrepreneurial logic (value chains) and workflow detail, and (2) horizontally in terms of type and commitment images of enterprise economic phenomena. A strong emphasis throughout the paper is given to the microeconomic foundations of the category definitions.

Question: What is the ontological foundation of REA Enterprise Information Systems?


Science-based views of drug addiction and its treatment..

Question: What is the science-based view of drug addiction and its treatment?


Mechanical design of a gravity-balancing wearable exoskeleton for the motion enhancement of human upper limb. Powered exoskeletons can provide motion enhancement for both healthy and physically challenged people. Upper limb exoskeletons are required to have multiple degrees-of-freedom and can still produce sufficient force to augment the upper limb motion. The design using serial mechanisms usually results in a complicated and bulky exoskeleton that prevents itself from being wearable. This paper presents a new exoskeleton design aimed to achieve compactness and wearability. We consider a shoulder exoskeleton that consists of a parallel spherical mechanism with two slider crank mechanisms. The actuators can be placed on a stationary platform and attached closely to human body. Thus a better inertia property can be obtained while maintaining lightweight. Through the use of a gravity-balancing mechanism, the required actuator power becomes smaller and with better efficiency. A static model is developed to analyze and optimize the exoskeleton. Through illustrations of a prototype, the exoskeleton is shown to be wearable and can provide adequate motion enhancement of a human's upper limb.

Question: What is a gravity-balancing mechanism?


An Innovative Approach to Investigate Various Software Testing Techniques and Strategies. Software testing is a way of finding errors from the system. It helps us to identify and debug mistakes, errors, faults and failures of a system. There are many techniques and strategies emerged since the concept of software development emerged. The aim of testing is to make the quality of software as efficient as possible.in this paper we discuss most widely used techniques and strategies. Where they can be used and how they can be used. How they work and how they differ (from each other).They are the following. Techniques: Black Box Testing, White Box Testing, And Grey Box Testing. Strategies: Unit Testing, System Testing, And Acceptance Testing.

Question: What are the most widely used software testing techniques?


Chimera: Collaborative Preemption for Multitasking on a Shared GPU. The demand for multitasking on graphics processing units (GPUs) is constantly increasing as they have become one of the default components on modern computer systems along with traditional processors (CPUs). Preemptive multitasking on CPUs has been primarily supported through context switching. However, the same preemption strategy incurs substantial overhead due to the large context in GPUs. The overhead comes in two dimensions: a preempting kernel suffers from a long preemption latency, and the system throughput is wasted during the switch. Without precise control over the large preemption overhead, multitasking on GPUs has little use for applications with strict latency requirements.  In this paper, we propose Chimera, a collaborative preemption approach that can precisely control the overhead for multitasking on GPUs. Chimera first introduces streaming multiprocessor (SM) flushing, which can instantly preempt an SM by detecting and exploiting idempotent execution. Chimera utilizes flushing collaboratively with two previously proposed preemption techniques for GPUs, namely context switching and draining to minimize throughput overhead while achieving a required preemption latency. Evaluations show that Chimera violates the deadline for only 0.2% of preemption requests when a 15us preemption latency constraint is used. For multi-programmed workloads, Chimera can improve the average normalized turnaround time by 5.5x, and system throughput by 12.2%.

Question: How does Chimera work?


Guidelines for conducting systematic mapping studies in software engineering: An update.

Question: What is a systematic mapping study?


Towards efficient content-aware search over encrypted outsourced data in cloud. With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.

Question: How to design a content-based search scheme and make semantic search more effective and context-aware?


DudeTM: Building Durable Transactions with Decoupling for Persistent Memory. Emerging non-volatile memory (NVM) offers non-volatility, byte-addressability and fast access at the same time. To make the best use of these properties, it has been shown by empirical evidence that programs should access NVM directly through CPU load and store instructions, so that the overhead of a traditional file system or database can be avoided. Thus, durable transactions become a common choice of applications for accessing persistent memory data in a crash consistent manner. However, existing durable transaction systems employ either undo logging, which requires a fence for every memory write, or redo logging, which requires intercepting all memory reads within transactions.  This paper presents DUDETM, a crash-consistent durable transaction system that avoids the drawbacks of both undo logging and redo logging. DUDETM uses shadow DRAM to decouple the execution of a durable transaction into three fully asynchronous steps. The advantage is that only minimal fences and no memory read instrumentation are required. This design also enables an out-of-the-box transactional memory (TM) to be used as an independent component in our system. The evaluation results show that DUDETM adds durability to a TM system with only 7.4 ~ 24.6% throughput degradation. Compared to the existing durable transaction systems, DUDETM provides 1.7times to 4.4times higher throughput. Moreover, DUDETM can be implemented with existing hardware TMs with minor hardware modifications, leading to a further 1.7times speedup.

Question: What is a durable transaction?


Estimation of Presentations Skills Based on Slides and Audio Features. This paper proposes a simple estimation of the quality of student oral presentations. It is based on the study and analysis of features extracted from the audio and digital slides of 448 presentations. The main goal of this work is to automatically predict the values assigned by professors to different criteria in a presentation evaluation rubric. Machine Learning methods were used to create several models that classify students in two clusters: high and low performers. The models created from slide features were accurate up to 65%. The most relevant features for the slide-base models were: number of words, images, and tables, and the maximum font size. The audio-based models reached up to 69% of accuracy, with pitch and filled pauses related features being the most significant. The relatively high degrees of accuracy obtained with these very simple features encourage the development of automatic estimation tools for improving presentation skills.

Question: How to estimate the quality of a presentation?


GP-GAN: Towards Realistic High-Resolution Image Blending. Recent advances in generative adversarial networks (GANs) have shown promising potentials in conditional image generation. However, how to generate high-resolution images remains an open problem. In this paper, we aim at generating high-resolution well-blended images given composited copy-and-paste ones, i.e. realistic highresolution image blending. To achieve this goal, we propose Gaussian-Poisson GAN (GP-GAN), a framework that combines the strengths of classical gradient-based approaches and GANs, which is the first work that explores the capability of GANs in high-resolution image blending task to the best of our knowledge. Particularly, we propose GaussianPoisson Equation to formulate the high-resolution image blending problem, which is a joint optimisation constrained by the gradient and colour information. Gradient filters can obtain gradient information. For generating the colour information, we propose Blending GAN to learn the mapping between the composited image and the well-blended one. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artefacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that majority of workers are in favour of the proposed approach.

Question: How to generate high-resolution well-blended images?


Phase-Based Methods for Heart Rate Detection Using UWB Impulse Doppler Radar. Ultra-wideband (UWB) pulse Doppler radars can be used for noncontact vital signs monitoring of more than one subject. However, their detected signals typically have low signal-to-noise ratio (SNR) causing significant heart rate (HR) detection errors, as the spurious harmonics of respiration signals and mixed products of respiration and heartbeat signals (that can be relatively higher than heartbeat signals) corrupt conventional fast Fourier transform spectrograms. In this paper, we extend the complex signal demodulation (CSD) and arctangent demodulation (AD) techniques previously used for accurately detecting the phase variations of reflected signals of continuous wave radars to UWB pulse radars as well. These detection techniques reduce the impact of the interfering harmonic signals, thus improving the SNR of the detected vital sign signals. To further enhance the accuracy of the HR estimation, a recently developed state-space method has been successfully combined with CSD and AD techniques and over 10 dB improvements in SNR is demonstrated. The implementation of these various detection techniques has been experimentally investigated and full error and SNR analysis of the HR detection are presented.

Question: How to detect heart rate using ultra-wideband radar?


Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. Two critical limitations for using current satellite sensors in real-time crop management are the lack of imagery with optimum spatial and spectral resolutions and an unfavorable revisit time for most crop stress-detection applications. Alternatives based on manned airborne platforms are lacking due to their high operational costs. A fundamental requirement for providing useful remote sensing products in agriculture is the capacity to combine high spatial resolution and quick turnaround times. Remote sensing sensors placed on unmanned aerial vehicles (UAVs) could fill this gap, providing low-cost approaches to meet the critical requirements of spatial, spectral, and temporal resolutions. This paper demonstrates the ability to generate quantitative remote sensing products by means of a helicopter-based UAV equipped with inexpensive thermal and narrowband multispectral imaging sensors. During summer of 2007, the platform was flown over agricultural fields, obtaining thermal imagery in the 7.5-13-mum region (40-cm resolution) and narrowband multispectral imagery in the 400-800-nm spectral region (20-cm resolution). Surface reflectance and temperature imagery were obtained, after atmospheric corrections with MODTRAN. Biophysical parameters were estimated using vegetation indices, namely, normalized difference vegetation index, transformed chlorophyll absorption in reflectance index/optimized soil-adjusted vegetation index, and photochemical reflectance index (PRI), coupled with SAILH and FLIGHT models. As a result, the image products of leaf area index, chlorophyll content (C ab), and water stress detection from PRI index and canopy temperature were produced and successfully validated. This paper demonstrates that results obtained with a low-cost UAV system for agricultural applications yielded comparable estimations, if not better, than those obtained by tradi

Question: What is the difference between a UAV and a drone?


Learning Partially Contracting Dynamical Systems from Demonstrations. An algorithm for learning the dynamics of point-to-point motions from demonstrations using an autonomous nonlinear dynamical system, named contracting dynamical system primitives (CDSP), is presented. The motion dynamics are approximated using a Gaussian mixture model (GMM) and its parameters are learned subject to constraints derived from partial contraction analysis. Systems learned using the proposed method generate trajectories that accurately reproduce the demonstrations and are guaranteed to converge to a desired goal location. Additionally, the learned models are capable of quickly and appropriately adapting to unexpected spatial perturbations and changes in goal location during reproductions. The CDSP algorithm is evaluated on shapes from a publicly available human handwriting dataset and also compared with two state-of-the-art motion generation algorithms. Furthermore, the CDSP algorithm is also shown to be capable of learning and reproducing point-to-point motions directly from real-world demonstrations using a Baxter robot.

Question: How to learn a dynamical system from demonstrations?


Adaptive Nonlinear Model Inversion Control of a Twin Rotor System Using Artificial Intelligence. The paper investigates the development of an adaptive dynamic nonlinear model inversion control law for a twin rotor MIMO system (TRMS) utilizing artificial neural networks and genetic algorithms. The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicles. A highly nonlinear 1DOF mathematical model of the TRMS is considered in this study and a nonlinear inverse model is developed for the pitch channel. In the absence of model inversion errors, a genetic algorithm-tuned PD controller is used to enhance the tracking characteristics of the system. An adaptive neural network element is integrated thereafter with the feedback control system to compensate for model inversion errors. In order to show the effectiveness of the proposed method in the simulation environment an inversion error has deliberately been provided as an uncertainty in the real situation. Square and sinusoidal reference command signals are used to test the control system performance, and it is noted that an excellent tracking response is exhibited in the presence of inversion errors caused by model uncertainty.

Question: How to control a twin rotor system?


An efficient audio fingerprint search algorithm for music retrieval. The conventional audio fingerprinting system by Haitsma uses a lookup table to identify the candidate songs in the database, which contains the sub-fingerprints of songs, and searches the candidates to find a song whose bit error rate is the lowest. However, this approach has a drawback that the number of database accesses increases dramatically, especially when the database contains a large number of songs or when a matching sub-fingerprint is not found in the lookup table due to a heavily degraded input signal. In this paper, a novel search method is proposed to overcome these difficulties. The proposed method partitions each song found from the lookup table into blocks, assigns a weight to each block, and uses the weight as a search priority to speed up the search process while reducing the number of database accesses. Various results from our experiment show the significant improvement in search speed while maintaining the search accuracy comparable to the conventional method.

Question: How to search a large database of songs?


An Approach to Discovering Temporal Association Rules. The goal of discovering association rules is to discover aH possible associations that accomplish certain restrictions (minimum support and confidence and interesting). However, it is possible to find interesting associations with a high confidence level hut with little support. This problem is caused by the way support is calculated, as the denominator represents the total number of transactions in a time period when the involved items may have not existed. If, on the other hand, we limit the total transactions to the ones belonging to the items' lifetime, those associations would be now discovered, as they would count on enough support. Another difficulty is the large number of rules that could be generated, tbr which many solutions have been proposed. Using age as an obsolescence tactor for rules helps reduce the number of rules to be presented to the user. In this paper we expand the notion of association rules incorporating time to the frequent itemsets discovered. The concept of temporal support is introduced and. as an example, the known algorithm A priori is modified to incorporate the temporal notions.

Question: What is the temporal support of the following association rule?


Rocky 7: a next generation Mars rover prototype. This paper provides a system overview of a new Mars rover prototype, Rocky 7 1. We describe all system aspects: mechanical and electrical design, computer and software infrastructure , algorithms for navigation and manipulation, science data acquisition, and outdoor rover testing. In each area, the improved or added functionality is explained in a context of its path to ight, and within the constraints of desired science missions.

Question: What is the name of the rover?


Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping. Traditional indoor laser scanning trolley/backpacks with multi-laser scanner, panorama cameras, and an inertial measurement unit (IMU) installed are a popular solution to the 3D indoor mapping problem. However, the cost of those mapping suits is quite expensive, and can hardly be replicated by consumer electronic components. The consumer RGB-Depth (RGB-D) camera (e.g., Kinect V2) is a low-cost option for gathering 3D point clouds. However, because of the narrow field of view (FOV), its collection efficiency and data coverages are lower than that of laser scanners. Additionally, the limited FOV leads to an increase of the scanning workload, data processing burden, and risk of visual odometry (VO)/simultaneous localization and mapping (SLAM) failure. To find an efficient and low-cost way to collect 3D point clouds data with auxiliary information (i.e., color) for indoor mapping, in this paper we present a prototype indoor mapping solution that is built upon the calibration of multiple RGB-D sensors to construct an array with large FOV. Three time-of-flight (ToF)-based Kinect V2 RGB-D cameras are mounted on a rig with different view directions in order to form a large field of view. The three RGB-D data streams are synchronized and gathered by the OpenKinect driver. The intrinsic calibration that involves the geometry and depth calibration of single RGB-D cameras are solved by homography-based method and ray correction followed by range biases correction based on pixel-wise spline line functions, respectively. The extrinsic calibration is achieved through a coarse-to-fine scheme that solves the initial exterior orientation parameters (EoPs) from sparse control markers and further refines the initial value by an iterative closest point (ICP) variant minimizing the distance between the RGB-D point clouds and the referenced laser point clouds. The effectiveness and accuracy of the prop

Question: How to calibrate multiple consumer RGB-D cameras for low-cost and efficient 3D indoor mapping?


A new 2D static hand gesture colour image dataset for ASL gestures. It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset.

Question: What is the difference between a dataset and a dataset?


Neural measures of the causal role of observers' facial mimicry on visual working memory for facial expressions. Simulation models of facial expressions propose that sensorimotor regions may increase the clarity of facial expressions representations in extrastriate areas. We monitored the event-related potential marker of visual working memory (VWM) representations, namely the sustained posterior contralateral negativity (SPCN), also termed contralateral delay activity, while participants performed a change detection task including to-be-memorized faces with different intensities of anger. In one condition participants could freely use their facial mimicry during the encoding/VWM maintenance of the faces while in a different condition participants had their facial mimicry blocked by a gel. Notably, SPCN amplitude was reduced for faces in the blocked mimicry condition when compared to the free mimicry condition. This modulation interacted with the empathy levels of participants such that only participants with medium-high empathy scores showed such reduction of the SPCN amplitude when their mimicry was blocked. The SPCN amplitude was larger for full expressions when compared to neutral and subtle expressions, while subtle expressions elicited lower SPCN amplitudes than neutral faces. These findings provide evidence of a functional link between mimicry and VWM for faces and further shed light on how this memory system may receive feedbacks from sensorimotor regions during the processing of facial expressions.

Question: How does facial mimicry affect visual working memory for facial expressions?


Commonsense Reasoning in and Over Natural Language. ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use comparable to WordNet. To meet the dual challenge of having to encode complex higher-order concepts, and maintaining ease-of-use, we introduce a novel use of semi-structured natural language fragments as the knowledge representation of commonsense concepts. In this paper, we present a methodology for reasoning flexibly about these semi-structured natural language fragments. We also examine the tradeoffs associated with representing commonsense knowledge in formal logic versus in natural language. We conclude that the flexibility of natural language makes it a highly suitable representation for achieving practical inferences over text, such as context finding, inference chaining, and conceptual analogy. 1 What is ConceptNet? ConceptNet (www.conceptnet.org) is the largest freely available, machine-useable commonsense resource. Structured as a network of semi-structured natural language fragments, ConceptNet presently consists of over 250,000 elements of commonsense knowledge. We were inspired dually by the range of commonsense concepts and relations in Cyc (Lenat, 1995), and by the ease-of-use of WordNet (Fellbaum, 1998), and hoped to combine the best of both worlds. As a result, we adopted the semantic network representation of WordNet, but extended the representation in several key ways. First, we extended WordNet's lexical notion of nodes to a conceptual notion of nodes, but we kept the nodes in natural language, because one of the primary strengths of WordNet in the textual domain is that its knowledge representation is itself textual. ConceptNet's nodes are thus natural language fragments which are semi-structured accordi

Question: What is the largest freely available, machine-useable commonsense resource?


ULDBs: Databases with Uncertainty and Lineage. This paper introduces ULDBs, an extension of relational databases with simple yet expressive constructs for representing and manipulating both lineage and uncertainty. Uncertain data and data lineage are two important areas of data management that have been considered extensively in isolation, however many applications require the features in tandem. Fundamentally, lineage enables simple and consistent representation of uncertain data, it correlates uncertainty in query results with uncertainty in the input data, and query processing with lineage and uncertainty together presents computational benefits over treating them separately.We show that the ULDB representation is complete, and that it permits straightforward implementation of many relational operations. We define two notions of ULDB minimality--data-minimal and lineage-minimal--and study minimization of ULDB representations under both notions. With lineage, derived relations are no longer self-contained: their uncertainty depends on uncertainty in the base data. We provide an algorithm for the new operation of extracting a database subset in the presence of interconnected uncertainty. Finally, we show how ULDBs enable a new approach to query processing in probabilistic databases.ULDBs form the basis of the Trio system under development at Stanford.

Question: What is the lineage of the Canadian Armed Forces?


Implementation of a Restricted Boltzmann Machine in a Spiking Neural Network. Restricted Boltzmann Machines (RBMs) have been demonstrated to perform efficiently on a variety of applications, such as dimensionality reduction and classification. Implementing RBMs on neuromorphic hardware has certain advantages, particularly from a concurrency and lowpower perspective. This paper outlines some of the requirements involved for neuromorphic adaptation of an RBM and attempts to address these issues with suitably targeted modifications for sampling and weight updates. Results show the feasibility of such alterations which will serve as a guide for future implementation of such algorithms in VLSI arrays of spiking neurons.

Question: How to implement a restricted boltzmann machine in a spiking neural network?


Perceptual objects capture attention. A recent study has demonstrated that the mere organization of some elements in the visual field into an object attracts attention automatically [Kimchi, R., Yeshurun, Y., & Cohen-Savransky, A. (2007). Automatic, stimulus-driven attentional capture by objecthood. Psychonomic Bulletin & Review, 14(1), 166-172]. We tested whether similar results will emerge when the target is not a part of the object and with simplified task demands. A matrix of 16 black L elements in various orientations preceded the presentation of a Vernier target. The target was either added to the matrix (Experiment 1), or appeared after its offset (Experiment 2). On some trials four elements formed a square-like object, and on some of these trials the target appeared in the center of the object. No featural uniqueness or abrupt onset was associated with the object and it did not predict the target location or the direction of the target's horizontal offset. Performance was better when the target appeared in the center of the object than in a different location than the object, even when the target appeared after the matrix offset. These findings support the hypothesis that a perceptual object captures attention (Kimchi et al., 2007), and demonstrate that this automatic deployment of attention to the object is robust and involves a spatial component.

Question: What is the purpose of the study?


Nonexistence of Certain Spherical Designs of Odd Strengths and Cardinalities. A sphericalτ -design onSn−1 is a finite set such that, for all polynomials f of degree at most τ , the average of over the set is equal to the average of f over the sphere Sn−1. In this paper we obtain some necessary conditions for the existence of designs of odd strengths and cardinalities. This gives nonexistence results in many cases. Asymptotically, we derive a bound which is better than the corresponding estimation ensured by the Delsarte– Goethals–Seidel bound. We consider in detail the strengths τ = 3 andτ = 5 and obtain further nonexistence results in these cases. When the nonexistence argument does not work, we obtain bounds on the minimum distance of such designs.

Question: What is a sphericalτ -design?


Substrate Integrated Waveguide Cavity-Backed Self-Triplexing Slot Antenna. In this letter, a novel substrate integrated waveguide (SIW) cavity-backed self-triplexing slot antenna is proposed for triple-band communication. It is realized on a single-layer printed circuit board. The proposed antenna consists of a pair of bow-tie slots etched on an SIW cavity, and three different resonant modes are excited by two microstrip lines and a coaxial probe to operate at three distinct frequencies (7.89, 9.44, and 9.87 GHz). The band around 9.48 GHz is obtained due to radiation from one bow-tie slot fed by adjacent microstrip feedline and band around 9.87 GHz is due to radiation from other bow-tie slot fed by its adjacent microstrip feedline. On the other hand, lower band around 7.89 GHz is achieved because of radiation from both bow-tie slots excited by coaxial probe feedline. The measured isolation between any two feedlines is better than 22.5 dB. The measured realized gain is more than 7.2 dB at all the bands. Cross polarization below 36.5, 29.3, and 24.45 dB in broad sight direction and high front-to-back ratio of more than 17.3 dB at each resonant frequency are obtained.

Question: What is a bow-tie slot?


A Case Study on User Experience (UX) Evaluation of Mobile Augmented Reality Prototypes. Mobile Augmented Reality (MAR) blends the real world with digital objects especially in ubiquitous devices such as smartphones. The MAR applications provide an intelligent interface for users. In this, valuable digital information is advertised in physical spaces. However, the success of these applications is tied directly to the degree of user acceptance. This makes understanding the needs and expectations of the MAR's potential users of paramount importance for designing and building the proper application. The objective of the paper is to expose an important gap in the development of novel applications in the virtual world. Previous research has shown that it is essential to study and understand the needs and expectations of the potential users of the upcoming application or system. Studying user needs and expectations before offering the developed application ensures a minimum level of acceptance and, of course, success. This paper presents a detailed study comprising of a userexperience (UX) evaluation of different prototypes through the use of three different UX evaluation methods. This kind of evaluation allows new developments to offer systems, which do not fail. The main contributions of this study are that it: 1) solicits expectations when consumers use MAR applications, 2) assesses the UX over different prototypes using three different metrics, 3) provides methodological insights on UX evaluation experiments and, 4) is useful for anyone who wants to develop handheld applications after understanding user expectations and how his experience should progress. The results of the study show that users value concreteness, realizability, personalization, novelty, intuitiveness and the usefulness of presented information. Paying attention to these factors can help develop more acceptable MAR applications and lead to more novel future designs.

Question: What is the difference between a case study and a study?


Wideband Excitation Technology of ${\rm TE}_{20}$ Mode Substrate Integrated Waveguide (SIW) and Its Applications. The higher order mode substrate integrated waveguide (SIW) has advantages in the applications, which are: simplifying the structure with reduced fabrication cost and enhancing the stability of performance by relaxed fabrication tolerance. In this paper, two wideband TE20 mode excitation structures are presented and investigated for the SIW. A slot along the mid line in the longitudinal direction of the SIW is employed to convert the electromagnetic field pattern between slotline and the TE20 mode SIW. The second structure is based on the slot aperture coupling and can realize wideband direct transition between the TE20 mode SIW and microstrip line. Both transitions have a simple and compact structure, as well as broadband characteristics. The wideband transition performance is demonstrated in this paper. As application examples of the transitions, two broadband substrate integrated baluns are presented and fabricated based on the TE20 mode excitation structures and TE20 mode characteristics. The 180 ° out-of-phase and equal magnitude of the balun outputs in a wide frequency range can be achieved inherently. The balun based on the slotline excitation has a fractional bandwidth of 50.2%, from 7.3 to 12.2 GHz, with measured return loss, amplitude imbalance, and phase imbalance better than 10 and 0.45 dB and 3.8 °. The balun based on the aperture coupling excitation shows a fractional bandwidth of 50.3%, from 7 to 11.7 GHz, with measured return loss, insertion loss, amplitude imbalance, and phase imbalance better than 10, 1, and 0.27 dB and 2.4 °, respectively. Both baluns not only show good performance, but also demonstrate the existence of the TE20 mode in the SIW.

Question: What is the difference between the TE20 mode and the TE10 mode?


Novel Vector Control Method for Three-Stage Hybrid Cascaded Multilevel Inverter. A three-stage 18-level hybrid inverter circuit and its innovative control method have been presented. The three hybrid inverter stages are the high-, medium-, and low-voltage stages. The high-voltage stage is made of a three-phase conventional inverter to reduce dc source cost and losses. The medium- and low-voltage stages are made of three-level inverters constructed using cascaded H-bridge units. The novelty of the proposed algorithm is to avoid the undesirable high switching frequency for high- and medium-voltage stages despite the fact that the inverter's dc sources are selected to maximize the inverter levels by eliminating redundant voltage states. Switching algorithms of the high- and medium-voltage stages have been developed to assure fundamental switching frequency operation of the high-voltage stages and not more than few times this frequency for the medium-voltage stage. The low-voltage stage is controlled using SVM to achieve the reference voltage vector exactly and to set the order of dominant harmonics as desired. The realization of this control approach has been enabled by considering the vector space plane in the state selection rather than individual phase levels. The inverter has been constructed, and the control algorithm has been implemented. Test results show that the proposed algorithm achieves the claimed features, and all major hypotheses have been verified.

Question: What is the novel vector control method for three-stage hybrid cascaded multilevel inverter?


Defects of the nose, lip, and cheek: rebuilding the composite defect.. BACKGROUND The face can be divided into regions (units) with characteristic skin quality, border outline, and three-dimensional contour. A defect may lie entirely within a single major unit or encompass several adjacent units, creating unique problems for repair. Composite defects overlap two or more facial units. Nasal defects often extend into the adjacent lip and cheek. The simplest solution may appear to be to simply "fill the hole"--just replace the missing bulk. Poor contour, incorrect dimension, malposition, asymmetry, poor blending into adjacent normal tissues, and a patch-like repair often follow.   METHODS The following principles of regional unit repair were applied to guide these complex reconstructions: (1) reconstruct units, not defects; (2) alter the wound in site, size, shape, and depth; (3) consider using separate grafts and flaps for each unit and subunit, if appropriate; (4) use "like" tissue for "like" tissue; (5) restore a stable platform; (6) build in stages; (7) use distant tissue for "invisible" needs and local skin for resurfacing; and (8) disregard old scars.   RESULTS Clinical cases of increasing composite complexity were repaired with local, regional, and distant tissues. Excellent aesthetics and function were obtained.   CONCLUSIONS Careful visual analysis of the normal face and the defect can direct the choice, timing, and technique of facial repair. Regional unit principles provide a coordinated approach to the vision, planning, and fabrication of these difficult wounds. The entire repair should be intellectually planned, designed step by step, and laid out in a series of coordinated steps, with general principles applied to successfully repair composite defects of the nose, lip, and cheek.

Question: How to repair a composite defect of the nose, lip, and cheek?


Parallel Database Recovery for Multicore Main-Memory Databases. Main-memory database systems for multicore servers can ach ieve excellent performance when processing massive volume of OL TP workloads. But crash-resilience mechanism, or namely logg ingand-replay, can either degrade the performance of transact ion processing or slow down the process of failure recovery. In this paper, we show that, by exploiting application semantics, it i s possible to achieve speedy failure recovery without introduci ng any costly logging overhead to the execution of online transact ions. We propose TARS, a parallel log-recovery mechanism that is specifically designed for lightweight, coarse-grained commandlogging approach. TARS leverages a combination of static and dynamic analyses to parallelize the log replay: at compile time, T ARS decomposes stored procedures by carefully analyzing depende ncies within and across programs; at recovery time, a re-executio n schedule with a high degree of parallelism is attained through lig tweight one-pass scans over transaction parameter values. As such, recovery latency is remarkably minimized. We evaluated T ARS in a main-memory database prototype running on a 40-core serve r. Compared to the state-of-the-art mechanisms, T ARS yields significantly higher recovery rate without compromising the effici en y of transaction processing.

Question: What is the recovery rate of TARS?


EMDR: a putative neurobiological mechanism of action.. Numerous studies have provided evidence for the efficacy of eye movement desensitization and reprocessing therapy (EMDR) in the treatment of posttraumatic stress disorder (PTSD), including recent studies showing it to be more efficient than therapist-directed flooding. But few theoretical explanations of how EMDR might work have been offered. Shapiro, in her original description of EMDR, proposed that its directed eye movements mimic the saccades of rapid eye movement sleep (REM), but provided no clear explanation of how such mimicry might lead to clinical improvement. We now revisit her original proposal and present a complete model for how EMDR could lead to specific improvement in PTSD and related conditions. We propose that the repetitive redirecting of attention in EMDR induces a neurobiological state, similar to that of REM sleep, which is optimally configured to support the cortical integration of traumatic memories into general semantic networks. We suggest that this integration can then lead to a reduction in the strength of hippocampally mediated episodic memories of the traumatic event as well as the memories' associated, amygdala-dependent, negative affect. Experimental data in support of this model are reviewed and possible tests of the model are suggested.

Question: How does EMDR work?


Analysis of the Paragraph Vector Model for Information Retrieval. Previous studies have shown that semantically meaningful representations of words and text can be acquired through neural embedding models. In particular, paragraph vector (PV) models have shown impressive performance in some natural language processing tasks by estimating a document (topic) level language model. Integrating the PV models with traditional language model approaches to retrieval, however, produces unstable performance and limited improvements. In this paper, we formally discuss three intrinsic problems of the original PV model that restrict its performance in retrieval tasks. We also describe modifications to the model that make it more suitable for the IR task, and show their impact through experiments and case studies. The three issues we address are (1) the unregulated training process of PV is vulnerable to short document over-fitting that produces length bias in the final retrieval model; (2) the corpus-based negative sampling of PV leads to a weighting scheme for words that overly suppresses the importance of frequent words; and (3) the lack of word-context information makes PV unable to capture word substitution relationships.

Question: What is the difference between a paragraph vector and a word vector?


Bijective parameterization with free boundaries. We present a fully automatic method for generating guaranteed bijective surface parameterizations from triangulated 3D surfaces partitioned into charts. We do so by using a distortion metric that prevents local folds of triangles in the parameterization and a barrier function that prevents intersection of the chart boundaries. In addition, we show how to modify the line search of an interior point method to directly compute the singularities of the distortion metric and barrier functions to maintain a bijective map. By using an isometric metric that is efficient to compute and a spatial hash to accelerate the evaluation and gradient of the barrier function for the boundary, we achieve fast optimization times. Unlike previous methods, we do not require the boundary be constrained by the user to a non-intersecting shape to guarantee a bijection, and the boundary of the parameterization is free to change shape during the optimization to minimize distortion.

Question: How to generate a bijective parameterization of a surface?


The neuroscience of social decision-making.. Given that we live in highly complex social environments, many of our most important decisions are made in the context of social interactions. Simple but sophisticated tasks from a branch of experimental economics known as game theory have been used to study social decision-making in the laboratory setting, and a variety of neuroscience methods have been used to probe the underlying neural systems. This approach is informing our knowledge of the neural mechanisms that support decisions about trust, reciprocity, altruism, fairness, revenge, social punishment, social norm conformity, social learning, and competition. Neural systems involved in reward and reinforcement, pain and punishment, mentalizing, delaying gratification, and emotion regulation are commonly recruited for social decisions. This review also highlights the role of the prefrontal cortex in prudent social decision-making, at least when social environments are relatively stable. In addition, recent progress has been made in understanding the neural bases of individual variation in social decision-making.

Question: What is the neuroscience of social decision-making?


Use of assistance and therapy dogs for children with autism spectrum disorders: a critical review of the current evidence.. BACKGROUND Autism spectrum disorders (ASD) are characterized by deficits in social reciprocity and communication, and by unusually restricted, repetitive behaviors. Intervention strategies based on the exploitation of the emotional aspects of human-dog relationships hold the potential to overcome the difficulty of subjects with ASD to relate and interact effectively with others, targeting core symptoms of this disorder.   METHODS This review summarizes the results of six published studies on the effects of brief interactions with dogs and the effects of introducing dogs in families with a child diagnosed with ASD, with an emphasis on social behaviors and language use. Furthermore, the possible mechanisms responsible for the beneficial effects observed are discussed.   CONCLUSIONS Although the studies described here are encouraging, further research with better designs and using larger samples is needed to strengthen translation of such interventions to the clinic. In addition, potential applications of analyzing child-dog interactions are highlighted to screen for early signs of the disorder.

Question: What is the current evidence on the use of assistance and therapy dogs for children with autism spectrum disorders?


Attacking Fieldbus Communications in ICS: Applications to the SWaT Testbed.

Question: What is a fieldbus?


A survey of decision tree classifier methodology. Decision Tree Classifiers (DTC's) are used successfully in many diverse areas such as radar signal classification, character recognition, remote sensing, medical diagnosis, expert systems, and speech recognition, to name only a few. Perhaps, the most important feature of DTC's is their capability to break down a complex decision-making process into a collection of simpler decisions, thus providing a solution which is often easier to interpret. This paper presents a survey of current methods for DTC designs and the various existing issues. After considering potential advantages of DTC's over single stage classifiers, subjects of tree structure design, feature selection at each internal node, and decision and search strategies are discussed_ Finally, several remarks are made concerning possible future research directions.

Question: What is a decision tree classifier?


Variable impedance actuators: A review. Variable Impedance Actuators (VIA) have received increasing attention in recent years as many novel applications involving interactions with an unknown and dynamic environment including humans require actuators with dynamics that are not well-achieved by classical stiff actuators. This paper presents an overview of the different VIAs developed and proposes a classification based on the principles through which the variable stiffness and damping are achieved. The main classes are active impedance by control, inherent compliance and damping actuators, inertial actuators, and combinations of them, which are then further divided into subclasses. This classification allows for designers of new devices to orientate and take inspiration and users of VIA's to be guided in the design and implementation process for their targeted application.

Question: What is a variable impedance actuator?


Learning Environmental Sounds with Multi-scale Convolutional Neural Network. Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.

Question: How to learn environmental sounds?


Macro process of knowledge management for continuous innovation. Purpose The purpose of this research is to understand the mechanisms of knowledge management (KM) for innovation and provide an approach for enterprises to leverage KM activities into continuous innovation. Design/methodology/approach – By reviewing the literature from multidisciplinary fields, the concepts of knowledge, KM and innovation are investigated. The physical, human and technological perspectives of KM are distinguished with the identification of two core activities for innovation: knowledge creation and knowledge usage. Then an essential requirement for continuous innovation –an internalization phase is defined. The systems thinking and human-centred perspectives are adopted for providing a comprehensive understanding about the mechanisms of KM for innovation. Findings – A networking process of continuous innovation based on KM is proposed by incorporating the phase of internalization. According to the three perspectives of KM, three sources of organizational knowledge assets in innovation are identified. Then based on the two core activities of innovation, a meta-model and a macro process of KM are proposed to model the mechanisms of KM for continuous innovation. Then, in order to operationalize the KM mechanisms, a hierarchical model is constructed by integrating three sources of knowledge assets, the meta-model and the macro process into the process of continuous innovation. This model decomposes the complex relationships between knowledge and innovation into four layers. Practical implications – According to the lessons learned about KM practices in previous research, the three perspectives of KM should collaborate with each other for successful implementation of KM projects for innovation; and the hierarchical model provides a suitable architecture to implement systems of KM for innovation. Originality/value – The meta-model and macro process of KM explain how the next generation of KM

Question: What is the purpose of this research?


Question Generation Shared Task and Evaluation Challenge - Status Report. The First Shared Task Evaluation Challenge on Question Generation took place in 2010 as part of the 3 workshop on Question Generation. The campaign included two tasks: Question Generation from Sentences and Question Generation from Paragraphs. This status report briefly summarizes the motivation, tasks and results. Lessons learned relevant to future QG-STECs are also offered.

Question: What is the first shared task evaluation challenge?


Scientific Workflow: A Survey and Research Directions. Workflow technologies are emerging as the dominant approach to coordinate groups of distributed services. However with a space filled with competing specifications, standards and frameworks from multiple domains, choosing the right tool for the job is not always a straightforward task. Researchers are often unaware of the range of technology that already exists and focus on implementing yet another proprietary workflow system. As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems.

Question: What is a workflow technology?


Removing rolling shutter wobble. We present an algorithm to remove wobble artifacts from a video captured with a rolling shutter camera undergoing large accelerations or jitter. We show how estimating the rapid motion of the camera can be posed as a temporal super-resolution problem. The low-frequency measurements are the motions of pixels from one frame to the next. These measurements are modeled as temporal integrals of the underlying high-frequency jitter of the camera. The estimated high-frequency motion of the camera is then used to re-render the sequence as though all the pixels in each frame were imaged at the same time. We also present an auto-calibration algorithm that can estimate the time between the capture of subsequent rows in the camera.

Question: How to remove rolling shutter wobble?


Mechanics and energetics of level walking with powered ankle exoskeletons.. Robotic lower limb exoskeletons that can alter joint mechanical power output are novel tools for studying the relationship between the mechanics and energetics of human locomotion. We built pneumatically powered ankle exoskeletons controlled by the user's own soleus electromyography (i.e. proportional myoelectric control) to determine whether mechanical assistance at the ankle joint could reduce the metabolic cost of level, steady-speed human walking. We hypothesized that subjects would reduce their net metabolic power in proportion to the average positive mechanical power delivered by the bilateral ankle exoskeletons. Nine healthy individuals completed three 30 min sessions walking at 1.25 m s(-1) while wearing the exoskeletons. Over the three sessions, subjects' net metabolic energy expenditure during powered walking progressed from +7% to -10% of that during unpowered walking. With practice, subjects significantly reduced soleus muscle activity (by approximately 28% root mean square EMG, P<0.0001) and negative exoskeleton mechanical power (-0.09 W kg(-1) at the beginning of session 1 and -0.03 W kg(-1) at the end of session 3; P=0.005). Ankle joint kinematics returned to similar patterns to those observed during unpowered walking. At the end of the third session, the powered exoskeletons delivered approximately 63% of the average ankle joint positive mechanical power and approximately 22% of the total positive mechanical power generated by all of the joints summed (ankle, knee and hip) during unpowered walking. Decreases in total joint positive mechanical power due to powered ankle assistance ( approximately 22%) were not proportional to reductions in net metabolic power ( approximately 10%). The ;apparent efficiency' of the ankle joint muscle-tendon system during human walking ( approximately 0.61) was much greater than reported values of the ;muscular efficiency' of positive mechanical

Question: What is the apparent efficiency of the ankle joint muscle-tendon system during human walking?


TEXT MINING : PROMISES AND CHALLENGES. Text mining, also known as knowledge discovery from text, and document information mining, refers to the process of extracting interesting patterns from very large text corpus for the purposes of discovering knowledge. Text mining is an interdisciplinary field involving information retrieval, text understanding, information extraction, clustering, categorization, visualization, database technology, machine learning, and data mining. Regarded by many as the next wave of knowledge discovery, text mining has a very high commercial value. This talk presents a general framework for text mining, consisting of two stages: text refining that transforms unstructured text documents into an intermediate form; and knowledge distill ation that deduces patterns or knowledge from the intermediate form. We then survey the state-of-the-art text mining approaches, products, and applications by aligning them based on the text refining and knowledge distill ation functions as well as the intermediate form that they adopt. In conclusion, we highlight the upcoming challenges of text mining and the opportunities it offers.

Question: What is text mining?


Institutionally Distributed Deep Learning Networks. Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In such cases, sharing a deep learning model is a more attractive alternative. The best method of performing such a task is unclear, however. In this study, we simulate the dissemination of learning deep learning network models across four institutions using various heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in three independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance (testing accuracy = 77.3%) that was closest to that of centrally hosted patient data (testing accuracy = 78.7%). We also found that there is an improvement in the performance of cyclical weight transfer heuristic with high frequency of weight transfer.

Question: How to share a deep learning model?


Fundamental Limitations of Small Antennas. A capacitor or inductor operating as a small antenna is theoretically capable of intercepting a certain amount of power, independent of its size, on the assumption of tuning without circuit loss. The practical efficiency relative to this ideal is limited by the "radiation power factor" of the antenna as compared with the power factor and bandwidth of the antenna tuning. The radiation power factor of either kind of antenna is somewhat greater than (1/6π) (Ab/l2) in which Ab is the cylindrical volume occupied by the antenna, and l is the radianlength (defined as 1/2π wavelength) at the operating frequency. The efficiency is further limited by the closeness of coupling of the antenna with its tuner. Other simple formulas are given for the more fundamental properties of small antennas and their behavior in a simple circuit. Examples for 1-Mc. operation in typical circuits indicate a loss of about 35 db for the I.R.E. standard capacitive antenna, 43 db for a large loop occupying a volume of 1 meter square by 0.5 meter axial length, and 64 db for a loop of 1/5 these dimensions.

Question: What is the radiation power factor of a small antenna?


ACE: An Efficient Asynchronous Corner Tracker for Event Cameras. The emergence of bio-inspired event cameras has opened up new exciting possibilities in high-frequency tracking, overcoming some of the limitations of traditional frame-based vision (e.g. motion blur during high-speed motions or saturation in scenes with high dynamic range). As a result, research has been focusing on the processing of their unusual output: an asynchronous stream of events. With the majority of existing techniques discretizing the event-stream into frame-like representations, we are yet to harness the true power of these cameras. In this paper, we propose the ACE tracker: a purely asynchronous framework to track corner-event features. Evaluation on benchmarking datasets reveals significant improvements in accuracy and computational efficiency in comparison to state-of-the-art event-based trackers. ACE achieves robust performance even in challenging scenarios, where traditional frame-based vision algorithms fail.

Question: What is the name of the event camera?


Action recognition in the premotor cortex.. We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5 neurons ("mirror neurons', n = 92) all of which became active both when the monkey performed a given action and when it observed a similar action performed by the experimenter. Mirror neurons, in order to be visually triggered, required an interaction between the agent of the action and the object of it. The sight of the agent alone or of the object alone (three-dimensional objects, food) were ineffective. Hand and the mouth were by far the most effective agents. The actions most represented among those activating mirror neurons were grasping, manipulating and placing. In most mirror neurons (92%) there was a clear relation between the visual action they responded to and the motor response they coded. In approximately 30% of mirror neurons the congruence was very strict and the effective observed and executed actions corresponded both in terms of general action (e.g. grasping) and in terms of the way in which that action was executed (e.g. precision grip). We conclude by proposing that mirror neurons form a system for matching observation and execution of motor actions. We discuss the possible role of this system in action recognition and, given the proposed homology between F5 and human Brocca's region, we posit that a matching system, similar to that of mirror neurons exists in humans and could be involved in recognition of actions as well as phonetic gestures.

Question: What is the function of mirror neurons?


Trustworthiness Attributes and Metrics for Engineering Trusted Internet-Based Software Systems. Trustworthiness of Internet-based software systems, apps, services and platform is a key success factor for their use and acceptance by organizations and end-users. The notion of trustworthiness, though, is subject to individual interpretation and preference, e.g., organizations require confidence about how their business critical data is handled whereas end-users may be more concerned about usability. As one main contribution, we present an extensive list of software quality attributes that contribute to trustworthiness. Those software quality attributes have been identified by a systematic review of the research literature and by analyzing two real-world use cases. As a second contribution, we sketch an approach for systematically deriving metrics to measure the trustworthiness of software system. Our work thereby contributes to better understanding which software quality attributes should be considered and assured when engineering trustworthy Internet-based software systems.

Question: What is the trustworthiness of internet based software systems?


Agnostic Estimation of Mean and Covariance. We consider the problem of estimating the mean and covariance of a distribution from i.i.d. samples in the presence of a fraction of malicious noise. This is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type. The agnostic problem includes many interesting special cases, e.g., learning the parameters of a single Gaussian (or finding the best-fit Gaussian) when a fraction of data is adversarially corrupted, agnostically learning mixtures, agnostic ICA, etc. We present polynomial-time algorithms to estimate the mean and covariance with error guarantees in terms of information-theoretic lower bounds. As a corollary, we also obtain an agnostic algorithm for Singular Value Decomposition.

Question: What is the agnostic estimation of mean and covariance?


Metrics for Polyphonic Sound Event Detection. This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously. The system output in this case contains overlapping events, marked as multiple sounds detected as being active at the same time. The polyphonic system output requires a suitable procedure for evaluation against a reference. Metrics from neighboring fields such as speech recognition and speaker diarization can be used, but they need to be partially redefined to deal with the overlapping events. We present a review of the most common metrics in the field and the way they are adapted and interpreted in the polyphonic case. We discuss segment-based and event-based definitions of each metric and explain the consequences of instance-based and class-based averaging using a case study. In parallel, we provide a toolbox containing implementations of presented metrics.

Question: What is the difference between a polyphonic sound event detection system and a polyphonic sound event detection system?


Performance of PMASynRM With Ferrite Magnets for EV/HEV Applications Considering Productivity. Although motors that use rare-earth permanent magnets typically exhibit high performance, their cost is high, and there are concerns about the stability of the raw material supply. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with a ferrite magnet that does not use rare-earth materials considering productivity. The performance of the proposed PMASynRM is evaluated based on the finite-element method and an experiment using a prototype machine. The analysis results reveal that the proposed PMASynRM has the same power density and an equivalent efficiency as rare-earth permanent-magnet synchronous motors for hybrid electric vehicles (2003 Toyota Prius). Furthermore, some experimental results are presented in order to validate the analytical results. As a result, the proposed PMASynRM was found to achieve high-power-density and high-efficiency performance.

Question: How does the performance of the proposed PMASynRM compare to that of a rare-earth permanent-magnet synchronous motor?


Image recognition for visually impaired people by sound. The aim of this paper is to represent a method where a blind person can get information about the shape of an image through speech signal. In this paper we proposed an algorithm for image recognition by speech sound. Blind people face a number of challenges when interacting with their environments because so much information is encoded visually. The proposed method enables the visually impaired people to see with the help of ears. The novelty of this paper is to convert the image to sound using the methodology of edge detection.

Question: How to convert image to sound?


Comparison of EEG signal features and ensemble learning methods for motor imagery classification. Classifying electroencephalogram (EEG) signal in Brain Computer Interface (BCI) is a useful methods to analysis different organs of human body and it can be used for communicate with the outside world and controlling external device. Accuracy classification of extracted features from EEG signals is a problem which many researcher try to improve it. Although many methods for extracting feature and classifying EEG signal have been proposed and developed, many of them suffer from extracting less accurate data from EEG signals. In this work, four signal feature extraction and three ensemble learning method have been reviewed and performances of classification techniques are compared for motor imagery task.

Question: What is the difference between ensemble learning and classification?


An Orbital Angular Momentum (OAM) Mode Reconfigurable Antenna for Channel Capacity Improvement and Digital Data Encoding. For purpose of utilizing orbital angular momentum (OAM) mode diversity, multiple OAM beams should be generated preferably by a single antenna. In this paper, an OAM mode reconfigurable antenna is proposed. Different from the existed OAM antennas with multiple ports for multiple OAM modes transmitting, the proposed antenna with only a single port, but it can be used to transmit mode 1 or mode −1 OAM beams arbitrary by controlling the PIN diodes on the feeding network through a programmable microcontroller which control by a remote controller. Simulation and measurement results such as return loss, near-field and far-field radiation patterns of two operating states for mode 1 and mode −1, and OAM mode orthogonality are given. The proposed antenna can serve as a candidate for utilizing OAM diversity, namely phase diversity to increase channel capacity at 2.4 GHz. Moreover, an OAM-mode based encoding method is experimentally carried out by the proposed OAM mode reconfigurable antenna, the digital data are encoded and decoded by different OAM modes. At the transmitter, the proposed OAM mode reconfigurable antenna is used to encode the digital data, data symbol 0 and 1 are mapped to OAM mode 1 and mode −1, respectively. At the receiver, the data symbols are decoded by phase gradient method.

Question: How to encode digital data by OAM mode?


An open-label study to evaluate sildenafil for the treatment of lymphatic malformations.. BACKGROUND Lymphatic malformations can be challenging to treat. Mainstay interventions including surgery and sclerotherapy are invasive and can result in local recurrence and complications.   OBJECTIVE We sought to assess the effect of 20 weeks of oral sildenafil on reducing lymphatic malformation volume and symptoms in children.   METHODS Seven children (4 boys, 3 girls; ages 13-85 months) with lymphatic malformations were given oral sildenafil for 20 weeks in this open-label study. The volume of the lymphatic malformation was calculated blindly using magnetic resonance imaging performed before and after 20 weeks of sildenafil. Lymphatic malformations were assessed clinically on weeks 4, 12, 20, and 32. Both the physician and parents evaluated the lymphatic malformation in comparison with baseline.   RESULTS Four subjects had a lymphatic malformation volume decrease (1.0%-31.7%). In 2 subjects, despite a lymphatic malformation volume increase (1.1%-3.7%), clinical improvement was noted while on sildenafil. One subject had a 29.6% increase in lymphatic malformation volume and no therapeutic response. Lymphatic malformations of all 6 subjects who experienced a therapeutic response on sildenafil softened and became easily compressible. Adverse events were minimal.   LIMITATIONS A randomized controlled trial will be necessary to verify the effects of sildenafil on lymphatic malformations.   CONCLUSIONS Sildenafil can reduce lymphatic malformation volume and symptoms in some children.

Question: What is the effect of sildenafil on lymphatic malformations?


IT-Forensic Automotive Investigations on the Example of Route Reconstruction on Automotive System and Communication Data.

Question: What is the IT-Forensic Automotive Investigations on the Example of Route Reconstruction on Automotive System and Communication Data?


Deep learning methods for knowledge base population. xxi Zusammenfassung xxiii

Question: What is the difference between knowledge base population and knowledge base induction?


Generating spiking time series with Generative Adversarial Networks : an application on banking transactions by Luca Simonetto 11413522 September 2018. The task of data generation using Generative Models has recently gained more and more attention from the scientific community, as the number of applications in which these models work surprisingly well is constantly increasing. Some examples are image and video generation, speech synthesis and style transfer, pose guided image generation, cross-domain transfer and super resolution. Contrarily to such tasks generating data coming from the banking domain poses a different challenge, due to its atypical structure when compared with traditional data and its limited availability due to privacy restrictions. In this work, we analyze the feasibility of generating spiking time series patterns appearing in the banking environment using Generative Adversarial Networks. We develop a novel end-to-end framework for training, testing and comparing different generative models using both quantitative and qualitative metrics. Finally, we propose a novel approach that combines Variational Autoencoders with Generative Adversarial Networks in order to learn a loss function for datasets in which good similarity metrics are difficult to define.

Question: Generating spiking time series with Generative Adversarial Networks : an application on banking transactions by Luca Simonetto 11413522 September 2018. The task of data generation using Generative Models has recently gained more and more attention from the scientific community, as the number of applications in which these models work surprisingly well


Forecasting stock market movement direction with support vector machine. Support vector machine (SVM) is a very speci1c type of learning algorithms characterized by the capacity control of the decision function, the use of the kernel functions and the sparsity of the solution. In this paper, we investigate the predictability of 1nancial movement direction with SVM by forecasting the weekly movement direction of NIKKEI 225 index. To evaluate the forecasting ability of SVM, we compare its performance with those of Linear Discriminant Analysis, Quadratic Discriminant Analysis and Elman Backpropagation Neural Networks. The experiment results show that SVM outperforms the other classi1cation methods. Further, we propose a combining model by integrating SVM with the other classi1cation methods. The combining model performs best among all the forecasting methods. ? 2004 Elsevier Ltd. All rights reserved.

Question: What is the best forecasting method for stock market movement direction?


Outlier Detection over Massive-Scale Trajectory Streams. The detection of abnormal moving objects over high-volume trajectory streams is critical for real-time applications ranging from military surveillance to transportation management. Yet this outlier detection problem, especially along both the spatial and temporal dimensions, remains largely unexplored. In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real-time applications. Our theoretical analysis and empirical study on two real-world datasets—the Beijing Taxi trajectory data and the Ground Moving Target Indicator data stream—and one generated Moving Objects dataset demonstrate the effectiveness of our taxonomy in effectively capturing different types of abnormal moving objects. Furthermore, we propose a general strategy for efficiently detecting these new outlier classes called the <underline>m</underline>inimal <underline>ex</underline>amination (MEX) framework. The MEX framework features three core optimization principles, which leverage spatiotemporal as well as the predictability properties of the neighbor evidence to minimize the detection costs. Based on this foundation, we design algorithms that detect the outliers based on these classes of new outlier semantics that successfully leverage our optimization principles. Our comprehensive experimental study demonstrates that our proposed MEX strategy drives the detection costs 100-fold down into the practical realm for applications that analyze high-volume trajectory streams in near real time.

Question: What is the taxonomy of neighbor-based trajectory outlier detection?


A 0.5V supply, 49nW band-gap reference and crystal oscillator in 40nm CMOS. This paper presents the co-design of 0.5V operational band-gap reference (BGR) and 32kHz crystal oscillator (XO) in 40nm CMOS process. The proposed BJT-based BGR offers 10× resistor area reduction compared to the conventional BGR. The proposed XO provides trans-conductance enhancement without the need for large coupling capacitor and uses a feedback loop to regulate its bias current, oscillation amplitude across PVT. The total power consumption is less than 49nW from −40°C to 120°C. Without any trimming, the BGR achieves temperature coefficients of 8ppm/°C and XO has temperature coefficient of 0.25ppm/°C. The BGR offers 5× lower area, 2× lower power consumption at 120°C compared to prior work on resistor-based, non-duty cycled BGR. The XO improves power consumption by at least 2× compared to prior work at 120°C, 0.5V supply.

Question: What is the area of a transistor?


Position Sensorless Control for Four-Switch Three-Phase Brushless DC Motor Drives. This paper proposes a position sensorless control scheme for four-switch three-phase (FSTP) brushless DC (BLDC) motor drives using a field programmable gate array (FPGA). A novel sensorless control with six commutation modes and novel pulsewidth modulation scheme is developed to drive FSTP BLDC motors. The low cost BLDC driver is achieved by the reduction of switch device count, cost down of control, and saving of hall sensors. The feasibility of the proposed sensorless control for FSTP BLDC motor drives is demonstrated by analysis and experimental results.

Question: How to drive a four-switch three-phase brushless DC motor?


Exploring the relationships between college students' cell phone use, personality and leisure.

Question: What is the relationship between cell phone use and personality?


Resource Management for Device-to-Device Underlay Communication. Device-to-Device (D2D) communication is a technology component for LTE-A. The existing researches allow D2D as an underlay to the cellular network to increase the spectral efficiency. In this book, D2D communication underlaying cellular networks is studied. Some physical-layer techniques and cross-layer optimization methods on resource management and interference avoidance are proposed and discussed. WINNER II channel models is applied to be the signal and interference model and simulation results show that the performance of D2D link is closely related to the distance between D2D transmitter and receiver and that between interference source and the receiver. Besides, by power control, D2D SINR degrades, which will naturally contribute to low interference to cellular communication. A simple mode selection method of D2D communication is introduced. Based on path-loss (PL) mode selection criterion, D2D gives better performance than traditional cellular system. When D2D pair is farther away from the BS, a better results can be obtained. Game theory, which offers a wide variety of analytical tools to study the complex interactions of players and predict their choices, can be used for power and radio resource management in D2D communication. A reverse iterative combinatorial auction is formulated as a mechanism to allocate the spectrum resources for D2D communications with multiple user pairs sharing the same channel. In addition, a game theoretic approach is developed to implement joint scheduling, power control and channel allocation for D2D communication. Finally, joint power and spectrum resource allocation method is studied under consideration of battery lifetime, which is an important application of D2D communication on increasing user's energy efficiency. The simulation results show that all these methods have beneficial effects on improving the system performance.

Question: What is the difference between D2D and LTE-A?


Brain–machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm. Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.

Question: How does brain-machine interface work?


PAPILLON: expressive eyes for interactive characters. PAPILLON is a technology for designing highly expressive animated eyes for interactive characters, robots and toys. Expressive eyes are essential in any form of face-to-face communication [2] and designing them has been a critical challenge in robotics, as well as in interactive character and toy development.

Question: What is the technology for designing highly expressive animated eyes for interactive characters?


Air Craft Winglet Design and Performance : Cant Angle Effect. A winglet is a device used to improve the efficiency of aircraft by lowering the lift induced drag caused by wingtip vortices. It is a vertical or angled extension at the tips of each wing. Winglets improve efficiency by diffusing the shed wingtip vortex, which in turn reduces the drag due to lift and improves the wing's lift over drag ratio Winglets increase the effective aspect ratio of a wing without adding greatly to the structural stress and hence necessary weight of its structure. In this research, a numerical validation procedure (by FLUENT ®, computational fluid dynamics software with The Spalart-Allmaras turbulence model) is described for determination and estimation aerodynamic characteristics of three dimension subsonic rectangular wing (with NACA653218airfoil cross section). It was observed that at the present work a good agreement between the numerical study and the experimental work. This paper describes a CFD 3-dimensional winglets analysis that was performed on a Cessna wing of NACA2412 cross sectional airfoil. The wing has span 13.16 m, root chord 1.857 m, tip chord 0.928 m, sweep angle 11 degree and taper ratio 0.5. The present study shows wing without winglet and wing with winglet at cant angle 0, 30 and 45 degree. A CFD simulation performs by to compare of aerodynamics characteristics of lift coefficient CL, drag coefficient CD and lift to drag ratio, L/D lift, pathlines and pressure contours. The models run at a Mach number of 0.2 at sea level. The pressure and temperature of air at this height are 101.325 kPa and 288.2 K respectively. The results show that the wing with winglet can increase the lift by ratio approximately 12%. The wing with winglet can decrease the drag by ratio approximately 4%. The wing with winglet can increase lift to drag, L/D by about 11% along different phases of flight.

Question: What is the cant angle effect?


Cultural adaptation and validation of the Health Literacy Questionnaire (HLQ): robust nine-dimension Danish language confirmatory factor model. Health literacy is an important construct in population health and healthcare requiring rigorous measurement. The Health Literacy Questionnaire (HLQ), with nine scales, measures a broad perception of health literacy. This study aimed to adapt the HLQ to the Danish setting, and to examine the factor structure, homogeneity, reliability and discriminant validity. The HLQ was adapted using forward-backward translation, consensus conference and cognitive interviews (n = 15). Psychometric properties were examined based on data collected by face-to-face interview (n = 481). Tests included difficulty level, composite scale reliability and confirmatory factor analysis (CFA). Cognitive testing revealed that only minor re-wording was required. The easiest scale to respond to positively was 'Social support for health', and the hardest were 'Navigating the healthcare system' and 'Appraisal of health information'. CFA of the individual scales showed acceptably high loadings (range 0.49-0.93). CFA fit statistics after including correlated residuals were good for seven scales, acceptable for one. Composite reliability and Cronbach's α were >0.8 for all but one scale. A nine-factor CFA model was fitted to items with no cross-loadings or correlated residuals allowed. Given this restricted model, the fit was satisfactory. The HLQ appears robust for its intended application of assessing health literacy in a range of settings. Further work is required to demonstrate sensitivity to measure changes.

Question: How do you measure health literacy?


Unsupervised Image-to-Image Translation Networks. Most of the existing image-to-image translation frameworks—mapping an image in one domain to a corresponding image in another—are based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework, which is based on variational autoencoders and generative adversarial networks. The proposed framework can learn the translation function without any corresponding images in two domains. We enable this learning capability by combining a weight-sharing constraint and an adversarial training objective. Through visualization results from various unsupervised image translation tasks, we verify the effectiveness of the proposed framework. An ablation study further reveals the critical design choices. Moreover, we apply the UNIT framework to the unsupervised domain adaptation task and achieve better results than competing algorithms do in benchmark datasets.

Question: How does unsupervised image-to-image translation work?