A Matrix Decomposition based Webshell Detection Method. WebShell is a web based network backdoor. With the help of WebShells, the hacker can take any control of the web services illegally. The current method of detecting WebShells is just matching the eigenvalues or detecting the produced flow or services, which is hard to find new kinds of WebShells. To solve these problems, this paper analyzes the different features of a page and proposes a novel matrix decomposition based WebShell detection algorithm. The algorithm is a supervised machine learning algorithm. By analyzing and learning features of known existing and non-existing WebShell pages, the algorithm can make predictions on the unknown pages. The experimental results show that, compared with traditional detection methods, this algorithm spends less time, has higher accuracy and recall rate, and can detect new kinds of WebShells with a certain probability, overcoming the shortcomings of the traditional feature matching based method, improving the accuracy and recalling rate of WebShell detection.

Question: What is a matrix decomposition based webshell detection method?


Making Decisions about Self-Disclosure in Online Social Networks. This paper explores privacy calculus decision making processes for online social networks (OSN). Content analysis method is applied to analyze data obtained from face-to-face interviews and online survey with open-ended questions of 96 OSN users from different countries. The factors users considered before self-disclosing are explored. The perceived benefits and risks of using OSN and their impact on self-disclosure are also identified. We determine that the perceived risks of OSN usage hinder selfdisclosure. It is not clear, however, whether the perceived benefits offset the impact of the risks on selfdisclosure behavior. The findings as a whole do not support privacy calculus in OSN settings.

Question: What are the factors that users consider before self-disclosing?


A hybrid RF and vibration energy harvester for wearable devices. This paper describes a hybrid radio frequency (RF) and piezoelectric thin film polyvinylidene fluoride (PVDF) vibration energy harvester for wearable devices. By exploiting the impedance characteristics of parasitic capacitances and discrete inductors, the proposed harvester not only scavenges 15 Hz vibration energy but also works as a 915 MHz flexible silver-ink RF dipole antenna. In addition, an interface circuit including a 6-stage Dickson RF-to-DC converter and a diode bridge rectifier to convert the RF and vibration outputs of the hybrid harvester into DC signals to power resistive loads is evaluated. A maximum DC output power of 20.9 μ", when using the RF to DC converter and −8 dBm input RF power, is achieved at 36 % of the open-circuit output voltage while the DC power harvested from 3 g vibration excitation reaches a maximum of 2.8 μW at 51% of open-circuit voltage. Experimental results show that the tested hybrid harvesting system simultaneously generates 7.3 μW DC power, when the distance from the harvester to a 3 W EIRP 915 MHz transmitter is 5.5 m, and 1.8 μW DC power from a 1.8 g vibration acceleration peak.

Question: What is a hybrid harvester?


Learning Dexterous In-Hand Manipulation. We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The training is performed in a simulated environment in which we randomize many of the physical properties of the system like friction coefficients and an object's appearance. Our policies transfer to the physical robot despite being trained entirely in simulation. Our method does not rely on any human demonstrations, but many behaviors found in human manipulation emerge naturally, including finger gaiting, multi-finger coordination, and the controlled use of gravity. Our results were obtained using the same distributed RL system that was used to train OpenAI Five [43]. We also include a video of our results: https://youtu. be/jwSbzNHGflM.

Question: How do we learn dexterous in-hand manipulation policies?


Mersenne Twister A Pseudo-Random Number Generator. Pseudo random number generators have been widely used in numbers of applications, particularly simulation and cryptography. Among them is a Mersenne Twister. Introduced in 1998 by Makoto Matsumoto and Takuji Nishimura, it has been a highly preferred generator since it provides long period, high order of dimensional equidistribution, speed and reliability. In this paper, we will focus mainly on describing the theories and algorithm of the Mersenne Twister. We also briefly introduce its variants and discuss its limitations and improvements.

Question: What is a pseudo random number generator?


Multi-phase Word Sense Embedding Learning Using a Corpus and a Lexical Ontology. Word embeddings play a significant role in many modern NLP systems. However, most prevalent word embedding learning methods learn one representation per word which is problematic for polysemous words and homonymous words. To address this problem, we propose a multi-phase word sense embedding learning method which utilizes both a corpus and a lexical ontology to learn one embedding per word sense. We use word sense definitions and relations between word senses defined in a lexical ontology in a different way from existing systems. Experimental results on word similarity task show that our approach produces word sense embeddings of high quality.

Question: What is a word sense embedding?


Mechanics and energetics of human locomotion on sand.. Moving about in nature often involves walking or running on a soft yielding substratum such as sand, which has a profound effect on the mechanics and energetics of locomotion. Force platform and cinematographic analyses were used to determine the mechanical work performed by human subjects during walking and running on sand and on a hard surface. Oxygen consumption was used to determine the energetic cost of walking and running under the same conditions. Walking on sand requires 1.6-2.5 times more mechanical work than does walking on a hard surface at the same speed. In contrast, running on sand requires only 1.15 times more mechanical work than does running on a hard surface at the same speed. Walking on sand requires 2.1-2.7 times more energy expenditure than does walking on a hard surface at the same speed; while running on sand requires 1.6 times more energy expenditure than does running on a hard surface. The increase in energy cost is due primarily to two effects: the mechanical work done on the sand, and a decrease in the efficiency of positive work done by the muscles and tendons.

Question: What is the difference between walking and running on sand?


Semantic Spaces: Measuring the Distance between Different Subspaces. Semantic Space models, which provide a numerical representation of words' meaning extracted from corpus of documents, have been formalized in terms of Hermitian operators over real valued Hilbert spaces by Bruza et al. [1]. The collapse of a word into a particular meaning has been investigated applying the notion of quantum collapse of superpositional states [2]. While the semantic association between words in a Semantic Space can be computed by means of the Minkowski distance [3] or the cosine of the angle between the vector representation of each pair of words, a new procedure is needed in order to establish relations between two or more Semantic Spaces. We address the question: how can the distance between different Semantic Spaces be computed? By representing each Semantic Space as a subspace of a more general Hilbert space, the relationship between Semantic Spaces can be computed by means of the subspace distance. Such distance needs to take into account the difference in the dimensions between subspaces. The availability of a distance for comparing different Semantic Subspaces would enable to achieve a deeper understanding about the geometry of Semantic Spaces which would possibly translate into better effectiveness in Information Retrieval tasks.

Question: How can the distance between different semantic spaces be computed?


Knowledge-aided adaptive radar at DARPA: an overview. For the past several years, the Defense Advanced Research Projects Agency (DARPA) has been pioneering the development of the first ever real-time knowledge-aided (KA) adaptive radar architecture. The impetus for this program is the ever increasingly complex missions and operational environments encountered by modern radars and the inability of traditional adaptation methods to address rapidly varying interference environments. The DARPA KA sensor signal processing and expert reasoning (KASSPER) program has as its goal the demonstration of a high performance embedded computing (HPEC) architecture capable of integrating high-fidelity environmental knowledge (i.e., priors) into the most computationally demanding subsystem of a modern radar: the adaptive space-time beamformer. This is no mean feat as environmental knowledge is a memory quantity that is inherently difficult (if not impossible) to access at the rates required to meet radar front-end throughput requirements. In this article, we will provide an overview of the KASSPER program highlighting both the benefits of KA adaptive radar, key algorithmic concepts, and the breakthrough look-ahead radar scheduling approach that is the keystone to the KASSPER HPEC architecture.

Question: What is the DARPA KA radar?


Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets. Social interactions take place in environments that influence people's behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users' textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.

Question: What is the lexicon-based coefficient attenuation?


Information Processing in Medical Imaging. In this paper, we present novel algorithms to compute robust statistics from manifold-valued data. Specifically, we present algorithms for estimating the robust Fréchet Mean (FM) and performing a robust exact-principal geodesic analysis (ePGA) for data lying on known Riemannian manifolds. We formulate the minimization problems involved in both these problems using the minimum distance estimator called the L2E. This leads to a nonlinear optimization which is solved efficiently using a Riemannian accelerated gradient descent technique. We present competitive performance results of our algorithms applied to synthetic data with outliers, the corpus callosum shapes extracted from OASIS MRI database, and diffusion MRI scans from movement disorder patients respectively.

Question: What is the corpus callosum?


Neural representations for object perception: structure, category, and adaptive coding.. Object perception is one of the most remarkable capacities of the primate brain. Owing to the large and indeterminate dimensionality of object space, the neural basis of object perception has been difficult to study and remains controversial. Recent work has provided a more precise picture of how 2D and 3D object structure is encoded in intermediate and higher-level visual cortices. Yet, other studies suggest that higher-level visual cortex represents categorical identity rather than structure. Furthermore, object responses are surprisingly adaptive to changes in environmental statistics, implying that learning through evolution, development, and also shorter-term experience during adulthood may optimize the object code. Future progress in reconciling these findings will depend on more effective sampling of the object domain and direct comparison of these competing hypotheses.

Question: What is the neural basis of object perception?


Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface. In motor imagery-based brain computer interfaces (BCI), discriminative patterns can be extracted from the electroencephalogram (EEG) using the common spatial pattern (CSP) algorithm. However, the performance of this spatial filter depends on the operational frequency band of the EEG. Thus, setting a broad frequency range, or manually selecting a subject-specific frequency range, are commonly used with the CSP algorithm. To address this problem, this paper proposes a novel filter bank common spatial pattern (FBCSP) to perform autonomous selection of key temporal-spatial discriminative EEG characteristics. After the EEG measurements have been bandpass-filtered into multiple frequency bands, CSP features are extracted from each of these bands. A feature selection algorithm is then used to automatically select discriminative pairs of frequency bands and corresponding CSP features. A classification algorithm is subsequently used to classify the CSP features. A study is conducted to assess the performance of a selection of feature selection and classification algorithms for use with the FBCSP. Extensive experimental results are presented on a publicly available dataset as well as data collected from healthy subjects and unilaterally paralyzed stroke patients. The results show that FBCSP, using a particular combination feature selection and classification algorithm, yields relatively higher cross-validation accuracies compared to prevailing approaches.

Question: How to select a feature selection and classification algorithm for use with the FBCSP?


Smart Supervisory Control for Optimized Power Management System of hybrid Micro-Grid. Micro grid have been accepted concept widely for the better interconnection of distributed generators (DGs). Corresponding to the conventional power system ac microgrid have been proposed, particularly increasing the use of renewable energy sources generate dc power which is need a dc link for the purpose of grid connection and as a result of increasing modern dc loads. Dc micro grid has been recently emerged for their benefits in terms of efficiency cost and no of conversion stages.. During the islanding operation of the hybrid ac/dc microgrid, the IC is intended to take the role of supplier to one microgrid and at the same time acts as a load to the other microgrid and the power management system should be able to share the power demand between the exiting ac and dc sources in both the microgrids. This paper considers the power flow control and management issues amongst multiple sources distributed throughout both ac and dc microgrids. The paper proposes a decentralized power sharing method in order to eliminate the need for communication between DGs or microgrids. The performance of the proposed power control strategy is validated for different operating conditions, using MATLAB/SIMULATION environment. Keyword: Mixed integer linear programming, hybrid ac/dc microgrid, interlinking ac/dc converter, power management. Introduction Nowadays, electrical grids are more distributed, intelligent and flexible. They are not only driven by the growing environmental concern and the energy security, but also by the ornamenting of the electricity market. Over 100 years the three phase AC power systems existing due to its different operating voltage levels and over long distance. Newly more renewable power conversion systems are connected in ac distribution systems due to environmental issues caused by fueled power plants. Nowadays, more DC loads like LED and Electric vehicles are connected to

Question: How to interlink ac and dc microgrids?


Critical period regulation.. Neuronal circuits are shaped by experience during critical periods of early postnatal life. The ability to control the timing, duration, and closure of these heightened levels of brain plasticity has recently become experimentally accessible, especially in the developing visual system. This review summarizes our current understanding of known critical periods across several systems and species. It delineates a number of emerging principles: functional competition between inputs, role for electrical activity, structural consolidation, regulation by experience (not simply age), special role for inhibition in the CNS, potent influence of attention and motivation, unique timing and duration, as well as use of distinct molecular mechanisms across brain regions and the potential for reactivation in adulthood. A deeper understanding of critical periods will open new avenues to "nurture the brain"-from international efforts to link brain science and education to improving recovery from injury and devising new strategies for therapy and lifelong learning.

Question: What is the critical period for the visual system?


Semi-Supervised Learning with the Deep Rendering Mixture Model. Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a probabilistic generative model that models latent nuisance variation, and whose inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to learn from both labeled and unlabeled data. Guided by the theory of the DRMM, we introduce a novel nonnegativity constraint and a variational inference term. We report state-of-the-art performance on MNIST and SVHN and competitive results on CIFAR10. We also probe deeper into how a DRMM trained in a semi-supervised setting represents latent nuisance variation using synthetically rendered images. Taken together, our work provides a unified framework for supervised, unsupervised, and semisupervised learning.

Question: What is the difference between supervised, unsupervised, and semi-supervised learning?


Interactive Neural Translation Assistance for Human Translators. We present the first user study on neural interactive translation prediction. Our neural translation aid was built into existing online translation software. Depending on the prefix already typed by the user the system suggests a list of continuations. We assessed the impact of the system on human translators in a user study with both professional and non-professional translators. The analysis was done with respect to translation speed and translation accuracy as assessed by human judges. We find that our neural translation aid enables relatively fast translations and does not compromise on quality.

Question: What is the first user study on neural interactive translation prediction?


A Portable SDR Non-Orthogonal Multiple Access Testbed for 5G Networks. Non-orthogonal multiple access (NOMA) is envisioned to be one of the promising radio access techniques for the fifth generation (5G) mobile networks. In this paper, a portable NOMA testbed based on software defined radio (SDR) is developed by transporting our NOMA system to mini personal computers (PCs). Moreover, the NOMA testbed has been enhanced from 5 MHz bandwidth to 10 MHz bandwidth. As the computation complexity grows higher with the increase of system bandwidth, the portable NOMA testbed is not competent to fully demonstrate the performance of our NOMA system due to the limitation of mini PC. For a better understanding of the NOMA testbed, the system architecture and scenario are introduced in brief. Then, the implementation of NOMA transceiver and the protocol stack are described separately. Finally, a series of experiments are carried out to evaluate its performance loss compared with the original NOMA system based on desktops. The experimental results indicate that the performance of processors may be a short slab for the development of portable SDR-based testbeds towards 5G networks.

Question: What is the difference between NOMA and orthogonal multiple access (OMA)?


Analysis of first-order anti-aliasing integration sampler. Performance of the first-order anti-aliasing integration sampler used in software-defined radio (SDR) receivers is analyzed versus all practical nonidealities. The nonidealities that are considered in this paper are transconductor finite output resistance, switch resistance, nonzero rise and fall times of the sampling clock, charge injection, clock jitter, and noise. It is proved that the filter is quite robust to all of these nonidealities except for transconductor finite output resistance. Furthermore, linearity and noise performances are all limited to design of a low-noise and highly linear transconductor.

Question: What is a software-defined radio?


Shreds: Fine-Grained Execution Units with Private Memory. Once attackers have injected code into a victim program's address space, or found a memory disclosure vulnerability, all sensitive data and code inside that address space are subject to thefts or manipulation. Unfortunately, this broad type of attack is hard to prevent, even if software developers wish to cooperate, mostly because the conventional memory protection only works at process level and previously proposed in-process memory isolation methods are not practical for wide adoption. We propose shreds, a set of OS-backed programming primitives that addresses developers' currently unmet needs for fine-grained, convenient, and efficient protection of sensitive memory content against in-process adversaries. A shred can be viewed as a flexibly defined segment of a thread execution (hence the name). Each shred is associated with a protected memory pool, which is accessible only to code running in the shred. Unlike previous works, shreds offer in-process private memory without relying on separate page tables, nested paging, or even modified hardware. Plus, shreds provide the essential data flow and control flow guarantees for running sensitive code. We have built the compiler toolchain and the OS module that together enable shreds on Linux. We demonstrated the usage of shreds and evaluated their performance using 5 non-trivial open source software, including OpenSSH and Lighttpd. The results show that shreds are fairly easy to use and incur low runtime overhead (4.67%).

Question: What is the size of the canadian military?


A pre-collision control strategy for human-robot interaction based on dissipated energy in potential inelastic impacts. Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.

Question: What is a pre-collision control strategy?


Locally Weighted Regression : An Approach to Regression Analysis by Local Fitting. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/astata.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.

Question: What is the publisher of this document?


THE EXPERIENCE FACTORY. This article presents an infrastructure, called the experience factory , aimed at capitalization and reuse of life cycle experience and products. The experience factory is a logical and physical organization, and its activities are independent from the ones of the development organization. The activities of the development organization and of the experience factory can be outlined in the following way:

Question: What is the experience factory?


X-band 200W AlGaN/GaN HEMT for high power application. A 200 Watts GaN high electron mobility transistor (HEMT) has been developed for X-band applications. The device consists of four dies of 0.35 um-gate GaN HEMT of 16 mm gate periphery together with input and output 2-stage impedance transformers assembled into a low thermal resistance package. The developed GaN HEMT provides 204W output power and 12dB small signal gain at 9.3 GHz with power added efficiency of 32% under pulse condition at a duty of 10% with a pulse width of 100 usec.

Question: What is the power of the device?


Finite-time posture control of a unicycle robot. This paper deals with the problem of posture control for a unicycle (single-wheel) robot by designing and analyzing a finite-time posture control strategy. The unicycle robot consists of a lower rotating wheel, an upper rotating disk, and a robot body. The rotating wheel enables the unicycle robot to move forward and backward to obtain pitch (longitudinal) balance. The rotating disk is used for obtaining roll (lateral) balance. Therefore, the unicycle robot can be viewed as a mobile inverted pendulum for the pitch axis and a reaction-wheel pendulum for the roll axis. The dynamics models for unicycle robots at roll and pitch axes are derived using the Lagrange equation. According to the dynamics models, two finite-time controller are designed, which includes the finite-time roll controller and the finite-time pitch controller. Moreover, the stability on the unicycle robot with the proposed finite-time posture control strategy is also analyzed. Finally, simulations are worked out to illustrate the effectiveness of the finite-time posture control strategy for the unicycle robot at roll and pitch axes.

Question: How to control the posture of a unicycle robot?


Human facial illustrations: Creation and psychophysical evaluation. We present a method for creating black-and-white illustrations from photographs of human faces. In addition an interactive technique is demonstrated for deforming these black-and-white facial illustrations to create caricatures which highlight and exaggerate representative facial features. We evaluate the effectiveness of the resulting images through psychophysical studies to assess accuracy and speed in both recognition and learning tasks. These studies show that the facial illustrations and caricatures generated using our techniques are as effective as photographs in recognition tasks. For the learning task we find that illustrations are learned two times faster than photographs and caricatures are learned one and a half times faster than photographs. Because our techniques produce images that are effective at communicating complex information, they are useful in a number of potential applications, ranging from entertainment and education to low bandwidth telecommunications and psychology research.

Question: How to create a caricature of a face?


Predictive coding under the free-energy principle.. This paper considers prediction and perceptual categorization as an inference problem that is solved by the brain. We assume that the brain models the world as a hierarchy or cascade of dynamical systems that encode causal structure in the sensorium. Perception is equated with the optimization or inversion of these internal models, to explain sensory data. Given a model of how sensory data are generated, we can invoke a generic approach to model inversion, based on a free energy bound on the model's evidence. The ensuing free-energy formulation furnishes equations that prescribe the process of recognition, i.e. the dynamics of neuronal activity that represent the causes of sensory input. Here, we focus on a very general model, whose hierarchical and dynamical structure enables simulated brains to recognize and predict trajectories or sequences of sensory states. We first review hierarchical dynamical models and their inversion. We then show that the brain has the necessary infrastructure to implement this inversion and illustrate this point using synthetic birds that can recognize and categorize birdsongs.

Question: What is the free-energy principle?


A Modified CFOA and Its Applications to Simulated Inductors, Capacitance Multipliers, and Analog Filters. In this paper, using a minimum number of passive components, i.e., new grounded and floating inductance simulators, grounded capacitance multipliers, and frequency-dependent negative resistors (FDNRs) based on one/two modified current-feedback operational amplifiers (MCFOAs), are proposed. The type of the simulators depends on the passive element selection used in the structure of the circuit without requiring critical active and passive component-matching conditions and/or cancellation constraints. In order to show the flexibility of the proposed MCFOA, a single-input three-output (SITO) voltage-mode (VM) filter, two three-input single-output (TISO) VM filters, and an SITO current-mode (CM) filter employing a single MCFOA are reported. The layout of the proposed MCFOA is also given. A number of simulations using the SPICE program and some experimental tests are performed to exhibit the performance of the introduced structures.

Question: What is a modified current-feedback operational amplifier?


The Formulation of Parameters for Type Design of Indian Script Based on Calligraphic Studies. A number of parameters were formulated for better analysing the anatomy of Indic letterforms. This methodology contributes to the understanding of the intricacies of type design of complex Indian scripts.

Question: What is the methodology of type design of complex Indian scripts?


A geometry-based soft shadow volume algorithm using graphics hardware. Most previous soft shadow algorithms have either suffered from aliasing, been too slow, or could only use a limited set of shadow casters and/or receivers. Therefore, we present a strengthened soft shadow volume algorithm that deals with these problems. Our critical improvements include robust penumbra wedge construction, geometry-based visibility computation, and also simplified computation through a four-dimensional texture lookup. This enables us to implement the algorithm using programmable graphics hardware, and it results in images that most often are indistinguishable from images created as the average of 1024 hard shadow images. Furthermore, our algorithm can use both arbitrary shadow casters and receivers. Also, one version of our algorithm completely avoids sampling artifacts which is rare for soft shadow algorithms. As a bonus, the four-dimensional texture lookup allows for small textured light sources, and, even video textures can be used as light sources. Our algorithm has been implemented in pure software, and also using the GeForce FX emulator with pixel shaders. Our software implementation renders soft shadows at 0.5--5 frames per second for the images in this paper. With actual hardware, we expect that our algorithm will render soft shadows in real time. An important performance measure is bandwidth usage. For the same image quality, an algorithm using the accumulated hard shadow images uses almost two orders of magnitude more bandwidth than our algorithm.

Question: What is a geometry-based soft shadow volume algorithm?


Unsupervised Visual Domain Adaptation Using Subspace Alignment. In this paper, we introduce a new domain adaptation (DA) algorithm where the source and target domains are represented by subspaces described by eigenvectors. In this context, our method seeks a domain adaptation solution by learning a mapping function which aligns the source subspace with the target one. We show that the solution of the corresponding optimization problem can be obtained in a simple closed form, leading to an extremely fast algorithm. We use a theoretical result to tune the unique hyper parameter corresponding to the size of the subspaces. We run our method on various datasets and show that, despite its intrinsic simplicity, it outperforms state of the art DA methods.

Question: What is the name of the algorithm?


Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT). Many e-recruitment tools for recruiting candidates for jobs have significantly spread in recent years. Companies often receive resumes from candidates using e-recruitment tools or via job portal for each job posting and manually short list qualified applicants. The existing e-recruitment tools have been mainly used for the storage of applicant contact data. In this paper, we present EXPERT, an intelligent tool for screening candidates for recruitment using ontology mapping. EXPERT has three phases in screening candidates for recruitment. In first phase, the system collects candidates' resumes and constructs ontology document for the features of the candidates. Job openings/job requirements are represented as ontology in the second phase and in third phase, EXPERT maps the job requirement ontology onto the candidate ontology document and retrieves the eligible candidates. Experiment results show that this model improves the accuracy of matching candidates with job requirement.

Question: What is ontology mapping?


7th order sub-millimeter wave frequency multiplier based on graphene implemented using a microstrip transition between two rectangular waveguides. In this work, a 7th order sub-millimeter wave frequency multiplier for Terahertz applications is presented. The multiplier is based on a microstrip line with a small gap where a graphene film layer is deposited. The multiplication phenomena is reached via the nonlinear behavior of the graphene film using an input signal provided by the WR28 standard waveguide operating in the Ka frequency band (31 to 40 GHz). A prototype of the multiplier has been manufactured and the behavior of the output power in the 220-280 GHz band versus the input power has been experimentally characterized.

Question: What is a sub-millimeter wave frequency multiplier?


Probabilistic neural network and its application. Nuclear marine apparatus is a huge complicated system, most of which equipments are of nonlin-earity, time varying, coupling and inexactness. Neuralnet is widely applied in nuclear fault diagnosis for its approaching any kinds of nonlinearity mapping. At present, BP neural net is used more widely, but the layers of the net and the neurones on each layer can not be delimited easily; such net may fall into the minimum point in the course of training. In this essay, PNN proves effective in diagnosing faults on nuclearmarine apparatus.

Question: What is the difference between BP neural net and PNN?


Aspect based sentiment analysis in social media with classifier ensembles. The analysis of user generated content on social media and the accurate specification of user opinions towards products and events is quite valuable to many applications. With the proliferation of Web 2.0 and the rapid growth of user-generated content on the web, approaches on aspect level sentiment analysis that yield fine grained information are of great interest. In this work, a classifier ensemble approach for aspect based sentiment analysis is presented. The approach is generic and utilizes latent dirichlet allocation to model a topic and to specify the main aspects that users address. Then, each comment is further analyzed and word dependencies that indicate the interactions between words and aspects are extracted. An ensemble classifier formulated by naive bayes, maximum entropy and support vector machines is designed to recognize the polarity of the user's comment towards each aspect. The evaluation results show sound improvement compared to individual classifiers and indicate that the ensemble system is scalable and accurate in analyzing user generated content and in specifying users' opinions and attitudes.

Question: How to extract aspect based sentiment analysis from social media?


Video2GIF: Automatic Generation of Animated GIFs from Video. We introduce the novel problem of automatically generating animated GIFs from video. GIFs are short looping video with no sound, and a perfect combination between image and video that really capture our attention. GIFs tell a story, express emotion, turn events into humorous moments, and are the new wave of photojournalism. We pose the question: Can we automate the entirely manual and elaborate process of GIF creation by leveraging the plethora of user generated GIF content? We propose a Robust Deep RankNet that, given a video, generates a ranked list of its segments according to their suitability as GIF. We train our model to learn what visual content is often selected for GIFs by using over 100K user generated GIFs and their corresponding video sources. We effectively deal with the noisy web data by proposing a novel adaptive Huber loss in the ranking formulation. We show that our approach is robust to outliers and picks up several patterns that are frequently present in popular animated GIFs. On our new large-scale benchmark dataset, we show the advantage of our approach over several state-of-the-art methods.

Question: Can we automate the entirely manual and elaborate process of GIF creation by leveraging the plethora of user generated GIF content?


Higher-Order Inference for Multi-class Log-Supermodular Models. Higher-order models have been shown to be very useful for a plethora of computer vision tasks. However, existing techniques have focused mainly on MAP inference. In this paper, we present the first efficient approach towards approximate Bayesian marginal inference in a general class of high-order, multi-label attractive models, where previous techniques slow down exponentially with the order (clique size). We formalize this task as performing inference in log-supermodular models under partition constraints, and present an efficient variational inference technique. The resulting optimization problems are convex and yield bounds on the partition function. We also obtain a fully factorized approximation to the posterior, which can be used in lieu of the true complicated distribution. We empirically demonstrate the performance of our approach by comparing it to traditional inference methods on a challenging high-fidelity multi-label image segmentation dataset. We obtain state-of-the-art classification accuracy for MAP inference, and substantially improved ROC curves using the approximate marginals.

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


Statistical limits of spiked tensor models. We study the statistical limits of both detecting and estimating a rank-one deformation of a symmetric random Gaussian tensor. We establish upper and lower bounds on the critical signal-to-noise ratio, under a variety of priors for the planted vector: (i) a uniformly sampled unit vector, (ii) i.i.d. ±1 entries, and (iii) a sparse vector where a constant fraction ρ of entries are i.i.d. ±1 and the rest are zero. For each of these cases, our upper and lower bounds match up to a 1+o(1) factor as the order d of the tensor becomes large. For sparse signals (iii), our bounds are also asymptotically tight in the sparse limit ρ → 0 for any fixed d (including the d = 2 case of sparse PCA). Our upper bounds for (i) demonstrate a phenomenon reminiscent of the work of Baik, Ben Arous and Péché: an 'eigenvalue' of a perturbed tensor emerges from the bulk at a strictly lower signal-to-noise ratio than when the perturbation itself exceeds the bulk; we quantify the size of this effect. We also provide some general results for larger classes of priors. In particular, the large d asymptotics of the threshold location differs between problems with discrete priors versus continuous priors. Finally, for priors (i) and (ii) we carry out the replica prediction from statistical physics, which is conjectured to give the exact information-theoretic threshold for any fixed d. Of independent interest, we introduce a new improvement to the second moment method for contiguity, on which our lower bounds are based. Our technique conditions away from rare 'bad' events that depend on interactions between the signal and noise. This enables us to close √ 2-factor gaps present in several previous works.

Question: What is the critical signal-to-noise ratio for detecting a rank-one deformation of a symmetric random Gaussian tensor?


Emergency Response Information System Interoperability: Development of Chemical Incident Response Data Model. Special Issue

Question: What is the development of chemical incident response data model?


Table of Contents Recognition and Extraction for Heterogeneous Book Documents. Existing work on book table of contents (TOC) recognition has been almost all on small size, application-dependent, and domain-specific datasets. However, TOC of books from different domains differ significantly in their visual layout and style, making TOC recognition a challenging problem for a large scale collection of heterogeneous books. We observed that TOCs can be placed into three basic styles, namely "flat", "ordered", and "divided", giving insights into how to achieve effective TOC parsing. As such, we propose a new TOC recognition approach which adaptively decides the most appropriate TOC parsing rules based on the classification of these three TOC styles. Evaluation on large number, over 25,000, of book documents from various domains demonstrates its effectiveness and efficiency.

Question: How to recognize the table of contents of a book?


Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification. RSSI-based indoor localization is getting much attention. Thanks to a number of researchers, the localization accuracy has already reached a sufficient level. However, it is still not easy-to-use technology because of its heavy installation cost. When an indoor localization system is installed, it needs to collect RSSI data for training classifiers. Existing techniques need to collect enough data at each location. This is why the installation cost is very heavy. We propose a technique to gather data efficiently by using machine learning techniques. Our proposed algorithm is based on multi-task learning and Bayesian optimization. This algorithm can remove the need to collect data of all location labels and select location labels to acquire new data efficiently. We verify this algorithm by using a Wi-Fi RSSI dataset collected in a building. The empirical results suggest that the algorithm is superior to an existing algorithm applying single-task learning and Active Class Selection.

Question: How to calibrate rssi-based indoor localization?


Automatic PCB Defects Detection and Classification using Matlab. The importance of the Printed Circuit Board inspection process has been magnified by requirements of the modern manufacturing environment. In electronics mass production manufacturing facilities, an attempt is often to achieve 100% quality assurance. In this work Machine Vision PCB Inspection System is applied at the first step of manufacturing. In this system a PCB inspection system is proposed and the inspection algorithm mainly focuses on the defect detection and defect classification of the defects. Defect classification is essential to the identification of the defect sources. The purpose of the system is to provide the automatic defect detection of PCB and relieve the human inspectors from the tedious task of finding the defects in PCB which may lead to electric failure. We first compare a standard PCB inspection image with a PCB image to be inspected. The MATLAB tool is used to detect the defects and to classify the defects. With the solutions designed and implemented in this thesis the algorithm designed in the proposed system is able to detect and classify all the known 14 types of defects successfully with greater accuracy. The algorithm makes use of image subtraction method for defect detection and kNN classification algorithm for the classification of the defects. This thesis will present and analyze the performance of the proposed inspection algorithm. The experiment will measure the accuracy of the system.

Question: What is the purpose of the PCB inspection system?


A broadband substrate integrated waveguide (SIW) filter. An eleven-stage broadband SIW filter has been designed, fabricated and measured. The measured results show that the SIW filter can be operated from 9.25 GHz to 11.25 GHz with good selectivity performance. The filter has a small size, low weight, and can be integrated with other SIW components without any transitions. It's suitable for the design of microwave and millimeter wave integrated circuits

Question: What is a broadband SIW filter?


Social network sites: definition, history, and scholarship. This publication contains reprint articles for which IEEE does not hold copyright. Full text is not available on IEEE Xplore for these articles.

Question: What is the definition of social network sites?


Enhancing linear system theory curriculum with an inverted pendulum robot. The demands on both delivery methodology and content of control curriculum continue to evolve as more applications in our world incorporate control theory and the need for continuing education increases. Not only has the content evolved, but the application of social-behavioral science research has resulted in cooperative and active learning practices by faculty. In response to these shifts in education, an open-source inverted pendulum robot was used in a linear systems theory (LST) class taught as part of a professional master's program (PMP). The robot had to have a low cost, and enough capability to enable the students to explore and test the ideas presented in lecture and to engage in collaborative learning experiences. This paper discusses the robot, describes the key control theory experiments, and reviews the lessons learned from this experience.

Question: What is a linear system theory?


An approach to online identification of Takagi-Sugeno fuzzy models. An approach to the online learning of Takagi-Sugeno (TS) type models is proposed in the paper. It is based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning. The rule-base and parameters of the TS model continually evolve by adding new rules with more summarization power and by modifying existing rules and parameters. In this way, the rule-base structure is inherited and up-dated when new data become available. By applying this learning concept to the TS model we arrive at a new type adaptive model called the Evolving Takagi-Sugeno model (ETS). The adaptive nature of these evolving TS models in combination with the highly transparent and compact form of fuzzy rules makes them a promising candidate for online modeling and control of complex processes, competitive to neural networks. The approach has been tested on data from an air-conditioning installation serving a real building. The results illustrate the viability and efficiency of the approach. The proposed concept, however, has significantly wider implications in a number of fields, including adaptive nonlinear control, fault detection and diagnostics, performance analysis, forecasting, knowledge extraction, robotics, behavior modeling.

Question: What is the difference between a fuzzy rule and a fuzzy model?


Patterns and dynamics of users' behavior and interaction: Network analysis of an online community. This research draws on longitudinal network data from an online community to examine patterns of users' behavior and social interaction, and infer the processes underpinning dynamics of system use. The online community represents a prototypical example of a complex evolving social network in which connections between users are established over time by online messages. We study the evolution of a variety of properties since the inception of the system, including how users create, reciprocate, and deepen relationships with one another, variations in users' gregariousness and popularity, reachability and typical distances among users, and the degree of local redundancy in the system. Results indicate that the system is a "small world"characterized by the emergence, in its early stages,of a hub-dominated structure with heterogeneity in users' behavior. We investigate whether hubs are responsible for holding the system together and facilitating information flow, examine first-mover advantages underpinning users'ability to rise to system prominence, and uncover gender differences in users' gregariousness, popularity, and local redundancy. We discuss the implications of the results for research on system use and evolving social networks, and for a host of applications, including information diffusion, communities of practice, and the security and robustness of information systems.

Question: What is the typical distance between users in an online community?


MapReduce: Simplified Data Processing on Large Clusters. MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

Question: What is mapreduce?


Resource Sharing in Continuous Sliding-Window Aggregates. We consider the problem of resource sharing when processing large numbers of continuous queries. We specifically address sliding-window aggregates over data streams, an important class of continuous operators for which sharing has not been addressed. We present a suite of sharing techniques that cover a wide range of possible scenarios: different classes of aggregation functions (algebraic, distributive, holistic), different window types (time-based, tuple-based, suffix, historical), and different input models (single stream, multiple substreams). We provide precise theoretical performance guarantees for our techniques, and show their practical effectiveness through experimental study.

Question: What is the difference between algebraic and distributive aggregation?


Identifying and Eliminating Mislabeled Training Instances. This paper presents a new approach to identifying and eliminating mislabeled training instances. The goal of this technique is to improve classiication accuracies produced by learning algorithms by improving the quality of the training data. The approach employs an ensemble of clas-siiers that serve as a lter for the training data. Using an n-fold cross validation, the training data is passed through the lter. Only instances that the lter classiies correctly are passed to the-nal learning algorithm. We present an empirical evaluation of the approach for the task of automated land cover mapping from remotely sensed data. Labeling error arises in these data from a multitude of sources including lack of consistency in the vegetation classiication used, variable measurement techniques, and variation in the spatial sampling resolution. Our evaluation shows that for noise levels of less than 40%, lter-ing results in higher predictive accuracy than not ltering, and for levels of class noise less than or equal to 20% ltering allows the base-line accuracy to be retained. Our empirical results suggest that the ensemble lter approach is an eeective method for identifying labeling errors, and further , that the approach will signiicantly beneet ongoing research to develop accurate and robust remote sensing-based methods to map land cover at global scales.

Question: How to identify and eliminate mislabeled training instances?


On the Margin Theory of Feedforward Neural Networks. Past works have shown that, somewhat surprisingly, over-parametrization can help generalization in neural networks. Towards explaining this phenomenon, we adopt a margin-based perspective. We establish: 1) for multilayer feedforward relu networks, the global minimizer of a weakly-regularized cross-entropy loss has the maximum normalized margin among all networks, 2) as a result, increasing the over-parametrization improves the normalized margin and generalization error bounds for two-layer networks. In particular, an infinite-size neural network enjoys the best generalization guarantees. The typical infinite feature methods are kernel methods; we compare the neural net margin with that of kernel methods and construct natural instances where kernel methods have much weaker generalization guarantees. We validate this gap between the two approaches empirically. Finally, this infinite-neuron viewpoint is also fruitful for analyzing optimization. We show that a perturbed gradient flow on infinite-size networks finds a global optimizer in polynomial time.

Question: What is the margin theory of feedforward neural networks?


A Secure Low-Cost Edge Device Authentication Scheme for the Internet of Things. Because of the enhanced capability of adversaries, edge devices of Internet of Things (IoT) infrastructure are now increasingly vulnerable to counterfeiting and piracy. Ensuring the authenticity of such devices is of great concern since an adversary can create a backdoor either to bypass the security, and/or to leak secret information over an unsecured communication channel. The reliability of such devices could also be called into question because they might be counterfeit, defective and/or of inferior quality. It is of prime importance to design and develop solutions for authenticating such edge devices. In this paper, we present a novel low-cost solution for authenticating edge devices. We use SRAM based PUF to generate unique "digital fingerprints" for every device, which can be used to generate a unique device ID. We propose a novel ID matching scheme to verify the identity of an edge device even though the PUF is extremely unreliable. We show that the probability of impersonating an ID by an adversary is extremely low. In addition, our proposed solution is resistant to various known attacks.

Question: What is a PUF?


A VLSI Implementation of an Analog Neural Network Suited for Genetic Algorithms. The usefulness of an artificial analog neural network is closely bound to its trainability. This paper introduces a new analog neural network architecture using weights determined by a genetic algorithm. The first VLSI implementation presented in this paper achieves 200 giga connections per second with 4096 synapses on less than 1 mm silicon area. Since the training can be done at the full speed of the network, several hundred individuals per second can be tested by the genetic algorithm. This makes it feasible to tackle problems that require large multi-layered networks.

Question: What is a genetic algorithm?


Aspect-level opinion mining of online customer reviews. This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspect-dependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspect- dependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.

Question: How to improve aspect-level opinion mining for online customer reviews?


The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan. With the rapid change of business competitive environment, enterprise resource integration and innovative issues of business operation have gradually become the most important issues for businesses. Furthermore, many enterprises have implemented novel information technology and developing the innovative e-business applications systems such as enterprise resource planning (ERP), customer relationship management (CRM), knowledge management (KM) and supply chain management (SCM) to enhance their competitive advantages. CRM systems can help organizations to gain the potential new customers, promote the existing customers' purchase, maintain good relationship with customers as well as to enhance the customer value, thus can improve the enterprise images. Moreover, the development and applications of CRM systems have also been considered as important issues for researchers and practitioners in recent years. For Taiwan's industry, it has been gradually transferred from manufacturing-oriented to a service-oriented. Thus, the service industry has higher percentage in the GDP and in which the distribution service industry is the biggest one and be a core industry in the whole of service industry. The main purpose of this study is to explore the factors affecting the acceptance and use of CRM systems. Furthermore, the proposed research model was built on the basis of unified theory of acceptance and use of technology (UTAUT) and task-technology fit (TTF) framework as well as technological and managerial theories. The implications of findings for practice will be discussed. 2010 Elsevier Ltd. All rights reserved.

Question: What is the difference between CRM and ERP?


Supervised LogEuclidean Metric Learning for Symmetric Positive Definite Matrices. Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem of metric learning for symmetric positive definite (SPD) matrices such as covariance matrices, which arise in many real-world applications. Naively using standard Mahalanobis metric learning methods under the Euclidean geometry for SPD matrices is not appropriate, because the difference of SPD matrices can be a non-SPD matrix and thus the obtained solution can be uninterpretable. To cope with this problem, we propose to use a properly parameterized LogEuclidean distance and optimize the metric with respect to kernel-target alignment, which is a supervised criterion for kernel learning. Then the resulting non-trivial optimization problem is solved by utilizing the Riemannian geometry. Finally, we experimentally demonstrate the usefulness of our LogEuclidean metric learning algorithm on real-world classification tasks for EEG signals and texture patches.

Question: What is the difference between the Euclidean and LogEuclidean distance?


Holographic Photolysis for Multiple Cell Stimulation in Mouse Hippocampal Slices. BACKGROUND Advanced light microscopy offers sensitive and non-invasive means to image neural activity and to control signaling with photolysable molecules and, recently, light-gated channels. These approaches require precise and yet flexible light excitation patterns. For synchronous stimulation of subsets of cells, they also require large excitation areas with millisecond and micrometric resolution. We have recently developed a new method for such optical control using a phase holographic modulation of optical wave-fronts, which minimizes power loss, enables rapid switching between excitation patterns, and allows a true 3D sculpting of the excitation volumes. In previous studies we have used holographic photololysis to control glutamate uncaging on single neuronal cells. Here, we extend the use of holographic photolysis for the excitation of multiple neurons and of glial cells.   METHODS/PRINCIPAL FINDINGS The system combines a liquid crystal device for holographic patterned photostimulation, high-resolution optical imaging, the HiLo microscopy, to define the stimulated regions and a conventional Ca(2+) imaging system to detect neural activity. By means of electrophysiological recordings and calcium imaging in acute hippocampal slices, we show that the use of excitation patterns precisely tailored to the shape of multiple neuronal somata represents a very efficient way for the simultaneous excitation of a group of neurons. In addition, we demonstrate that fast shaped illumination patterns also induce reliable responses in single glial cells.   CONCLUSIONS/SIGNIFICANCE We show that the main advantage of holographic illumination is that it allows for an efficient excitation of multiple cells with a spatiotemporal resolution unachievable with other existing approaches. Although this paper focuses on the photoactivation of caged molecules, our approach will surely prove very efficient for

Question: What is the difference between a holographic and a hologram?


Statistical Decision Theory and Bayesian Analysis, 2nd Edition.

Question: What is the difference between Bayesian and Frequentist analysis?


Autonomous Object Manipulation Using a Soft Planar Grasping Manipulator. This article presents the development of an autonomous motion planning algorithm for a soft planar grasping manipulator capable of grasp-and-place operations by encapsulation with uncertainty in the position and shape of the object. The end effector of the soft manipulator is fabricated in one piece without weakening seams using lost-wax casting instead of the commonly used multilayer lamination process. The soft manipulation system can grasp randomly positioned objects within its reachable envelope and move them to a desired location without human intervention. The autonomous planning system leverages the compliance and continuum bending of the soft grasping manipulator to achieve repeatable grasps in the presence of uncertainty. A suite of experiments is presented that demonstrates the system's capabilities.

Question: What is the soft planar grasping manipulator?


A comparison of Deep Learning methods for environmental sound detection. Environmental sound detection is a challenging application of machine learning because of the noisy nature of the signal, and the small amount of (labeled) data that is typically available. This work thus presents a comparison of several state-of-the-art Deep Learning models on the IEEE challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 challenge task and data, classifying sounds into one of fifteen common indoor and outdoor acoustic scenes, such as bus, cafe, car, city center, forest path, library, train, etc. In total, 13 hours of stereo audio recordings are available, making this one of the largest datasets available.

Question: What is the largest dataset available?


Mostly Exploration-Free Algorithms for Contextual Bandits. The contextual bandit literature has traditionally focused on algorithms that address the explorationexploitation tradeoff. In particular, greedy algorithms that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy algorithms are desirable in practical settings where exploration may be costly or unethical (e.g., clinical trials). Surprisingly, we find that a simple greedy algorithm can be rate-optimal (achieves asymptotically optimal regret) if there is sufficient randomness in the observed contexts (covariates). We prove that this is always the case for a two-armed bandit under a general class of context distributions that satisfy a condition we term covariate diversity. Furthermore, even absent this condition, we show that a greedy algorithm can be rate-optimal with nonzero probability. Thus, standard bandit algorithms may unnecessarily explore. Motivated by these results, we introduce Greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy algorithm or to explore. We prove that this algorithm is rate-optimal without any additional assumptions on the context distribution or the number of arms. Extensive simulations demonstrate that Greedy-First successfully reduces experimentation and outperforms existing (exploration-based) contextual bandit algorithms such as Thompson sampling or UCB.

Question: What is the difference between exploration and exploitation?


Mobility of an in-pipe robot with screw drive mechanism inside curved pipes. This paper presents motion analyses and experiments of an in-pipe robot with screw drive mechanism while it moves inside curved pipes. This robot is driven by only one motor and composed of two units. One unit works as a rotator and the another one works as a stator. Screw drive mechanism is a better way to travel inside pipelines of small diameter for inspection, because the area for moving of the robot is narrow and the number of actuators can be reduced (minimum is just one). Therefore, the robot can be smaller and lighter. Furthermore, the control becomes easier. Although many kinds of in-pipe robots with screw drive mechanism have been reported to date, the most important problem of such drive mechanism is the difficulty of traveling in pipes other than straight ones. Examples of such pipes are curved pipes like elbow and bent, branch pipes like a T-shape and the pipe where diameter changes. A concrete analysis has not been done yet. In this paper, we concentrate on a helical driving motion of the robot inside the curved pipe and finally perform experiments to determine the characteristics of it.

Question: What is the difference between a screw drive mechanism and a worm drive mechanism?


Multi-task low-rank affinity pursuit for image segmentation. This paper investigates how to boost region-based image segmentation by pursuing a new solution to fuse multiple types of image features. A collaborative image segmentation framework, called multi-task low-rank affinity pursuit, is presented for such a purpose. Given an image described with multiple types of features, we aim at inferring a unified affinity matrix that implicitly encodes the segmentation of the image. This is achieved by seeking the sparsity-consistent low-rank affinities from the joint decompositions of multiple feature matrices into pairs of sparse and low-rank matrices, the latter of which is expressed as the production of the image feature matrix and its corresponding image affinity matrix. The inference process is formulated as a constrained nuclear norm and ℓ2;1-norm minimization problem, which is convex and can be solved efficiently with the Augmented Lagrange Multiplier method. Compared to previous methods, which are usually based on a single type of features, the proposed method seamlessly integrates multiple types of features to jointly produce the affinity matrix within a single inference step, and produces more accurate and reliable segmentation results. Experiments on the MSRC dataset and Berkeley segmentation dataset well validate the superiority of using multiple features over single feature and also the superiority of our method over conventional methods for feature fusion. Moreover, our method is shown to be very competitive while comparing to other state-of-the-art methods.

Question: How to fuse multiple types of features?


NL-InSAR: Nonlocal Interferogram Estimation. Interferometric synthetic aperture radar (SAR) data provide reflectivity, interferometric phase, and coherence images, which are paramount to scene interpretation or low-level processing tasks such as segmentation and 3-D reconstruction. These images are estimated in practice from a Hermitian product on local windows. These windows lead to biases and resolution losses due to the local heterogeneity caused by edges and textures. This paper proposes a nonlocal approach for the joint estimation of the reflectivity, the interferometric phase, and the coherence images from an interferometric pair of coregistered single-look complex (SLC) SAR images. Nonlocal techniques are known to efficiently reduce noise while preserving structures by performing the weighted averaging of similar pixels. Two pixels are considered similar if the surrounding image patches are "resembling." Patch similarity is usually defined as the Euclidean distance between the vectors of graylevels. In this paper, a statistically grounded patch-similarity criterion suitable to SLC images is derived. A weighted maximum likelihood estimation of the SAR interferogram is then computed with weights derived in a data-driven way. Weights are defined from the intensity and interferometric phase and are iteratively refined based both on the similarity between noisy patches and on the similarity of patches from the previous estimate. The efficiency of this new interferogram construction technique is illustrated both qualitatively and quantitatively on synthetic and true data.

Question: How to estimate the interferometric phase from a single look complex SAR image?


Calibrating Noise to Sensitivity in Private Data Analysis. We continue a line of research initiated in [10, 11] on privacypreserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = P i g(xi), where xi denotes the ith row of the database and g maps database rows to [0, 1]. We extend the study to general functions f , proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f . Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.

Question: What is the difference between the true answer and the true answer plus noise?


Advances in Instance Selection for Instance-Based Learning Algorithms. The basic nearest neighbour classifier suffers from the indiscriminate storage of all presented training instances. With a large database of instances classification response time can be slow. When noisy instances are present classification accuracy can suffer. Drawing on the large body of relevant work carried out in the past 30 years, we review the principle approaches to solving these problems. By deleting instances, both problems can be alleviated, but the criterion used is typically assumed to be all encompassing and effective over many domains. We argue against this position and introduce an algorithm that rivals the most successful existing algorithm. When evaluated on 30 different problems, neither algorithm consistently outperforms the other: consistency is very hard. To achieve the best results, we need to develop mechanisms that provide insights into the structure of class definitions. We discuss the possibility of these mechanisms and propose some initial measures that could be useful for the data miner.

Question: What is the nearest neighbour classifier?


Anger detection in call center dialogues. We present a method to classify fixed-duration windows of speech as expressing anger or not, which does not require speech recognition, utterance segmentation, or separating the utterances of different speakers and can, thus, be easily applied to real-world recordings. We also introduce the task of ranking a set of spoken dialogues by decreasing percentage of anger duration, as a step towards helping call center supervisors and analysts identify conversations requiring further action. Our work is among the very few attempts to detect emotions in spontaneous human-human dialogues recorded in call centers, as opposed to acted studio recordings or human-machine dialogues. We show that despite the non-perfect performance (approx. 70% accuracy) of the window-level classifier, its decisions help produce a ranking of entire conversations by decreasing percentage of anger duration that is clearly better than a random ranking, which represents the case where supervisors and analysts randomly select conversations to inspect.

Question: How to detect anger in call center dialogues?


A Baseline for General Music Object Detection with Deep Learning. Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We consider three datasets of heterogeneous typology but with the same annotation format, three neural models of different nature, and establish their performance in terms of a common evaluation standard. The experimental results confirm that the direct music object detection with deep learning is indeed promising, but at the same time illustrates some of the domain-specific shortcomings of the general detectors. A qualitative comparison then suggests avenues for OMR improvement, based both on properties of the detection model and how the datasets are defined. To the best of our knowledge, this is the first time that competing music object detection systems from the machine learning paradigm are directly compared to each other. We hope that this work will serve as a reference to measure the progress of future developments of OMR in music object detection.

Question: What is the difference between a baseline and a baseline for music object detection?


Ultra-wideband, short-pulse ground-penetrating radar: simulation and measurement. Ultra-wideband (UWB), short-pulse (SP) radar is investigated theoretically and experimentally for the detection and identification of targets buried in and placed atop soil. The calculations are performed using a rigorous, three-dimensional (3-D) Method of Moments algorithm for perfectly conducting bodies of revolution. Particular targets investigated theoretically include anti-personnel mines, anti-tank mines, and a 55-gallon drum, for which we model the time-domain scattered fields and the buried-target late-time resonant frequencies. With regard to the latter, the computed resonant frequencies are utilized to assess the feasibility of resonance-based buried-target identification for this class of targets. The measurements are performed using a novel UWB, SP synthetic aperture radar (SAR) implemented on a mobile boom. Experimental and theoretical results are compared.

Question: What is the difference between a radar and a sonar?


DataVizard: Recommending Visual Presentations for Structured Data. Selecting the appropriate visual presentation of the data such that it not only preserves the semantics but also provides an intuitive summary of the data is an important, often the final step of data analytics. Unfortunately, this is also a step involving significant human effort starting from selection of groups of columns in the structured results from analytics stages, to the selection of right visualization by experimenting with various alternatives. In this paper, we describe our DataVizard system aimed at reducing this overhead by automatically recommending the most appropriate visual presentation for the structured result. Specifically, we consider the following two scenarios: first, when one needs to visualize the results of a structured query such as SQL; and the second, when one has acquired a data table with an associated short description (e.g., tables from the Web). Using a corpus of real-world database queries (and their results) and a number of statistical tables crawled from the Web, we show that DataVizard is capable of recommending visual presentations with high accuracy.

Question: What is the most appropriate visual presentation for the structured result?


Trends in computer science research. Keywords in the ACM Digital Library and IEEE Xplore digital library and in NSF grants anticipate future CS research.

Question: What are the keywords in the ACM Digital Library?


Active Learning for Convolutional Neural Networks: A Core-Set Approach. Convolutional neural networks (CNNs) have been successfully applied to many recognition and learning tasks using a universal recipe; training a deep model on a very large dataset of supervised examples. However, this approach is rather restrictive in practice since collecting a large set of labeled images is very expensive. One way to ease this problem is coming up with smart ways for choosing images to be labelled from a very large collection (i.e. active learning). Our empirical study suggests that many of the active learning heuristics in the literature are not effective when applied to CNNs in batch setting. Inspired by these limitations, we define the problem of active learning as core-set selection, i.e. choosing set of points such that a model learned over the selected subset is competitive for the remaining data points. We further present a theoretical result characterizing the performance of any selected subset using the geometry of the datapoints. As an active learning algorithm, we choose the subset which is expected to yield best result according to our characterization. Our experiments show that the proposed method significantly outperforms existing approaches in image classification experiments by a large margin.

Question: What is the geometry of the datapoints?


A mobile vision system for robust multi-person tracking. We present a mobile vision system for multi-person tracking in busy environments. Specifically, the system integrates continuous visual odometry computation with tracking-by-detection in order to track pedestrians in spite of frequent occlusions and egomotion of the camera rig. To achieve reliable performance under real-world conditions, it has long been advocated to extract and combine as much visual information as possible. We propose a way to closely integrate the vision modules for visual odometry, pedestrian detection, depth estimation, and tracking. The integration naturally leads to several cognitive feedback loops between the modules. Among others, we propose a novel feedback connection from the object detector to visual odometry which utilizes the semantic knowledge of detection to stabilize localization. Feedback loops always carry the danger that erroneous feedback from one module is amplified and causes the entire system to become instable. We therefore incorporate automatic failure detection and recovery, allowing the system to continue when a module becomes unreliable. The approach is experimentally evaluated on several long and difficult video sequences from busy inner-city locations. Our results show that the proposed integration makes it possible to deliver stable tracking performance in scenes of previously infeasible complexity.

Question: What is the difference between a mobile vision system and a mobile vision system for multi-person tracking?


Advances in direct transesterification of algal oils from wet biomass.. An interest in biodiesel as an alternative fuel for diesel engines has been increasing because of the issue of petroleum depletion and environmental concerns related to massive carbon dioxide emissions. Researchers are strongly driven to pursue the next generation of vegetable oil-based biodiesel. Oleaginous microalgae are considered to be a promising alternative oil source. To commercialize microalgal biodiesel, cost reductions in oil extraction and downstream biodiesel conversion are stressed. Herein, starting from an investigation of oil extraction from wet microalgae, a review is conducted of transesterification using enzymes, homogeneous and heterogeneous catalysts, and yield enhancement by ultrasound, microwave, and supercritical process. In particular, there is a focus on direct transesterification as a simple and energy efficient process that omits a separate oil extraction step and utilizes wet microalgal biomass; however, it is still necessary to consider issues such as the purification of microalgal oils and upgrading of biodiesel properties.

Question: What is the difference between direct and indirect transesterification?


Marijuana Use in the Elderly: Implications and Considerations.. OBJECTIVE This article reviews the literature on the use of marijuana in the elderly. Pharmacists play an important role in the management of medications including drug use of potentially illegal drugs, including marijuana. The use of both recreational and medical marijuana has grown exponentially in the general population, including in older adults. As of 2017, marijuana for medical use is legal in 26 states and the District of Columbia.   DATA SOURCES PubMed and Internet search using the following terms: marijuana, cannabis, delta-9-tetrhydrocannabinol (THC), cannabidiol, cannabinoid, elderly, geriatric, and pharmacology. Findings are based on data collected from older adults (65 years of age and older) through August 2016.   STUDY SELECTION Because of the lack of research and funding, reputable literature on the impact of marijuana on older adults is scarce. The available evidence suggests that elderly individuals should be cautious when consuming marijuana, especially those who have certain comorbid conditions.   DATA EXTRACTION The geriatric population has a higher likelihood of having multiple comorbidities and is subject to polypharmacy. Marijuana use, medicinal or recreational, complicates the picture with additive central nervous system side effects.   DATA SYNTHESIS This article reviews the growing information on marijuana use and discusses issues to consider and cautions in usage that can apply to day-to-day clinical practice and geriatric care. The role of the pharmacist in educating patients, caregivers, and health care providers is expanding with the growing number of states that have legalized medical marijuana (26 states and the District of Columbia, as of 2017). Important education points including drug-drug interactions, drug-disease interactions, and signs and symptoms of acute overdose should be considered.   CONCLUSION With this review, pharmacists will be informed on recommendation

Question: What is the role of the pharmacist in educating patients, caregivers, and health care providers?


Energy Minimization Using Multiple Supply Voltages. We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data ow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.

Question: What is the average energy consumption of a data-path?


LDAExplore: Visualizing Topic Models Generated Using Latent Dirichlet Allocation. We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstract's of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.

Question: How does LDAExplore work?


A Reorder Buffer Design for High Performance Processors. Modern reorder buffers (ROBs) were conceived to improve processor performance by allowing instruction execution out of the original program order and run ahead of sequential instruction code exploiting existing instruction level parallelism (ILP). The ROB is a functional structure of a processor execution engine that supports speculative execution, physical register recycling, and precise exception recovering. Traditionally, the ROB is considered as a monolithic circular buffer with incoming instructions at the tail pointer after the decoding stage and completing instructions at the head pointer after the commitment stage. The latter stage verifies instructions that have been dispatched, issued, executed, and are not completed speculatively. This paper presents a design of distributed reorder buffer microarchitecture by using small structures near building blocks which work together, using the same tail and head pointer values on all structures for synchronization. The reduction of area, and therefore, the reduction of power and delay make this design suitable for both embedded and high performance microprocessors.

Question: What is a reorder buffer?


A Personalized Company Recommender System for Job Seekers. Our team intends to develop a recommendation system for job seekers based on the information of current employees in big companies. Several models are implemented to achieve over 60% success rate in classifying employees, and we use these models to help job seekers identify their best fitting company.

Question: What is a recommender system?


A Novel Differential-Fed Patch Antenna on Stepped-Impedance Resonator With Enhanced Bandwidth Under Dual-Resonance. A novel design concept to enhance the bandwidth of a differential-fed patch antenna using the dual-resonant radiation of a stepped-impedance resonator (SIR) is proposed. The SIR is composed of two distinctive portions: the radiating patch and a pair of open stubs. Initially, based on the transmission line model, the first and second odd-order radiative resonant modes, i.e., TM10 and TM30, of this SIR-typed patch antenna are extensively investigated. It is demonstrated that the frequency ratio between the dual-resonant modes can be fully controlled by the electrical length and the impedance ratios between the open stub and radiating patch. After that, the SIR-typed patch antenna is reshaped with stepped ground plane in order to increase the impedance ratio as highly required for wideband radiation. With this arrangement, these two radiative modes are merged with each other, resulting in a wide impedance bandwidth with a stable radiation pattern under dual-resonant radiation. Finally, the proposed antenna is designed, fabricated, and measured. It is verified in experiment that the impedance bandwidth (|Sdd11| <; -10 dB) of the proposed antenna has gained tremendous increment up to 10% (0.85-0.94 GHz) with two attenuation poles. Most importantly, the antenna has achieved a stable gain varying from 7.4 to 8.5 dB within the whole operating band, while keeping low-cross polarization.

Question: What is the novel differential-fed patch antenna?


Semantic Web User Interfaces : A Systematic Mapping Study and Review. Context : Exploration of user interfaces has attracted a considerable attention in Semantic Web research in the last several years. However, the overall impact of these research efforts and the level of the maturity of their results are still unclear. Objective: This study aims at surveying existing research on Semantic Web user interfaces in order to synthesise their results, assess the level of the maturity of the research results, and identify needs for future research. Method : This paper reports the results of a systematic literature review and mapping study on Semantic Web user interfaces published between 2003 and 2011. After collecting a corpus of 87 papers, the collected papers are analysed through four main dimensions: general information such as sources, types and time of publication; covered research topics; quality of evaluations; and finally, used research methods and reported rigour of their use. Results: Based on only the obtained data, the paper provides a high level overview of the works reviewed, a detailed quality assessment of papers involving human subjects and a discussion of the identified gaps in the analysed papers and draws promising venues for future research. Conclusion: We arrive at two main conclusions: important gaps exist which hinder a wide adoption of Semantic Web technologies (e.g. lack user interfaces based on semantic technologies for social and mobile applications) and experimental research hardly used any proven theories and instruments specifically designed for investigating technology acceptance.

Question: What is the canadian military?


Organizational behavior: affect in the workplace.. The study of affect in the workplace began and peaked in the 1930s, with the decades that followed up to the 1990s not being particularly fertile. Whereas job satisfaction generally continues to be loosely but not carefully thought of and measured as an affective state, critical work in the 1990s has raised serious questions about the affective status of job satisfaction in terms of its causes as well as its definition and measurement. Recent research has focused on the production of moods and emotions at work, with an emphasis, at least conceptually, on stressful events, leaders, work groups, physical settings, and rewards/punishment. Other recent research has addressed the consequences of workers' feelings, in particular, a variety of performance outcomes (e.g., helping behaviors and creativity). Even though recent interest in affect in the workplace has been intense, many theoretical and methodological opportunities and challenges remain.

Question: What is the affect in the workplace?


Quantitative functional evaluation of a 3D-printed silicone-embedded prosthesis for partial hand amputation: A case report.. STUDY DESIGN A male patient with partial hand amputation of his nondominant hand, with only stumps of the proximal phalanx of the first and fifth finger, was evaluated. The performance of using two alternative 3D printed silicone-embedded personalized prostheses was evaluated using the quantitative Jebsen Hand Function Test.   INTRODUCTION Custom design and fabrication of 3D printed prostheses appears to be a good technique for improving the clinical treatment of patients with partial hand amputations. Despite its importance the literature shows an absence of studies reporting on quantitative functional evaluations of 3D printed hand prostheses.   PURPOSE OF THE STUDY We aim at producing the first quantitative assessment of the impact of using 3D printed silicone-embedded prostheses that can be fabricated and customized within the clinical environment.   METHODS Alginate molds and computed tomographic scans were taken from the patient's hand. Each candidate prosthesis was modeled in Computer Aided Design software and then fabricated using a combination of 3D printed parts and silicone-embedded components.   DISCUSSION Incorporating the patient's feedback during the design loop was very important for obtaining a good aid on his work activities. Although the explored patient-centered design process still requires a multidisciplinary team, functional benefits are large.   CONCLUSION(S) Quantitative data demonstrates better hand performance when using 3D printed silicone-embedded prosthesis vs not using any aid. The patient accomplished complex tasks such as driving a nail and opening plastic bags. This was impossible without the aid of produced prosthesis.

Question: How does a 3D printed silicone-embedded prosthesis compare to a conventional prosthesis?


M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. A new access method called M tree is pro posed to organize and search large data sets from a generic metric space i e where ob ject proximity is only de ned by a distance function satisfying the positivity symmetry and triangle inequality postulates We detail algorithms for insertion of objects and split management which keep the M tree always balanced several heuristic split alternatives are considered and experimentally evaluated Algorithms for similarity range and k nearest neighbors queries are also described Re sults from extensive experimentation with a prototype system are reported considering as the performance criteria the number of page I O s and the number of distance computa tions The results demonstrate that the M tree indeed extends the domain of applica bility beyond the traditional vector spaces performs reasonably well in high dimensional data spaces and scales well in case of growing les

Question: What is the M tree?


The Impact of Interactive Touchscreens on Physics Education in Upper Secondary School – A systematic literature review. Interactive touchscreens such as tablet PCs (TPC) and interactive whiteboards (IWB) are becoming more and more common in classrooms around the world. To date, very little research has been conducted on the impact of the new technology on physics education. This systematic literature review aims to investigate research on what impact tablet PCs and interactive whiteboards might have on the education in upper Secondary School. The review was performed in response to the following questions: 1. What is the influence of IWBs and TPCs on students' active participation in physics education? 2. How can an IWB or TPC improve students' learning about physics concepts? 3. How can educational research on touchscreen technology help inform effective teaching strategies in physics education? To respond to the questions of the study, relevant research about interactive whiteboards and/or tablet PCs was consulted and analysed. Twelve articles were located, mainly through the ERIC and Scopus databases, but also through Google Scholar. The included articles reported empirical research about physics education with interactive whiteboards or tablet PCs. The results from the articles indicate that interactive touchscreens might help improve learners' active participation in physics education. Interactive whiteboards can, for example, be used to display interactive simulations during group work, something students are found to appreciate and easily engage in. A tablet PC can be used in the same way, but also allows students to receive anonymous support and feedback from the teacher during class which seems to be beneficial for learning. Results show that it is possible to improve students' understanding of physics concepts by using interactive whiteboards or tablet PCs. However, further research is required to compare results from students using touch technology and st

Question: What is the impact of interactive touchscreens on physics education?