Point Cloud Segmentation and Denoising via Constrained Nonlinear Least Squares Normal Estimates.

Question: What is the difference between a point cloud and a cloud of points?


Preserving the Archival Bond in Distributed Ledgers: A Data Model and Syntax. Distributed cryptographic ledgers, such as the blockchain, are now being used in recordkeeping. However, they lack a key feature of more traditional recordkeeping systems needed to establish the authenticity of records and enable reliance on them for trustworthy recordkeeping. The missing feature is known in archival science as the archival bond – the mutual relationship that exists among documents by virtue of the actions in which they participate. In this paper, we propose a novel data model and syntax using core web principles that can be used to address this shortcoming in distributed ledgers as recordkeeping systems.

Question: What is the archival bond?


Conclusions of Ghaheri's Study That Laser Surgery for Posterior Tongue and Lip Ties Improves Breastfeeding Are Not Substantiated.. Editor's Note: The diagnosis and treatment of tongue and lip ties remains a topic of much and at times heated debate. The following correspondences, which take opposite sides of the argument as to the value of laser surgery, are symptomatic of this still unresolved and hotly contested subject. Breastfeeding Medicine is of the opinion that it should serve as a platform for the presentation of both sides of the issue so as to provide information that will assist clinicians in their management decision.

Question: What is the value of laser surgery for tongue and lip ties?


Synthetic aperture radar interferometry. Synthetic aperture radar interferometry is an imaging technique for measuring the topography of a surface, its changes over time, and other changes in the detailed characteristic of the surface. By exploiting the phase of the coherent radar signal, interferometry has transformed radar remote sensing from a largely interpretive science to a quantitative tool, with applications in cartography, geodesy, land cover characterization, and natural hazards. This paper reviews the techniques of interferometry, systems and limitations, and applications in a rapidly growing area of science and engineering.

Question: What is synthetic aperture radar interferometry?


Performance of H.264, H.265, VP8 and VP9 Compression Standards for High Resolutions. Recently multimedia services increase, especially in video domain, leads to requirements of quality assessment. The main factors effected that quality are compression technology and transmission link. This paper presents a coding efficiency comparison of the well-known video compression standards Advanced Video Coding (H.264/AVC), High-Efficiency Video Coding (H.265/HEVC), VP8 and VP9 using objective metrics. An extensive range of bitrates from low to high bitrates was selected and encoded. The evaluation was done for four types of sequences in high resolutions depending on content. All four video sequences were encoded by using same encoding configurations for all the examined video codecs. The results showed that the quality of all compression standards rises logarithmically with increasing bitrate - in low bitrates the quality grows faster than in high bitrates. The efficiency of the new compression standards outperforms the older ones. The efficiency of VP8 compression standard outperforms the H.264/AVC compression standard. The efficiency of H.265/HEVC and VP9 compression standards is almost the same. The results also showed that VP8 codec cannot allow encoding by low bitrates. According to the results can be also said that the efficiency of the new compression standards decreases with the resolution. The results showed that the effectiveness of compression depends on the type of test sequence.

Question: What is the difference between H.264, H.265, VP8 and VP9?


A novel coaxial probe waveguide to microstrip transition. In this paper, a modified coaxial probe waveguide-to-microstrip transition structure at K-band is proposed. The transition uses two kinds of coaxial probes and has hermetic characteristics. It can also be used in other band, such as Ka-band. The structure is demonstrated for designing the transition. HFSS simulation and measurement results for the transition structure have been compared in terms of |S/sub 21/|. The comparison shows that the measurement agrees well with the simulation result. The measurement has shown 0.5 dB insertion loss.

Question: What is a coaxial probe waveguide?


Specification Inference from Demonstrations. Learning from expert demonstrations has received a lot of attention in artificial intelligence and machine learning. The goal is to infer the underlying reward function that an agent is optimizing given a set of observations of the agent's behavior over time in a variety of circumstances, the system state trajectories, and a plant model specifying the evolution of the system state for different agent's actions. The system is often modeled as a Markov decision process (Puterman 2014), that is, the next state depends only on the current state and agent's action, and the the agent's choice of action depends only on the current state. While the former is a Markovian assumption on the evolution of system state, the later assumes that the target reward function is itself Markovian. In this work, we explore learning a class of non-Markovian reward functions, known in the formal methods literature as specifications. These specifications offer better composition, transferability, and interpretability. We then show that inferring the specification can be done efficiently without unrolling the transition system. We demonstrate on a 2-d grid world example.

Question: What is a specification?


SAP HANA - From Relational OLAP Database to Big Data Infrastructure. SAP HANA started as one of the best-performing database engines for OLAP workloads strictly pursuing a main-memory centric architecture and exploiting hardware developments like large number of cores and main memories in the TByte range. Within this paper, we outline the steps from a traditional relational database engine to a Big Data infrastructure comprising different methods to handle data of different volume, coming in with different velocity, and showing a fairly large degree of variety. In order to make the presentation of this transformation process more tangible, we discuss two major technical topics–HANA native integration points as well as extension points for collaboration with Hadoop-based data management infrastructures. The overall of goal of this paper is to (a) review current application patterns and resulting technical challenges as well as to (b) paint the big picture for upcoming architectural designs with SAP HANA database as the core of a SAP Big Data infrastructure.

Question: What is the big picture for upcoming architectural designs with SAP HANA database as the core of a SAP Big Data infrastructure?


A vision-based method for weeds identification through the Bayesian decision theory. One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

Question: What is the difference between a priori and a posteriori?


Design aspects of network assisted device-to-device communications. Device-to-device (D2D) communications underlaying a cellular infrastructure has been proposed as a means of taking advantage of the physical proximity of communicating devices, increasing resource utilization, and improving cellular coverage. Relative to the traditional cellular methods, there is a need to design new peer discovery methods, physical layer procedures, and radio resource management algorithms that help realize the potential advantages of D2D communications. In this article we use the 3GPP Long Term Evolution system as a baseline for D2D design, review some of the key design challenges, and propose solution approaches that allow cellular devices and D2D pairs to share spectrum resources and thereby increase the spectrum and energy efficiency of traditional cellular networks. Simulation results illustrate the viability of the proposed design.

Question: What is the design of network assisted device-to-device communications?


Multi-modal Mean-Fields via Cardinality-Based Clamping. Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.

Question: What is the difference between mean field and mean field inference?


All-digital PLL and transmitter for mobile phones. We present the first all-digital PLL and polar transmitter for mobile phones. They are part of a single-chip GSM/EDGE transceiver SoC fabricated in a 90 nm digital CMOS process. The circuits are architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrateable with a digital baseband and application processor. To achieve this, we exploit the new paradigm of a deep-submicron CMOS process environment by leveraging on the fast switching times of MOS transistors, the fine lithography and the precise device matching, while avoiding problems related to the limited voltage headroom. The transmitter architecture is fully digital and utilizes the wideband direct frequency modulation capability of the all-digital PLL. The amplitude modulation is realized digitally by regulating the number of active NMOS transistor switches in accordance with the instantaneous amplitude. The conventional RF frequency synthesizer architecture, based on a voltage-controlled oscillator and phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter. The transmitter performs GMSK modulation with less than 0.5/spl deg/ rms phase error, -165 dBc/Hz phase noise at 20 MHz offset, and 10 /spl mu/s settling time. The 8-PSK EDGE spectral mask is met with 1.2% EVM. The transmitter occupies 1.5 mm/sup 2/ and consumes 42 mA at 1.2 V supply while producing 6 dBm RF output power.

Question: What is the difference between a digital PLL and a digital phase locked loop?


A near-threshold 7T SRAM cell with high write and read margins and low write time for sub-20 nm FinFET technologies. In this paper, a 7T SRAM cell with differential write and single ended read operations working in the near-threshold region is proposed. The structure is based on modifying a recently proposed 5T cell which uses high and low VTH transistors to improve the read and write stability. To enhance the read static noise margin (RSNM) while keeping the high write margin and low write time, an extra access transistor is used and the threshold voltages of the SRAM transistors are appropriately set. In addition, to maintain the low leakage power of the cell and increase the Ion/Ioff ratio of its access transistors, a high VTH transistor is used in the pull down path of the cell. To assess the efficacy of the proposed cell, its characteristics are compared with those of 5T, 6T, 8T, and 9T SRAM cells. The characteristics are obtained from HSPICE simulations using 20 nm, 16 nm, 14 nm, 10 nm, and 7 nm FinFET technologies assuming a supply voltage of 500 mV. The results reveal high write and read margins, the highest Ion/Ioff ratio, a fast write, and ultra-low leakage power in the hold "0" state for the cell. Therefore, the suggested 7T cell may be considered as one of the better design choices for both high performance and low power applications. Also, the changes of cell parameters when the temperature rises from 40 1C to 100 1C are investigated. Finally, the write margin as well as the read and hold SNMs of the cell in the presence of the process variations are studied at two supply voltages of 400 mV and 500 mV. The study shows that the proposed cell meets the required cell sigma value (6σ) under all conditions. & 2015 Elsevier B.V. All rights reserved.

Question: What is the difference between a near-threshold SRAM cell and a near-threshold SRAM cell?


Neural Variational Inference and Learning in Belief Networks. •We introduce a simple, efficient, and general method for training directed latent variable models. – Can handle both discrete and continuous latent variables. – Easy to apply – requires no model-specific derivations. •Key idea: Train an auxiliary neural network to perform inference in the model of interest by optimizing the variational bound. – Was considered before for Helmholtz machines and rejected as infeasible due to high variance of inference net gradient estimates. •We make the approach practical using simple and general variance reduction techniques. •Promising document modelling results using sigmoid belief networks.

Question: What is a belief network?


CSLIM: contextual SLIM recommendation algorithms. Context-aware recommender systems (CARS) take contextual conditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also incorporates contextual conditions. In this paper, we introduce another matrix factorization approach for contextual recommendations, the contextual SLIM (CSLIM) recommendation approach. It is derived from the sparse linear method (SLIM) which was designed for Top-N recommendations in traditional recommender systems. Based on the experimental evaluations over several context-aware data sets, we demonstrate that CLSIM can be an effective approach for context-aware recommendations, in many cases outperforming state-of-the-art CARS algorithms in the Top-N recommendation task.

Question: What is contextual SLIM?


An Ontology-based Summarization System for Arabic Documents (OSSAD). With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper an Ontology-based Summarization System for Arabic Documents, OSSAD, is introduced. Domain knowledge is extracted from an Arabic corpus and represented by topic related concepts/keywords and the lexical relations among them. The user's query is first expanded by using the Arabic WordNet and then by adding the domain-specific knowledge base to the expansion. For summarization, decision tree algorithm (C4.5) is used, which was trained by a set of features extracted from the original documents. For the testing dataset, Essex Arabic Summaries Corpus (EASC) was used. Recall Oriented Understudy for Gisting Evaluation (ROUGE) was used to compare OSSAD summaries with the human summaries along with other automatic summarization systems, showing that the proposed approach demonstrated promising results.

Question: What is the ontology-based summarization system for Arabic documents?


Learning to Detect Human-Object Interactions. We study the problem of detecting human-object interactions (HOI) in static images, defined as predicting a human and an object bounding box with an interaction class label that connects them. HOI detection is a fundamental problem in computer vision as it provides semantic information about the interactions among the detected objects. We introduce HICO-DET, a new large benchmark for HOI detection, by augmenting the current HICO classification benchmark with instance annotations. To solve the task, we propose Human-Object Region-based Convolutional Neural Networks (HO-RCNN). At the core of our HO-RCNN is the Interaction Pattern, a novel DNN input that characterizes the spatial relations between two bounding boxes. Experiments on HICO-DET demonstrate that our HO-RCNN, by exploiting human-object spatial relations through Interaction Patterns, significantly improves the performance of HOI detection over baseline approaches.

Question: How to detect human-object interactions?


Real-Time Hand Tracking under Occlusion from an Egocentric RGB-D Sensor. We present an approach for real-time, robust and accurate hand pose estimation from moving egocentric RGB-D cameras in cluttered real environments. Existing methods typically fail for hand-object interactions in cluttered scenes imaged from egocentric viewpoints—common for virtual or augmented reality applications. Our approach uses two subsequently applied Convolutional Neural Networks (CNNs) to localize the hand and regress 3D joint locations. Hand localization is achieved by using a CNN to estimate the 2D position of the hand center in the input, even in the presence of clutter and occlusions. The localized hand position, together with the corresponding input depth value, is used to generate a normalized cropped image that is fed into a second CNN to regress relative 3D hand joint locations in real time. For added accuracy, robustness and temporal stability, we refine the pose estimates using a kinematic pose tracking energy. To train the CNNs, we introduce a new photorealistic dataset that uses a merged reality approach to capture and synthesize large amounts of annotated data of natural hand interaction in cluttered scenes. Through quantitative and qualitative evaluation, we show that our method is robust to self-occlusion and occlusions by objects, particularly in moving egocentric perspectives.

Question: How to train a neural network to recognize hand gestures?


Glove Based User Interaction Techniques for Augmented Reality in an Outdoor Environment. This paper presents a set of pinch glove-based user interface tools for an outdoor wearable augmented reality computer system. The main form of user interaction is the use of hand and head gestures. We have developed a set of augmented reality information presentation techniques. To support direct manipulation, the following three selection techniques have been implemented: two-handed framing, line of sight and laser beam. A new glove-based text entry mechanism has been developed to support symbolic manipulation. A scenario for a military logistics task is described to illustrate the functionality of this form of interaction.

Question: What is a scenario?


A Low-Power Wideband Transmitter Front-End Chip for 80 GHz FMCW Radar Systems With Integrated 23 GHz Downconverter VCO. A low-power FMCW 80 GHz radar transmitter front-end chip is presented, which was fabricated in a SiGe bipolar production technology ( fT=180 GHz, fmax=250 GHz ). Additionally to the fundamental 80 GHz VCO, a 4:1-frequency divider (up to 100 GHz), a 23 GHz local oscillator (VCO) with a low phase noise of -112 dBc/Hz (1 MHz offset), a PLL-mixer and a static frequency divider is integrated together with several output buffers. This chip was designed for low power consumption (in total <; 0.5 W, i.e., 100 mA at 5 V supply voltage), which is dominated by the 80 GHz VCO due to the demands for high output power (≈ 12 dBm) and low phase noise (minimum -97 dBc/Hz at 1 MHz offset) within the total wide tuning range from 68 GHz to 92.5 GHz (Δf = 24.5 GHz). Measurements of the double-PLL system at 80 GHz showed a low phase noise of -88 dBc/Hz at 10 kHz offset frequency.

Question: What is the frequency of the local oscillator?


Portable and Efficient Auto-vectorized Bytecode: a Look at the Interaction between Static and JIT Compilers. Heterogeneity is a confirmed trend of computing systems. Bytecode formats and just-in-time compilers have been proposed to deal with the diversity of the platforms. By hiding architectural details and giving software developers a unified view of the machine, they help improve portability and manage the complexity of large software. Independently, careful exploitation of SIMD instructions has become crucial for the performance of many applications. However, auto-vectorizing compilers need detailed information about the architectural features of the processor to generate efficient code. We propose to reconcile the use of architecture neutral bytecode formats with the need to generate highly efficient vectorized native code. We make three contributions. 1) We show that vectorized bytecode is a viable approach that can deliver portable performance in the presence of SIMD extensions, while incurring only minor penalty when SIMD is not supported. In other words, the information that a loop can be vectorized is the vectorized loop itself. 2) We analyze the interaction between the static and just-in-time compilers and we derive conditions to deliver performance. 3) We add vectorization capabilities to the CLI port of the GCC compiler.

Question: What is the difference between a static and a just-in-time compiler?


Black-Box Calibration for ADCs With Hard Nonlinear Errors Using a Novel INL-Based Additive Code: A Pipeline ADC Case Study. This paper presents a digital nonlinearity calibration technique for ADCs with strong input–output discontinuities between adjacent codes, such as pipeline, algorithmic, and SAR ADCs with redundancy. In this kind of converter, the ADC transfer function often involves multivalued regions, where conventional integral-nonlinearity (INL)-based calibration methods tend to miscalibrate, negatively affecting the ADC's performance. As a solution to this problem, this paper proposes a novel INL-based calibration which incorporates information from the ADC's internal signals to provide a robust estimation of static nonlinear errors for multivalued ADCs. The method is fully generalizable and can be applied to any existing design as long as there is access to internal digital signals. In pipeline or subranging ADCs, this implies access to partial subcodes before digital correction; for algorithmic or SAR ADCs, conversion bit/bits per cycle are used. As a proof-of-concept demonstrator, the experimental results for a 1.2 V 23 mW 130 nm-CMOS pipeline ADC with a SINAD of 58.4 dBc (in nominal conditions without calibration) is considered. In a stressed situation with 0.95 V of supply, the ADC has SINAD values of 47.8 dBc and 56.1 dBc, respectively, before and after calibration (total power consumption, including the calibration logic, being 15.4 mW).

Question: What is a pipeline ADC?


An Introduction to the Aesop System. As the design of software architectures emerges as a discipline within software engineering, it becomes increasingly important to support architectural description and analysis with tools and environments. This paper provides a brief introduction to Aesop, a set of tools for developing architectural design environments that exploit architectural styles to guide software architects in producing speci c systems.

Question: What is a software architecture?


Generalized distributed rate limiting. The Distributed Rate Limiting (DRL) paradigm is a recently proposed mechanism for decentralized control of cloud-based services. DRL is a simple and efficient approach to resolve the issues of pricing and resource control/engineering of cloud based services. The existing DRL schemes focus on very specific performance metrics (such as loss rate and fair-share) and their design heavily depends on the assumption that the traffic is generated by elastic TCP sources. In this paper we tackle the DRL problem for general workloads and performance metrics and propose an analytic framework for the design of stable DRL algorithms. The closed-form nature of our results allows simple design rules which, together with extremely low communication overhead, makes the presented algorithms practical and easy to deploy with guaranteed convergence properties under a wide range of possible scenarios.

Question: What is the difference between the existing DRL schemes and the proposed analytic framework?


Human skin as data transmission medium for improved privacy and usability in wearable electronics. Wearable electronics have multiple benefits in medical applications, such as feedback, monitoring and prevention. The number of these devices in circulation is rapidly growing, emphasizing increasing problems in their use caused by one of two standard communication types - wireless communications and wired communications. These problems include obtrusiveness and inconvenient usage, limited bandwidth, privacy and security issues and relatively high energy consumption. Wireless communications are less secure, with a more limited bandwidth but more unobtrusive and convenient than wired ones. On the other hand, wired communications don't need so many batteries, thereby devices can be charged more rarely and provide more privacy. A third, emerging technology for wearable sensor network communication using the body itself as communication medium tries to solve these problems. The body coupled communications benefit from the communication privacy of the wired networks, while not requiring obtrusive wiring. Unfortunately this type of communication requires complex signal processing for reliable communications as the signal changes depending on the body parts connected. To solve this problem and allow more rapid adoption of body coupled communications in wearable electronics we develop a transfer function for calculating the parameters of human skin as data transmission medium, allowing more usable and privacy oriented communications between the wearable nodes. Additionally this has the potential of reducing the energy consumption of wearable devices making them more practical to use for a prolonged period of time as well as opening new applications as secure personal data exchange using touch.

Question: What is the difference between wired and wireless communications?


Deep Convolutional Factor Analyser for Multivariate Time Series Modeling. Deep generative models can perform dramatically better than traditional graphical models in a number of machine learning tasks. However, training such models remains challenging because their latent variables typically do not have an analytical posterior distribution, largely due to the nonlinear activation nodes. We present a deep convolutional factor analyser (DCFA) for multivariate time series modeling. Our network is constructed in a way that bottom layer nodes are independent. Through a process of up-sampling and convolution, higher layer nodes gain more temporal dependency. Our model can thus give a time series different representations at different depths. DCFA only consists of linear Gaussian nodes. Therefore, the posterior distributions of latent variables are also Gaussian and can be estimated easily using standard variational Bayes algorithm. We show that even without nonlinearity the proposed deep model can achieve state-of-the-art results in anomaly detection, classification and clustering using both synthetic and real-world datasets.

Question: What is a deep convolutional factor analyser?


Factorization meets the neighborhood: a multifaceted collaborative filtering model. Recommender systems provide users with personalized suggestions for products or services. These systems often rely on Collaborating Filtering (CF), where past transactions are analyzed in order to establish connections between users and products. The two more successful approaches to CF are latent factor models, which directly profile both users and products, and neighborhood models, which analyze similarities between products or users. In this work we introduce some innovations to both approaches. The factor and neighborhood models can now be smoothly merged, thereby building a more accurate combined model. Further accuracy improvements are achieved by extending the models to exploit both explicit and implicit feedback by the users. The methods are tested on the Netflix data. Results are better than those previously published on that dataset. In addition, we suggest a new evaluation metric, which highlights the differences among methods, based on their performance at a top-K recommendation task.

Question: What is the Netflix data?


Group Buying on the Web: A Comparison of Price-Discovery Mechanisms. Web-based Group-Buying mechanisms, a refinement of quantity discounting, are being used for both Business-to-Business (B2B) and Business-toConsumer (B2C) transactions. In this paper, we survey currently operational online Group-Buying markets, and then study this phenomenon using analytical models. We surveyed over fifty active Group-Buying sites, and provide a comprehensive review of Group-Buying practices in the B2B, B2C and non-profit sectors, across three continents. On the modeling side, we build on the coordination literature in Information Economics and the quantity-discounts literature in Operations to develop an analytical model of a monopolist who uses web-based Group-Buying mechanisms under different kinds of demand uncertainty. We derive the monopolist's optimal Group-Buying schedule, and compare his profits with those that obtain under the more conventional posted-price mechanism. We also study the effect of heterogeneity in the demand regimes, in combination with uncertainty, on the relative performance of the two mechanisms. We further study the impact of the timing of the pricing decision (vis-à-vis the production decision) by modeling it as a two-stage game between the monopolist and buyers. Finally, we investigate how Group-Buying schemes compare with posted price markets when buyers can revise their prior valuation of products based on information received from third parties (infomediaries). In all cases, we characterize the conditions under which one mechanism outperforms the other, and those under which the posted price and Group-Buy mechanisms lead to identical seller revenues. Our results have implications for firms' choice of price discovery mechanisms in electronic markets and scheduling of production and pricing decisions in the presence (and absence) of scale economies of production. 1 Email: anandk@wharton.upenn.edu; Tel: (215) 898 1175. 2 Email: raviaron@wharton.upenn.e

Question: What is the difference between a B2B and a B2C?


Android smartphone vulnerabilities: A survey. Round the globe mobile devices like Smartphone, PDAs & tablets are playing an essential role in every person day to day lives. Various operating systems such as android, ios, blackberry etc provides a platform for smart devices. Google's Android is a one of the most popular and user friendly open source software platform for mobile devices. Along with its convenience people are likely to download and install malicious applications developed to misguide the user which will create a security gap to penetrate for the attacker. Hackers are inclined to discover and exploit the new vulnerabilities which will bring forth with the latest version of Android. In this paper, we concentrate on examining and understanding the vulnerabilities exist in android operating system. We will also suggest a metadata model which gives the information of all the related terms required for vulnerability assessment. Here, analyzing data is extracted from Open Source Vulnerability Database (OSVDB) and National Vulnerability Database (NVD).

Question: What is the metadata model?


Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks. This study introduces a novel conditional recycle generative adversarial network for facial attribute transformation, which can transform high-level semantic face attributes without changing the identity. In our approach, we input a source facial image to the conditional generator with target attribute condition to generate a face with the target attribute. Then we recycle the generated face back to the same conditional generator with source attribute condition. A face which should be similar to that of the source face in personal identity and facial attributes is generated. Hence, we introduce a recycle reconstruction loss to enforce the final generated facial image and the source facial image to be identical. Evaluations on the CelebA dataset demonstrate the effectiveness of our approach. Qualitative results show that our approach can learn and generate high-quality identity-preserving facial images with specified attributes.

Question: How to generate a face with a specified attribute?


A Computer-Aided Diagnosis System of Nuclear Cataract. Cataracts are the leading cause of blindness worldwide, and nuclear cataract is the most common form of cataract. An algorithm for automatic diagnosis of nuclear cataract is investigated in this paper. Nuclear cataract is graded according to the severity of opacity using slit lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model. On the basis of the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine regression is employed for grade prediction. This is the first time that the nucleus region can be detected automatically in slit lamp images. The system is validated using clinical images and clinical ground truth on >5000 images. The success rate of structure detection is 95% and the average grading difference is 0.36 on a 5.0 scale. The automatic diagnosis system can improve the grading objectivity and potentially be used in clinics and population studies to save the workload of ophthalmologists.

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


Demand Queries with Preprocessing. Given a set of items and a submodular set-function f that determines the value of every subset of items, a demand query assigns prices to the items, and the desired answer is a set S of items that maximizes the pro t, namely, the value of S minus its price. The use of demand queries is well motivated in the context of combinatorial auctions. However, answering a demand query (even approximately) is NP-hard. We consider the question of whether exponential time preprocessing of f prior to receiving the demand query can help in later answering demand queries in polynomial time. We design a preprocessing algorithm that leads to approximation ratios that are NP-hard to achieve without preprocessing. We also prove that there are limitations to the approximation ratios achievable after preprocessing, unless NP ⊂ P/poly.

Question: What is the approximation ratio of the preprocessing algorithm?


Learning Sparse Feature Representations for Music Annotation and Retrieval. We present a data-processing pipeline based on sparse feature learning and describe its applications to music annotation and retrieval. Content-based music annotation and retrieval systems process audio starting with features. While commonly used features, such as MFCC, are handcrafted to extract characteristics of the audio in a succinct way, there is increasing interest in learning features automatically from data using unsupervised algorithms. We describe a systemic approach applying feature-learning algorithms to music data, in particular, focusing on a highdimensional sparse-feature representation. Our experiments show that, using only a linear classifier, the newly learned features produce results on the CAL500 dataset comparable to state-of-the-art music annotation and retrieval systems.

Question: What is a sparse feature representation?


Unsupervised Discovery of Morphemes. We present two methods for unsupervised segmentation of words into morphemelike units. The model utilized is especially suited for languages with a rich morphology, such as Finnish. The first method is based on the Minimum Description Length (MDL) principle and works online. In the second method, Maximum Likelihood (ML) optimization is used. The quality of the segmentations is measured using an evaluation method that compares the segmentations produced to an existing morphological analysis. Experiments on both Finnish and English corpora show that the presented methods perform well compared to a current stateof-the-art system.

Question: What is the minimum description length principle?


Should TED talks be teaching us something?. The nonprofit phenomenon "TED," the brand name for the concepts of Technology Education and Design, was born in 1984. It launched into pop culture stardom in 2006 when the organization's curators began offering short, free, unrestricted, and educational video segments. Known as "TEDTalks," these informational segments are designed to be no longer than 18 minutes in length and provide succinct, targeted enlightenment on various topics or ideas that are deemed "worth spreading." TED Talks, often delivered in sophisticated studios with trendy backdrops, follow a format that focuses learners on the presenter and limited, extremely purposeful visual aids. Topics range from global warming to running to the developing world. Popular TED Talks, such as Sir Ken Robinson's "Schools Kill Creatively" or Dan Gilbert's "Why Are We Happy?" can easily garner well over a million views. TED Talks are a curious phenomenon for educators to observe. They are in many ways the antithesis of traditional lectures, which are typically 60-120 minutes in length and delivered in cavernous halls by faculty members engaged in everyday academic lives. Perhaps the formality of the lecture is the biggest superficial difference in comparison to casual TEDTalks (Table 1). However, TED Talks are not as unstructured as they may appear. Presenters are well coached and instructed to follow a specific presentation formula, whichmaximizes storyboarding and highlights passion for the subject. While learning is not formally assessed, TED Talks do seem to accomplish their goals of spreading ideas while sparking curiosity within the learner. The fact that some presentations have been viewed more than 16 million times points to the effectiveness of the platform in at least reaching learners and stimulating a desire to click, listen, and learn.Moreover, the TEDTalks website is the fourth most popular technology website and the single most popular conference and events w

Question: What is the difference between a TED talk and a lecture?


A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them. The accuracy of optical flow estimation algorithms has been improving steadily as evidenced by results on the Middlebury optical flow benchmark. The typical formulation, however, has changed little since the work of Horn and Schunck. We attempt to uncover what has made recent advances possible through a thorough analysis of how the objective function, the optimization method, and modern implementation practices influence accuracy. We discover that "classical" flow formulations perform surprisingly well when combined with modern optimization and implementation techniques. One key implementation detail is the median filtering of intermediate flow fields during optimization. While this improves the robustness of classical methods it actually leads to higher energy solutions, meaning that these methods are not optimizing the original objective function. To understand the principles behind this phenomenon, we derive a new objective function that formalizes the median filtering heuristic. This objective function includes a non-local smoothness term that robustly integrates flow estimates over large spatial neighborhoods. By modifying this new term to include information about flow and image boundaries we develop a method that can better preserve motion details. To take advantage of the trend towards video in wide-screen format, we further introduce an asymmetric pyramid downsampling scheme that enables the estimation of longer range horizontal motions. The methods are evaluated on the Middlebury, MPI Sintel, and KITTI datasets using the same parameter settings.

Question: What is the difference between the classical and median flow formulations?


Face Recognition with Local Binary Patterns, Spatial Pyramid Histograms and Naive Bayes Nearest Neighbor Classification. Face recognition algorithms commonly assume that face images are well aligned and have a similar pose -- yet in many practical applications it is impossible to meet these conditions. Therefore extending face recognition to unconstrained face images has become an active area of research. To this end, histograms of Local Binary Patterns (LBP) have proven to be highly discriminative descriptors for face recognition. Nonetheless, most LBP-based algorithms use a rigid descriptor matching strategy that is not robust against pose variation and misalignment. We propose two algorithms for face recognition that are designed to deal with pose variations and misalignment. We also incorporate an illumination normalization step that increases robustness against lighting variations. The proposed algorithms use descriptors based on histograms of LBP and perform descriptor matching with spatial pyramid matching (SPM) and Naive Bayes Nearest Neighbor (NBNN), respectively. Our contribution is the inclusion of flexible spatial matching schemes that use an image-to-class relation to provide an improved robustness with respect to intra-class variations. We compare the accuracy of the proposed algorithms against Ahonen's original LBP-based face recognition system and two baseline holistic classifiers on four standard datasets. Our results indicate that the algorithm based on NBNN outperforms the other solutions, and does so more markedly in presence of pose variations.

Question: How to perform face recognition with local binary patterns?


SCRUM: An extension pattern language for hyper productive software development. The patterns of the SCRUM development method are presented as an extension pattern language to the existing organizational pattern languages. In the last few years, the SCRUM development method has rapidly gained recognition as an effective tool to hyper-productive software development. However, when SCRUM patterns are combined with other existing organizational patterns, they lead to highly adaptive, yet well-structured software development organizations. Also, decomposing SCRUM into patterns can guide adoption of only those parts of SCRUM that are applicable to a specific situation.

Question: What is SCRUM?


A Critical Review of Recurrent Neural Networks for Sequence Learning. Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translating natural language, engaging in dialogue, and controlling a robot, often demand both capabilities. Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes. Unlike standard feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window. Although recurrent neural networks have traditionally been difficult to train, and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with them. In recent years, systems based on long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition. In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we reconcile conflicting notation and nomenclature. Our goal is to provide a selfcontained explication of the state of the art together with a historical perspective and references to primary research.

Question: What is the state of the art in recurrent neural networks?


Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications. Image segmentation remains one of the major challenges in image analysis. In medical applications, skilled operators are usually employed to extract the desired regions that may be anatomically separate but statistically indistinguishable. Such manual processing is subject to operator errors and biases, is extremely time consuming, and has poor reproducibility. We propose a robust algorithm for the segmentation of three-dimensional (3-D) image data based on a novel combination of adaptive K-mean clustering and knowledge-based morphological operations. The proposed adaptive K-mean clustering algorithm is capable of segmenting the regions of smoothly varying intensity distributions. Spatial constraints are incorporated in the clustering algorithm through the modeling of the regions by Gibbs random fields. Knowledge-based morphological operations are then applied to the segmented regions to identify the desired regions according to the a priori anatomical knowledge of the region-of-interest. This proposed technique has been successfully applied to a sequence of cardiac CT volumetric images to generate the volumes of left ventricle chambers at 16 consecutive temporal frames. Our final segmentation results compare favorably with the results obtained using manual outlining. Extensions of this approach to other applications can be readily made when a priori knowledge of a given object is available.

Question: How to segment a 3D image?


Emotion Regulation Ability 1 Running head : EMOTION REGULATION The Ability to Regulate Emotion is Associated with Greater Well-Being , Income , and Socioeconomic Status. Are people who are best able to implement strategies to regulate their emotional expressive behavior happier and more successful than their counterparts? Although past research has examined individual variation in knowledge of the most effective emotion regulation strategies, little is known about how individual differences in the ability to actually implement these strategies, as assessed objectively in the laboratory, is associated with external criteria. In two studies, we examined how individual variation in the ability to modify emotional expressive behavior in response to evocative stimuli is related to well-being and financial success. Study 1 showed that individuals who can best suppress their emotional reaction to an acoustic startle are happiest with their lives. Study 2 showed that individuals who can best amplify their emotional reaction to a disgust-eliciting movie are happiest with their lives and have the highest disposable income and socioeconomic status. Thus, being able to implement emotion regulation strategies in the laboratory is closely linked to well-being and financial success. Emotion Regulation Ability 3 The Ability to Regulate Emotion is Associated with Greater Well-Being, Income, and Socioeconomic Status Individual variation in cognitive abilities, such as language and mathematics, has been shown to relate strongly to a number of important life criteria, including performance at school and at work (Kunzel, Hezlett, & Ones, 2004; Schmidt & Hunter, 1998). Research in recent years has suggested that there is also important variation among individuals in emotional abilities (see Mayer, Roberts, & Barsade, 2008; Mayer, Salovey, & Caruso, 2008, for reviews). In particular, the ability to regulate emotions reflects variation in how well people adjust emotional responses to meet

Question: What is the relationship between emotion regulation ability and well-being?


Wide-band microstrip antenna with an H-shaped coupling aperture. Theoretical and experimental results of a wide-band planar antenna are presented. This antenna can achieve a wide bandwidth, low cross-polarization levels, and low backward radiation levels. For wide bandwidth and easy integration with active circuits, it uses aperture-coupled stacked square patches. The coupling aperture is an H-shaped aperture. Based on the finite-difference time-domain method, a parametric study of the input impedance of the antenna is presented, and effects of each parameter on the antenna impedance are illustrated. One antenna is also designed, fabricated, and measured. The measured return loss exhibits an impedance bandwidth of 21.7%. The cross-polarization levels in both and planes are better than 23 dB. The front-to-back ratio of the antenna radiation pattern is better than 22 dB. Both theoretical and experimental results of parameters and radiation patterns are presented and discussed.

Question: What is the wide-band microstrip antenna with an H-shaped coupling aperture?


Childhood Obesity Perceptions Among African American Caregivers in a Rural Georgia Community: A Mixed Methods Approach. Given the pivotal role of African American caregiver's perceptions of childhood obesity in rural areas, the inclusion of caregiver's perceptions could potentially reduce childhood obesity rates. The objective of the current study was to explore childhood obesity perceptions among African Americans in a rural Georgia community. This concurrent mixed methods study utilized two theoretical frameworks: Social Cognitive Theory and Social Ecological Model. Using a convenience sample, caregivers ages 22–65 years completed a paper-based survey (n = 135) and a face-to-face interview (n = 12) to explore perceptions of obesity risk factors, health complications, weight status, built environment features, and obesity prevention approaches. Descriptive statistics were generated and a six-step process was used for qualitative analysis. Participants commonly cited behavioral risk factors; yet, social aspects and appearance of the community were not considered contributing factors. Chronic diseases were reported as obesity health complications. Caregivers had a distorted view of their child's weight status. In addition, analysis revealed that caregivers assessed child's weight and height measurements by the child's appearance or a recent doctor visit. Environmental barriers reported by caregivers included safety concerns and insufficient physical activity venues and programs. Also, caregivers conveyed parents are an imperative component of preventing obesity. Although this study found caregivers were aware of obesity risk factors, health complications, built environment features, and prevention approaches their obesity perceptions were not incorporated into school or community prevention efforts. Findings suggest that children residing in rural areas are in need of tailored efforts that address caregiver perceptions of obesity.

Question: What are the perceptions of childhood obesity among caregivers in a rural community?


Sentiment Analyzer: Extracting Sentiments about a Given Topic using Natural Language Processing Techniques. We present Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. Our sentiment analysis consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. The performance of the algorithms was verified on online product review articles ("digital camera" and "music" reviews), and more general documents including general webpages and news articles.

Question: What is the performance of the algorithms?


Exploiting Electronic Health Records to Mine Drug Effects on Laboratory Test Results. The proliferation of Electronic Health Records (EHRs) challenges data miners to discover potential and previously unknown patterns from a large collection of medical data. One of the tasks that we address in this paper is to reveal previously unknown effects of drugs on laboratory test results. We propose a method that leverages drug information to find a meaningful list of drugs that have an effect on the laboratory result. We formulate the problem as a convex non smooth function and develop a proximal gradient method to optimize it. The model has been evaluated on two important use cases: lowering low-density lipoproteins and glycated hemoglobin test results. The experimental results provide evidence that the proposed method is more accurate than the state-of-the-art method, rediscover drugs that are known to lower the levels of laboratory test results, and most importantly, discover additional potential drugs that may also lower these levels.

Question: How to mine drug effects on laboratory test results?


Rough Set Theory in analyzing the attributes of combination values for the insurance market. Based on Rough Set Theory, this research addresses the effect of attributes/features on the combination values of decisions that insurance companies make to satisfy customers' needs. Attributes impact on combination values by yielding sets with fewer objects (such as one or two objects), which increases both the lower and upper approximations. It also increases the decision rules, and degrades the precision of decisions. Our approach redefines the value set of attributes through expert knowledge by reducing the independent data set and reclassifying it. This approach is based on an empirical study. The results demonstrate that the redefined combination values of attributes can contribute to the precision of decisions in insurance marketing. Following an empirical analysis, we use a hit test that incorporates 50 validated sample data into the decision rule so that the hit rate reaches 100%. The results of the empirical study indicate that the generated decision rules can cover all new data. Consequently, we believe that the effects of attributes on combination values can be fully applied in research into insurance marketing. 2005 Elsevier Ltd. All rights reserved.

Question: What is rough set theory?


SpatialHadoop: towards flexible and scalable spatial processing using mapreduce. Recently, MapReduce frameworks, e.g., Hadoop, have been used extensively in different applications that include tera-byte sorting, machine learning, and graph processing. With the huge volumes of spatial data coming from different sources, there is an increasing demand to exploit the efficiency of Hadoop, coupled with the flexibility of the MapReduce framework, in spatial data processing. However, Hadoop falls short in supporting spatial data efficiently as the core is unaware of spatial data properties. This paper describes SpatialHadoop; a full-edged MapReduce framework with native support for spatial data. SpatialHadoop is a comprehensive extension to Hadoop that injects spatial data awareness in each Hadoop layer, namely, the language, storage, MapReduce, and operations layers. In the language layer, SpatialHadoop adds a simple and ex- pressive high level language for spatial data types and operations. In the storage layer, SpatialHadoop adapts traditional spatial index structures, Grid, R-tree and R+-tree, to form a two-level spatial index. SpatialHadoop enriches the MapReduce layer by two new components, SpatialFileSplitter and SpatialRecordReader, for efficient and scalable spatial data processing. In the operations layer, SpatialHadoop is already equipped with a dozen of operations, including range query, kNN, and spatial join. The flexibility and open source nature of SpatialHadoop allows more spatial operations to be implemented efficiently using MapReduce. Extensive experiments on a real system prototype and real datasets show that SpatialHadoop achieves orders of magnitude better performance than Hadoop for spatial data processing.

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


Decision-Making in a Robotic Architecture for Autonomy. This paper presents an overview of the intelligent decision-making capabilities of the CLARAty robotic architecture for autonomy. CLARAty is a two layered architecture where the top Decision Layer contains techniques for autonomously creating a plan of robot commands and the bottom Functional Layer provides standard robot capabilities that interface to system hardware. This paper focuses on the Decision Layer organization and capabilities. Specifically, the Decision Layer provides a framework for utilizing AI planning and executive techniques, which provide onboard, autonomous command generation and replanning for planetary rovers. The Decision Layer also provides a flexible interface to the Functional Layer, which can be tailored based on user preferences and domain features. This architecture is currently being tested on several JPL rovers.

Question: What is the decision layer of a robotic architecture?


Simple Customization of Recursive Neural Networks for Semantic Relation Classification. In this paper, we present a recursive neural network (RNN) model that works on a syntactic tree. Our model differs from previous RNN models in that the model allows for an explicit weighting of important phrases for the target task. We also propose to average parameters in training. Our experimental results on semantic relation classification show that both phrase categories and task-specific weighting significantly improve the prediction accuracy of the model. We also show that averaging the model parameters is effective in stabilizing the learning and improves generalization capacity. The proposed model marks scores competitive with state-of-the-art RNN-based models.

Question: What is the difference between a syntactic tree and a syntactic parse?


Computational design of linkage-based characters. We present a design system for linkage-based characters, combining form and function in an aesthetically-pleasing manner. Linkage-based character design exhibits a mix of discrete and continuous problems, making for a highly unintuitive design space that is difficult to navigate without assistance. Our system significantly simplifies this task by allowing users to interactively browse different topology options, thus guiding the discrete set of choices that need to be made. A subsequent continuous optimization step improves motion quality and, crucially, safeguards against singularities. We demonstrate the flexibility of our method on a diverse set of character designs, and then realize our designs by physically fabricating prototypes.

Question: How do you design a linkage-based character?


An Ultralow-Power Low-Noise CMOS Biopotential Amplifier for Neural Recording. This brief presents a design strategy for a neural recording amplifier array with ultralow-power low-noise operation that is suitable for large-scale integration. The topology combines a highly efficient but supply-sensitive single-ended first stage with a shared reference channel and a differential second stage to effect feedforward supply noise cancellation, combining the low power of single-ended amplifiers with improved supply rejection. For a two-channel amplifier, the measurements show a midband gain of 58.7 dB and a passband from 490 mHz to 10.5 kHz. The amplifier consumes 2.85 μA per channel from a 1-V supply and exhibits an input-referred noise of 3.04 μVrms from 0.1 Hz to 100 kHz, corresponding to a noise efficiency factor of 1.93. The power supply rejection ratio is better than 50 dB in the passband. The amplifier is fabricated in a 90-nm CMOS process and occupies 0.137 mm2 of chip area.

Question: What is the ultralow-power low-noise operation?


E-GOVERNMENT EVALUATION FACTORS: CITIZEN'S PERSPECTIVE. The e-government field is growing to a considerable size, both in its contents and position with respect to other research fields. The government to citizen segment of egovernment is taking the lead in terms of its importance and size. Like the evaluation of all other information systems initiatives, the evaluation of egovernments in both theory and practice has proved to be important but complex. The complexity of evaluation is mostly due to the multiple perspectives involved, the difficulties of quantifying benefits, and the social and technical context of use. The importance of e-government evaluation is due to the enormous investment of governments on delivering e-government services, and to the considerable pace of growing in the e-government field. However, despite the importance of the evaluation of e-government services, literature shows that e-government evaluation is still an immature area in terms of development and management. This work is part of a research effort that aims to develop a holistic evaluation framework for e-government systems. The main aim of this paper is to investigate the citizen' perspective in evaluating e-government services, and present a set of evaluating factors that influence citizens' utilization of e-government services. These evaluation factors can serve as part of an e-government evaluation framework. Moreover, the evaluation factors can also be used as means of providing valuable feedback for the planning of future egovernment initiatives.

Question: What is the e-government field?


Measuring Machine Intelligence Through Visual Question Answering. As machines have become more intelligent, there has been a renewed interest in methods for measuring their intelligence. A common approach is to propose tasks for which a human excels, but one which machines find difficult. However, an ideal task should also be easy to evaluate and not be easily gameable. We begin with a case study exploring the recently popular task of image captioning and its limitations as a task for measuring machine intelligence. An alternative and more promising task is Visual Question Answering that tests a machine's ability to reason about language and vision. We describe a dataset unprecedented in size created for the task that contains over 760,000 human generated questions about images. Using around 10 million human generated answers, machines may be easily evaluated.

Question: What is the largest dataset for visual question answering?


Phylogeography of Y-chromosome haplogroup I reveals distinct domains of prehistoric gene flow in europe.. To investigate which aspects of contemporary human Y-chromosome variation in Europe are characteristic of primary colonization, late-glacial expansions from refuge areas, Neolithic dispersals, or more recent events of gene flow, we have analyzed, in detail, haplogroup I (Hg I), the only major clade of the Y phylogeny that is widespread over Europe but virtually absent elsewhere. The analysis of 1,104 Hg I Y chromosomes, which were identified in the survey of 7,574 males from 60 population samples, revealed several subclades with distinct geographic distributions. Subclade I1a accounts for most of Hg I in Scandinavia, with a rapidly decreasing frequency toward both the East European Plain and the Atlantic fringe, but microsatellite diversity reveals that France could be the source region of the early spread of both I1a and the less common I1c. Also, I1b*, which extends from the eastern Adriatic to eastern Europe and declines noticeably toward the southern Balkans and abruptly toward the periphery of northern Italy, probably diffused after the Last Glacial Maximum from a homeland in eastern Europe or the Balkans. In contrast, I1b2 most likely arose in southern France/Iberia. Similarly to the other subclades, it underwent a postglacial expansion and marked the human colonization of Sardinia approximately 9,000 years ago.

Question: What is the distribution of haplogroup I?


Detecting Insider Threats Using RADISH: A System for Real-Time Anomaly Detection in Heterogeneous Data Streams. We present a scalable system for high-throughput real-time analysis of heterogeneous data streams. Our architecture enables incremental development of models for predictive analytics and anomaly detection as data arrives into the system. In contrast with batch data-processing systems, such as Hadoop, that can have high latency, our architecture allows for ingest and analysis of data on the fly, thereby detecting and responding to anomalous behavior in near real time. This timeliness is important for applications such as insider threat, financial fraud, and network intrusions. We demonstrate an application of this system to the problem of detecting insider threats, namely, the misuse of an organization's resources by users of the system and present results of our experiments on a publicly available insider threat dataset.

Question: How do you detect insider threats?


Idiopathic isolated clitoromegaly: A report of two cases. BACKGROUND: Clitoromegaly is a frequent congenital malformation, but acquired clitoral enlargement is relatively rare. METHODS: Two acquired clitoromegaly cases treated in Ataturk Training Hospital, Izmir, Turkey are presented. RESULTS: History from both patients revealed clitoromegaly over the last three years. Neither gynecological nor systemic abnormalities were detected in either patient. Karyotype analyses and hormonal tests were normal. Abdominal and gynaecological ultrasound did not show any cystic lesion or other abnormal finding. Computerized tomography scan of the adrenal glands was normal. Clitoroplasty with preservation of neurovascular pedicles was performed for the treatment of clitoromegaly. CONCLUSION: The patients were diagnosed as "idiopathic isolated" clitoromegaly. To the best of our knowledge, there has been no detailed report about idiopathic clitoromegaly in the literature.

Question: What is clitoromegaly?


Modelling and testing consumer trust dimensions in e-commerce. Prior research has found trust to play a significant role in shaping purchase intentions of a consumer. However there has been limited research where consumer trust dimensions have been empirically defined and tested. In this paper we empirically test a path model such that Internet vendors would have adequate solutions to increase trust. The path model presented in this paper measures the three main dimensions of trust, i.e. competence, integrity, and benevolence. And assesses the influence of overall trust of consumers. The paper also analyses how various sources of trust, i.e. consumer characteristics, firm characteristic, website infrastructure and interactions with consumers, influence dimensions of trust. The model is tested using 365 valid responses. Findings suggest that consumers with high overall trust demonstrate a higher intention to purchase online. © 2017 Elsevier Ltd. All rights reserved.

Question: How to increase trust in e-commerce?


A Full English Sentence Database for Off-line Handwriting Recognition. In this paper we present a new database for off-line handwriting recognition, together with a few preprocessing and text segmentation procedures. The database is based on the Lancaster-Oslo/Bergen(LOB) corpus. This corpus is a collection of texts that were used to generate forms, which subsequently were filled out by persons with their handwriting. Up to now (December 1998) the database includes 556 forms produced by approximately 250 different writers. The database consists of full English sentences. It can serve as a basis for a variety of handwriting recognition tasks. The main focus, however, is on recognition techniques that use linguistic knowledge beyond the lexicon level. This knowledge can be automatically derived from the corpus or it can be supplied from external sources.

Question: What is a database for off-line handwriting recognition?


The DEXMART hand: Mechatronic design and experimental evaluation of synergy-based control for human-like grasping. This paper summarizes recent activities carried out for the development of an innovative anthropomorphic robotic hand called the DEXMART Hand. The main goal of this research is to face the problems that affect current robotic hands by introducing suitable design solutions aimed at achieving simplification and cost reduction while possibly enhancing robustness and performance. While certain aspects of the DEXMART Hand development have been presented in previous papers, this paper is the first to give a comprehensive description of the final hand version and its use to replicate humanlike grasping. In this paper, particular emphasis is placed on the kinematics of the fingers and of the thumb, the wrist architecture, the dimensioning of the actuation system, and the final implementation of the position, force and tactile sensors. The paper focuses also on how these solutions have been integrated into the mechanical structure of this innovative robotic hand to enable precise force and displacement control of the whole system. Another important aspect is the lack of suitable control tools that severely limits the development of robotic hand applications. To address this issue, a new method for the observation of human hand behavior during interaction with common day-to-day objects by means of a 3D computer vision system is presented in this work together with a strategy for mapping human hand postures to the robotic hand. A simple control strategy based on postural synergies has been used to reduce the complexity of the grasp planning problem. As a preliminary evaluation of the DEXMART Hand's capabilities, this approach has been adopted in this paper to simplify and speed up the transfer of human actions to the robotic hand, showing its effectiveness in reproducing human-like grasping.

Question: What is the DEXMART hand?


The Reorganization of Human Brain Networks Modulated by Driving Mental Fatigue. The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroencephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (<italic>Cp</italic>) for beta, alpha, and delta bands and the character path length (<italic> Lp</italic>) for all EEG bands. The normalized measures <inline-formula><tex-math notation="LaTeX"> $\boldsymbol{\gamma}$</tex-math></inline-formula> showed significant increases in beta, alpha, and delta bands, and <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\lambda}$</tex-math></inline-formula> showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

Question: How does driving mental fatigue affect the brain functional network topology?


Pricing Services Subject to Congestion: Charge Per-Use Fees or Sell Subscriptions?. Should a firm charge on a per-use basis or sell subscriptions when its service experiences congestion? Queueing-based models of pricing primarily focus on charging a fee per use for the service, in part because per-use pricing enables the firm to regulate congestion—raising the per-use price naturally reduces how frequently customers use a service. The firm has less control over usage with subscription pricing (by definition, with subscription pricing customers are not charged proportional to their actual usage), and this is a disadvantage when customers dislike congestion. However, we show that subscription pricing is more effective at earning revenue. Consequently, the firm may be better off with subscription pricing, even, surprisingly, when congestion is intuitively most problematic for the firm: e.g., as congestion becomes more disliked by consumers. We show that the absolute advantage of subscription pricing relative to per-use pricing can be substantial, whereas the potential advantage of per-use pricing is generally modest. Subscription pricing becomes relatively more attractive if consumers become more heterogeneous in their service rates (e.g., some know they are "heavy" users and others know they are "light" users) as long as capacity is fixed, the potential utilization is high, and the two segments have substantially different usage rates. Otherwise, heterogeneity in usage rates makes subscription pricing less attractive relative to per-use pricing. We conclude that subscription pricing can be effective even if congestion is relevant for the overall quality of a service. Keywords service operations, operations strategy, pricing and revenue management, game theory, queueing theory Disciplines Business | Business Administration, Management, and Operations | Business Analytics | Management Sciences and Quantitative Methods | Marketing | Operations and Supply Chain Management

Question: What is the difference between subscription and per-use pricing?


Atrial Fibrillation Classification from a Short Single Lead ECG Recording Using a Deep Convolutional Neural Network. In this work, a deep convolutional neural network (CNN) is proposed to detect atrial fibrillation (AF) among the normal, noisy and other categories of cardiac arrhythmias electrocardiogram (ECG) recordings. The proposed CNN is trained by stochastic gradient descent with the categorical cross-entropy loss function. The network performance is evaluated on training (75%) and validation (25%) data sets that are obtained from 2017 Physionet/CinC challenge database. The proposed CNN model respectively achieves the average accuracy and F1 score of 87% and 0.84 on validation data set. One of the main advantages of this work besides high accuracy and reliability, is to simplify the feature extraction process and to remove the need for detecting ECG signal fiducial points and extracting hand-crafted features unlike conventional methods available in the literature. Moreover, it provides an opportunity for ECG screening in a large population, especially for atrial fibrillation screening, using wearable devices such as OM apparel that records high-quality single channel ECG signal.

Question: How to detect atrial fibrillation from a single lead ECG?


Ensemble Coordination Approach in Multi-AGV Systems Applied to Industrial Warehouses. This paper deals with a holistic approach to coordinate a fleet of automated guided vehicles (AGVs) in an industrial environment. We propose an ensemble approach based on a two layer control architecture and on an automatic algorithm for the definition of the roadmap. The roadmap is built by considering the path planning algorithm implemented on the hierarchical architecture and vice versa. In this way, we want to manage the coordination of the whole system in order to increase the flexibility and the global efficiency. Furthermore, the roadmap is computed in order to maximize the redundancy, the coverage and the connectivity. The architecture is composed of two layers. The low-level represents the roadmap itself. The high-level describes the topological relationship among different areas of the environment. The path planning algorithm works on both these levels and the subsequent coordination among AGVs is obtained exploiting shared resource (i.e., centralized information) and local negotiation (i.e., decentralized coordination). The proposed approach is validated by means of simulations and comparison using real plants. Note to Practitioners-The motivation of this work grows from the need to increase the flexibility and efficiency of current multi-AGV systems. In particular, in the current state-of-the-art the AGVs are guided through a manually defined roadmap with ad-hoc strategies for coordination. This is translated into high setup time requested for installation and the impossibility to easily respond to dynamic changes of the environment. The proposed method aims at managing all the design, setup and control process in an automatic manner, decreasing the time for setup and installation. The flexibility is also increased by considering a coordination strategy for the fleet of AGVs not based on manual ad-hoc rules. Simulations are performed in order to compare the proposed app

Question: What is the motivation of this work?


Learning from other subjects helps reducing Brain-Computer Interface calibration time. A major limitation of Brain-Computer Interfaces (BCI) is their long calibration time, as much data from the user must be collected in order to tune the BCI for this target user. In this paper, we propose a new method to reduce this calibration time by using data from other subjects. More precisely, we propose an algorithm to regularize the Common Spatial Patterns (CSP) and Linear Discriminant Analysis (LDA) algorithms based on the data from a subset of automatically selected subjects. An evaluation of our approach showed that our method significantly outperformed the standard BCI design especially when the amount of data from the target user is small. Thus, our approach helps in reducing the amount of data needed to achieve a given performance level.

Question: How to reduce the calibration time of a brain computer interface?


Role of Broca's Area in Implicit Motor Skill Learning: Evidence from Continuous Theta-burst Magnetic Stimulation. Complex actions can be regarded as a concatenation of simple motor acts, arranged according to specific rules. Because the caudal part of the Broca's region (left Brodmann's area 44, BA 44) is involved in processing hierarchically organized behaviors, we aimed to test the hypothesis that this area may also play a role in learning structured motor sequences. To address this issue, we investigated the inhibitory effects of a continuous theta-burst TMS (cTBS) applied over left BA 44 in healthy subjects, just before they performed a serial RT task (SRTT). SRTT has been widely used to study motor skill learning and is also of interest because, for complex structured sequences, subjects spontaneously organize them into smaller subsequences, referred to as chunks. As a control, cTBS was applied over the vertex in another group, which underwent the same experiment. Control subjects showed both a general practice learning effect, evidenced by a progressive decrease in RT across blocks and a sequence-specific learning effect, demonstrated by a significant RT increase in a pseudorandom sequence. In contrast, when cTBS was applied over left BA 44, subjects lacked both the general practice and sequence-specific learning effects. However, surprisingly, their chunking pattern was preserved and remained indistinguishable from controls. The present study indicates that left BA 44 plays a role in motor sequence learning, but without being involved in elementary chunking. This dissociation between chunking and sequence learning could be explained if we postulate that left BA 44 intervenes in high hierarchical level processing, possibly to integrate elementary chunks together.

Question: What is the role of Broca's area in implicit motor skill learning?


Towards Discipline-Independent Argumentative Zoning : Evidence from Chemistry and Computational Linguistics. Argumentative Zoning (AZ) is an analysis of the argumentative and rhetorical structure of a scientific paper. It has been shown to be reliably used by independent human coders, and has proven useful for various information access tasks. Annotation experiments have however so far been restricted to one discipline, computational linguistics (CL). Here, we present a more informative AZ scheme with 15 categories in place of the original 7, and show that it can be applied to the life sciences as well as to CL. We use a domain expert to encode basic knowledge about the subject (such as terminology and domain specific rules for individual categories) as part of the annotation guidelines. Our results show that non-expert human coders can then use these guidelines to reliably annotate this scheme in two domains, chemistry and computational linguistics.

Question: What is the difference between a domain expert and a non-expert?


Data Governance Maturity Model for Micro Financial Organizations in Peru. Micro finance organizations play an important role since they facilitate integration of all social classes to sustained economic growth. Against this background, exponential growth of data, resulting from transactions and operations carried out with these companies on a daily basis, becomes imminent. Appropriate management of this data is therefore necessary because, otherwise, it will result in a competitive disadvantage due to the lack of valuable and quality information for decision-making and process improvement. Data Governance provides a different approach to data management, as seen from the perspective of business assets. In this regard, it is necessary that the organization have the ability to assess the extent to which that management is correct or is generating expected results. This paper proposes a data governance maturity model for micro finance organizations, which frames a series of formal requirements and criteria providing an objective diagnosis. This model was implemented based on the information of a Peruvian micro finance organization. Four domains, out of the seven listed in the model, were evaluated. Finally, after validation of the proposed model, it was evidenced that it serves as a means for identifying the gap between data management and objectives set.

Question: What is a micro finance organization?


Characterizing Machines and Workloads on a Google Cluster. Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a large-scale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, job- and task-level workload behavior, and how the overall cluster resources are utilized.

Question: What is the frequency of machine maintenance?


Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization. Recently, there has been considerable progress towards algorithms for approximating Nash equilibrium strategies in extensive games. One such algorithm, Counterfactual Regret Minimization (CFR), has proven to be effective in two-player zero-sum poker domains. While the basic algorithm is iterative and performs a full game traversal on each iteration, sampling based approaches are possible. For instance, chance-sampled CFR considers just a single chance outcome per traversal, resulting in faster but less precise iterations. While more iterations are required, chance-sampled CFR requires less time overall to converge. In this work, we present new sampling techniques that consider sets of chance outcomes during each traversal to produce slower, more accurate iterations. By sampling only the public chance outcomes seen by all players, we take advantage of the imperfect information structure of the game to (i) avoid recomputation of strategy probabilities, and (ii) achieve an algorithmic speed improvement, performing O(n) work at terminal nodes in O(n) time. We demonstrate that this new CFR update converges more quickly than chance-sampled CFR in the large domains of poker and Bluff.

Question: What is the difference between Monte Carlo and chance-sampled CFR?


NAWMS: nonintrusive autonomous water monitoring system. Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users.  The goal of our work is to furnish users with an easy-to-install self-calibrating system that provides information on when, where, and how much water they are using. The system uses wireless vibration sensors attached to pipes and, thus, neither plumbing nor special expertise is necessary for its installation. By implementing a non-intrusive, autonomous, and adaptive system using commodity hardware, we believe it is cost-effective and widely deployable.  NAWMS makes use of the existing household water flow meter, which is considered accurate, but lacks spatial granularity, and adds vibration sensors on individual water pipes to estimate the water flow to each individual outlet. Compensating for manufacturing, installation, and material variabilities requires calibration of these low cost sensors to achieve a reasonable level of accuracy. We have devised an adaptive auto-calibration procedure, which attempts to solve a two phase linear programming and mixed linear geometric programming problem.  We show through experiments on a three pipe testbed that such a system is indeed feasible and adapts well to minimize error in the water usage estimate. We report an accuracy, over likely domestic flow-rate scenarios, with long-term stability and a mean absolute error of 7%.

Question: What is the nonintrusive autonomous water monitoring system?


Didier Dubey Suarez Medina Assessment of Web-based Information Security Awareness Courses. Information security awareness web-based courses are commonly recommended in cyber security strategies to help build a security culture capable of addressing information systems breaches caused by user mistakes whose negligence or ignorance of policies may endanger information systems assets. A research gap exists on the impact of Information Security Awareness Web-Based Courses: these are failing in changing to a significant degree the behavior of participants regarding compliance and diligence, which translates into continuous vulnerabilities. The aim of this work is to contribute with a theoretical and empirical analysis on the potential strengths and weaknesses of Information Security Awareness Web-Based Courses and with two practical tools readily applicable for designers and reviewers of web-based or mediatized courses on information security awareness and education. The research design seeks to respond two research questions. The first on the formulation of a minimum set of criteria that could be applied to Information Security Awareness Web-Based Courses, to support their real impact on employee's diligence and compliance, resulting in eleven criteria for courses' assessment and a checklist. The second, about a controlled experiment to explore the actual impact of an existing course, in respect to diligence and compliance using phishing emails as educational tools, that reaffirms the theoretical assumptions arrived to earlier. The development of minimum criteria and their systematic implementation pursue behavioral change, emphasizes the importance of disciplinary integration in cyber security research, and advocates for the development of a solid security culture of diligence and compliance, capable of supporting the protection of organizations from information system threats. The results gathered in this study suggest that achieving positive results in the existing i

Question: What is the impact of information security awareness web-based courses?


A Tutorial on Logistic Regression. Many procedures in SAs/STAT~ can be used to perform 10' gistic regression analysis: CATMOD, GENMOD,LOGISTIC, al)d PROBIT. Each procedure has special features that make. ~ useful for gertain applications. For most applica· tions, PROC LOGISTIC is the preferred choice,. It fits binary response or proportional odds models, provides various model·selection methods to identify important prognostic variables from a large number of candidate variables, and computes regression diagnostic statistics. This tutorial dis· cusses some of the problems users encountered when they used the LOGISTIC procedure.

Question: What is a proportional odds model?


Joint Deep Modeling of Users and Items Using Reviews for Recommendation. A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.

Question: What is the name of the recommender system?


Surface Plasmon Resonance Imaging Sensors : A Review. Surface plasmon resonance (SPR) imaging sensors realize label free, real-time, highly sensitive, quantitative, high throughput biological interaction monitoring and the binding profiles from multi-analytes further provide the binding kinetic parameters between different bio-molecules. In the past two decades, SPR imaging sensors found rapid increasing applications in fundamental biological studies, medical diagnostics, drug discovery, food safety, precision measurement and environmental monitoring. In this paper, we review the recent advances of SPR imaging sensor technology towards high throughput multi-analyte screening. Finally, we describe our multiplex colorimetric SPR imaging biosensor based on polarization orientation for high throughput bio-sensing applications.

Question: What is the difference between SPR and SPR imaging?


Consistent inference of probabilities in layered networks: predictions and generalizations. The problem of learning a general input-output relation using a layered neural network is discussed in a statistical framework. By imposing the consistency condition that the error minimization be equivalent to a likelihood maximization for training the network, the authors arrive at a Gibbs distribution on a canonical ensemble of networks with the same architecture. This statistical description enables them to evaluate the probability of a correct prediction of an independent example, after training the network on a given training set. The prediction probability is highly correlated with the generalization ability of the network, as measured outside the training set. This suggests a general and practical criterion for training layered networks by minimizing prediction errors. The authors demonstrate the utility of this criterion for selecting the optimal architecture in the continuity problem. As a theoretical application of the statistical formalism, they discuss the question of learning curves and estimate the sufficient training size needed for correct generalization, in a simple example.<<ETX>>

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


VINERy: A Visual IDE for Information Extraction. Information Extraction (IE) is the key technology enabling analytics over unstructured and semi-structured data. Not surprisingly, it is becoming a critical building block for a wide range of emerging applications. To satisfy the rising demands for information extraction in real-world applications, it is crucial to lower the barrier to entry for IE development and enable users with general computer science background to develop higher quality extractors. In this demonstration, we present VINERY, an intuitive yet expressive visual IDE for information extraction. We show how it supports the full cycle of IE development without requiring a single line of code and enables a wide range of users to develop high quality IE extractors with minimal efforts. The extractors visually built in VINERY are automatically translated into semantically equivalent extractors in a state-of-the-art declarative language for IE. We also demonstrate how the auto-generated extractors can then be imported into a conventional Eclipse-based IDE for further enhancement. The results of our user studies indicate that VINERY is a significant step forward in facilitating extractor development for both expert and novice IE developers.

Question: What is the name of the visual IDE for information extraction?


Scale out for large minibatch SGD: Residual network training on ImageNet-1K with improved accuracy and reduced time to train. For the past 5 years, the ILSVRC competition and the ImageNet dataset have attracted a lot of interest from the Computer Vision community, allowing for state-of-the-art accuracy to grow tremendously. This should be credited to the use of deep artificial neural network designs. As these became more complex, the storage, bandwidth, and compute requirements increased. This means that with a non-distributed approach, even when using the most high-density server available, the training process may take weeks, making it prohibitive. Furthermore, as datasets grow, the representation learning potential of deep networks grows as well by using more complex models. This synchronicity triggers a sharp increase in the computational requirements and motivates us to explore the scaling behaviour on petaflop scale supercomputers. In this paper we will describe the challenges and novel solutions needed in order to train ResNet50 in this large scale environment. We demonstrate above 90% scaling efficiency and a training time of 28 minutes using up to 104K x86 cores. This is supported by software tools from Intel's ecosystem. Moreover, we show that with regular 90 120 epoch train runs we can achieve a top-1 accuracy as high as 77% for the unmodified ResNet-50 topology. We also introduce the novel Collapsed Ensemble (CE) technique that allows us to obtain a 77.5% top-1 accuracy, similar to that of a ResNet-152, while training a unmodified ResNet-50 topology for the same fixed training budget. All ResNet-50 models as well as the scripts needed to replicate them will be posted shortly. Keywords—deep learning, scaling, convergence, large minibatch, ensembles.

Question: How large is the canadian military?