Driver fatigue detection based on saccadic eye movements. The correct determination of driver's level of fatigue has been of vital importance for the safety of driving. There are various methods, such as analyzing facial expression, eyelid activity, and head movements to assess the fatigue level of drivers. This paper describes the design and prototype implementation of a driver fatigue level determination system based on detection of saccadic eye movements. Driver's eye movement speed is used to assess driver's fatigue level. The information about eyes is obtained via infrared led camera device. Movements of pupils were recorded in two driving scenarios with different traffic density. In the first scenario, the traffic density was set to low while the second scenario was based on high density and aggressive traffic. Based on the movements of pupils, the data on saccadic eye movement was analyzed to determine fatigue level of the driver. Acceleration, speed, and size of pupils at both traffic scenarios were compared with data mining techniques, such as segmentation adaptive peak, entropy, and data distribution analyses. Significantly different levels of fatigue were found between the tired and vigorous driver for the different types of scenarios.

Question: How to detect driver fatigue based on saccadic eye movements?


Emotion recognition from text using semantic labels and separable mixture models. This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.

Question: How to automatically recognize emotion from text?


Positive Definite Kernels in Machine Learning. This survey is an introduction to positive definite kernels and the set of methods they have inspired in the machine learning literature, namely kernel methods. We first discuss some properties of positive definite kernels as well as reproducing kernel Hibert spaces, the natural extension of the set of functions {k(x, ·), x ∈ X} associated with a kernel k defined on a space X . We discuss at length the construction of kernel functions that take advantage of well-known statistical models. We provide an overview of numerous data-analysis methods which take advantage of reproducing kernel Hilbert spaces and discuss the idea of combining several kernels to improve the performance on certain tasks. We also provide a short cookbook of different kernels which are particularly useful for certain datatypes such as images, graphs or speech segments. Remark: This report is a draft. I apologize in advance for the numerous mistakes, typos, and not always well written parts it contains. Comments and suggestions will be highly appreciated.

Question: What is a positive definite kernel?


Deliberation Networks: Sequence Generation Beyond One-Pass Decoding. The encoder-decoder framework has achieved promising progress for many sequence generation tasks, including machine translation, text summarization, dialog system, image captioning, etc. Such a framework adopts an one-pass forward process while decoding and generating a sequence, but lacks the deliberation process: A generated sequence is directly used as final output without further polishing. However, deliberation is a common behavior in human's daily life like reading news and writing papers/articles/books. In this work, we introduce the deliberation process into the encoder-decoder framework and propose deliberation networks for sequence generation. A deliberation network has two levels of decoders, where the first-pass decoder generates a raw sequence and the second-pass decoder polishes and refines the raw sentence with deliberation. Since the second-pass deliberation decoder has global information about what the sequence to be generated might be, it has the potential to generate a better sequence by looking into future words in the raw sentence. Experiments on neural machine translation and text summarization demonstrate the effectiveness of the proposed deliberation networks. On the WMT 2014 English-to-French translation task, our model establishes a new state-of-the-art BLEU score of 41.5.

Question: How does the deliberation network work?


A Review of the Status of Brain Structure Research in Transsexualism. The present review focuses on the brain structure of male-to-female (MtF) and female-to-male (FtM) homosexual transsexuals before and after cross-sex hormone treatment as shown by in vivo neuroimaging techniques. Cortical thickness and diffusion tensor imaging studies suggest that the brain of MtFs presents complex mixtures of masculine, feminine, and demasculinized regions, while FtMs show feminine, masculine, and defeminized regions. Consequently, the specific brain phenotypes proposed for MtFs and FtMs differ from those of both heterosexual males and females. These phenotypes have theoretical implications for brain intersexuality, asymmetry, and body perception in transsexuals as well as for Blanchard's hypothesis on sexual orientation in homosexual MtFs. Falling within the aegis of the neurohormonal theory of sex differences, we hypothesize that cortical differences between homosexual MtFs and FtMs and male and female controls are due to differently timed cortical thinning in different regions for each group. Cross-sex hormone studies have reported marked effects of the treatment on MtF and FtM brains. Their results are used to discuss the early postmortem histological studies of the MtF brain.

Question: What is the difference between male and female brains?


Ontology-Based Information Extraction for Knowledge Enrichment and Validation. Ontology is widely used as a mean to represent and share common concepts and knowledge from a particular domain or specialisation. As a knowledge representation, the knowledge within an ontology must be able to evolve along with the recent changes and updates within the community practice. In this paper, we propose a new Ontology-based Information Extraction (OBIE) system that extends existing systems in order to enrich and validate an ontology. Our model enables the ontology to find related recent knowledge in the domain from communities, by exploiting their underlying knowledge as keywords. The knowledge extraction process uses ontology-based and pattern-based information extraction technique. Not only the extracted knowledge enriches the ontology, it also validates contradictory instance-related statements within the ontology that is no longer relevant to recent practices. We determine a confidence value during the enrichment and validation process to ensure the stability of the enriched ontology. We implement the model and present a case study in herbal medicine domain. The result of the enrichment and validation process shows promising results. Moreover, we analyse how our proposed model contributes to the achievement of a richer and stable ontology.

Question: What is the ontology-based information extraction?


Detection of Ellipses by a Modified Hough Transformation. The Hough transformation can detect straight lines in an edge-enhanced picture, however its extension to recover ellipses requires too long a computing time. This correspondence proposes a modified method which utilizes two properties of an ellipse in such a way that it iteratively searches for clusters in two different parameter spaces to find almost complete ellipses, then evaluates their parameters by the least mean squares method.

Question: How to detect ellipses?


Intelligent Unmanned Air Vehicle Flight Systems. This paper describes an intelligent autonomous airborne flight capability that is being used as a test bed for future technology development. The unmanned air vehicles (UAVs) fly under autonomous control of both an onboard computer and an autopilot. The onboard computer provides the mission control and runs the autonomous Intelligent Controller (IC) software while the autopilot controls the vehicle navigation and flight control. The autonomous airborne flight system is described in detail. An IC architecture directly applicable to the design of unmanned vehicles is also presented. The UAVs may operate independently or in cooperation with one another to carry out a specified mission. The intelligent UAV flight system is used to evaluate and study autonomous UAV control as well as multi-vehicle collaborative control.

Question: What is the difference between an autonomous and a non-autonomous UAV?


A monad for deterministic parallelism. We present a new programming model for deterministic parallel computation in a pure functional language. The model is monadic and has explicit granularity, but allows dynamic construction of dataflow networks that are scheduled at runtime, while remaining deterministic and pure. The implementation is based on monadic concurrency, which has until now only been used to simulate concurrency in functional languages, rather than to provide parallelism. We present the API with its semantics, and argue that parallel execution is deterministic. Furthermore, we present a complete work-stealing scheduler implemented as a Haskell library, and we show that it performs at least as well as the existing parallel programming models in Haskell.

Question: What is a monad?


Survey of Insurance Fraud Detection Using Data Mining Techniques. 62 Abstract— With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.

Question: What is the role of insurance sector in the economy?


Achieving IT-Business Strategic Alignment via Enterprise-Wide Implementation of Balanced Scorecards. Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Question: What is the difference between a publisher and an agent?


Goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance. In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent's trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.

Question: What is the goal-based trajectory analysis for unusual behaviour detection in intelligent surveillance?


Effects of bacterial inoculants on the indigenous microbiome and secondary metabolites of chamomile plants. Plant-associated bacteria fulfill important functions for plant growth and health. However, our knowledge about the impact of bacterial treatments on the host's microbiome and physiology is limited. The present study was conducted to assess the impact of bacterial inoculants on the microbiome of chamomile plants Chamomilla recutita (L.) Rauschert grown in a field under organic management in Egypt. Chamomile seedlings were inoculated with three indigenous Gram-positive strains (Streptomyces subrutilus Wbn2-11, Bacillus subtilis Co1-6, Paenibacillus polymyxa Mc5Re-14) from Egypt and three European Gram-negative strains (Pseudomonas fluorescens L13-6-12, Stenotrophomonas rhizophila P69, Serratia plymuthica 3Re4-18) already known for their beneficial plant-microbe interaction. Molecular fingerprints of 16S rRNA gene as well as real-time PCR analyses did not show statistically significant differences for all applied bacterial antagonists compared to the control. In contrast, a pyrosequencing analysis of the 16S rRNA gene libraries revealed significant differences in the community structure of bacteria between the treatments. These differences could be clearly shown by a shift within the community structure and corresponding beta-diversity indices. Moreover, B. subtilis Co1-6 and P. polymyxa Mc5Re-14 showed an enhancement of the bioactive secondary metabolite apigenin-7-O-glucoside. This indicates a possible new function of bacterial inoculants: to interact with the plant microbiome as well as to influence the plant metabolome.

Question: What is the impact of bacterial inoculants on the microbiome of chamomile plants?


Augmented reality-based exergames for rehabilitation. Rehabilitation for stroke afflicted patients, through exercises tailored for individual needs, aims at relearning basic motor skills, especially in the extremities. Rehabilitation through Augmented Reality (AR) based games engage and motivate patients to perform exercises which, otherwise, maybe boring and monotonic. Also, mirror therapy allows users to observe one's own movements in the game providing them with good visual feedback. This paper presents an augmented reality based system for rehabilitation by playing four interactive, cognitive and fun Exergames (exercise and gaming).  The system uses low-cost RGB-D cameras such as Microsoft Kinect V2 to capture and generate 3D model of the person by extracting him/her from the entire captured data and immersing it in different interactive virtual environments. Animation based limb movement enhancement along with cognitive aspects incorporated in the game can help in positive reinforcement, progressive challenges and motion improvement. Recording module of the toolkit allows future reference and facilitates feedback from the physician. 10 able-bodied users, 2 psychological experts and 2 Physical Medicine and Rehabilitation physicians evaluated the user experience and usability aspects of the exergames. Results obtained shows the games to be fun and realistic, and at the same time engaging and motivating for performing exercises.

Question: What is the augmented reality based exergames for rehabilitation?


Decision making regarding Smith-Petersen vs. pedicle subtraction osteotomy vs. vertebral column resection for spinal deformity.. STUDY DESIGN Author experience and literature review.   OBJECTIVES To investigate and discuss decision-making on when to perform a Smith-Petersen osteotomy as opposed to a pedicle subtraction procedure and/or a vertebral column resection.   SUMMARY OF BACKGROUND DATA Articles have been published regarding Smith-Petersen osteotomies, pedicle subtraction procedures, and vertebral column resections. Expectations and complications have been reviewed. However, decision-making regarding which of the 3 procedures is most useful for a particular spinal deformity case is not clearly investigated.   METHODS Discussed in this manuscript is the author's experience and the literature regarding the operative options for a fixed coronal or sagittal deformity.   RESULTS There are roles for Smith-Petersen osteotomy, pedicle subtraction, and vertebral column resection. Each has specific applications and potential complications.   CONCLUSION As the magnitude of resection increases, the ability to correct deformity improves, but also the risk of complication increases. Therein, an understanding of potential applications and complications is helpful.

Question: What is the difference between a Smith-Petersen osteotomy and a pedicle subtraction osteotomy?


Design and implementation of fine pitch COB LED display. LED displays are widely used in advertisement, transportation, and commercial signages. Conventional LED display modules consist of a LED array assembly and a driving circuit board, inside an external housing. The dot pitch of conventional LED displays is typically 2.0 mm or above, which is relatively coarse. In some applications, such as the screen of personal mobile LED devices, a higher resolution with the dot pitch smaller than <;1.5 mm is needed due to the short viewing distance. Therefore, technologies for implementing fine pitch LED displays are in demand. This paper introduces a new type of LED display with the chip-on-board (COB) technology. The dot pitch of this new type of COB LED display may be reduced to 0.5 mm. The configurations for monochromatic and tricolored display modules are discussed. The reliability studies on the COB LED display are also presented. The results of ball bond shear, wire pull, junction temperature and thermal cycling tests indicate that the fine pitch COB LED display is feasible and reliable.

Question: What is the dot pitch of a COB LED display?


Evaluation of LoRa receiver performance under co-technology interference. LoRa networks achieve a remarkably wide coverage as compared to that of the conventional wireless systems by employing the chirp spread spectrum (CSS). However, single-hop LoRa networks have limited penetration in indoor environments and cannot efficiently utilize the multiple-access dimensions arising from different spreading factors (SF) because the star topology is typically used for LoRa. On the other hand, a multi-hop LoRa network has the potential to realize extensive network coverage by utilizing packet relaying and improving the network capacity by utilizing different SFs. We evaluated the LoRa receiver performance under co-technology interference via simulation and experiments to realize these potentials. The results show that LoRa can survive interference from time-synchronized packets transmitted by multiple transmitters with an identical SF. Furthermore, we examined the orthogonality between different SFs by evaluating the required signal-to-interference ratio (SIR). Finally, we demonstrated the possibility of employing different SFs to construct the pipeline in a multi-hop relay network to increase network efficiency.

Question: How can LoRa networks be used to improve the performance of wireless networks?


NEUROPSI: a brief neuropsychological test battery in Spanish with norms by age and educational level.. The purpose of this research was to develop, standardize, and test the reliability of a short neuropsychological test battery in the Spanish language. This neuropsychological battery was named "NEUROPSI," and was developed to assess briefly a wide spectrum of cognitive functions, including orientation, attention, memory, language, visuoperceptual abilities, and executive functions. The NEUROPSI includes items that are relevant for Spanish-speaking communities. It can be applied to illiterates and low educational groups. Administration time is 25 to 30 min. Normative data were collected from 800 monolingual Spanish-speaking individuals, ages 16 to 85 years. Four age groups were used: (1) 16 to 30 years, (2) 31 to 50 years, (3) 51 to 65 years, and (4) 66 to 85 years. Data also are analyzed and presented within 4 different educational levels that were represented in this sample; (1) illiterates (zero years of school); (2) 1 to 4 years of school; (2) 5 to 9 years of school; and (3) 10 or more years of formal education. The effects of age and education, as well as the factor structure of the NEUROPSI are analyzed. The NEUROPSI may fulfill the need for brief, reliable, and objective evaluation of a broad range of cognitive functions in Spanish-speaking populations.

Question: What is the purpose of this research?


Of animals and objects: men's implicit dehumanization of women and likelihood of sexual aggression.. Although dehumanizing women and male sexual aggression are theoretically aligned, the present research provides the first direct support for this assumption, using the Implicit Association Test to assess two forms of female dehumanization: animalization and objectification. In Study 1, men who automatically associated women more than men with primitive constructs (e.g., animals, instinct, nature) were more willing to rape and sexually harass women, and to report negative attitudes toward female rape victims. In Study 2, men who automatically associated women with animals (e.g., animals, paw, snout) more than with humans scored higher on a rape-behavioral analogue, as well as rape proclivity. Automatically objectifying women by associating them with objects, tools, and things was also positively correlated with men's rape proclivity. In concert, the research demonstrates that men who implicitly dehumanize women (as either animals or objects) are also likely to sexually victimize them.

Question: What is the first direct support for this assumption?


Dune: Safe User-level Access to Privileged CPU Features. Dune is a system that provides applications with direct but safe access to hardware features such as ring protection, page tables, and tagged TLBs, while preserving the existing OS interfaces for processes. Dune uses the virtualization hardware in modern processors to provide a process, rather than a machine abstraction. It consists of a small kernel module that initializes virtualization hardware and mediates interactions with the kernel, and a user-level library that helps applications manage privileged hardware features. We present the implementation of Dune for 64bit x86 Linux. We use Dune to implement three userlevel applications that can benefit from access to privileged hardware: a sandbox for untrusted code, a privilege separation facility, and a garbage collector. The use of Dune greatly simplifies the implementation of these applications and provides significant performance advantages.

Question: What is a sandbox?


Low-latency adaptive streaming over tcp. Media streaming over TCP has become increasingly popular because TCP's congestion control provides remarkable stability to the Internet. Streaming over TCP requires adapting to bandwidth availability, but unforunately, TCP can introduce significant latency at the application level, which causes unresponsive and poor adaptation. This article shows that this latency is not inherent in TCP but occurs as a result of throughput-optimized TCP implementations. We show that this latency can be minimized by dynamically tuning TCP's send buffer. Our evaluation shows that this approach leads to better application-level adaptation and it allows supporting interactive and other low-latency applications over TCP.

Question: How to minimize latency in TCP?


A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases. The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper , we introduce the new clustering algorithm DBCLASD (Distribution Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS, discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the clustering algorithm DBSCAN requiring two input parameters which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.

Question: What is the distribution based clustering algorithm for mining in large spatial databases?


Very High Resolution Spaceborne SAR Tomography in Urban Environment. Synthetic aperture radar tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. It uses stacks of several acquisitions from slightly different viewing angles (the elevation aperture) to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. The new class of meter-resolution spaceborne SAR systems (TerraSAR-X and COSMO-Skymed) offers a tremendous improvement in tomographic reconstruction of urban areas and man-made infrastructure. The high resolution fits well to the inherent scale of buildings (floor height, distance of windows, etc.). This paper demonstrates the tomographic potential of these SARs and the achievable quality on the basis of TerraSAR-X spotlight data of urban environment. A new Wiener-type regularization to the singular-value decomposition method-equivalent to a maximum a posteriori estimator-for TomoSAR is introduced and is extended to the differential case (4-D, i.e., space-time). Different model selection schemes for the estimation of the number of scatterers in a resolution cell are compared and proven to be applicable in practice. Two parametric estimation algorithms of the scatterers' elevation and their velocities are evaluated. First 3-D and 4-D reconstructions of an entire building complex (including its radar reflectivity) with very high level of detail from spaceborne SAR data by pixelwise TomoSAR are presented.

Question: What is the resolution of the tomographic reconstruction?


An upper limb exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint. This paper presents an upper limb rehabilitation exoskeleton with an optimized 4R spherical wrist mechanism for the shoulder joint. Traditional designs of shoulder exoskeletons use 3R mechanisms to replicate the spherical motion of the shoulder. However, due to the exceptionally large range of motion of the human shoulder, the 3R mechanism is required to operate at a singular configuration at some point in the shoulder workspace. In this configuration, the 3R mechanism loses the ability to rotate the shoulder about one DOF. To overcome this problem, the use of a kinematically redundant 4R mechanism is proposed. The 4R mechanism has been optimized in a previous work and is used in the design of the presented exoskeleton prototype. This new shoulder mechanism allows the exoskeleton to achieve the entire human shoulder workspace without mechanical interference and while operating well away from singular configurations. Numerous features have also been included in the exoskeleton design to ensure it is safe, comfortable and easy to use. The shoulder mechanism of the exoskeleton is analyzed to demonstrate the capabilities of the 4R mechanism.

Question: What is the difference between a 3R and a 4R mechanism?


Infinite-Horizon Policy-Gradient Estimation. Gradient-based approaches to direct policy search in reinf orcement learning have received much recent attention as a means to solve problems of partial observability and to avoid some of the problems associated with policy degradation in value-f unction methods. In this paper we introduceGPOMDP, a simulation-based algorithm for generating a bi sedestimate of the gradient of theaverage rewardin Partially Observable Markov Decision Processes ( POMDPs) controlled by parameterized stochastic policies. A similar algorithm wa s proposed by Kimura, Yamamura, and Kobayashi (1995). The algorithm's chief advantages are tha t i requires storage of only twice the number of policy parameters, uses one free parameter 2 [0; 1) (which has a natural interpretation in terms of bias-variance trade-off), and requires no knowl edge of the underlying state. We prove convergence of GPOMDP, and show how the correct choice of the parameter is related to the mixing timeof the controlledPOMDP. We briefly describe extensions of GPOMDP to controlled Markov chains, continuous state, observation and control s paces, multiple-agents, higher-order derivatives, and a version for training stochastic policie s with internal states. In a companion paper (Baxter, Bartlett, & Weaver, 2001) we show how the gradient e stimates generated by GPOMDP can be used in both a traditional stochastic gradient algori thm and a conjugate-gradient procedure to find local optima of the average reward.

Question: What is the average reward in a partially observable Markov decision process?


Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks. Early detection of pulmonary cancer is the most promising way to enhance a patient's chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection(average FROC-score of 0.891, ranking the 1st place over all submitted results).

Question: How to detect pulmonary nodules in CT images?


Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization. Feature selection is an important component of many machine learning applications. Especially in many bioinformatics tasks, efficient and robust feature selection methods are desired to extract meaningful features and eliminate noisy ones. In this paper, we propose a new robust feature selection method with emphasizing joint `2,1-norm minimization on both loss function and regularization. The `2,1-norm based loss function is robust to outliers in data points and the `2,1norm regularization selects features across all data points with joint sparsity. An efficient algorithm is introduced with proved convergence. Our regression based objective makes the feature selection process more efficient. Our method has been applied into both genomic and proteomic biomarkers discovery. Extensive empirical studies are performed on six data sets to demonstrate the performance of our feature selection method.

Question: What is the difference between the `2,1-norm and the `1-norm?


Self-Supervised Visual Planning with Temporal Skip Connections. In order to autonomously learn wide repertoires of complex skills, robots must be able to learn from their own autonomously collected data, without human supervision. One learning signal that is always available for autonomously collected data is prediction. If a robot can learn to predict the future, it can use this predictive model to take actions to produce desired outcomes, such as moving an object to a particular location. However, in complex open-world scenarios, designing a representation for prediction is difficult. In this work, we instead aim to enable self-supervised robot learning through direct video prediction: instead of attempting to design a good representation, we directly predict what the robot will see next, and then use this model to achieve desired goals. A key challenge in video prediction for robotic manipulation is handling complex spatial arrangements such as occlusions. To that end, we introduce a video prediction model that can keep track of objects through occlusion by incorporating temporal skipconnections. Together with a novel planning criterion and action space formulation, we demonstrate that this model substantially outperforms prior work on video prediction-based control. Our results show manipulation of objects not seen during training, handling multiple objects, and pushing objects around obstructions. These results represent a significant advance in the range and complexity of skills that can be performed entirely with self-supervised robot learning.

Question: What is a temporal skip connection?


Compiling Knowledge into Decomposable Negation Normal Form. We propose a method for compiling proposit ional theories into a new tractable form that we refer to as decomposable negation normal form (DNNF). We show a number of results about our compilation approach. First, we show that every propositional theory can be compiled into D N N F and present an algorithm to this effect. Second, we show that if a clausal form has a bounded treewidth, then its DNNF compilation has a linear size and can be computed in linear t ime — treewidth is a graphtheoretic parameter which measures the connectivity of the clausal form. Th i rd , we show that once a propositional theory is compiled into DNNF, a number of reasoning tasks, such as satisfiability and forgett ing, can be performed in linear t ime. Finally, we propose two techniques for approximating the DNNF compilat ion of a theory when the size of such compilation is too large to be practical. One of the techniques generates a sound but incomplete compilation, while the other generates a complete but unsound compilation. Together, these approximations bound the exact compilation from below and above in terms for their abil i ty to answer queries.

Question: What is the size of the compilation of a propositional theory?


Small-world networks and functional connectivity in Alzheimer's disease.. We investigated whether functional brain networks are abnormally organized in Alzheimer's disease (AD). To this end, graph theoretical analysis was applied to matrices of functional connectivity of beta band-filtered electroencephalography (EEG) channels, in 15 Alzheimer patients and 13 control subjects. Correlations between all pairwise combinations of EEG channels were determined with the synchronization likelihood. The resulting synchronization matrices were converted to graphs by applying a threshold, and cluster coefficients and path lengths were computed as a function of threshold or as a function of degree K. For a wide range of thresholds, the characteristic path length L was significantly longer in the Alzheimer patients, whereas the cluster coefficient C showed no significant changes. This pattern was still present when L and C were computed as a function of K. A longer path length with a relatively preserved cluster coefficient suggests a loss of complexity and a less optimal organization. The present study provides further support for the presence of "small-world" features in functional brain networks and demonstrates that AD is characterized by a loss of small-world network characteristics. Graph theoretical analysis may be a useful approach to study the complexity of patterns of interrelations between EEG channels.

Question: What is the characteristic path length?


An optimal method for stochastic composite optimization. This paper considers an important class of convex programming (CP) problems, namely, the stochastic composite optimization (SCO), whose objective function is given by the summation of general nonsmooth and smooth stochastic components. Since SCO covers non-smooth, smooth and stochastic CP as certain special cases, a valid lower bound on the rate of convergence for solving these problems is known from the classic complexity theory of convex programming. Note however that the optimization algorithms that can achieve this lower bound had never been developed. In this paper, we show that the simple mirror-descent stochastic approximation method exhibits the best-known rate of convergence for solving these problems. Our major contribution is to introduce the accelerated stochastic approximation (AC-SA) algorithm based on Nesterov's optimal method for smooth CP [32,34], and show that the AC-SA algorithm can achieve the aforementioned lower bound on the rate of convergence for SCO. To the best of our knowledge, it is also the first universally optimal algorithm in the literature for solving non-smooth, smooth and stochastic CP problems. We illustrate the significant advantages of the AC-SA algorithm over existing methods in the context of solving a special but broad class of stochastic programming problems.

Question: What is the optimal method for stochastic composite optimization?


Dynamics simulation toolbox for industrial robot manipulators. A new robot toolbox for dynamics simulation based on MATLAB Graphical User Interface (GUI) is developed for educational purposes. It is built on the previous version named as ROBOLAB performing only the kinematics analysis of robot manipulators. The toolbox presented in this paper provides interactive real-time simulation and visualization of the industrial robot manipulator's dynamics based on Langrange Euler and Newton Euler formulations. Since most of the industrial robot manipulators are produced with six degrees of freedom (DOF) such as the PUMA 560, the Fanuc ArcMate 120iB and Stanford Arm, the library of the toolbox includes sixteen fundamental 6-DOF robot manipulators with Euler wrist. The software can be used to interactively perform robotics analysis and off-line programming of robot dynamics such as forward and inverse dynamics as well as to interactively teach and simulate the basic principles of robot dynamics in a very realistic way. In order to demonstrate the user-friendly features of the toolbox much better, simulation of the NS robot manipulator (Stanford Arm) is provided as an example. 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 319 330, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20262

Question: What is the dynamics simulation toolbox for industrial robot manipulators?


Markov logic networks. We propose a simple approach to combining first-order logic and probabilistic graphical models in a single representation. A Markov logic network (MLN) is a first-order knowledge base with a weight attached to each formula (or clause). Together with a set of constants representing objects in the domain, it specifies a ground Markov network containing one feature for each possible grounding of a first-order formula in the KB, with the corresponding weight. Inference in MLNs is performed by MCMC over the minimal subset of the ground network required for answering the query. Weights are efficiently learned from relational databases by iteratively optimizing a pseudo-likelihood measure. Optionally, additional clauses are learned using inductive logic programming techniques. Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach.

Question: What is a markov logic network?


Threshold-Dependent Camouflaged Cells to Secure Circuits Against Reverse Engineering Attacks. With current tools and technology, someone who has physical access to a chip can extract the detailed layout of the integrated circuit (IC). By using advanced visual imaging techniques, reverse engineering can reveal details that are meant to be kept secret, such as a secure protocol or novel implementation that offers a competitive advantage. A promising solution to defend against reverse engineering attacks is IC camouflaging. In this work, we propose a new camouflaging technique based on the threshold voltage of the transistors. We refer to these cells as threshold dependent camouflaged cells. Our work differs from current commercial solutions in that the latter use look-alike cells, with the assumption that it is difficult for the reverse engineer to identify the cell's functionality. Yet, if a structural distinction between cells exists, then these are still vulnerable, especially as reverse engineers use more advanced and precise techniques. On the other hand, the proposed threshold dependent standard cells are structurally identical regardless of the cells' functionality. Detailed circuit simulations of our proposed threshold dependent camouflaged cells demonstrate that they can be used to cost-effectively and robustly camouflage large netlists. Corner analysis of process, temperature, and supply voltage (PVT) variations show that our cells operate as expected over all PVT corners simulated.

Question: How to protect a circuit from reverse engineering attacks?


A MODERN TAKE. We revisit the bias-variance tradeoff for neural networks in light of modern empirical findings. The traditional bias-variance tradeoff in machine learning suggests that as model complexity grows, variance increases. Classical bounds in statistical learning theory point to the number of parameters in a model as a measure of model complexity, which means the tradeoff would indicate that variance increases with the size of neural networks. However, we empirically find that variance due to training set sampling is roughly constant (with both width and depth) in practice. Variance caused by the non-convexity of the loss landscape is different. We find that it decreases with width and increases with depth, in our setting. We provide theoretical analysis, in a simplified setting inspired by linear models, that is consistent with our empirical findings for width. We view bias-variance as a useful lens to study generalization through and encourage further theoretical explanation from this perspective.

Question: What is the bias-variance tradeoff in neural networks?


Analysis of level design 'push & pull' within 21 games. This paper investigates the differences between 3D level designs in 21 popular games. We have developed a framework to analyze 3D level designs based on patterns extracted from level designers and game play sessions. We then use these patterns to analyze several game play sessions. Results of this analysis reveal methods by which designers push and pull players through levels. We discuss an analysis of these patterns in terms of three level affordance configurations, combat, environmental resistance, and mixed goals in 21 different games using one walkthrough play session per game. By looking at the variety of games, we can further explore the level similarities and differences between games.

Question: What is a level affordance configuration?


Optimizing Players' Expected Enjoyment in Interactive Stories. In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player's expected enjoyment without removing player agency. Our personalized drama manager models a player's preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player's expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players' enjoyment ratings in an interactive storytelling testbed, compared to drama managers in pre-

Question: What is a drama manager?


Pattern Reconfigurable Antenna Based on Morphing Bistable Composite Laminates. In this paper, a novel pattern reconfigurable antenna based on morphing bistable composite laminates is presented. The bistable asymmetric glass-fiber reinforced polymer (GFRP) composite laminates have two stable configurations with curvatures of opposite signs. The antenna pattern is reconfigured by transforming the configuration of the bistable GFRP laminate which acts as the substrate of the antenna. The coplanar waveguide transmission lines feeding technique is used for the microstrip quasi-Yagi antenna. A prototype of the proposed antenna is fabricated using a semi-automatic screen printer and an autoclave. The transformation between the two stable states of the proposed antenna using Ni/Ti shape memory alloy springs is investigated experimentally. The out-of-plane displacements, reflection coefficients and radiation patterns for the two stable configurations of the antenna are measured, which agree well with the simulated results. The main beam direction is 89° and 59° for the two stable configurations, respectively. In addition, the influences of various bending radii on the radiation patterns are investigated to gain a thorough understanding of the reconfigurable mechanism of the proposed antenna. Finally, a two-element array of such an antenna is presented and measured. The proposed antenna provides a potential application in multifunctional, conformal, morphing, and integrated structures.

Question: What is the difference between a quasi-Yagi antenna and a Yagi antenna?


A Comparison of Push and Pull Techniques for AJAX. AJAX applications are designed to have high user interactivity and low user-perceived latency. Real-time dynamic Web data such as news headlines, stock tickers, and auction updates need to be propagated to the users as soon as possible. However, AJAX still suffers from the limitations of the Web's request/response architecture which prevents servers from pushing real-time dynamic web data. Such applications usually use a pull style to obtain the latest updates, where the client actively requests the changes based on a predefined interval. It is possible to overcome this limitation by adopting a push style of interaction where the server broadcasts data when a change occurs on the server side. Both these options have their own trade-offs. This paper explores the fundamental limits of browser-based applications and analyzes push solutions for AJAX technology. It also shows the results of an empirical study comparing push and pull.

Question: What is the difference between push and pull?


The nucleotide sequence of a HMW glutenin subunit gene located on chromosome 1A of wheat (Triticum aestivum L.).. A cloned 8.2 kb EcoRI fragment has been isolated from a genomic library of DNA derived from Triticum aestivum L. cv. Cheyenne. This fragment contains sequences related to the high molecular weight (HMW) subunits of glutenin, proteins considered to be important in determining the elastic properties of gluten. The cloned HMW subunit gene appears to be derived from chromosome 1A. The nucleotide sequence of this gene has provided new information on the structure and evolution of the HMW subunits. However, hybrid-selection translation experiments suggest that this gene is silent.

Question: What is the name of the gene?


Millimeter-wave large-scale phased-arrays for 5G systems. This talk will present our latest work on silicon RFICs for phased-array applications with emphasis on very large chips with built-in-self-test capabilities for 5G systems. SiGe is shown to be ideal for mm-wave applications due to its high temperature performance (automotive radars, base-stations, defense systems, etc.) and lower power consumption. These chips drastically reduce the cost of microwave and millimeter-wave phased arrays by combining many elements on the same chip, together with digital control and some cases, high-efficiency antennas. The phased-array chips also result in an easier packaging scheme using either a multi-layer PCB or wafer-level packages. We believe that this family of chips will be essential for millimeter-wave 5G communication systems.

Question: What is the cost of a mm-wave phased array?


Foot pressure distribution and contact duration pattern during walking at self-selected speed in young adults. Foot load observed as pressure distribution is examined in relation to a foot and ankle functions within the gait cycle. This load defines the modes of healthy and pathological gait. Determination of the patterns of healthy, i.e. "normal" walk is the basis for classification of pathological modes. Eleven healthy participants were examined in this initial study. Participants walked over pressure plate barefoot at their self - selected speed. Maximal values of the pressure were recorded in the heel, the first, the second and the third metatarsal joints and in the hallux region. Largest contact duration is recorded in the metatarsal region.

Question: What is the largest contact duration?


Navigation in a small world. The small-world phenomenon — the principle that most of us are linked by short chains of acquaintances — was first investigated as a question in sociology and is a feature of a range of networks arising in nature and technology. Experimental study of the phenomenon revealed that it has two fundamental components: first, such short chains are ubiquitous, and second, individuals operating with purely local information are very adept at finding these chains. The first issue has been analysed, and here I investigate the second by modelling how individuals can find short chains in a large social network.

Question: How can I find short chains in a large social network?


Spin Transfer Torques. This tutorial article introduces the physics of spin transfer torques in magnetic devices. We provide an elementary discussion of the mechanism of spin transfer torque, and review the theoretical and experimental progress in this field. Our intention is to be accessible to beginning graduate students. This is the introductory paper for a cluster of "Current Perspectives" articles on spin transfer torques published in volume 320 of the Journal of Magnetism and Magnetic Materials. This article is meant to set the stage for the others which follow it in this cluster; they focus in more depth on particularly interesting aspects of spin-torque physics and highlight unanswered questions that might be productive topics for future research.

Question: What is spin transfer torque?


Why the sunny side is up: association between affect and vertical position.. Metaphors linking spatial location and affect (e.g., feeling up or down) may have subtle, but pervasive, effects on evaluation. In three studies, participants evaluated words presented on a computer. In Study 1, evaluations of positive words were faster when words were in the up rather than the down position, whereas evaluations of negative words were faster when words were in the down rather than the up position. In Study 2, positive evaluations activated higher areas of visual space, whereas negative evaluations activated lower areas of visual space. Study 3 revealed that, although evaluations activate areas of visual space, spatial positions do not activate evaluations. The studies suggest that affect has a surprisingly physical basis.

Question: What is the sunny side up?


Applying multi-agent technique in multi-section flexible manufacturing system. In the highly competitive market, cooperative multi-agent transaction and negotiation mechanism have become an important research topic. This paper uses multi-agent technology to construct a multi-section flexible manufacturing system (FMS) model, and utilizes simulation to build a manufacturing environment based on JADE framework for multi-agent to combine with dispatching rules, such as shortest imminent processing time (SIPT), first come first serve (FCFS) earliest due date (EDD), and Buffer Sequence. This paper finds that using multi-agent technique for multi-section FMS model can enhance the production efficiency in practice. Meanwhile, in this study, multi-agent systems combined with dynamic dispatching can be used to identify the best dispatching rules combination for achieving largest throughput, and thus it can provide the reference for production scheduling in advance. 2010 Elsevier Ltd. All rights reserved.

Question: How to apply multi-agent technique in multi-section flexible manufacturing system?


Link prediction in complex networks: a local naïve Bayes model. Common-neighbor-based method is simple yet effective to predict missing links, which assume that two nodes are more likely to be connected if they have more common neighbors. In such method, each common neighbor of two nodes contributes equally to the connection likelihood. In this Letter, we argue that different common neighbors may play different roles and thus lead to different contributions, and propose a local näıve Bayes model accordingly. Extensive experiments were carried out on eight real networks. Compared with the common-neighbor-based methods, the present method can provide more accurate predictions. Finally, we gave a detailed case study on the US air transportation network. Introduction. – The problem of link prediction aims at estimating the likelihood of the existence of a link between two nodes in a given network, based on the observed links [1]. Recently, the study of link prediction has attracted much attention from disparate scientific communities. In the theoretical aspect, accurate prediction indeed gives evidence to some underlying mechanisms that drives the network evolution [2]. Moveover, it is very possible to build a fair evaluation platform for network modeling under the framework of link prediction, which might be interested by network scientists [1, 3]. In the practical aspect, for biological networks such as protein-protein interaction networks and metabolic networks [4–6], the experiments of uncovering the new links or interactions are costly, and thus to predict in advance and focus on the links most likely to exist can sharply reduce the experimental costs [7]. In addition, some of the representative methods of link prediction have been successfully applied to address the classification problem in partially labeled networks [8, 9], as well as identified the spurious links resulted from the inaccurate information in the data [10]. Motivated by the theoretical interests a

Question: What is the difference between a local naïve Bayes model and a common-neighbor-based method?


Interventions in Small Food Stores to Change the Food Environment, Improve Diet, and Reduce Risk of Chronic Disease. INTRODUCTION Many small-store intervention trials have been conducted in the United States and other countries to improve the food environment and dietary behaviors associated with chronic disease risk. However, no systematic reviews of the methods and outcomes of these trials have been published. The objective of this study was to identify small-store interventions and to determine their impact on food availability, dietary behaviors, and psychosocial factors that influence chronic disease risk.   METHODS From May 2009 through September 2010, we used PubMed, web-based searches, and listservs to identify small-store interventions that met the following criteria: 1) a focus on small food stores, 2) a completed impact evaluation, and 3) English-written documentation (peer-reviewed articles or other trial documents). We initially identified 28 trials; 16 met inclusion criteria and were used for analysis. We conducted interviews with project staff to obtain additional information. Reviewers extracted and reported data in a table format to ensure comparability between data.   RESULTS Reviewed trials were implemented in rural and urban settings in 6 countries and primarily targeted low-income racial/ethnic minority populations. Common intervention strategies included increasing the availability of healthier foods (particularly produce), point-of-purchase promotions (shelf labels, posters), and community engagement. Less common strategies included business training and nutrition education. We found significant effects for increased availability of healthy foods, improved sales of healthy foods, and improved consumer knowledge and dietary behaviors.   CONCLUSION Trial impact appeared to be linked to the increased provision of both healthy foods (supply) and health communications designed to increase consumption (demand).

Question: What is the impact of small food store interventions on diet and chronic disease risk?


RABAC: Role-Centric Attribute-Based Access Control. Role-based access control (RBAC) is a commercially dominant model, standardized by the National Institute of Standards and Technology (NIST). Although RBAC provides compelling benefits for security management it has several known deficiencies such as role explosion, wherein multiple closely related roles are required (e.g., attendingdoctor role is separately defined for each patient). Numerous extensions to RBAC have been proposed to overcome these shortcomings. Recently NIST announced an initiative to unify and standardize these extensions by integrating roles with attributes, and identified three approaches: use attributes to dynamically assign users to roles, treat roles as just another attribute, and constrain the permissions of a role via attributes. The first two approaches have been previously studied. This paper presents a formal model for the third approach for the first time in the literature. We propose the novel role-centric attribute-based access control (RABAC) model which extends the NIST RBAC model with permission filtering policies. Unlike prior proposals addressing the role-explosion problem, RABAC does not fundamentally modify the role concept and integrates seamlessly with the NIST RBAC model. We also define an XACML profile for RABAC based on the existing XACML profile for RBAC.

Question: What is the role-centric attribute-based access control model?


Joint precision optimization and high level synthesis for approximate computing. Approximate computing has been recognized as an effective low power technique for applications with intrinsic error tolerance, such as image processing and machine learning. Existing efforts on this front are mostly focused on approximate circuit design, approximate logic synthesis or processor architecture approximation techniques. This work aims at how to make good use of approximate circuits at system and block level. In particular, approximation aware scheduling, functional unit allocation and binding algorithms are developed for data intensive applications. Simple yet credible error models, which are essential for precision control in the optimizations, are investigated. The algorithms are further extended to include bitwidth optimization in fixed point computations. Experimental results, including those from Verilog simulations, indicate that the proposed techniques facilitate desired energy savings under latency and accuracy constraints.

Question: What is approximate computing?


Uniport: A Uniform Programming Support Framework for Mobile Cloud Computing. Personal mobile devices (PMDs) have become the most used computing devices for many people. With the introduction of mobile cloud computing, we can augment the storage and computing capabilities of PMDs via cloud support. However, there are many challenges in developing mobile cloud applications (MCAs) that incorporate cloud computing efficiently, especially for developers targeting multiple mobile platforms. This paper presents Uniport, a uniform framework for developing MCAs. We introduce a uniform architecture for MCAs based on the Model-View-Controller (MVC) pattern and a set of programming primitives and runtime libraries. Not only can Uniport support the creation of new MCAs, it can also help transform existing mobile applications to MCAs efficiently. We demonstrate the applicability and flexibility of Uniport in a case study to transform three existing mobile applications on iOS, Android and Windows Phone, to their mobile cloud versions respectively. Evaluation results show that, with very few modifications, we can easily transform mobile applications to MCAs that can exploit the cloud support to improve performance by 3 - 7x and save more than half of their energy consumption.

Question: What is the difference between a mobile cloud application and a mobile application?


Practical privacy: the SuLQ framework. We consider a statistical database in which a trusted administrator introduces noise to the query responses with the goal of maintaining privacy of individual database entries. In such a database, a query consists of a pair (S, f) where S is a set of rows in the database and f is a function mapping database rows to {0, 1}. The true answer is ΣiεS f(di), and a noisy version is released as the response to the query. Results of Dinur, Dwork, and Nissim show that a strong form of privacy can be maintained using a surprisingly small amount of noise -- much less than the sampling error -- provided the total number of queries is sublinear in the number of database rows. We call this query and (slightly) noisy reply the SuLQ (Sub-Linear Queries) primitive. The assumption of sublinearity becomes reasonable as databases grow increasingly large.We extend this work in two ways. First, we modify the privacy analysis to real-valued functions f and arbitrary row types, as a consequence greatly improving the bounds on noise required for privacy. Second, we examine the computational power of the SuLQ primitive. We show that it is very powerful indeed, in that slightly noisy versions of the following computations can be carried out with very few invocations of the primitive: principal component analysis, k means clustering, the Perceptron Algorithm, the ID3 algorithm, and (apparently!) all algorithms that operate in the in the statistical query learning model [11].

Question: What is the SuLQ framework?


1 TOURISM ONTOLOGY AND SEMANTIC MANAGEMENT SYSTEM : STATE-OFTHE-ARTS ANALYSIS. The global importance of tourism is steadily rising,creating new job opportunities in many countries. Today's information management solutions for the complex tasks of tourism intermediaries are still at an early stage from a semantic point of view. This paper presents some preliminary results of OnTourism Project. OnTourism is aimed at (1) applying, concretizing and evaluating Semantic Web technologies such as ontologies, semantic annotation of content, and semantic search to the information-rich and economically important tourism domain, (2) identifying, developing and integrating reference ontologies for the tourism industry, and (3) showing the proof-of-concept in a real-world scenario of the Austrian tourism industry. First results presented in this paper identify publicly available tourism ontologies and existing freely available ontology management tools for the tourism domain. We identify seven tourism ontologies which are suitable as a basis for creating problem-specific ontologies. Furthermore we review and evaluate five freely available ontology management tools that are suited for application in the tourism domain.

Question: What is the tourism ontology?


ViziQuer: A Tool to Explore and Query SPARQL Endpoints. The presented tool uses a novel approach to explore and query a SPARQL endpoint. The tool is simple to use as a user needs only to enter an address of a SPARQL endpoint of one's interest. The tool will extract and visualize graphically the data schema of the endpoint. The user will be able to overview the data schema and use it to construct a SPARQL query according to the data schema. The tool can be downloaded from http://viziquer.lumii.lv. There is also additional information and help on how to use it in practice.

Question: How to use the tool?


Your liking is my curiosity : a social popularity intervention to induce curiosity. Our actions and decisions are regularly influenced by the social environment around us. Can social environment be leveraged to induce curiosity and facilitate subsequent learning? Across two experiments, we show that curiosity is contagious: social environment can influence people's curiosity about the answers to scientific questions. Our findings show that people are more likely to become curious about the answers to more popular questions, which in turn influences the information they choose to reveal. Given that curiosity has been linked to better learning, these findings have important implications for education.

Question: How can social environment be leveraged to induce curiosity and facilitate subsequent learning?


Programming algorithms for multilevel phase-change memory. Phase-change memory (PCM) has emerged as one among the most promising technologies for next-generation nonvolatile solid-state memory. Multilevel storage, namely storage of non-binary information in a memory cell, is a key factor for reducing the total cost-per-bit and thus increasing the competitiveness of PCM technology in the nonvolatile memory market. In this paper, we present a family of advanced programming schemes for multilevel storage in PCM. The proposed schemes are based on iterative write-and-verify algorithms that exploit the unique programming characteristics of PCM in order to achieve significant improvements in resistance-level packing density, robustness to cell variability, programming latency, energy-per-bit and cell storage capacity. Experimental results from PCM test-arrays are presented to validate the proposed programming schemes. In addition, the reliability issues of multilevel PCM in terms of resistance drift and read noise are discussed.

Question: What is the difference between a programming scheme and a programming algorithm?


Topological Data Analysis of Biological Aggregation Models. We apply tools from topological data analysis to two mathematical models inspired by biological aggregations such as bird flocks, fish schools, and insect swarms. Our data consists of numerical simulation output from the models of Vicsek and D'Orsogna. These models are dynamical systems describing the movement of agents who interact via alignment, attraction, and/or repulsion. Each simulation time frame is a point cloud in position-velocity space. We analyze the topological structure of these point clouds, interpreting the persistent homology by calculating the first few Betti numbers. These Betti numbers count connected components, topological circles, and trapped volumes present in the data. To interpret our results, we introduce a visualization that displays Betti numbers over simulation time and topological persistence scale. We compare our topological results to order parameters typically used to quantify the global behavior of aggregations, such as polarization and angular momentum. The topological calculations reveal events and structure not captured by the order parameters.

Question: What is topological data analysis?


Sentiment classification of Internet restaurant reviews written in Cantonese. Cantonese is an important dialect in some regions of Southern China. Local online users often represent their opinions and experiences on the web with written Cantonese. Although the information in those reviews is valuable to potential consumers and sellers, the huge amount of web reviews make it difficult to give an unbiased evaluation to a product and the Cantonese reviews are unintelligible for Mandarin Chinese speakers. In this paper, standard machine learning techniques naive Bayes and SVM are incorporated into the domain of online Cantonese-written restaurant reviews to automatically classify user reviews as positive or negative. The effects of feature presentations and feature sizes on classification performance are discussed. We find that accuracy is influenced by interaction between the classification models and the feature options. The naive Bayes classifier achieves as well as or better accuracy than SVM. Character-based bigrams are proved better features than unigrams and trigrams in capturing Cantonese sentiment orientation. 2010 Elsevier Ltd. All rights reserved.

Question: How to classify restaurant reviews written in Cantonese?


Underwater Vehicle Obstacle Avoidance and Path Planning Using a MultiBeam Forward Looking Sonar. This paper describes a new framework for segmentation of sonar images, tracking of underwater objects and motion estimation. This framework is applied to the design of an obstacle avoidance and path planning system for underwater vehicles based on a multi-beam forward looking sonar sensor. The real-time data flow (acoustic images) at the input of the system is first segmented and relevant features are extracted. We also take advantage of the real-time data stream to track the obstacles in following frames to obtain their dynamic characteristics. This allows us to optimize the preprocessing phases in segmenting only the relevant part of the images. Once the static (size and shape) as well as dynamic characteristics (velocity, acceleration, ...) of the obstacles have been computed, we create a representation of the vehicle's workspace based on these features. This representation uses constructive solid geometry(CSG) to create a convex set of obstacles defining the workspace. The tracking takes also into account obstacles which are no longer in the field of view of the sonar in the path planning phase. A well-proven nonlinear search (sequential quadratic programming) is then employed, where obstacles are expressed as constraints in the search space. This approach is less affected by local minima than classical methods using potential fields. The proposed system is not only capable of obstacle avoidance but also of path planning in complex environments which include fast moving obstacles. Results obtained on real sonar data are shown and discussed. Possible applications to sonar servoing and real-time motion estimation are also discussed.

Question: What is a sonar servo?


A delay-tolerant network architecture for challenged internets. The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.

Question: What is a delay-tolerant network architecture?


How to gamify software engineering. Software development, like any prolonged and intellectually demanding activity, can negatively affect the motivation of developers. This is especially true in specific areas of software engineering, such as requirements engineering, test-driven development, bug reporting and fixing, where the creative aspects of programming fall short. The developers' engagement might progressively degrade, potentially impacting their work's quality.

Question: How to gamify software engineering?


Overview of the EVS codec architecture. The recently standardized 3GPP codec for Enhanced Voice Services (EVS) offers new features and improvements for low-delay real-time communication systems. Based on a novel, switched low-delay speech/audio codec, the EVS codec contains various tools for better compression efficiency and higher quality for clean/noisy speech, mixed content and music, including support for wideband, super-wideband and full-band content. The EVS codec operates in a broad range of bitrates, is highly robust against packet loss and provides an AMR-WB interoperable mode for compatibility with existing systems. This paper gives an overview of the underlying architecture as well as the novel technologies in the EVS codec and presents listening test results showing the performance of the new codec in terms of compression and speech/audio quality.

Question: What is the EVS codec?


A unified approach to statistical tomography using coordinate descent optimization. Over the past years there has been considerable interest in statistically optimal reconstruction of cross-sectional images from tomographic data. In particular, a variety of such algorithms have been proposed for maximum a posteriori (MAP) reconstruction from emission tomographic data. While MAP estimation requires the solution of an optimization problem, most existing reconstruction algorithms take an indirect approach based on the expectation maximization (EM) algorithm. We propose a new approach to statistically optimal image reconstruction based on direct optimization of the MAP criterion. The key to this direct optimization approach is greedy pixel-wise computations known as iterative coordinate decent (ICD). We propose a novel method for computing the ICD updates, which we call ICD/Newton-Raphson. We show that ICD/Newton-Raphson requires approximately the same amount of computation per iteration as EM-based approaches, but the new method converges much more rapidly (in our experiments, typically five to ten iterations). Other advantages of the ICD/Newton-Raphson method are that it is easily applied to MAP estimation of transmission tomograms, and typical convex constraints, such as positivity, are easily incorporated.

Question: What is a statistically optimal reconstruction algorithm?


Depth driven people counting using deep region proposal network. People counting is a crucial subject in video surveillance application. Factors such as severe occlusions, scene perspective distortions in real application scenario make this task challenging. In this paper, we carefully designed a deep detection framework based on depth information for people counting in crowded environments. Our system performs head detection on depth images collected by an overhead vertical Kinect sensor. To the best of our knowledge, this is the first attempt to use the deep convolutional neural networks on depth images for people counting. We explored the impact of the number and quality of RPN positive anchors on the performance of Faster R-CNN and proposed a solution. Our method is very simple but effective, not only showing promising results but also efficiency as it runs in real-time at a frame rate of about 110 frames per second on a GPU.

Question: How to count people in a crowded environment?


A solution to the simultaneous localization and map building (SLAM) problem. The simultaneous localisation and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle location. Starting from the estimation-theoretic foundations of this problem developed in [1], [2], [3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative map with zero uncertainty is then developed. It is then shown that the absolute accuracy of the map and the vehicle location reach a lower bound defined only by the initial vehicle uncertainty. Together, these results show that it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and, using relative observations only, incrementally build a perfect map of the world and to compute simultaneously a bounded estimate of vehicle location. This paper also describes a substantial implementation of the SLAM algorithm on a vehicle operating in an outdoor environment using millimeter-wave (MMW) radar to provide relative map observations. This implementation is used to demonstrate how some key issues such as map management and data association can be handled in a practical environment. The results obtained are cross-compared with absolute locations of the map landmarks obtained by surveying. In conclusion, this paper discusses a number of key issues raised by the solution to the SLAM problem including sub-optimal map-building algorithms and map management.

Question: How to solve the simultaneous localization and map building problem?


Design of an anthropomorphic prosthetic hand driven by Shape Memory Alloy actuators. This paper presents the mechanical design of an ultra-lightweight, biomimetically actuated anthropomorphic hand for prosthetic purposes. The proposed design is based on an underactuated configuration of 16 joints and 7 active degrees of freedom. Shape Memory Alloy wires are used as motive elements of a specially designed actuation system installed within the envelope of the hand and forearm; due to their inherent contraction ability when heated, these innovative micro-actuators produce linear motion which is imparted to the fingers via a tendon transmission system. The overall design is completed with the integration of the necessary locking mechanisms for power saving. An experimental prototype of the suggested prosthesis has been fabricated using rapid prototyping techniques and it will be used as an evaluation platform for further research towards the development of a truly multifunctional, silent and cosmetically appealing hand for upper limb amputees.

Question: What is the design of an anthropomorphic prosthetic hand?


Hipster Wars: Discovering Elements of Fashion Styles. The clothing we wear and our identities are closely tied, revealing to the world clues about our wealth, occupation, and socioidentity. In this paper we examine questions related to what our clothing reveals about our personal style. We first design an online competitive Style Rating Game called Hipster Wars to crowd source reliable human judgments of style. We use this game to collect a new dataset of clothing outfits with associated style ratings for 5 style categories: hipster, bohemian, pinup, preppy, and goth. Next, we train models for betweenclass and within-class classification of styles. Finally, we explore methods to identify clothing elements that are generally discriminative for a style, and methods for identifying items in a particular outfit that may indicate a style.

Question: What is the largest clothing brand in the world?


Location-Based Services for Mobile Telephony: a Study of Users' Privacy Concerns. Context -aware computing often involves tracking peoples' location. Many studies and applications highlight the importance of keeping people's location information private. We discuss two types of locationbased services; location-tracking services that are based on other parties tracking the user's location and position-aware services that rely on the device's knowledge of its own location. We present an experimental case study that examines people's concern for location privacy and compare this to the use of location-based services. We find that even though the perceived usefulness of the two different types of services is the same, locationtracking services generate more concern for privacy than posit ion-aware services. We conclude that development emphasis should be given to position-aware services but that location-tracking services have a potential for success if users are given a simple option for turning the location-tracking off.

Question: What is the difference between location-based services and position-aware services?


Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots. Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

Question: What is the difference between a capsule endoscope and a capsule robot?


High Performance Clustering Based on the Similarity Join. A broad class of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms perform a similarity query for each point stored in the database. This approach causes serious performance degenerations if the considered data set does not fit into main memory. Usual cache strategies such as LRU fail because the locality of KDD algorithms is typically not high enough. In this paper, we propose to replace repeated similarity queries by the similarity join, a database primitive prevalent in multimedia database systems. We present a schema to transform query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. In order to perform a comprehensive experimental evaluation of our approach, we apply the proposed transformation to the clustering algorithm DBSCAN and to the hierarchical cluster structure analysis method OPTICS. Our technique allows the application of any similarity join algorithm, which may be based on index structures or not. In our experiments, we use a similarity join algorithm based on a variant of the X-tree. The experiments yield substantial performance improvements of our technique over the original algorithms. The traditional techniques are outperformed by factors of up to 33 for the X-tree and 54 for the R*-tree.

Question: What is the performance of the similarity join?


A risk taxonomy proposal for software maintenance. There can be no doubt that risk management is an important activity in the software engineering area. One proof of this is the large body of work existing in this area. However, when one takes a closer look at it, one perceives that almost all this work is concerned with risk management for software development projects. The literature on risk management for software maintenance is much scarcer. On the other hand, software maintenance projects do present specificities that imply they offer different risks than development. This suggests that maintenance projects could greatly benefit from better risk management tools. One step in this direction would be to help identifying potential risk factors at the beginning of a maintenance project. For this, we propose taxonomy of possible risks for software management projects. The ontology was created from: i) an extensive survey of risk management literature, to list known risk factors for software development; and, ii) an extensive survey of maintenance literature, to list known problems that may occur during maintenance.

Question: What is a risk taxonomy?


Mining Vessel Tracking Data for Maritime Domain Applications. The growing number of remote sensing systems and ship reporting technologies (e.g. Automatic Identification System, Long Range Identification and Tracking, radar tracking, Earth Observation) are generating an overwhelming amount of spatio-temporal and geographically distributed data related to vessels and their movements. Research on reliable data mining techniques has proven essential to the discovery of knowledge from such increasingly available information on ship traffic at sea. Data driven knowledge discovery has very recently demonstrated its value in fields that go beyond the original maritime safety and security remits of such data. They include, but are not limited to, fisheries management, maritime spatial planning, gridding ship emissions, mapping activities at sea, risk assessment of offshore platforms, and trade indicators. The extraction of useful information from maritime Big Data is thus a key element in providing operational authorities, policy-makers and scientists with supporting tools to understand what is happening at sea and improve maritime knowledge. This work will provide a survey of the recent JRC research activities relevant to automatic anomaly detection and knowledge discovery in the maritime domain. Data mining, data analytics and predictive analysis examples are introduced using real data. In addition, this paper presents approaches to detect anomalies in reporting messages and unexpected behaviours at sea.

Question: What is the canadian military?


Performance Evaluation of SVM and K-Nearest Neighbor Algorithm over Medical Data set. In this age of computer science each and every thing becomes intelligent and perform task as human. For that purpose there are various tools, techniques and methods are proposed. Support vector machine is a model for statistics and computer science, to perform supervised learning, methods that are used to make analysis of data and recognize patterns. SVM is mostly used for classification and regression analysis. And in the same way k-nearest neighbor algorithm is a classification algorithm used to classify data using training examples. In this paper we use SVM and KNN algorithm to classify data and get prediction (find hidden patterns) for target. Here we use medical patients nominal data to classify and discover the data pattern to predict future disease, Uses data mining which is use to classify text analysis in future.

Question: What is the difference between SVM and KNN?


Towards Internet of Things : Survey and Future Vision. Internet of things is a promising research due to its importance in many commerce, industry, and education applications. Recently, new applications and research challenges in numerous areas of Internet of things are fired. In this paper, we discuss the history of Internet of things, different proposed architectures of Internet of things, research challenges and open problems related to the Internet of things. We also introduce the concept of Internet of things database and discuss about the future vision of Internet of things. These are the manuscript preparation guidelines used as a standard template for all journal submissions. Author must follow these instructions while preparing/modifying these guidelines.

Question: What is the history of Internet of things?


Evaluation of Multi-view 3D Reconstruction Software. A number of software solutions for reconstructing 3D models from multi-view image sets have been released in recent years. Based on an unordered collection of photographs, most of these solutions extract 3D models using structure-from-motion (SFM) algorithms. In this work, we compare the resulting 3D models qualitatively and quantitatively. To achieve these objectives, we have developed different methods of comparison for all software solutions. We discuss the perfomance and existing drawbacks. Particular attention is paid to the ability to create printable 3D models or 3D models usable for other applications.

Question: What is the best multi-view reconstruction software?


A multiperiod set covering location model for dynamic redeployment of ambulances. Emergency medical service (EMS) providers continually seek ways to improve system performance particularly the response time to incidents. The demand for ambulances fluctuate throughout the week, depending on the day of week, and even the time of day, therefore EMS operators can improve system performance by dynamic relocation/redeployment of ambulances in response to fluctuating demand patters. The objective of the model is to determine the minimum number of ambulances and their locations for each time cluster in which significant changes in demand pattern occur while meeting coverage requirement with a predetermined reliability. The model is further enhanced by calculating ambulance specific busy probabilities and validated by a comprehensive simulation model. Computational results on experimental data sets and data from an EMS agency are provided. 2006 Elsevier Ltd. All rights reserved.

Question: What is the objective of the model?


DBpedia - A crystallization point for the Web of Data. The DBpedia project is a community effort to extract structured information from Wikipedia and to make this information accessible on the Web. The resulting DBpedia knowledge base currently describes over 2.6 million entities. For each of these entities, DBpedia defines a globally unique identifier that can be dereferenced over the Web into a rich RDF description of the entity, including human-readable definitions in 30 languages, relationships to other resources, classifications in four concept hierarchies, various facts as well as data-level links to other Web data sources describing the entity. Over the last year, an increasing number of data publishers have begun to set data-level links to DBpedia resources, making DBpedia a central interlinking hub for the emerging Web of data. Currently, the Web of interlinked data sources around DBpedia provides approximately 4.7 billion pieces of information and covers domains such as geographic information, people, companies, films, music, genes, drugs, books, and scientific publications. This article describes the extraction of the DBpedia knowledge base, the current status of interlinking DBpedia with other data sources on the Web, and gives an overview of applications that facilitate the Web of Data around DBpedia.

Question: What is the DBpedia project?


On-Line Analytical Processing. On-line analytical processing (OLAP) describes an approach to decision support, which aims to extract knowledge from a data warehouse, or more specifically, from data marts. Its main idea is providing navigation through data to non-expert users, so that they are able to interactively generate ad hoc queries without the intervention of IT professionals. This name was introduced in contrast to on-line transactional processing (OLTP), so that it reflected the different requirements and characteristics between these classes of uses. The concept falls in the area of business intelligence.

Question: What is the difference between OLAP and OLTP?


Whose Book is it Anyway ? Using Machine Learning to Identify the Author of Unknown Texts. In this paper, we present an implementation of a new method for text classification that achieves significantly faster results than most existing classifiers. We extract features that are computationally efficient and forgo more computationally expensive ones used by many other text classifiers, most notably n-grams (contiguous sequences of n words). We then analyze the feature vectors through a hybrid SVM technique that sequentially classifies them with a one-versus-all classifier and a sequence of binary classifiers. Our overall method finishes execution within seconds on large novels, with accuracy comparable to that of standard classifiers but with a significantly shorter runtime.

Question: What is a hybrid SVM?


Power-constrained CMOS scaling limits. The scaling of CMOS technology has progressed rapidly for three decades, but may soon come to an end because of powerdissipation constraints. The primary problem is static power dissipation, which is caused by leakage currents arising from quantum tunneling and thermal excitations. The details of these effects, along with other scaling issues, are discussed in the context of their dependence on application. On the basis of these considerations, the limits of CMOS scaling are estimated for various application scenarios.

Question: What is the power dissipation of CMOS?


Understanding Internet Banking Adoption and Use Behavior: A Hong Kong Perspective. Hong Kong is an international financial center well known for its efficiency and its ability to adapt and keep up with the times. Recently, however, the Hong Kong banking industry has been losing competitive advantages in some areas, with the adoption of Internet Banking being one of them. Hong Kong banks have been slower than some other international banks in joining the e-commerce evolution, which first emerged in the United States in mid-90s. Financial institutions in the U.S. have introduced and promoted online banking to provide better customer services. Many property and stock investment firms in Hong Kong have also jumped on the bandwagon and adopted the Internet as a channel for providing better and more efficient services ABSTRACT

Question: What is the difference between Hong Kong and the United States?


Malware detection using adaptive data compression. A popular approach in current commercial anti-malware software detects malicious programs by searching in the code of programs for scan strings that are byte sequences indicative of malicious code. The scan strings, also known as the signatures of existing malware, are extracted by malware analysts from known malware samples, and stored in a database often referred to as a virus dictionary. This process often involves a significant amount of human efforts. In addition, there are two major limitations in this technique. First, not all malicious programs have bit patterns that are evidence of their malicious nature. Therefore, some malware is not recorded in the virus dictionary and can not be detected through signature matching. Second, searching for specific bit patterns will not work on malware that can take many forms--obfuscated malware. Signature matching has been shown to be incapable of identifying new malware patterns and fails to recognize obfuscated malware. This paper presents a malware detection technique that discovers malware by means of a learning engine trained on a set of malware instances and a set of benign code instances. The learning engine uses an adaptive data compression model--prediction by partial matching (PPM)--to build two compression models, one from the malware instances and the other from the benign code instances. A code instance is classified, either as "malware" or "benign", by minimizing its estimated cross entropy. Our preliminary results are very promising. We achieved about 0.94 true positive rate with as low as 0.016 false positive rate. Our experiments also demonstrate that this technique can effectively detect unknown and obfuscated malware.

Question: What is the difference between a virus and a malware?


Computational Capabilities of Graph Neural Networks. In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G, n) isin R m that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.

Question: What is the computational capabilities of graph neural networks?