Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks. Brain Computer Interface (BCI) has been recently increased in research. Functional Near Infrared Spectroscope (fNIRs) is one the latest technologies which utilize light in the near-infrared range to determine brain activities. Because near infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems, fNIRs monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI. We applied two different mathematics tools separately, Wavelets analysis for preprocessing as signal filters and feature extractions and Neural networks for cognition brain tasks as a classification module. We also discuss and compare with other methods while our proposals perform better with an average accuracy of 99.9% for classification. Keywords—functional near infrared spectroscope (fNIRs), braincomputer interface (BCI), wavelets, neural networks, brain activity, neuroimaging.

Question: What is the difference between a brain computer interface and a brain computer?


Cerebral bases of subliminal speech priming. While the neural correlates of unconscious perception and subliminal priming have been largely studied for visual stimuli, little is known about their counterparts in the auditory modality. Here we used a subliminal speech priming method in combination with fMRI to investigate which regions of the cerebral network for language can respond in the absence of awareness. Participants performed a lexical decision task on target items preceded by subliminal primes, which were either phonetically identical or different from the target. Moreover, the prime and target could be spoken by the same speaker or by two different speakers. Word repetition reduced the activity in the insula and in the left superior temporal gyrus. Although the priming effect on reaction times was independent of voice manipulation, neural repetition suppression was modulated by speaker change in the superior temporal gyrus while the insula showed voice-independent priming. These results provide neuroimaging evidence of subliminal priming for spoken words and inform us on the first, unconscious stages of speech perception.

Question: What is the first unconscious stage of speech perception?


Real-Time Tracking of Non-Rigid Objects Using Mean Shift. A new method for r eal-time tracking of non-rigid objects seen from a moving camera is proposed. The central computational module is based on the mean shift iterations and nds the most probable tar get p osition in the current frame. The dissimilarity between the target model (its c olor distribution) and the target candidates is expr essed by a metric derive d from the Bhattacharyya coe cient. The theoretical analysis of the approach shows that it relates to the Bayesian framework while providing a practical, fast and e cient solution. The capability of the tracker to handle in real-time partial occlusions, signi cant clutter, and target scale variations, is demonstrated for several image sequences.

Question: What is the difference between mean shift and Bayesian framework?


Understanding Availability. This paper addresses a simple, yet fundamental question in the design of peer-to-peer systems: What does it mean when we say "availability" and how does this understanding impact the engineering of practical systems? We argue that existing measurements and models do not capture the complex timevarying nature of availability in today's peer-to-peer environments. Further, we show that unforeseen methodological shortcomings have dramatically biased previous analyses of this phenomenon. As the basis of our study, we empirically characterize the availability of a large peer-to-peer system over a period of 7 days, analyze the dependence of the underlying availability distributions, measure host turnover in the system, and discuss how these results may affect the design of high-availability peer-to-peer services.

Question: What is the availability of a peer-to-peer system?


Techniques for Assessing Polygonal Approximations of Curves. Given the enormous number of available methods for finding po lygonal approximations to curves techniques are required to assess different algorithms. Some of the standard approaches are shown to be unsuitable if the approximations contain varying numbers of lines. Ins tead, we suggest assessing an algorithm's results relative to an optimal polygon, and describe a measure which combines the relative fidelity and efficiency of a curve segmentation. We use this measure to compare the appl ication of 23 algorithms to a curve first used by Teh and Chin [37]; their ISEs are assessed relative to the o ptimal ISE. In addition, using an example of pose estimation, it is shown how goal-directed evaluation c a be used to select an appropriate assessment criterion.

Question: What is the optimal ise?


Penoscrotal plication as a uniform approach to reconstruction of penile curvature.. OBJECTIVE To present our 4-year experience of using a minimally invasive technique, penoscrotal plication (PSP), as a uniform treatment for men with debilitating penile curvature resulting from Peyronie's disease.   PATIENTS AND METHODS In 48 men (median age 58.7 years) with penile curvature the penis was reconstructed by imbricating the tunica albuginea opposite the curvature with multiple nonabsorbable sutures. All patients, regardless of the degree or direction of curvature, were approached through a small penoscrotal incision made without degloving the penis. Detailed measurements of penile shaft angle and stretched penile length were recorded and analysed before and after reconstruction, and the numbers of sutures required for correction were documented.   RESULTS Nearly all patients had dorsal and/or lateral deformities that were easily corrected via a ventral penoscrotal incision. The median (range) degree of correction was 28 (18-55) degrees and number of sutures used was 6 (4-17). Stretched penile length measurements before and after plication showed no significant difference. A single PSP procedure was successful in 45/48 (93%) patients; two were dissatisfied with the correction, one having repeat plication and the other a penile prosthesis; one other required a suture release for pain.   CONCLUSIONS PSP is safe and effective and should be considered even for cases with severe or biplanar curvature.

Question: What is the penoscrotal plication?


Research in Education ( CPRE ) 1-2001 Teacher Turnover , Teacher Shortages , and the Organization of Schools. Contemporary educational theory holds that one of the pivotal causes of inadequate school performance is the inability of schools to adequately staff classrooms with qualified teachers. Contemporary theory also holds that these staffing problems are primarily due to shortages of teachers, which, in turn, are primarily due to recent increases in teacher retirements and student enrollments. This analysis investigates the possibility that there are other factors that might have an impact on teacher turnover levels, and, in turn, the staffing problems of schools, factors rooted in the organizational characteristics and conditions of schools. The data utilized in this investigation are from the Schools and Staffing Survey and its supplement, the Teacher Followup Survey, a large, comprehensive, nationally representative survey of teachers and schools conducted by the National Center for Education Statistics. The results of this analysis show that, net of teacher effects, there are significant effects of school characteristics and organizational conditions on teacher turnover which have largely been overlooked by previous research. For example, the data show that while high-poverty public schools have moderately higher rates, contrary to conventional wisdom, neither larger schools, nor public schools in large school districts, nor urban public schools have especially high rates of teacher turnover. In contrast, small private schools stand out for their high rates of turnover. Moreover, the data show, again contrary to popular wisdom, that the amount of turnover accounted for by retirement is relatively minor, especially when compared to that resulting from two related causes – teacher job dissatisfaction and teachers pursuing other jobs. The data show that, in particular, low salaries, inadequate support from the school administration, student discipline problems,

Question: What are the causes of teacher turnover?


The short-form McGill pain questionnaire. A short form of the McGill Pain Questionnaire (SF-MPQ) has been developed. The main component of the SF-MPQ consists of 15 descriptors (11 sensory; 4 affective) which are rated on an intensity scale as 0 = none, 1 = mild, 2 = moderate or 3 = severe. Three pain scores are derived from the sum of the intensity rank values of the words chosen for sensory, affective and total descriptors. The SF-MPQ also includes the Present Pain Intensity (PPI) index of the standard MPQ and a visual analogue scale (VAS). The SF-MPQ scores obtained from patients in post-surgical and obstetrical wards and physiotherapy and dental departments were compared to the scores obtained with the standard MPQ. The correlations were consistently high and significant. The SF-MPQ was also shown to be sufficiently sensitive to demonstrate differences due to treatment at statistical levels comparable to those obtained with the standard form. The SF-MPQ shows promise as a useful tool in situations in which the standard MPQ takes too long to administer, yet qualitative information is desired and the PPI and VAS are inadequate.

Question: What is the short form of the McGill pain questionnaire?


Simulating Human Game Play for Level Difficulty Estimation with Convolutional Neural Networks. This thesis presents an approach to predict the difficulty of levels in a game by simulating game play following a policy learned from human game play. Using state-action pairs tracked from players of the game Candy Crush Saga, we train a Convolutional Neural Network to predict an action given a game state. The trained model then acts as a policy. Our goal is to predict the success rate (SR) of players, from the SR obtained by simulating game play. Previous state-ofthe-art was using Monte Carlo tree search (MCTS) or handcrafted heuristics for game play simulation. We benchmark our suggested approach against one using MCTS. The hypothesis is that, using our suggested approach, predicting the players' SR from the SR obtained through the simulation, leads to better estimations of the players' SR. Our results show that we could not only significantly improve the predictions of the players' SR, but also decrease the time for game play simulation by at least 50 times. Referat Simulering av mänskligt spelande för bedömning av spelbanors svårighetsgrad med Convolutional Neural Networks. Den här avhandlingen presenterar ett tillvägagångssätt för att förutse svårighetsgrad av spelbanor genom spelsimulering enligt en strategi lärd från mänskligt spelande. Med användning av tillstånd-handlings par insamlade från spelare av spelet Candy Crush Saga, tränar vi ett Convolutional Neural Network att förutse en handling från ett givet tillstånd. Den tränade modellen agerar sedan som strategi. Vårt mål är att förutse success rate (SR) av spelare, från SR erhållen från spelsimulering. Tidigare state-of-the-art använde Monte Carlo tree search (MCTS) eller handgjorda heuristiker för spelsimulering. Vi jämför vårt tillvägagångssätt med MCTS. Hypotesen är att vårt föreslagna tillvägagångssätt leder till bättre förutsägelser av mänsklig SR från SR erhållen från spelsimulering. Våra resultat visar a

Question: How large is the canadian military?


Daily Mindful Responding Mediates the Effect of Meditation Practice on Stress and Mood: The Role of Practice Duration and Adherence.. OBJECTIVE Although meditation practice is an important component of many mindfulness-based interventions (MBIs), empirical findings of its effects on psychological functioning are mixed and the mechanisms for the effects remain unclear. Responding with mindfulness (i.e., returning one's attention back to a nonjudgmental, present-oriented awareness) is a fundamental skill practiced in meditations. With repeated meditation practice, this skill is thought to become internalized and be applied to one's daily life. We thus hypothesized that the extent to which individuals responded to daily events with mindfulness would mediate the effects of meditation practice (instance, duration, and adherence to instructions) on psychological well-being.   METHOD Using a daily diary methodology, we tracked the meditation practice, use of mindful responding during the day, and psychological outcomes (perceived stress, negative and positive affect) of 117 mindfulness-based stress reduction program participants.   RESULTS We found that on days when participants meditated, they responded with greater mindfulness to daily events, which accounted for the beneficial effects of meditating on psychological outcomes. Furthermore, findings suggest that on meditation days, longer and more closely adhered meditation practices were independently associated with increases in mindful responding, which in turn were associated with better psychological outcomes.   CONCLUSION These results suggest that regular, longer, and more closely adhered meditation practice is an important component of MBIs, in part because it leads to responding more mindfully in daily life, which promotes well-being.

Question: How long does it take to meditate?


Does working memory training work? The promise and challenges of enhancing cognition by training working memory.. A growing body of literature shows that one's working memory (WM) capacity can be expanded through targeted training. Given the established relationship between WM and higher cognition, these successful training studies have led to speculation that WM training may yield broad cognitive benefits. This review considers the current state of the emerging WM training literature, and details both its successes and limitations. We identify two distinct approaches to WM training, strategy training and core training, and highlight both the theoretical and practical motivations that guide each approach. Training-related increases in WM capacity have been successfully demonstrated across a wide range of subject populations, but different training techniques seem to produce differential impacts upon the broader landscape of cognitive abilities. In particular, core WM training studies seem to produce more far-reaching transfer effects, likely because they target domain-general mechanisms of WM. The results of individual studies encourage optimism regarding the value of WM training as a tool for general cognitive enhancement. However, we discuss several limitations that should be addressed before the field endorses the value of this approach.

Question: Does working memory training work?


Reducing the energy cost of human walking using an unpowered exoskeleton. With efficiencies derived from evolution, growth and learning, humans are very well-tuned for locomotion. Metabolic energy used during walking can be partly replaced by power input from an exoskeleton, but is it possible to reduce metabolic rate without providing an additional energy source? This would require an improvement in the efficiency of the human–machine system as a whole, and would be remarkable given the apparent optimality of human gait. Here we show that the metabolic rate of human walking can be reduced by an unpowered ankle exoskeleton. We built a lightweight elastic device that acts in parallel with the user's calf muscles, off-loading muscle force and thereby reducing the metabolic energy consumed in contractions. The device uses a mechanical clutch to hold a spring as it is stretched and relaxed by ankle movements when the foot is on the ground, helping to fulfil one function of the calf muscles and Achilles tendon. Unlike muscles, however, the clutch sustains force passively. The exoskeleton consumes no chemical or electrical energy and delivers no net positive mechanical work, yet reduces the metabolic cost of walking by 7.2 ± 2.6% for healthy human users under natural conditions, comparable to savings with powered devices. Improving upon walking economy in this way is analogous to altering the structure of the body such that it is more energy-effective at walking. While strong natural pressures have already shaped human locomotion, improvements in efficiency are still possible. Much remains to be learned about this seemingly simple behaviour.

Question: How can we improve the efficiency of human walking?


Deep Relevance Ranking using Enhanced Document-Query Interactions. We explore several new models for document relevance ranking, building upon the Deep Relevance Matching Model (DRMM) of Guo et al. (2016). Unlike DRMM, which uses context-insensitive encodings of terms and query-document term interactions, we inject rich context-sensitive encodings throughout our models, inspired by PACRR's (Hui et al., 2017) convolutional n-gram matching features, but extended in several ways including multiple views of query and document inputs. We test our models on datasets from the BIOASQ question answering challenge (Tsatsaronis et al., 2015) and TREC ROBUST 2004 (Voorhees, 2005), showing they outperform BM25-based baselines, DRMM, and PACRR.

Question: What is the deep relevance ranking?


Knowledge-Based Distant Regularization in Learning Probabilistic Models. Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization of such domain knowledge in popular regularization methods. In this paper, we propose the knowledge-based distant regularization framework, in which we utilize the distant information encoded in a knowledge graph for regularization of probabilistic model estimation. In particular, we propose to impose prior distributions on model parameters specified by knowledge graph embeddings. As an instance of the proposed framework, we present the factor analysis model with the knowledge-based distant regularization. We show the results of preliminary experiments on the improvement of the generalization capability of such model.

Question: What is knowledge-based distant regularization?


Towards Safe Deep Learning: Unsupervised Defense Against Generic Adversarial Attacks. Recent advances in adversarial Deep Learning (DL) have opened up a new and largely unexplored surface for malicious attacks jeopardizing the integrity of autonomous DL systems. We introduce a novel automated countermeasure called Parallel Checkpointing Learners (PCL) to thwart the potential adversarial attacks and significantly improve the reliability (safety) of a victim DL model. The proposed PCL methodology is unsupervised, meaning that no adversarial sample is leveraged to build/train parallel checkpointing learners. We formalize the goal of preventing adversarial attacks as an optimization problem to minimize the rarely observed regions in the latent feature space spanned by a DL network. To solve the aforementioned minimization problem, a set of complementary but disjoint checkpointing modules are trained and leveraged to validate the victim model execution in parallel. Each checkpointing learner explicitly characterizes the geometry of the input data and the corresponding high-level data abstractions within a particular DL layer. As such, the adversary is required to simultaneously deceive all the defender modules in order to succeed. We extensively evaluate the performance of the PCL methodology against the state-of-the-art attack scenarios, including Fast-Gradient-Sign (FGS), Jacobian Saliency Map Attack (JSMA), Deepfool, and Carlini&WagnerL2. Extensive proof-of-concept evaluations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate the effectiveness of our proposed defense mechanism against adversarial samples.

Question: What is the difference between adversarial and generic adversarial attacks?


The Cityscapes Dataset for Semantic Urban Scene Understanding. Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes. To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations, 20 000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.

Question: What is the size of the dataset?


Tutorial on Answering Questions about Images with Deep Learning. Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. With small tweaks the models that we present here can achieve a competitive performance on both datasets, in fact, they are among the best methods that use a combination of LSTM with a global, full frame CNN representation of an image. We hope that after reading this tutorial, the reader will be able to use Deep Learning frameworks, such as Keras and introduced Kraino, to build various architectures that will lead to a further performance improvement on this challenging task. 1 Preface In this tutorial1 we build a few architectures that can answer questions about images. The architectures are based on our two papers on this topic: Malinowski et al. [2015] and Malinowski et al. [2016]; and more broadly, on our project towards a Visual Turing Test2. In particular, an encoder-decoder perspective of Malinowski et al. [2016] allows us to effectively experiment with various design choices. For the sake of simplicity, we only consider a classification-based approach to answer questions about images, although an approach that generate answers word-by-word is also studied in the community [Malinowski et al., 2015]. In the tutorial, we mainly focus on the DAQUAR dataset [Malinowski and Fritz, 2014a], but a few possible directions to apply learnt techniques to VQA [Antol et al., 2015] are also pointed. First, we will get familiar with the task of answering questions about images, and a dataset that implements the task (due to a small size, we mainly use DAQUAR as it better serves an educational purpose that we aim at this tutorial). Next, we buil

Question: What is the size of the canadian military?


Attention training and attention state training. The ability to attend and to exercise cognitive control are vital aspects of human adaptability. Several studies indicate that attention training using computer based exercises can lead to improved attention in children and adults. Randomized control studies of exposure to nature, mindfulness and integrative body-mind training (IBMT) yield improved attention and self-regulation. Here, we ask how attention training and attention state training might be similar and different in their training methods, neural mechanisms and behavioral outcomes. Together these various methods lead to practical ways of improving attention and self-regulation.

Question: What is attention training?


Negative pressure wave based pipeline Leak Detection: Challenges and algorithms. Negative pressure wave is a popular method to detect the occurrence and location of leak incidents in oil/gas pipeline. Three core technical challenges and related algorithm are discussed in this paper. The first is data quality. The balance between noise level and locating precision is discussed in filter design. The second one is dynamic slope in anomaly detection, whence a bi-SPC (Static Process Control) algorithms is proposed to make the threshold be adaptive. The third one is the false alarm caused normal working condition changes. Multiple-sensor paring algorithms is presented. With these algorithms, the robustness and locating precision of LDS (Leak Detection System) can be ensured.

Question: What is the negative pressure wave based pipeline leak detection?


Word Segmentation in Sanskrit Using Path Constrained Random Walks. In Sanskrit, the phonemes at the word boundaries undergo changes to form new phonemes through a process called as sandhi. A fused sentence can be segmented into multiple possible segmentations. We propose a word segmentation approach that predicts the most semantically valid segmentation for a given sentence. We treat the problem as a query expansion problem and use the path-constrained random walks framework to predict the correct segments.

Question: How to segment a sentence in Sanskrit?


Analyzing and Managing Role-Based Access Control Policies. Today more and more security-relevant data is stored on computer systems; security-critical business processes are mapped to their digital counterparts. This situation applies to various domains such as health care industry, digital government, and financial service institutes requiring that different security requirements must be fulfilled. Authorisation constraints can help the policy architect design and express higher-level organisational rules. Although the importance of authorisation constraints has been addressed in the literature, there does not exist a systematic way to verify and validate authorisation constraints. In this paper, we specify both non-temporal and history-based authorisation constraints in the Object Constraint Language (OCL) and first-order linear temporal logic (LTL). Based upon these specifications, we attempt to formally verify role-based access control policies with the help of a theorem prover and to validate policies with the USE system, a validation tool for OCL constraints. We also describe an authorisation engine, which supports the enforcement of authorisation constraints.

Question: How to validate role-based access control policies?


Motion-based grasp selection: Improving traditional control strategies of myoelectric hand prosthesis. This paper introduces a novel prosthetic hand control architecture using inertial information for grasp prediction in order to reduce the cognitive burden of amputees. A pair of inertial measurement sensors (IMUs) are fitted on the wrist and bicep to record arm trajectory when reaching to grasp an object. Each object class can be associated with different methods for grasping and manipulation. An observation experiment was conducted to find the most common grasping methods for generic object classes: Very Small (VS), Small (S), and Medium (M). A Cup (CP) class was also examined to find differences in grasping habits for pick and place, and drinking applications. The resulting grasps were used to test the discriminatory ability of inertial motion features in the upper limb for VS, S and CP object classes. Subject experiments demonstrated an average classification success rate of 90.8%, 69.2% and 88.1% for VS, S and CP classes respectively when using a k-nearest neighbors algorithm with a Euclidean distance metric. The results suggest that inertial motion features have great potential to predict the grasp pattern during reach, and to the authors' knowledge, is the first IMU-based control strategy to utilize natural motion that is aimed at hand prosthesis control.

Question: How to improve the grasping of a prosthetic hand?


YouTube-8M: A Large-Scale Video Classification Benchmark. Many recent advancements in Computer Vision are attributed to large datasets. Open-source software packages for Machine Learning and inexpensive commodity hardware have reduced the barrier of entry for exploring novel approaches at scale. It is possible to train models over millions of examples within a few days. Although large-scale datasets exist for image understanding, such as ImageNet, there are no comparable size video classification datasets. In this paper, we introduce YouTube-8M, the largest multi-label video classification dataset, composed of∼8 million videos—500K hours of video—annotated with a vocabulary of 4800 visual entities. To get the videos and their (multiple) labels, we used a YouTube video annotation system, which labels videos with the main topics in them. While the labels are machine-generated, they have high-precision and are derived from a variety of human-based signals including metadata and query click signals, so they represent an excellent target for content-based annotation approaches. We filtered the video labels (Knowledge Graph entities) using both automated and manual curation strategies, including asking human raters if the labels are visually recognizable. Then, we decoded each video at one-frame-per-second, and used a Deep CNN pretrained on ImageNet to extract the hidden representation immediately prior to the classification layer. Finally, we compressed the frame features and make both the features and video-level labels available for download. The dataset contains frame-level features for over 1.9 billion video frames and 8 million videos, making it the largest public multi-label video dataset. We trained various (modest) classification models on the dataset, evaluated them using popular evaluation metrics, and report them as baselines. Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using the publicly-avai

Question: What is the largest video classification dataset?


Fractal Merkle Tree Representation and Traversal. We introduce a technique for traversal of Merkle trees, and propose an efficient algorithm that generates a sequence of leaves along with their associated authentication paths. For one choice of parameters, and a total of N leaves, our technique requires a worst-case computational effort of 2 log N/loglog N hash function evaluations per output, and a total storage capacity of less than 1.5 log N/loglog N hash values. This is a simultaneous improvement both in space and time complexity over any previously published algorithm.

Question: What is a fractal merkle tree?


Algorithmic Prediction of Health-Care Costs. The rising cost of health care is one of the world's most important problems. Accordingly, predicting such costs with accuracy is a significant first step in addressing this problem. Since the 1980s, there has been research on the predictive modeling of medical costs based on (health insurance) claims data using heuristic rules and regression methods. These methods, however, have not been appropriately validated using populations that the methods have not seen. We utilize modern data-mining methods, specifically classification trees and clustering algorithms, along with claims data from over 800,000 insured individuals over three years, to provide rigorously validated predictions of health-care costs in the third year, based on medical and cost data from the first two years. We quantify the accuracy of our predictions using unseen (out-of-sample) data from over 200,000 members. The key findings are: (a) our data-mining methods provide accurate predictions of medical costs and represent a powerful tool for prediction of health-care costs, (b) the pattern of past cost data is a strong predictor of future costs, and (c) medical information only contributes to accurate prediction of medical costs of high-cost members.

Question: What is the accuracy of the predictions?


A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums. In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule.

Question: What is the total amount of pictures?


The HEXACO Honesty-Humility, Agreeableness, and Emotionality factors: a review of research and theory.. We review research and theory on the HEXACO personality dimensions of Honesty-Humility (H), Agreeableness (A), and Emotionality (E), with particular attention to the following topics: (1) the origins of the HEXACO model in lexical studies of personality structure, and the content of the H, A, and E factors in those studies; (2) the operationalization of the H, A, and E factors in the HEXACO Personality Inventory-Revised; (3) the construct validity of self-reports on scales measuring the H factor; (4) the theoretical distinction between H and A; (5) similarity and assumed similarity between social partners in personality, with a focus on H and A; (6) the extent to which H (and A and E) variance is represented in instruments assessing the "Five-Factor Model" of personality; and (7) the relative validity of scales assessing the HEXACO and Five-Factor Model dimensions in predicting criteria conceptually relevant to H, A, and E.

Question: What is the HEXACO personality inventory?


How Maps Work - Representation, Visualization, and Design. how maps work representation visualization and design. Book lovers, when you need a new book to read, find the book here. Never worry not to find what you need. Is the how maps work representation visualization and design your needed book now? That's true; you are really a good reader. This is a perfect book that comes from great author to share with you. The book offers the best experience and lesson to take, not only take, but also learn.

Question: How maps work?


Operative strategy in postero-medial fracture-dislocation of the proximal tibia.. OBJECTIVE In 1981, Moore introduced a new classification for dislocation-type fractures caused by high-energy mechanisms. The most common medial Moore-type fractures are entire condyle fractures with the avulsion of the median eminence (tibial anterior cruciate ligament (ACL) insertion). They are usually associated with a posterolateral depression of the tibial plateau and an injury of the lateral menisco-tibial capsule. This uniform injury of the knee is increasingly observed in the recent years after skiing injuries due to the high-speed carving technique. This uprising technique uses shorter skis with more sidecut allowing much higher curve speeds and increases the forces on the knee joint. The aim of this study was to describe the injury pattern, our developed operative approach for reconstruction and outcome.   METHODS A total of 28 patients with 29 postero-medial fracture dislocation of the proximal tibia from 2001 until 2009 were analysed. Clinical and radiological follow-up was performed after 4 years on average (1 year in minimum). Evaluation criteria included the Lysholm score for everyday knee function and the Tegner score evaluating the activity level. All fractures were stabilised post primarily. The surgical main approach was medial. First, the exposure of the entire medial condyle fracture was performed following the fracture line to the articular border. The posterolateral impaction was addressed directly through the main fracture gap from anteromedial to posterolateral. Small plateau fragments were removed, larger fragments reduced and preliminarily fixed with separate K-wire(s). The postero-medial part of the condyle was then prepared for main reduction and application of a buttress T-plate in a postero-medial position, preserving the pes anserinus and medial collateral ligament. In addition, a parapatellar medial mini-arthrotomy through the same main medial approach

Question: What is the surgical approach for postero-medial fracture-dislocation of the proximal tibia?


Survey of Event Correlation Techniques for Attack Detection in Early Warning Systems. In the context of early warning systems for detecting Internet worms and other attacks, event correlation techniques are needed for two reasons. First, network attack detection is usually based on distributed sensors, e.g. intrusion detection systems. During attacks but even in normal operation, the generated amount of events is hard to handle in order to evaluate the current attack situation for a larger network. Thus, the concept of event or alert correlation has been introduced. This survey was motivated by recent work on early warning systems. We summarize and clarify the typical terminology used in this context and present a requirement analysis from an early warning system's point of view. In the main part of this survey, we summarize and classify event correlation techniques as described in the literature.

Question: What is an early warning system?


Automatic collision avoidance during parking and maneuvering — An optimal control approach. In order to reduce the great number of parking incidences and other collisions in low speed scenarios, an obstacle avoidance algorithm is proposed. Since collision avoidance can be achieved by sole braking when driving slowly this algorithm's objective is a comfort orientated braking routine. Therefore, an optimization problem is formulated leading to jerk and time optimal trajectories for braking. In addition to that, based on the prediction of the future vehicle motion, this algorithm compensates for a significant actuator time delay. Using an occupancy grid for representing the static vehicle environment and the current driving state, possible collision points are determined not only for the vehicle front or rear, but also for both vehicle sides, where no sensors are located. The algorithm's performance is demonstrated in a real-world scenario.

Question: How to avoid collisions in parking?


Detection of camera artifacts from camera images. Cameras are frequently used in state-of-the-art systems in order to get detailed information of the environment. However, when cameras are used outdoors they easily get dirty or scratches on the lens which leads to image artifacts that can deteriorate a system's performance. Little work has yet been done on how to detect and cope with such artifacts. Most of the previous work has concentrated on detecting a specific artifact like rain drops on the windshield of a car. In this paper, we show that on moving systems most artifacts can be detected by analyzing the frames in a stream of images from a camera for static image parts. Based on the observation that most artifacts are temporally stable in their position in the image we compute pixel-wise correlations between images. Since the system is moving the static artifacts will lead to a high correlation value while the pixels showing only scene elements will have a low correlation value. For testing this novel algorithm, we recorded some outdoor data with the three different artifacts: raindrops, dirt and scratches. The results of our new algorithm on this data show, that it reliably detects all three artifacts. Moreover, we show that our algorithm can be implemented efficiently by means of box-filters which allows it to be used as a self-checking routine running in background on low-power systems such as autonomous field robots or advanced driver assistant systems on a vehicle.

Question: How to detect camera artifacts from camera images?


An Overview of 3D Software Visualization. Software visualization studies techniques and methods for graphically representing different aspects of software. Its main goal is to enhance, simplify and clarify the mental representation a software engineer has of a computer system. During many years, visualization in 2D space has been actively studied, but in the last decade, researchers have begun to explore new 3D representations for visualizing software. In this article, we present an overview of current research in the area, describing several major aspects like: visual representations, interaction issues, evaluation methods and development tools. We also perform a survey of some representative tools to support different tasks, i.e., software maintenance and comprehension, requirements validation and algorithm animation for educational purposes, among others. Finally, we conclude identifying future research directions.

Question: What is the main goal of software visualization?


Search mode and purchase intention in online shopping behavior. This study focuses on the effect of website visitors' goal-oriented search mode on purchase intention in online environments. In a study of 874 respondents recruited from 13 online shops representing a diversity of product categories and customer segments, the effect of visitors' goal-oriented search mode on purchase intention is found to be moderated by product involvement and product risk. Furthermore, product involvement, product risk, product knowledge and Internet experience are found to have positive effects on the degree of goaloriented search mode of the visitors. Also, product knowledge and Internet experience are reported to have direct positive effects on purchase intention. The results point to the importance of understanding the characteristics of website visitors, and to customize the support and search services offered on the website to the characteristics and preferences of the individual visitor. Customization of this kind may partly be based on immediate visitor history (referrer) and may be used to increase purchase intention, and eventually online sales.

Question: What is the effect of website visitors' goal-oriented search mode on purchase intention in online environments?


Contactless Energy Transfer to a Moving Actuator. In this paper a new topology for contactless energy transfer is proposed and tested that can transfer energy to a moving actuator using inductive coupling. The proposed topology provides long-stroke contactless energy transfer capability in a plane and a short-stroke movement of a few millimeters perpendicular to the plane. In addition, it is tolerant to small rotations. The experimental setup consists of a platform with one secondary coil, which is attached to a linear actuator and a 3-phase brushless electromotor. Underneath the platform is an array of primary coils, which are each connected to a half-bridge square wave power supply. The energy transfer to the electromotor is measured while the platform is moved over the array of primary coils by the linear actuator. The secondary coil moves with a stroke of 18 cm at speeds over 1 m/s, while up to 33 W power is transferred with 90% efficiency

Question: How does the energy transfer work?


Network Hardware-Accelerated Consensus. Consensus protocols are the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be gained by offering consensus as a network service (CAANS). CAANS leverages recent advances in commodity networking hardware design and programmability to implement consensus protocol logic in network devices. CAANS provides a complete Paxos protocol, is a dropin replacement for software-based implementations of Paxos, makes no restrictions on network topologies, and is implemented in a higher-level, data-plane programming language, allowing for portability across a range of target devices. At the same time, CAANS significantly increases throughput and reduces latency for consensus operations. Consensus logic executing in hardware can transmit consensus messages at line speed, with latency only slightly higher than simply forwarding packets. Report Info

Question: What is consensus protocol?


Underwater Image Super-Resolution by Descattering and Fusion. Underwater images are degraded due to scatters and absorption, resulting in low contrast and color distortion. In this paper, a novel self-similarity-based method for descattering and super resolution (SR) of underwater images is proposed. The traditional approach of preprocessing the image using a descattering algorithm, followed by application of an SR method, has the limitation that most of the high-frequency information is lost during descattering. Consequently, we propose a novel high turbidity underwater image SR algorithm. We first obtain a high resolution (HR) image of scattered and descattered images by using a self-similarity-based SR algorithm. Next, we apply a convex fusion rule for recovering the final HR image. The super-resolved images have a reasonable noise level after descattering and demonstrate visually more pleasing results than conventional approaches. Furthermore, numerical metrics demonstrate that the proposed algorithm shows a consistent improvement and that edges are significantly enhanced.

Question: How to super-resolve underwater images?


Talking to machines (statistically speaking). Statistical methods have long been the dominant approach in speech recognition and probabilistic modelling in ASR is now a mature technology. The use of statistical methods in other areas of spoken dialogue is however more recent and rather less mature. This paper reviews spoken dialogue systems from a statistical modelling perspective. The complete system is first presented as a partially observable Markov decision process. The various sub-components are then exposed by introducing appropriate intermediate variables. Samples of existing work are reviewed within this framework, including dialogue control and optimisation, semantic interpretation, goal detection, natural language generation and synthesis.

Question: What is a partially observable Markov decision process?


A survey of trust and reputation systems for online service provision. Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyze the current trends and developments in this area, and to propose a research agenda for trust and reputation systems. Josang, A., R. Ismal, and C. Boyd (2007). A Survey of Trust and Reputation Systems for Online Service Provision, Journal of Decision Support Systems, 43(2):618-644 Citation: Outline of Presentation • Section 1: Introduction • Section 2: Define trust and reputation • Section 3: Trust and Reputation relationship as security mechanisms • Section 4: Collaborative filtering and reputation • Section 5: Trust Classes • Section 6: Four categories of reputation and trust semantics • Section 7: Centralized and distributed architectures for reputation Outline of Presentation Continued • Section 8: Reputation computation methods (Day 1 Goal) • Section 9: Reputation systems used in commercial appl

Question: What is the purpose of this article?


Data privacy through optimal k-anonymization. Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimal k-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a non-trivial dataset under a general model of the problem.

Question: What is the optimal k-anonymization of a dataset?


68 Implementing Dubins Airplane Pathson Fixed-Wing UAVs !. A well-known path-planning technique for mobile robots or planar aerial vehicles is to use Dubins paths, which are minimum-distance paths between two configurations subject to the constraints of the Dubins car model. An extension of this method to a three-dimensional Dubins airplane model has recently been proposed. This chapter builds on that work showing a complete architecture for implementing Dubins airplane paths on small fixed-wing UAVs. The existing Dubins airplane model is modified to be more consistent with the kinematics of a fixed-wing aircraft. The chapter then shows how a recently proposed vector-field method can be used to design a guidance law that causes the Dubins airplane model to follow straight-line and helical paths. Dubins airplane paths are more complicated than Dubins car paths because of the altitude component. Based on the difference between the altitude of the start and end configurations, Dubins airplane paths can be classified as low, medium, or high altitude gain. While for medium and high altitude gain there are many different Dubins airplane paths, this chapter proposes selecting the path that maximizes the average altitude throughout the maneuver. The proposed architecture is implemented on a six degree-of-freedom Matlab/Simulink simulation of an Aerosonde UAV, and results from this simulation demonstrate the effectiveness of the technique. 68.

Question: How many degrees of freedom does a fixed-wing UAV have?


An improved one-time password authentication scheme. The rapid development of the Internet facilitates our lives in many aspects. More and more business will be done through Internet. Under such circumstances, enough attention must be given to the information security, of which the identity authentication is one important problem. In the traditional authentication scheme, the user provides the username and static password to service provider, but there are some inherent shortcomings of this method-static passwords maybe guessed, forgotten, and eavesdropped. One-Time Password (OTP) is considered as the strongest authentication scheme among all password-based solutions. In this paper, a novel two-factor authentication scheme based OTP is proposed. The scheme not only satisfies the mutual authentication between the user and service provider, but also presents higher security and lower computational cost than traditional schemes based OTP.

Question: What is the difference between the traditional authentication scheme and the proposed scheme?


Industrial Management & Data Systems Transformational leadership and innovative work behavior. Purpose – The purpose of this paper is to explore the mediating role of psychological empowerment and the moderating role of self-construal (independent and interdependent) on the relationship between transformational leadership and employees' innovative work behavior (IWB). Design/methodology/approach – A total of 639 followers and 87 leaders filled out questionnaires from cross-industry sample of five most innovative companies of China. Structural equation modeling was used to analyze the relations. Findings – Results revealed that psychological empowerment mediated the relationship between transformational leadership and IWB. The research established that transformational leadership positively influences IWB which includes idea generation as well as idea implementation. The results also showed that the relationship between transformational leadership and IWB was stronger among employees with a higher interdependent self-construal and a lower independent self-construal. Originality/value – This study adds to IWB literature by empirically testing the moderating role of self-construal and the mediating role of psychological empowerment on transformational leadership-IWB link.

Question: What is the purpose of this paper?


Security and compliance challenges in complex IT outsourcing arrangements: A multi-stakeholder perspective. Complex IT outsourcing arrangements promise numerous benefits such as increased cost predictability and reduced costs, higher flexibility and scalability upon demand. Organizations trying to realize these benefits, however, face several security and compliance challenges. In this article, we investigate the pressure to take action with respect to such challenges and discuss avenues toward promising responses. We collected perceptions on security and compliance challenges from multiple stakeholders by means of a series of interviews and an online survey, first, to analyze the current and future relevance of the challenges as well as potential adverse effects on organizational performance and, second, to discuss the nature and scope of potential responses. The survey participants confirmed the current and future relevance of the six challenges auditing clouds, managing heterogeneity of services, coordinating involved parties, managing relationships between clients and vendors, localizing and migrating data and coping with lack of security awareness. Additionally, they perceived these challenges as affecting organizational performance adversely in case they are not properly addressed. Responses in form of organizational measures were considered more promising than technical ones concerning all challenges except localizing and migrating data, for which the opposite was true. Balancing relational and contractual governance as well as employing specific client and vendor capabilities is essential for the success of IT outsourcing arrangements, yet do not seem sufficient to overcome the investigated challenges. Innovations connecting the technical perspective of utility software with the business perspective of application software relevant for security and compliance management, however, nourish the hope that the benefits associated with complex IT outsourcing arran

Question: What are the security and compliance challenges in complex IT outsourcing arrangements?


Enhancing information retrieval and resource discovery from data using the Semantic Web. Data are everywhere. Often the sheer quantities of data that is stored or archived in repositories or digital libraries make it difficult to navigate data; information in the data is obscured, particularly where we have Big Data. Traditionally, we record metadata on our data items to assist data classification and some information retrieval. The Semantic Web enables us to further unlock and enrich our data, by exploring how different data are related or connected. Using ontologies and Linked Data we can declare, navigate and discover semantic relationships. Relationships exist both locally, within the data, and globally, such that we can enhance our data with information retrieved from a wider context. To illustrate how the application of Semantic Web technologies aids data discovery and information retrieval, I discuss two case studies: (1) Sharing Ancient Wisdoms (SAWS), a Dynamic Library of information on selected ancient wisdom literature; and (2) the DEFRA DTC archive, a repository of data about freshwater quality in the UK.

Question: What is a Dynamic Library of information on selected ancient wisdom literature?


Avionic Data acquisition system using MIL STD 1553B controller with IRIG-B timecode decoder. Many critical parameters of rockets and satellite are obtained by means of telemetry system present onboard. For meaningful analysis of data these parameters are to be time stamped. This is achieved by means IRIG B time code proposed by Inter-range instrumentation group. The data obtained need to be transmitted to various sub systems present in ground. Bus based configuration is planned for this inter connection. This is achieved by MIL STD 1553B protocol. A data bus is used to provide a medium for the exchange of data and information between various systems. The MIL STD 1553B bus consists of Bus Controller, Remote Terminals and transmission media. The data received from telemetry system are Manchester encoded signals which needs to be decoded. The objective of the project was to design and develop MIL STD 1553B Bus controller to transmit command and data words to various other remote terminals. An IRIG B time decoder module was developed to decode the timing information transmitted from a reference so that the Data acquisition system can be used to process the data worldwide. A Manchester Data Decoder for decoding the data was developed. The system had better synchronization capability and clock independence.

Question: What is the purpose of the project?


Decentralized Control for Parallel Operation of Distributed Generation Inverters Using Resistive Output Impedance. In this paper, a novel wireless load-sharing controller for islanding parallel inverters in an ac-distributed system is proposed. This paper explores the resistive output impedance of the parallel-connected inverters in an island microgrid. The control loops are devised and analyzed, taking into account the special nature of a low-voltage microgrid, in which the line impedance is mainly resistive and the distance between the inverters makes the control intercommunication between them difficult. In contrast with the conventional droop-control method, the proposed controller uses resistive output impedance, and as a result, a different control law is obtained. The controller is implemented by using a digital signal processor board, which only uses local measurements of the unit, thus increasing the modularity, reliability, and flexibility of the distributed system. Experimental results are provided from two 6-kVA inverters connected in parallel, showing the features of the proposed wireless control

Question: How to control the inverters in parallel?


Food Balance Estimation by Using Personal Dietary Tendencies in a Multimedia Food Log. We have investigated the "FoodLog" multimedia food-recording tool, whereby users upload photographs of their meals and a food diary is constructed using image-processing functions such as food-image detection and food-balance estimation. In this paper, following a brief introduction to FoodLog, we propose a Bayesian framework that makes use of personal dietary tendencies to improve both food-image detection and food-balance estimation. The Bayesian framework facilitates incremental learning. It incorporates three personal dietary tendencies that influence food analysis: likelihood, prior distribution, and mealtime category. In the evaluation of the proposed method using images uploaded to FoodLog, both food-image detection and food-balance estimation are improved. In particular, in the food-balance estimation, the mean absolute error is significantly reduced from 0.69 servings to 0.28 servings on average for two persons using more than 200 personal images, and 0.59 servings to 0.48 servings on average for four persons using 100 personal images. Among the works analyzing food images, this is the first to make use of statistical personal bias to improve the performance of the analysis.

Question: How does the Bayesian framework improve food-balance estimation?


A framework for easing the development of applications embedding answer set programming. Answer Set Programming (ASP) is a well-established declarative problem solving paradigm which became widely used in AI and recognized as a powerful tool for knowledge representation and reasoning (KRR), especially for its high expressiveness and the ability to deal also with incomplete knowledge.  Recently, thanks to the availability of a number of robust and efficient implementations, ASP has been increasingly employed in a number of different domains, and used for the development of industrial-level and enterprise applications. This made clear the need for proper development tools and interoperability mechanisms for easing interaction and integration with external systems in the widest range of real-world scenarios, including mobile applications and educational contexts.  In this work we present a framework for integrating the KRR capabilities of ASP into generic applications. We show the use of the framework by illustrating proper specializations for some relevant ASP systems over different platforms, including the mobile setting; furthermore, the potential of the framework for educational purposes is illustrated by means of the development of several ASP-based applications.

Question: What is a framework for easing the development of applications embedding answer set programming?


A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments. Current progress in wearable and implanted health monitoring technologies has strong potential to alter the future of healthcare services by enabling ubiquitous monitoring of patients. A typical health monitoring system consists of a network of wearable or implanted sensors that constantly monitor physiological parameters. Collected data are relayed using existing wireless communication protocols to a base station for additional processing. This article provides researchers with information to compare the existing low-power communication technologies that can potentially support the rapid development and deployment of WBAN systems, and mainly focuses on remote monitoring of elderly or chronically ill patients in residential environments.

Question: What is the purpose of this article?


Fault Detection, Isolation, and Service Restoration in Distribution Systems: State-of-the-Art and Future Trends. This paper surveys the conceptual aspects, as well as recent developments in fault detection, isolation, and service restoration (FDIR) following an outage in an electric distribution system. This paper starts with a discussion of the rationale for FDIR, and then investigates different areas of the FDIR problem. Recently reported approaches are compared and related to discussions on current practices. This paper then addresses some of the often-cited associated technical, environmental, and economic challenges of implementing self-healing for the distribution grid. The review concludes by pointing toward the need and directions for future research.

Question: What is the difference between fault detection, isolation, and service restoration?


A two-dimensional model for the study of interpersonal attraction.. We describe a model for understanding interpersonal attraction in which attraction can be understood as a product of the initial evaluations we make about others. The model posits that targets are evaluated on two basic dimensions, capacity and willingness, such that affective and behavioral attraction result from evaluations of (a) a target's capacity to facilitate the perceiver's goals/needs and (b) a target's potential willingness to facilitate those goals/needs. The plausibility of the two-dimensional model of attraction is evaluated vis-à-vis the extant literature on various attraction phenomena including the reciprocity of liking effect, pratfall effect, matching hypothesis, arousal effects, and similarity effect. We conclude that considerable evidence across a wide range of phenomena supports the idea that interpersonal attraction is principally determined by inferences about the target's capacity and willingness.

Question: What is the plausibility of the two-dimensional model of attraction?


phishGILLNET — phishing detection methodology using probabilistic latent semantic analysis , AdaBoost , and co-training. Identity theft is one of the most profitable crimes committed by felons. In the cyber space, this is commonly achieved using phishing. We propose here robust server side methodology to detect phishing attacks, called phishGILLNET, which incorporates the power of natural language processing and machine learning techniques. phishGILLNET is a multi-layered approach to detect phishing attacks. The first layer (phishGILLNET1) employs Probabilistic Latent Semantic Analysis (PLSA) to build a topic model. The topic model handles synonym (multiple words with similar meaning), polysemy (words with multiple meanings), and other linguistic variations found in phishing. Intentional misspelled words found in phishing are handled using Levenshtein editing and Google APIs for correction. Based on term document frequency matrix as input PLSA finds phishing and non-phishing topics using tempered expectation maximization. The performance of phishGILLNET1 is evaluated using PLSA fold in technique and the classification is achieved using Fisher similarity. The second layer of phishGILLNET (phishGILLNET2) employs AdaBoost to build a robust classifier. Using probability distributions of the best PLSA topics as features the classifier is built using AdaBoost. The third layer (phishGILLNET3) further expands phishGILLNET2 by building a classifier from labeled and unlabeled examples by employing Co-Training. Experiments were conducted using one of the largest public corpus of email data containing 400,000 emails. Results show that phishGILLNET3 outperforms state of the art phishing detection methods and achieves F-measure of 100%. Moreover, phishGILLNET3 requires only a small percentage (10%) of data be annotated thus saving significant time, labor, and avoiding errors incurred in human annotation.

Question: What is the purpose of phishing?


A reconfigurable low-noise dynamic comparator with offset calibration in 90nm CMOS. This paper presents a reconfigurable, low offset, low noise and high speed dynamic clocked-comparator for medium to high resolution Analog to Digital Converters (ADCs). The proposed comparator reduces the input referred noise by half and shows a better output driving capability when compared with the previous work. The offset, noise and power consumption can be controlled by a clock delay which allows simple reconfiguration. Moreover, the proposed offset calibration technique improves the offset voltage from 11.6mV to 533μV at 1 sigma. A prototype of the comparator is implemented in 90nm 1P8M CMOS with experimental results showing 320μV input referred noise at 1.5GHz with 1.2V supply.

Question: What is a comparator?


Towards a Distributed Large-Scale Dynamic Graph Data Store. In many graph applications, the structure of the graph changes dynamically over time and may require real time analysis. However, constructing a large graph is expensive, and most studies for large graphs have not focused on a dynamic graph data structure, but rather a static one. To address this issue, we propose DegAwareRHH, a high performance dynamic graph data store designed for scaling out to store large, scale-free graphs by leveraging compact hash tables with high data locality. We extend DegAwareRHH for multiple processes and distributed memory, and perform dynamic graph construction on large scale-free graphs using emerging 'Big Data HPC' systems such as the Catalyst cluster at LLNL. We demonstrate that DegAwareRHH processes a request stream 206.5x faster than a state-of-the-art shared-memory dynamic graph processing framework, when both implementations use 24 threads/processes to construct a graph with 1 billion edge insertion requests and 54 million edge deletion requests. DegAwareRHH also achieves a processing rate of over 2 billion edge insertion requests per second using 128 compute nodes to construct a large-scale web graph, containing 128 billion edges, the largest open-source real graph dataset to our knowledge.

Question: How large is the graph?


Security/privacy of wearable fitness tracking IoT devices. As wearable fitness trackers gain widespread acceptance among the general population, there is a concomitant need to ensure that associated privacy and security vulnerabilities are kept to a minimum. We discuss potential vulnerabilities of these trackers, in general, and specific vulnerabilities in one such tracker - Fitbit - identified by Rahman et al. (2013) who then proposed means to address identified vulnerabilities. However, the `fix' has its own vulnerabilities. We discuss possible means to alleviate related issues.

Question: What are the security and privacy issues of wearable fitness trackers?


Robot-aided neurorehabilitation of the upper extremities. Task-oriented repetitive movements can improve muscle strength and movement co-ordination in patients with impairments due to neurological lesions. The application of robotics and automation technology can serve to assist, enhance, evaluate and document the rehabilitation of movements. The paper provides an overview of existing devices that can support movement therapy of the upper extremities in subjects with neurological pathologies. The devices are critically compared with respect to technical function, clinical applicability, and, if they exist, clinical outcomes.

Question: What is the difference between a robot and a robot-aided neurorehabilitation of the upper extremities?


Classification of Brain MR Images using Texture Feature Extraction. Alzheimer's disease (AD), is a degenerative disease which leads to memory loss and problems with thinking and behaviour.AD is a type of dementia which accounts for an estimated 60% to 80% of cases. Accurate diagnosis depends on the identification of discriminative features of AD. Recently, different feature extraction methods are used for the classification of AD. In this paper, we proposed a classification framework to select features, which are extracted using Gray-Level Co-occurrence Matrix (GLCM) method to distinguish between the AD and the Normal Control (NC). In order to evaluate the proposed method, we have performed evaluations on the MRI acquiring from the OASIS database. The proposed method yields an average testing accuracy of 75.71% which indicates that the proposed method can differentiate AD and NC satisfactorily.

Question: How to classify brain MR images using texture feature extraction?


A Web Scraping Methodology for Bypassing Twitter API Restrictions. Retrieving information from social networks is the first and primordial step many data analysis fields such as Natural Language Processing, Sentiment Analysis and Machine Learning. Important data science tasks relay on historical data gathering for further predictive results. Most of the recent works use Twitter API, a public platform for collecting public streams of information, which allows querying chronological tweets for no more than three weeks old. In this paper, we present a new methodology for collecting historical tweets within any date range using web scraping techniques bypassing for Twitter API restrictions.

Question: How to bypass twitter api restrictions?


Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem. In recent years, the task of reformulating natural language queries has received considerable attention from both industry and academic communities. Because of the lexical chasm problem between natural language queries and web documents, if we directly use natural language queries as inputs for retrieval, the results are usually unsatisfactory. In this work, we formulated the task as a translation problem to convert natural language queries into keyword queries. Since the nature language queries users input are diverse and multi-faceted, general encoder-decoder models cannot effectively handle low-frequency words and out-of-vocabulary words. We propose a novel encoder-decoder method with two decoders: the pointer decoder firstly extracts query terms directly from the source text via copying mechanism, then the generator decoder generates query terms using two attention modules simultaneously considering the source text and extracted query terms. For evaluation and training, we also proposed a semi-automatic method to construct a large-scale dataset about natural language query-keyword query pairs. Experimental results on this dataset demonstrated that our model could achieve better performance than the previous state-of-the-art methods.

Question: What is the lexical chasm problem?


Securing wireless sensor networks: a survey. The significant advances of hardware manufacturing technology and the development of efficient software algorithms make technically and economically feasible a network composed of numerous, small, low-cost sensors using wireless communications, that is, a wireless sensor network. WSNs have attracted intensive interest from both academia and industry due to their wide application in civil and military scenarios. In hostile scenarios, it is very important to protect WSNs from malicious attacks. Due to various resource limitations and the salient features of a wireless sensor network, the security design for such networks is significantly challenging. In this article, we present a comprehensive survey of WSN security issues that were investigated by researchers in recent years and that shed light on future directions for WSN security.

Question: What is the security of wireless sensor networks?


A Fiber Tracking Method for Building Patient Specific Dynamic Musculoskeletal Models from Diffusion Tensor Data. A new musculoskeltal simulation primitive, the strand, can provide an efficient way of simulating complicated muscle fiber architectures [1]. In this paper we present a new fiber tracking algorithm based on an energy minimizing active curve that is well suited for building these strand type models from diffusion tensor imaging data. We present fiber tracking results for the Brachioradialis muscle in the left forearm of a male subject. The algorithm produces a space filling arrangement of fibers that are uniformly distributed throughout the muscle and aligned with the underlying muscle fiber direction.

Question: How to build a patient specific dynamic musculoskeletal model from diffusion tensor imaging data?


Memory encoding in Alzheimer's disease: an fMRI study of explicit and implicit memory.. Alzheimer's disease is the most common cause of dementia in older adults. Although the cognitive deficits and pathologic hallmarks of Alzheimer's disease have been well characterized, few functional imaging studies have examined the functional competency of specific brain regions and their relationship to specific behavioural memory deficits in Alzheimer's disease. We used functional MRI (fMRI) to examine seven early stage Alzheimer's disease patients and seven healthy age-matched neurologically normal control subjects during intentional encoding of scenes. Subjects viewed blocks of novel scenes, repeated scenes or baseline. Data were analysed using whole-brain statistical parametric mapping and region of interest approaches. The Alzheimer's disease group demonstrated impaired explicit recognition memory, but intact implicit memory (repetition priming), for the scenes. Alzheimer's disease patients demonstrated a graded deficit in activation for novel versus repeated scenes along the ventral visual stream, with most impaired activation changes in the mesial temporal lobe (MTL) and fusiform regions, most preserved activations in primary visual cortex and variably affected activations in secondary visual areas. Group-level correlations with behavioural measures of explicit memory were found in MTL, lingual and fusiform areas, whereas correlations with priming were found in lateral occipital, parietal and frontal areas. Together, these fMRI findings indicate a dissociation in Alzheimer's disease between impaired explicit memory encoding in MTL and fusiform regions and intact implicit encoding in earlier-stage occipital cortex.

Question: What is the difference between explicit and implicit memory?


Quantum clustering algorithms. By the term "quantization", we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.

Question: What is a quantum clustering algorithm?


3D Face Recognition Using Shapes of Facial Curves. Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The differences between shapes of curves are quantified using lengths of geodesic paths between them on a pre-defined curve shape space. The metric for comparing facial surfaces is a composition of the metric involving individual facial curves. These ideas are demonstrated in the context of face recognition using the nearest-neighbor classifier

Question: How to recognize faces using curves?


A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future. A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to PWs and the communities they serve. Then, we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.

Question: What is a smart wheelchair?


Hand3D: Hand Pose Estimation using 3D Neural Network. We propose a novel 3D neural network architecture for 3D hand pose estimation from a single depth image. Different from previous works that mostly run on 2D depth image domain and require intermediate or post process to bring in the supervision from 3D space, we convert the depth map to a 3D volumetric representation, and feed it into a 3D convolutional neural network(CNN) to directly produce the pose in 3D requiring no further process. Our system does not require the ground truth reference point for initialization, and our network architecture naturally integrates both local feature and global context in 3D space. To increase the coverage of the hand pose space of the training data, we render synthetic depth image by transferring hand pose from existing real image datasets. We evaluation our algorithm on two public benchmarks and achieve the state-ofthe-art performance. The synthetic hand pose dataset will be available.

Question: How to estimate hand pose from a single depth image?


Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy. Deep learning networks have achieved state-of-the-art accuracies on computer vision workloads like image classification and object detection. The performant systems, however, typically involve big models with numerous parameters. Once trained, a challenging aspect for such top performing models is deployment on resource constrained inference systems — the models (often deep networks or wide networks or both) are compute and memory intensive. Low-precision numerics and model compression using knowledge distillation are popular techniques to lower both the compute requirements and memory footprint of these deployed models. In this paper, we study the combination of these two techniques and show that the performance of low-precision networks can be significantly improved by using knowledge distillation techniques. Our approach, Apprentice, achieves state-of-the-art accuracies using ternary precision and 4-bit precision for variants of ResNet architecture on ImageNet dataset. We present three schemes using which one can apply knowledge distillation techniques to various stages of the train-and-deploy pipeline.

Question: What is knowledge distillation?


A PVT-insensitive CMOS output driver with constant slew rate. In this paper, we propose an output driver which has constant slew rate over PVT variation. To make output driver's slew rate to be constant, rising and failing time of pre-driving node of output driver is kept constant by predriver, which has constant pull-up and pull-down path resistance. To make output voltage swing level to be constant, another replica bias circuit was used with CMOS push-pull output driver. HSPICE simulations show that the slew rate is 3.21 V/nS on fast condition and 3.12V/nS on slow condition with 1.4K pre-driver's path resistance. This circuit was implemented with 0.18 /spl mu/m CMOS process.

Question: How to make output driver's slew rate constant?


Oscillator phase noise: a tutorial. Linear time-invariant (LTI) phase noise theories provide important qualitative design insights but are limited in their quantitative predictive power. Part of the difficulty is that device noise undergoes multiple frequency translations to become oscillator phase noise. A quantitative understanding of this process requires abandoning the principle of time invariance assumed in most older theories of phase noise. Fortunately, the noise-to-phase transfer function of oscillators is still linear, despite the existence of the nonlinearities necessary for amplitude stabilization. In addition to providing a quantitative reconciliation between theory and measurement, the time-varying phase noise model presented in this tutorial identifies the importance of symmetry in suppressing the upconversion of 1/f noise into close-in phase noise, and provides an explicit appreciation of cyclostationary effects and AM-PM conversion. These insights allow a reinterpretation of why the Colpitts oscillator exhibits good performance, and suggest new oscillator topologies. Tuned LC and ring oscillator circuit examples are presented to reinforce the theoretical considerations developed. Simulation issues and the accommodation of amplitude noise are considered in appendixes.

Question: What is the difference between a linear time-invariant phase noise model and a linear time-varying phase noise model?


Investigating the usability of iPad mobile tablet in formative assessment of a mathematics course. Formative assessment allows students to judge their learning progress against a given standard or assessment criteria during the course of study. There are challenges related to the generation and delivery of feedback for formative purposes, for instance the students' engagement in their learning, the pedagogy adopted and the usability of the technology tools used in this regard. This paper presents a study of iPad mobile tablet technology-supported peer-to-peer assessment (P2PASS), which is a modern form of formative assessment. The experimental work was undertaken at Kigali Institute of Science and Technology (KIST) in Rwanda. The implementation of P2PASS in this study shows the students' engagement in learning mathematics, and allowed the investigation on the possibilities of improving their mastery of the subject. This paper further discusses the students' attitudes towards the P2PASS process as well as the technological tools used. The usability problems of the iPad applications are discussed, together with the implications of using mobile tablet technology in providing the feedback on the mathematical work.

Question: What is the difference between formative and summative assessment?


Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics. Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce. This is the case of the on-line signature verification task. In this paper, we propose a novel writer-independent on-line signature verification systems based on Recurrent Neural Networks (RNNs) with a Siamese architecture whose goal is to learn a dissimilarity metric from the pairs of signatures. To the best of our knowledge, this is the first time these recurrent Siamese networks are applied to the field of on-line signature verification, which provides our main motivation. We propose both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) systems with a Siamese architecture. In addition, a bidirectional scheme (which is able to access both past and future context) is considered for both LSTM- and GRU-based systems. An exhaustive analysis of the system performance and also the time consumed during the training process for each recurrent Siamese network is carried out in order to compare the advantages and disadvantages for practical applications. For the experimental work, we use the BiosecurID database comprised of 400 users who contributed a total of 11,200 signatures in four separated acquisition sessions. Results achieved using our proposed recurrent Siamese networks have outperformed the state-of-the-art on-line signature verification systems using the same database.

Question: What is the size of the canadian military?


Depth-Gated LSTM. In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a linear dependence between lower and upper layer recurrent units. Importantly, the linear dependence is gated through a gating function, which we call depth gate. This gate is a function of the lower layer memory cell, the input to and the past memory cell of this layer. We conducted experiments and verified that this new architecture of LSTMs was able to improve machine translation and language modeling performances.

Question: How does the depth gate work?


Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking. The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.

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


Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles. This paper describes the application of state-estimation techniques for the real-time prediction of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, approaches based on the well-known Kalman Filter (KF) and Extended Kalman Filter (EKF), are presented, using a generic cell model, to provide correction for offset, drift, and long-term state divergence-an unfortunate feature of more traditional coulomb-counting techniques. The underlying dynamic behavior of each cell is modeled using two capacitors (bulk and surface) and three resistors (terminal, surface, and end), from which the SoC is determined from the voltage present on the bulk capacitor. Although the structure of the model has been previously reported for describing the characteristics of lithium-ion cells, here it is shown to also provide an alternative to commonly employed models of lead-acid cells when used in conjunction with a KF to estimate SoC and an EKF to predict state-of-health (SoH). Measurements using real-time road data are used to compare the performance of conventional integration-based methods for estimating SoC with those predicted from the presented state estimation schemes. Results show that the proposed methodologies are superior to more traditional techniques, with accuracy in determining the SoC within 2% being demonstrated. Moreover, by accounting for the nonlinearities present within the dynamic cell model, the application of an EKF is shown to provide verifiable indications of SoH of the cell pack.

Question: How do you predict the state of health of a lead-acid battery?


Properties of cutaneous mechanoreceptors in the human hand-related to touch sensation. Recordings from single peripheral nerve fibres made it possible to analyse the functional properties of tactile afferent units supplying the glabrous skin of the human hand and to assess directly the relation between impulse discharge and perceptive experiences. The 17,000 tactile units in this skin area of the human hand are of four different types: two fast adapting types, FA I and FA I1 (formerly RA and PC), and two slowly adapting types, SA I and SA 11. The receptive field characteristics and the densities in the skin of the type I units (FA I and SA I) indicate that these account for the detailed spatial resolution that is of paramount importance for the motor skill and the explorative role of the hand. The relationship between the stimulus amplitude and perceived intensity during sustained skin indentations did not match the corresponding stimulus response functions of SA units suggesting non-linear transformations within the central nervous system. These transformations, in turn, appear to vary between subjects. A single impulse in a single FA I unit may be felt when originating from the most important tactile regions of the hand, indicating that the psychophysical detection may be set by the threshold of the sense organs. Moreover, no significant noise seems to be superimposed in the respective central sensory pathways.

Question: What is the function of the human hand?


Flicker-Free Electrolytic Capacitor-Less Universal Input Offline LED Driver With PFC. Recent developments in improving lighting efficiency and cost reduction of LEDs have made them suitable alternatives to the current lighting systems. In this paper, a novel offline structure is proposed to drive LEDs. The proposed circuit has a high-input power factor, high efficiency, a long lifetime, and it produces no flicker. To increase the lifetime of the converter, the proposed circuit does not include any electrolytic capacitors in the power stage. The proposed circuit consists of a transition mode flyback converter in order to improve power factor. Additionally, a buck converter is added to the third winding of the flyback transformer in order to create two parallel paths for the electrical power to feed the output load. DC power reaches the load through one stage (flyback) and ac power reaches the load through two stages of conversion (flyback + buck). Therefore, in the proposed one-and-a-half stage circuit, the efficiency is improved compared to a regular two-stage circuit. Although the proposed structure has some output current ripple, it is low enough (less than 8%) that the structure can be rendered flicker free, as shall be discussed. Principles of operation and design equations are presented as well as experimental results for a 700 mA/20 W universal input prototype.

Question: What is the difference between a flyback converter and a buck converter?


SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems. Direct methods for visual odometry (VO) have gained popularity for their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, in which established feature-based methods succeed instead. Based on these considerations, we propose a semidirect VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients, but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.

Question: What is the difference between direct and semidirect visual odometry?


ontoAGA: Ontology to Support Educational Systems Interoperability. Looking forward to promote the exchange and integration of information in order to build a unique reference model for the universities it is essencial to assure commom concepts of higher education institutions in Brazil. This paper proposes an University Support Management Ontology, named ontoAGA, to help the operation of legacy data in Semantic Web in a standardized manner and to allow the integration of databases with different technologies.To evaluate the research questions two usages cenarios were defined and the results point to the proposal viability. Resumo. Para promover o intercâmbio e a integração de informações de forma a se construir um modelo de referência para as universidades há necessidade de se estruturar conceitos comuns às instituições de ensino superior no Brasil. Este artigo propõe uma ontologia, no domínio da gestão acadêmica nas universidades, denominada ontoAGA, para ajudar a exploração de dados legados na web Semântica de forma padronizada e para possibilitar a integração de bases de dados de diferentes tecnologias. Para avaliação das questões de pesquisa foram definidos dois cenários de uso, cujos resultados apontam para a viabilidade da proposta.

Question: Ontology to support educational systems interoperability.


Community structure in social and biological networks.. A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.

Question: What is the community structure of the world wide web?