Accelerometer Based Joint Step Detection and Adaptive Step Length Estimation Algorithm Using Handheld Devices. —The pedestrian inertial navigation systems are generally based on Pedestrian Dead Reckoning (PDR) algorithm. Considering the physiological characteristics of pedestrian movement, we use the cyclical characteristics and statistics of acceleration waveform and features which are associated with the walking speed to estimate the stride length. Due to the randomness of the pedestrian hand-held habit, the step events cannot always be detected by using the periods of zero velocity updates (ZUPTs). Furthermore, the signal patterns of the sensor could differ significantly depending on the carrying modes and the user's hand motion. Hence, the step detection and the associated adaptive step length model using a handheld device equipped with accelerometer is required to obtain high-accurate measurements. To achieve this goal, a compositional algorithm of empirical formula and Back-propagation neural network by using handheld devices is proposed to estimate the step length, as well as achieve the accuracy of step detection higher than 98%. Furthermore, the proposed joint step detection and adaptive step length estimation algorithm can help much in the development of Pedestrian Navigation Devices (PNDs) based on the handheld inertial sensors.

Question: How to detect the step events using the accelerometer?


Introduction: attachment theory and psychotherapy.. In this introduction to the JCLP: In Session 69(11) issue on attachment theory and psychotherapy, the key points of attachment theory (Bowlby, , , 1981) and its relevance to psychotherapy are briefly described. The aim of this issue is to provide case illustrations of how an attachment theory perspective and principles can expand our understanding of psychotherapy practice.

Question: What is attachment theory?


IAP Guidelines on Rickettsial Diseases in Children.. OBJECTIVE To formulate practice guidelines on rickettsial diseases in children for pediatricians across India.   JUSTIFICATION Rickettsial diseases are increasingly being reported from various parts of India. Due to low index of suspicion, nonspecific clinical features in early course of disease, and absence of easily available, sensitive and specific diagnostic tests, these infections are difficult to diagnose. With timely diagnosis, therapy is easy, affordable and often successful. On the other hand, in endemic areas, where healthcare workers have high index of suspicion for these infections, there is rampant and irrational use of doxycycline as a therapeutic trial in patients of undifferentiated fevers. Thus, there is a need to formulate practice guidelines regarding rickettsial diseases in children in Indian context.   PROCESS A committee was formed for preparing guidelines on rickettsial diseases in children in June 2016. A meeting of consultative committee was held in IAP office, Mumbai and scientific content was discussed. Methodology and results were scrutinized by all members and consensus was reached. Textbook references and published guidelines were also used in few instances to make recommendations. Various Indian and international publications pertinent to present study were collated and guidelines were approved by all committee members. Future updates in these guidelines will be dictated by new scientific data in the field of rickettsial diseases in children.   RECOMMENDATIONS Indian tick typhus and scrub typhus are commonly seen rickettsial diseases in India. It is recommended that practicing pediatricians should be well conversant with compatible clinical scenario, suggestive epidemiological features, differential diagnoses and suggestive laboratory features to make diagnosis and avoid over diagnosis of these infections, as suggested in these guidelines. Doxycycline is the drug of choice and treatm

Question: What is the recommended treatment for rickettsial diseases in children?


Canine computer interaction: towards designing a touchscreen interface for working dogs. Touchscreens can provide a way for service dogs to relay emergency information about their handlers from a home or office environment. In this paper, we build on work exploring the ability of canines to interact with touchscreen interfaces. We observe new requirements for training and explain best practices found in training techniques. Learning from previous work, we also begin to test new dog interaction techniques such as lift-off selection and sliding gestural motions. Our goal is to understand the affordances needed to make touchscreen interfaces usable for canines and help the future design of touchscreen interfaces for assistance dogs in the home.

Question: What is the canadian military?


Real time visual traffic lights recognition based on Spot Light Detection and adaptive traffic lights templates. This paper introduces a new real-time traffic light recognition system for on-vehicle camera applications. This approach has been tested with good results in urban scenes. Thanks to the use of our generic "Adaptive Templates" it would be possible to recognize different kinds of traffic lights from various countries.

Question: What is a generic "Adaptive Templates"?


An Energy-Efficient , Wireless Top-of-Rack to Top-of-Rack Datacenter Network using 60 GHz Links. Datacenters have become the digital backbone of the modern society and consume enormous amounts of power. Significant portion of the power consumption is due to the power hungry switching fabric necessary for communication in the datacenter. Additionally, the complex cabling in traditional datacenters pose design and maintenance challenges and increase the energy cost of the cooling infrastructure by obstructing the flow of chilled air. Recent research on wireless datacenters have proposed interconnecting rows of racks at the top-of-rack (ToR) level via wireless links to eliminate the need for complex network of power hungry routers and cables. Links are established using either highly directional phased array antennas or narrow-beam horned antennas between those ToR entities. ToR-to-ToR wireless links have also been used to augment existing wired networks, improving overall performance characteristics. All these wireless approaches advocate the use of 60GHz line-of-sight (LoS) communication paths between antennas for the establishment of reliable wireless channels. In this work, we explore the feasibility of a ToR-to-ToR wireless network for a small to medium-scale datacenter from the perspective of system-level performance. We evaluate a ToR-to-ToR wireless datacenter network (DCN) for network-level data rate and overall power consumption and compare it to a traditional fat-tree based DCN. We find that the ToR-to-ToR wireless DCN can sustain similar data rates for typical query based applications and consume less power compared to that of traditional datacenters. KeywordsWireless datacenter; Energy Efficiency; 60GHz; IEEE 802.11ad; Top-of-Rack;

Question: What is the feasibility of a ToR-to-ToR wireless datacenter network?


A 300 nW , 15 ppm / C , 20 ppm / V CMOS Voltage Reference Circuit Consisting of Subthreshold MOSFETs. A low-power CMOS voltage reference was developed using a 0.35 m standard CMOS process technology. The device consists of MOSFET circuits operated in the subthreshold region and uses no resistors. It generates two voltages having opposite temperature coefficients and adds them to produce an output voltage with a near-zero temperature coefficient. The resulting voltage is equal to the extrapolated threshold voltage of a MOSFET at absolute zero temperature, which was about 745 mV for the MOSFETs we used. The temperature coefficient of the voltage was 7 ppm/ C at best and 15 ppm/ C on average, in a range from 20 to 80 C. The line sensitivity was 20 ppm/V in a supply voltage range of 1.4–3 V, and the power supply rejection ratio (PSRR) was 45 dB at 100 Hz. The power dissipation was 0.3 W at 80 C. The chip area was 0.05 mm . Our device would be suitable for use in subthreshold-operated, power-aware LSIs.

Question: What is a CMOS voltage reference?


Mathematical Formulas Extraction. As a universal technical language, mathematics has been widely applied in many fields, and it is more accurate than any other languages in describing information. Therefore, numerous mathematical formulas exist in all kinds of documents. There is no doubt that automatic mathematical formulas processing is very important and necessary, of which extract formulas from document images is the first step. In this paper, formulas extraction methods which are not based on recognition results are presented: isolated formulas are extracted based on Parzen window and embedded expressions are extracted based on 2-D structures detection. Experiments show that our methods are very effective in formulas extraction.

Question: What is the first step in extracting mathematical formulas?


Developing an Assessment Method of Active Aging: University of Jyvaskyla Active Aging Scale.. OBJECTIVE To develop an assessment method of active aging for research on older people.   METHOD A multiphase process that included drafting by an expert panel, a pilot study for item analysis and scale validity, a feedback study with focus groups and questionnaire respondents, and a test-retest study. Altogether 235 people aged 60 to 94 years provided responses and/or feedback.   RESULTS We developed a 17-item University of Jyvaskyla Active Aging Scale with four aspects in each item (goals, ability, opportunity, and activity; range 0-272). The psychometric and item properties are good and the scale assesses a unidimensional latent construct of active aging.   DISCUSSION Our scale assesses older people's striving for well-being through activities pertaining to their goals, abilities, and opportunities. The University of Jyvaskyla Active Aging Scale provides a quantifiable measure of active aging that may be used in postal questionnaires or interviews in research and practice.

Question: How do you measure active aging?


Learning to Detect Misleading Content on Twitter. The publication and spread of misleading content is a problem of increasing magnitude, complexity and consequences in a world that is increasingly relying on user-generated content for news sourcing. To this end, multimedia analysis techniques could serve as an assisting tool to spot and debunk misleading content on the Web. In this paper, we tackle the problem of misleading multimedia content detection on Twitter in real time. We propose a number of new features and a new semi-supervised learning event adaptation approach that helps generalize the detection capabilities of trained models to unseen content, even when the event of interest is of different nature compared to that used for training. Combined with bagging, the proposed approach manages to outperform previous systems by a significant margin in terms of accuracy. Moreover, in order to communicate the verification process to end users, we develop a web-based application for visualizing the results.

Question: How to detect misleading content on twitter?


Facial Expression Detection using Filtered Local Binary Pattern Features with ECOC Classifiers and Platt Scaling. We outline a design for a FACS-based facial expression recognition system and describe in more detail the implementation of two of its main components. Firstly we look at how features that are useful from a pattern analysis point of view can be extracted from a raw input image. We show that good results can be obtained by using the method of local binary patterns (LPB) to generate a large number of candidate features and then selecting from them using fast correlation-based filtering (FCBF). Secondly we show how Platt scaling can be used to improve the performance of an error-correcting output code (ECOC) classifier.

Question: How to extract features from an image?


The Fast-Forward Planning System. planner in the Fifth International Conference on Artificial Intelligence Planning and Scheduling (AIPS'00) planning systems competition. Like the well-known HSP system, FF relies on forward search in the state space, guided by a heuristic that estimates goal distances by ignoring delete lists. It differs from HSP in a number of important details. This article describes the algorithmic techniques used in FF in comparison to HSP and evaluates their benefits in terms of run-time and solution-length behavior.

Question: What is the fast-forward planning system?


Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth. When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.

Question: How can we predict the performance of a segmentation method on new data?


Multiple extended object tracking using Gaussian processes. The goal of multi-object tracking is to estimate the number of objects and their states recursively over time. In the presence of extended objects, i.e. objects with an extent that is not negligible in comparison to sensor resolution and which give rise to multiple measurements per time step, the tracking task is even more challenging: To fully utilize all available information and to achieve accurate estimation results, measurement models that are able to represent the object shape as well as algorithms that tackle the aggravated track-to-measurements association problem are necessary. In this work, the Gaussian process measurement model is integrated in the recently developed labeled multi-Bernoulli filter for extended objects. Thus, the ability of Gaussian processes to estimate and represent a wide range of free-from shapes is combined with a principled approach to the multiple extended objects tracking problem. The filter performance is demonstrated for simulated and experimental vehicle tracking using a laser scanner. For this particular application, a new, approximately axis-symmetric covariance function is additionally introduced.

Question: What is the difference between a Gaussian process and a Gaussian mixture model?


Mushroom immunomodulators: unique molecules with unlimited applications.. For centuries, mushrooms have been used as food and medicine in different cultures. More recently, many bioactive compounds have been isolated from different types of mushrooms. Among these, immunomodulators have gained much interest based on the increasing growth of the immunotherapy sector. Mushroom immunomodulators are classified under four categories based on their chemical nature as: lectins, terpenoids, proteins, and polysaccharides. These compounds are produced naturally in mushrooms cultivated in greenhouses. For effective industrial production, cultivation is carried out in submerged culture to increase the bioactive compound yield, decrease the production time, and reduce the cost of downstream processing. This review provides a comprehensive overview on mushroom immunomodulators in terms of chemistry, industrial production, and applications in medical and nonmedical sectors.

Question: What is the difference between lectins and polysaccharides?


Feature Extraction Methods for Time Series Data in SAS ® Enterprise Miner TM. Because time series data have a unique data structure, it is not easy to apply some existing data mining tools directly to the data. For example, in classification and clustering problems, each time point is often considered a variable and each time series is considered an observation. As the time dimension increases, the number of variables also increases, in proportion to the time dimension. Therefore, data mining tasks require some feature extraction techniques to summarize each time series in a form that has a significantly lower dimension. This paper describes various feature extraction methods for time series data that are implemented in SAS Enterprise MinerTM. INTRODUCTION Time series data mining has four major tasks: clustering, indexing, classification, and segmentation. Clustering finds groups of time series that have similar patterns. Indexing finds similar time series in order, given a query series. Classification assigns each time series to a known category by using a trained model. Segmentation partitions time series. Time series data can be considered multidimensional data; this means that there is one observation per time unit, and each time unit makes one dimension. Some common data mining techniques are used for the tasks. However, in the real world, each time series is usually a high-dimensional sequence, such as stock market data whose prices change over time or data collected by a medical device. When the time unit is seconds, the dimensions of data that accumulate in just one hour are 3,600. Moreover, the dimensions are highly correlated to one another. The number of variables in training data increases proportionally as the time dimension increases. Most existing data mining tools cannot be used efficiently on time series data without a dimension reduction. Therefore, a dimension reduction is required through feature extraction techniques that map each time series to

Question: What is the difference between time series and multidimensional data?


Web Spam Taxonomy. Web spamming refers to actions intended to mislead search engines into ranking some pages higher than they deserve. Recently, the amount of web spam has increased dramatically, leading to a degradation of search results. This paper presents a comprehensive taxonomy of current spamming techniques, which we believe can help in developing appropriate countermeasures.

Question: What is web spam?


Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neural mechanisms of cognitive control enable us to initiate, coordinate and update behaviour. Central to successful control is the ability to suppress actions that are no longer relevant or required. In this article, we review the contribution of cognitive neuroscience, molecular genetics and clinical investigations to understanding how response inhibition is mediated in the human brain. In Section 1, we consider insights into the neural basis of inhibitory control from the effects of neural interference, neural dysfunction, and drug addiction. In Section 2, we explore the functional specificity of inhibitory mechanisms among a range of related processes, including response selection, working memory, and attention. In Section 3, we focus on the contribution of response inhibition to understanding flexible behaviour, including the effects of learning and individual differences. Finally, in Section 4, we propose a series of technical and conceptual objectives for future studies addressing the neural basis of inhibition.

Question: What is the neural basis of response inhibition?


Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke.. OBJECTIVE To compare the effects of robot-assisted movement training with conventional techniques for the rehabilitation of upper-limb motor function after stroke.   DESIGN Randomized controlled trial, 6-month follow-up.   SETTING A Department of Veterans Affairs rehabilitation research and development center.   PARTICIPANTS Consecutive sample of 27 subjects with chronic hemiparesis (>6mo after cerebrovascular accident) randomly allocated to group.   INTERVENTIONS All subjects received twenty-four 1-hour sessions over 2 months. Subjects in the robot group practiced shoulder and elbow movements while assisted by a robot manipulator. Subjects in the control group received neurodevelopmental therapy (targeting proximal upper limb function) and 5 minutes of exposure to the robot in each session.   MAIN OUTCOME MEASURES Fugl-Meyer assessment of motor impairment, FIMtrade mark instrument, and biomechanic measures of strength and reaching kinematics. Clinical evaluations were performed by a therapist blinded to group assignments.   RESULTS Compared with the control group, the robot group had larger improvements in the proximal movement portion of the Fugl-Meyer test after 1 month of treatment (P<.05) and also after 2 months of treatment (P<.05). The robot group had larger gains in strength (P<.02) and larger increases in reach extent (P<.01) after 2 months of treatment. At the 6-month follow-up, the groups no longer differed in terms of the Fugl-Meyer test (P>.30); however, the robot group had larger improvements in the FIM (P<.04).   CONCLUSIONS Compared with conventional treatment, robot-assisted movements had advantages in terms of clinical and biomechanical measures. Further research into the use of robotic manipulation for motor rehabilitation is justified.

Question: What is the difference between robot-assisted movement training and conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke?


Spectrum Sensing in Cognitive Radio Networks. The rising number and capacity requirements of wireless systems bring increasing demand for RF spectrum. Cognitive radio (CR) system is an emerging concept to increase the spectrum efficiency. CR system aims to enable opportunistic usage of the RF bands that are not occupied by their primary licensed users in spectrum overlay approach. In this approach, the major challenge in realizing the full potential of CR systems is to identify the spectrum opportunities in the wide band regime reliably and optimally. In the spectrum underlay approach, CR systems enable dynamic spectrum access by co-existing and transmitting simultaneously with licensed primary users without creating harmful interference to them. In this case, the challenge is to transmit with low power so as not to exceed the tolerable interference level to the primary users. Spectrum sensing and estimation is an integral part of the CR system, which is used to identify the spectrum opportunities in spectrum overlay and to identify the interference power to primary users in spectrum underlay approach. In this chapter, the authors present a comprehensive study of signal detection techniques for spectrum sensing proposed for CR systems. Specifically, they outline the state of the art research results, challenges, and future perspectives of spectrum sensing in CR systems, and also present a comparison of different methods. With this chapter, readers can have a comprehensive insight of signal processing methods of spectrum sensing for cognitive radio networks and the ongoing research and development in this area.

Question: What is the difference between spectrum overlay and spectrum underlay?


Tagging The Web: Building A Robust Web Tagger with Neural Network. In this paper, we address the problem of web-domain POS tagging using a twophase approach. The first phase learns representations that capture regularities underlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters of the neural network are trained using guided learning in the second phase. Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers.

Question: What is a twophase approach?


Application of COReS to Compute Research Papers Similarity. Over the decades, the immense growth has been reported in research publications due to continuous developments in science. To date, various approaches have been proposed that find similarity between research papers by applying different similarity measures collectively or individually based on the content of research papers. However, the contemporary schemes are not conceptualized enough to find related research papers in a coherent manner. This paper is aimed at finding related research papers by proposing a comprehensive and conceptualized model via building ontology named COReS: Content-based Ontology for Research Paper Similarity. The ontology is built by finding the explicit relationships (i.e., super-type sub-type, disjointedness, and overlapping) between state-of-the-art similarity techniques. This paper presents the applications of the COReS model in the form of a case study followed by an experiment. The case study uses InText citation-based and vector space-based similarity measures and relationships between these measures as defined in COReS. The experiment focuses on the computation of comprehensive similarity and other content-based similarity measures and rankings of research papers according to these measures. The obtained Spearman correlation coefficient results between ranks of research papers for different similarity measures and user study-based measure, justify the application of COReS for the computation of document similarity. The COReS is in the process of evaluation for ontological errors. In the future, COReS will be enriched to provide more knowledge to improve the process of comprehensive research paper similarity computation.

Question: What is the difference between a research paper and a research paper?


A trust-risk perspective on social commerce use: an examination of the biasing role of habit. Purpose – Social commerce websites have emerged as new platforms which integrate social media features with traditional commerce aspects to enhance users' purchasing experience. The purpose of this paper is to examine the role of social factors such as trust toward site members in determining users' trust and risk evaluations, and the role of social commerce use habit in attenuating users' rational risk and trust considerations for developing purchase intentions. Design/methodology/approach – Relying on the risk deterrence perspective and rational decision-making models involving trust and habit, this study proposes a set of hypotheses which are tested through analyzing survey data using structural equation modeling techniques. Findings – Results show that commerce risk deters purchasing intentions; trust toward the social commerce website increases users' purchasing intentions; and trust toward the site members indirectly increases purchasing intentions. Moreover, trust toward site members reduces perceived commerce risk. Findings also show that habit modulates trust and risk effects on use decisions in this context; habit moderates (weakens) the relationships between commerce risk and purchase intentions and between trust toward the social commerce site and purchase intentions. Originality/value – This study extends theories on decision making in social settings such as in the case of social commerce. It does so by accounting for unique modulating effects of habit in social settings in which social aspects such as trust in other members and risk are unique and important.

Question: How does social commerce affect trust and risk?


Normative data for the Rey-Osterrieth and the Taylor complex figure tests in Quebec-French people.. The Rey-Osterrieth (ROCF) and Taylor (TCF) complex figure tests are widely used to assess visuospatial and constructional abilities as well as visual/non-verbal memory. Normative data adjusted to the cultural and linguistic reality of older Quebec-French individuals is still nonexistent for these tests. In this article, we report the results of two studies that aimed to establish normative data for Quebec-French people (aged at least 50 years) for the copy, immediate recall, and delayed recall trials of the ROCF (Study 1) and the TCF (Study 2). For both studies, the impact of age, education, and sex on test performance was examined. Moreover, the impact of copy time on test performance, the impact of copy score on immediate and delayed recall score, and the impact of immediate recall score on delayed recall performance were examined. Based on regression models, equations to calculate Z scores for copy and recall scores are provided for both tests.

Question: Normative data for the Rey-Osterrieth and the Taylor complex figure tests in Quebec-French people.


An investigation of factors associated with the health and well-being of HIV-infected or HIV-affected older people in rural South Africa. BACKGROUND Despite the severe impact of HIV in sub-Saharan Africa, the health of older people aged 50+ is often overlooked owing to the dearth of data on the direct and indirect effects of HIV on older people's health status and well-being. The aim of this study was to examine correlates of health and well-being of HIV-infected older people relative to HIV-affected people in rural South Africa, defined as participants with an HIV-infected or death of an adult child due to HIV-related cause.   METHODS Data were collected within the Africa Centre surveillance area using instruments adapted from the World Health Organization (WHO) Study on global AGEing and adult health (SAGE). A stratified random sample of 422 people aged 50+ participated. We compared the health correlates of HIV-infected to HIV-affected participants using ordered logistic regressions. Health status was measured using three instruments: disability index, quality of life and composite health score.   RESULTS Median age of the sample was 60 years (range 50-94). Women HIV-infected (aOR 0.15, 95% confidence interval (CI) 0.08-0.29) and HIV-affected (aOR 0.20, 95% CI 0.08-0.50), were significantly less likely than men to be in good functional ability. Women's adjusted odds of being in good overall health state were similarly lower than men's; while income and household wealth status were stronger correlates of quality of life. HIV-infected participants reported better functional ability, quality of life and overall health state than HIV-affected participants.   DISCUSSION AND CONCLUSIONS The enhanced healthcare received as part of anti-retroviral treatment as well as the considerable resources devoted to HIV care appear to benefit the overall well-being of HIV-infected older people; whereas similar resources have not been devoted to the general health needs of HIV uninfe

Question: What is the difference between HIV-infected and HIV-affected people?


Ring oscillators for CMOS process tuning and variability control. Test structures utilizing ring oscillators to monitor MOSFET ac characteristics for digital CMOS circuit applications are described. The measurements provide information on the average behavior of sets of a few hundred MOSFETs under high speed switching conditions. The design of the ring oscillators is specifically tailored for process centering and monitoring of variability in circuit performance in the manufacturing line as well as in the product. The delay sensitivity to key MOSFET parameter variations in a variety of ring oscillator designs is studied using a compact model for partially depleted silicon on insulator(PD-SOI) technology, but the analysis is equally valid for conventional bulk Si technology. Examples of hardware data illustrating the use of this methodology are taken primarily from experimental hardware in the 90-nm CMOS technology node in PD-SOI. The design and data analysis techniques described here allow very rapid investigation of the sources of variations in circuit delays.

Question: What is the difference between a ring oscillator and a delay line?


Neural Turing Machines. We extend the capabilities of neural networks by coupling them to external memory resources, which they can interact with by attentional processes. The combined system is analogous to a Turing Machine or Von Neumann architecture but is differentiable end-toend, allowing it to be efficiently trained with gradient descent. Preliminary results demonstrate that Neural Turing Machines can infer simple algorithms such as copying, sorting, and associative recall from input and output examples.

Question: What is a neural Turing machine?


Imago: presence and emotion in virtual reality. As virtual reality becomes available to a wider audience, filmmakers are being challenged to create ever more immersive and creative narratives for this quickly evolving medium. While many of the early attempts at virtual reality filmmaking have been devoted to the creation of quick paced and aw-inspiring experiences, filmmakers such as Chris Milk and Oculus Story Studio have been more focused on more story driven experiences, using the medium to tell narratives in a new and exciting way.  Project Hypnos was created at Carnegie Mellon University to explore immersive narrative filmmaking techniques for virtual reality. The resulting proof-of-concept film, Imago, was created using the techniques developed during this research. Imago plays with the concept of a viewer's presence in a film in an attempt to elicit both negative and positive emotions. The result is a story-driven experience that comes to life through live-action drama, dance, and abstract CG imagery.

Question: What is the difference between a proof-of-concept film and a proof-of-concept?


Automatic Room Detection and Room Labeling from Architectural Floor Plans. This paper presents an automatic system for analyzing and labeling architectural floor plans. In order to detect the locations of the rooms, the proposed systems extracts both, structural and semantic information from given floor plans. Furthermore, OCR is applied on the text layer to retrieve the meaningful room labeling. Finally, a novel post-processing is proposed to split rooms into several sub-regions if several semantic rooms share the same physical room. Our fully automatic system is evaluated on a publicly available dataset of architectural floor plans. In our experiments, we could clearly outperform other state-of-the-art approaches for room detection.

Question: How does the system detect the rooms?


HAWK : Halting Anomaly with Weighted ChoKing to Rescue Well-Behaved TCP Sessions from Shrew DoS Attacks. 1 Manuscript submitted to ICDCS 2005 on October 8, 2004. All rights reserved by the authors. This research was supported by an NSF ITR Research Grant under contract number ACI-0325409. Corresponding Author: Kai Hwang, Email: kaihwang@usc.edu, Tel: 213-740-4470, Fax: 213-740-4418. Abstract—High availability in network services is crucial for effective large-scale distributed computing. While denial-ofservice (DoS) attacks through massive packet flooding have baffled researchers for years, a new type of even more detrimental attack—shrew attacks (periodic intensive packet bursts with low average rate)—has recently been identified. Shrew attacks can significantly degrade well-behaved TCP sessions and are very difficult to detect, not to mention defend against, due to its low average rate. We propose a new stateful adaptive queue management technique called HAWK (Halting Anomaly with Weighted choKing) which works by judiciously identifying malicious shrew packet flows using a small flow table and dropping such packets decisively to halt the attack such that well-behaved TCP sessions can re-gain their bandwidth shares. Our NS-2 based extensive performance results indicate that HAWK is highly agile in the sense that it can swiftly (e.g., within 5 seconds) identify and then halt shrew attacks with a flow table of tiny size (e.g., 600 bytes). Thus, HAWK is readily amenable to practical implementation in a real environment.

Question: What is a shrew attack?


Demystifying automotive safety and security for semiconductor developer. Advances in both semiconductor and automotive industry are today enabling the next generation of vehicles with significant electronics content than ever before. Consumers can now avail vehicle offerings in the form of Electric and Hybrid Electric Vehicles (EV/HEV) that have improved fuel efficiency, provide enhanced driver-passenger comfort and experience through Advance Driver Assistance Systems (ADAS) and car infotainment systems, and more. Increasing electronics, software content, and connectivity drive two consumer concerns — "functional safety" and "security" — to the forefront. In this tutorial, we dissect these concerns from an end application perspective and translate the system level requirements and standards into semiconductor development requirements. We indicate both current and emerging practices, and touch upon areas requiring new or optimal design and electronic design automation (EDA) solutions. While functional safety is the primary focus for deep-dive in this tutorial, we also examine key facets of automotive security which is now emerging as a critical area for further understanding and standardization.

Question: What is the difference between functional safety and security?


BAG: Managing GPU as Buffer Cache in Operating Systems. This paper presents the design, implementation and evaluation of BAG, a system that manages GPU as the buffer cache in operating systems. Unlike previous uses of GPUs, which have focused on the computational capabilities of GPUs, BAG is designed to explore a new dimension in managing GPUs in heterogeneous systems where the GPU memory is an exploitable but always ignored resource. With the carefully designed data structures and algorithms, such as concurrent hashtable, log-structured data store for the management of GPU memory, and highly-parallel GPU kernels for garbage collection, BAG achieves good performance under various workloads. In addition, leveraging the existing abstraction of the operating system not only makes the implementation of BAG non-intrusive, but also facilitates the system deployment.

Question: What is the design of BAG?


Link Prediction via Matrix Factorization. We propose to solve the link prediction problem in graphs using a supervised matrix factorization approach. The model learns latent features from the topological structure of a (possibly directed) graph, and is shown to make better predictions than popular unsupervised scores. We show how these latent features may be combined with optional explicit features for nodes or edges, which yields better performance than using either type of feature exclusively. Finally, we propose a novel approach to address the class imbalance problem which is common in link prediction by directly optimizing for a ranking loss. Our model is optimized with stochastic gradient descent and scales to large graphs. Results on several datasets show the efficacy of our approach.

Question: What is the link prediction problem?


Why do humans reason? Arguments for an argumentative theory.. Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views. This explains the notorious confirmation bias. This bias is apparent not only when people are actually arguing, but also when they are reasoning proactively from the perspective of having to defend their opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs to persist. Proactively used reasoning also favors decisions that are easy to justify but not necessarily better. In all these instances traditionally described as failures or flaws, reasoning does exactly what can be expected of an argumentative device: Look for arguments that support a given conclusion, and, ceteris paribus, favor conclusions for which arguments can be found.

Question: What is the function of reasoning?


Automatic scoring of pronunciation quality. We present a paradigm for the automatic assessment of pronunciation quality by machine. In this scoring paradigm, both native and nonnative speech data is collected and a database of human-expert ratings is created to enable the development of a variety of machine scores. We ®rst discuss issues related to the design of speech databases and the reliability of human ratings. We then address pronunciation evaluation as a prediction problem, trying to predict the grade a human expert would assign to a particular skill. Using the speech and the expert-ratings databases, we build statistical models and introduce di€erent machine scores that can be used as predictor variables. We validate these machine scores on the Voice Interactive Language Training System (VILTS) corpus, evaluating the pronunciation of American speakers speaking French and we show that certain machine scores, like the log-posterior and the normalized duration, achieve a correlation with the targeted human grades that is comparable to the human-to-human correlation when a sucient amount of speech data is available. Ó 2000 Elsevier Science B.V. All rights reserved.

Question: How to automatically score pronunciation quality?


Literature Review of Automatic Multiple Documents Text Summarization. For the blessing of World Wide Web, the corpus of online information is gigantic in its volume. Search engines have been developed such as Google, AltaVista, Yahoo, etc., to retrieve specific information from this huge amount of data. But the outcome of search engine is unable to provide expected result as the quantity of information is increasing enormously day by day and the findings are abundant. So, the automatic text summarization is demanded for salient information retrieval. Automatic text summarization is a system of summarizing text by computer where a text is given to the computer as input and the output is a shorter and less redundant form of the original text. An informative précis is very much helpful in our daily life to save valuable time. Research was first started naively on single document abridgement but recently information is found from various sources about a single topic in different website, journal, newspaper, text book, etc., for which multi-document summarization is required. In this paper, automatic multiple documents text summarization task is addressed and different procedure of various researchers are discussed. Various techniques are compared here that have done for multi-document summarization. Some promising approaches are indicated here and particular concentration is dedicated to describe different methods from raw level to similar like human experts, so that in future one can get significant instruction for further analysis.

Question: What is the difference between a précis and a summary?


Estimating alertness from the EEG power spectrum. In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air traffic control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these fluctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and artificial neural networks, the authors show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEC; measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.

Question: How can we estimate alertness from the EEG power spectrum?


CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only single high-confidence rule. In this study, we propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large database efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted analysis using multiple strong association rules. Our extensive experiments on databases from UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.

Question: What is the classification accuracy of CMAR?


"I am active": effects of a program to promote active aging. BACKGROUND Active aging involves a general lifestyle strategy that allows preservation of both physical and mental health during the aging process. "I am Active" is a program designed to promote active aging by increased physical activity, healthy nutritional habits, and cognitive functioning. The purpose of this study was to assess the effectiveness of this program.   METHODS Sixty-four healthy adults aged 60 years or older were recruited from senior centers and randomly allocated to an experimental group (n=31) or a control group (n=33). Baseline, post-test, and 6-month follow-up assessments were performed after the theoretical-practical intervention. Effect sizes were calculated.   RESULTS At the conclusion of the program, the experimental group showed significant improvement compared with the control group in the following domains: physical activity (falls risk, balance, flexibility, self-efficacy), nutrition (self-efficacy and nutritional status), cognitive performance (processing speed and self-efficacy), and quality of life (general, health and functionality, social and economic status). Although some declines were reported, improvements at follow-up remained in self-efficacy for physical activity, self-efficacy for nutrition, and processing speed, and participants had better nutritional status and quality of life overall.   CONCLUSION Our findings show that this program promotes improvements in domains of active aging, mainly in self-efficacy beliefs as well as in quality of life in healthy elders.

Question: What is the purpose of this study?


Broad-Band High-Power Amplifier Using Spatial Power-Combining Technique. High power, broad bandwidth, high linearity, and low noise are among the most important features in amplifier design. The broad-band spatial power-combining technique addresses all these issues by combining the output power of a large quantity of microwave monolithic integrated circuit (MMIC) amplifiers in a broad-band coaxial waveguide environment, while maintaining good linearity and improving phase noise of the MMIC amplifiers. A coaxial waveguide was used as the host of the combining circuits for broader bandwidth and better uniformity by equally distributing the input power to each element. A new compact coaxial combiner with much smaller size is investigated. Broad-band slotline to microstrip-line transition is integrated for better compatibility with commercial MMIC amplifiers. Thermal simulations are performed and an improved thermal management scheme over previous designs is employed to improve the heat sinking in high-power application. A high-power amplifier using the compact combiner design is built and demonstrated to have a bandwidth from 6 to 17 GHz with 44-W maximum output power. Linearity measurement has shown a high third-order intercept point of 52 dBm. Analysis shows the amplifier has the ability to extend spurious-free dynamic range by 2 3 times. The amplifier also has shown a residual phase floor close to 140 dBc at 10-kHz offset from the carrier with 5–6-dB reductions compared to a single MMIC amplifier it integrates.

Question: What is the broad-band spatial power-combining technique?


Modeling crowdsourcing systems: design and analysis of incentive mechanism and rating system. Over the past few years, we have seen an increasing popularity of crowdsourcing services [5]. Many companies are now providing such services, e.g., Amazon Mechanical Turk [1], Google Helpouts [3], and Yahoo! Answers [8], etc. Briefly speaking, crowdsourcing is an online, distributed problem solving paradigm and business production platform. It uses the power of today's Internet to solicit the collective intelligence of large number of users. Relying on the wisdom of the crowd to solve posted tasks (or problems), crowdsourcing has become a promising paradigm to obtain "solutions" which can have higher quality or lower costs than the conventional method of solving problems via specialized employees or contractors in a company. Typically, a crowdsourcing system operates with three basic components: Users, tasks and rewards. Users are classified into requesters and workers. A user can be a requester or a worker, and in some cases, a user can be a requester/worker at the same time. Requesters outsource tasks to workers and associate each task with certain rewards, which will be granted to the workers who solve the task. Workers, on the other hand, solve the assigned tasks and reply to requesters with solutions, and then take the reward, which can be in form of money [1], entertainment [7] or altruism [8], etc. To have a successful crowdsourcing website, it is pertinent to attract high volume of participation of users (requesters and workers), and at the same time, solutions by workers have to be of high quality. In this paper we design a rating system and a mechanism to encourage users to participate, and incentivize workers to high quality solutions. First, we develop a game-theoretic model to characterize workers' strategic behavior. We then design a class effective incentive mechanisms which consist of a task bundling scheme and a rating system, and pay workers according to s

Question: What is the difference between a crowdsourcing system and a crowdsourcing website?


Compressed Nonparametric Language Modelling. Hierarchical Pitman-Yor Process priors are compelling methods for learning language models, outperforming point-estimate based methods. However, these models remain unpopular due to computational and statistical inference issues, such as memory and time usage, as well as poor mixing of sampler. In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees. Then, we develop an efficient approximate inference scheme in this framework that has a much lower memory footprint compared to full HPYP and is fast in the inference time. The experimental results illustrate that our model can be built on significantly larger datasets compared to previous HPYP models, while being several orders of magnitudes smaller, fast for training and inference, and outperforming the perplexity of the state-of-the-art Modified Kneser-Ney countbased LM smoothing by up to 15%.

Question: What is the size of the compressed suffix tree?


Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module. Emergence of Controlled Environment Agriculture (CEA) ranging from computer controlled water irrigation system to lightning and ventilation has changed the conventional scenario of farming. This paper proposes and demonstrate an economical and easy to use arduino based controlled irrigation system. The designed system deals with various environmental factors such as moisture, temperature and amount of water required by the crops using sensors like water flow sensor, temperature sensor and soil moisture sensor. Datas are collected and received by arduino which can be linked to an interactive website which show the real time values along with the standard values of different factor required by a crop. This allows user to control irrigation pumps and sprinklers from far distance through a website and to meet the standard values which would help the farmer to yield maximum and quality crops. Studies conducted on laboratory prototype suggested the designed system to be applicable which can be implemented.

Question: How to control the irrigation system?


The 2 nd Workshop on Arabic Corpora and Processing Tools 2016 Theme :. Speech datasets and corpora are crucial for both developing and evaluating accurate Natural Language Processing systems. While Modern Standard Arabic has received more attention, dialects are drastically underestimated, even they are the most used in our daily life and the social media, recently. In this paper, we present the methodology of building an Arabic Speech Corpus for Algerian dialects, and the preliminary version of that dataset of dialectal arabic speeches uttered by Algerian native speakers selected from different Algeria's departments. In fact, by means of a direct recording way, we have taken into acount numerous aspects that foster the richness of the corpus and that provide a representation of phonetic, prosodic and orthographic varieties of Algerian dialects. Among these considerations, we have designed a rich speech topics and content. The annotations provided are some useful information related to the speakers, time-aligned orthographic word transcription. Many potential uses can be considered such as speaker/dialect identification and computational linguistic for Algerian sub-dialects. In its preliminary version, our corpus encompasses 17 sub-dialects with 109 speakers and more than 6 K utterances.

Question: What is the size of the corpus?


Proposal Encoding and Decoding Graph Representations of Natural Language.

Question: What is the difference between a graph and a tree?


Modeling Complex Air Traffic Management Systems. In this work, we propose the use of multi-agent system (MAS) models as the basis for predictive reasoning about various safety conditions and the performance of Air Traffic Management (ATM) Systems. To this end, we describe the engineering of a domain-specific MAS model that provides constructs for creating scenarios related to ATM systems and procedures; we then instantiate the constructs in the ATM model for different scenarios. As a case study we generate a model for a concept that provides the ability to maximize departure throughput at La Guardia airport (LGA) without impacting the flow of the arrival traffic; the model consists of approximately 1.5 hours real time flight data. During this time, between 130 and 150 airplanes are managed by four en-route controllers, three TRACON controllers, and one tower controller at LGA who is responsible for departures and arrivals. The planes are landing at approximately 36 to 40 planes an hour. A key contribution of this work is that the model can be extended to various air-traffic management scenarios and can serve as a template for engineering large-scale models in other domains.

Question: What is the difference between a multi-agent system and a multi-agent model?


Multimodal deep learning for robust RGB-D object recognition. Robust object recognition is a crucial ingredient of many, if not all, real-world robotics applications. This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition. Our architecture is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network. We focus on learning with imperfect sensor data, a typical problem in real-world robotics tasks. For accurate learning, we introduce a multi-stage training methodology and two crucial ingredients for handling depth data with CNNs. The first, an effective encoding of depth information for CNNs that enables learning without the need for large depth datasets. The second, a data augmentation scheme for robust learning with depth images by corrupting them with realistic noise patterns. We present state-of-the-art results on the RGB-D object dataset [15] and show recognition in challenging RGB-D real-world noisy settings.

Question: What is the canadian military?


Regression approaches to voice quality controll based on one-to-many eigenvoice conversion. This paper proposes techniques for flexibly controlling voice quality of converted speech from a particular source speaker based on one-to-many eigenvoice conversion (EVC). EVC realizes a voice quality control based on the manipulation of a small number of parameters, i.e., weights for eigenvectors, of an eigenvoice Gaussian mixture model (EV-GMM), which is trained with multiple parallel data sets consisting of a single source speaker and many pre-stored target speakers. However, it is difficult to control intuitively the desired voice quality with those parametersbecause each eigenvector doesn't usually represent a specific physical meaning. In order to cope with this problem, we propose regression approaches to the EVC-based voice quality controller. The tractable voice quality control of the converted speech is achieved with a low-dimensionalvoice quality control vector capturing specific voice characteristics. We conducted experimental verifications of each of the proposed approaches.

Question: How to control voice quality of converted speech?


Power optimization for FinFET-based circuits using genetic algorithms. Reducing power consumption is one of the important design goals for circuit designers. Power optimization techniques for bulk CMOS-based circuit designs have been extensively studied. As technology scales, FinFET has been proposed as an alternative for bulk CMOS when technology scales beyond 32 nm technology (E.J. Nowak et al., 2004). In this paper, we propose a power optimization framework for FinFET based circuit design, based on genetic algorithms. We exploit the unique feature of independent gate (IG) controls for FinFET devices to reduce the power consumption, and combine with other low power techniques such as multi-Vdd and gate sizing to achieve power optimization for FinFET-based circuits. We use 32 nm PTM FinFET device model (W. Zhao and Y. Cao, 2006) and conduct experiments on ISCAS benchmarks. The experimental results show that our methodology can achieve over 80% power reduction while satisfying the same performance constraints, comparing to the case that all the FinFET transistors are tuned to be the fastest ones.

Question: How to reduce power consumption for FinFET-based circuits?


Automatic fine tuning of cavity filters Automatisk finjustering av kavitetsfilter. Cavity filters are a necessary component in base stations used for telecommunication. Without these filters it would not be possible for base stations to send and receive signals at the same time. Today these cavity filters require fine tuning by humans before they can be deployed. This thesis have designed and implemented a neural network that can tune cavity filters. Different types of design parameters have been evaluated, such as neural network architecture, data presentation and data preprocessing. While the results was not comparable to human fine tuning, it was shown that there was a relationship between error and number of weights in the neural network. The thesis also presents some rules of thumb for future designs of neural network used for filter tuning.

Question: How to tune cavity filters?


Rewarded Outcomes Enhance Reactivation of Experience in the Hippocampus. Remembering experiences that lead to reward is essential for survival. The hippocampus is required for forming and storing memories of events and places, but the mechanisms that associate specific experiences with rewarding outcomes are not understood. Event memory storage is thought to depend on the reactivation of previous experiences during hippocampal sharp wave ripples (SWRs). We used a sequence switching task that allowed us to examine the interaction between SWRs and reward. We compared SWR activity after animals traversed spatial trajectories and either received or did not receive a reward. Here, we show that rat hippocampal CA3 principal cells are significantly more active during SWRs following receipt of reward. This SWR activity was further enhanced during learning and reactivated coherent elements of the paths associated with the reward location. This enhanced reactivation in response to reward could be a mechanism to bind rewarding outcomes to the experiences that precede them.

Question: How does the hippocampus store memories?


User Interface Menu Design Performance and User Preferences: A Review and Ways Forward. This review paper is about menus on web pages and applications and their positioning on the user screen. The paper aims to provide the reader with a succinct summary of the major research in this area along with an easy to read tabulation of the most important studies. Furthermore, the paper concludes with some suggestions for future research regarding how menus and their positioning on the screen could be improved. The two principal suggestions concern trying to use more qualitative methods for investigating the issues and to develop in the future more universally designed menus. Keywords—Menus; navigation of interfaces; universal design;

Question: What is the difference between a menu and a navigation bar?


Multisensor data fusion: Target tracking with a doppler radar and an Electro-Optic camera. This paper addresses the problem of multisensor data fusion for target tracking using a Doppler radar with range rate measurements and an Electro-Optic (EO) camera. We present three fusion architectures, named FA1-FA3, to perform data fusion using the above mentioned sensors. FA1 and FA2 are distributed fusion architectures employing the information matrix fusion method with dynamic feedback. In FA1, radar and camera pseudo measurements are formed that allow us to make use of a linear Kalman Filter (KF) for the radar local filter and an Extended Kalman Filter (EKF) for the EO camera local filter. In FA2, the radar and camera measurements are used directly and therefore the system comprises two EKFs. FA3 is a centralised architecture where the data fusion is performed by way of the measurement fusion method. The final contribution of this paper is a performance comparison of these sensor data fusion techniques when making use of range rate measurements. In order to evaluate the performance of the fusion architectures, Monte Carlo simulations are performed and two filter metrics are presented: an absolute metric - the root mean squared error (RMSE) and a performance metric - the average normalised estimation error squared (ANEES). The results show that the fusion architectures presented are accurate, stable and credible.

Question: What is the difference between a radar and an electro-optic camera?


Ranking in context using vector spaces. This paper presents a principled approach to the problem of retrieval in context. The notion of basis of a vector space was introduced previously to describe context. Any basis vectors represents a distinct piece of context, such as time, space, or word meaning. A vector is generated by a basis just as an informative object or an information need is generated in a context. As a consequence a different basis generates a different vector as a different context would generate different information needs or informative objects. Also the Vector Space Model (VSM) describes information needs and informative objects as query vectors and document vectors, respectively. However the VSM assumes that there is a unique basis, which is the set of versors and always generates the same vector provided the same coefficients. Thus a drawback of the VSM is that the vectors are insensitive to context, i.e. they are generated and ranked in the same way independently of the context in which information need and informative objects are. This paper also proposes a function to rank documents in context. Since a basis spans a subspace, which includes all the vectors of object being in the same context, the ranking function is a distance measure between the document vector and the subspace. Even though ranking is still based on an inner product between two vectors, the basic difference is that projection and distance depend on the basis, i.e. on the pieces of context and then ultimately on context. Since an informative object can be produced by different contexts, different bases can arise and then ranking can change. Mathematically, object vector generation is given by x = p1b1 + · · · p k b k = x = B · p, where B is a n × k (k ≤ n) complex matrix and p is a k × 1 real vector. The b's are independent vectors and as such form a basis B of a subspace L(B) of C n. The basis generates all the vectors in L(B) and every vector in it describes an informative

Question: What is the difference between a basis and a basis vector?


Multi-domain spoken language understanding with transfer learning. This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domaindependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems. 2009 Elsevier B.V. All rights reserved.

Question: What is the difference between a triangular-chain structured model and a chain structured model?


Intrusion Detection with Neural Networks. With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to detect illegitimate use is through monitoring unusual user activity. Methods of intrusion detection based on hand-coded rule sets or predicting commands on-line are laborous to build or not very reliable. This paper proposes a new way of applying neural networks to detect intrusions. We believe that a user leaves a 'print' when using the system; a neural network can be used to learn this print and identify each user much like detectives use thumbprints to place people at crime scenes. If a user's behavior does not match his/her print, the system administrator can be alerted of a possible security breech. A backpropagation neural network called NNID (Neural Network Intrusion Detector) was trained in the identification task and tested experimentally on a system of 10 users. The system was 96% accurate in detecting unusual activity, with 7% false alarm rate. These results suggest that learning user profiles is an effective way for detecting intrusions.

Question: What is a neural network?


Online human gesture recognition from motion data streams. Online human gesture recognition has a wide range of applications in computer vision, especially in human-computer interaction applications. Recent introduction of cost-effective depth cameras brings on a new trend of research on body-movement gesture recognition. However, there are two major challenges: i) how to continuously recognize gestures from unsegmented streams, and ii) how to differentiate different styles of a same gesture from other types of gestures. In this paper, we solve these two problems with a new effective and efficient feature extraction method that uses a dynamic matching approach to construct a feature vector for each frame and improves sensitivity to the features of different gestures and decreases sensitivity to the features of gestures within the same class. Our comprehensive experiments on MSRC-12 Kinect Gesture and MSR-Action3D datasets have demonstrated a superior performance than the stat-of-the-art approaches.

Question: How to recognize gestures from motion data streams?


On automatic differentiation. During these last years, the environmental problems have acquired a growing place in our society. It becomes necessary to find a lasting way to the nuclear waste storage. The waste storage is in relation to the characteristics of wastes according to their activity and the life of the radionuclide. After many studies on the topic, led in particular by ANDRA, the storage in deep level is considered as the reference strategy for wastes with high or medium activity and long-lived. We have to make simulations of the radionucleide transport in underground in order to determine the impact of a possible propagation of radioelements. The modelling of the flow in porous media around the storage requires to know the physical parameters of the different geological layers. Those parameters (porosity and diffusion) are not directly accessible by measurements, hence we have to solve an inverse problem to recover them.

Question: How to solve an inverse problem?


Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs. Battery energy storage effectively staBIlizes the electric grid and aids renewable integration by balancing supply and demand in real time. The importance of such storage is especially crucial in densely populated urban areas, where traditional storage techniques such as pumped hydroelectric energy storage and compressed-air energy storage are often not feasible.

Question: What is the importance of battery energy storage?


A Graph Rewriting Approach for Converting Asynchronous ROMs into Synchronous Ones. Most of FPGAs have Configurable Logic Blocks (CLBs) to implement combinational and sequential circuits and block RAMs to implement Random Access Memories (RAMs) and Read Only Memories (ROMs). Circuit design that minimizes the number of clock cycles is easy if we use asynchronous read operations. However, most of FPGAs support synchronous read operations, but do not support asynchronous read operations. The main contribution of this paper is to provide one of the potent approaches to resolve this problem. We assume that a circuit using asynchronous ROMs designed by a non-expert or quickly designed by an expert is given. Our goal is to convert this circuit with asynchronous ROMs into an equivalent circuit with synchronous ones. The resulting circuit with synchronous ROMs can be embedded into FPGAs. We also discuss several techniques to decrease the latency and increase the clock frequency of the resulting circuits. key words: FPGA, block RAMs, asynchronous read operations, rewriting algorithm

Question: How to convert asynchronous ROMs into synchronous ones?


Generative Local Metric Learning for Nearest Neighbor Classification. We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian.

Question: What is the asymptotic theoretical analysis in this work?


Saliency-Guided Detection of Unknown Objects in RGB-D Indoor Scenes. This paper studies the problem of detecting unknown objects within indoor environments in an active and natural manner. The visual saliency scheme utilizing both color and depth cues is proposed to arouse the interests of the machine system for detecting unknown objects at salient positions in a 3D scene. The 3D points at the salient positions are selected as seed points for generating object hypotheses using the 3D shape. We perform multi-class labeling on a Markov random field (MRF) over the voxels of the 3D scene, combining cues from object hypotheses and 3D shape. The results from MRF are further refined by merging the labeled objects, which are spatially connected and have high correlation between color histograms. Quantitative and qualitative evaluations on two benchmark RGB-D datasets illustrate the advantages of the proposed method. The experiments of object detection and manipulation performed on a mobile manipulator validate its effectiveness and practicability in robotic applications.

Question: What is the saliency-guided detection of unknown objects in rgb-d indoor scenes?


Programming cells by multiplex genome engineering and accelerated evolution. The breadth of genomic diversity found among organisms in nature allows populations to adapt to diverse environments. However, genomic diversity is difficult to generate in the laboratory and new phenotypes do not easily arise on practical timescales. Although in vitro and directed evolution methods have created genetic variants with usefully altered phenotypes, these methods are limited to laborious and serial manipulation of single genes and are not used for parallel and continuous directed evolution of gene networks or genomes. Here, we describe multiplex automated genome engineering (MAGE) for large-scale programming and evolution of cells. MAGE simultaneously targets many locations on the chromosome for modification in a single cell or across a population of cells, thus producing combinatorial genomic diversity. Because the process is cyclical and scalable, we constructed prototype devices that automate the MAGE technology to facilitate rapid and continuous generation of a diverse set of genetic changes (mismatches, insertions, deletions). We applied MAGE to optimize the 1-deoxy-d-xylulose-5-phosphate (DXP) biosynthesis pathway in Escherichia coli to overproduce the industrially important isoprenoid lycopene. Twenty-four genetic components in the DXP pathway were modified simultaneously using a complex pool of synthetic DNA, creating over 4.3 billion combinatorial genomic variants per day. We isolated variants with more than fivefold increase in lycopene production within 3 days, a significant improvement over existing metabolic engineering techniques. Our multiplex approach embraces engineering in the context of evolution by expediting the design and evolution of organisms with new and improved properties.

Question: How many combinatorial genomic variants can be generated in a day?


Comparator Networks. The objective of this work is set-based verification, e.g. to decide if two sets of images of a face are of the same person or not. The traditional approach to this problem is to learn to generate a feature vector per image, aggregate them into one vector to represent the set, and then compute the cosine similarity between sets. Instead, we design a neural network architecture that can directly learn set-wise verification. Our contributions are: (i) We propose a Deep Comparator Network (DCN) that can ingest a pair of sets (each may contain a variable number of images) as inputs, and compute a similarity between the pair – this involves attending to multiple discriminative local regions (landmarks), and comparing local descriptors between pairs of faces; (ii) To encourage highquality representations for each set, internal competition is introduced for recalibration based on the landmark score; (iii) Inspired by image retrieval, a novel hard sample mining regime is proposed to control the sampling process, such that the DCN is complementary to the standard image classification models. Evaluations on the IARPA Janus face recognition benchmarks show that the comparator networks outperform the previous state-of-the-art results by a large margin.

Question: How to compare two sets of images?


Local Indicators of Spatial Association. The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the "spatial" aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G; statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify "outliers," as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.

Question: What is a local indicator of spatial association?


A proactive approach based on online reliability prediction for adaptation of service-oriented systems. Service computing is an emerging technology in System of Systems Engineering (SoS Engineering or SoSE), which regards System as a Service (i.e. SaaS), and aims to construct a robust and value-added complex system by outsourcing external component systems through service composition technology. A service-oriented SoS runs under a dynamic and uncertain environment. To successfully deploy SoS's run-time quality assurance, online reliability time series prediction, which aims to predict the reliability in near future for a service-oriented SoS arises as a grand challenge in SoS research. In this paper, we tackle the prediction challenge by exploiting two novel prediction models. We adopt motifs-based Dynamic Bayesian Networks (or m DBNs) model to perform one-step-ahead time series prediction, and propose a multi-steps trajectories DBNs (or multi DBNs) to further revise the future reliability prediction. Finally, an proactive adaption strategy is achieved based on the reliability prediction results. Extensive experiments conducted on real-world Web services demonstrate that our models outperform other well-known approaches consistently.

Question: What is a service-oriented SoS?


CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings. This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying fine-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.

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


Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques. A new spectral-spatial classification scheme for hyperspectral images is proposed. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images. The developed classification scheme improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification. The proposed method performs particularly well for classification of images with large spatial structures and when different classes have dissimilar spectral responses and a comparable number of pixels.

Question: How to classify hyperspectral images?


Private Empirical Risk Minimization: Efficient Algorithms and Tight Error Bounds. Convex empirical risk minimization is a basic tool in machine learning and statistics. We provide new algorithms and matching lower bounds for differentially private convex empirical risk minimization assuming only that each data point's contribution to the loss function is Lipschitz and that the domain of optimization is bounded. We provide a separate set of algorithms and matching lower bounds for the setting in which the loss functions are known to also be strongly convex. Our algorithms run in polynomial time, and in some cases even match the optimal nonprivate running time (as measured by oracle complexity). We give separate algorithms (and lower bounds) for (ε, 0)and (ε, δ)-differential privacy; perhaps surprisingly, the techniques used for designing optimal algorithms in the two cases are completely different. Our lower bounds apply even to very simple, smooth function families, such as linear and quadratic functions. This implies that algorithms from previous work can be used to obtain optimal error rates, under the additional assumption that the contributions of each data point to the loss function is smooth. We show that simple approaches to smoothing arbitrary loss functions (in order to apply previous techniques) do not yield optimal error rates. In particular, optimal algorithms were not previously known for problems such as training support vector machines and the high-dimensional median.

Question: What is the difference between convex and strongly convex?


A Taxonomy of Attacks on the DNP3 Protocol. Distributed Network Protocol (DNP3) is the predominant SCADA protocol in the energy sector – more than 75% of North American electric utilities currently use DNP3 for industrial control applications. This paper presents a taxonomy of attacks on the protocol. The attacks are classified based on targets (control center, outstation devices and network/communication paths) and threat categories (interception, interruption, modification and fabrication). To facilitate risk analysis and mitigation strategies, the attacks are associated with the specific DNP3 protocol layers they exploit. Also, the operational impact of the attacks is categorized in terms of three key SCADA objectives: process confidentiality, process awareness and process control. The attack taxonomy clarifies the nature and scope of the threats to DNP3 systems, and can provide insights into the relative costs and benefits of implementing mitigation strategies.

Question: What is the DNP3 protocol?


Live-fly, large-scale field experimentation for large numbers of fixed-wing UAVs. In this paper, we present extensive advances in live-fly field experimentation capabilities of large numbers of fixed-wing aerial robots, and highlight both the enabling technologies as well as the challenges addressed in such largescale flight operations. We showcase results from recent field tests, including the autonomous launch, flight, and landing of 50 UAVs, which illuminate numerous operational lessons learned and generate rich multi-UAV datasets. We detail the design and open architecture of the testbed, which intentionally leverages low-cost and open-source components, aimed at promoting continued advances and alignment of multi-robot systems research and practice.

Question: What is the largest number of UAVs that have been launched in a single flight?


Axe UX: Exploring long-term user experience with iScale and AttrakDiff. Positive user experience (UX) is an important goal in product design. Positive long-term UX is believed to improve customer loyalty, therefore being vital for continuous commercial success. Most UX research investigates momentary or short-term UX although the relationship between the user and the product evolves over time. There is a need to develop methods for measuring long-term UX and evaluate their feasibility in different product contexts. In this explorative study, 18 customers reported their experiences during the first three months of use of a non-interactive design tool, an axe. UX was evaluated with retrospective iScale tool and monthly repeated AttrakDiff questionnaire. iScale demonstrated the long-term trend of the attractiveness of the product well and provided information about the causes of the change in the experience. The AttrakDiff questionnaire was a good indicator of attractiveness during a longer period of time and is also well suited to remote studies.

Question: What is the relationship between UX and long-term customer loyalty?


Modeling of Item-Difficulty for Ontology-based MCQs. Multiple choice questions (MCQs) that can be generated from a domain ontology can significantly reduce human effort & time re quired for authoring & administering assessments in an e-Learning envir o ment. Even though there are various methods for generating MCQs from ontologi es, methods for determining the difficulty-levels of such MCQs are less explor ed. In this paper, we study various aspects and factors that are involved in deter mining the difficultyscore of an MCQ, and propose an ontology-based model for the p rediction. This model characterizes the difficulty values associated with t he s em and choice set of the MCQs, and describes a measure which combines both the s cor s. Furthermore, the notion of assigning difficultly-scores based on th e skill level of the test taker is utilized for predicating difficulty-score of a stem . We studied the effectiveness of the predicted difficulty-scores with the help of a psychometric model from the Item Response Theory, by involving real-students a d domain experts. Our results show that, the predicated difficulty-levels of t he MCQs are having high correlation with their actual difficulty-levels.

Question: What is the difference between ontology-based MCQs and traditional MCQs?


What Yelp Fake Review Filter Might Be Doing?. Online reviews have become a valuable resource for decision making. However, its usefulness brings forth a curse ‒ deceptive opinion spam. In recent years, fake review detection has attracted significant attention. However, most review sites still do not publicly filter fake reviews. Yelp is an exception which has been filtering reviews over the past few years. However, Yelp's algorithm is trade secret. In this work, we attempt to find out what Yelp might be doing by analyzing its filtered reviews. The results will be useful to other review hosting sites in their filtering effort. There are two main approaches to filtering: supervised and unsupervised learning. In terms of features used, there are also roughly two types: linguistic features and behavioral features. In this work, we will take a supervised approach as we can make use of Yelp's filtered reviews for training. Existing approaches based on supervised learning are all based on pseudo fake reviews rather than fake reviews filtered by a commercial Web site. Recently, supervised learning using linguistic n-gram features has been shown to perform extremely well (attaining around 90% accuracy) in detecting crowdsourced fake reviews generated using Amazon Mechanical Turk (AMT). We put these existing research methods to the test and evaluate performance on the real-life Yelp data. To our surprise, the behavioral features perform very well, but the linguistic features are not as effective. To investigate, a novel information theoretic analysis is proposed to uncover the precise psycholinguistic difference between AMT reviews and Yelp reviews (crowdsourced vs. commercial fake reviews). We find something quite interesting. This analysis and experimental results allow us to postulate that Yelp's filtering is reasonable and its filtering algorithm seems to be correlated with abnormal spamming behaviors.

Question: What Yelp Fake Review Filter Might Be Doing?


Cutting Corners and Working Overtime: Quality Erosion in the Service Industry. The erosion of service quality throughout the economy is a frequent concern in the popular press. The American Customer Satisfaction Index for services fell in 2000 to 69.4%, down 5 percentage points from 1994. We hypothesize that the characteristics of services— inseparability, intangibility, and labor intensity—interact with management practices to bias service providers toward reducing the level of service they deliver, often locking entire industries into a vicious cycle of eroding service standards. To explore this proposition we develop a formal model that integrates the structural elements of service delivery. We use econometric estimation, interviews, observations, and archival data to calibrate the model for a consumer-lending service center in a major bank in the United Kingdom. We find that temporary imbalances between service capacity and demand interact with decision rules for effort allocation, capacity management, overtime, and quality aspirations to yield permanent erosion of the service standards and loss of revenue. We explore policies to improve performance and implications for organizational design in the service sector. (Organizational Learning; Service Management Performance; Service Operations; Service Quality; Simulation; System Dynamics)

Question: What is the service industry?


A regression-based radar-mote system for people counting. People counting is key to a diverse set of sensing applications. In this paper, we design a mote-scale event-driven solution that uses a low-power pulsed radar to estimate the number of people within the ~10m radial range of the radar. In contrast to extant solutions, most of which use computer vision, our solution is light-weight and private. It also better tolerates the presence of obstacles that partially or fully impair line of sight; this is achieved by accounting for "small" indirect radio reflections via joint time-frequency domain features. The counter itself is realized using Support Vector Regression; the regression map is learned from a medium sized dataset of 0-~40 people in various indoor room settings. 10-fold cross validation of our counter yields a mean absolute error of 2.17 between the estimated count and the ground truth and a correlation coefficient of 0.97.We compare the performance of our solution with baseline counters.

Question: What is a regression-based radar-mote system for people counting?


Music, Intelligence and Artificiality. The discipline of Music-AI is defined as that activity which seeks to program computers to perform musical tasks in an intelligent, which possibly means humanlike way. A brief historical survey of different approaches within the discipline is presented. Two particular issues arise: the explicit representation of knowledge; and symbolic and subsymbolic representation and processing. When attempting to give a precise definition of Music-AI, it is argued that all musical processes must make some reference to human behaviour, and so Music-AI is a central rather than a peripheral discipline for musical computing. However, it turns out that the goals of Music-AI as first expressed, the mimicking of human behaviour, are impossible to achieve in full, and that it is impossible, in principle, for computers to pass a musical version of the Turing test. In practice, however, computers are used for their non-human-like behaviour just as much as their human-like behaviour, so the real goal of Music-AI must be reformulated. Furthermore, it is argued that the non-holistic analysis of human behaviour which this reformulation entails is actually informative for our understanding of human behaviour. Music-AI could also be fruitfully concerned with developing musical intelligences which were explicitly not human. Music-AI is then seen to be as much a creative enterprise as a scientific one.

Question: What is the difference between music and intelligence?


Learning influence probabilities in social networks. Recently, there has been tremendous interest in the phenomenon of influence propagation in social networks. The studies in this area assume they have as input to their problems a social graph with edges labeled with probabilities of influence between users. However, the question of where these probabilities come from or how they can be computed from real social network data has been largely ignored until now. Thus it is interesting to ask whether from a social graph and a log of actions by its users, one can build models of influence. This is the main problem attacked in this paper. In addition to proposing models and algorithms for learning the model parameters and for testing the learned models to make predictions, we also develop techniques for predicting the time by which a user may be expected to perform an action. We validate our ideas and techniques using the Flickr data set consisting of a social graph with 1.3M nodes, 40M edges, and an action log consisting of 35M tuples referring to 300K distinct actions. Beyond showing that there is genuine influence happening in a real social network, we show that our techniques have excellent prediction performance.

Question: How can we predict the time by which a user may be expected to perform an action?


Real-time pen-and-ink illustration of landscapes. Non-photorealistic rendering has been proven to be particularly efficient in conveying and transmitting selected visual information. Our paper presents a NPR rendering pipeline that supports pen-and-ink illustration for, but not limited to, complex landscape scenes in real time. This encompasses a simplification framework using clustering which enables new approaches to efficient and coherent rendering of stylized silhouettes, hatching and abstract shading. Silhouette stylization is performed in image-space. This avoids explicit computation of connected lines. Further, coherent hatching of the tree foliage is performed using an approximate view-dependent parameterization computed on-the-fly within the same simplification framework. All NPR algorithms are integrated with photorealistic rendering, allowing seamless transition and combination between a variety of photorealistic and non-photorealistic drawing styles.

Question: What is a non-photorealistic rendering?


What is the amygdala?. 'Amygdala' and 'amygdalar complex' are terms that now refer to a highly differentiated region near the temporal pole of the mammalian cerebral hemisphere. Cell groups within it appear to be differentiated parts of the traditional cortex, the claustrum, or the striatum, and these parts belong to four obvious functional systems--accessory olfactory, main olfactory, autonomic and frontotemporal cortical. In rats, the central nucleus is a specialized autonomic-projecting motor region of the striatum, whereas the lateral and anterior basolateral nuclei together are a ventromedial extension of the claustrum for major regions of the temporal and frontal lobes. The rest of the amygdala forms association parts of the olfactory system (accessory and main), with cortical, claustral and striatal parts. Terms such as 'amygdala' and 'lenticular nucleus' combine cell groups arbitrarily rather than according to the structural and functional units to which they now seem to belong. The amygdala is neither a structural nor a functional unit.

Question: What is the amygdala?