CDM protection design for CMOS applications using RC-triggered rail clamps. A new ESD protection design method is introduced that optimizes the CDM circuit response of an RC-triggered rail clamp network for I/O protection. The design is verified by VF-TLP and CDM test data of I/O test rings with two different output driver options.

Question: What is the CDM protection design for CMOS applications using RC-triggered rail clamps?


Critical review of the e-loyalty literature: a purchase-centred framework.

Question: What is a purchase-centred framework?


Performance comparison of FPGA, GPU and CPU in image processing. Many applications in image processing have high inherent parallelism. FPGAs have shown very high performance in spite of their low operational frequency by fully extracting the parallelism. In recent micro processors, it also becomes possible to utilize the parallelism using multi-cores which support improved SIMD instructions, though programmers have to use them explicitly to achieve high performance. Recent GPUs support a large number of cores, and have a potential for high performance in many applications. However, the cores are grouped, and data transfer between the groups is very limited. Programming tools for FPGA, SIMD instructions on CPU and a large number of cores on GPU have been developed, but it is still difficult to achieve high performance on these platforms. In this paper, we compare the performance of FPGA, GPU and CPU using three applications in image processing; two-dimensional filters, stereo-vision and k-means clustering, and make it clear which platform is faster under which conditions.

Question: What is the difference between FPGA, GPU and CPU?


Comparative analysis of RADAR-IR sensor fusion methods for object detection. This paper presents the Radar and IR sensor fusion method for objection detection. The infrared camera parameter calibration with Levenberg-Marquardt (LM) optimization method is proposed based on the Radar ranging data represented by Cartesian coordinate compared with 6 fusion methods. The proposed method firstly performs the estimation of the intrinsic parameter matrix of infrared camera with some optical trick. Then the method searches the extrinsic parameters using the generative approach. The initial angle and translation of the extrinsic parameters are optimized by the LM method with the geometrical cost function. In the experiments, the performance of proposed method outperforms by a maximum 13 times the performance of the other baseline methods on the averaged Euclidian distance error. In future work, the angular noise of the Radar information will be improved and the proposed method will provide the effective proposals for the deep neural network.

Question: What is the difference between radar and infrared?


Evidences for the Anti-panic Actions of Cannabidiol. BACKGROUND Panic disorder (PD) is a disabling psychiatry condition that affects approximately 5% of the worldwide population. Currently, long-term selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for PD; however, the common side-effect profiles and drug interactions may provoke patients to abandon the treatment, leading to PD symptoms relapse. Cannabidiol (CBD) is the major non-psychotomimetic constituent of the Cannabis sativa plant with antianxiety properties that has been suggested as an alternative for treating anxiety disorders. The aim of the present review was to discuss the effects and mechanisms involved in the putative anti-panic effects of CBD.   METHODS electronic database was used as source of the studies selected selected based on the studies found by crossing the following keywords: cannabidiol and panic disorder; canabidiol and anxiety, cannabidiol and 5-HT1A receptor).   RESULTS In the present review, we included both experimental laboratory animal and human studies that have investigated the putative anti-panic properties of CBD. Taken together, the studies assessed clearly suggest an anxiolytic-like effect of CBD in both animal models and healthy volunteers.   CONCLUSIONS CBD seems to be a promising drug for the treatment of PD. However, novel clinical trials involving patients with the PD diagnosis are clearly needed to clarify the specific mechanism of action of CBD and the safe and ideal therapeutic doses of this compound.

Question: What is the mechanism of action of cannabidiol?


Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives. Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area

Question: What is the difference between a vehicular ad hoc network and a mobile ad hoc network?


Stacked Graphs – Geometry & Aesthetics. In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.

Question: How to make a stacked graph?


Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience.. For over 25 years, personal assistant robots for severely disabled individuals have been in development. More recently, using robots to deliver rehabilitation therapy has been proposed. This paper summarizes the development and clinical testing of three mechatronic systems for post-stroke therapy conducted at the VA Palo Alto in collaboration with Stanford University. We describe the philosophy and experiences that guided their evolution. Unique to the Palo Alto approach is provision for bimanual, mirror-image, patient-controlled therapeutic exercise. Proof-of-concept was established with a 2-degree-of-freedom (DOF) elbow/forearm manipulator. Tests of a second-generation therapy robot producing planar forearm movements in 19 hemiplegic and control subjects confirmed the validity and reliability of interaction forces during mechanically assisted upper-limb movements. Clinical trials comparing 3-D robot-assisted therapy to traditional therapy in 21 chronic stroke subjects showed significant improvement in the Fugl-Meyer (FM) measure of motor recovery in the robot group, which exceeded improvements in the control group.

Question: What is the difference between a robot and a personal assistant?


Updating freeze: Aligning animal and human research. Freezing is widely used as the main outcome measure for fear in animal studies. Freezing is also getting attention more frequently in human stress research, as it is considered to play an important role in the development of psychopathology. Human models on defense behavior are largely based on animal models. Unfortunately, direct translations between animal and human studies are hampered by differences in definitions and methods. The present review therefore aims to clarify the conceptualization of freezing. Neurophysiological and neuroanatomical correlates are discussed and a translational model is proposed. We review the upcoming research on freezing in humans that aims to match animal studies by using physiological indicators of freezing (bradycardia and objective reduction in movement). Finally, we set the agenda for future research in order to optimize mutual animal-human translations and stimulate consistency and systematization in future empirical research on the freezing phenomenon.

Question: What is the definition of freezing?


All-printed diode operating at 1.6 GHz.. Printed electronics are considered for wireless electronic tags and sensors within the future Internet-of-things (IoT) concept. As a consequence of the low charge carrier mobility of present printable organic and inorganic semiconductors, the operational frequency of printed rectifiers is not high enough to enable direct communication and powering between mobile phones and printed e-tags. Here, we report an all-printed diode operating up to 1.6 GHz. The device, based on two stacked layers of Si and NbSi2 particles, is manufactured on a flexible substrate at low temperature and in ambient atmosphere. The high charge carrier mobility of the Si microparticles allows device operation to occur in the charge injection-limited regime. The asymmetry of the oxide layers in the resulting device stack leads to rectification of tunneling current. Printed diodes were combined with antennas and electrochromic displays to form an all-printed e-tag. The harvested signal from a Global System for Mobile Communications mobile phone was used to update the display. Our findings demonstrate a new communication pathway for printed electronics within IoT applications.

Question: What is the frequency of the printed diode?


The Chinese Wall Security Policy. Everyone who has seen the movie Wall Street wi~l have seen a commercial security policy in action. The recent work of Clark and Wilson and the WIPCIS initiative (the Workshop on Integrity Policy for Computer Information Systems) has drawn attention to the existence of a wide range of commercial security policies which are both significantly different from each other and quite alien to current "military" thin king as implemented in products for the security mari<et place. This paper presents a basic mathematical theory which implements one such policy, the Chinese Wall, and shows that it cannot be correctly represented by a 13e11-LaPadula model. The Chinese Wall policy combines commercial discretion with legally enforceable mandatory con trols. It is required in the operation of many financial services organizations and is, therefore, perhaps as significant t to the financial world as Bel!-LaPadula 's policies are to the military. MIRQK?LK7T,K2N Until recently, military security policy thinking has dominated the direction of computer security research both in the US and the UK. Clark and Wilson's seminal paper [1] has, however, drawn attention to the fact that commercial security needs are just as important as those of the de fence community and, through the WIPCIS initiative [2], that the problems of the commercial community are at least as diverse and relevant to the computer scientist. There are many well defined commercial security policies covering all aspects of Clark and Wilson's model [3]. One of these, the Chinese Wall security policy is perhaps as significant to some parts of the commercial world as Bell and LaPadula's policies [4, 5] are to the military. It can be most easily visualized as the code of practice that must be followed by a market analyst working for a financial institution providing corporate business services. Such an analyst must uphold the confidentiality of information provided to him by his firm's client

Question: What is the Chinese Wall security policy?


An overview on crime prediction methods. In the recent past, crime analyses are required to reveal the complexities in the crime dataset. This process will help the parties that involve in law enforcement in arresting offenders and directing the crime prevention strategies. The ability to predict the future crimes based on the location, pattern and time can serve as a valuable source of knowledge for them either from strategic or tactical perspectives. Nevertheless, to predict future crime accurately with a better performance, it is a challenging task because of the increasing numbers of crime in present days. Therefore, crime prediction method is important to identify the future crime and reduces the numbers of crime. Currently, some researchers have been conducted a study to predict crime based on particular inputs. The performance of prediction models can be evaluated using a variety of different prediction methods such as support vector machine, multivariate time series and artificial neural network. However, there are still some limitations on their findings to provide an accurate prediction for the location of crimes. A large number of research papers on this topic have already been published previously. Thus, in this paper, we thoroughly review each of them and summarized the outcomes. Our objective is to identify current implementations of crime prediction method and the possibility to enhance it for future needs.

Question: What is the difference between crime prediction and crime forecasting?


PMSG fault diagnosis in marine application. This paper presents a fault diagnosis (FD) method for on line monitoring of stator winding partial inter-turn fault and severe faults, in permanent magnet synchronous generator (PMSG) used in shaft generator system in marine applications. The faulty machine is represented in state space model based on the dq-frame transformation. The Extended Kalman Filter (EKF) parameters estimation technique is used to estimate the value of stator winding short-circuit parameters of the machine. The proposed technique has been simulated for different fault scenarios using Matlab®/Simulink®. Simulation results show that the proposed technique is robust and effective. Moreover it can be applied at different operating conditions to prevent development of complete failure.

Question: How to diagnose faults in a PMSG?


Automatic Identification and Classification of Misogynistic Language on Twitter. Hate speech may take different forms in online social media. Most of the investigations in the literature are focused on detecting abusive language in discussions about ethnicity, religion, gender identity and sexual orientation. In this paper, we address the problem of automatic detection and categorization of misogynous language in online social media. The main contribution of this paper is two-fold: (1) a corpus of misogynous tweets, labelled from different perspective and (2) an exploratory investigations on NLP features and ML models for detecting and classifying misogynistic language.

Question: What is the main contribution of this paper?


Chemical crystal identification with deep learning machine vision. This study was carried out with the purpose of testing the ability of deep learning machine vision to identify microscopic objects and geometries found in chemical crystal structures. A database of 6994 images taken with a light microscope showing microscopic crystal details of selected chemical compounds along with 180 images of an unknown chemical was created to train and test, respectively the deep learning models. The models used were GoogLeNet (22 layers deep network) and VGG-16 (16 layers deep network), based on the Caffe framework (University of California, Berkeley, CA) of the DIGITS platform (NVIDIA Corporation, Santa Clara, CA). The two models were successfully trained with the images, having validation accuracy values of 97.38% and 99.65% respectively. Finally, both models were able to correctly identify the unknown chemical sample with a high probability score of 93.34% (GoogLeNet) and 99.41% (VGG-16). The positive results found in this study can be further applied to other unknown sample identification tasks using light microscopy coupled with deep learning machine vision.

Question: What is the chemical crystal identification with deep learning machine vision?


Single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp. A single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp is proposed. In the proposed converter, the operation principle is quite different from other boost bridgeless three-level converters. The charging stage is presented in the low side while the discharging stage in the high side and vice versa. As a result, nature voltage clamp is achieved. And the slow diodes are 'shared'. They operate not only as rectifying diodes, but also as voltage clamping diodes. Besides, only single inductor is required and the device utilization factor is high. Besides, the common-mode(CM) noise is rather low. First, the operational principle are analyzed in detail. Then the performance evaluation and design consideration are presented. Finally, a 1kW experimental prototype is built. The result shows that the efficiency is above 94% at full load under 90V and the maximum efficiency is 98.4%.

Question: What is a single inductor three-level boost bridgeless PFC rectifier with nature voltage clamp?


Neighborhood-Level LGBT Hate Crimes and Bullying Among Sexual Minority Youths: A Geospatial Analysis.. The goal of this study was to evaluate a novel measure of environmental risk factors for bullying among sexual minority youths. Data on lesbian, gay, bisexual, and transgender (LGBT) assault hate crimes were obtained from police records, geocoded, and then linked to individual-level data on bullying and sexual orientation from the 2008 Boston Youth Survey Geospatial Dataset (N = 1,292; 108 sexual minorities). Results indicated that sexual minority youths who reported relational and electronic bullying were more likely to reside in neighborhoods with higher LGBT assault hate crime rates. There was no asso- ciation between LGBT assault hate crimes and bullying among heterosexual youths, pro- viding evidence for specificity to sexual minority youth. Moreover, no relationships were observed between sexual minority bullying and neighborhood-level violent and property crimes, indicating that the results were specific to LGBT assault hate crimes.

Question: What is the goal of this study?


X-shaped inferior extensor retinaculum and its doubtful use in the Bröstrom–Gould procedure. The inferior extensor retinaculum (IER) is an aponeurotic structure located in the anterior aspect of the ankle. According to the literature, it can be used to reinforce a repair of the anterior talofibular ligament in ankle instability. Despite its usual description as an Y-shaped structure, it is still unclear which part of the retinaculum is used for this purpose, or if it is instead the crural fascia that is being used. The purpose of this study is to define the anatomical characteristics of the IER to better understand its role in the Broström–Gould procedure. Twenty-one ankles were dissected. The morphology of the IER and its relationship with neighbouring structures were recorded. Seventeen (81%) of the IER in this study had an X-shaped morphology, with the presence of an additional oblique superolateral band. This band, by far the thinnest of the retinaculum, is supposed to be used to reinforce the repair of the anterior talofibular ligament. The intermediate dorsal cutaneous nerve (lateral branch of the superficial peroneal nerve) was found to cross the retinaculum in all cases. The IER is most commonly seen as an X-shaped structure, but the fact that the oblique superolateral band is a thin band of tissue probably indicates that it may not add significant strength to ankle stability. Furthermore, the close relationship of the retinaculum with the superficial peroneal nerve is another factor to consider before deciding to perform a Broström–Gould procedure. These anatomical findings advise against the use of the Gould augmentation.

Question: What is the purpose of the IER?


Gender Identity and Adjustment in Middle Childhood. Gender identity is a central construct in many accounts of psychosocial development, yet it has been defined in diverse ways. Kohlberg (1966) and Zucker et al. (1993) viewed gender identity as knowing that one is a member of one sex rather than the other; Kagan (1964) regarded gender identity as the degree to which one perceives the self as conforming to cultural stereotypes for one's gender; Bem (1981) saw gender identity as the degree to which one internalizes societal pressures for gender conformity; Green (1974) and Spence (1985) viewed gender identity as a fundamental sense of acceptance of, and of belonging to, one's gender. It is conceivable that all of the foregoing (and still other) conceptualizations of gender identity have merit but that different varieties or facets of gender identity serve different psychological functions or affect adjustment in different ways. Thus, it may be fruitful to regard gender identity as a multidimensional construct and to define gender identity as the collection of thoughts and feelings one has about one's gender category and one's membership in it. A recent study by Egan and Perry (2001) was built on this premise. Egan and Perry proposed that gender identity is composed of five major components: (a) membership knowledge (knowledge of membership in a gender category); (b) gender typicality (the degree to which one feels one is a typical member of one's gender category); (c) gender contentedness (the degree to which one is happy with one's gender assignment); (d) felt pressure for gender conformity (the degree to which one feels pressure from parents, peers, and self for conformity to gender stereotypes); and (e) intergroup bias (the extent to which one believes one's own sex is superior to the other). Egan and Perry (2001) measured the last four of these components of gender identity in preadolescent children and found the components to be relatively independent, to be fair

Question: What is the difference between gender identity and gender role?


Heart rate variability biofeedback increases baroreflex gain and peak expiratory flow.. OBJECTIVE We evaluated heart rate variability biofeedback as a method for increasing vagal baroreflex gain and improving pulmonary function among 54 healthy adults.   METHODS We compared 10 sessions of biofeedback training with an uninstructed control. Cognitive and physiological effects were measured in four of the sessions.   RESULTS We found acute increases in low-frequency and total spectrum heart rate variability, and in vagal baroreflex gain, correlated with slow breathing during biofeedback periods. Increased baseline baroreflex gain also occurred across sessions in the biofeedback group, independent of respiratory changes, and peak expiratory flow increased in this group, independently of cardiovascular changes. Biofeedback was accompanied by fewer adverse relaxation side effects than the control condition.   CONCLUSIONS Heart rate variability biofeedback had strong long-term influences on resting baroreflex gain and pulmonary function. It should be examined as a method for treating cardiovascular and pulmonary diseases. Also, this study demonstrates neuroplasticity of the baroreflex.

Question: What is the difference between vagal and sympathetic baroreflex?


Optimizing esthetics for implant restorations in the anterior maxilla: anatomic and surgical considerations.. The placement of dental implants in the anterior maxilla is a challenge for clinicians because of patients' exacting esthetic demands and difficult pre-existing anatomy. This article presents anatomic and surgical considerations for these demanding indications for implant therapy. First, potential causes of esthetic implant failures are reviewed, discussing anatomic factors such as horizontal or vertical bone deficiencies and iatrogenic factors such as improper implant selection or the malpositioning of dental implants for an esthetic implant restoration. Furthermore, aspects of preoperative analysis are described in various clinical situations, followed by recommendations for the surgical procedures in single-tooth gaps and in extended edentulous spaces with multiple missing teeth. An ideal implant position in all 3 dimensions is required. These mesiodistal, apicocoronal, and orofacial dimensions are well described, defining "comfort" and "danger" zones for proper implant position in the anterior maxilla. During surgery, the emphasis is on proper implant selection to avoid oversized implants, careful and low-trauma soft tissue handling, and implant placement in a proper position using either a periodontal probe or a prefabricated surgical guide. If missing, the facial bone wall is augmented using a proper surgical technique, such as guided bone regeneration with barrier membranes and appropriate bone grafts and/or bone substitutes. Finally, precise wound closure using a submerged or a semi-submerged healing modality is recommended. Following a healing period of between 6 and 12 weeks, a reopening procedure is recommended with a punch technique to initiate the restorative phase of therapy.

Question: What is the ideal implant position in the anterior maxilla?


The partograph for the prevention of obstructed labor.. Obstructed labor is an important cause of maternal and perinatal mortality and morbidity. The partograph graphically represents key events in labor and provides an early warning system. The World Health Organization partographs are the best known partographs in low resource settings. Experiences with World Health Organization and other types of partographs in low resource settings suggest that when used with defined management protocols, this inexpensive tool can effectively monitor labor and prevent obstructed labor. However, challenges to implementation exist and these should be addressed urgently.

Question: What is the partograph?


Rain Streak Removal Using Layer Priors. This paper addresses the problem of rain streak removal from a single image. Rain streaks impair visibility of an image and introduce undesirable interference that can severely affect the performance of computer vision algorithms. Rain streak removal can be formulated as a layer decomposition problem, with a rain streak layer superimposed on a background layer containing the true scene content. Existing decomposition methods that address this problem employ either dictionary learning methods or impose a low rank structure on the appearance of the rain streaks. While these methods can improve the overall visibility, they tend to leave too many rain streaks in the background image or over-smooth the background image. In this paper, we propose an effective method that uses simple patch-based priors for both the background and rain layers. These priors are based on Gaussian mixture models and can accommodate multiple orientations and scales of the rain streaks. This simple approach removes rain streaks better than the existing methods qualitatively and quantitatively. We overview our method and demonstrate its effectiveness over prior work on a number of examples.

Question: How to remove rain streaks from an image?


Accurate human motion capture in large areas by combining IMU- and laser-based people tracking. A large number of applications use motion capture systems to track the location and the body posture of people. For instance, the movie industry captures actors to animate virtual characters that perform stunts. Today's tracking systems either operate with statically mounted cameras and thus can be used in confined areas only or rely on inertial sensors that allow for free and large-scale motion but suffer from drift in the pose estimate. This paper presents a novel tracking approach that aims to provide globally aligned full body posture estimates by combining a mobile robot and an inertial motion capture system. In our approach, a mobile robot equipped with a laser scanner is used to anchor the pose estimates of a person given a map of the environment. It uses a particle filter to globally localize a person wearing a motion capture suit and to robustly track the person's position. To obtain a smooth and globally aligned trajectory of the person, we solve a least squares optimization problem formulated from the motion capture suite and tracking data. Our approach has been implemented on a real robot and exhaustively tested. As the experimental evaluation shows, our system is able to provide locally precise and globally aligned estimates of the person's full body posture.

Question: How accurate is the human motion capture?


A Survey of Computation Offloading for Mobile Systems. Mobile systems have limited resources, such as battery life, network bandwidth, storage capacity, and processor performance. These restrictions may be alleviated by computation of f loading: sending heavy computation to resourceful servers and receiving the results from these servers. Many issues related to offloading have been investigated in the past decade. This survey paper provides an overview of the background, techniques, systems, and research areas for offloading computation. We also describe directions for future research.

Question: What is the background of offloading computation?


Localized forgery detection in hyperspectral document images. Hyperspectral imaging is emerging as a promising technology to discover patterns that are otherwise hard to identify with regular cameras. Recent research has shown the potential of hyperspectral image analysis to automatically distinguish visually similar inks. However, a major limitation of prior work is that automatic distinction only works when the number of inks to be distinguished is known a priori and their relative proportions in the inspected image are roughly equal. This research work aims at addressing these two problems. We show how anomaly detection combined with unsupervised clustering can be used to handle cases where the proportions of pixels belonging to the two inks are highly unbalanced. We have performed experiments on the publicly available UWA Hyperspectral Documents dataset. Our results show that INFLO anomaly detection algorithm is able to best distinguish inks for highly unbalanced ink proportions.

Question: How to detect localized forgery in hyperspectral images?


Least Squares Support Vector Machine Classifiers. In this letter we discuss a least squares version for support vector machine (SVM) classifiers. Due to equality type constraints in the formulation, the solution follows from solving a set of linear equations, instead of quadratic programming for classical SVM's. The approach is illustrated on a two-spiral benchmark classification problem.

Question: What is a least squares support vector machine?


Grammatical error correction in non-native English. Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Previous research has mainly focussed on individual error types and current commercial proofreading tools only target limited error types. As sentences produced by learners may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this thesis, we investigate GEC for learners of English as a Second Language (ESL). Specifically, we treat GEC as a translation task from incorrect into correct English, explore new models for developing end-to-end GEC systems for all error types, study system performance for each error type, and examine model generali-sation to different corpora. First, we apply Statistical Machine Translation (SMT) to GEC and prove that it can form the basis of a competitive all-errors GEC system. We implement an SMT-based GEC system which contributes to our winning system submitted to a shared task in 2014. Next, we propose a ranking model to re-rank correction candidates generated by an SMT-based GEC system. This model introduces new linguistic information and we show that it improves correction quality. Finally, we present the first study using Neural Machine Translation (NMT) for GEC. We demonstrate that NMT can be successfully applied to GEC and help capture new errors missed by an SMT-based GEC system. While we focus on GEC for English, our methods presented in this thesis can be easily applied to any language. Acknowledgements First and foremost, I owe a huge debt of gratitude to my supervisor, Ted Briscoe, who has patiently guided me through my PhD and always been very helpful, understanding and supportive. I cannot thank him enough for providing me with opportunities that helped me grow as a researcher and a critical thinker. I am immensely grateful to my examiners, Paula Buttery and Stephen Pulman, for their

Question: What is the difference between a proofreader and a grammatical error correction system?


Natural Language Generation in Interactive Systems. An informative and comprehensive overview of the state-of-the-art in natural language generation (NLG) for interactive systems, this guide introduces graduate students and new researchers to the field of natural language processing and artificial intelligence, while inspiring them with ideas for future research. Detailing the techniques and challenges of NLG for interactive applications, it focuses on research into systems that model collaborativity and uncertainty, are capable of being scaled incrementally, and can engage with the user effectively. A range of real-world case studies is also included. The book and the accompanying website feature a comprehensive bibliography, and refer the reader to corpora, data, software, and other resources for pursuing research on natural language generation and interactive systems, including dialogue systems, multimodal interfaces, and assistive technologies. It is an ideal resource for students and researchers in computational linguistics, natural language processing, and related fields.

Question: What is a comprehensive overview of the state-of-the-art in natural language generation for interactive systems?


Dendrites, deep learning, and sequences in the hippocampus.. The hippocampus places us both in time and space. It does so over remarkably large spans: milliseconds to years, and centimeters to kilometers. This works for sensory representations, for memory, and for behavioral context. How does it fit in such wide ranges of time and space scales, and keep order among the many dimensions of stimulus context? A key organizing principle for a wide sweep of scales and stimulus dimensions is that of order in time, or sequences. Sequences of neuronal activity are ubiquitous in sensory processing, in motor control, in planning actions, and in memory. Against this strong evidence for the phenomenon, there are currently more models than definite experiments about how the brain generates ordered activity. The flip side of sequence generation is discrimination. Discrimination of sequences has been extensively studied at the behavioral, systems, and modeling level, but again physiological mechanisms are fewer. It is against this backdrop that I discuss two recent developments in neural sequence computation, that at face value share little beyond the label "neural." These are dendritic sequence discrimination, and deep learning. One derives from channel physiology and molecular signaling, the other from applied neural network theory - apparently extreme ends of the spectrum of neural circuit detail. I suggest that each of these topics has deep lessons about the possible mechanisms, scales, and capabilities of hippocampal sequence computation.

Question: How does the hippocampus generate sequences?


Coccydynia: an overview of the anatomy, etiology, and treatment of coccyx pain.. BACKGROUND Despite its small size, the coccyx has several important functions. Along with being the insertion site for multiple muscles, ligaments, and tendons, it also serves as one leg of the tripod-along with the ischial tuberosities-that provides weight-bearing support to a person in the seated position. The incidence of coccydynia (pain in the region of the coccyx) has not been reported, but factors associated with increased risk of developing coccydynia include obesity and female gender.   METHODS This article provides an overview of the anatomy, physiology, and treatment of coccydynia.   RESULTS Conservative treatment is successful in 90% of cases, and many cases resolve without medical treatment. Treatments for refractory cases include pelvic floor rehabilitation, manual manipulation and massage, transcutaneous electrical nerve stimulation, psychotherapy, steroid injections, nerve block, spinal cord stimulation, and surgical procedures.   CONCLUSION A multidisciplinary approach employing physical therapy, ergonomic adaptations, medications, injections, and, possibly, psychotherapy leads to the greatest chance of success in patients with refractory coccyx pain. Although new surgical techniques are emerging, more research is needed before their efficacy can be established.

Question: What is the incidence of coccydynia?


Action recognition with deep neural networks. In this work, we have invesigated the action recognition problem using the Charades Action Recognition Dataset with 157 action classes. We have compared the results of different techniques such as extreme learning machines, support vector machines, and decision trees, applied on the features extracted with deep neural networks and the scene-action conditional probabilities.

Question: What is the action recognition problem?


Design of a novel dual band patch antenna loaded with CELC resonator for WLAN/WiMAX applications. A Complementary Electric Field Coupled (CELC) resonator, loaded in the ground plane of a conventional patch antenna to achieve dual band operation is presented here. Two antenna designs have been presented. In the first design, one CELC is etched out from the ground plane of patch antenna which leads to generation of a lower order mode in addition to the conventional patch mode. In order to achieve better impedance bandwidth for the lower order resonant mode two CELC resonators with closely spaced resonant frequencies are etched out from the ground plane of patch antenna in the second design. The proposed antennas can be used in WLAN (Wireless Local Area Network) and WiMAX (Worldwide Interoperability for Microwave Access) applications.

Question: What is a CELC resonator?


Classification methods for activity recognition. Activity recognition is an important subject in different areas of research, like e-health and ubiquitous computing. In this paper we give an overview of recent work in the field of activity recognition from both body mounted sensors and ambient sensors. We discuss some of the differences among approaches and present guidelines for choosing a suitable approach under various circumstances.

Question: What is activity recognition?


Semantics in Visual Information Retrieval. V isual information retrieval systems have entered a new era. First-generation systems allowed access to images and videos through textual data. 1,2 Typical searches for these systems include, for example, " all images of paintings of the Florentine school of the 15th century " or " all images by Cezanne with landscapes. " Such systems expressed information through alphanumeric keywords or scripts. They employed representation schemes like relational models, frame models, and object-oriented models. On the other hand, current-generation retrieval systems support full retrieval by visual content. 3,4 Access to visual information is not only performed at a conceptual level, using keywords as in the textu-al domain, but also at a perceptual level, using objective measurements of visual content. In these systems, image processing, pattern recognition , and computer vision constitute an integral part of the system's architecture and operation. They objectively analyze pixel distribution and extract the content descriptors automatically from raw sensory data. Image content descriptors are commonly represented as feature vectors, whose elements correspond to significant parameters that model image attributes. Therefore, visual attributes are regarded as points in a multidimen-sional feature space, where point closeness reflects feature similarity. These advances (for comprehensive reviews of the field, see the " Further Reading " sidebar) have paved the way for third-generation systems, featuring full multimedia data management and networking support. Forthcoming standards such as MPEG-4 and MPEG-7 (see the Nack and Lindsay article in this issue) provide the framework for efficient representation, processing, and retrieval of visual information. Yet many problems must still be addressed and solved before these technologies can emerge. An important issue is the design of indexing structures for efficient retrieval from large, possi

Question: What is the difference between first- and third-generation visual information retrieval systems?


Zero-forcing beamforming with block diagonalization scheme for Coordinated Multi-Point transmission. Coordinated Multi-Point (CoMP) transmission is a technology targeted for Long Term Evolution Advanced (LTE-A). The Joint Processing (JP) technique for CoMP can maximize system performance, which is achieved mainly with channel information-based beamforming algorithms. Precoding methods perform differently in various CoMP scenarios. In this paper, we propose a joint processing scheme in downlink CoMP transmission system. We apply block diagonal beamforming to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point.

Question: What is the difference between precoding and beamforming?


Energy-efficient deployment of Intelligent Mobile sensor networks. Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.

Question: How can we deploy sensors in an energy-efficient manner?


Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks. Recurrent neural network (RNN) models are widely used for processing sequential data governed by a latent tree structure. Previous work shows that RNN models (especially Long Short-Term Memory (LSTM) based models) could learn to exploit the underlying tree structure. However, its performance consistently lags behind that of tree-based models. This work proposes a new inductive bias Ordered Neurons, which enforces an order of updating frequencies between hidden state neurons. We show that the ordered neurons could explicitly integrate the latent tree structure into recurrent models. To this end, we propose a new RNN unit: ON-LSTM, which achieve good performances on four different tasks: language modeling, unsupervised parsing, targeted syntactic evaluation, and logical inference1.

Question: What is the difference between a tree-based model and a recurrent neural network model?


Efficient and Low Latency Detection of Intruders in Mobile Active Authentication. Active authentication (AA) refers to the problem of continuously verifying the identity of a mobile device user for the purpose of securing the device. We address the problem of quickly detecting intrusions with lower false detection rates in mobile AA systems with higher resource efficiency. Bayesian and MiniMax versions of the quickest change detection (QCD) algorithms are introduced to quickly detect intrusions in mobile AA systems. These algorithms are extended with an update rule to facilitate low-frequency sensing which leads to low utilization of resources. Effectiveness of the proposed framework is demonstrated using three publicly available unconstrained face and touch gesture-based AA datasets. It is shown that the proposed QCD-based intrusion detection methods can perform better than many state-of-the-art AA methods in terms of latency and low false detection rates. Furthermore, it is shown that employing the proposed resource-efficient extension further improves the performance of the QCD-based setup.

Question: How to detect intruders in mobile active authentication?


Integrated system of face recognition and sound localization for a smart door phone. This paper proposes a smart system using both face recognition and sound localization techniques to identify foreign faces through a door phone in a more efficient and accurate way. In particular, a visitor's voice is used to localize the proper location of the speaker. The location information is then used to adjust the door phone camera position to frame the face in its field of view. In order to accurately locate the facial position of the visitor, 4 microphones are positioned in a cross configuration. Furthermore, face detection and face recognition algorithms as well as a wireless interface are used to recognize the visitor and send the information to the home owner's phone. The entire control system is built using an FPGA chip and tested for actual use in door phone environments1.

Question: How to build a smart door phone?


On the role of color in the perception of motion in animated visualizations. Although luminance contrast plays a predominant role in motion perception, significant additional effects are introduced by chromatic contrasts. In this paper, relevant results from psychophysical and physiological research are described to clarify the role of color in motion detection. Interpreting these psychophysical experiments, we propose guidelines for the design of animated visualizations, and a calibration procedure that improves the reliability of visual motion representation. The guidelines are applied to examples from texture-based flow visualization, as well as graph and tree visualization.

Question: What is the role of color in the perception of motion in animated visualizations?


A 280-KBytes Twin-Bit-Cell Embedded NOR Flash Memory With a Novel Sensing Current Protection Enhanced Technique and High-Voltage Generating Systems. A novel sensing current protection enhanced (SCPE) technique and high-voltage (HV) generating systems are adopted to greatly enhance the advantages of the twin-bit cell. The SCPE technique provides the tradeoff of sensing margin loss between "1" and "0" bits sensing case to realize fast read access. The read speed is improved by 7.7%. HV generating systems with parallel-series-transform and capacitance-shared techniques are proposed to satisfy the complicated requirements of output capability and current drivability with lower area penalty. The HV periphery area decreases by 71%. A 1.5-V 280-KBytes embedded NOR flash memory IP has been fabricated in HHGrace (Shanghai Huahong Grace Semiconductor Manufacturing Corporation) 90-nm 4 poly 4 metal CMOS process. The complicated operation voltages and access time of 23.5 ns have been achieved at 1.5 V. The die size of the proposed IP is 0.437 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> and the area size of charge pump has been reduced to 0.006 mm<inline-formula> <tex-math notation="LaTeX">$^{2}$ </tex-math></inline-formula> on average.

Question: What is the area size of the charge pump?


Intentional attunement: A neurophysiological perspective on social cognition and its disruption in autism. A direct form of experiential understanding of others, "intentional attunement", is achieved by modeling their behavior as intentional experiences on the basis of the activation of shared neural systems underpinning what the others do an feel and what we do and feel. This modeling mechanism is embodied simulation. In parallel with the detached sensory description of the observed social stimuli, internal representations of the body states associated with actions, emotions, and sensations are evoked in the observer, as if he/she would be doing a similar action or experiencing a similar emotion or sensation. Mirror neuron systems are likely the neural correlate of this mechanism. By means of a shared neural state realized in two different bodies, the "objectual other" becomes "another self". A defective intentional attunement caused by a lack of embodies simulation might cause some of the social impairments of autistic individuals.

Question: What is the neural correlate of intentional attunement?


Virgin soil in irony research : Personality , humor , and the " sense of irony ". he aim of the paper is fourfold: (a) show why humor scholars should study irony, (b) explore the need for considering interindividual differences in healthy adults' irony performance, (c) stress the necessity for developing tools assessing habitual differences in irony performance, and (d) indicate future directions for joint irony and humor research and outline possible applications. Verbal irony is often employed with a benevolent humorous intent by speakers, but can also serve as a means of disparagement humor. In both cases, encoding and decoding activities entailing irony need to be considered in the context of the psychology of humor. We argue that verbal irony performance can be considered a phenomenon native to the realm of humor and individual differences. We point out that research has widely neglected the meaningfulness of variance in irony performance within experimental groups when looking at determinants of irony detection and production. Based on theoretical considerations and previous empirical findings we show that this variance can be easily related to individual-differences variables such as the sense of humor, dispositions toward laughter and ridicule (e.g., gelotophobia), and general mental ability. Furthermore, we hypothesize that there is an enduring trait determining irony performance we will label the sense of irony. The sense of irony possibly goes along with inclinations toward specific affective and cognitive processing patterns when dealing with verbal irony. As an application, novel irony performance tests can help to study psychological and neurophysiological correlates of irony performance more feasibly, that is, in nonclinical groups. DOI: https://doi.org/10.1037/tps0000054 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-124094 Accepted Version Originally published at: Bruntsch, Richard; Hofma

Question: What is the sense of irony?


Semantic Linking of Learning Object Repositories to DBpedia. Large-sized repositories of learning objects (LOs) are difficult to create and also to maintain. In this paper we propose a way to reduce this drawback by improving the classification mechanisms of the LO repositories. Specifically, we present a solution to automate the LO classification of the Universia repository, a collection of more than 15 million of LOs represented according to the IEEE LOM standard. Although a small part of these LOs is correctly classified, most are unclassified and therefore searching and accessing information is difficult. Our solution makes use of the categories provided by DBpedia, a linked data repository, to automatically improve its classification through a graph-based filtering algorithm, which selects the most suitable categories for describing a LO. Once selected, these categories will classify the LO, linking its classification metadata with a set of DBpedia categories.

Question: How to classify a LO?


A survey of datasets for visual tracking. For 15 years now, visual tracking has been a very active research area of the computer vision community. But an increasing amount of works can be observed in the last five years. This has led to the development of numerous algorithms that can deal with more and more complex video sequences. Each of them has its own strengths and weaknesses. That is the reason why it becomes necessary to compare those algorithms. For this purpose, some datasets dedicated to visual tracking as well as, sometimes, their ground truth annotation files are regularly made publicly available by researchers. However, each dataset has its own specificities and is sometimes dedicated to test the ability of some algorithms to tackle only one or a few specific visual tracking subproblems. This article provides an overview of some of the datasets that are most used by the visual tracking community, but also of others that address specific tasks. We also propose a cartography of these datasets from a novel perspective, namely that of the difficulties the datasets present for visual tracking.

Question: What is a dataset for visual tracking?


Coping theory and research: past, present, and future.. In this essay in honor of Donald Oken, I emphasize coping as a key concept for theory and research on adaptation and health. My focus will be the contrasts between two approaches to coping, one that emphasizes style—that is, it treats coping as a personality characteristic—and another that emphasizes process—that is, efforts to manage stress that change over time and are shaped by the adaptational context out of which it is generated. I begin with an account of the style and process approaches, discuss their history briefly, set forth the principles of a process approach, describe my own efforts at measurement, and define coping and its functions from a process standpoint. This is followed by a digest of major generalizations that resulted from coping process research. The essay concludes with a discussion of special issues of coping measurement, in particular, the limitations of both coping style and process approaches and how these limitations might be dealt with. There has been a prodigious volume of coping research in the last decade or two, which I can only touch on very selectively. In this essay, I also ignore a host of important developmental issues that have to do with the emergence of coping and its cognitive and motivational bases in infants, as well as a growing literature on whether, how, and why the coping process changes with aging.

Question: What is coping?


The nude image identification with adaptive skin chromatic distribution matching scheme. This paper presents a new nude image identification system. We propose an adaptive chroma-distribution matching scheme based on face detection to on-line determine the image's skin chromatic distribution such that it can tolerate the color deviation coming from special lighting without increasing false alarm. The object detection based on machine learning approach is used to locate the face region in the test image. According to the color information of face, the matched skin chromatic distribution is selected to detect the skin objects in the test image. The texture feature, namely coarseness, is used to acquire accurate skin segmentation. The low-level but reliable geometrical constraints and the mug shot exclusion procedure are employed to further examine the skin regions. Experimental results that the overall detection rate is 86.3% show our method can achieve satisfactory performance for detecting nude images under special lighting conditions.

Question: What is the nude image identification with adaptive skin chromatic distribution matching scheme?


Application-aware prioritization mechanisms for on-chip networks. Network-on-Chips (NoCs) are likely to become a critical shared resource in future many-core processors. The challenge is to develop policies and mechanisms that enable multiple applications to efficiently and fairly share the network, to improve system performance. Existing local packet scheduling policies in the routers fail to fully achieve this goal, because they treat every packet equally, regardless of which application issued the packet.  This paper proposes prioritization policies and architectural extensions to NoC routers that improve the overall application-level throughput, while ensuring fairness in the network. Our prioritization policies are application-aware, distinguishing applications based on the stall-time criticality of their packets. The idea is to divide processor execution time into phases, rank applications within a phase based on stall-time criticality, and have all routers in the network prioritize packets based on their applications' ranks. Our scheme also includes techniques that ensure starvation freedom and enable the enforcement of system-level application priorities.  We evaluate the proposed prioritization policies on a 64-core CMP with an 8x8 mesh NoC, using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 25.6% over age-based arbitration and 18.4% over round-robin arbitration. Averaged over 96 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 9.1% over the best existing prioritization policy, while also reducing application-level unfairness.

Question: What is the average system throughput?


Neural Question Answering at BioASQ 5B. This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-ofthe-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.

Question: What is the neural question answering system?


Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology. The rapid growth of investment in information technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation a d management issue. While such acceptance has received fairly extensive attention from previous research, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts. In response, this paper reports a research work that examined the applicability of the Technology Acceptance Model (ΤΑΜ) in explaining physicians' decisions to accept telemedicine technology in the health-care context. The technology, the user group, and the organizational context are all new to IT acceptance/adoption research. The study also addressed a pragmatic technology management need resulting from millions of dollars invested by healthcare organizations in developing and implementing telemedicine programs in recent years. The model's overall fit, explanatory power, and the individual causal links that it postulates were evaluated by examining the acceptance of telemedicine technology among physicians practicing at public tertiary hospitals in Hong Kong. Our results suggested that ΤΑΜ was able to provide a reasonable depiction of physicians' intention to use telemedicine technology. Perceived usefulness was found to be a significant determinant ofattitude and intention but perceived ease of use was not. The relatively low R-square of the model suggests both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context. Based on the study findings, implications for user technology acceptance research and telemedicine management are discussed.

Question: What is the technology acceptance model?


High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis. Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.

Question: What is the inpainting accuracy?


A Generalized Representer Theorem. Wahba's classical representer theorem states that the solutions of certain risk minimization problems involving an empirical risk term and a quadratic regularizer can be written as expansions in terms of the training examples. We generalize the theorem to a larger class of regularizers and empirical risk terms, and give a self-contained proof utilizing the feature space associated with a kernel. The result shows that a wide range of problems have optimal solutions that live in the finite dimensional span of the training examples mapped into feature space, thus enabling us to carry out kernel algorithms independent of the (potentially infinite) dimensionality of the feature space.

Question: What is a generalized representer theorem?


Running on the bare metal with GeekOS. Undergraduate operating systems courses are generally taught using one of two approaches: abstract or concrete. In the abstract approach, students learn the concepts underlying operating systems theory, and perhaps apply them using user-level threads in a host operating system. In the concrete approach, students apply concepts by working on a real operating system kernel. In the purest manifestation of the concrete approach, students implement operating system projects that run on real hardware.GeekOS is an instructional operating system kernel which runs on real hardware. It provides the minimum functionality needed to schedule threads and control essential devices on an x86 PC. On this foundation, we have developed projects in which students build processes, semaphores, a multilevel feedback scheduler, paged virtual memory, a filesystem, and inter-process communication. We use the Bochs emulator for ease of development and debugging. While this approach (tiny kernel run on an emulator) is not new, we believe GeekOS goes further towards the goal of combining realism and simplicity than previous systems have.

Question: What is the difference between abstract and concrete?


Detection , localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors. Permanently attached piezoelectric sensors arranged in a spatially distributed array are under consideration for structural health monitoring systems incorporating active ultrasonic methods. Most damage detection and localization methods that have been proposed are based upon comparing monitored signals to baselines recorded from the structure prior to initiation of damage. To be effective, this comparison process must take into account any conditions other than damage that have changed the ultrasonic signals. Proposed here is a two-step process whereby damage is first detected and is then localized and characterized. The detection strategy considers the long time behavior of the signals in the diffuse-like regime where distinct echoes can no longer be identified. The localization strategy is to generate images of damage based upon the early time regime when discrete echoes from boundary reflections and scattering sites are meaningful. Results are shown for an aluminum plate with artificial damage introduced in combination with temperature variations. The loss of local temporal coherence combined with an optimal baseline selection procedure is shown to be effective for the detection of damage, and a delay-and-sum imaging method applied to the residual signals both localizes the damage and provides characterization information. (Some figures in this article are in colour only in the electronic version)

Question: What is the difference between a piezoelectric sensor and a piezoelectric transducer?


Learning to Detect Anomalies in Surveillance Video. Detecting anomalies in surveillance videos, that is, finding events or objects with low probability of occurrence, is a practical and challenging research topic in computer vision community. In this paper, we put forward a novel unsupervised learning framework for anomaly detection. At feature level, we propose a Sparse Semi-nonnegative Matrix Factorization (SSMF) to learn local patterns at each pixel, and a Histogram of Nonnegative Coefficients (HNC) can be constructed as local feature which is more expressive than previously used features like Histogram of Oriented Gradients (HOG). At model level, we learn a probability model which takes the spatial and temporal contextual information into consideration. Our framework is totally unsupervised requiring no human-labeled training data. With more expressive features and more complicated model, our framework can accurately detect and localize anomalies in surveillance video. We carried out extensive experiments on several benchmark video datasets for anomaly detection, and the results demonstrate the superiority of our framework to state-of-the-art approaches, validating the effectiveness of our framework.

Question: How to detect anomalies in surveillance video?


Children's engagement with educational iPad apps: Insights from a Spanish classroom. This study investigates the effects of a story-making app called Our Story and a selection of other educational apps on the learning engagement of forty-one Spanish 4–5-year-olds. Children were observed interacting in small groups with the story-making app and this was compared to their engagement with a selection of construction and drawing apps. Children's engagement was analysed in two ways: it was categorised using Bangert-Drowns and Pyke's taxonomy for individual hands-on engagement with educational software, and using the concept of exploratory talk as developed by Mercer et al. to analyse peer engagement. For both approaches, quantitative and qualitative indices of children's engagement were considered. The overall findings suggested that in terms of the BangertDrowns and Pyke taxonomy, the quality of children's individual engagement was higher with the OS app in contrast to their engagement with other app software. The frequency of children's use of exploratory talk was similar with the OS and colouring and drawing apps, and a detailed qualitative analysis of the interaction transcripts revealed several instances of the OS and drawing apps supporting joint problem-solving and collaborative engagement. We suggest that critical indices of an app's educational value are the extent to which the app supports opportunities for open-ended content and children's independent use of increasingly difficult features. 2013 Elsevier Ltd. All rights reserved.

Question: What is the educational value of an educational iPad app?


Implicit Subjective and Sentimental Usages in Multi-sense Word Embeddings. In multi-sense word embeddings, contextual variations in corpus may cause a univocal word to be embedded into different sense vectors. Shi et al. (2016) show that this kind of pseudo multi-senses can be eliminated by linear transformations. In this paper, we show that pseudo multi-senses may come from a uniform and meaningful phenomenon such as subjective and sentimental usage, though they are seemingly redundant. In this paper, we present an unsupervised algorithm to find a linear transformation which can minimize the transformed distance of a group of sense pairs. The major shrinking direction of this transformation is found to be related with subjective shift. Therefore, we can not only eliminate pseudo multi-senses in multisense embeddings, but also identify these subjective senses and tag the subjective and sentimental usage of words in the corpus automatically.

Question: What is the major shrinking direction of this transformation?


RF-MEMS Switches for Reconfigurable Integrated Circuits. This paper deals with a relatively new area of radio-frequency (RF) technology based on microelectromechanical systems (MEMS). RF MEMS provides a class of new devices and components which display superior high-frequency performance relative to conventional (usually semiconductor) devices, and which enable new system capabilities. In addition, MEMS devices are designed and fabricated by techniques similar to those of very large-scale integration, and can be manufactured by traditional batch-processing methods. In this paper, the only device addressed is the electrostatic microswitch—perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capacitor banks, and time-delay networks. The superior performance combined with ultra-low-power dissipation and large-scale integration should enable new system functionality as well. Two possibilities addressed here are quasi-optical beam steering and electrically reconfigurable antennas.

Question: What is the difference between a microswitch and a switch?


100 W GaN HEMT power amplifier module with > 60% efficiency over 100–1000 MHz bandwidth. We have demonstrated a decade bandwidth 100 W GaN HEMT power amplifier module with 15.5–18.6 dB gain, 104–121 W CW output power and 61.4–76.6 % drain efficiency over the 100–1000 MHz band. The 2 × 2 inch compact power amplifier module combines four 30 W lossy matched broadband GaN HEMT PAs packaged in a ceramic SO8 package. Each of the 4 devices is fully matched to 50 Ω and obtains 30.8–35.7 W with 68.6–79.6 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 48V drain voltage, alongside GaAs integrated passive matching circuitry. The four devices are combined using a broadband low loss coaxial balun. We believe this combination of output power, bandwidth and efficiency is the best reported to date. These amplifiers are targeted for use in multi-band public mobile radios and for instrumentation applications.

Question: What is the best reported to date?


Pileus: protecting user resources from vulnerable cloud services. Cloud computing platforms are now constructed as distributed, modular systems of cloud services, which enable cloud users to manage their cloud resources. However, in current cloud platforms, cloud services fully trust each other, so a malicious user may exploit a vulnerability in a cloud service to obtain unauthorized access to another user's data. To date, over 150 vulnerabilities have been reported in cloud services in the OpenStack cloud. Research efforts in cloud security have focused primarily on attacks originating from user VMs or compromised operating systems rather than threats caused by the compromise of distributed cloud services, leaving cloud users open to attacks from these vulnerable cloud services. In this paper, we propose the Pileus cloud service architecture, which isolates each user's cloud operations to prevent vulnerabilities in cloud services from enabling malicious users to gain unauthorized access. Pileus deploys stateless cloud services "on demand" to service each user's cloud operations, limiting cloud services to the permissions of individual users. Pileus leverages the decentralized information flow control (DIFC) model for permission management, but the Pileus design addresses special challenges in the cloud environment to: (1) restrict how cloud services may be allowed to make security decisions; (2) select trustworthy nodes for access enforcement in a dynamic, distributed environment; and (3) limit the set of nodes a user must trust to service each operation. We have ported the OpenStack cloud platform to Pileus, finding that we can systematically prevent compromised cloud services from attacking other users' cloud operations with less than 3% additional latency for the operation. Application of the Pileus architecture to Open-Stack shows that confined cloud services can service users' cloud operations effectively for a modest overhead.

Question: What is the overhead of the pileus architecture?


Scaling to Very Very Large Corpora for Natural Language Disambiguation. The amount of readily available on-line text has reached hundreds of billions of words and continues to grow. Yet for most core natural language tasks, algorithms continue to be optimized, tested and compared after training on corpora consisting of only one million words or less. In this paper, we evaluate the performance of different learning methods on a prototypical natural language disambiguation task, confusion set disambiguation, when trained on orders of magnitude more labeled data than has previously been used. We are fortunate that for this particular application, correctly labeled training data is free. Since this will often not be the case, we examine methods for effectively exploiting very large corpora when labeled data comes at a cost.

Question: How to scale to very very large corpora for natural language disambiguation?


Effects of concurrent endurance and strength training on running economy and .VO(2) kinetics.. PURPOSE It has been suggested that endurance training influences the running economy (CR) and the oxygen uptake (.VO(2)) kinetics in heavy exercise by accelerating the primary phase and attenuating the .VO(2) slow component. However, the effects of heavy weight training (HWT) in combination with endurance training remain unclear. The purpose of this study was to examine the influence of a concurrent HWT+endurance training on CR and the .VO(2) kinetics in endurance athletes.   METHODS Fifteen triathletes were assigned to endurance+strength (ES) or endurance-only (E) training for 14 wk. The training program was similar, except ES performed two HWT sessions a week. Before and after the training period, the subjects performed 1) an incremental field running test for determination of .VO(2max) and the velocity associated (V(.VO2max)), the second ventilatory threshold (VT(2)); 2) a 3000-m run at constant velocity, calculated to require 25% of the difference between .VO(2max) and VT(2), to determine CR and the characteristics of the VO(2) kinetics; 3) maximal hopping tests to determine maximal mechanical power and lower-limb stiffness; 4) maximal concentric lower-limb strength measurements.   RESULTS After the training period, maximal strength were increased (P < 0.01) in ES but remained unchanged in E. Hopping power decreased in E (P < 0.05). After training, economy (P < 0.05) and hopping power (P < 0.001) were greater in ES than in E. .VO(2max), leg hopping stiffness and the .VO(2) kinetics were not significantly affected by training either in ES or E.   CONCLUSION Additional HWT led to improved maximal strength and running economy with no significant effects on the .VO(2) kinetics pattern in heavy exercise.

Question: What is the difference between a triathlete and a triathlete?


Measurement-Based Harmonic Modeling of an Electric Vehicle Charging Station Using a Three-Phase Uncontrolled Rectifier. The harmonic pollution of electric vehicle chargers is increasing rapidly as the scale of electric vehicle charging stations enlarges to meet the rising demand for electric vehicles. This paper investigates the operating characteristics of a three-phase uncontrolled rectification electric vehicle charger with passive power factor correction. A method for estimating the equivalent circuit parameters of chargers is proposed based on the measured feature data of voltage and current at the ac side and on the circuit constraint during the conduction charging process. A harmonic analytical model of the charging station is then established by dividing the same charger types into groups. The parameter estimation method and the harmonic model of the charging station are verified through simulation, experiment, and field test. The parameter sensitivities of the equivalent circuit to the charging current harmonic are also studied.

Question: How to measure the harmonic pollution of an electric vehicle charging station?


Hybrid Forest: A Concept Drift Aware Data Stream Mining Algorithm. Nowadays with a growing number of online controlling systems in the organization and also a high demand of monitoring and stats facilities that uses data streams to log and control their subsystems, data stream mining becomes more and more vital. Hoeffding Trees (also called Very Fast Decision Trees a.k.a. VFDT) as a Big Data approach in dealing with the data stream for classification and regression problems showed good performance in handling facing challenges and making the possibility of any-time prediction. Although these methods outperform other methods e.g. Artificial Neural Networks (ANN) and Support Vector Regression (SVR), they suffer from high latency in adapting with new concepts when the statistical distribution of incoming data changes. In this article, we introduced a new algorithm that can detect and handle concept drift phenomenon properly. This algorithms also benefits from fast startup ability which helps systems to be able to predict faster than other algorithms at the beginning of data stream arrival. We also have shown that our approach will overperform other controversial approaches for classification and regression tasks.

Question: What is a data stream mining algorithm?


Word Translation Without Parallel Data. State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data supervision can be alleviated with character-level information. While these methods showed encouraging results, they are not on par with their supervised counterparts and are limited to pairs of languages sharing a common alphabet. In this work, we show that we can build a bilingual dictionary between two languages without using any parallel corpora, by aligning monolingual word embedding spaces in an unsupervised way. Without using any character information, our model even outperforms existing supervised methods on cross-lingual tasks for some language pairs. Our experiments demonstrate that our method works very well also for distant language pairs, like English-Russian or EnglishChinese. We finally describe experiments on the English-Esperanto low-resource language pair, on which there only exists a limited amount of parallel data, to show the potential impact of our method in fully unsupervised machine translation. Our code, embeddings and dictionaries are publicly available1.

Question: How can we build a bilingual dictionary without using parallel corpora?


Control Knowledge to Improve Plan Quality. Generating production-quality plans is an essential element in transforming planners from research tools into real-world applications. However most of the work to date on learning planning control knowledge has been aimed at improving the efficiency of planning; this work has been termed "speed-up learning". This paper focuses on learning control knowledge to guide a planner towards better solutions, i.e. to improve the quality of the plans produced by the planner, as its problem solving experience increases. We motivate the use of quality-enhancing search control knowledge and its automated acquisition from problem solving experience. We introduce an implemented mechanism for learning such control knowledge and some of our preliminary results in a process planning domain.

Question: How to improve the quality of the plans produced by the planner?


A (sub)graph isomorphism algorithm for matching large graphs. We present an algorithm for graph isomorphism and subgraph isomorphism suited for dealing with large graphs. A first version of the algorithm has been presented in a previous paper, where we examined its performance for the isomorphism of small and medium size graphs. The algorithm is improved here to reduce its spatial complexity and to achieve a better performance on large graphs; its features are analyzed in detail with special reference to time and memory requirements. The results of a testing performed on a publicly available database of synthetically generated graphs and on graphs relative to a real application dealing with technical drawings are presented, confirming the effectiveness of the approach, especially when working with large graphs.

Question: What is a graph isomorphism algorithm?


Math Anxiety : Personal , Educational , and Cognitive Consequences. Highly math-anxious individuals are characterized by a strong tendency to avoid math, which ultimately undercuts their math competence and forecloses important career paths. But timed, on-line tests reveal math-anxiety effects on w h o l e-n u m b e r a r i t h m e t i c p r o b l e m s (e. g. , 4 6 ϩ 2 7) , whereas achievement tests show no competence differences. Math anxiety disrupts cognitive processing by compromising ongoing activity in working memory. Although the causes of math anxiety are undetermined, some teaching styles are implicated as risk factors. We need research on the origins of math anxiety and on its " signature " in brain activity , to examine both its emo-t i o n a l a n d i t s c o g n i t i v e components. My graduate assistant recently told me about a participant he had tested in the lab. She exhibited increasing discomfort and nervousness as the testing session progressed , eventually becoming so distraught that she burst into tears. My assistant remarked that many of our participants show some un-ease or apprehension during test-ing—trembling hands, nervous laughter, and so forth. Many ask, defensively, if their performance says anything about their overall intelligence. These occasionally extreme emotional reactions are not triggered by deliberately provocative procedures—there are no personally sensitive questions or intentional manipulations of stress. Instead, we merely ask college adults to solve elementary-school arithmetic problems, such as 46 ϩ 18 ϭ ? and 34 Ϫ 19 ϭ ? The reactions are obvious symptoms of anxiety, in this case math anxiety induced by ordinary arithmetic problems presented in timed tasks. On the one hand, it is almost unbelievable that tests on such fundamental topics can be so upsetting ; knowing that 15 Ϫ 8 ϭ 7 ought to be as basic as knowing how to spell " cat. " On the other hand, U.S. culture abounds with attitudes that foster math anxiety: Mat

Question: What is the difference between a math anxiety and a math phobia?


Soft decoding of JPEG 2000 compressed images using bit-rate-driven deep convolutional neural networks. Lossy image compression methods always introduce various unpleasant artifacts into the compressed results, especially at low bit-rates. In recent years, many effective soft decoding methods for JPEG compressed images have been proposed. However, to the best of our knowledge, very few works have been done on soft decoding of JPEG 2000 compressed images. Inspired by the outstanding performance of Convolution Neural Network (CNN) in various computer vision tasks, we presents a soft decoding method for JPEG 2000 by using multiple bit-rate-driven deep CNNs. More specifically, in training stage, we train a series of deep CNNs using lots of high quality training images and the corresponding JPEG 2000 compressed images at different coding bit-rates. In testing stage, for an input compressed image, the CNN trained with the nearest coding bit-rate is selected to perform soft decoding. Extensive experiments demonstrate the effectiveness of the presented soft decoding framework, which greatly improves the visual quality and objective scores of JPEG 2000 compressed images.

Question: What is the soft decoding of JPEG 2000 compressed images?


Multimodal Deep Learning. Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train a deep network that learns features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. We validate our methods on the CUAVE and AVLetters datasets with an audio-visual speech classification task, demonstrating superior visual speech classification on AVLetters and effective multimodal fusion.

Question: How to train a deep network that learns features to address these tasks.


Comparison of supervised learning methods for spike time coding in spiking neural networks. In this review we focus our attention on the supervised learning methods for spike time coding in Spiking Neural Networks (SNN). This study is motivated by the recent experimental results on information coding in the biological neural systems which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We pose a fundamental question, what paradigms of neural temporal coding can be implemented with the recent learning methods. In order to answer this question we discuss various approaches to the considered learning task. We shortly describe the particular learning algorithms and report the results of experiments. Finally we discuss properties, assumptions and limitations of each method. We complete this review with a comprehensive list of pointers to the literature.

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


Knowledge discovery and data mining in toxicology.. Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.

Question: What is the most important application area of knowledge discovery and data mining in toxicology?


Based Emotion Recognition : A Survey. Human Computer interaction is a very powerful and most current area of research because the human world is getting more digitize. This needs the digital systems to imitate the human behaviour correctly. Emotion is one aspect of human behaviour which plays an important role in human computer interaction, the computer interfaces need to recognize the emotion of the users in order to exhibit a truly intelligent behaviour. Human express the emotion in the form facial expression, speech, and writing text. Every day, massive amount of textual data is gathered into internet such as blogs, social media etc. This comprise a challenging style as it is formed with both plaint text and short messaging language. This paper is mainly focused on an overview of emotion detection from text and describes the emotion detection methods. These methods are divided into the following four main categories: keyword-based, Lexical Affinity method, learning based, and hybrid based approach. Limitations of these emotion recognition methods are presented in this paper and also, addresses the text normalization using different handling techniques for both plaint text and short messaging language.

Question: What is the difference between keyword-based, Lexical Affinity method, learning based, and hybrid based approach?


Computationally modeling human emotion. Computer models of emotion inform theories of human intelligence and advance human-centric applications.

Question: What is a computer model of emotion?


Role of Emotional Intelligence for Academic Achievement for Students. In the current competitive environment where students are expected to perform multi roles with efficiency and effectiveness, it is highly needed to develop their right attitude and emotional intelligence towards the unseen complexities of life and quality education. As emotional intelligence is a subset of social intelligence with the ability to understand and monitor one's own feelings and others too which allows a student to mine the required data for his academic achievement which is an outcome of education and the extent at which the educational goal has been achieved. The emphasis of this paper was to determine the factors which are affecting the development of emotional intelligence and its role in academic achievement for students. In this research secondary data has been collected out of which we find out the correlation between emotional intelligence and academic achievement and teaching emotional and social skills at school not only positively influence academic achievement during the year when these were taught but also leaves the impact in long term achievement. Findings of this paper present that academic achievement without emotional intelligence does not indicate future success and absence of emotional intelligence also indicate the week personality and ability to build relations at working place as well in schools and it is highly important for quality education.

Question: What is the role of emotional intelligence in academic achievement?


Performance anomaly of 802.11b. We analyze the performance of the IEEE 802.11b wireless local area networks. We have observed that when some mobile hosts use a lower bit rate than the others, the performance of all hosts is considerably degraded. Such a situation is a common case in wireless local area networks in which a host far away from an Access Point is subject to important signal fading and interference. To cope with this problem, the host changes its modulation type, which degrades its bit rate to some lower value. Typically, 802.11b products degrade the bit rate from 11 Mb/s to 5.5, 2, or 1 Mb/s when repeated unsuccessful frame transmissions are detected. In such a case, a host transmitting for example at 1 Mb/s reduces the throughput of all other hosts transmitting at 11 Mb/s to a low value below 1 Mb/s. The basic CSMA/CA channel access method is at the root of this anomaly: it guarantees an equal long term channel access probability to all hosts. When one host captures the channel for a long time because its bit rate is low, it penalizes other hosts that use the higher rate. We analyze the anomaly theoretically by deriving simple expressions for the useful throughput, validate them by means of simulation, and compare with several performance measurements.

Question: What is the 802.11b performance anomaly?


Shakeout: A New Regularized Deep Neural Network Training Scheme. Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. The invention of effective training techniques largely contributes to this success. The so-called "Dropout" training scheme is one of the most powerful tool to reduce over-fitting. From the statistic point of view, Dropout works by implicitly imposing an L2 regularizer on the weights. In this paper, we present a new training scheme: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, our method randomly chooses to enhance or inverse the contributions of each unit to the next layer. We show that our scheme leads to a combination of L1 regularization and L2 regularization imposed on the weights, which has been proved effective by the Elastic Net models in practice. We have empirically evaluated the Shakeout scheme and demonstrated that sparse network weights are obtained via Shakeout training. Our classification experiments on real-life image datasets MNIST and CIFAR10 show that Shakeout deals with over-fitting effectively.

Question: What is the difference between L1 and L2 regularization?


An Anomaly Detection Method for Spacecraft Using Relevance Vector Learning. This paper proposes a novel anomaly detection system for spacecrafts based on data mining techniques. It constructs a nonlinear probabilistic model w.r.t. behavior of a spacecraft by applying the relevance vector regression and autoregression to massive telemetry data, and then monitors the on-line telemetry data using the model and detects anomalies. A major advantage over conventional anomaly detection methods is that this approach requires little a priori knowledge on the system.

Question: What is a spacecraft?


Teaching the science of learning. The science of learning has made a considerable contribution to our understanding of effective teaching and learning strategies. However, few instructors outside of the field are privy to this research. In this tutorial review, we focus on six specific cognitive strategies that have received robust support from decades of research: spaced practice, interleaving, retrieval practice, elaboration, concrete examples, and dual coding. We describe the basic research behind each strategy and relevant applied research, present examples of existing and suggested implementation, and make recommendations for further research that would broaden the reach of these strategies.

Question: What are the cognitive strategies of learning?


Phoneme visem mapping for Marathi language using linguistic approach. Visual speech is transcribed using visems. Visem is a set of visibly different phonemes in a language. There is high correlation between Phoneme and visem and phoneme to visem mapping is many to one because of the fact that many phonemes cannot be distinguished visually. Since each language has its own phonetic set, visem classification becomes language dependant process. Most of the phoneme visem maps available in literature are developed for English language. This paper presents phoneme visem mapping of consonants and vowels of Marathi language based on linguistic approach. This is the first attempt towards Marathi phoneme visem mapping and will be useful for visual speech recognition in Marathi language.

Question: What is the first attempt towards Marathi phoneme visem mapping?


Local sensor system for badminton smash analysis. This paper presents a development of a sensory system for analysis of badminton smashes. During a badminton game, the ability to execute a powerful smash is fundamental for a player to be competitive. In most games, the winning factor for the game is often attributed to a high shuttle speed during the execution of a smash. It was envisioned that the shuttle speed can be correlated from the speed of the rackets head. To help analyze the key relationship between the racket speed and the shuttle ball speed, a series of sensors were integrated into a conventional racket. The aim of this investigation is to develop a sensor system that will facilitate the quantifiable analysis of the badminton smash. It was determined in a previous investigation that a single accelerometer is insufficient to determine the two or three dimensional trajectory of the racket. Therefore a low mass compact, 2-axes piezoelectric accelerometers was applied to the head of the racket. An acoustic shock sensor was also mounted on the racket head to identify the instant of the contact event. It was hypothesized that the magnitude of the acoustic signal, associated with the hitting event, and the final speed of the racket when fused could provide a good correlation with the shuttle speed. The fuzzy inference system and ANFIS (Adaptive Neuro-Fuzzy Inferior System) sensor fusion techniques was investigated. It has been demonstrated that it is possible to analyze the performance of the smashing stroke based on the system developed in this investigation which in turn may, with further develop by a useful aid to badminton coaches and the methods developed may also be applicable to other applications.

Question: What is the difference between a sensor and a sensor system?