AccessMiner: using system-centric models for malware protection. Models based on system calls are a popular and common approach to characterize the run-time behavior of programs. For example, system calls are used by intrusion detection systems to detect software exploits. As another example, policies based on system calls are used to sandbox applications or to enforce access control. Given that malware represents a significant security threat for today's computing infrastructure, it is not surprising that system calls were also proposed to distinguish between benign processes and malicious code.  Most proposed malware detectors that use system calls follows program-centric analysis approach. That is, they build models based on specific behaviors of individual applications. Unfortunately, it is not clear how well these models generalize, especially when exposed to a diverse set of previously-unseen, real-world applications that operate on realistic inputs. This is particularly problematic as most previous work has used only a small set of programs to measure their technique's false positive rate. Moreover, these programs were run for a short time, often by the authors themselves.  In this paper, we study the diversity of system calls by performing a large-scale collection (compared to previous efforts) of system calls on hosts that run applications for regular users on actual inputs. Our analysis of the data demonstrates that simple malware detectors, such as those based on system call sequences, face significant challenges in such environments. To address the limitations of program-centric approaches, we propose an alternative detection model that characterizes the general interactions between benign programs and the operating system (OS). More precisely, our system-centric approach models the way in which benign programs access OS resources (such as files and registry entries). Our experiments demonstrate that this approach captures well the behavior of benign pro

Question: What is the difference between a system-centric model and a program-centric model?


Transanal endoscopic total mesorectal excision: technical aspects of approaching the mesorectal plane from below—a preliminary report. Laparoscopic total mesorectal excision (TME) for low rectal cancer can be technically challenging. This report describes our initial experience with a hybrid laparoscopic and transanal endoscopic technique for TME in low rectal cancer. Between December 2012 and October 2013, we identified patients with rectal cancer < 5 cm from the anorectal junction (ARJ) who underwent laparoscopic-assisted TME with a transanal minimally invasive surgery (TAMIS) technique. A standardized stepwise approach was used in all patients. Resection specimens were examined for completeness and measurement of margins. Preoperative magnetic resonance imaging (MRI) characteristics and short-term postoperative outcomes were examined. All values are mean ± standard deviation. Ten patients (8 males; median age: 60.5 (range 36–70) years) were included. On initial MRI, all tumors were T2 or T3, mean tumor height from the ARJ was 28.9 ± 12.2 mm, mean circumferential resection margin was 5.3 ± 3.1 mm , and the mean angle between the anal canal and the levator ani was 83.9° ± 9.7°. All patients had had preoperative chemoradiotherapy, TME via TAMIS, and distal anastomosis. There were no intraoperative complications, anastomotic leaks, or 30-day mortality. The pathologic quality of all mesorectal specimens was excellent. The distal resection margin was 19.4 ± 10.4 mm, the mean circumferential resection margin was 13.8 ± 5.1 mm, and the median lymph node harvest was 10.5 (range 5–15) nodes. A combined laparoscopic and transanal approach can achieve a safe and oncologically complete TME dissection for low rectal tumors. This approach may improve clinical outcomes in these technically difficult cases, but larger prospective studies are needed.

Question: What is the difference between a laparoscopic and transanal approach?


Extending IEEE 802.1 AVB with time-triggered scheduling: A simulation study of the coexistence of synchronous and asynchronous traffic. In-car networks based on Ethernet are expected to be the first choice for future applications in the domain of info-and entertainment. However, the full benefit of a technologically integrated in-car network will only become rewarding with an Ethernet-based backbone, unifying several automotive domains in a single infrastructure. Today, there is remarkable interest in the IEEE 802.1 Audio/Video Bridging (AVB) protocol suite, that provides end-to-end performance guarantees in Ethernet networks. But for the strict timing requirements of automotive control-traffic, these guarantees are too weak. An extension of Ethernet AVB with synchronous time-triggered traffic can overcome these limitations. In this paper, we investigate the coexistence of synchronous and asynchronous traffic by experimentally adding time-triggered messages to the credit-based shaper of AVB in a straightforward way. Based on simulations and analytical evaluations, we quantify the impact of such integration concepts for a reasonable design range. Our results demonstrate the feasibility of a shaping strategy with concurrent AVB and time-triggered message, but show a significant impact of the schedule design on the asynchronous AVB streams. Based on our findings, we provide recommendations for configurations that can improve end-to-end network performance for in-car applications by over 100%.

Question: What is the impact of time-triggered messages on AVB?


Intelligent UAV-assisted routing protocol for urban VANETs. The process of routing in Vehicular Ad hoc Networks (VANET) is a challenging task in city environments. Finding the shortest end-to-end connected path satisfying delay restriction and minimal overhead is confronted with many constraints and difficulties. Such difficulties are due to the high mobility of vehicles, the frequent path failures, and the various obstructions, which may affect the reliability of the data transmission and routing. Commercial Unmanned Aerial Vehicles (UAVs) or what are commonly referred to as drones can come in handy in dealing with these constraints. In this paper, we study how UAVs operating in ad hoc mode can cooperate with VANET on the ground so as to assist in the routing process and improve the reliability of the data delivery by bridging the communication gap whenever it is possible. In a previous work, we have proposed UVAR – a UAV-Assisted VANETs routing protocol, which improves data routing and connectivity of the vehicles on the ground through the use of UAVs. However, UVAR does not fully exploit UAVs in the sky for data forwarding because it uses UAVs only when the network is poorly dense. In ∗Corresponding author Email addresses: s.oubbati@lagh-univ.dz (Omar Sami Oubbati), alakas@uaeu.ac.ae (Abderrahmane Lakas), fen.zhou@univ-avignon.fr (Fen Zhou), mesut.guenes@ovgu.de (Mesut Güneş), n.lagraa@lagh-univ.dz (Nasreddine Lagraa), m.yagoubi@lagh-univ.dz (Mohamed Bachir Yagoubi) Preprint submitted to Computer Communications April 4, 2017

Question: What is the difference between a UAV and a drone?


Fuzzy Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Stochastic Systems. This paper studies an adaptive tracking control for a class of nonlinear stochastic systems with unknown functions. The considered systems are in the nonaffine pure-feedback form, and it is the first to control this class of systems with stochastic disturbances. The fuzzy-neural networks are used to approximate unknown functions. Based on the backstepping design technique, the controllers and the adaptation laws are obtained. Compared to most of the existing stochastic systems, the proposed control algorithm has fewer adjustable parameters and thus, it can reduce online computation load. By using Lyapunov analysis, it is proven that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded in probability and the system output tracks the reference signal to a bounded compact set. The simulation example is given to illustrate the effectiveness of the proposed control algorithm.

Question: What is a nonaffine pure-feedback form?


Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm. Projective adaptive resonance theory (PART) neural network developed by Cao and Wu recently has been shown to be very effective in clustering data sets in high dimensional spaces. The PART algorithm is based on the assumptions that the model equations of PART (a large scale and singularly perturbed system of differential equations coupled with a reset mechanism) have quite regular computational performance. This paper provides a rigorous proof of these regular dynamics of the PART model when the signal functions are special step functions, and provides additional simulation results to illustrate the computational performance of PART.

Question: What is the PART algorithm?


Polygon mesh repairing: An application perspective. Nowadays, digital 3D models are in widespread and ubiquitous use, and each specific application dealing with 3D geometry has its own quality requirements that restrict the class of acceptable and supported models. This article analyzes typical defects that make a 3D model unsuitable for key application contexts, and surveys existing algorithms that process, repair, and improve its structure, geometry, and topology to make it appropriate to case-by-case requirements.  The analysis is focused on polygon meshes, which constitute by far the most common 3D object representation. In particular, this article provides a structured overview of mesh repairing techniques from the point of view of the application context. Different types of mesh defects are classified according to the upstream application that produced the mesh, whereas mesh quality requirements are grouped by representative sets of downstream applications where the mesh is to be used. The numerous mesh repair methods that have been proposed during the last two decades are analyzed and classified in terms of their capabilities, properties, and guarantees. Based on these classifications, guidelines can be derived to support the identification of repairing algorithms best-suited to bridge the compatibility gap between the quality provided by the upstream process and the quality required by the downstream applications in a given geometry processing scenario.

Question: What is a polygon mesh?


Kernel estimation for motion blur removal using deep convolutional neural network. Blind deblurring can restore the sharp image from the blur version when the blur kernel is unknown, which is a challenging task. Kernel estimation is crucial for blind deblurring. In this paper, a novel blur kernel estimation method based on regression model is proposed for motion blur. The motion blur features are firstly mined through convolutional neural network (CNN), and then mapped to motion length and orientation by support vector regression (SVR). Experiments show that the proposed model, namely CNNSVR, can give more accurate kernel estimation and generate better deblurring result compared with other state-of-the-art algorithms.

Question: How to estimate the kernel of a motion blur?


Entailment-based Fully Automatic Technique for Evaluation of Summaries. We propose a fully automatic technique for evaluating text summaries without the need to prepare the gold standard summaries manually. A standard and popular summary evaluation techniques or tools are not fully automatic; they all need some manual process or manual reference summary. Using recognizing textual entailment (TE), automatically generated summaries can be evaluated completely automatically without any manual preparation process. We use a TE system based on a combination of lexical entailment module, lexical distance module, Chunk module, Named Entity module and syntactic text entailment (TE) module. The documents are used as text (T) and summary of these documents are taken as hypothesis (H). Therefore, the more information of the document is entailed by its summary the better the summary. Comparing with the ROUGE 1.5.5 evaluation scores over TAC 2008 (formerly DUC, conducted by NIST) dataset, the proposed evaluation technique predicts the ROUGE scores with a accuracy of 98.25% with respect to ROUGE-2 and 95.65% with respect to ROUGE-SU4.

Question: How to evaluate the summary?


The thought translation device (TTD) for completely paralyzed patients.. The thought translation device trains locked-in patients to self-regulate slow cortical potentials (SCP's) of their electroencephalogram (EEG). After operant learning of SCP self-control, patients select letters, words or pictograms in a computerized language support program. Results of five respirated, locked-in-patients are described, demonstrating the usefulness of the thought translation device as an alternative communication channel in motivated totally paralyzed patients with amyotrophic lateral sclerosis.

Question: What is the thought translation device?


SOCIAL DIALOGUE WITH EMBODIED CONVERSATIONAL AGENTS. The functions of social dialogue between people in the context of performing a task is discussed, as well as approaches to modelling such dialogue in embodied conversational agents. A study of an agent's use of social dialogue is presented, comparing embodied interactions with similar interactions conducted over the phone, assessing the impact these media have on a wide range of behavioural, task and subjective measures. Results indicate that subjects' perceptions of the agent are sensitive to both interaction style (social vs. task-only dialogue) and

Question: What is the impact of social dialogue on a wide range of behavioural, task and subjective measures?


A Data Mining view on Class Room Teaching Language. From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.

Question: What is association rule?


Can Smartwatches Replace Smartphones for Posture Tracking?. This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch's ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches' ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed.

Question: How can a smartwatch be used to track posture?


Bach 10 Dataset---- A Versatile Polyphonic Music Dataset. This is a polyphonic music dataset which can be used for versatile research problems, such as Multi-pitch Estimation and Tracking, Audio-score Alignment, Source Separation, etc. This dataset consists of the audio recordings of each part and the ensemble of ten pieces of four-part J.S. Bach chorales, as well as their MIDI scores, the ground-truth alignment between the audio and the score, the ground-truth pitch values of each part and the ground-truth notes of each piece. The audio recordings of the four parts (Soprano, Alto, Tenor and Bass) of each piece are performed by violin, clarinet, saxophone and bassoon, respectively.

Question: What is a polyphonic music dataset?


Semi-supervised Sequence Learning. We present two approaches that use unlabeled data to improve sequ nce learning with recurrent networks. The first approach is to predict wha t comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which r eads the input sequence into a vector and predicts the input sequence again. T hese two algorithms can be used as a "pretraining" step for a later supervised seq uence learning algorithm. In other words, the parameters obtained from the unsu pervised step can be used as a starting point for other supervised training model s. In our experiments, we find that long short term memory recurrent networks after b eing pretrained with the two approaches are more stable and generalize bette r. With pretraining, we are able to train long short term memory recurrent network s up to a few hundred timesteps, thereby achieving strong performance in ma ny text classification tasks, such as IMDB, DBpedia and 20 Newsgroups.

Question: What is a sequence autoencoder?


Joint pose estimation and action recognition in image graphs. Human analysis in images and video is a hard problem due to the large variation in human pose, clothing, camera view-points, lighting and other factors. While the explicit modeling of this variability is difficult, the huge amount of available person images motivates for the implicit, data-driven approach to human analysis. In this work we aim to explore this approach using the large amount of images spanning a subspace of human appearance. We model this subspace by connecting images into a graph and propagating information through such a graph using a discriminatively-trained graphical model. We particularly address the problems of human pose estimation and action recognition and demonstrate how image graphs help solving these problems jointly. We report results on still images with human actions from the KTH dataset.

Question: How to solve the problem of human pose estimation and action recognition in image graphs?


A Comparative Study of Staff Removal Algorithms. This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization-based approach. We define three different error metrics, compare the algorithms with respect to these metrics, and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation, our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal but applicable to other segmentation problems as well.

Question: What is a staff removal algorithm?


Seventh sense technology. Seventh Sense Technology is a technology used for the human electronic interaction using natural hand gestures. The hand gestures can be used to control any micro controller based devices or robots at distant places using Zigbee or GSM Module or Satellite transceiver. The "Seventh Sense Technology" is easy to implement with reduced implementation cost and it opens a wide area of research for the next generation and a greater development in almost all areas of current interest. Seventh Sense Technology uses the Sixth Sense Technology and its advancements to control the autonomous robots or any microcontroller based devices on human will. Currently autonomous robots or any microcontroller based devices are designed for a single purpose, but with this technology robots and those devices can be made to achieve multidimensional purposes. Even the costlier sensors can be replaced with the low cost night vision cameras. It opens a way to control robots or any microcontroller based devices at any distance with natural hand gestures of human beings. It also has the provision of customizable virtual keyboard for giving more actions to distant devices. It is going to be a greater field of research that also paves a lot of attention in this current world of technology.

Question: What is the seventh sense technology?


A fast garment fitting algorithm using skeleton-based error metric. Funding information National Natural Science Foundation of China, Grant/Award Number: 61732015; National Key R&D Program of China, Grant/Award Number: 2017YFB1002600; Key Research and Development Program of Zhejiang Province, Grant/Award Number: 2018C01090; National Natural Science Foundation of China, Grant/Award Number: 61472355 Abstract We present a fast and automatic method to fit a given 3D garment onto a human model with various shapes and poses, without using a reference human model. Our approach uses a novel skeleton-based error metric to find the pose that best fits the input garment. Specifically, we first generate the skeleton of the given human model and its corresponding skinning weights. Then, we iteratively rotate each bone to find its best position to fit the garment. After that, we rig the surface of the human model according to the transformations of the skeleton. Potential penetrations are resolved using collision handling and physically based simulation. Finally, we restore the human model back to the original pose in order to obtain the desired fitting result. Our experiment results show that besides its efficiency and automation, our method is about two orders of magnitudes faster than existing approaches, and it can handle various garments, including jacket, trousers, skirt, a suit of clothing, and even multilayered clothing.

Question: What is a fast garment fitting algorithm?


Generating Neural Networks with Neural Networks. Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation generalizes variational inference. We use multi-layered perceptrons to form the mapping from the low dimensional input random vector to the high dimensional weight space, and demonstrate how to reduce the number of parameters in this mapping by parameter sharing. We perform experiments and show that the generated weights are diverse and lie on a non-trivial manifold.

Question: How to generate neural networks with neural networks?


Question Answering on SQuAD. In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention vectors to get the answers. Our best model gets 62.23% F1 score and 48.72% EM score on the test set.

Question: What is the best model for question answering?


Peristaltic locomotion with antagonistic actuators in soft robotics. This paper presents a soft robotic platform that exhibits peristaltic locomotion. The design principle is based on the unique antagonistic arrangement of radial/circular and longitudinal muscle groups of Oligochaeta. Sequential antagonistic motion is achieved in a flexible braided mesh-tube structure with NiTi coil actuators. A numerical model for the mesh structure describes how peristaltic motion induces robust locomotion and details the deformation by the contraction of NiTi actuators. Several peristaltic locomotion modes are modeled, tested, and compared on the basis of locomotion speed. The entire mechanical structure is made of flexible mesh materials and can withstand significant external impacts during locomotion. This approach can enable a completely soft robotic platform by employing a flexible control unit and energy sources.

Question: What is the difference between antagonistic and non-antagonistic actuators?


Secure Border Gateway Protocol (Secure-BGP). The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet's routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This document describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture with other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.

Question: What is the border gateway protocol?


Natural Language Aggregate Query over RDF Data. Natural language question/answering over RDF data has received widespread attention. Although there have been several studies that have dealt with a small number of aggregate queries, they have many restrictions (i.e., interactive information, controlled question or query template). Thus far, there has been no natural language querying mechanism that can process general aggregate queries over RDF data. Therefore, we propose a framework called NLAQ (Natural Language Aggregate Query). First, we propose a novel algorithm to automatically understand a user's query intention, which mainly contains semantic relations and aggregations. Second, to build a better bridge between the query intention and RDF data, we propose an extended paraphrase dictionary ED to obtain more candidate mappings for semantic relations, and we introduce a predicate-type adjacent set PT to filter out inappropriate candidate mapping combinations in semantic relations and basic graph patterns. Third, we design a suitable translation plan for each aggregate category and effectively distinguish whether an aggregate item is numeric or not, which will greatly affect the aggregate result. Finally, we conduct extensive experiments over real datasets (QALD benchmark and DBpedia), and the experimental results demonstrate that our solution is effective.

Question: What is the largest country in the world?


A Review on Performance Analysis of Cloud Computing Services for Scientific Computing. Cloud computing has emerged as a very important commercial infrastructure that promises to reduce the need for maintaining costly computing facilities by organizations and institutes. Through the use of virtualization and time sharing of resources, clouds serve with a single set of physical resources as a large user base with altogether different needs. Thus, the clouds have the promise to provide to their owners the benefits of an economy of calibration and, at the same time, become a substitute for scientists to clusters, grids, and parallel production conditions. However, the present commercial clouds have been built to support web and small database workloads, which are very different from common scientific computing workloads. Furthermore, the use of virtualization and resource time sharing may introduce significant performance penalties for the demanding scientific computing workloads. In this paper, we analyze the performance of cloud computing services for scientific computing workloads. This paper evaluate the presence in real scientific computing workloads of Many-Task Computing users, that is, of users who employ loosely coupled applications comprising many tasks to achieve their scientific goals. Our effective method demonstrates to yield comparative and even better results than the more complex state-of-the-art techniques but has the advantage to be appropriate for real-time applications.

Question: What is the difference between cloud computing and virtualization?


Multi-user lax communications: A multi-armed bandit approach. Inspired by cognitive radio networks, we consider a setting where multiple users share several channels modeled as a multi-user multi-armed bandit (MAB) problem. The characteristics of each channel are unknown and are different for each user. Each user can choose between the channels, but her success depends on the particular channel chosen as well as on the selections of other users: if two users select the same channel their messages collide and none of them manages to send any data. Our setting is fully distributed, so there is no central control. As in many communication systems, the users cannot set up a direct communication protocol, so information exchange must be limited to a minimum. We develop an algorithm for learning a stable configuration for the multi-user MAB problem. We further offer both convergence guarantees and experiments inspired by real communication networks, including comparison to state-of-the-art algorithms.

Question: How to learn a stable configuration for the multi-user MAB problem?


When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks. Social bots are automatic or semi-automatic computer programs that mimic humans and/or human behavior in online social networks. Social bots can attack users (targets) in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011 an experiment conducted by the WebEcologyProject during 2011 in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users' level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users.

Question: What is the canadian military?


Overview of Permanent-Magnet Brushless Drives for Electric and Hybrid Electric Vehicles. With ever-increasing concerns on our environment, there is a fast growing interest in electric vehicles (EVs) and hybrid EVs (HEVs) from automakers, governments, and customers. As electric drives are the core of both EVs and HEVs, it is a pressing need for researchers to develop advanced electric-drive systems. In this paper, an overview of permanent-magnet (PM) brushless (BL) drives for EVs and HEVs is presented, with emphasis on machine topologies, drive operations, and control strategies. Then, three major research directions of the PM BL drive systems are elaborated, namely, the magnetic-geared outer-rotor PM BL drive system, the PM BL integrated starter-generator system, and the PM BL electric variable-transmission system.

Question: What is the difference between an electric vehicle and a hybrid electric vehicle?


Automatic question answering using the web: Beyond the Factoid. In this paper we describe and evaluate a Question Answering (QA) system that goes beyond answering factoid questions. Our approach to QA assumes no restrictions on the type of questions that are handled, and no assumption that the answers to be provided are factoids. We present an unsupervised approach for collecting question and answer pairs from FAQ pages, which we use to collect a corpus of 1 million question/answer pairs from FAQ pages available on the Web. This corpus is used to train various statistical models employed by our QA system: a statistical chunker used to transform a natural language-posed question into a phrase-based query to be submitted for exact match to an off-the-shelf search engine; an answer/question translation model, used to assess the likelihood that a proposed answer is indeed an answer to the posed question; and an answer language model, used to assess the likelihood that a proposed answer is a well-formed answer. We evaluate our QA system in a modular fashion, by comparing the performance of baseline algorithms against our proposed algorithms for various modules in our QA system. The evaluation shows that our system achieves reasonable performance in terms of answer accuracy for a large variety of complex, non-factoid questions.

Question: What is the difference between a factoid question and a non-factoid question?


Wideband Circular Polarization Reconfigurable Antenna. This communication introduces a polarization reconfigurable wideband circularly polarized (CP) antenna, which consists of four radiating arms connected to a reconfigurable feeding network. The four radiating arms excited by the feeding network are able to generate wideband CP waves with bidirectional radiation patterns in free space. In order to increase the gain and obtain a broadside radiation pattern, the proposed antenna is placed above a metallic reflector with the distance of quarter wavelength at the center frequency. In addition, polarization reconfigurability is realized by utilizing PIN diodes in the feeding transmission lines such that left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) modes can be selectively excited by controlling the on/off states of the PIN diodes. The proposed antenna exhibits a wide impedance bandwidth of 80% and an overlapped axial ratio (AR) bandwidth of 23.5% for both modes. The antenna gain is stable across the operating bandwidth with the peak gain of 4.8 dBic. The antenna has a wide AR beamwidth of 90°. The presented work is suitable for GPS, CNSS, or RFID applications.

Question: What is the name of the antenna?


Right nulled GLR parsers. The right nulled generalized LR parsing algorithm is a new generalization of LR parsing which provides an elegant correction to, and extension of, Tomita's GLR methods whereby we extend the notion of a reduction in a shift-reduce parser to include right nulled items. The result is a parsing technique which runs in linear time on LR(1) grammars and whose performance degrades gracefully to a polynomial bound in the presence of nonLR(1) rules. Compared to other GLR-based techniques, our algorithm is simpler and faster.

Question: What is a right nulled GLR parser?


30 + years of research and practice of outsourcing – Exploring the past and anticipating the future. Article history: Received 7 January 2008 Received in revised form 24 June 2008 Accepted 31 July 2008 Available online 5 April 2009 Outsourcing is a phenomenon that as a practice originated in the 1950s, but it was not until the 1980s when the strategy became widely adopted in organizations. Since then, the strategy has evolved from a strictly cost focused approach towards more cooperative nature, in which cost is only one, often secondary, decision-making criterion. In the development of the strategy, three broad and somewhat overlapping, yet distinct phases can be identified: the era of the Big Bang, the era of the Bandwagon, and the era of Barrierless Organizations. This paper illustrates that the evolution of the practice has caused several contradictions among researchers, as well as led to the situation where the theoretical background of the phenomenon has recently become much richer. Through examining existing research, this paper intends to identify the development of outsourcing strategy from a practical as well as a theoretical perspective from its birth up to today. In addition, through providing insights from managers in the information technology industry, this paper aims at providing a glimpse from the future – that is – what may be the future directions and research issues in this complex phenomenon? © 2009 Elsevier Inc. All rights reserved.

Question: What is the history of outsourcing?


A New Modular Bipolar High-Voltage Pulse Generator. Adapting power-electronic converters that are used in pulsed-power application attracted considerable attention during recent years. In this paper, a modular bipolar high-voltage pulse generator is proposed based on the voltage multipliers and half-bridge converters concept by using power electronics switches. This circuit is capable to generate repetitive high voltage bipolar pulses with a flexible output pattern, by using low voltage power supply and elements. The proposed topology was simulated in MATLAB/Simulink. To verify the circuit operation a four-stage laboratory prototype has been assembled and tested. The results confirm the theoretical analysis and show the validity of the converter scheme.

Question: What is a pulse generator?


A review of split-bolus single-pass CT in the assessment of trauma patients. The purpose of this study was to review and compare the image quality and radiation dose of split-bolus single-pass computed tomography(CT) in the assessment of trauma patients in comparison to standard multi-phase CT techniques. An online electronic database was searched using the MESH terms "split-bolus," "dual phase," and "single pass." Inclusion criteria required the research article to compare a split contrast bolus protocol in a single-pass scan in the assessment of trauma patients. Studies using split-bolus CT technique in non-traumatic injury assessment were excluded. Six articles met the inclusion criteria. Parenchymal and vascular image qualities, as well as subjective image quality assessments, were equal or superior in comparison to non-split-bolus multi-phase trauma CT protocols. Split-bolus single-pass CT decreased radiation exposure in all studies. Further research is required to determine the superior split-bolus protocol and the specificity and sensitivity of detecting blunt cerebrovascular injury screening, splenic parenchymal vascular lesions, and characterization of pelvic vascular extravasation.

Question: What is the purpose of this study?


Photovoltaic based water pumping system. In this paper a stand-alone Photovoltaic (PV) systems is presented for water pumping. Solar PV water pumping systems are used for irrigation and drinking water. PV based pumping systems without battery can provide a cost-effective use of solar energy. For the purpose of improving efficiency of the system perturb and observe (P&O) algorithm based Maximum Power Point Tracker (MPPT) is connected to this system. The aim of this paper is to show how to achieve an effective photovoltaic pumping system without battery storage. Results are presented based on different cases of irrigation pumping application and availability of solar irradiance. Simulation results using MATLAB/SIMULINK show that the performance of the controllers both in transient as well as in steady state is quite satisfactory.

Question: How to achieve an effective photovoltaic pumping system without battery storage?


Galvanic Skin Response : A Physiological Sensor System for Affective Computing. system is the change in electrical properties of skin due to variation in physiological and psychological conditions. The change is caused by the degree to which a person's sweat glands are active. Psychological status of a person tends to make the glands active and this change the skin resistance. Drier the skin is, higher will be the skin resistance. This variation in skin resistance ranges from 5kΩ to 25kΩ. In the current work, a subject whose Galvanic skin response (GSR) is to be measured, has been shown/played a movie clipping, images or recorded audio signals. Depending upon the theme, emotion will be evoked in the subject. Due to the change in emotion, GSR varies. This variation in GSR is recorded for a fixed time interval. In the present work, a total of 75 subjects are selected. People from different age groups and social background, both male and female have been carefully considered. Data acquisition and analysis is carried out by using LabVIEW. The results obtained are convincing and follows the ground reality with a high level of accuracy. As the subject also gives the feedback about the emotions he/she experienced, the results obtained are validated.

Question: How does galvanic skin response work?


Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders. RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical foundations have remained unclear. In this work we make progress towards that by giving proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives and we give bounds on the running time. We then design experiments to compare the performances of RMSProp and ADAM against Nesterov Accelerated Gradient method on a variety of autoencoder setups. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter β1. We show that in terms of getting lower training and test losses, at very high values of the momentum parameter (β1 = 0.99) (and large enough nets if using mini-batches) ADAM outperforms NAG at any momentum value tried for the latter. On the other hand, NAG can sometimes do better when ADAM's β1 is set to the most commonly used value: β1 = 0.9. We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms and finding weights which increase the minimum eigenvalue of the Hessian of the loss function.

Question: What is the convergence guarantee for RMSProp and ADAM in non-convex optimization?


Automatic assessment of macular edema from color retinal images. Diabetic macular edema (DME) is an advanced symptom of diabetic retinopathy and can lead to irreversible vision loss. In this paper, a two-stage methodology for the detection and classification of DME severity from color fundus images is proposed. DME detection is carried out via a supervised learning approach using the normal fundus images. A feature extraction technique is introduced to capture the global characteristics of the fundus images and discriminate the normal from DME images. Disease severity is assessed using a rotational asymmetry metric by examining the symmetry of macular region. The performance of the proposed methodology and features are evaluated against several publicly available datasets. The detection performance has a sensitivity of 100% with specificity between 74% and 90%. Cases needing immediate referral are detected with a sensitivity of 100% and specificity of 97%. The severity classification accuracy is 81% for the moderate case and 100% for severe cases. These results establish the effectiveness of the proposed solution.

Question: How can we detect macular edema?


Ethnicity estimation with facial images. We have advanced an effort to develop vision based human understanding technologies for realizing human-friendly machine interfaces. Visual information, such as gender, age ethnicity, and facial expression play an important role in face-to-face communication. This paper addresses a novel approach for ethnicity classification with facial images. In this approach, the Gabor wavelets transformation and retina sampling are combined to extract key facial features, and support vector machines that are used for ethnicity classification. Our system, based on this approach, has achieved approximately 94% for ethnicity estimation under various lighting conditions.

Question: How to estimate ethnicity with facial images?


A Load Balancing Model based for Cloud Computing using Public Cloud. It is difficult to maintain all the resources and services on single system. So by using Load balancing strategy we distribute load on single system to multiple system providing efficient response to user, resulting in improve user satisfaction. Load balancing in the cloud computing has an important impact on the performance. To improve user satisfaction there is a need of efficient cloud computing for that we need a good load balancing algorithm. This article shows

Question: What is load balancing in cloud computing?


Indoors forensic entomology: colonization of human remains in closed environments by specific species of sarcosaprophagous flies.. Fly species that are commonly recovered on human corpses concealed in houses or other dwellings are often dependent on human created environments and might have special features in their biology that allow them to colonize indoor cadavers. In this study we describe nine typical cases involving forensically relevant flies on human remains found indoors in southern Finland. Eggs, larvae and puparia were reared to adult stage and determined to species. Of the five species found the most common were Lucilia sericata Meigen, Calliphora vicina Robineau-Desvoidy and Protophormia terraenovae Robineau-Desvoidy. The flesh fly Sarcophaga caerulescens Zetterstedt is reported for the first time to colonize human cadavers inside houses and a COI gene sequence based DNA barcode is provided for it to help facilitate identification in the future. Fly biology, colonization speed and the significance of indoors forensic entomological evidence are discussed.

Question: What is the significance of indoors forensic entomology?


Universal Blind Quantum Computation. We present a protocol which allows a client to have a server carry out a quantum computation for her such that the client's inputs, outputs and computation remain perfectly private, and where she does not require any quantum computational power or memory. The client only needs to be able to prepare single qubits randomly chosen from a finite set and send them to the server, who has the balance of the required quantum computational resources. Our protocol is interactive: after the initial preparation of quantum states, the client and server use two-way classical communication which enables the client to drive the computation, giving single-qubit measurement instructions to the server, depending on previous measurement outcomes. Our protocol works for inputs and outputs that are either classical or quantum. We give an authentication protocol that allows the client to detect an interfering server; our scheme can also be made fault-tolerant. We also generalize our result to the setting of a purely classical client who communicates classically with two non-communicating entangled servers, in order to perform a blind quantum computation. By incorporating the authentication protocol, we show that any problem in BQP has an entangled two-prover interactive proof with a purely classical verifier. Our protocol is the first universal scheme which detects a cheating server, as well as the first protocol which does not require any quantum computation whatsoever on the client's side. The novelty of our approach is in using the unique features of measurement-based quantum computing which allows us to clearly distinguish between the quantum and classical aspects of a quantum computation.

Question: What is the difference between a quantum and classical computer?


Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion. In this paper, the automatic medical annotation task of the 2007 CLEF cross-language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The paper also describes the evolution of the task from its origin in 2005 to 2007. The 2007 task, comprising 11,000 fully annotated training images and 1,000 test images to be annotated, is a realistic task with a large number of possible classes at different levels of detail. Detailed analysis of the methods across participating groups is presented with respect to the (i) image representation, (ii) classification method, and (iii) use of the class hierarchy. The results show that methods which build on local image descriptors and discriminative models are able to provide good predictions of the image classes, mostly by using techniques that were originally developed in the machine learning and computer vision domain for object recognition in non-medical images.

Question: What is the medical image annotation task in ImageCLEF 2007?


A term-based methodology for query reformulation understanding. Key to any research involving session search is the understanding of how a user's queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user's information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user's motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.

Question: What is a term-based methodology for query reformulation?


Architectural optimizations for high performance and energy efficient Smith-Waterman implementation on FPGAs using OpenCL. Smith-Waterman is a dynamic programming algorithm that plays a key role in the modern genomics pipeline as it is guaranteed to find the optimal local alignment between two strings of data. The state of the art presents many hardware acceleration solutions that have been implemented in order to exploit the high degree of parallelism available in this algorithm. The majority of these implementations use heuristics to increase the performance of the system at the expense of the accuracy of the result. In this work, we present an implementation of the pure version of the algorithm. We include the key architectural optimizations to achieve highest possible performance for a given platform and leverage the Berkeley roofline model to track the performance and guide the optimizations. To achieve scalability, our custom design comprises of systolic arrays, data compression features and shift registers, while a custom port mapping strategy aims to maximize performance. Our designs are built leveraging an OpenCL-based design entry, namely Xilinx SDAccel, in conjunction with a Xilinx Virtex 7 and Kintex Ultrascale platform. Our final design achieves a performance of 42.47 GCUPS (giga cell updates per second) with an energy efficiency of 1.6988 GCUPS/W. This represents an improvement of 1.72x in performance and energy efficiency over previously published FPGA implementations and 8.49x better in energy efficiency over comparable GPU implementations.

Question: What is the best way to implement a Smith-Waterman algorithm?


IoT security attacks using reverse engineering methods on WSN applications. With the rapid technological advancements of sensors, Wireless Sensor Networks (WSNs) have become a popular technology for the Internet of Things (IoT). We investigated the security of WSNs in an environmental monitoring application with the goal to demonstrate the overall security. We implemented a Secure Temperature Monitoring System (STMS), which served as our WSN application. Our results revealed a security flaw found in the bootstrap loader (BSL) password used to protect MSP430 micro-controller units (MCUs). We demonstrated how the BSL password could be brute forced in a matter of days. Furthermore, we illustrate how an attacker can reverse engineer WSN applications to obtain critical security information such as encryption keys. We contribute a solution to patch the weak BSL password security flaw and improve the security of MSP430 MCU chips. The Secure-BSL patch we contribute allows the randomization of the BSL password. Our solution increases the brute force time to decades. The impractical brute force time enhances the security of the MSP430 and prevents future reverse engineering tactics. Our research serves as proof that the security of WSNs and the overall IoT technology is broken if we cannot protect these everyday objects at the physical layer.

Question: How to secure WSNs?


Optimizing the Hadoop MapReduce Framework with high-performance storage devices. Solid-state drives (SSDs) are an attractive alternative to hard disk drives (HDDs) to accelerate the Hadoop MapReduce Framework. However, the SSD characteristics and today's Hadoop framework exhibit mismatches that impede indiscriminate SSD integration. This paper explores how to optimize a Hadoop MapReduce Framework with SSDs in terms of performance, cost, and energy consumption. It identifies extensible best practices that can exploit SSD benefits within Hadoop when combined with high network bandwidth and increased parallel storage access. Our Terasort benchmark results demonstrate that Hadoop currently does not sufficiently exploit SSD throughput. Hence, using faster SSDs in Hadoop does not enhance its performance. We show that SSDs presently deliver significant efficiency when storing intermediate Hadoop data, leaving HDDs for Hadoop Distributed File System (HDFS). The proposed configuration is optimized with the JVM reuse option and frequent heartbeat interval option. Moreover, we examined the performance of a state-of-the-art non-volatile memory express interface SSD within the Hadoop MapReduce Framework. While HDFS read and write throughput increases with high-performance SSDs, achieving complete system performance improvement requires carefully balancing CPU, network, and storage resource capabilities at a system level.

Question: How to optimize the Hadoop MapReduce Framework with high-performance storage devices?


Studying weak central coherence at low levels: children with autism do not succumb to visual illusions. A research note.. While anecdotal reports of abnormal perceptual experiences in autism abound, there have been to date no experimental studies showing fundamental perceptual peculiarities. The present paper reports results from a first study of low-level visual integration in autism. Twenty-five subjects with autism, 21 normal 7- and 8-year-olds, and 26 children with learning difficulties were asked to make simple judgements about six well-known visual illusions. Two conditions were used, in an attempt to explore group differences; standard two-dimensional black and white line drawings, and the same figures augmented with raised coloured lines. The subjects with autism were less likely to succumb to the two-dimensional illusions than were the other groups, and were less aided by the three-dimensional 'disembedded' condition. These striking results are discussed with reference to the 'central coherence' account of autism.

Question: What is the central coherence account of autism?


3D Face Recognition using Kinect. 2D Face recognition systems bound to fail on images with varying pose angles and occlusions. Many pose invariant methods are proposed in recent years but they are still not able to achieve very good accuracies. So in order to achieve a better accuracy we need to extend algorithms over 3D faces. Due to the high cost involved in acquisition of 3D faces we developed our approach for low-cost and low-quality Microsoft Kinect Sensor and propose an algorithm to produce better results than existing 2D Face recognition techniques even after compromising on the quality of the images from the sensor. Our proposed algorithm is based on modified SURF descriptors on RGB images combined with various enhancements on automatically generated training images using Depth and Color images. We compare our results obtained with State Of The Art Techniques obtained on publicly available RGB-D Face databases. Our System obtained recognition rate of 98.07% on 30° CurtinFace Database, 89.28% on EURECOM Database, 98.00% on 15° Internal Database and 81.00% on 30° Internal Database.

Question: How to recognize faces using Kinect?


Gated convolutional networks based hybrid acoustic models for low resource speech recognition. In acoustic modeling for large vocabulary speech recognition, recurrent neural networks (RNN) have shown great abilities to model temporal dependencies. However, the performance of RNN is not prominent in resource limited tasks, even worse than the traditional feedforward neural networks (FNN). Furthermore, training time for RNN is much more than that for FNN. In recent years, some novel models are provided. They use non-recurrent architectures to model long term dependencies. In these architectures, they show that using gate mechanism is an effective method to construct acoustic models. On the other hand, it has been proved that using convolution operation is a good method to learn acoustic features. We hope to take advantages of both these two methods. In this paper we present a gated convolutional approach to low resource speech recognition tasks. The gated convolutional networks use convolutional architectures to learn input features and a gate to control information. Experiments are conducted on the OpenKWS, a series of low resource keyword search evaluations. From the results, the gated convolutional networks relatively decrease the WER about 6% over the baseline LSTM models, 5% over the DNN models and 3% over the BLSTM models. In addition, the new models accelerate the learning speed by more than 1.8 and 3.2 times compared to that of the baseline LSTM and BLSTM models.

Question: What is the gated convolutional networks?


Mining Offensive Language on Social Media. English. The present research deals with the automatic annotation and classification of vulgar ad offensive speech on social media. In this paper we will test the effectiveness of the computational treatment of the taboo contents shared on the web, the output is a corpus of 31,749 Facebook comments which has been automatically annotated through a lexicon-based method for the automatic identification and classification of taboo expressions. Italiano. La presente ricerca affronta il tema dell'annotazione e della classificazione automatica dei contenuti volgari e offensivi espressi nei social media. Lo scopo del nostro lavoro consiste nel testare l'efficacia del trattamento computazionale dei contenuti tabù condivisi in rete. L'output che forniamo un corpus di 31,749 commenti generati dagli utenti di Facebook e annotato automaticamente attraverso un metodo basato sul lessico per l'identificazione e la classificazione delle

Question: What is the output of the research?


Psychological Well-Being in Adult Life. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Question: How do I use the JSTOR archive?


Space-Time block codes from orthogonal designs. We introduce space–time block coding , a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space–time block code and the encoded data is split inton streams which are simultaneously transmitted usingn transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximumlikelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space–time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space–time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm. The classical mathematical framework of orthogonal designs is applied to construct space–time block codes. It is shown that space–time block codes constructed in this way only exist for few sporadic values ofn. Subsequently, a generalization of orthogonal designs is shown to provide space–time block codes for both real and complex constellations for any number of transmit antennas. These codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as PAM. For an arbitrary complex constellation such as PSK and QAM, space–time block codes are designed that achieve1=2 of the maximum possible transmission rate for any number of transmit antennas. For the specific cases of two, three, and four transmit antennas, space–time block codes are designed that achieve, respectively, all, 3=4, and 3=4 of maximum possible transmission rate using arbitrary complex constellations. The best tradeoff between the decoding delay and the number of transmit anten

Question: What is the maximum possible transmission rate for any number of transmit antennas?


A MATLAB-based Convolutional Neural Network Approach for Face Recognition System.

Question: What is a convolutional neural network?


Classification of Age Groups Based on Facial Features. An age group classification system for gray-scale facial images is proposed in this paper. Four age groups, including babies, young adults, middle-aged adults, and old adults, are used in the classification system. The process of the system is divided into three phases: location, feature extraction, and age classification. Based on the symmetry of human faces and the variation of gray levels, the positions of eyes, noses, and mouths could be located by applying the Sobel edge operator and region labeling. Two geometric features and three wrinkle features from a facial image are then obtained. Finally, two back-propagation neural networks are constructed for classification. The first one employs the geometric features to distinguish whether the facial image is a baby. If it is not, then the second network uses the wrinkle features to classify the image into one of three adult groups. The proposed system is experimented with 230 facial images on a Pentium II 350 processor with 128 MB RAM. One half of the images are used for training and the other half for test. It takes 0.235 second to classify an image on an average. The identification rate achieves 90.52% for the training images and 81.58% for the test images, which is roughly close to human's subjective justification.

Question: How to classify age groups based on facial features?


A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers. This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user's sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the user's intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The user's mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol. August 25, 2016 DRAFT

Question: How to control a powered exoskeleton using brain signals?


Markov Chain Monte Carlo Maximum Likelihood. Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood estimation is explained, and its performance is compared with maximum pseudo likelihood estimation.

Question: What is Markov chain Monte Carlo?


Enhanced Computer Vision With Microsoft Kinect Sensor: A Review. With the invention of the low-cost Microsoft Kinect sensor, high-resolution depth and visual (RGB) sensing has become available for widespread use. The complementary nature of the depth and visual information provided by the Kinect sensor opens up new opportunities to solve fundamental problems in computer vision. This paper presents a comprehensive review of recent Kinect-based computer vision algorithms and applications. The reviewed approaches are classified according to the type of vision problems that can be addressed or enhanced by means of the Kinect sensor. The covered topics include preprocessing, object tracking and recognition, human activity analysis, hand gesture analysis, and indoor 3-D mapping. For each category of methods, we outline their main algorithmic contributions and summarize their advantages/differences compared to their RGB counterparts. Finally, we give an overview of the challenges in this field and future research trends. This paper is expected to serve as a tutorial and source of references for Kinect-based computer vision researchers.

Question: What is the difference between the Kinect and the RGB camera?


Size dependency of nanocrystalline TiO 2 on its optical property and photocatalytic reactivity exemplified by 2-chlorophenol. Anatase TiO2 nanocrystallines (17–29 nm) were successfully synthesized by the metal–organic chemical vapor deposition method (MOCVD). Moderate manipulation of system parameters of MOCVD can control the particle size. The electro-optical and photocatalytic properties of the synthesized TiO2 nanoparticles were studied along with several commercially available ultra-fine TiO2 particles (e.g., 3.8–5.7 nm). The band gap of the TiO2 crystallines was determined using the transformed diffuse reflectance technique according to the Kubelka–Munk theory. Results showed that the band gap of TiO2 monotonically decreased from 3.239 to 3.173 eV when the particle size decreased from 29 to 17 nm and then increased from 3.173 to 3.289 eV as the particle size decreased from 17 to 3.8 nm. The results of band gap change as a function of particle size agreed well with what was predicted by the Brus' equation, i.e., the effective mass model (EMM). However, results of the photocatalytic oxidation of 2-chlorophenol (2-CP), showed that the smaller the particle size, the faster the degradation rate. This is attributed in part to the combined effect of band gap change relative to the spectrum of the light source and the specific surface area (or particle size) of the photocatalysts. The change of band gap due to particle size represents only a small optical absorption window with respect to the total spectrum of the light source, i.e., from 380 to 400 nm versus>280 nm. Consequently, the gain in optical property of the larger particles was severely compromised by their decrease in specific surface area. Our results clearly indicated the importance of specific surface area in controlling the photocatalytic reactivity of photocatalysts. Results also showed that the secondary particle size grew with time due mainly to particle aggregation. The photocatalytic rate constants d

Question: What is the band gap of titanium dioxide?


Mining hidden community in heterogeneous social networks. Social network analysis has attracted much attention in recent years. Community mining is one of the major directions in social network analysis. Most of the existing methods on community mining assume that there is only one kind of relation in the network, and moreover, the mining results are independent of the users' needs or preferences. However, in reality, there exist multiple, heterogeneous social networks, each representing a particular kind of relationship, and each kind of relationship may play a distinct role in a particular task. Thus mining networks by assuming only one kind of relation may miss a lot of valuable hidden community information and may not be adaptable to the diverse information needs from different users.In this paper, we systematically analyze the problem of mining hidden communities on heterogeneous social networks. Based on the observation that different relations have different importance with respect to a certain query, we propose a new method for learning an optimal linear combination of these relations which can best meet the user's expectation. With the obtained relation, better performance can be achieved for community mining. Our approach to social network analysis and community mining represents a major shift in methodology from the traditional one, a shift from single-network, user-independent analysis to multi-network, user-dependant, and query-based analysis. Experimental results on Iris data set and DBLP data set demonstrate the effectiveness of our method.

Question: What is the difference between the traditional social network analysis and the proposed method?


Power Modeling and Optimization for OLED Displays. Emerging organic light-emitting diode (OLED)-based displays obviate external lighting, and consume drastically different power when displaying different colors, due to their emissive nature. This creates a pressing need for OLED display power models for system energy management, optimization as well as energy-efficient GUI design, given the display content or even the graphical-user interface (GUI) code. In this work, we study this opportunity using commercial QVGA OLED displays and user studies. We first present a comprehensive treatment of power modeling of OLED displays, providing models that estimate power consumption based on pixel, image, and code, respectively. These models feature various tradeoffs between computation efficiency and accuracy so that they can be employed in different layers of a mobile system. We validate the proposed models using a commercial QVGA OLED module and a mobile device with a QVGA OLED display. Then, based on the models, we propose techniques that adapt GUIs based on existing mechanisms as well as arbitrarily under usability constraints. Our measurement and user studies show that more than 75 percent display power reduction can be achieved with user acceptance.

Question: How to model power consumption of OLED displays?


SEISA: set expansion by iterative similarity aggregation. In this paper, we study the problem of expanding a set of given seed entities into a more complete set by discovering other entities that also belong to the same concept set. A typical example is to use "Canon" and "Nikon" as seed entities, and derive other entities (e.g., "Olympus") in the same concept set of camera brands. In order to discover such relevant entities, we exploit several web data sources, including lists extracted from web pages and user queries from a web search engine. While these web data are highly diverse with rich information that usually cover a wide range of the domains of interest, they tend to be very noisy. We observe that previously proposed random walk based approaches do not perform very well on these noisy data sources. Accordingly, we propose a new general framework based on iterative similarity aggregation, and present detailed experimental results to show that, when using general-purpose web data for set expansion, our approach outperforms previous techniques in terms of both precision and recall.

Question: What is the set expansion problem?


CCDN: Content-Centric Data Center Networks. Data center networks continually seek higher network performance to meet the ever increasing application demand. Recently, researchers are exploring the method to enhance the data center network performance by intelligent caching and increasing the access points for hot data chunks. Motivated by this, we come up with a simple yet useful caching mechanism for generic data centers, i.e., a server caches a data chunk after an application on it reads the chunk from the file system, and then uses the cached chunk to serve subsequent chunk requests from nearby servers. To turn the basic idea above into a practical system and address the challenges behind it, we design content-centric data center networks CCDNs, which exploits an innovative combination of content-based forwarding and location [Internet Protocol IP]-based forwarding in switches, to correctly locate the target server for a data chunk on a fully distributed basis. Furthermore, CCDN enhances traditional content-based forwarding to determine the nearest target server, and enhances traditional location IP-based forwarding to make high utilization of the precious memory space in switches. Extensive simulations based on real-world workloads and experiments on a test bed built with NetFPGA prototypes show that, even with a small portion of the server's storage as cache e.g., 3% and with a modest content forwarding information base size e.g., 1000 entries in switches, CCDN can improve the average throughput to get data chunks by 43% compared with a pure Hadoop File System HDFS system in a real data center.

Question: What is a content-centric data center network?


Emotion regulation: affective, cognitive, and social consequences.. One of life's great challenges is successfully regulating emotions. Do some emotion regulation strategies have more to recommend them than others? According to Gross's (1998, Review of General Psychology, 2, 271-299) process model of emotion regulation, strategies that act early in the emotion-generative process should have a different profile of consequences than strategies that act later on. This review focuses on two commonly used strategies for down-regulating emotion. The first, reappraisal, comes early in the emotion-generative process. It consists of changing the way a situation is construed so as to decrease its emotional impact. The second, suppression, comes later in the emotion-generative process. It consists of inhibiting the outward signs of inner feelings. Experimental and individual-difference studies find reappraisal is often more effective than suppression. Reappraisal decreases emotion experience and behavioral expression, and has no impact on memory. By contrast, suppression decreases behavioral expression, but fails to decrease emotion experience, and actually impairs memory. Suppression also increases physiological responding for suppressors and their social partners. This review concludes with a consideration of five important directions for future research on emotion regulation processes.

Question: What are the consequences of emotion regulation?


Art-Bots: Toward Chat-Based Conversational Experiences in Museums.

Question: What is the name of the art-bot?


Local optima smoothing for global optimization. It is widely believed that in order to solve large scale global optimization problems an appropriate mixture of local approximation and global exploration is necessary. Local approximation, if first order information on the objective function is available, is efficiently performed by means of local optimization methods. Unfortunately, global exploration, in absence of some kind of global information on the problem, is a "blind" procedure, aimed at placing observations as evenly as possible in the search domain. Often this procedure reduces to uniform random sampling (like in Multistart algorithms, or in techniques based on clustering). In this paper we propose a new framework for global exploration which tries to guide random exploration towards the region of attraction of low-level local optima. The main idea originated by the use of smoothing techniques (based on gaussian convolutions): the possibility of applying a smoothing transformation not to the objective function but to the result of local searches seems to have never been explored yet. Although an exact smoothing of the results of local searches is impossible to implement, in this paper we propose a computational approximation scheme which has proven to be very efficient and (maybe more important) extremely robust in solving large scale global optimization problems with huge numbers of local optima. ∗Email: b.addis@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze †Email: locatell@di.unito.it Dip. Informatica Università di Torino ‡Email: schoen@ing.unifi.it Dip. Sistemi e Informatica Università di Firenze

Question: What is the difference between local optima smoothing and local optima clustering?


Can Computers Overcome Humans? Consciousness Interaction and its Implications. Can computers overcome human capabilities? This is a paradoxical and controversial question, particularly because there are many hidden assumptions. This article focuses on that issue putting on evidence some misconception related to future generations of machines and the understanding of the brain. It will discuss to what extent computers might reach human capabilities, and how it would be possible only if the computer is a conscious machine. However, it will be shown that if the computer is conscious, an interference process due to consciousness would affect the information processing of the system. Therefore, it might be possible to make conscious machines to overcome human capabilities, which will have similar limitations than humans. In other words, trying to overcome human capabilities with computers implies the paradoxical conclusion that a computer will never overcome human capabilities at all, or if the computer does, it should not be considered as a computer anymore.

Question: Can computers overcome humans?


Time Series Forecasting Based on Augmented Long Short-Term Memory. In this paper, we use recurrent autoencoder model to predict the time series in single and multiple steps ahead. Previous prediction methods, such as recurrent neural network (RNN) and deep belief network (DBN) models, cannot learn long term dependencies. And conventional long short-term memory (LSTM) model doesn't remember recent inputs. Combining LSTM and autoencoder (AE), the proposed model can capture long-term dependencies across data points and uses features extracted from recent observations for augmenting LSTM at the same time. Based on comprehensive experiments, we show that the proposed methods significantly improves the state-of-art performance on chaotic time series benchmark and also has better performance on real-world data. Both single-output and multiple-output predictions are investigated.

Question: How to predict time series?


Effective Design Parameters on the End Effect in Single-Sided Linear Induction Motors. Linear induction motors are used in various industries but they have some specific phenomena which are the causes for some problems. The most important phenomenon is called end effect. End effect decreases efficiency, power factor and output force and unbalances the phase currents. This phenomenon is more important in medium and high speeds machines. In this paper a factor, EEF , is obtained by an accurate equivalent circuit model, to determine the end effect intensity. In this way, all of effective design parameters on end effect is described. Accuracy of this equivalent circuit model is evaluated by two dimensional finite-element analysis using ANSYS. The results show the accuracy of the equivalent circuit model. Keywords—Linear induction motor, end effect, equivalent circuit model, finite-element method.

Question: What is the end effect in linear induction motors?


Mathematical models of cancer metabolism.. Metabolism is essential for life, and its alteration is implicated in multiple human diseases. The transformation from a normal to a cancerous cell requires metabolic changes to fuel the high metabolic demands of cancer cells, including but not limited to cell proliferation and cell migration. In recent years, there have been a number of new discoveries connecting known aberrations in oncogenic and tumour suppressor pathways with metabolic alterations required to sustain cell proliferation and migration. However, an understanding of the selective advantage of these metabolic alterations is still lacking. Here, we review the literature on mathematical models of metabolism, with an emphasis on their contribution to the identification of the selective advantage of metabolic phenotypes that seem otherwise wasteful or accidental. We will show how the molecular hallmarks of cancer can be related to cell proliferation and tissue remodelling, the two major physiological requirements for the development of a multicellular structure. We will cover different areas such as genome-wide gene expression analysis, flux balance models, kinetic models, reaction diffusion models and models of the tumour microenvironment. We will also highlight current challenges and how their resolution will help to achieve a better understanding of cancer metabolism and the metabolic vulnerabilities of cancers.

Question: What is the difference between a mathematical model and a mathematical model of cancer metabolism?


Partial Multi-Modal Sparse Coding via Adaptive Similarity Structure Regularization. Multi-modal sparse coding has played an important role in many multimedia applications, where data are usually with multiple modalities. Recently, various multi-modal sparse coding approaches have been proposed to learn sparse codes of multi-modal data, which assume that data appear in all modalities, or at least there is one modality containing all data. However, in real applications, it is often the case that some modalities of the data may suffer from missing information and thus result in partial multi-modality data. In this paper, we propose to solve the partial multi-modal sparse coding problem via multi-modal similarity structure regularization. Specifically, we propose a partial multi-modal sparse coding framework termed Adaptive Partial Multi-Modal Similarity Structure Regularization for Sparse Coding (AdaPM2SC), which preserves the similarity structure within the same modality and between different modalities. Experimental results conducted on two real-world datasets demonstrate that AdaPM2SC significantly outperforms the state-of-the-art methods under partial multi-modality scenario.

Question: What is the difference between AdaPM2SC and other multi-modal sparse coding methods?


The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data. BACKGROUND Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies.   NEW METHODS We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps - from flexible read-in of data formats to GLM regressor/contrast creation - without any manual intervention.   RESULTS We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N=35).   COMPARISON WITH EXISTING METHODS The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data.   CONCLUSIONS Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.

Question: How do you model physiological noise?


Anchors aweigh: A demonstration of cross-modality anchoring and magnitude priming. Research has shown that judgments tend to assimilate to irrelevant "anchors." We extend anchoring effects to show that anchors can even operate across modalities by, apparently, priming a general sense of magnitude that is not moored to any unit or scale. An initial study showed that participants drawing long "anchor" lines made higher numerical estimates of target lengths than did those drawing shorter lines. We then replicated this finding, showing that a similar pattern was obtained even when the target estimates were not in the dimension of length. A third study showed that an anchor's length relative to its context, and not its absolute length, is the key to predicting the anchor's impact on judgments. A final study demonstrated that magnitude priming (priming a sense of largeness or smallness) is a plausible mechanism underlying the reported effects. We conclude that the boundary conditions of anchoring effects may be much looser than previously thought, with anchors operating across modalities and dimensions to bias judgment.

Question: Anchors aweigh: A demonstration of cross-modality anchoring and magnitude priming.


User simulation for spoken dialogue systems: learning and evaluation. We propose the "advanced" n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and "normal" n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of〈 speech act, task 〉 pairs) are selected, based on features of the current dialogue state which encodes the whole history of the dialogue. The user simulation based on "normal" n-grams treats a dialogue as a sequence of lists of 〈 speech act, task 〉 pairs. Here the length of the history considered is restricted by the order of the n-gram. The "advanced" n-grams are a variation of the normal ngrams, where user actions are conditioned not only on speech acts and tasks but also on the current status of the tasks, i.e. whether the information needed by the application (in our case flight booking) has been provided and confirmed by the user. This captures elements of goal-directed user behaviour. All models were trained and evaluated on the C OMMUNICATOR corpus, to which we added annotations for user actions and dialogue context. We then evaluate how closely the synthetic responses resemble the real user responses by comparing the user response generated by each user simulation model in a given dialogue context (taken from the annotated corpus) with the actual user response. We propose the expected accuracy, expected precision, and expected recall evaluation metrics as opposed to standard precision and recall used in prior work. We also discuss why they are more appropriate metrics for evaluating user simulation models compared to their standard counterparts. The advanced n-grams produce higher scores than the normal n-grams for small values of n, which proves their strength when little amount of data is available t

Question: What is the difference between "normal" and "advanced" n-grams?


Big Scholarly Data in CiteSeerX: Information Extraction from the Web. We examine CiteSeerX, an intelligent system designed with the goal of automatically acquiring and organizing large-scale collections of scholarly documents from the world wide web. From the perspective of automatic information extraction and modes of alternative search, we examine various functional aspects of this complex system with an eye towards ongoing and future research developments.

Question: What is the goal of the system?


Chatbot for admissions. The communication of potential students with a university department is performed manually and it is a very time consuming procedure. The opportunity to communicate with on a one-to-one basis is highly valued. However with many hundreds of applications each year, one-to-one conversations are not feasible in most cases. The communication will require a member of academic staff to expend several hours to find suitable answers and contact each student. It would be useful to reduce his costs and time. The project aims to reduce the burden on the head of admissions, and potentially other users, by developing a convincing chatbot. A suitable algorithm must be devised to search through the set of data and find a potential answer. The program then replies to the user and provides a relevant web link if the user is not satisfied by the answer. Furthermore a web interface is provided for both users and an administrator. The achievements of the project can be summarised as follows. To prepare the background of the project a literature review was undertaken, together with an investigation of existing tools, and consultation with the head of admissions. The requirements of the system were established and a range of algorithms and tools were investigated, including keyword and template matching. An algorithm that combines keyword matching with string similarity has been developed. A usable system using the proposed algorithm has been implemented. The system was evaluated by keeping logs of questions and answers and by feedback received by potential students that used it. 3 Acknowledgements I would like to thank Dr Peter Hancox for his immeasurable help and support throughout this project. I also need to express my thanks to the computer support team for their excellent help and instructions. Finally, I feel the need to acknowledge the constant support offered by my parents. Introduction This chapter covers an introduction to the project including the conte

Question: What is the project about?


Magnetic field effects on plant growth, development, and evolution. The geomagnetic field (GMF) is a natural component of our environment. Plants, which are known to sense different wavelengths of light, respond to gravity, react to touch and electrical signaling, cannot escape the effect of GMF. While phototropism, gravitropism, and tigmotropism have been thoroughly studied, the impact of GMF on plant growth and development is not well-understood. This review describes the effects of altering magnetic field (MF) conditions on plants by considering plant responses to MF values either lower or higher than those of the GMF. The possible role of GMF on plant evolution and the nature of the magnetoreceptor is also discussed.

Question: What is the role of magnetic field on plant growth?


An introduction to millimeter-wave mobile broadband systems. Almost all mobile communication systems today use spectrum in the range of 300 MHz-3 GHz. In this article, we reason why the wireless community should start looking at the 3-300 GHz spectrum for mobile broadband applications. We discuss propagation and device technology challenges associated with this band as well as its unique advantages for mobile communication. We introduce a millimeter-wave mobile broadband (MMB) system as a candidate next generation mobile communication system. We demonstrate the feasibility for MMB to achieve gigabit-per-second data rates at a distance up to 1 km in an urban mobile environment. A few key concepts in MMB network architecture such as the MMB base station grid, MMB interBS backhaul link, and a hybrid MMB + 4G system are described. We also discuss beamforming techniques and the frame structure of the MMB air interface.

Question: What is the millimeter-wave mobile broadband?


GradNets: Dynamic Interpolation Between Neural Architectures. In machine learning, there is a fundamental trade-off between ease of optimization and expressive power. Neural Networks, in particular, have enormous expressive power and yet are notoriously challenging to train. The nature of that optimization challenge changes over the course of learning. Traditionally in deep learning, one makes a static trade-off between the needs of early and late optimization. In this paper, we investigate a novel framework, GradNets, for dynamically adapting architectures during training to get the benefits of both. For example, we can gradually transition from linear to non-linear networks, deterministic to stochastic computation, shallow to deep architectures, or even simple downsampling to fully differentiable attention mechanisms. Benefits include increased accuracy, easier convergence with more complex architectures, solutions to test-time execution of batch normalization, and the ability to train networks of up to 200 layers.

Question: What is a neural network?


Linear permanent magnet motor for reciprocating compressor applications. This paper investigates the performance of a three designs of tubular linear permanent magnet motors (TLPMMs) with quasi-Halbach magnetized moving-magnet armature and slotted stator with a single-slot. The motors were developed for reciprocating compressor applications such as household refrigerators. The three designs have different magnet shape structures, and the stator core of all designs have employed a soft magnetic composite (SMC) material, Somaloy 700. Based on finite element analysis (FEA) an extensively analysis on the opencircuit magnetic field distribution, back-EMF, thrust force, fluxlinkage, winding inductance and cogging force in the three designs have been established. The simulation results indicate the effectiveness of the designs to drive a linear reciprocating compressor in household refrigerators, and also the simulation results have shown the superiority of the two designs with T-shape and trapezoidal magnet configurations, over the TLPMM with conventional rectangular magnet. Moreover, the permanent magnets (PMs) array arrangement has significant effects on the motor performance.

Question: What is the difference between a tubular linear permanent magnet motor and a tubular linear induction motor?


Characterization and Modeling of Nonfilamentary Ta/TaOx/TiO2/Ti Analog Synaptic Device. A two-terminal analog synaptic device that precisely emulates biological synaptic features is expected to be a critical component for future hardware-based neuromorphic computing. Typical synaptic devices based on filamentary resistive switching face severe limitations on the implementation of concurrent inhibitory and excitatory synapses with low conductance and state fluctuation. For overcoming these limitations, we propose a Ta/TaOx/TiO2/Ti device with superior analog synaptic features. A physical simulation based on the homogeneous (nonfilamentary) barrier modulation induced by oxygen ion migration accurately reproduces various DC and AC evolutions of synaptic states, including the spike-timing-dependent plasticity and paired-pulse facilitation. Furthermore, a physics-based compact model for facilitating circuit-level design is proposed on the basis of the general definition of memristor devices. This comprehensive experimental and theoretical study of the promising electronic synapse can facilitate realizing large-scale neuromorphic systems.

Question: What is the difference between a synapse and a memristor?


CitySpectrum: a non-negative tensor factorization approach. People flow at a citywide level is in a mixed state with several basic patterns (e.g. commuting, working, commercial), and it is therefore difficult to extract useful information from such a mixture of patterns directly. In this paper, we proposed a novel tensor factorization approach to modeling city dynamics in a basic life pattern space (CitySpectral Space). To obtain the CitySpectrum, we utilized Non-negative Tensor Factorization (NTF) to decompose a people flow tensor into basic life pattern tensors, described by three bases i.e. the intensity variation among different regions, the time-of-day and the sample days. We apply our approach to a big mobile phone GPS log dataset (containing 1.6 million users) to model the fluctuation in people flow before and after the Great East Japan Earthquake from a CitySpectral perspective. In addition, our framework is extensible to a variety of auxiliary spatial-temporal data. We parametrize a people flow with a spatial distribution of the Points of Interest (POIs) to quantitatively analyze the relationship between human mobility and POI distribution. Based on the parametric people flow, we propose a spectral approach for a site-selection recommendation and people flow simulation in another similar area using POI distribution.

Question: What is the people flow?