HEVC/H.265 vs. VP9 state-of-the-art video coding comparison for HD and UHD applications. High Efficiency Video Coding (HEVC) and VP9 are the latest state-of-the-art high-efficiency video coding standards. Both standards aim to decrease the bit-rate while improving the compression efficiency. HEVC/H.265 video standard was collaboratively developed by ITU-T and ISO/IEC with up to 50% bit-rate reduction as compared to its predecessor H.264/AVC. On the other hand, VP9 is an open source video standard developed by Google. While maintaining the same video quality, VP9 has achieved 50% bit-rate reduction as compared to its predecessor VP8. This research paper focuses on the comparison of HEVC and VP9 based on both subjective as well as objective assessments. First, detailed differences in design and specification of the two standards are discussed. Later experiment results are provided, which compare the coding efficiency of both standards on various high-definition 720p, 1090p, and 2160p sample test bench videos. According to our experimental results, it has been empirically proved that HEVC/H.265 outperforms VP9 in bit-rate savings.

Question: What is the difference between HEVC and VP9?


GRAVITY-ASSIST: A series elastic body weight support system with inertia compensation. We present GRAVITY-ASSIST, a series elastic active body weight support and inertia compensation system for use in robot assisted gait rehabilitation. The device consists of a single degree of freedom series elastic actuator that connects to the trunk of a patient. The series elastic system is novel in that, it can provide the desired level of dynamic unloading such that the patient experiences only a percentage of his/her weight and inertia. Inertia compensation is important, since the inertial forces can cause significant deviations from the desired unloading force, specially at low support forces and fast walking speeds. Furthermore, this feature enables the inertia of the harness and force sensing unit attached to the patient to be compensated for, making sure that the device does not interfere with the natural gait cycle. We present a functional prototype of the device, its characterization and experimental verification of the approach.

Question: What is a series elastic body weight support system?


A Comprehensive Survey of Recent Trends in Cloud Robotics Architectures and Applications. Cloud robotics has recently emerged as a collaborative technology between cloud computing and service robotics enabled through progress in wireless networking, large scale storage and communication technologies, and the ubiquitous presence of Internet resources over recent years. Cloud computing empowers robots by offering them faster and more powerful computational capabilities through massively parallel computation and higher data storage facilities. It also offers access to open-source, big datasets and software, cooperative learning capabilities through knowledge sharing, and human knowledge through crowdsourcing. The recent progress in cloud robotics has led to active research in this area spanning from the development of cloud robotics architectures to its varied applications in different domains. In this survey paper, we review the recent works in the area of cloud robotics technologies as well as its applications. We draw insights about the current trends in cloud robotics and discuss the challenges and limitations in the current literature, open research questions and future research directions.

Question: What is cloud robotics?


Local force cues for strength and stability in a distributed robotic construction system. Construction of spatially extended, self-supporting structures requires a consideration of structural stability throughout the building sequence. For collective construction systems, where independent agents act with variable order and timing under decentralized control, ensuring stability is a particularly pronounced challenge. Previous research in this area has largely neglected considering stability during the building process. Physical forces present throughout a structure may be usable as a cue to inform agent actions as well as an indirect communication mechanism (stigmergy) to coordinate their behavior, as adding material leads to redistribution of forces which then informs the addition of further material. Here we consider in simulation a system of decentralized climbing robots capable of traversing and extending a two-dimensional truss structure, and explore the use of feedback based on force sensing as a way for the swarm to anticipate and prevent structural failures. We consider a scenario in which robots are tasked with building an unsupported cantilever across a gap, as for a bridge, where the goal is for the swarm to build any stable spanning structure rather than to construct a specific predetermined blueprint. We show that access to local force measurements enables robots to build cantilevers that span significantly farther than those built by robots without access to such information. This improvement is achieved by taking measures to maintain both strength and stability, where strength is ensured by paying attention to forces during locomotion to prevent joints from breaking, and stability is maintained by looking at how loads transfer to the ground to ensure against toppling. We show that swarms that take both kinds of forces into account have improved building performance, in both structured settings with flat ground and unpredictable environments with rough

Question: How can we build a bridge?


Lifelong Machine Learning Systems: Beyond Learning Algorithms. Lifelong Machine Learning, or LML, considers systems that can learn many tasks from one or more domains over its lifetime. The goal is to sequentially retain learned knowledge and to selectively transfer that knowledge when learning a new task so as to develop more accurate hypotheses or policies. Following a review of prior work on LML, we propose that it is now appropriate for the AI community to move beyond learning algorithms to more seriously consider the nature of systems that are capable of learning over a lifetime. Reasons for our position are presented and potential counter-arguments are discussed. The remainder of the paper contributes by defining LML, presenting a reference framework that considers all forms of machine learning, and listing several key challenges for and benefits from LML research. We conclude with ideas for next steps to advance the field.

Question: What is lifelong machine learning?


Futures of elderly care in Iran: A protocol with scenario approach. Background: The number of people aged 60 and older is increasing faster than other age groups worldwide. Iran will experience a sharp aging population increase in the next decades, and this will pose new challenges to the healthcare system. Since providing high quality aged-care services would be the major concern of the policymakers, this question arises that what types of aged care services should be organized in the coming 10 years? This protocol has been designed to develop a set of scenarios for the future of elderly care in Iran. Methods: In this study, intuitive logics approach and Global Business Network (GBN) model were used to develop scenarios for elderly care in Iran. In terms of perspective, the scenarios in this approach are normative, qualitative with respect to methodology and deductive in constructing the process of scenarios. The three phases of GBN model are as follows: 1) Orientation: Identifying strategic levels, stakeholders, participants and time horizon; 2) Exploration: Identifying the driving forces and key uncertainties; 3) Synthesis: Defining the scenario logics and constructing scenario storyline. Results: Presently, two phases are completed and the results will be published in mid-2016. Conclusion: This study delivers a comprehensive framework for taking appropriate actions in providing care for the elderly in the future. Moreover, policy makers should specify and provide the full range of services for the elderly, and in doing so, the scenarios and key findings of this study could be of valuable help.

Question: What is the future of elderly care in Iran?


Propagation Models for Trust and Distrust in Social Networks. Semantic Web endeavors have mainly focused on issues pertaining to knowledge representation and ontology design. However, besides understanding information metadata stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements.

Question: What is the difference between trust and distrust?


Towards Effective Log Summarization. Database access logs are the canonical go-to resource for tasks ranging from performance tuning to security auditing. Unfortunately, they are also large, unwieldy, and it can be difficult for a human analyst to divine the intent behind typical queries in the log. With an eye towards creating tools for ad-hoc exploration of queries by intent, we analyze techniques for clustering queries by intent. Although numerous techniques have already been developed for log summarization, they target specific goals like query recommendation or storage layout optimization rather than the more fuzzy notion of query intent. In this paper, we first survey a variety of log summarization techniques, focusing on a class of approaches that use query similarity metrics. We then propose DCABench, a benchmark that evaluates how well query similarity metrics capture query intent, and use it to evaluate three similarity metrics. DCABench uses student answers to query construction assignments to capture a wide range of distinct SQL queries that all have the same intent. Next, we propose and evaluate a query regularization process that standardizes query representations, significantly improving the effectiveness of the three similarity metrics tested. Finally, we propose an entirely new similarity metric based on the Weisfeiler-Lehman (WL) approximate graph isomorphism algorithm, which identifies salient features of a graph — or in our case, of the abstract syntax tree of a query. We show experimentally that distances in WL-feature space capture a meaningful notion of similarity, while still retaining competitive performance.

Question: What is the canonical go-to resource for tasks ranging from performance tuning to security auditing?


BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks. In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Coarse guidance using deformation fields obtained by an existing registration method; and (2) Fine guidance using image similarity. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.

Question: How to register brain images?


Designing Media Architecture: Tools and Approaches for Addressing the Main Design Challenges. Media Architecture is reaching a level of maturity at which we can identify tools and approaches for addressing the main challenges for HCI practitioners working in this field. While previous influential contributions within Media Architecture have identified challenges for designers and offered case studies of specific approaches, here, we (1) provide guidance on how to tackle the domain-specific challenges of Media Architecture design -- pertaining to the interface, integration, content, context, process, prototyping, and evaluation -- on the basis of the development of numerous installations over the course of seven years, and thorough studies of related work, and (2) present five categories of tools and approaches -- software tools, projection, 3D models, hardware prototyping, and evaluation tools -- developed to address these challenges in practice, exemplified through six concrete examples from real-life cases.

Question: What is the difference between a software tool and a software library?


Action and Interaction Recognition in First-Person Videos. In this work, we evaluate the performance of the popular dense trajectories approach on first-person action recognition datasets. A person moving around with a wearable camera will actively interact with humans and objects and also passively observe others interacting. Hence, in order to represent real-world scenarios, the dataset must contain actions from first-person perspective as well as third-person perspective. For this purpose, we introduce a new dataset which contains actions from both the perspectives captured using a head-mounted camera. We employ a motion pyramidal structure for grouping the dense trajectory features. The relative strengths of motion along the trajectories are used to compute different bag-of-words descriptors and concatenated to form a single descriptor for the action. The motion pyramidal approach performs better than the baseline improved trajectory descriptors. The method achieves 96.7% on the JPL interaction dataset and 61.8% on our NUS interaction dataset. The same is used to detect actions in long video sequences and achieves average precision of 0.79 on JPL interaction dataset.

Question: What is the motion pyramidal approach?


Online continuous stereo extrinsic parameter estimation. Stereo visual odometry and dense scene reconstruction depend critically on accurate calibration of the extrinsic (relative) stereo camera poses. We present an algorithm for continuous, online stereo extrinsic re-calibration operating only on sparse stereo correspondences on a per-frame basis. We obtain the 5 degree of freedom extrinsic pose for each frame, with a fixed baseline, making it possible to model time-dependent variations. The initial extrinsic estimates are found by minimizing epipolar errors, and are refined via a Kalman Filter (KF). Observation covariances are derived from the Cramer-Rao lower bound of the solution uncertainty. The algorithm operates at frame rate with unoptimized Matlab code with over 1000 correspondences per frame. We validate its performance using a variety of real stereo datasets and simulations.

Question: How to estimate the extrinsic parameters of a stereo camera?


Discriminative Training of Deep Fully Connected Continuous CRFs With Task-Specific Loss. Recent works on deep conditional random fields (CRFs) have set new records on many vision tasks involving structured predictions. Here, we propose a fully connected deep continuous CRF model with task-specific losses for both discrete and continuous labeling problems. We exemplify the usefulness of the proposed model on multi-class semantic labeling (discrete) and the robust depth estimation (continuous) problems. In our framework, we model both the unary and the pairwise potential functions as deep convolutional neural networks (CNNs), which are jointly learned in an end-to-end fashion. The proposed method possesses the main advantage of continuously valued CRFs, which is a closed-form solution for the maximum a posteriori (MAP) inference. To better take into account the quality of the predicted estimates during the cause of learning, instead of using the commonly employed maximum likelihood CRF parameter learning protocol, we propose task-specific loss functions for learning the CRF parameters. It enables direct optimization of the quality of the MAP estimates during the learning process. Specifically, we optimize the multi-class classification loss for the semantic labeling task and the Tukey's biweight loss for the robust depth estimation problem. Experimental results on the semantic labeling and robust depth estimation tasks demonstrate that the proposed method compare favorably against both baseline and state-of-the-art methods. In particular, we show that although the proposed deep CRF model is continuously valued, with the equipment of task-specific loss, it achieves impressive results even on discrete labeling tasks.

Question: What is the difference between a deep continuous CRF and a deep conditional random field?


Pharmacist-managed medication therapy adherence clinics: The Malaysian experience.. In Malaysia, the first pharmacist-managed clinic was established in 2004. This type of clinics is locally known as a medication therapy adherence clinic (MTAC).1e3 In fact, these clinics were introduced by the Pharmaceutical Services Division (PSD), Ministry of Health Malaysia (MOH) as part of the clinical pharmacy services to improve the quality, safety, and cost-effectiveness of patient care through better medicines management at ambulatory care settings in hospitals and MOH clinics.2 The major aims of the clinics include optimization of medication therapy, improvement of medication adherence, and prevention or reduction of adverse drug events and other medication related problems.1 Over a decade, the number of MTACs has increased tremendously to 660 by the end of 2013.1 Moreover, currently, there are 13 types of clinics being operated in Malaysia that include neurology (stroke), diabetes, heart failure, respiratory, psoriasis, anticoagulation, rheumatology, nephrology, hepatitis, geriatrics, retroviral disease, hemophilia, and psychiatry.1,3,4 The pharmacists operate the clinics and provide pharmaceutical care services to the patients in collaboration with physicians and other healthcare professionals according to specific protocols. The specific roles of the pharmacist include providing drug information, patient education and counseling, identifying and solving drugrelated problems, monitoring patients' drug therapy and treatment outcomes, offering feedback on patient progress, making appropriate recommendations to physicians to individualize the patients' drug regimens according to patient-related factors including clinical, humanistic, and economic factors.1e4 Several studies have been conducted to evaluate the impact of pharmacist-managed clinics on patient care in Malaysia.5e10 These studies have demonstrated that the clinics are beneficial in optimizing medication therapy, i

Question: What is the impact of pharmacist-managed medication therapy adherence clinics on patient care in Malaysia?


VIPS: a Vision-based Page Segmentation Algorithm. A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Experiments show satisfactory results.

Question: What is a tag-tree independent approach?


Enhanced SAR ADC energy efficiency from the early reset merged capacitor switching algorithm. The early reset merged capacitor switching algorithm (EMCS) is proposed as an energy reducing switching technique for a binary weighted, capacitive successive approximation (SAR) analog to digital converter (ADC). The method uses the merged capacitor switching (MCS) architecture and optimizes the use of the VCM level during the SAR conversion. This algorithm can reduce switching power by over 12% with no additional DAC driver activity when compared to the MCS scheme. The MCS and EMCS approaches are analyzed mathematically and the EMCS energy consumption is shown to be lower than or equal to that of the MCS technique for every digital code. Static linearity improvements for this structure are also shown with the integral non-linearity (INL) reducing by a factor of two due to the utilization of the MCS three level DAC. The EMCS implementation methodology is also described.

Question: What is the difference between the MCS and EMCS approaches?


Accident prediction model for railway-highway interfaces.. Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.

Question: What is the relationship between the number of accidents and the number of trains?


Regional coherence changes in the early stages of Alzheimer's disease: A combined structural and resting-state functional MRI study. Recent functional imaging studies have indicated that the pathophysiology of Alzheimer's disease (AD) can be associated with the changes in spontaneous low-frequency (<0.08 Hz) blood oxygenation level-dependent fluctuations (LFBF) measured during a resting state. The purpose of this study was to examine regional LFBF coherence patterns in early AD and the impact of regional brain atrophy on the functional results. Both structural MRI and resting-state functional MRI scans were collected from 14 AD subjects and 14 age-matched normal controls. We found significant regional coherence decreases in the posterior cingulate cortex/precuneus (PCC/PCu) in the AD patients when compared with the normal controls. Moreover, the decrease in the PCC/PCu coherence was correlated with the disease progression measured by the Mini-Mental State Exam scores. The changes in LFBF in the PCC/PCu may be related to the resting hypometabolism in this region commonly detected in previous positron emission tomography studies of early AD. When the regional PCC/PCu atrophy was controlled, these results still remained significant but with a decrease in the statistical power, suggesting that the LFBF results are at least partly explained by the regional atrophy. In addition, we also found increased LFBF coherence in the bilateral cuneus, right lingual gyrus and left fusiform gyrus in the AD patients. These regions are consistent with previous findings of AD-related increased activation during cognitive tasks explained in terms of a compensatory-recruitment hypothesis. Finally, our study indicated that regional brain atrophy could be an important consideration in functional imaging studies of neurodegenerative diseases.

Question: What is the impact of regional brain atrophy on the functional results?


RY-ON WITH CLOTHING REGION. We propose a virtual try-on method based on generative adversarial networks (GANs). By considering clothing regions, this method enables us to reflect the pattern of clothes better than Conditional Analogy GAN (CAGAN), an existing virtual try-on method based on GANs. Our method first obtains the clothing region on a person by using a human parsing model learned with a large-scale dataset. Next, using the acquired region, the clothing part is removed from a human image. A desired clothing image is added to the blank area. The network learns how to apply new clothing to the area of people's clothing. Results demonstrate the possibility of reflecting a clothing pattern. Furthermore, an image of the clothes that the person is originally wearing becomes unnecessary during testing. In experiments, we generate images using images gathered from Zaland (a fashion E-commerce site).

Question: How to generate images of clothes?


Towards detecting fake user accounts in facebook. People are highly dependent on online social networks (OSNs) which have attracted the interest of cyber criminals for carrying out a number of malicious activities. An entire industry of black-market services has emerged which offers fake accounts based services for sale. We, therefore, in our work, focus on detecting fake accounts on a very popular (and difficult for data collection) online social network, Facebook. Key contributions of our work are as follows. The first contribution has been collection of data related to real and fake accounts on Facebook. Due to strict privacy settings and ever evolving API of Facebook with each version adding more restrictions, collecting user accounts data became a major challenge. Our second contribution is the use of user-feed information on Facebook to understand user profile activity and identifying an extensive set of 17 features which play a key role in discriminating fake users on Facebook with real users. Third contribution is the use these features and identifying the key machine learning based classifiers who perform well in detection task out of a total of 12 classifiers employed. Fourth contribution is the identifying which type of activities (like, comment, tagging, sharing, etc) contribute the most in fake user detection. Results exhibit classification accuracy of 79% among the best performing classifiers. In terms of activities, likes and comments contribute well towards detection task. Although the accuracy is not very high, however, our work forms a baseline for further improvement. Our results indicate that many fake users are classified as real suggesting clearly that fake accounts are mimicking real user behavior to evade detection mechanisms. Our work concludes by enlisting a number of future course of actions that can be undertaken.

Question: How to detect fake accounts on facebook?


Statistical mechanics of complex networks. Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and computers connected by physical links. While traditionally these systems have been modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks are governed by robust organizing principles. This article reviews the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, the authors discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, the emerging theory of evolving networks, and the interplay between topology and the network's robustness against failures and attacks.

Question: What is the statistical mechanics of complex networks?


Key Risk Indicators – Their Role in Operational Risk Management and Measurement. In comparison with other operational risk management and measurement tools such as loss data (internal, public and consortium), risk-and-control assessments, capital allocation and performance measurement, key risk indicators (KRIs) remain one of the outstanding action items on most firms' to-do lists, along with scenario analysis. KRIs are not new, but, up until recently, attempts to implement such programmes have often been characterised as less than effective. We believe that there are emerging best practices for KRI programmes that overcome some of the traditional challenges in KRI implementations. Further, we believe that investment in KRI programmes will reap many benefits, including the ability to clearly convey risk appetite, optimise risk and return, and improve the likelihood of achieving primary business goals through more effective operational risk management. In this chapter, we will seek to demystify KRIs, understand the basic fundamentals in identifying, specifying, selecting and implementing quality indicators, and consider how to monitor and report on them, in conjunction with other useful operational risk management information, to create powerful management reporting. We will also examine the potential for more advanced applications of KRIs, touching on the measurement of risk for correlation purposes, and the potential for composite indicators as well as KRI benchmarking. Finally, we will overview an industry KRI 10

Question: What is the role of key risk indicators in operational risk management?


Fibonacci Exposure Bracketing for High Dynamic Range Imaging. Exposure bracketing for high dynamic range (HDR) imaging involves capturing several images of the scene at different exposures. If either the camera or the scene moves during capture, the captured images must be registered. Large exposure differences between bracketed images lead to inaccurate registration, resulting in artifacts such as ghosting (multiple copies of scene objects) and blur. We present two techniques, one for image capture (Fibonacci exposure bracketing) and one for image registration (generalized registration), to prevent such motion-related artifacts. Fibonacci bracketing involves capturing a sequence of images such that each exposure time is the sum of the previous N(N > 1) exposures. Generalized registration involves estimating motion between sums of contiguous sets of frames, instead of between individual frames. Together, the two techniques ensure that motion is always estimated between frames of the same total exposure time. This results in HDR images and videos which have both a large dynamic range and minimal motion-related artifacts. We show, by results for several real-world indoor and outdoor scenes, that the proposed approach significantly outperforms several existing bracketing schemes.

Question: How to capture HDR images?


ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES FACULTY OF INFORMATICS DEPARTMENT OF INFORMATION SCIENCE DESIGNING A STEMMER FOR GE'EZ TEXT USING RULE BASED APPROACH.

Question: How to design a stemmer for ge'ez text using rule based approach?


Depth-Dependent Halos: Illustrative Rendering of Dense Line Data. We present a technique for the illustrative rendering of 3D line data at interactive frame rates. We create depth-dependent halos around lines to emphasize tight line bundles while less structured lines are de-emphasized. Moreover, the depth-dependent halos combined with depth cueing via line width attenuation increase depth perception, extending techniques from sparse line rendering to the illustrative visualization of dense line data. We demonstrate how the technique can be used, in particular, for illustrating DTI fiber tracts but also show examples from gas and fluid flow simulations and mathematics as well as describe how the technique extends to point data. We report on an informal evaluation of the illustrative DTI fiber tract visualizations with domain experts in neurosurgery and tractography who commented positively about the results and suggested a number of directions for future work.

Question: How to render dense line data?


An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods. Despite the great developments in information technology, particularly the Internet, computer networks, global information exchange, and its positive impact in all areas of daily life, it has also contributed to the development of penetration and intrusion which forms a high risk to the security of information organizations, government agencies, and causes large economic losses. There are many techniques designed for protection such as firewall and intrusion detection systems (IDS). IDS is a set of software and/or hardware techniques used to detect hacker's activities in computer systems. Two types of anomalies are used in IDS to detect intrusive activities different from normal user behavior. Misuse relies on the knowledge base that contains all known attack techniques and intrusion is discovered through research in this knowledge base. Artificial intelligence techniques have been introduced to improve the performance of these systems. The importance of IDS is to identify unauthorized access attempting to compromise confidentiality, integrity or availability of the computer network. This paper investigates the Intrusion Detection (ID) problem using three machine learning algorithms namely, BayesNet algorithm, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM). The algorithms are applied on a real, Management Information Based (MIB) dataset that is collected from real life environment. To enhance the detection process accuracy, a set of feature selection approaches is used; Infogain (IG), ReleifF (RF), and Genetic Search (GS). Our experiments show that the three feature selection methods have enhanced the classification performance. GS with bayesNet, MLP and SVM give high accuracy rates, more specifically the BayesNet with the GS accuracy rate is 99.9%.

Question: What is the importance of intrusion detection systems?


Foot orthoses for plantar heel pain: a systematic review and meta-analysis.. OBJECTIVE To investigate the effectiveness of foot orthoses for pain and function in adults with plantar heel pain.   DESIGN Systematic review and meta-analysis. The primary outcome was pain or function categorised by duration of follow-up as short (0 to 6 weeks), medium (7 to 12 weeks) or longer term (13 to 52 weeks).   DATA SOURCES Medline, CINAHL, SPORTDiscus, Embase and the Cochrane Library from inception to June 2017.   ELIGIBILITY CRITERIA FOR SELECTING STUDIES Studies must have used a randomised parallel-group design and evaluated foot orthoses for plantar heel pain. At least one outcome measure for pain or function must have been reported.   RESULTS A total of 19 trials (1660 participants) were included. In the short term, there was very low-quality evidence that foot orthoses do not reduce pain or improve function. In the medium term, there was moderate-quality evidence that foot orthoses were more effective than sham foot orthoses at reducing pain (standardised mean difference -0.27 (-0.48 to -0.06)). There was no improvement in function in the medium term. In the longer term, there was very low-quality evidence that foot orthoses do not reduce pain or improve function. A comparison of customised and prefabricated foot orthoses showed no difference at any time point.   CONCLUSION There is moderate-quality evidence that foot orthoses are effective at reducing pain in the medium term, however it is uncertain whether this is a clinically important change.

Question: What is the difference between customised and prefabricated foot orthoses?


REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems. The package REGULARIZATION TOOLS consists of 54 Matlab routines for analysis and solution of discrete ill-posed problems, i.e., systems of linear equations whose coefficient matrix has the properties that its condition number is very large, and its singular values decay gradually to zero. Such problems typically arise in connection with discretization of Fredholm integral equations of the first kind, and similar ill-posed problems. Some form of regularization is always required in order to compute a stabilized solution to discrete ill-posed problems. The purpose of REGULARIZATION TOOLS is to provide the user with easy-to-use routines, based on numerical robust and efficient algorithms, for doing experiments with regularization of discrete ill-posed problems. By means of this package, the user can experiment with different regularization strategies, compare them, and draw conclusions from these experiments that would otherwise require a major programming effert. For discrete ill-posed problems, which are indeed difficult to treat numerically, such an approach is certainly superior to a single black-box routine. This paper describes the underlying theory gives an overview of the package; a complete manual is also available.

Question: What is a discrete ill-posed problem?


Speech recognition in adverse conditions : A review. Speech recognition in adverse conditions: A review Sven L. Mattys a , Matthew H. Davis b , Ann R. Bradlow c & Sophie K. Scott d a Department of Psychology, University of York, York, UK b Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK c Department of Linguistics, Northwestern University, Evanston, IL, USA d Institute of Cognitive Neuroscience, University College London, London, UK

Question: What is the speech recognition in adverse conditions?


Weighted-to-Spherically-Uniform Quality Evaluation for Omnidirectional Video. Omnidirectional video records a scene in all directions around one central position. It allows users to select viewing content freely in all directions. Assuming that viewing directions are uniformly distributed, the isotropic observation space can be regarded as a sphere. Omnidirectional video is commonly represented by different projection formats with one or multiple planes. To measure objective quality of omnidirectional video in observation space more accurately, a weighted-to-spherically-uniform quality evaluation method is proposed in this letter. The error of each pixel on projection planes is multiplied by a weight to ensure the equivalent spherical area in observation space, in which pixels with equal mapped spherical area have the same influence on distortion measurement. Our method makes the quality evaluation results more accurate and reliable since it avoids error propagation caused by the conversion from resampling representation space to observation space. As an example of such quality evaluation method, weighted-to-spherically-uniform peak signal-to-noise ratio is described and its performance is experimentally analyzed.

Question: What is the weighted-to-spherically-uniform quality evaluation method?


Design of Incoherent Frames via Convex Optimization. This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.

Question: How to design an incoherent frame?


Evaluation of Quranic text retrieval system based on manually indexed topics. This paper investigates the effectiveness of a state of the art information retrieval (IR) system in the verse retrieval problem for Quranic text. The evaluation is based on manually indexed topics of the Quran that provides both the queries and the relevance judgments. Furthermore, the system is evaluated in both Malay and English environment. The performance of the system is measured based on the MAP, the precision at 1, 5 and 10, and the MRR scores. The results of the evaluation are promising, showing the IR system has many potential for the Quranic text retrieval.

Question: What is the performance of the system?


Design and prototype of monolithic compliant grippers for adaptive grasping. This study presents the designs of monolithic compliant grippers made by silicon rubber for adaptive grasping applications. The proposed grippers are symmetric two-finger designs actuated by one linear actuator. An optimal design procedure including topology and size optimization methods is used to synthesize the rubber grippers with the objective to maximize mechanical advantage, which is defined as the ratio of output force to input force. Three grippers synthesized with different design parameters are prototyped. The best design is identified through the adaptability test, and a robotic gripper assembly is developed to demonstrate the effectiveness of the design through the test of grasping irregular objects. The maximum payload for the gripper assembly is identified as 2.5kg.

Question: What is the maximum payload of the gripper?


End-to-End Channel Capacity of a Wireless Sensor Network Under Reachback. Many wireless sensor network applications will impose a many-to-one traffic flow pattern. This phenomena in communication networks is referred to as reachback. The higher volume of traffic in traffic hotspots, which usually form around the base station, limits the number of sensors that can participate in the formation of a wireless sensor network with a fixed per sensor data rate. To alleviate the effects of reachback, spatial correlation of sensor data has enabled the use of Slepian-Wolf coding as a compression technique to reduce the volume of traffic in sensor networks. Within a cluster, Slepian-Wolf coding is used to compress the sensor readings. Clusterheads then transmit the compressed sensor readings over the overlay network which is formed by all clusterheads. We develop an expression for the end-to-end (sensor-to-base station) channel capacity of such a wireless sensor network and observe the effect of variations in the number of clusterheads on end-to-end capacity.

Question: How many clusterheads are needed to form a wireless sensor network?


Sequence Covering for Efficient Host-Based Intrusion Detection. This paper introduces a new similarity measure, the covering similarity, which we formally define for evaluating the similarity between a symbolic sequence and a set of symbolic sequences. A pairwise similarity can also be directly derived from the covering similarity to compare two symbolic sequences. An efficient implementation to compute the covering similarity is proposed which uses a suffix-tree data structure, but other implementations, based on suffix array for instance, are possible and are possibly necessary for handling very large-scale problems. We have used this similarity to isolate attack sequences from normal sequences in the scope of host-based intrusion detection. We have assessed the covering similarity on two well-known benchmarks in the field. In view of the results reported on these two datasets for the state-of-the-art methods, according to the comparative study, we have carried out based on three challenging similarity measures commonly used for string processing, or in bioinformatics, we show that the covering similarity is particularly relevant to address the detection of anomalies in sequences of system calls.

Question: What is the covering similarity?


Comparing Entities in RDF Graphs. The Semantic Web has fuelled the appearance of numerous open-source knowledge bases. Knowledge bases enable new types of information search, going beyond classical query answering and into the realm of exploratory search, and providing answers to new types of user questions. One such question is how two entities are comparable, i.e., what are similarities and differences between the information known about the two entities. Entity comparison is an important task and a widely used functionality available in many information systems. Yet it is usually domain-specific and depends on a fixed set of aspects to compare. In this paper we propose a formal framework for domain-independent entity comparison that provides similarity and difference explanations for input entities. We model explanations as conjunctive queries, we discuss how multiple explanations for an entity pair can be ranked and we provide a polynomial-time algorithm for generating most specific similarity explanations.

Question: What is the difference between a knowledge base and a semantic web?


Conservative treatment of carpal tunnel syndrome: comparison between laser therapy and Fascial Manipulation(®).. The etiopathogenesis of Carpal Tunnel Syndrome (CTS) is multifactorial and most cases are classified as idiopathic (Thurston 2013). A randomized controlled trial was performed to compare the effectiveness of Fascial Manipulation(®) (FM) and Low-Level Laser Therapy (LLLT) for CTS. This prospective trial included 42 patients (70 hands with symptoms) with clinical and electroneuromyographic diagnosis of CTS. The patients were randomly assigned to receive multiple sessions of FM or multiple session of LLLT. The Visual Analogic Scale (VAS) and Boston Carpal Tunnel Questionnaire (BCTQ) were performed at baseline, end of treatment and after three months. The group that received FM showed a significant reduction in subjective pain perception and an increased function assessed by BCTQ at the end of the treatment and follow-up. The group that received LLLT showed an improvement in the BCTQ at the end of the treatment but the improvement level was not sustained at the three month follow-up. FM is a valid alternative treatment for CTS.

Question: What is the difference between LLLT and FM?


An algorithm for treatment of the drooping nose.. UNLABELLED Nasal tip ptosis ("drooping" or long nose) occurs when the tip of the nose is more caudal than what is deemed ideal. Intrinsic factors, such as elongated or caudally-rotated lower lateral cartilages, can lead to nasal tip ptosis. Extrinsic factors, such as elongated upper lateral cartilages or excessive caudal anterior septum and heavy nasal skin, can push the nasal tip caudally and lead to drooping of the nasal tip. The loss of maxillary or nasal spine support may enhance the potential for tip ptosis. In addition, a hyperactive depressor nasi septi could, as a result of continuous pull on the tip, result in tip ptosis. Aging or previous nasal procedures (such as the Goldman-type tip surgery) where the continuity of the lateral and medial crura of the lower lateral cartilages have been violated may cause a weakening of the tip-supporting mechanisms and de-rotation of the nasal tip. Correction of this deformity is challenging and rewarding; it can resolve both the cosmetic deformity and nasal obstruction symptoms related to this entity. The goal of this article is to present our current principles of diagnosis and treatment of nasal tip ptosis, as well as to introduce and algorithm of preferred methods and techniques for its reliable and stable correction.   RESULTS Correction of the nasal tip ptosis requires accurate diagnosis, a recognition of the interplay between various anatomic components, specific strategy planning, and a correction of anatomic abnormalities.

Question: How to treat a drooping nose?


E-S-QUAL A Multiple-Item Scale for Assessing Electronic Service Quality. Using the means-end framework as a theoretical foundation, this article conceptualizes, constructs, refines, and tests a multiple-item scale (E-S-QUAL) for measuring the service quality delivered by Web sites on which customers shop online. Two stages of empirical data collection revealed that two different scales were necessary for capturing electronic service quality. The basic E-S-QUAL scale developed in the research is a 22-item scale of four dimensions: efficiency, fulfillment, system availability, and privacy. The second scale, E-RecS-QUAL, is salient only to customers who had nonroutine encounters with the sites and contains 11 items in three dimensions: responsiveness, compensation, and contact. Both scales demonstrate good psychometric properties based on findings from a variety of reliability and validity tests and build on the research already conducted on the topic. Directions for further research on electronic service quality are offered. Managerial implications stemming from the empirical findings about E-S-QUAL are also discussed.

Question: What is the difference between the E-S-QUAL and E-RecS-QUAL scales?


Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading. We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as "powergrading." We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small "budget" of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.

Question: How does powergrading amplify human effort?


Direction matters: hand pose estimation from local surface normals. We present a hierarchical regression framework for estimating hand joint positions from single depth images based on local surface normals. The hierarchical regression follows the tree structured topology of hand from wrist to finger tips. We propose a conditional regression forest, i.e. the Frame Conditioned Regression Forest (FCRF) which uses a new normal difference feature. At each stage of the regression, the frame of reference is established from either the local surface normal or previously estimated hand joints. By making the regression with respect to the local frame, the pose estimation is more robust to rigid transformations. We also introduce a new efficient approximation to estimate surface normals. We verify the effectiveness of our method by conducting experiments on two challenging real-world datasets and show consistent improvements over previous discriminative pose estimation methods.

Question: How to estimate hand pose from single depth images?


PZT-Actuated and -Sensed Resonant Micromirrors with Large Scan Angles Applying Mechanical Leverage Amplification for Biaxial Scanning. This article presents design, fabrication and characterization of lead zirconate titanate (PZT)-actuated micromirrors, which enable extremely large scan angle of up to 106° and high frequency of 45 kHz simultaneously. Besides the high driving torque delivered by PZT actuators, mechanical leverage amplification has been applied for the micromirrors in this work to reach large displacements consuming low power. Additionally, fracture strength and failure behavior of poly-Si, which is the basic material of the micromirrors, have been studied to optimize the designs and prevent the device from breaking due to high mechanical stress. Since comparing to using biaxial micromirror, realization of biaxial scanning using two independent single-axial micromirrors shows considerable advantages, a setup combining two single-axial micromirrors for biaxial scanning and the results will also be presented in this work. Moreover, integrated piezoelectric position sensors are implemented within the micromirrors, based on which closed-loop control has been developed and studied.

Question: What is the difference between a single-axial and a biaxial micromirror?


Modified post-filter to recover modulation spectrum for HMM-based speech synthesis. This paper proposes a modified post-filter to recover a Modulation Spectrum (MS) in HMM-based speech synthesis. To alleviate the over-smoothing effect which is one of the major problems in HMM-based speech synthesis, the MS-based post-filter has been proposed. It recovers the utterance-level MS of the generated speech trajectory, and we have reported its benefit to the quality improvement. However, this post-filter is not applicable to various lengths of speech parameter trajectories, such as phrases or segments, which are shorter than an utterance. To address this problem, we propose two modified post-filters, (1) the time-invariant filter with a simplified conversion form and (2) the segment-level post-filter which applicable to a short-term parameter sequence. Furthermore, we also propose (3) the post-filter to recover the phoneme-level MS of HMM-state duration. Experimental results show that the modified post-filters also yield significant quality improvements in synthetic speech as yielded by the conventional post-filter.

Question: How to recover the modulation spectrum of a short-term parameter sequence?


Velocity determination in scenes containing several moving objects. A method is described which quantifies the speed and direction of several moving objects in a sequence of digital images. A relationship between the time variation of intensity, the spatial gradient, and velocity has been developed which allows the determination of motion using clustering techniques. This paper describes these relationships, the clustering technique, and provides examples of the technique on real images containing several moving objects.

Question: How do you determine the velocity of several moving objects in a sequence of digital images?


Grammatical Error Correction. Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Earlier attempts to grammatical error correction involve rule-based and classifier approaches which are limited to correcting only some particular type of errors in a sentence. As sentences may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this report, we investigate GEC as a translation task from incorrect to correct English and explore some machine translation approaches for developing end-to-end GEC systems for all error types. We apply Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) approaches to GEC and show that they can correct multiple errors of different types in a sentence when compared to the earlier methods which focus on individual errors. We also discuss some of the weakness of machine translation approaches. Finally, we also experiment on a candidate re-ranking technique to re-rank the hypotheses generated by machine translation systems. With regression models, we try to predict a grammaticality score for each candidate hypothesis and re-rank them according to the score.

Question: What is grammatical error correction?


Synthetic Population Generation Without a Sample.

Question: How to generate synthetic population without a sample?


Pors: proofs of retrievability for large files. In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.  A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.  In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.  We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

Question: What is a POR?


LabelMe video: Building a video database with human annotations. Currently, video analysis algorithms suffer from lack of information regarding the objects present, their interactions, as well as from missing comprehensive annotated video databases for benchmarking. We designed an online and openly accessible video annotation system that allows anyone with a browser and internet access to efficiently annotate object category, shape, motion, and activity information in real-world videos. The annotations are also complemented with knowledge from static image databases to infer occlusion and depth information. Using this system, we have built a scalable video database composed of diverse video samples and paired with human-guided annotations. We complement this paper demonstrating potential uses of this database by studying motion statistics as well as cause-effect motion relationships between objects.

Question: What is the size of the video database?


Question: What is a core of semantic knowledge?


On the Spectral Bias of Neural Networks. Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.

Question: What is the bias of neural networks?


TopicViz: interactive topic exploration in document collections. Existing methods for searching and exploring large document collections focus on surface-level matches to user queries, ignoring higher-level semantic structure. In this paper we show how topic modeling - a technique for identifying latent themes across a large collection of documents - can support semantic exploration. We present TopicViz: an interactive environment which combines traditional search and citation-graph exploration with a force-directed layout that links documents to the latent themes discovered by the topic model. We describe usage scenarios in which TopicViz supports rapid sensemaking on large document collections.

Question: How can topic modeling help with sensemaking?


Open Database of Epileptic EEG with MRI and Postoperational Assessment of Foci—a Real World Verification for the EEG Inverse Solutions. This paper introduces a freely accessible database http://eeg.pl/epi , containing 23 datasets from patients diagnosed with and operated on for drug-resistant epilepsy. This was collected as part of the clinical routine at the Warsaw Memorial Child Hospital. Each record contains (1) pre-surgical electroencephalography (EEG) recording (10–20 system) with inter-ictal discharges marked separately by an expert, (2) a full set of magnetic resonance imaging (MRI) scans for calculations of the realistic forward models, (3) structural placement of the epileptogenic zone, recognized by electrocorticography (ECoG) and post-surgical results, plotted on pre-surgical MRI scans in transverse, sagittal and coronal projections, (4) brief clinical description of each case. The main goal of this project is evaluation of possible improvements of localization of epileptic foci from the surface EEG recordings. These datasets offer a unique possibility for evaluating different EEG inverse solutions. We present preliminary results from a subset of these cases, including comparison of different schemes for the EEG inverse solution and preprocessing. We report also a finding which relates to the selective parametrization of single waveforms by multivariate matching pursuit, which is used in the preprocessing for the inverse solutions. It seems to offer a possibility of tracing the spatial evolution of seizures in time.

Question: What is the difference between the 10-20 system and the international 10-20 system?


Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection. This paper describes our approach to the SemEval-2017 shared task of determining question-question similarity in a community question-answering setting (Task 3B). We extracted both syntactic and semantic similarity features between candidate questions, performed pairwise-preference learning to optimize for ranking order, and then trained a random forest classifier to predict whether the candidate questions were paraphrases of each other. This approach achieved a MAP of 45.7% out of max achievable 67.0% on the test set.

Question: What is the MAP of the SemEval-2017 shared task?


Low-Power, Adaptive Neuromorphic Systems: Recent Progress and Future Directions. In this paper, we present a survey of recent works in developing neuromorphic or neuro-inspired hardware systems. In particular, we focus on those systems which can either learn from data in an unsupervised or online supervised manner. We present algorithms and architectures developed specially to support on-chip learning. Emphasis is placed on hardware friendly modifications of standard algorithms, such as backpropagation, as well as novel algorithms, such as structural plasticity, developed specially for low-resolution synapses. We cover works related to both spike-based and more traditional non-spike-based algorithms. This is followed by developments in novel devices, such as floating-gate MOS, memristors, and spintronic devices. CMOS circuit innovations for on-chip learning and CMOS interface circuits for post-CMOS devices, such as memristors, are presented. Common architectures, such as crossbar or island style arrays, are discussed, along with their relative merits and demerits. Finally, we present some possible applications of neuromorphic hardware, such as brain–machine interfaces, robotics, etc., and identify future research trends in the field.

Question: What is a neuromorphic system?


Development of an Autonomous Remote Access Water Quality Monitoring System. Due to the vast increase in global industrial output, rural to urban drift and the over-utilization of land and sea resources, the quality of water available to people has deteriorated greatly. Before the sensor based approach to water quality monitoring, water quality was tested by collecting the samples of water and experimentally analyzing it in the laboratories. However, in today, with time being a scarce resource, the traditional method of water quality testing is not efficient anymore. To tackle this issue, several electronic (microcontroller and sensor based) water quality monitoring systems were developed in the past decade. However, an in depth study of this current water quality testing technology shows that there are some limitations that should be taken into consideration. Therefore, an automatic, remote, and low cost water quality monitoring system has been developed. This system consists of a core microcontroller, multiple sensors, GSM module, LCD display screen, and an alarm subsystem. The quality of water is read from the physical world through the water quality testing sensors and sent to the microcontroller. The data is then analyzed by the microcontroller and the result is displayed on the LCD screen on the device. At the same time, another copy of the sensor readings is sent remotely to the user's mobile phone in the form of SMS. If an abnormal water quality parameter is detected by any sensor, the alarm system will turn on the respective red LED for that parameter and the buzzer will give warning sound. At the same time, the abnormality of the water parameter is reported to the user through SMS. The system is aimed to be used for wide applications and by all categories of users. It can facilitate the process of water quality monitoring autonomously and with low cost; to help people improve their quality of drinking water, household water supplies and aquaculture farms, e

Question: What is the difference between a microcontroller and a microprocessor?


A robot's spatial perception communicated via human touch. A robot perceives space in order to navigate, map, search and investigate its surroundings. One application area where humans interact with robots is search and rescue. The robot may have unique capabilities such as seeing outside the visible spectrum and being able to navigate in tight and hazardous spaces. In such an operation, the human may also be an effective search agent. Thus, it would be beneficial if the robot's spatial perception was conveyed to the human via a secondary peripheral modality. We have worked on developing a visual to tactile substitution.device for people who are visually impaired or blind. We propose to use this similar technique to the remote robot in order that it convey its spatial perception to the human operator or co-worker.

Question: What is a robot's spatial perception?


Person Re-identification with Deep Similarity-Guided Graph Neural Network. The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probegallery pairs (nodes) and utilizes such relationships to update the probegallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes' information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.

Question: How to re-identify a person with deep similarity?


Pick-by-Vision comes on age: evaluation of an augmented reality supported picking system in a real storage environment. Order picking is one of the most important process steps in logistics. Because of their flexibility human beings cannot be replaced by machines. But if workers in order picking systems are equipped with a head-mounted display, Augmented Reality can improve the information visualization.  In this paper the development of such a system -- called Pick-by-Vision - is presented. The system is evaluated in a user study performed in a real storage environment. Important logistics figures as well as subjective figures were measured. The results show that a Pick-by-Vision system can improve considerably industrial order picking processes.

Question: What is the development of a Pick-by-Vision system?


Cave or Community? An Empirical Examination of 100 Mature Open Source Projects. Starting with Eric Raymond's groundbreaking work, The Cathedral and the Bazaar, open-source software (OSS) has commonly been regarded as work produced by a community of developers. Yet, given the nature of software programs, one also hears of developers with no lives that work very hard to achieve great product results. In this paper, I sought empirical evidence that would help us understand which is more commonthe cave (i.e., lone producer) or the community. Based on a study of the top 100 mature products on Sourceforge, I find a few surprising things. First, most OSS programs are developed by individuals, rather than communities. The median number of developers in the 100 projects I looked at was 4 and the mode was 1numbers much lower than previous ones reported for highly successful projects! Second, most OSS programs do not generate a lot of discussion. Third, products with more developers tend to be viewed and downloaded more often. Fourth, the number of developers associated with a project is unrelated to the age of the project. Fifth, the larger the project, the smaller the percent of project administrators.

Question: What is the median number of developers in open source projects?


Optimization for allocating BEV recharging stations in urban areas by using hierarchical clustering. The Battery Electric Vehicle (BEV) prototypes are evolving into a reality in a foreseeable future. Before BEV can embark on a mass adoption by road commuters, a new infrastructure for battery charging will have to be ready in place. By 2015, access to BEV charging will be available at nearly one million charge points in the United States, as claimed by CleanMPG. Early BEV adopters will primarily charge their vehicles at home, in their private garages. However, for many Asia-Pacific regions where people live in concrete ghettos, public charging will play a more central role due to reduced access to convenient home charging. This research focuses on planning BEV charging locations to be installed in urbanized areas where they are usually characterized by dense traffic concentrations, restricted street spaces and other complex factors such as the distribution of power grids. We proposed a two-steps model that first quantified the road information into data points, and subsequently they are converged into 'demand clusters' over an urbanized area by hierarchical clustering analysis. Optimization techniques then applied on the demand clusters with the aim of meeting the supplies and demands, while at the same time certain constraints and cost factors are considered. The model is believed to be an important decision-support tool for city planning and BEV charging stations allocation.

Question: How to optimize the allocation of BEV charging stations in urban areas?


OpenPAWS: An open source PAWS and UHF TV White Space database implementation for India. TV white space (TVWS) geolocation database is being used for the protection of the terrestrial TV broadcast receivers, and the coexistence of secondary devices. To the best of our knowledge, though TV White Space calculations are available, an active online database does not exist for India. In this paper, the development of the first TVWS database for India is detailed and is released for public access. A standardized protocol to access the TVWS database on a readily available hardware platform is implemented. A hardware prototype, which is capable of querying the TVWS database and operating in the TV band without causing harmful interference to the TV receivers in UHF TV bands, is developed. The source code of our implementation has been released under the GNU general public license version 2.0.

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


Being There: The Subjective Experience of Presence.

Question: What is the subject of the book?


Memory versus non-linearity in reservoirs. Reservoir Computing (RC) is increasingly being used as a conceptually simple yet powerful method for using the temporal processing of recurrent neural networks (RNN). However, because fundamental insight in the exact functionality of the reservoir is as yet still lacking, in practice there is still a lot of manual parameter tweaking or brute-force searching involved in optimizing these systems. In this contribution we aim to enhance the insights into reservoir operation, by experimentally studying the interplay of the two crucial reservoir properties, memory and non-linear mapping. For this, we introduce a novel metric which measures the deviation of the reservoir from a linear regime and use it to define different regions of dynamical behaviour. Next, we study the relationship of two important reservoir parameters, input scaling and spectral radius, on two properties of an artificial task, namely memory and non-linearity.

Question: What is the relationship between input scaling and spectral radius?


A Genetic NewGreedy Algorithm for Influence Maximization in Social Network. A user may be influenced by the other users of a social network by sharing information. Influence maximization is one of the critical research topics aimed at knowing the current circumstances of a social network, such as the general mood of the society. The goal of this problem is to find a seed set which has a maximum influence with respect to a propagation model. For the influence maximization problem is NP-Hard, it is obvious that an exhausted search algorithm is not able to find the solution in a reasonable time. It is also obvious that a greedy algorithm may not find a solution that satisfies all the requirements. Hence, a high-performance algorithm for solving the influence maximization problem, which leverages the strength of the greedy method and the genetic algorithm (GA) is presented in this paper. Experimental results show that the proposed algorithm can provide a better result than simple GA by about 10% in terms of the quality.

Question: What is the influence maximization problem?


Artificial Intelligence techniques: An introduction to their use for modelling environmental systems. Knowledge-based or Artificial Intelligence techniques are used increasingly as alternatives to more classical techniques to model environmental systems. We review some of them and their environmental applicability, with examples and a reference list. The techniques covered are case-based reasoning, rule-based systems, artificial neural networks, fuzzy models, genetic algorithms, cellular automata, multi-agent systems, swarm intelligence, reinforcement learning and hybrid systems. © 2008 IMACS. Published by Elsevier B.V. All rights reserved.

Question: What is the environmental applicability of artificial intelligence techniques?


Improving Image Classification Using Semantic Attributes. The Bag-of-Words (BoW) model—commonly used for image classification—has two strong limitations: on one hand, visual words are lacking of explicit meanings, on the other hand, they are usually polysemous. This paper proposes to address these two limitations by introducing an intermediate representation based on the use of semantic attributes. Specifically, two different approaches are proposed. Both approaches consist in predicting a set of semantic attributes for the entire images as well as for local image regions, and in using these predictions to build the intermediate level features. Experiments on four challenging image databases (PASCAL VOC 2007, Scene-15, MSRCv2 and SUN-397) show that both approaches improve performance of the BoW model significantly. Moreover, their combination achieves the state-of-the-art results on several of these image databases.

Question: What is the state-of-the-art image classification method?


Generalized state-space model for an n-phase interleaved buck-boost converter. An n-phase interleaved buck-boost converter (IBBC) with generalized steady state model has been presented in this paper. Development in distributed generation system requires low cost, high efficient converters for power conversion. Paralleling of converters is done so as to minimize the ripples at the output and reduce the size of passive components and improve the overall efficiency and cost of the system. An interleaved buck boost converter finds application in fuel cells, electric vehicles and various solar charging applications. The steady state and small signal analysis of an n phase buck-boost converter in CCM mode have been carried out and average state space equations have been developed. In averaging, parasitic elements have been excluded for simplifying the calculations. A simulation model for a three phase interleaved buck boost converter has been presented initially which is then generalized.

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


A Tractable Approach to Coverage and Rate in Cellular Networks. Cellular networks are usually modeled by placing the base stations on a grid, with mobile users either randomly scattered or placed deterministically. These models have been used extensively but suffer from being both highly idealized and not very tractable, so complex system-level simulations are used to evaluate coverage/outage probability and rate. More tractable models have long been desirable. We develop new general models for the multi-cell signal-to-interference-plus-noise ratio (SINR) using stochastic geometry. Under very general assumptions, the resulting expressions for the downlink SINR CCDF (equivalent to the coverage probability) involve quickly computable integrals, and in some practical special cases can be simplified to common integrals (e.g., the Q-function) or even to simple closed-form expressions. We also derive the mean rate, and then the coverage gain (and mean rate loss) from static frequency reuse. We compare our coverage predictions to the grid model and an actual base station deployment, and observe that the proposed model is pessimistic (a lower bound on coverage) whereas the grid model is optimistic, and that both are about equally accurate. In addition to being more tractable, the proposed model may better capture the increasingly opportunistic and dense placement of base stations in future networks.

Question: What is the coverage gain from static frequency reuse?


Managing Context Information for Adaptive Security in IoT Environments. Pervasive computing is becoming a reality due to the rise of the so-called Internet of Things (IoT). In this paradigm, everyday and physical objects are being equipped with capabilities to detect and communicate information they receive from their environment, turning them into smart objects. However, such entities are usually deployed on environments with changing and dynamic conditions, which can be used by them to modify their operation or behavior. Under the foundations of EU FP7 SocIoTal project, this work provides an overview about how contextual information can be taken into account by smart objects when making security decisions, by considering such information as a first-class component, in order to realize the so-called context-aware security on IoT scenarios.

Question: What is the context-aware security on IoT scenarios?


Competing for Attention in Social Media under Information Overload Conditions. Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.

Question: How does the spread of information differ from the spread of disease?


Ka band spatial power-combining amplifier structures. 2.24 w power-amplifier (PA) module at 35 GHz presented using broad-band spatial power-combining system. The combiner can accommodate more monolithic microwave integrated-circuit (MMIC) PA with stagger placement structure on limited microstrip space in Ka-band waveguide structure with good return losses, and heat can dissipated into aluminum carrier quickly. This combiner is based on a slotline-to-microstrip transition structure, which also serves as a four-way power combiner. The proposed 2*2 combining structure combined by vertical stacking inside the waveguide was analyzed and optimized by finite-element-method (FEM) simulations and experiments.

Question: What is the structure of the amplifier?


Scene Understanding by Reasoning Stability and Safety. This paper presents a new perspective for 3D scene understanding by reasoning object stability and safety using intuitive mechanics. Our approach utilizes a simple observation that, by human design, objects in static scenes should be stable in the gravity field and be safe with respect to various physical disturbances such as human activities. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Given a 3D point cloud captured for a static scene by depth cameras, our method consists of three steps: (i) recovering solid 3D volumetric primitives from voxels; (ii) reasoning stability by grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior; and (iii) reasoning safety by evaluating the physical risks for objects under physical disturbances, such as human activity, wind or earthquakes. We adopt a novel intuitive physics model and represent the energy landscape of each primitive and object in the scene by a disconnectivity graph (DG). We construct a contact graph with nodes being 3D volumetric primitives and edges representing the supporting relations. Then we adopt a Swendson–Wang Cuts algorithm to partition the contact graph into groups, each of which is a stable object. In order to detect unsafe objects in a static scene, our method further infers hidden and situated causes (disturbances) in the scene, and then introduces intuitive physical mechanics to predict possible effects (e.g., falls) as consequences of the disturbances. In experiments, we demonstrate that the algorithm achieves a substantially better performance for (i) object segmentation, (ii) 3D volumetric recovery, and (iii) scene understanding with respect to other state-of-the-art methods. We also compare the safety prediction from the intuitive mechanics model with human judgement.

Question: What is the canadian military?


New York University 2016 System for KBP Event Nugget: A Deep Learning Approach. This is the first time New York University (NYU) participates in the event nugget (EN) evaluation of the Text Analysis Conference (TAC). We developed EN systems for both subtasks of event nugget, i.e, EN Task 1: Event Nugget Detection and EN Task 2: Event Nugget Detection and Coreference. The systems are mainly based on our recent research on deep learning for event detection (Nguyen and Grishman, 2015a; Nguyen and Grishman, 2016a). Due to the limited time we could devote to system development this year, we only ran the systems on the English evaluation data. However, we expect that the adaptation of the current systems to new languages can be done quickly. The development experiments show that although our current systems do not rely on complicated feature engineering, they significantly outperform the reported systems last year for the EN subtasks on the 2015 evaluation data.

Question: What is the first time New York University (NYU) participates in the event nugget (EN) evaluation of the Text Analysis Conference (TAC)?


A lightweight symbolic virtual machine for solver-aided host languages. Solver-aided domain-specific languages (SDSLs) are an emerging class of computer-aided programming systems. They ease the construction of programs by using satisfiability solvers to automate tasks such as verification, debugging, synthesis, and non-deterministic execution. But reducing programming tasks to satisfiability problems involves translating programs to logical constraints, which is an engineering challenge even for domain-specific languages.  We have previously shown that translation to constraints can be avoided if SDSLs are implemented by (traditional) embedding into a host language that is itself solver-aided. This paper describes how to implement a symbolic virtual machine (SVM) for such a host language. Our symbolic virtual machine is lightweight because it compiles to constraints only a small subset of the host's constructs, while allowing SDSL designers to use the entire language, including constructs for DSL embedding. This lightweight compilation employs a novel symbolic execution technique with two key properties: it produces compact encodings, and it enables concrete evaluation to strip away host constructs that are outside the subset compilable to constraints. Our symbolic virtual machine architecture is at the heart of Rosette, a solver-aided language that is host to several new SDSLs.

Question: What is a symbolic virtual machine?


Ee364a: Convex Optimization I Final Exam. This is a 24 hour take-home final. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. You may use any books, notes, or computer programs, but you may not discuss the exam with anyone until March 16, after everyone has taken the exam. The only exception is that you can ask us for clarification, via the course staff email address. We've tried pretty hard to make the exam unambiguous and clear, so we're unlikely to say much. Please make a copy of your exam, or scan it, before handing it in. Please attach the cover page to the front of your exam. Assemble your solutions in order (problem 1, problem 2, problem 3,. . .), starting a new page for each problem. Put everything associated with each problem (e.g., text, code, plots) together; do not attach code or plots at the end of the final. We will deduct points from long needlessly complex solutions, even if they are correct. Our solutions are not long, so if you find that your solution to a problem goes on and on for many pages, you should try to figure out a simpler one. We expect neat, legible exams from everyone, including those enrolled Cr/N. When a problem involves computation you must give all of the following: a clear discussion and justification of exactly what you did, the source code that produces the result, and the final numerical results or plots. Files containing problem data can be found in the usual place, Please respect the honor code. Although we allow you to work on homework assignments in small groups, you cannot discuss the final with anyone, at least until everyone has taken it. All problems have equal weight. Some are easy. Others, not so much. Be sure you are using the most recent version of CVX, CVXPY, or Convex,jl. Check your email often during the exam, just in case we need to send out an important announcement. Some problems involve applications. But you do not need to know anything about the problem area to solve th

Question: What is the honor code?


Team assembly mechanisms determine collaboration network structure and team performance.. Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.

Question: How does team size affect team performance?


A possible mechanism of horseback riding on dynamic trunk alignment. The study aimed to clarify the regularity of the motions of horse's back, rider's pelvis and spine associated with improvement of rider's dynamic trunk alignment. The study used a crossover design, with exercise using the horseback riding simulator (simulator hereafter) as the control condition. The experiments were conducted at Tokyo University of Agriculture Bio-therapy Center. The sample consisted of 20 healthy volunteers age 20-23 years. Participants performed 15-min sessions of horseback riding with a Hokkaido Pony and exercise using the simulator in experiments separated by ≥2 weeks. Surface electromyography (EMG) after horseback riding revealed decreased activity in the erector spinae. Exploratory data analysis of acceleration and angular velocity inferred associations between acceleration (Rider's neck/longitudinal axis [Y hereafter]) and angular velocity (Horse saddle/Y) as well as angular velocity (Rider's pelvis/Y) and angular velocity (Horse saddle/Y). Acceleration (Rider's neck/Y) tended to be associated with angular velocity (Rider's pelvis/Y). Surface EMG following exercise revealed decreased activity in the rectus abdominis and erector spinae after the simulator exercise. Horseback riding improved the rider's dynamic trunk alignment with a clear underlying mechanism, which was not observed with the simulator.

Question: What is the mechanism of horseback riding on dynamic trunk alignment?


Novel Feed Network for Circular Polarization Antenna Diversity. In this letter, we propose a novel feed network for circular polarization (CP) diversity antennas to enhance communications performances. The previous CP diversity systems adopted dual polarization diversity using right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) within a small space. However, the same rotational CPs need further antenna distance. To overcome the space limitation for the same CP diversity antenna, we propose a feed network for CP antenna using conventional CP with orthogonal polarization radiation characteristics of a linear polarization (LP) diversity system without additional antennas. The port isolation characteristics and impedance matching conditions are numerically verified for the diversity system and antenna characteristics. The proposed feed network with CP patch antennas is fabricated and measured in a reverberation chamber for diversity performances, and their validity is verified.

Question: How to design a feed network for circular polarization antenna diversity?


Spatial Attention Deep Net with Partial PSO for Hierarchical Hybrid Hand Pose Estimation. [3] Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Leichter, D.K.C.R.I., Wei, A.V.Y., Krupka, D.F.P.K.E., Fitzgibbon, A., Izadi, S.: Accurate, robust, and flexible real-time hand tracking. In: CHI (2015) [4] Oberweger, M., Wohlhart, P., Lepetit, V.: Training a feedback loop for hand pose estimation. In: ICCV (2015) [18] Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation. arXiv preprint arXiv:1502.06807 (2015) [19] Tang, D., Taylor, J., Kohli, P., Keskin, C., Kim, T.K., Shotton, J.: Opening the black box: Hierarchical sampling optimization for estimating human hand pose. In: ICCV (2015) * Indicates equal contribution. Results T θ

Question: What is the difference between the Canadian Armed Forces and the Canadian Army?


Adverse Effects Associated with Protein Intake above the Recommended Dietary Allowance for Adults. Background. While high-protein consumption-above the current recommended dietary allowance for adults (RDA: 0.8 g protein/kg body weight/day)-is increasing in popularity, there is a lack of data on its potential adverse effects. Objective. To determine the potential disease risks due to high protein/high meat intake obtained from diet and/or nutritional supplements in humans. Design. Review. Subjects. Healthy adult male and female subjects. Method. In order to identify relevant studies, the electronic databases, Medline and Google Scholar, were searched using the terms:"high protein diet," "protein overconsumption," "protein overuse," and "high meat diet." Papers not in English were excluded. Further studies were identified by citations in retrieved papers. Results. 32 studies (21 experimental human studies and 11 reviews) were identified. The adverse effects associated with long-term high protein/high meat intake in humans were (a) disorders of bone and calcium homeostasis, (b) disorders of renal function, (c) increased cancer risk, (d) disorders of liver function, and (e) precipitated progression of coronary artery disease. Conclusions. The findings of the present study suggest that there is currently no reasonable scientific basis in the literature to recommend protein consumption above the current RDA (high protein diet) for healthy adults due to its potential disease risks. Further research needs to be carried out in this area, including large randomized controlled trials.

Question: What are the adverse effects associated with high protein intake?


Generalizing Data to Provide Anonymity when Disclosing Information (Abstract). The proliferation of information on the Internet and access to fast computers with large storage capacities has increased the volume of information collected and disseminated about individuals. The existence os these other data sources makes it much easier to re-identify individuals whose private information is released in data believed to be anonymous. At the same time, increasing demands are made on organizations to release individualized data rather than aggregate statistical information. Even when explicit identi ers, such as name and phone number, are removed or encrypted when releasing individualized data, other characteristic data, which we term quasi-identi ers, can exist which allow the data recipient to re-identify individuals to whom the data refer. In this paper, we provide a computational disclosure technique for releasing information from a private table such that the identity of any individual to whom the released data refer cannot be de nitively recognized. Our approach protects against linking to other data. It is based on the concepts of generalization, by which stored values can be replaced with semantically consistent and truthful but less precise alternatives, and of k-anonymity . A table is said to provide k-anonymity when the contained data do not allow the recipient to associate the released information to a set of individuals smaller than k. We introduce the notions of generalized table and of minimal generalization of a table with respect to a k-anonymity requirement. As an optimization problem, the objective is to minimally distort the data while providing adequate protection. We describe an algorithm that, given a table, e ciently computes a preferred minimal generalization to provide anonymity.

Question: How to anonymize data?


Geographic and Genetic Population Differentiation of the Amazonian Chocolate Tree (Theobroma cacao L). Numerous collecting expeditions of Theobroma cacao L. germplasm have been undertaken in Latin-America. However, most of this germplasm has not contributed to cacao improvement because its relationship to cultivated selections was poorly understood. Germplasm labeling errors have impeded breeding and confounded the interpretation of diversity analyses. To improve the understanding of the origin, classification, and population differentiation within the species, 1241 accessions covering a large geographic sampling were genotyped with 106 microsatellite markers. After discarding mislabeled samples, 10 genetic clusters, as opposed to the two genetic groups traditionally recognized within T. cacao, were found by applying Bayesian statistics. This leads us to propose a new classification of the cacao germplasm that will enhance its management. The results also provide new insights into the diversification of Amazon species in general, with the pattern of differentiation of the populations studied supporting the palaeoarches hypothesis of species diversification. The origin of the traditional cacao cultivars is also enlightened in this study.

Question: What is the origin of the traditional cacao cultivars?