Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search. Similarity indices for high-dimensional data are very desirable for building content-based search systems for featurerich data such as audio, images, videos, and other sensor data. Recently, locality sensitive hashing (LSH) and its variations have been proposed as indexing techniques for approximate similarity search. A significant drawback of these approaches is the requirement for a large number of hash tables in order to achieve good search quality. This paper proposes a new indexing scheme called multi-probe LSH that overcomes this drawback. Multi-probe LSH is built on the well-known LSH technique, but it intelligently probes multiple buckets that are likely to contain query results in a hash table. Our method is inspired by and improves upon recent theoretical work on entropy-based LSH designed to reduce the space requirement of the basic LSH method. We have implemented the multi-probe LSH method and evaluated the implementation with two different high-dimensional datasets. Our evaluation shows that the multi-probe LSH method substantially improves upon previously proposed methods in both space and time efficiency. To achieve the same search quality, multi-probe LSH has a similar timeefficiency as the basic LSH method while reducing the number of hash tables by an order of magnitude. In comparison with the entropy-based LSH method, to achieve the same search quality, multi-probe LSH uses less query time and 5 to 8 times fewer number of hash tables.

Question: What is the space efficiency of multi-probe LSH?


Group Linguistic Bias Aware Neural Response Generation. For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users' preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoderdecoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.

Question: How to generate responses with customized styles?


A comparative study of metamodeling methods for multiobjective crashworthiness optimization. The response surface methodology (RSM), which typically uses quadratic polynomials, is predominantly used for metamodeling in crashworthiness optimization because of the high computational cost of vehicle crash simulations. Research shows, however, that RSM may not be suitable for modeling highly nonlinear responses that can often be found in impact related problems, especially when using limited quantity of response samples. The radial basis functions (RBF) have been shown to be promising for highly nonlinear problems, but no application to crashworthiness problems has been found in the literature. In this study, metamodels by RSM and RBF are used for multiobjective optimization of a vehicle body in frontal collision, with validations by finite element simulations using the full-scale vehicle model. The results show that RSM is able to produce good approximation models for energy absorption, and the model appropriateness can be well predicted by ANOVA. However, in the case of peak acceleration, RBF is found to generate better models than RSM based on the same number of response samples, with the multiquadric function identified to be the most stable RBF. Although RBF models are computationally more expensive, the optimization results of RBF models are found to be more accurate. 2005 Elsevier Ltd. All rights reserved.

Question: What is the difference between RSM and RBF?


Homography-based 2D Visual Tracking and Servoing. The objective of this paper is to propose a new homography-based approach to image-based visual tracking and servoing. The visual tracking algorithm proposed in the paper is based on a new efficient second-order minimization method. Theoretical analysis and comparative experiments with other tracking approaches show that the proposed method has a higher convergence rate than standard first-order minimization techniques. Therefore, it is well adapted to real-time robotic applications. The output of the visual tracking is a homography linking the current and the reference image of a planar target. Using the homography, a task function isomorphic to the camera pose has been designed. A new image-based control law is proposed which does not need any measure of the 3D structure of the observed target (e.g. the normal to the plane). The theoretical proof of the existence of the isomorphism between the task function and the camera pose and the theoretical proof of the stability of the control law are provided. The experimental results, obtained with a 6 d.o.f. robot, show the advantages of the proposed method with respect to the existing approaches. KEY WORDS—visual tracking, visual servoing, efficient second-order minimization, homography-based control law

Question: What is the difference between a homography and a projective transformation?


Turret: A Platform for Automated Attack Finding in Unmodified Distributed System Implementations. Security and performance are critical goals for distributed systems. The increased design complexity, incomplete expertise of developers, and limited functionality of existing testing tools often result in bugs and vulnerabilities that prevent implementations from achieving their design goals in practice. Many of these bugs, vulnerabilities, and misconfigurations manifest after the code has already been deployed making the debugging process difficult and costly. In this paper, we present Turret, a platform for automatically finding performance attacks in unmodified implementations of distributed systems. Turret does not require the user to provide any information about vulnerabilities and runs the implementation in the same operating system setup as the deployment, with an emulated network. Turret uses a new attack finding algorithm and several optimizations that allow it to find attacks in a matter of minutes. We ran Turret on 5 different distributed system implementations specifically designed to tolerate insider attacks, and found 30 performance attacks, 24 of which were not previously reported to the best of our knowledge.

Question: What is a platform for automatically finding performance attacks in unmodified implementations of distributed systems?


Efficiency and harmonics generation in microwave to DC conversion circuits of half-wave and full-wave rectifier types. A Rectifying Antenna (Rectenna) is one of the most important components for a wireless power transmission. It has been developed for many applications such as Space Solar Power System (SSPS), and Radio Frequency Identification (RFID) etc. The Rectenna consisting of RF-DC conversion circuits and receiving antennas needs to be designed for high conversion efficiency to achieve efficient power transmission. we try to design the mw-Class RF-DC conversion circuit using half-and full-wave rectification. The measured conversion efficiencies is 59.3 % and 65.3 % athalf- and full-wave type, respectively. And the full-wave type has lower 2nd harmonic reflection coefficient.

Question: How to design a rectifying antenna?


Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics. Computational phenotyping is the process of converting heterogeneous electronic health records (EHRs) into meaningful clinical concepts. Unsupervised phenotyping methods have the potential to leverage a vast amount of labeled EHR data for phenotype discovery. However, existing unsupervised phenotyping methods do not incorporate current medical knowledge and cannot directly handle missing, or noisy data.  We propose Rubik, a constrained non-negative tensor factorization and completion method for phenotyping. Rubik incorporates 1) guidance constraints to align with existing medical knowledge, and 2) pairwise constraints for obtaining distinct, non-overlapping phenotypes. Rubik also has built-in tensor completion that can significantly alleviate the impact of noisy and missing data. We utilize the Alternating Direction Method of Multipliers (ADMM) framework to tensor factorization and completion, which can be easily scaled through parallel computing.  We evaluate Rubik on two EHR datasets, one of which contains 647,118 records for 7,744 patients from an outpatient clinic, the other of which is a public dataset containing 1,018,614 CMS claims records for 472,645 patients. Our results show that Rubik can discover more meaningful and distinct phenotypes than the baselines. In particular, by using knowledge guidance constraints, Rubik can also discover sub-phenotypes for several major diseases. Rubik also runs around seven times faster than current state-of-the-art tensor methods. Finally, Rubik is scalable to large datasets containing millions of EHR records.

Question: What is the rubik algorithm?


ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]. This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a "two-way communication" style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.

Question: What is the nearest neighbor method?


Semantic Segmentation using Adversarial Networks. Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.

Question: What is adversarial training?


Convolution Kernels for Natural Language. We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional representations of these structures. We show how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and we give experimental results on the ATIS corpus of parse trees.

Question: What is a kernel?


Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. In this paper, we propose a machine-learning solution to problems consisting of many similar prediction tasks. Each of the individual tasks has a high risk of overrtting. We combine two types of knowledge transfer between tasks to reduce this risk: multi-task learning and hierarchical Bayesian modeling. Multi-task learning is based on the assumption that there exist features typical to the task at hand. To nd these features, we train a huge two-layered neural network. Each task has its own output, but shares the weights from the input to the hidden units with all other tasks. In this way a relatively large set of possible explanatory variables (the network inputs) is reduced to a smaller and easier to handle set of features (the hidden units). Given this set of features and after an appropriate scale transformation, we assume that the tasks are exchangeable. This assumption allows for a hierarchical Bayesian analysis in which the hyperparameters can be estimated from the data. EEectively, these hyperpa-rameters act as regularizers and prevent over-tting. We describe how to make the system robust against nonstationarities in the time series and give directions for further improvement. We illustrate our ideas on a database regarding the prediction of newspaper sales.

Question: How to solve a huge number of similar tasks?


Compact hashing with joint optimization of search accuracy and time. Similarity search, namely, finding approximate nearest neighborhoods, is the core of many large scale machine learning or vision applications. Recently, many research results demonstrate that hashing with compact codes can achieve promising performance for large scale similarity search. However, most of the previous hashing methods with compact codes only model and optimize the search accuracy. Search time, which is an important factor for hashing in practice, is usually not addressed explicitly. In this paper, we develop a new scalable hashing algorithm with joint optimization of search accuracy and search time simultaneously. Our method generates compact hash codes for data of general formats with any similarity function. We evaluate our method using diverse data sets up to 1 million samples (e.g., web images). Our comprehensive results show the proposed method significantly outperforms several state-of-the-art hashing approaches.

Question: What is the difference between a hash function and a hash table?


In Search of the Uncanny Valley. Recent advances in computer animation and robotics have lead to greater and greater realism of human appearance to be obtained both on screen and in physical devices. A particular issue that has arisen in this pursuit is whether increases in realism necessarily lead to increases in acceptance. The concept of the uncanny valley suggests that high, though not perfect, levels of realism will result in poor acceptance. We review this concept and its psychological basis.

Question: What is the uncanny valley?


A compact cavity filter with novel TM mode dielectric resonator structure. A novel transverse magnetic (TM) mode dielectric resonator and its filter realization are proposed for mobile communication system miniaturization applications. Single dielectric resonator and an eight-pole filter with three-transmission-zeros are modeled and designed for desired specification by using efficient optimization technique. About 30% horizontal dimensions are saved comparing with the common coaxial filter. Excellent filter responses are obtained and measured.

Question: What is a compact cavity filter?


Supporting Iterative Cohort Construction with Visual Temporal Queries. Many researchers across diverse disciplines aim to analyze the behavior of cohorts whose behaviors are recorded in large event databases. However, extracting cohorts from databases is a difficult yet important step, often overlooked in many analytical solutions. This is especially true when researchers wish to restrict their cohorts to exhibit a particular temporal pattern of interest. In order to fill this gap, we designed COQUITO, a visual interface that assists users defining cohorts with temporal constraints. COQUITO was designed to be comprehensible to domain experts with no preknowledge of database queries and also to encourage exploration. We then demonstrate the utility of COQUITO via two case studies, involving medical and social media researchers.

Question: How to define a cohort with temporal constraints?


Integrated Architectures for Learning, Planning, and Reacting Based on Approximating Dynamic Programming. This paper extends previous work with Dyna a class of architectures for intelligent systems based on approximating dynamic program ming methods Dyna architectures integrate trial and error reinforcement learning and execution time planning into a single process operating alternately on the world and on a learned model of the world In this paper I present and show results for two Dyna archi tectures The Dyna PI architecture is based on dynamic programming s policy iteration method and can be related to existing AI ideas such as evaluation functions and uni versal plans reactive systems Using a nav igation task results are shown for a simple Dyna PI system that simultaneously learns by trial and error learns a world model and plans optimal routes using the evolving world model The Dyna Q architecture is based on Watkins s Q learning a new kind of rein forcement learning Dyna Q uses a less famil iar set of data structures than does Dyna PI but is arguably simpler to implement and use We show that Dyna Q architectures are easy to adapt for use in changing environments Introduction to Dyna How should a robot decide what to do The traditional answer in AI has been that it should deduce its best action in light of its current goals and world model i e that it should plan However it is now widely recognized that planning s usefulness is limited by its computational complexity and by its dependence on an accurate world model An alternative approach is to do the planning in advance and compile its result into a set of rapid reactions or situation action rules which are then used for real time decision making Yet a third approach is to learn a good set of reactions by trial and error this has the advantage of eliminating the dependence on a world model In this paper I brie y introduce Dyna a class of simple architectures integrating and permitting tradeo s among these thre

Question: What is the difference between Dyna PI and Dyna Q?


Wearable Real-Time Stereo Vision for the Visually Impaired. In this paper, a development of image processing, stereovision methodology and a sonification procedure for image sonification system for vision substitution are presented. The hardware part consists of a sunglass fitted with two mini cameras, laptop computer and stereo earphones. The image of the scene in front of blind people is captured by stereo cameras. The captured image is processed to enhance the important features in the scene in front of blind user. The image processing is designed to extract the objects from the image and the stereo vision method is applied to calculate the disparity which is required to determine the distance between the blind user and the objects. The processed image is mapped on to stereo sound for the blind's understanding of the scene in front. Experimentations were conducted in the indoor environment and the proposed methodology is found to be effective for object identification, and thus the sound produced will assists the visually impaired for their collision free navigation.

Question: How to make a stereo vision system for the visually impaired?


The Treatment of Missing Values and its Effect on Classifier Accuracy. The presence of missing values in a dataset can affect the performance of a classifier constructed using that dataset as a training sample. Several methods have been proposed to treat missing data and the one used most frequently deletes instances containing at least one missing value of a feature. In this paper we carry out experiments with twelve datasets to evaluate the effect on the misclassification error rate of four methods for dealing with missing values: the case deletion method, mean imputation, median imputation, and the KNN imputation procedure. The classifiers considered were the Linear Discriminant Analysis (LDA) and the KNN classifier. The first one is a parametric classifier whereas the second one is a non parametric classifier.

Question: What is the effect of missing values on classifier accuracy?


Hybrid decision tree. In this paper, a hybrid learning approach named HDT is proposed. HDT simulates human reasoning by using symbolic learning to do qualitative analysis and using neural learning to do subsequent quantitative analysis. It generates the trunk of a binary hybrid decision tree according to the binary information gain ratio criterion in an instance space defined by only original unordered attributes. If unordered attributes cannot further distinguish training examples falling into a leaf node whose diversity is beyond the diversity-threshold, then the node is marked as a dummy node. After all those dummy nodes are marked, a specific feedforward neural network named FANNC that is trained in an instance space defined by only original ordered attributes is exploited to accomplish the learning task. Moreover, this paper distinguishes three kinds of incremental learning tasks. Two incremental learning procedures designed for example-incremental learning with different storage requirements are provided, which enables HDT to deal gracefully with data sets where new data are frequently appended. Also a hypothesis-driven constructive induction mechanism is provided, which enables HDT to generate compact concept descriptions.

Question: What is a hybrid decision tree?


Band-pass shielding enclosure for 2.4/5 GHz WLAN applications. This paper presents a design of band-pass shielding enclosure for two frequency bands of WLAN. The proposed design employs a compact frequency selective surface to highlight the advantages of these miniaturized structures for the selective shielding of such enclosures that are electrically small. The results indicate that the proposed shielding enclosure has high transmittance in both the pass bands for horizontal and vertical polarizations, while offers a good amount of electromagnetic shielding outside these frequency bands.

Question: What is a band-pass shielding enclosure?


Evaluating iterative optimization across 1000 datasets. While iterative optimization has become a popular compiler optimization approach, it is based on a premise which has never been truly evaluated: that it is possible to learn the best compiler optimizations across data sets. Up to now, most iterative optimization studies find the best optimizations through repeated runs on the same data set. Only a handful of studies have attempted to exercise iterative optimization on a few tens of data sets.  In this paper, we truly put iterative compilation to the test for the first time by evaluating its effectiveness across a large number of data sets. We therefore compose KDataSets, a data set suite with 1000 data sets for 32 programs, which we release to the public. We characterize the diversity of KDataSets, and subsequently use it to evaluate iterative optimization.We demonstrate that it is possible to derive a robust iterative optimization strategy across data sets: for all 32 programs, we find that there exists at least one combination of compiler optimizations that achieves 86% or more of the best possible speedup across all data sets using Intel's ICC (83% for GNU's GCC). This optimal combination is program-specific and yields speedups up to 1.71 on ICC and 2.23 on GCC over the highest optimization level (-fast and -O3, respectively). This finding makes the task of optimizing programs across data sets much easier than previously anticipated, and it paves the way for the practical and reliable usage of iterative optimization. Finally, we derive pre-shipping and post-shipping optimization strategies for software vendors.

Question: What is the best compiler optimization for iterative optimization?


Human hand modelling: kinematics, dynamics, applications. An overview of mathematical modelling of the human hand is given. We consider hand models from a specific background: rather than studying hands for surgical or similar goals, we target at providing a set of tools with which human grasping and manipulation capabilities can be studied, and hand functionality can be described. We do this by investigating the human hand at various levels: (1) at the level of kinematics, focussing on the movement of the bones of the hand, not taking corresponding forces into account; (2) at the musculotendon structure, i.e. by looking at the part of the hand generating the forces and thus inducing the motion; and (3) at the combination of the two, resulting in hand dynamics as well as the underlying neurocontrol. Our purpose is to not only provide the reader with an overview of current human hand modelling approaches but also to fill the gaps with recent results and data, thus allowing for an encompassing picture.

Question: What is the purpose of human hand modelling?


Experimental validation of dual PPG local pulse wave velocity probe. A novel dual photoplethysmograph (PPG) probe and measurement system for local pulse wave velocity (PWV) is proposed and demonstrated. The developed probe design employs reflectance PPG transducers for non-invasive detection of blood pulse propagation waveforms from two adjacent measurement points (28 mm apart). Transit time delay between the continuously acquired dual pulse waveform was used for beat-to-beat local PWV measurement. An in-vivo experimental validation study was conducted on 10 healthy volunteers (8 male and 2 female, 21 to 33 years of age) to validate the PPG probe design and developed local PWV measurement system. The proposed system was able to measure carotid local PWV from multiple subjects. Beat-to-beat variation of baseline carotid PWV was less than 7.5% for 7 out of 10 subjects, a maximum beat-to-beat variation of 16% was observed during the study. Variation in beat-to-beat carotid local PWV and brachial blood pressure (BP) values during post-exercise recovery period was also examined. A statistically significant correlation between intra-subject local PWV variation and brachial BP parameters was observed (r > 0.85, p < 0.001). The results demonstrated the feasibility of proposed PPG probe for continuous beat-to-beat local PWV measurement from the carotid artery. Such a non-invasive local PWV measurement unit can be potentially used for continuous ambulatory BP measurements.

Question: What is the difference between a photoplethysmograph and a pulse oximeter?


Joint Learning Templates and Slots for Event Schema Induction. Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (templates) of event may exist in the raw text and what roles (slots) may exist in each event type. In this paper, we propose a joint entity-driven model to learn templates and slots simultaneously based on the constraints of templates and slots in the same sentence. In addition, the entities' semantic information is also considered for the inner connectivity of the entities. We borrow the normalized cut criteria in image segmentation to divide the entities into more accurate template clusters and slot clusters. The experiment shows that our model gains a relatively higher result than previous work.

Question: What is the difference between the normalized cut criteria in image segmentation and the joint entity-driven model?


Aletheia - An Advanced Document Layout and Text Ground-Truthing System for Production Environments. Large-scale digitisation has led to a number of new possibilities with regard to adaptive and learning based methods in the field of Document Image Analysis and OCR. For ground truth production of large corpora, however, there is still a gap in terms of productivity. Ground truth is not only crucial for training and evaluation at the development stage of tools but also for quality assurance in the scope of production workflows for digital libraries. This paper describes Aletheia, an advanced system for accurate and yet cost-effective ground truthing of large amounts of documents. It aids the user with a number of automated and semi-automated tools which were partly developed and improved based on feedback from major libraries across Europe and from their digitisation service providers which are using the tool in a production environment. Novel features are, among others, the support of top-down ground truthing with sophisticated split and shrink tools as well as bottom-up ground truthing supporting the aggregation of lower-level elements to more complex structures. Special features have been developed to support working with the complexities of historical documents. The integrated rules and guidelines validator, in combination with powerful correction tools, enable efficient production of highly accurate ground truth.

Question: What is the difference between a document layout and a document image?


Modeling of Overhead Transmission Lines for Lightning Studies. The aim of this paper is to analyze the influence of the models proposed for representing overhead transmission lines in lightning calculations. A full model of a transmission line for lightning overvoltage calculations can be split into several parts: wires (shield wires and phase conductors), towers, footing impedances and insulators. An additional component, the arrester, should be added if the study is aimed at selecting the arrester ratings needed to achieve a given performance of the line. This paper presents a sensitivity study whose main goal is to determine the effect that the model and the parameters selected for representing the main parts of a transmission line can have on the flashover rate.

Question: What is the effect of the model on the flashover rate?


The FEELING of WHAT HAPPENS : BODY AND EMOTION IN THE MAKING OF CONSCIOUSNESS. Antonio Damasio is a Portuguese-born American behavioral neurologist and neuroscientist. For a person without any knowledge of neuroscience, biology, or psychology, The Feeling of What Happens would presumably be a fascinating read. How often does one typically contemplate their own contemplation? The concepts presented by Damasio are abstract and uncommon, and undoubtedly are a new phenomenon for the average reader.

Question: What is the feeling of what happens?


Measuring Semantic Similarity in the Taxonomy of WordNet. This paper presents a new model to measure semantic similarity in the taxonomy of WordNet, using edgecounting techniques. We weigh up our model against a benchmark set by human similarity judgment, and achieve a much improved result compared with other methods: the correlation with average human judgment on a standard 28 word pair dataset is 0.921, which is better than anything reported in the literature and also significantly better than average individual human judgments. As this set has been effectively used for algorithm selection and tuning, we also cross-validate an independent 37 word pair test set (0.876) and present results for the full 65 word pair superset (0.897).

Question: What is the taxonomy of wordnet?


Empirical vulnerability analysis of automated smart contracts security testing on blockchains. Œe emerging blockchain technology supports decentralized computing paradigm shi‰ and is a rapidly approaching phenomenon. While blockchain is thought primarily as the basis of Bitcoin, its application has grown far beyond cryptocurrencies due to the introduction of smart contracts. Smart contracts are self-enforcing pieces of so‰ware, which reside and run over a hosting blockchain. Using blockchain-based smart contracts for secure and transparent management to govern interactions (authentication, connection, and transaction) in Internet-enabled environments, mostly IoT, is a niche area of research and practice. However, writing trustworthy and safe smart contracts can be tremendously challenging because of the complicated semantics of underlying domain-speci€c languages and its testability. Œere have been high-pro€le incidents that indicate blockchain smart contracts could contain various code-security vulnerabilities, instigating €nancial harms. When it involves security of smart contracts, developers embracing the ability to write the contracts should be capable of testing their code, for diagnosing security vulnerabilities, before deploying them to the immutable environments on blockchains. However, there are only a handful of security testing tools for smart contracts. Œis implies that the existing research on automatic smart contracts security testing is not adequate and remains in a very stage of infancy. With a speci€c goal to more readily realize the application of blockchain smart contracts in security and privacy, we should €rst understand their vulnerabilities before widespread implementation. Accordingly, the goal of this paper is to carry out a far-reaching experimental assessment of current static smart contracts security testing tools, for the most widely used blockchain, the Ethereum and its domain-speci€c programming language, Solidity, to provide the €rst

Question: What is the difference between a static and dynamic smart contract?


Blockchain — Literature survey. In the modern world that we live in everything is gradually shifting towards a more digitized outlook. From teaching methods to online transactions, every small aspect of our life is given a more technological touch. In such a case "money" is not left behind either. An approach towards digitizing money led to the creation of "bitcoin". Bitcoin is the most efficient crypto-currency so far in the history of digital currency. Ruling out the interference of any third party which monitors the cash flow, Bitcoin is a decentralized form of online currency and is widely accepted for internet transactions all over the world. The need for digital money would be extensive in the near future.

Question: What is the most efficient crypto-currency?


Automatic Code Completion. Code completion software is an important tool for many developers, but it traditionally fails to model any long term program dependencies such as scoped variables, instead settling for suggestions based on static libraries. In this paper we present a deep learning approach to code completion for non-terminals (program structural components) and terminals (program text) that takes advantage of running dependencies to improve predictions. We develop an LSTM model and augment it with several approaches to Attention in order to better capture the relative value of the input, hidden state, and context. After evaluating on a large dataset of JavaScript programs, we demonstrate that our Gated LSTM model significantly improves over a Vanilla LSTM baseline, achieving an accuracy of 77% on the non-terminal prediction problem and 46% accuracy on the terminal prediction problem.

Question: What is the difference between a deep learning approach and a traditional approach to code completion?


Kinematic analysis of electroactive polymer actuators as soft and smart structures with more DoF than inputs. Electroactive polymer (EAP) actuators have been attracting the attention of researchers due to their muscle-like behaviour and unusual properties. Several modelling methods have been proposed to understand their mechanical, chemical, electrical behaviours or `electro-chemo-mechanical' behaviour. However, estimating the whole shape or configuration of the EAP actuators has always been challenging due to their highly non-linear bending behaviour. This paper reports on an effective method to estimate the whole shape deflection of the EAP actuators by employing a so-called backbone approach. Tri-layer configured polypyrrole (PPy) based EAP actuators were used as a soft and smart structure with more degrees of freedom than its input. After deriving the inverse kinematic model of the actuator, its complete shape is estimated by solving the inverse kinematic model with an angle optimization (AngleOPT) method. The experimental results and numerical results have demonstrated the effectiveness of the method in estimating the highly non-linear bending behaviour of the PPy actuators and applicability of this modelling approach to other EAP actuators.

Question: What is the difference between a backbone and a backbone approach?


Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey. Swarm systems consist of large numbers of robots that collaborate autonomously. With an appropriate level of human control, swarm systems could be applied in a variety of contexts ranging from search-and-rescue situations to Cyber defence. The two decision making cycles of swarms and humans operate on two different time-scales, where the former is normally orders of magnitude faster than the latter. Closing the loop at the intersection of these two cycles will create fast and adaptive humanswarm teaming networks. This paper brings desperate pieces of the ground work in this research area together to review this multidisciplinary literature. We conclude with a framework to synthesize the findings and summarize the multi-modal indicators needed for closed-loop human-swarm adaptive systems.

Question: What is the difference between a swarm and a team?


A Blockchain-Based Approach to Health Information Exchange Networks. Sharing healthcare data between institutions is challenging. Heterogeneous data structures may preclude compatibility, while disparate use of healthcare terminology limits data comprehension. Even if structure and semantics could be agreed upon, both security and data consistency concerns abound. Centralized data stores and authority providers are attractive targets for cyber attack, and establishing a consistent view of the patient record across a data sharing network is problematic. In this work we present a Blockchain-based approach to sharing patient data. This approach trades a single centralized source of trust in favor of network consensus, and predicates consensus on proof of structural and semantic interoperability.

Question: What is a blockchain?


Least squares quantization in PCM. It has long been realized that in pulse-code modulation (PCM), with a given ensemble of signals to handle, the quantum values should be spaced more closely in the voltage regions where the signal amplitude is more likely to fall. It has been shown by Panter and Dite that, in the limit as the number of quanta becomes infinite, the asymptotic fractional density of quanta per unit voltage should vary as the one-third power of the probability density per unit voltage of signal amplitudes. In this paper the corresponding result for any finite number of quanta is derived; that is, necessary conditions are found that the quanta and associated quantization intervals of an optimum finite quantization scheme must satisfy. The optimization criterion used is that the average quantization noise power be a minimum. It is shown that the result obtained here goes over into the Panter and Dite result as the number of quanta become large. The optimum quantization schemes for 26 quanta, b = 1,2, t ,7, are given numerically for Gaussian and for Laplacian distribution of signal amplitudes.

Question: What is the quantization noise power?


A High-Performance Drive Circuit for All Solid-State Marx Generator. In recent years, all solid-state Marx generators have been found more and more extensive applications in industrial, environmental, and biological fields. All solid-state Marx generators have many requirements for their drive circuits, such as good synchrony for driving signals at different stages, fine isolation between control signals and the main circuits, adjustable pulsewidths and frequencies, and good driving abilities. This paper proposes a high-performance drive circuit for a 24-stage Marx generator based on IGBTs. A half-bridge circuit using IR2110 outputs a high-current turning-on drive signal (positive) and a turning-off drive signal (negative) with adjustable dead time. The control drivers are input to the common primary side of 24 nanocrystalline magnetic transformers, which isolate the control circuit and the main circuit. Through gate circuits at the second sides of the magnetic cores, the turning-on drive signal charges 24 gate-emitter capacitors to required voltages and consequently all IGBTs move into on state until the turning-off signal arrives. Similarly, the negative turning-off drive signal charges all gate-emitter capacitors to a negative voltage which ensures all IGBTs stay in the off state. Therefore, the pulsewidth is determined by the phase difference between the turning-on and turning-off drive signals. Equipped with this drive circuit, the 24-stage Marx generator is able to produce stable high voltage pulse with a peak value of -9.6 kV, PRF $0.05\sim 5$ kHz, and different pulsewidths. In this paper, the design details and experimental confirmation of the proposed drive circuit are illustrated.

Question: How to design a high-performance drive circuit for a 24-stage Marx generator?


Lock-free linked lists and skip lists. Lock-free shared data structures implement distributed objects without the use of mutual exclusion, thus providing robustness and reliability. We present a new lock-free implementation of singly-linked lists. We prove that the worst-case amortized cost of the operations on our linked lists is linear in the length of the list plus the contention, which is better than in previous lock-free implementations of this data structure. Our implementation uses backlinks that are set when a node is deleted so that concurrent operations visiting the deleted node can recover. To avoid performance problems that would arise from traversing long chains of backlink pointers, we introduce flag bits, which indicate that a deletion of the next node is underway. We then give a lock-free implementation of a skip list dictionary data structure that uses the new linked list algorithms to implement individual levels. Our algorithms use the single-word C&S synchronization primitive.

Question: What is a skip list?


Unconstrained Monocular 3D Human Pose Estimation by Action Detection and Cross-Modality Regression Forest. This work addresses the challenging problem of unconstrained 3D human pose estimation (HPE) from a novel perspective. Existing approaches struggle to operate in realistic applications, mainly due to their scene-dependent priors, such as background segmentation and multi-camera network, which restrict their use in unconstrained environments. We therfore present a framework which applies action detection and 2D pose estimation techniques to infer 3D poses in an unconstrained video. Action detection offers spatiotemporal priors to 3D human pose estimation by both recognising and localising actions in space-time. Instead of holistic features, e.g. silhouettes, we leverage the flexibility of deformable part model to detect 2D body parts as a feature to estimate 3D poses. A new unconstrained pose dataset has been collected to justify the feasibility of our method, which demonstrated promising results, significantly outperforming the relevant state-of-the-arts.

Question: How to detect actions in an unconstrained environment?


A NOVEL DUAL-POLARIZED DOUBLE-RIDGED HORN ANTENNA FOR WIDEBAND APPLICATIONS. Dual-polarized antenna is widely used in communication systems such as ECM and DF systems. In this paper a novel doubleridged horn antenna with dual polarizations is introduced for frequency range of 8–18 GHz. Common double ridged horn antennas have single polarization over the operating frequency. We have used five layers polarizer to provide dual polarizations performance of the doubleridged horn antenna. In order to achieve dual polarizations the strips width, strips spacing and layers distances are optimized. It is worth mentioning that the corresponding VSWR of the antenna during the optimization process should be maintain below a certain value (VSWR< 2). Simulation results show that the proposed antenna yields dual polarizations performance and low VSWR over the operating frequency. We have used CST software for antenna simulation which is based on the finite integral technique.

Question: What is the dual polarized doubleridged horn antenna?


The Rise of Bots: A Survey of Conversational Interfaces, Patterns, and Paradigms. This work documents the recent rise in popularity of messaging bots: chatterbot-like agents with simple, textual interfaces that allow users to access information, make use of services, or provide entertainment through online messaging platforms. Conversational interfaces have been often studied in their many facets, including natural language processing, artificial intelligence, human-computer interaction, and usability. In this work we analyze the recent trends in chatterbots and provide a survey of major messaging platforms, reviewing their support for bots and their distinguishing features. We then argue for what we call "Botplication", a bot interface paradigm that makes use of context, history, and structured conversation elements for input and output in order to provide a conversational user experience while overcoming the limitations of text-only interfaces.

Question: What is the rise of bots?


Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders. The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. Variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method "Tybalt" after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeare's "Romeo and Juliet". From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.

Question: What is a variational autoencoder?


A Novel Boil-Off Gas Re-Liquefaction Using a Spray Recondenser for Liquefied Natural-Gas Bunkering Operations. This study presents the design of a novel boil-off gas (BOG) re-liquefaction technology using a BOG recondenser system. The BOG recondenser system targets the liquefied natural gas (LNG) bunkering operation, in which the BOG phase transition occurs in a pressure vessel instead of a heat exchanger. The BOG that is generated during LNG bunkering operation is characterized as an intermittent flow with various peak loads. The system was designed to temporarily store the transient BOG inflow, condense it with subcooled LNG and store the condensed liquid. The superiority of the system was verified by comparing it with the most extensively employed conventional re-liquefaction system in terms of consumption energy and via an exergy analysis. Static simulations were conducted for three compositions; the results indicated that the proposed system provided 0 to 6.9% higher efficiencies. The exergy analysis indicates that the useful work of the conventional system is 24.9%, and the useful work of the proposed system is 26.0%. Process dynamic simulations of six cases were also performed to verify the behaviour of the BOG recondenser system. The results show that the pressure of the holdup in the recondenser vessel increased during the BOG inflow mode and decreased during the initialization mode. The maximum pressure of one of the bunkering cases was 3.45 bar. The system encountered a challenge during repetitive operations due to overpressurizing of the BOG recondenser vessel.

Question: What is the novel boil-off gas re-liquefaction using a spray recondenser for liquefied natural-gas bunkering operations?


Barbed sutures in facial rejuvenation.. Self-retaining barbed sutures, innovations for nonsurgical facial and neck rejuvenation, are currently available as short APTOS threads or long WOFFLES threads. The author uses APTOS threads for malar rounding, facial tightening and firming, and uses WOFFLES threads as a sling, suspending ptotic facial tissues to the firm, dense tissues of the temporal scalp.

Question: What is the difference between APTOS and WOFFLES threads?


Optimising ring oscillator-based true random number generators concept on FPGA. Random Number Generators are widely used in cryptographic applications (symmetric and asymmetric encryption, digital certificate generation, etc.) and other related areas. True Random Number Generators (TRNG) are based on physical non-deterministic phenomenon. Most of these generators are based on a large number of interconnected high frequency ring oscillators (RO), which are most commonly implemented in FPGA. This paper describes an optimized solution which increases the speed and the complexity of these ring oscillator-based TRNG. The proposed solution combines a personalized approach on the Ring Oscillator scheme, based on preprocessed raw data and a well-known one, based on the desynchronization of a large number of free running ROs, therefore minimizing the resource used within the FPGA. The paper covers the most known statistical testes (provided by the National Institute of Standards and Technology - NIST) applied on a large amount of data and proves the stability of a generator by providing positive experimental results that were obtained from measurements using different operating frequencies.

Question: How to generate a random number in FPGA?


Deep Learning based Large Scale Visual Recommendation and Search for E-Commerce. In this paper, we present a uni€ed end-to-end approach to build a large scale Visual Search and Recommendation system for ecommerce. Previous works have targeted these problems in isolation. We believe a more e'ective and elegant solution could be obtained by tackling them together. We propose a uni€ed Deep Convolutional Neural Network architecture, called VisNet 1, to learn embeddings to capture the notion of visual similarity, across several semantic granularities. We demonstrate the superiority of our approach for the task of image retrieval, by comparing against the state-of-the-art on the Exact Street2Shop [14] dataset. We then share the design decisions and trade-o's made while deploying the model to power Visual Recommendations across a catalog of 50M products, supporting 2K queries a second at Flipkart, India's largest e-commerce company. Œe deployment of our solution has yielded a signi€cant business impact, as measured by the conversion-rate.

Question: What is the state of the art in visual recommendation?


Nobody ' s watching ? Subtle cues affect generosity in an anonymous economic game. Models indicate that opportunities for reputation formation can play an important role in sustaining cooperation and prosocial behavior. Results from experimental economic games support this conclusion, as manipulating reputational opportunities affects prosocial behavior. Noting that some prosocial behavior remains even in anonymous noniterated games, some investigators argue that humans possess a propensity for prosociality independent of reputation management. However, decision-making processes often employ both explicit propositional knowledge and intuitive or affective judgments elicited by tacit cues. Manipulating game parameters alters explicit information employed in overt strategizing but leaves intact cues that may affect intuitive judgments relevant to reputation formation. To explore how subtle cues of observability impact prosocial behavior, we conducted five dictator games, manipulating both auditory cues of the presence of others (via the use of sound-deadening earmuffs) and visual cues (via the presentation of stylized eyespots). Although earmuffs appeared to reduce generosity, this effect was not significant. However, as predicted, eyespots substantially increased generosity, despite no differences in actual anonymity; when using a computer displaying eyespots, almost twice as many participants gave money to their partners compared with the

Question: How does the presence of eyespots affect generosity?


Emotion in the criminal psychopath: startle reflex modulation.. Startle-elicited blinks were measured during presentation of affective slides to test hypotheses concerning emotional responding in psychopaths. Subjects were 54 incarcerated sexual offenders divided into nonpsychopathic, psychopathic, and mixed groups based on file and interview data. Consistent with findings for normal college students, nonpsychopaths and mixed subjects showed a significant linear relationship between slide valence and startle magnitude, with startle responses largest during unpleasant slides and smallest during pleasant slides. This effect was absent in psychopaths. Group differences in startle modulation were related to affective features of psychopathy, but not to antisocial behavior per se. Psychopathy had no effect on autonomic or self-report responses to slides. These results suggest an abnormality in the processing of emotional stimuli by psychopaths that manifests itself independently of affective report.

Question: What is the difference between nonpsychopathic and psychopathic subjects?


A gene module associated with dysregulated TCR signaling pathways in CD4+ T cell subsets in rheumatoid arthritis.. We analyzed the transcriptome of detailed CD4+ T cell subsets including them after abatacept treatment, and examined the difference among CD4+ T cell subsets and identified gene sets that are closely associated disease activity and abatacept treatment. Seven CD4+ T cell subsets (naive, Th1, Th17, Th1/17, nonTh1/17, Tfh and Treg) were sorted from PBMCs taken from 10 RA patients and 10 healthy controls, and three RA patients donated samples before and 6 months after abatacept treatment. Paired-end RNA sequencing was performed using HiSeq 2500. A total of 149 samples except for 12 outliers were analyzed. Overview of expression pattern of RA revealed that administration of abatacept exerts a large shift toward the expression pattern of HC. Most of differentially expressed gene (DEG) upregulated in RA (n = 1776) were downregulated with abatacept treatment (n = 1349). Inversely, most of DEG downregulated in RA (n = 1860) were upregulated with abatacept treatment (n = 1294). This DEG-based analysis revealed shared pathway changes in RA CD4+ T cell subsets. Knowledge-based pathway analysis revealed the upregulation of activation-related pathways in RA that was substantially ameliorated by abatacept. Weighted gene co-expression network analysis (WGCNA) evaluated CD4+ T cells collectively and identified a gene module that consisted of 227 genes and was correlated with DAS28-CRP (Spearman's rho = 0.46, p = 4 × 10-9) and abatacept administration (Spearman's rho = -0.91, p = 5 × 10-57). The most highly connected 30 genes of this module included ZAP70 and JAK3, and pathway analysis of this module revealed dysregulation of the TCR signaling pathway network, which was ameliorated by abatacept.

Question: What is the difference between the gene expression of RA and HC?


Simulation of lightning impulse voltage stresses in underground cables. The goal of this work is to study the transient behavior of the cable against the application of standard and non-standard lightning impulse voltage waveforms. A 66 kV cable model has been developed in MATLAB Simulink and standard and non-standard impulse voltages are applied to it. A preliminary comparative study on the obtained voltages indicated that non-standard impulse voltage waveform developed higher voltage stress in the cable. Simulation results helped to investigate which impulses (standard or non-standard) represent the worst possible voltage stresses on the cables. This study provides the basis for further study of effects of non-standard impulse voltage waveforms and make necessary correction in the existing impulse testing standards.

Question: What is the goal of this work?


Sampling-Based Estimation of the Number of Distinct Values of an Attribute. We provide several new sampling-based estimators of the number of distinct values of an attribute in a relation. We compare these new estimators to estimators from the database and statistical literature empirically, using a large number of attribute-value distributions drawn from a variety of real-world databases. This appears to be the first extensive comparison of distinct-value estimators in either the database or statistical literature, and is certainly the first to use highlyskewed data of the sort frequently encountered in database applications. Our experiments indicate that a new "hybrid" estimator yields the highest precision on average for a given sampling fraction. This estimator explicitly takes into account the degree of skew in the data and combines a new "smoothed jackknife" estimator with an estimator due to Shlosser. We investigate how the hybrid estimator behaves as we scale up the size of the database.

Question: How many distinct values does a relation have?


A CMOS low-power low-offset and high-speed fully dynamic latched comparator. This paper presents a novel dynamic latched comparator that demonstrates lower offset voltage and higher load drivability than the conventional dynamic latched comparators. With two additional inverters inserted between the input- and output-stage of the conventional double-tail dynamic comparator, the gain preceding the regenerative latch stage is improved. The complementary version of the regenerative latch stage, which provides larger output drive current than the conventional one at a limited area, is implemented. The proposed circuit is designed using 90nm CMOS technology and 1V power supply voltage, and it demonstrates up to 19% less offset voltage and 62% less sensitivity of the delay to the input voltage difference (17ps/decade) than the conventional double-tail latched comparator at approximately the same area and power consumption.

Question: What is the difference between a dynamic and a regenerative latch?


Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate. Background: The Food and Drug Administration (FDA) in the United States and the European Medicines Agency (EMA) have recognized social media as a new data source to strengthen their activities regarding drug safety. Objective: Our objective in the ADR-PRISM project was to provide text mining and visualization tools to explore a corpus of posts extracted from social media. We evaluated this approach on a corpus of 21 million posts from five patient forums, and conducted a qualitative analysis of the data available on methylphenidate in this corpus. Methods: We applied text mining methods based on named entity recognition and relation extraction in the corpus, followed by signal detection using proportional reporting ratio (PRR). We also used topic modeling based on the Correlated Topic Model to obtain the list of the matics in the corpus and classify the messages based on their topics. Results: We automatically identified 3443 posts about methylphenidate published between 2007 and 2016, among which 61 adverse drug reactions (ADR) were automatically detected. Two pharmacovigilance experts evaluated manually the quality of automatic identification, and a f-measure of 0.57 was reached. Patient's reports were mainly neuro-psychiatric effects. Applying PRR, 67% of the ADRs were signals, including most of the neuro-psychiatric symptoms but also palpitations. Topic modeling showed that the most represented topics were related to Childhood and Treatment initiation, but also Side effects. Cases of misuse were also identified in this corpus, including recreational use and abuse. Conclusion: Named entity recognition combined with signal detection and topic modeling have demonstrated their complementarity in mining social media data. An in-depth analysis focused on methylphenidate showed that this approach was able to detect potential signals and to provide better un

Question: What is the f-measure of the text mining?


n ensemble learning framework for anomaly detection in building nergy consumption. During building operation, a significant amount of energy is wasted due to equipment and humanrelated faults. To reduce waste, today's smart buildings monitor energy usage with the aim of identifying abnormal consumption behaviour and notifying the building manager to implement appropriate energysaving procedures. To this end, this research proposes a new pattern-based anomaly classifier, the collective contextual anomaly detection using sliding window (CCAD-SW) framework. The CCAD-SW framework identifies anomalous consumption patterns using overlapping sliding windows. To enhance the anomaly detection capacity of the CCAD-SW, this research also proposes the ensemble anomaly detection (EAD) framework. The EAD is a generic framework that combines several anomaly detection classifiers

Question: What is the difference between the CCAD-SW and the EAD?


Can We Predict a Riot? Disruptive Event Detection Using Twitter. In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook, and YouTube. In these highly interactive systems, the general public are able to post real-time reactions to "real world" events, thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly small-scale incidents, using streamed data is a non-trivial task but would be of high value to public safety organisations such as local police, who need to respond accordingly. To address this challenge, we present an end-to-end integrated event detection framework that comprises five main components: data collection, pre-processing, classification, online clustering, and summarization. The integration between classification and clustering enables events to be detected, as well as related smaller-scale "disruptive events," smaller incidents that threaten social safety and security or could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely temporal, spatial, and textual content. We evaluate our framework on a large-scale, real-world dataset from Twitter. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We use ground-truth data based on intelligence gathered by the London Metropolitan Police Service, which provides a record of actual terrestrial events and incidents during the riots, and show that our system can perform as well as terrestrial sources, and even better in some cases.

Question: Can we predict a riot?


ACStream: Enforcing Access Control over Data Streams. In this demo proposal, we illustrate ACStream, a system built on top of Stream Base [1], to specify and enforce access control policies over data streams. ACStream supports a very flexible role-based access control model specifically designed to protect against unauthorized access to streaming data. The core component of ACStream is a query rewriting mechanism that, by exploiting a set of secure operators proposed by us in [2], rewrites a user query in such a way that it does not violate the specified access control policies during its execution. The demo will show how policies modelling a variety of access control requirements can be easily specified and enforced using ACStream.

Question: What is a data stream?


Applying Vector Space Model (VSM) Techniques in Information Retrieval for Arabic Language. i Acknowledgments vi

Question: What is the difference between vector space model and information retrieval?


A Novel Variable Reluctance Resolver for HEV/EV Applications. In order to simplify the manufacturing process of variable reluctance (VR) resolvers for hybrid electric vehicle/electric vehicle (HEV/EV) applications, a novel VR resolver with nonoverlapping tooth-coil windings is proposed in this paper. A comparison of the winding configurations is first carried out between the existing and the proposed designs, followed by the description of the operating principle. Furthermore, the influence of actual application conditions is investigated by finite-element (FE) analyses, including operating speed and assembling eccentricity. In addition, identical stator and windings of the novel design can be employed in three resolvers of different rotor saliencies. The voltage difference among the three rotor combinations, as well as the detecting accuracy, is further investigated. Finally, prototypes are fabricated and tested to verify the analyses.

Question: What is the novel variable reluctance resolver?


Fault tolerant three-phase AC motor drive topologies: a comparison of features, cost, and limitations. This paper compares the many fault tolerant three-phase ac motor drive topologies that have been proposed to provide output capacity for the inverter faults of switch short or open-circuits, phase-leg short-circuits, and single-phase open-circuits. Also included is a review of the respective control methods for fault tolerant inverters including two-phase and unipolar control methods. The output voltage and current space in terms of dq components is identified for each topology and fault. These quantities are then used to normalize the power capacity of each system during a fault to a standard inverter during normal operation. A silicon overrating cost factor is adopted as a metric to compare the relative switching device costs of the topologies compared to a standard three-phase inverter.

Question: What is the fault tolerance of a three-phase ac motor drive?


Towards Operational Measures of Computer Security. Ideally, a measure of the security of a system should capture quantitatively the intuitive notion of 'the ability of the system to resist attack'. That is, it should be operational, reflecting the degree to which the system can be expected to remain free of security breaches under particular conditions of operation (including attack). Instead, current security levels at best merely reflect the extensiveness of safeguards introduced during the design and development of a system. Whilst we might expect a system developed to a higher level than another to exhibit 'more secure behaviour' in operation, this cannot be guaranteed; more particularly, we cannot infer what the actual security behaviour will be from knowledge of such a level. In the paper we discuss similarities between reliability and security with the intention of working towards measures of 'operational security' similar to those that we have for reliability of systems. Very informally, these measures could involve expressions such as the rate of occurrence of security breaches (cf rate of occurrence of failures in reliability), or the probability that a specified 'mission' can be accomplished without a security breach (cf reliability function). This new approach is based on the analogy between system failure and security breach. A number of other analogies to support this view are introduced. We examine this duality critically, and have identified a number of important open questions that need to be answered before this quantitative approach can be taken further. The work described here is therefore somewhat tentative, and one of our major intentions is to invite discussion about the plausibility and feasibility of this new approach. Towards Operational Measures of Computer Security 2 _____________________________________________________________________

Question: What is the canadian military?


A comparison of methods for differential expression analysis of RNA-seq data. Finding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data. We conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data. Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the 'limma' method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Question: What is the difference between the methods for differential expression analysis of RNA-seq data?


Collaboration, creativity and the co-construction of oral and written texts. The Open University's repository of research publications and other research outputs Collaboration, creativity and the co-construction of oral and written texts Journal Article Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. (To be included in a special issue of Thinking Skills and Creativity, on " Collaborative Abstract In this paper we explore how primary school children 'learn to collaborate' and 'collaborate to learn' on creative writing projects by using diverse cultural artefacts-including oracy, literacy and ICT. We begin by reviewing some key socio-cultural concepts which serve as a theoretical framework for the research reported. Secondly, we describe the context in which the children talked and worked together to create their projects. This context is a 'learning community' developed as part of an innovative educational programme with the aim of promoting the social construction of knowledge among all participants. We then present microgenetic analyses of the quality of the interaction and dialogues taking place as peers worked together on their projects, and how these collaborative processes and uses of the mediational artefacts were taken up by the children. In order to exemplify these processes, our analyses centre on a selection of examples of dialogues, texts and multimedia products of stories created by groups of 4th grade (9-10 year-old) children. Overall, the work reveals the dynamic functioning in educational settings of some central socio-cultural concepts. These include: co-construction; intertextuality and intercontextuality amongst oracy, literacy and uses of ICT; collaborative creativity; development of dialogical and text production strategies and appropriation of diverse cul

Question: What is the difference between a collaborative and a collaborative learning project?


A Fast Parallel Algorithm for Thinning Digital Patterns. A fast parallel thinning algorithm is proposed in this paper. It consists of two subiterations: one aimed at deleting the south-east boundary points and the north-west corner points while the other one is aimed at deleting the north-west boundary points and the south-east corner points. End points and pixel connectivity are preserved. Each pattern is thinned down to a "skeleton" of unitary thickness. Experimental results show that this method is very effective.

Question: What is a skeleton?


Design and control of an actuated thumb exoskeleton for hand rehabilitation following stroke. Chronic hand impairment is common following stroke. This paper presents an actuated thumb exoskeleton (ATX) to facilitate research in hand rehabilitation therapy. The ATX presented in this work permits independent bi-directional actuation in each of the 5 degrees-of-freedom (DOF) of the thumb using a mechanism that has 5 active DOF and 3 passive DOF. The ATX is able to provide considerable joint torques for the user while still allowing backdrivability through flexible shaft transmission. A prototype has been built and experiments were conducted to evaluate the closed-loop position control. Further improvement and future work are discussed.

Question: What is the difference between a thumb exoskeleton and a hand exoskeleton?


CYCLOSA: Decentralizing Private Web Search through SGX-Based Browser Extensions. By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and accurate private Web search solution. CYCLOSA improves security by relying on trusted execution environments (TEEs) as provided by Intel SGX. Further, CYCLOSA proposes a novel adaptive privacy protection solution that reduces the risk of user re-identification. CYCLOSA sends fake queries to the search engine and dynamically adapts their count according to the sensitivity of the user query. In addition, CYCLOSA meets scalability as it is fully decentralized, spreading the load for distributing fake queries among other nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real query and the fake queries separately, in contrast to other existing solutions that mix fake and real query results.

Question: What is a secure, scalable and accurate private Web search solution?


Electricity Generation Using an Air-Cathode Single Chamber Microbial Fuel Cell in the Presence and Absence of a Proton Exchange Membrane. Microbial fuel cells (MFCs) are typically designed as a two-chamber system with the bacteria in the anode chamber separated from the cathode chamber by a polymeric proton exchange membrane (PEM). Most MFCs use aqueous cathodes where water is bubbled with air to provide dissolved oxygen to electrode. To increase energy output and reduce the cost of MFCs, we examined power generation in an air-cathode MFC containing carbon electrodes in the presence and absence of a polymeric proton exchange membrane (PEM). Bacteria present in domestic wastewater were used as the biocatalyst, and glucose and wastewater were tested as substrates. Power density was found to be much greater than typically reported for aqueous-cathode MFCs, reaching a maximum of 262 ( 10 mW/m2 (6.6 ( 0.3 mW/L; liquid volume) using glucose. Removing the PEM increased the maximum power density to 494 ( 21 mW/m2 (12.5 ( 0.5 mW/L). Coulombic efficiency was 40-55% with the PEM and 9-12% with the PEM removed, indicating substantial oxygen diffusion into the anode chamber in the absence of the PEM. Power output increased with glucose concentration according to saturation-type kinetics, with a half saturation constant of 79 mg/L with the PEM-MFC and 103 mg/L in the MFC without a PEM (1000 Ω resistor). Similar results on the effect of the PEM on power density were found using wastewater, where 28 ( 3 mW/m2 (0.7 ( 0.1 mW/L) (28% Coulombic efficiency) was produced with the PEM, and 146 ( 8 mW/m2 (3.7 ( 0.2 mW/L) (20% Coulombic efficiency) was produced when the PEM was removed. The increase in power output when a PEM was removed was attributed to a higher cathode potential as shown by an increase in the open circuit potential. An analysis based on available anode surface area and maximum bacterial growth rates suggests that mediatorless MFCs may have an upper order-of-magnitude lim

Question: What is the maximum power density of a microbial fuel cell?


Big Data Management Assessed Coursework Two Big Data vs Semantic Web F 21 BD.

Question: What is the difference between big data and semantic web?


Visual domain-specific modelling : Benefits and experiences of using metaCASE tools. 1 Introduction Jackson (Jackson 95) recognises the vital difference between an application's domain and its code: two different worlds, each with its own language, experts, ways of thinking etc. A finished application forms the intersection between these worlds. The difficult job of the software engineer is to build a bridge between these worlds, at the same time as solving problems in both worlds.

Question: What is metaCASE?


Improved Use of Continuous Attributes in C4.5. A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modi cations lead to smaller decision trees with higher predictive accuracies. Results also con rm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.

Question: How to improve the use of continuous attributes in C4.5?


Ensemble application of convolutional neural networks and multiple kernel learning for multimodal sentiment analysis. The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10-13% and 3-5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.

Question: How to extract user opinion and emotions from video content?


Articulation points guided redundancy elimination for betweenness centrality. Betweenness centrality (BC) is an important metrics in graph analysis which indicates critical vertices in large-scale networks based on shortest path enumeration. Typically, a BC algorithm constructs a shortest-path DAG for each vertex to calculate its BC score. However, for emerging real-world graphs, even the state-of-the-art BC algorithm will introduce a number of redundancies, as suggested by the existence of articulation points. Articulation points imply some common sub-DAGs in the DAGs for different vertices, but existing algorithms do not leverage such information and miss the optimization opportunity.  We propose a redundancy elimination approach, which identifies the common sub-DAGs shared between the DAGs for different vertices. Our approach leverages the articulation points and reuses the results of the common sub-DAGs in calculating the BC scores, which eliminates redundant computations. We implemented the approach as an algorithm with two-level parallelism and evaluated it on a multicore platform. Compared to the state-of-the-art implementation using shared memory, our approach achieves an average speedup of 4.6x across a variety of real-world graphs, with the traversal rates up to 45 ~ 2400 MTEPS (Millions of Traversed Edges per Second).

Question: What is the betweenness centrality?


MIMO for DVB-NGH, the next generation mobile TV broadcasting [Accepted From Open Call]. DVB-NGH (Digital Video Broadcasting - Next Generation Handheld) is the next generation technology for mobile TV broadcasting, which has been developed by the DVB project with the most advanced transmission technologies. DVB-NGH is the first broadcasting standard to incorporate multiple-input multiple-output (MIMO) as the key technology to overcome the Shannon limit of single antenna communications. MIMO techniques can be used to improve the robustness of the transmitted signal by exploiting the spatial diversity of the MIMO channel, but also to achieve increased data rates through spatial multiplexing. This article describes the benefits of MIMO that motivated its incorporation in DVB-NGH, reviews the MIMO schemes adopted, and discusses some aspects related to the deployment of MIMO networks in DVB-NGH. The article also provides a feature comparison with the multi-antenna techniques for 3GGP's LTE/LTE-Advanced for cellular networks. Finally, physical layer simulation results calibrated within the DVB-NGH standardization process are provided to illustrate the gain of MIMO for the next generation of mobile TV broadcasting.

Question: What is the next generation of mobile TV broadcasting?


Piaget ' s Constructivism , Papert ' s Constructionism : What ' s the difference ?. What is the difference between Piaget's constructivism and Papert's "constructionism"? Beyond the mere play on the words, I think the distinction holds, and that integrating both views can enrich our understanding of how people learn and grow. Piaget's constructivism offers a window into what children are interested in, and able to achieve, at different stages of their development. The theory describes how children's ways of doing and thinking evolve over time, and under which circumstance children are more likely to let go of—or hold onto— their currently held views. Piaget suggests that children have very good reasons not to abandon their worldviews just because someone else, be it an expert, tells them they're wrong. Papert's constructionism, in contrast, focuses more on the art of learning, or 'learning to learn', and on the significance of making things in learning. Papert is interested in how learners engage in a conversation with [their own or other people's] artifacts, and how these conversations boost self-directed learning, and ultimately facilitate the construction of new knowledge. He stresses the importance of tools, media, and context in human development. Integrating both perspectives illuminates the processes by which individuals come to make sense of their experience, gradually optimizing their interactions with the world

Question: What is the difference between Piaget's constructivism and Papert's "constructionism"?


Economic modelling using computational intelligence techniques. Attempting to successfully and accurately predict the financial market has long attracted the interests and attention of economists, bankers, mathematicians and scientists alike. The financial markets form the bedrock of any economy. There are a large number of factors and parameters that influence the direction, volume, price and flow of traded stocks. This coupled with the markets' vulnerability to external and non-finance related factors and the resulting intrinsic volatility makes the development of a robust and accurate financial market prediction model an interesting research and engineering problem. In an attempt to solve this engineering problem, the authors of this paper present a rough set theory based predictive model for the financial markets. Rough set theory has, as its base, imperfect data analysis and approximation. The theory is used to extract a set of reducts and a set of trading rules based on trading data of the Johannesburg Stock Exchange (JSE) for the period 1 April 2006 to 1 April 2011. To increase the efficiency of the model four of dicretization algorithms were used on the data set, namely (Equal Frequency Binning (EFB), Boolean Reasoning, Entropy and the Naïve Algorithm. The EFB algorithm gives the least number of rules and highest accuracy. Next, the reducts are extracted using the Genetic Algorithm and finally the set of dependency rules are generated from the set of reducts. A rough set confusion matrix is used to assess the accuracy of the model. The model gave a prediction accuracy of 80.4% using the Standard Voting classifier. Keywords-rough set theory; financial market modelling, neural networks, discretization, classification. INTRODUCTION Around the world, trading in the stock market has gained enormous popularity as a means through which one can reap huge profits. Attempting to successfully and accurately predict the financial market has long attracted the interests

Question: What is the financial market?


Understanding Citizen ' s Continuance Intention to Use e-Government Website : a Composite View of Technology Acceptance Model and Computer Self-Efficacy. This study aims to understand the fundamental factors influencing the citizen's continuance intention to use eGovernment websites by using the Technology Acceptance Model (TAM) as a based theoretical model. Computer selfefficacy is adopted as an additional factor that influences the citizen's continuance intention to use e-Government websites. To empirically test the proposed research model, the web-based survey was employed. The participants consisted of 614 country-wide citizens with at least a bachelor's degree and an experience with e-Government websites. Regression analysis was conducted to test the model. The results revealed that perceived usefulness and perceived ease of use of e-Government websites and citizen's computer self-efficacy directly enhanced citizen's continuance intention to use e-Government websites. In addition, perceived ease of use of e-Government websites indirectly enhanced citizen's continuance intention through perceived usefulness.

Question: What is the relationship between technology acceptance model and computer self-efficacy?


The mirror-neuron system.. A category of stimuli of great importance for primates, humans in particular, is that formed by actions done by other individuals. If we want to survive, we must understand the actions of others. Furthermore, without action understanding, social organization is impossible. In the case of humans, there is another faculty that depends on the observation of others' actions: imitation learning. Unlike most species, we are able to learn by imitation, and this faculty is at the basis of human culture. In this review we present data on a neurophysiological mechanism--the mirror-neuron mechanism--that appears to play a fundamental role in both action understanding and imitation. We describe first the functional properties of mirror neurons in monkeys. We review next the characteristics of the mirror-neuron system in humans. We stress, in particular, those properties specific to the human mirror-neuron system that might explain the human capacity to learn by imitation. We conclude by discussing the relationship between the mirror-neuron system and language.

Question: What is the mirror-neuron system?


MobiHide: A Mobilea Peer-to-Peer System for Anonymous Location-Based Queries. Modern mobile phones and PDAs are equipped with positioning capabilities (e.g., GPS). Users can access public location-based services (e.g., Google Maps) and ask spatial queries. Although communication is encrypted, privacy and confidentiality remain major concerns, since the queries may disclose the location and identity of the user. Commonly, spatial K-anonymity is employed to hide the query initiator among a group of K users. However, existing work either fails to guarantee privacy, or exhibits unacceptably long response time. In this paper we propose MobiHide, a Peer-to-Peer system for anonymous location-based queries, which addresses these problems. MobiHide employs the Hilbert space-filling curve to map the 2-D locations of mobile users to 1-D space. The transformed locations are indexed by a Chord-based distributed hash table, which is formed by the mobile devices. The resulting Peer-to-Peer system is used to anonymize a query by mapping it to a random group of K users that are consecutive in the 1-D space. Compared to existing state-of-the-art, MobiHide does not provide theoretical anonymity guarantees for skewed query distributions. Nevertheless, it achieves strong anonymity in practice, and it eliminates system hotspots. Our experimental evaluation shows that MobiHide has good load balancing and fault tolerance properties, and is applicable to real-life scenarios with numerous mobile users.

Question: What is the Hilbert space-filling curve?


Patch Autocorrelation Features: a translation and rotation invariant approach for image classification. The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are

Question: What is the difference between the PAF and TRIPAF approaches?


PreCog: Improving Crowdsourced Data Quality Before Acquisition. Quality control in crowdsourcing systems is crucial. It is typically done after data collection, often using additional crowdsourced tasks to assess and improve the quality. These post-hoc methods can easily add cost and latency to the acquisition process—particularly if collecting high-quality data is important. In this paper, we argue for pre-hoc interface optimizations based on feedback that helps workers improve data quality before it is submitted and is well suited to complement post-hoc techniques. We propose the Precog system that explicitly supports such interface optimizations for common integrity constraints as well as more ambiguous text acquisition tasks where quality is ill-defined. We then develop the Segment-Predict-Explain pattern for detecting low-quality text segments and generating prescriptive explanations to help the worker improve their text input. Our unique combination of segmentation and prescriptive explanation are necessary for Precog to collect 2× more high-quality text data than non-Precog approaches on two real domains.

Question: What is the canadian military?


Learning to Reason With Adaptive Computation. Multi-hop inference is necessary for machine learning systems to successfully solve tasks such as Recognising Textual Entailment and Machine Reading. In this work, we demonstrate the effectiveness of adaptive computation for learning the number of inference steps required for examples of different complexity and that learning the correct number of inference steps is difficult. We introduce the first model involving Adaptive Computation Time which provides a small performance benefit on top of a similar model without an adaptive component as well as enabling considerable insight into the reasoning process of the model.

Question: What is the first model involving adaptive computation?


Investigating Players' Engagement, Immersion, and Experiences in Playing Pokémon Go. In recent years, Augmented Reality (AR) based mobile games have become popular among players. The Pokémon Go is one of the well-known examples. Although Pokémon Go game has become a global phenomenon, there is a limited study about players' experiences in the gameplay. Especially, little is known about players' engagement and immersion in playing AR-based mobile games in which physical movements in the real world are largely required to play the game. In this study, we conducted a pilot study with eight participants to investigate players' engagement, immersion, and experiences in the Pokémon Go gameplay. We report and discuss the preliminary findings from the study, which can help game designers understand players' experiences in the gameplay so that they can design more creative AR games in the future. Furthermore, the findings are also useful for future studies with larger sample size.

Question: What is the purpose of this study?


Mis problems and failures: a socio-technical perspective. Many of the problems arjd failures of Management Information Systems (MIS) and Management Science/Operations Research (MS/OR) projects have been attributed to organizational behavioral problems. The millions of dollars organizations spend on MIS and MS/OR development are of little benefit because systems continue to fail. Steps can be taken to understand and solve these behavioral probiems. This article argues that in most oases these behavioral problems are the result of inadequate designs. These bad designs are attributed to the way MIS systems designers view organizations, their members, and the function of an MIS within them. I.e., systems designers' frames of reference. These frames of reference cause faulty design choices and failures to perceive better design alternatives. Seven conditions are discussed which reflect current systems designers' points of view. The discussion of these conditions demonstrates the need to refrawe MIS design methodology within the Socio-Technicai Systems (STS) design approach and change systems designers' perspectives. The STS approach is introduced as a realistic view of organizations and a way to change them. This article is the first of two to appear in consecutive issues of the MIS Quarterly. The purpose of this first article is to demonstrate the need for the srs approaj:h. The second will present the basic concepts and principles of the STS methodology and how it can be utilized in the design of an MIS.

Question: What is the purpose of this article?


Extending the Coverage of DBpedia Properties using Distant Supervision over Wikipedia. DBpedia is a Semantic Web project aiming to extract structured data from Wikipedia articles. Due to the increasing number of resources linked to it, DBpedia plays a central role in the Linked Open Data community. Currently, the information contained in DBpedia is mainly collected from Wikipedia infoboxes, a set of subject-attribute-value triples that represents a summary of the Wikipedia page. These infoboxes are manually compiled by the Wikipedia contributors, and in more than 50% of the Wikipedia articles the infobox is missing. In this article, we use the distant supervision paradigm to extract the missing information directly from the Wikipedia article, using a Relation Extraction tool trained on the information already present in DBpedia. We evaluate our system on a data set consisting of seven DBpedia properties, demonstrating the suitability of the approach in extending the DBpedia coverage.

Question: What is the DBpedia coverage of the DBpedia properties?