1 Marine anthropogenic litter on British beaches : a 10-year nationwide assessment using citizen science. Growing evidence suggests that anthropogenic litter, particularly plastic, represents a highly pervasive and persistent threat to global marine ecosystems. Multinational research is progressing to characterise its sources, distribution and abundance so that interventions aimed at reducing future inputs and clearing extant litter can be developed. Citizen science projects, whereby members of the public gather information, offer a low-cost method of collecting large volumes of data with considerable temporal and spatial coverage. Furthermore, such projects raise awareness of environmental issues and can lead to positive changes in behaviours and attitudes. We present data collected over a decade (2005-2014 inclusive) by Marine Conservation Society (MCS) volunteers during beach litter surveys carried along the British coastline, with the aim of increasing knowledge on the composition, spatial distribution and temporal trends of coastal debris. Unlike many citizen science projects, the MCS beach litter survey programme gathers information on the number of volunteers, duration of surveys and distances covered. This comprehensive information provides an opportunity to standardise data for variation in sampling effort among surveys, enhancing the value of outputs and robustness of findings. We found that plastic is the main constituent of anthropogenic litter on British beaches and the majority of traceable items originate from land-based sources, such as public littering. We identify the coast of the Western English Channel and Celtic Sea as experiencing the highest relative litter levels. Increasing trends over the 10-year time period were detected for a number of individual item categories, yet no statistically significant change in total (effort-corrected) litter was detected. We discuss the limitations of the dataset and make recommendations for future work. The s

Question: What is the largest marine litter study?


Automated reference resolution in legal texts. This paper investigates the task of reference resolution in the legal domain. This is a new interesting task in Legal Engineering research. The goal is to create a system which can automatically detect references and then extracts their referents. Previous work limits itself to detect and resolve references at the document targets. In this paper, we go a step further in trying to resolve references to sub-document targets. Referents extracted are the smallest fragments of texts in documents, rather than the entire documents that contain the referenced texts. Based on analyzing the characteristics of reference phenomena in legal texts, we propose a four-step framework to deal with the task: mention detection, contextual information extraction, antecedent candidate extraction, and antecedent determination. We also show how machine learning methods can be exploited in each step. The final system achieves 80.06 % in the F1 score for detecting references, 85.61 % accuracy for resolving them, and 67.02 % in the F1 score for the end-to-end setting task on the Japanese National Pension Law corpus.

Question: What is the goal of the paper?


Marginalized Denoising Autoencoders for Domain Adaptation. Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters — in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.

Question: What is the difference between a denoising autoencoder and a stacked denoising autoencoder?


Effects of lumbar stabilization exercise on functional disability and lumbar lordosis angle in patients with chronic low back pain. [Purpose] This study examined the effects of lumbar stabilization exercises on the functional disability and lumbar lordosis angles in patients with chronic low back pain. [Subjects] The subjects were 30 patients with chronic low back pain divided into a lumbar stabilization exercise group (n = 15) and a conservative treatment group (n = 15). [Methods] The lumbar stabilization exercise and conservative treatment groups performed an exercise program and conservative physical treatment, respectively. Both programs were performed 3 times a week for 6 weeks. The degree of functional disability was assessed by the Oswestry disability index, and lumbar lordosis angles were measured by plain radiography. [Results] The Oswestry disability index decreased significantly in the both groups; however, it was significantly lower in the lumbar stabilization exercise group. The lumbar lordosis angle increased significantly in the lumbar stabilization exercise group after treatment and was also significantly greater than that in the conservative treatment group. [Conclusion] Lumbar stabilization exercise is more effective than conservative treatment for improving functional disability and lumbar lordosis angles.

Question: What is the purpose of this study?


Numerically Grounded Language Models for Semantic Error Correction. Semantic error detection and correction is an important task for applications such as fact checking, speech-to-text or grammatical error correction. Current approaches generally focus on relatively shallow semantics and do not account for numeric quantities. Our approach uses language models grounded in numbers within the text. Such groundings are easily achieved for recurrent neural language model architectures, which can be further conditioned on incomplete background knowledge bases. Our evaluation on clinical reports shows that numerical grounding improves perplexity by 33% and F1 for semantic error correction by 5 points when compared to ungrounded approaches. Conditioning on a knowledge base yields further improvements.

Question: How does grounding numbers in the text improve the performance of semantic error correction?


Playing Stochastic Games Precisely. We study stochastic two-player games where the goal of one player is to achieve precisely a given expected value of the objective function, while the goal of the opponent is the opposite. Potential applications for such games include controller synthesis problems where the optimisation objective is to maximise or minimise a given payoff function while respecting a strict upper or lower bound, respectively. We consider a number of objective functions including reachability, ω-regular, discounted reward, and total reward. We show that precise value games are not determined, and compare the memory requirements for winning strategies. For stopping games we establish necessary and sufficient conditions for the existence of a winning strategy of the controller for a large class of functions, as well as provide the constructions of compact strategies for the studied objectives.

Question: What is the difference between a stochastic game and a stochastic game?


Tools to support systematic reviews in software engineering: a cross-domain survey using semi-structured interviews. Background: A number of software tools are being developed to support systematic reviewers within the software engineering domain. However, at present, we are not sure which aspects of the review process can most usefully be supported by such tools or what characteristics of the tools are most important to reviewers. Aim: The aim of the study is to explore the scope and practice of tool support for systematic reviewers in other disciplines. Method: Researchers with experience of performing systematic reviews in Healthcare and the Social Sciences were surveyed. Qualitative data was collected through semi-structured interviews and data analysis followed an inductive approach. Results: 13 interviews were carried out. 21 software tools categorised into one of seven types were identified. Reference managers were the most commonly mentioned tools. Features considered particularly important by participants were support for multiple users, support for data extraction and support for tool maintenance. The features and importance levels identified by participants were compared with those proposed for tools to support systematic reviews in software engineering. Conclusions: Many problems faced by systematic reviewers in other disciplines are similar to those faced in software engineering. There is general consensus across domains that improved tools are needed.

Question: What are the most common software tools used by systematic reviewers?


Food region segmentation in meal images using touch points. We propose an interactive scheme for segmenting meal images for automated dietary assessment. A smartphone user photographs a meal and marks a few touch points on the resulting image. The segmentation algorithm initializes a set of food segments with the touch points, and grows them using local image features. We evaluate the algorithm with a data set consisting of 300 manually segmented meal images. The precision of segmentation is 0.87, compared with 0.70 for fully automatic segmentation. The results show that the precision of segmentation was significantly improved by incorporating minimal user intervention.

Question: How to segment food images?


Learning to predict slip for ground robots. In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle's own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself

Question: How to predict slip for ground robots?


Paragraph: Thwarting Signature Learning by Training Maliciously. Defending a server against Internet worms and defending a user's email inbox against spam bear certain similarities. In both cases, a stream of samples arrives, and a classifier must automatically determine whether each sample falls into a malicious target class (e.g., worm network traffic, or spam email). A learner typically generates a classifier automatically by analyzing two labeled training pools: one of innocuous samples, and one of samples that fall in the malicious target class. Learning techniques have previously found success in settings where the content of the labeled samples used in training is either random, or even constructed by a helpful teacher, who aims to speed learning of an accurate classifier. In the case of learning classifiers for worms and spam, however, an adversary controls the content of the labeled samples to a great extent. In this paper, we describe practical attacks against learning, in which an adversary constructs labeled samples that, when used to train a learner, prevent or severely delay generation of an accurate classifier. We show that even a delusive adversary, whose samples are all correctly labeled, can obstruct learning. We simulate and implement highly effective instances of these attacks against the Polygraph [15] automatic polymorphic worm signature generation algorithms.

Question: How does the canadian military defend against worms?


Going Wild: Large-Scale Classification of Open DNS Resolvers. Since several years, millions of recursive DNS resolvers are-deliberately or not-open to the public. This, however, is counter-intuitive, since the operation of such openly accessible DNS resolvers is necessary in rare cases only. Furthermore, open resolvers enable both amplification DDoS and cache snooping attacks, and can be abused by attackers in multiple other ways. We thus find open recursive DNS resolvers to remain one critical phenomenon on the Internet.  In this paper, we illuminate this phenomenon by analyzing it from two different angles. On the one hand, we study the landscape of DNS resolvers based on empirical data we collected for over a year. We analyze the changes over time and classify the resolvers according to device type and software version. On the other hand, we take the viewpoint of a client and measure the response authenticity of these resolvers. Besides legitimate redirections (e.g., to captive portals or router login pages), we find millions of resolvers to deliberately manipulate DNS resolutions (i.e., return bogus IP address information). To understand this threat in more detail, we systematically analyze non-legitimate DNS responses and reveal open DNS resolvers that manipulate DNS resolutions to censor communication channels, inject advertisements, serve malicious files, perform phishing, or redirect to other kinds of suspicious or malicious activities.

Question: What is the largest dns resolver?


Reconstructing Storyline Graphs for Image Recommendation from Web Community Photos. In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs. The storyline graphs can be an effective summary that visualizes various branching narrative structure of events or activities recurring across the input photo sets of a topic class. In order to explore further the usefulness of the storyline graphs, we leverage them to perform the image sequential prediction tasks, from which photo recommendation applications can benefit. We formulate the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develop an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization. With experiments on more than 3.3 millions of images of 24 classes and user studies via Amazon Mechanical Turk, we show that the proposed algorithm improves other candidate methods for both storyline reconstruction and image prediction tasks.

Question: How to reconstruct storyline graphs from images?


Advanced Features for Enterprise-Wide Role-Based Access Control. The administration of users and access rights in large enterprises is a complex and challenging task. Roles are a powerful concept for simplifying access control, but their implementation is normally restricted to single systems and applications. In this article we define Enterprise Roles capable of spanning all IT systems in an organisation. We show how the Enterprise Role-Based Access Control (ERBAC) model exploits the RBAC model outlined in the NIST standard draft[5] and describe its extensions. We have implemented ERBAC as a basic concept of SAM Jupiter, a commercial security administration tool. Based on practical experience with the deployment of Enterprise Roles during SAM implementation projects in large organisations, we have enhanced the ERBAC model by including different ways of parametrising the roles. We show that using parameters can significantly reduce the number of roles needed in an enterprise and simplify the role structure, thereby reducing the administration effort considerably. The enhanced ERBAC features are illustrated by reallife examples.

Question: What is the difference between role-based access control and role-based access control?


A Comparison of Controllers for Balancing Two Wheeled Inverted Pendulum Robot. One of the challenging tasks concerning two wheeled inverted pendulum (TWIP) mobile robot is balancing its tilt to upright position, this is due to its inherently open loop instability. This paper presents an experimental comparison between model based controller and non-model based controllers in balancing the TWIP mobile robot. A Fuzzy Logic Controller (FLC) which is a non-model based controller and a Linear Quadratic Controller (LQR) which is a model-based controller, and the conventional controller, Proportional Integral Derivative (PID) were implemented and compared on a real time TWIP mobile robot. The FLC controller performance has given superior result as compared to LQR and PID in terms of speed response but consumes higher energy. Index Term-Two Wheeled Inverted Pendulum (TWIP), Fuzzy Logic Controller (FLC), Linear Quadratic Controller (LQR), Euler Lagrange equations.

Question: What is the difference between a model based controller and a non-model based controller?


Design of an Ultra-Wideband Pulse Generator Based on Avalanche Transistor. Based on the avalanche effect of avalanche transistor, a kind of ultra-wideband nanosecond pulse circuit has been designed, whose frequency, pulse width and amplitude are tunable. In this paper, the principle, structure and selection of components' parameters in the circuit are analyzed in detail. The circuit generates periodic negative pulse, whose pulse full width is 890 ps and pulse amplitude is -11.2 V in simulation mode. By setting up circuit for experiment and changing parameters properly, a kind of ultra-wideband pulse with pulse width of 2.131 ns and pulse amplitude of -9.23 V is achieved. With the features such as simple structure, stable and reliable performance and low cost, this pulse generator is applicable to ultra-wideband wireless communication system.

Question: What is the pulse width of an ultra-wideband pulse?


McRT-STM: a high performance software transactional memory system for a multi-core runtime. Applications need to become more concurrent to take advantage of the increased computational power provided by chip level multiprocessing. Programmers have traditionally managed this concurrency using locks (mutex based synchronization). Unfortunately, lock based synchronization often leads to deadlocks, makes fine-grained synchronization difficult, hinders composition of atomic primitives, and provides no support for error recovery. Transactions avoid many of these problems, and therefore, promise to ease concurrent programming.We describe a software transactional memory (STM) system that is part of McRT, an experimental Multi-Core RunTime. The McRT-STM implementation uses a number of novel algorithms, and supports advanced features such as nested transactions with partial aborts, conditional signaling within a transaction, and object based conflict detection for C/C++ applications. The McRT-STM exports interfaces that can be used from C/C++ programs directly or as a target for compilers translating higher level linguistic constructs.We present a detailed performance analysis of various STM design tradeoffs such as pessimistic versus optimistic concurrency, undo logging versus write buffering, and cache line based versus object based conflict detection. We also show a MCAS implementation that works on arbitrary values, coexists with the STM, and can be used as a more efficient form of transactional memory. To provide a baseline we compare the performance of the STM with that of fine-grained and coarse-grained locking using a number of concurrent data structures on a 16-processor SMP system. We also show our STM performance on a non-synthetic workload -- the Linux sendmail application.

Question: What is a transactional memory?


A Unified Framework for Multimodal Domain Adaptation. Domain adaptation aims to train a model on labeled data from a source domain while minimizing test error on a target domain. Most of existing domain adaptation methods only focus on reducing domain shift of single-modal data. In this paper, we consider a new problem of multimodal domain adaptation and propose a unified framework to solve it. The proposed multimodal domain adaptation neural networks(MDANN) consist of three important modules. (1) A covariant multimodal attention is designed to learn a common feature representation for multiple modalities. (2) A fusion module adaptively fuses attended features of different modalities. (3) Hybrid domain constraints are proposed to comprehensively learn domain-invariant features by constraining single modal features, fused features, and attention scores. Through jointly attending and fusing under an adversarial objective, the most discriminative and domain-adaptive parts of the features are adaptively fused together. Extensive experimental results on two real-world cross-domain applications (emotion recognition and cross-media retrieval) demonstrate the effectiveness of the proposed method.

Question: What is a unified framework for multimodal domain adaptation?


Combining Gradient Boosting Machines with Collective Inference to Predict Continuous Values. Gradient boosting of regression trees is a competitive procedure for learning predictive models of continuous data that fits the data with an additive non-parametric model. The classic version of gradient boosting assumes that the data is independent and identically distributed. However, relational data with interdependent, linked instances is now common and the dependencies in such data can be exploited to improve predictive performance. Collective inference is one approach to exploit relational correlation patterns and significantly reduce classification error. However, much of the work on collective learning and inference has focused on discrete prediction tasks rather than continuous. In this work, we investigate how to combine these two paradigms together to improve regression in relational domains. Specifically, we propose a boosting algorithm for learning a collective inference model that predicts a continuous target variable. In the algorithm, we learn a basic relational model, collectively infer the target values, and then iteratively learn relational models to predict the residuals. We evaluate our proposed algorithm on a real network dataset and show that it outperforms alternative boosting methods. However, our investigation also revealed that the relational features interact together to produce better predictions.

Question: How to combine gradient boosting and collective inference to predict continuous values?


Early Breast Cancer Detection using Mammogram Images: A Review of Image Processing Techniques. Breast cancer is one of the most common cancers worldwide among women so that one in eight women is affected by this disease during their lifetime. Mammography is the most effective imaging modality for early detection of breast cancer in early stages. Because of poor contrast and low visibility in the mammographic images, early detection of breast cancer is a significant step to efficient treatment of the disease. Different computeraided detection algorithms have been developed to help radiologists provide an accurate diagnosis. This paper reviews the most common image processing approaches developed for detection of masses and calcifications. The main focus of this review is on image segmentation methods and the variables used for early breast cancer detection. Texture analysis is the crucial step in any image segmentation techniques which are based on a local spatial variation of intensity or color. Therefore, various methods of texture analysis for mass and micro-calcification detection in mammography are discussed in details.

Question: What is the most common cancer among women?


Loop closure detection for visual SLAM using PCANet features. Loop closure detection benefits simultaneous localization and mapping (SLAM) in building a consistent map of the environment by reducing the accumulate error. Handcrafted features have been successfully used in traditional approaches, whereas in this paper, we show that unsupervised features extracted by deep learning models, can improves the accuracy of loop closure detection. In particular, we employ a cascaded deep network, namely the PCANet, to extract features as image descriptors. We tested the performance of our proposed method on open datasets to compare with traditional approaches. We found that the PCANet features outperform state-of-the-art handcrafted competitors, and are computational efficient to be implemented in practical robotics.

Question: What is a deep learning model?


Recurrent Residual Learning for Sequence Classification. In this paper, we explore the possibility of leveraging Residual Networks (ResNet), a powerful structure in constructing extremely deep neural network for image understanding, to improve recurrent neural networks (RNN) for modeling sequential data. We show that for sequence classification tasks, incorporating residual connections into recurrent structures yields similar accuracy to Long Short Term Memory (LSTM) RNN with much fewer model parameters. In addition, we propose two novel models which combine the best of both residual learning and LSTM. Experiments show that the new models significantly outperform LSTM.

Question: What is the difference between recurrent neural networks and recurrent residual networks?


A Language Modeling Approach to Predicting Reading Difficulty. We demonstrate a new research approach to the problem of predicting the reading difficulty of a text passage, by recasting readability in terms of statistical language modeling. We derive a measure based on an extension of multinomial naïve Bayes classification that combines multiple language models to estimate the most likely grade level for a given passage. The resulting classifier is not specific to any particular subject and can be trained with relatively little labeled data. We perform predictions for individual Web pages in English and compare our performance to widely-used semantic variables from traditional readability measures. We show that with minimal changes, the classifier may be retrained for use with French Web documents. For both English and French, the classifier maintains consistently good correlation with labeled grade level (0.63 to 0.79) across all test sets. Some traditional semantic variables such as type-token ratio gave the best performance on commercial calibrated test passages, while our language modeling approach gave better accuracy for Web documents and very short passages (less than 10 words).

Question: How to predict the reading difficulty of a text passage?


Linear Machine Decision Trees. This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks.

Question: What is a linear machine?


Scaled Agile Framework: Presentation and real world example. This case focuses on the applicability of the Scaled Agile Framework (SAFe) founded by Dean Leffingwell. Modern organizations often work with agile software engineering teams using traditional single team-level methods like Scrum, but multiple teams and the program or portfolio level are not part of methods like Scrum. SAFe tries to apply agile methodologies to the whole organisation. The real world example focuses on a key element of SAFe, the Program Increment (PI) planning meeting and how it can improve multiple team collaboration.

Question: What is the real world example?


Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks. This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.

Question: What is a voting ensemble?


Automatically refining the wikipedia infobox ontology. The combined efforts of human volunteers have recently extracted numerous facts from Wikipedia, storing them as machine-harvestable object-attribute-value triples in Wikipedia infoboxes. Machine learning systems, such as Kylin, use these infoboxes as training data, accurately extracting even more semantic knowledge from natural language text. But in order to realize the full power of this information, it must be situated in a cleanly-structured ontology. This paper introduces KOG, an autonomous system for refining Wikipedia's infobox-class ontology towards this end. We cast the problem of ontology refinement as a machine learning problem and solve it using both SVMs and a more powerful joint-inference approach expressed in Markov Logic Networks. We present experiments demonstrating the superiority of the joint-inference approach and evaluating other aspects of our system. Using these techniques, we build a rich ontology, integrating Wikipedia's infobox-class schemata with WordNet. We demonstrate how the resulting ontology may be used to enhance Wikipedia with improved query processing and other features.

Question: What is the combined efforts of human volunteers?


Persona: an online social network with user-defined privacy. Online social networks (OSNs) are immensely popular, with some claiming over 200 million users. Users share private content, such as personal information or photographs, using OSN applications. Users must trust the OSN service to protect personal information even as the OSN provider benefits from examining and sharing that information. We present Persona, an OSN where users dictate who may access their information. Persona hides user data with attribute-based encryption (ABE), allowing users to apply fine-grained policies over who may view their data. Persona provides an effective means of creating applications in which users, not the OSN, define policy over access to private data. We demonstrate new cryptographic mechanisms that enhance the general applicability of ABE. We show how Persona provides the functionality of existing online social networks with additional privacy benefits. We describe an implementation of Persona that replicates Facebook applications and show that Persona provides acceptable performance when browsing privacy-enhanced web pages, even on mobile devices.

Question: What is an online social network?


Simulating LTE/LTE-Advanced Networks with SimuLTE. In this work we present SimuLTE, an OMNeT++-based simulator for LTE and LTE-Advanced networks. Following well-established OMNeT++ programming practices, SimuLTE exhibits a fully modular structure, which makes it easy to be extended, verified and integrated. Moreover, it inherits all the benefits of such a widely-used and versatile simulation framework as OMNeT++, i.e., experiment support and seamless integration with the OMNeT++ network modules, such as INET. This allows SimuLTE users to build up mixed scenarios where LTE is only a part of a wider network. This paper describes the architecture of SimuLTE, with particular emphasis on the modeling choices at the MAC layer, where resource scheduling is located. Furthermore, we describe some of the verification and validation efforts and present an example of the performance analysis that can be carried out with SimuLTE.

Question: What is the simuLTE simulator?


A 29–30GHz 64-element Active Phased array for 5G Application. A 64-element 29–30GHz active phased array for 5G millimeter wave applications is presented in this paper. The proposed phased array composites of 64-element antennas, 64-chan-nel T/R modules, 4 frequency conversion links, beam controlling circuitry, power management circuits and cooling fans, and are integrated in a in a very compact size(135mmX 77mmX56mm). Hybrid integration of GaAs and Si circuits are employed to achieve better RF performance. The architecture of the proposed phased array and the detail design of the T/R modules and antennas are analyzed. By the OTA (over the air) measurement, the proposed phased array achieves a bandwidth of 1 GHz at the center frequency of 29.5GHz, and the azimuth beam-width is 12 deg with the scanning range of ±45deg. With the excitation of 800MHz 64QAM signals, the transmitter beam achieves a EVM of 5.5%, ACLR of −30.5dBc with the PA working at −10dB back off, and the measured saturated EIRP is 63 dBm.

Question: What is the size of the antenna?


Sequence-to-Dependency Neural Machine Translation. Nowadays a typical Neural Machine Translation (NMT) model generates translations from left to right as a linear sequence, during which latent syntactic structures of the target sentences are not explicitly concerned. Inspired by the success of using syntactic knowledge of target language for improving statistical machine translation, in this paper we propose a novel Sequence-to-Dependency Neural Machine Translation (SD-NMT) method, in which the target word sequence and its corresponding dependency structure are jointly constructed and modeled, and this structure is used as context to facilitate word generations. Experimental results show that the proposed method significantly outperforms state-of-the-art baselines on Chinese-English and JapaneseEnglish translation tasks.

Question: What is the difference between neural machine translation and statistical machine translation?


Removal of High Density Salt and Pepper Noise Through Modified Decision Based Unsymmetric Trimmed Median Filter. A modified decision based unsymmetrical trimmed median filter algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise is proposed in this paper. The proposed algorithm replaces the noisy pixel by trimmed median value when other pixel values, 0's and 255's are present in the selected window and when all the pixel values are 0's and 255's then the noise pixel is replaced by mean value of all the elements present in the selected window. This proposed algorithm shows better results than the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), and Progressive Switched Median Filter (PSMF). The proposed algorithm is tested against different grayscale and color images and it gives better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Question: How to remove salt and pepper noise from an image?


Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings. In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly inflectional Turkish and Czech languages. We improved the state-of-the-art F-score obtained for Turkish without using gazetteers by 2.26% and for Czech by 1.53%. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages.

Question: How to improve named entity recognition for morphologically rich languages?


Chest pathology detection using deep learning with non-medical training. In this work, we examine the strength of deep learning approaches for pathology detection in chest radiographs. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of CNN learned from a non-medical dataset to identify different types of pathologies in chest x-rays. We tested our algorithm on a 433 image dataset. The best performance was achieved using CNN and GIST features. We obtained an area under curve (AUC) of 0.87-0.94 for the different pathologies. The results demonstrate the feasibility of detecting pathology in chest x-rays using deep learning approaches based on non-medical learning. This is a first-of-its-kind experiment that shows that Deep learning with ImageNet, a large scale non-medical image database may be a good substitute to domain specific representations, which are yet to be available, for general medical image recognition tasks.

Question: What is the best way to detect chest pathology?


Ensembles of nested dichotomies for multi-class problems. Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.

Question: What is a multi-class classification problem?


Design Considerations and Performance Evaluation of Single-Stage TAIPEI Rectifier for HVDC Distribution Applications. Design considerations and performance evaluations of a three-phase, four-switch, single-stage, isolated zero-voltage-switching (ZVS) rectifier are presented. The circuit is obtained by integrating the three-phase, two-switch, ZVS, discontinuous-current-mode (DCM), boost power-factorcorrection (PFC) rectifier, named for short the TAIPEI rectifier, with the ZVS full-bridge (FB) phase-shift dc/dc converter. The performance was evaluated on a three-phase 2.7-kW prototype designed for HVDC distribution applications with the line-toline voltage range from 180 VRMS to 264 VRMS and with a tightly regulated variable dc output voltage from 200 V to 300 V. The prototype operates with ZVS over the entire input-voltage and load-current range and achieves less than 5% input-current THD with the efficiency in the 95% range.

Question: What is the name of the circuit?


Facilitating relational governance through service level agreements in IT outsourcing: An application of the commitment-trust theory. Article history: Received 18 June 2007 Received in revised form 9 June 2008 Accepted 20 June 2008 Available online 25 June 2008 Firms increasingly rely on outsourcing for strategic IT decisions, and themany sophisticated forms of outsourcing require significant management attention to ensure their success. Two forms of interorganizational governance–formal control and relational–have been used to examine the management of IT outsourcing relationships. Contrary to the conventional substitution view, recent studies have found that these two governance modes are complementary; however, the dynamics of their interactions remain unexplored. Based on the commitment–trust theory, this paper focuses on how the formal controlmechanism can influence the relational governance in an outsourcing engagement. Using service level agreements (SLAs) as a proxy for formal control, this studyfinds that eleven contractual elements, characterized as foundation, governance, and change management variables in an SLA, are positively related to the trust and relationship commitment among the parties. Trust and commitment, in turn, positively influence relational outcomes that we theorize would contribute to outsourcing success. Both research and practical implications of the results are discussed. © 2008 Elsevier B.V. All rights reserved.

Question: How can we facilitate relational governance through service level agreements in IT outsourcing?


ECNUCS: Measuring Short Text Semantic Equivalence Using Multiple Similarity Measurements. This paper reports our submissions to the Semantic Textual Similarity (STS) task in SemEval 2013 (Task 6). We submitted three Support Vector Regression (SVR) systems in core task, using 6 types of similarity measures, i.e., string similarity, number similarity, knowledge-based similarity, corpus-based similarity, syntactic dependency similarity and machine translation similarity. Our third system with different training data and different feature sets for each test data set performs the best and ranks 35 out of 90 runs. We also submitted two systems in typed task using string based measure and Named Entity based measure. Our best system ranks 5 out of 15 runs.

Question: What is the best system in SemEval 2013?


Accelerated Attributed Network Embedding. Network embedding is to learn low-dimensional vector representations for nodes in a network. It has shown to be effective in a variety of tasks such as node classification and link prediction. While embedding algorithms on pure networks have been intensively studied, in many real-world applications, nodes are often accompanied with a rich set of attributes or features, aka attributed networks. It has been observed that network topological structure and node attributes are often strongly correlated with each other. Thus modeling and incorporating node attribute proximity into network embedding could be potentially helpful, though non-trivial, in learning better vector representations. Meanwhile, real-world networks often contain a large number of nodes and features, which put demands on the scalability of embedding algorithms. To bridge the gap, in this paper, we propose an accelerated attributed network embedding algorithm AANE, which enables the joint learning process to be done in a distributed manner by decomposing the complex modeling and optimization into many sub-problems. Experimental results on several real-world datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Question: What is the difference between network embedding and attributed network embedding?


Loughborough University Institutional Repository Product attachment and replacement : implications for sustainable design. This research investigated a number of main areas of attachment in order to determine how consumer-product relationships are formed and to identify whether these feelings influence replacement decisions. Primary research comprised of interviews with consumers to discuss the topic area in relation to three possessions selected for their attachment qualities. The research highlighted how attachment is determined by multiple themes, many of which are circumstantial to consumers' experiences and therefore difficult for designers to control. Findings showed that memories were the most prominent theme of participants' attachment, closely followed by pleasure and usability. Enjoyment and pleasure were found to be the primary reason for attachment to newly purchased items, whereas nostalgia was highest for older possessions. Appearance and reliability were found to have considerable influence on participants' attitudes towards replacement.

Question: What is the difference between attachment and nostalgia?


Evaluation Of Linear Interpolation Smoothing On Naive Bayes Spam Classifier. The inconvenience associated with spams and the cost of having an important mail misclassified as spam have made all efforts at improving spam filtering worthwhile. The Naive Bayes algorithm has been found to be successful in properly classifying mails. However, they are not perfect. Recent researches have introduced the idea of smoothing into the Naive Bayes algorithm and they have been found to produce better classification. This study applies the concept of linear interpolation smoothing to Naive Bayes spam classification. The resulting classifier did well at improving spam classification and also reducing false positives.

Question: What is the Naive Bayes algorithm?


Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks. Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other machine learning techniques, is vulnerable to adversarial samples: inputs crafted to force a deep neural network (DNN) to provide adversary-selected outputs. Such attacks can seriously undermine the security of the system supported by the DNN, sometimes with devastating consequences. For example, autonomous vehicles can be crashed, illicit or illegal content can bypass content filters, or biometric authentication systems can be manipulated to allow improper access. In this work, we introduce a defensive mechanism called defensive distillation to reduce the effectiveness of adversarial samples on DNNs. We analytically investigate the generalizability and robustness properties granted by the use of defensive distillation when training DNNs. We also empirically study the effectiveness of our defense mechanisms on two DNNs placed in adversarial settings. The study shows that defensive distillation can reduce effectiveness of sample creation from 95% to less than 0.5% on a studied DNN. Such dramatic gains can be explained by the fact that distillation leads gradients used in adversarial sample creation to be reduced by a factor of 1030. We also find that distillation increases the average minimum number of features that need to be modified to create adversarial samples by about 800% on one of the DNNs we tested.

Question: How can we defend against adversarial samples?


The effects of a 6-week plyometric training program on agility.. The purpose of the study was to determine if six weeks of plyometric training can improve an athlete's agility. Subjects were divided into two groups, a plyometric training and a control group. The plyometric training group performed in a six week plyometric training program and the control group did not perform any plyometric training techniques. All subjects participated in two agility tests: T-test and Illinois Agility Test, and a force plate test for ground reaction times both pre and post testing. Univariate ANCOVAs were conducted to analyze the change scores (post - pre) in the independent variables by group (training or control) with pre scores as covariates. The Univariate ANCOVA revealed a significant group effect F2,26 = 25.42, p=0.0000 for the T-test agility measure. For the Illinois Agility test, a significant group effect F2,26 = 27.24, p = 0.000 was also found. The plyometric training group had quicker posttest times compared to the control group for the agility tests. A significant group effect F2,26 = 7.81, p = 0.002 was found for the Force Plate test. The plyometric training group reduced time on the ground on the posttest compared to the control group. The results of this study show that plyometric training can be an effective training technique to improve an athlete's agility. Key PointsPlyometric training can enhance agility of athletes.6 weeks of plyometric training is sufficient to see agility results.Ground reaction times are decreased with plyometric training.

Question: What is the difference between plyometric training and plyometric drills?


Implementation and Optimization of the Accelerator Based on FPGA Hardware for LSTM Network. Today, artificial neural networks (ANNs) are important machine learning methods which are widely used in a variety of applications. As the emerging field of ANNs, recurrent neural networks (RNNs) are often used for sequencerelated applications. And Long Short-Term Memory (LSTM) is an improved RNN which contains complex computational logic. To achieve high accuracy, researchers always build largescale LSTM networks which are time-consuming and powerconsuming. Thus the acceleration of LSTM networks, low power & energy consumption become the hot issues in today's research. In this paper, we present a hardware accelerator for the LSTM neural network layer based on FPGA Zedboard and use pipeline methods to parallelize the forward computing process. To optimize our implementation, we also use multiple methods including tiled matrix-vector multiplication, binary adder tree, and overlap of computation and data access. Through the acceleration and optimization methods, our accelerator is power-efficient and has a better performance than ARM Cortex A9 processor and Intel Core i5 processor.

Question: How to implement LSTM network on FPGA?


Density Based Smart Traffic System with Real Time Data Analysis Using IoT. This paper aims to develop a convenient traffic system that allows a smooth movement of cars which will help build a smarter city. The traffic system currently implemented in many areas is not based on the density of traffic and every road is allotted a preset time. This results in traffic congestion due to large red-light delays and timings allotted for roads in a city that should vary during peak on-off hours, but in reality don't. These traditional systems are not adaptable and fail to support traffic during an unexpected situation or during an accident, and this makes them inefficient. In order to calculate the density of traffic various sensors can be used, each having their merits and demerits. In our proposed system Ultrasound Sensors are used along with Image Processing(using live feed from a camera) that works on a Raspberry Pi platform and calculates the vehicle density and dynamically allots time for different levels of traffic. This in turn allows better signal control and effective management of traffic thereby reducing the probability of a collision. By using Internet Of Things(IoT) real time data from the system can be collected, stored and managed on a cloud. This data can be used to interpret the signal duration in-case any of the sensing equipment fail, and also for future analysis.

Question: What is IoT?


A Trusted Safety Verifier for Process Controller Code. Attackers can leverage security vulnerabilities in control systems to make physical processes behave unsafely. Currently, the safe behavior of a control system relies on a Trusted Computing Base (TCB) of commodity machines, firewalls, networks, and embedded systems. These large TCBs, often containing known vulnerabilities, expose many attack vectors which can impact process safety. In this paper, we present the Trusted Safety Verifier (TSV), a minimal TCB for the verification of safety-critical code executed on programmable controllers. No controller code is allowed to be executed before it passes physical safety checks by TSV. If a safety violation is found, TSV provides a demonstrative test case to system operators. TSV works by first translating assembly-level controller code into an intermediate language, ILIL. ILIL allows us to check code containing more instructions and features than previous controller code safety verification techniques. TSV efficiently mixes symbolic execution and model checking by transforming an ILIL program into a novel temporal execution graph that lumps together safetyequivalent controller states. We implemented TSV on a Raspberry Pi computer as a bump-in-the-wire that intercepts all controllerbound code. Our evaluation shows that it can test a variety of programs for common safety properties in an average of less than three minutes, and under six minutes in the worst case—a small one-time addition to the process engineering life cycle.

Question: What is a bump-in-the-wire?


An effective hybrid transactional memory system with strong isolation guarantees. We propose signature-accelerated transactional memory (SigTM), ahybrid TM system that reduces the overhead of software transactions. SigTM uses hardware signatures to track the read-set and write-set forpending transactions and perform conflict detection between concurrent threads. All other transactional functionality, including dataversioning, is implemented in software. Unlike previously proposed hybrid TM systems, SigTM requires no modifications to the hardware caches, which reduces hardware cost and simplifies support for nested transactions and multithreaded processor cores. SigTM is also the first hybrid TM system to provide strong isolation guarantees between transactional blocks and non-transactional accesses without additional read and write barriers in non-transactional code.  Using a set of parallel programs that make frequent use of coarse-grain transactions, we show that SigTM accelerates software transactions by 30% to 280%. For certain workloads, SigTM can match the performance of a full-featured hardware TM system, while for workloads with large read-sets it can be up to two times slower. Overall, we show that SigTM combines the performance characteristics and strong isolation guarantees of hardware TM implementations with the low cost and flexibility of software TM systems.

Question: What is a hybrid transactional memory system?


A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches. [1] Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajökull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events. Citation: Hooper, A., (2008), A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, Geophys. Res. Lett., 35, L16302, doi:10.1029/ 2008GL034654.

Question: What is the difference between a persistent scatterer and a small baseline approach?


Move Evaluation in Go Using Deep Convolutional Neural Networks. The game of Go is more challenging than other board games, due to the difficulty of constructing a position or move evaluation function. In this paper we investigate whether deep convolutional networks can be used to directly represent and learn this knowledge. We train a large 12-layer convolutional neural network by supervised learning from a database of human professional games. The network correctly predicts the expert move in 55% of positions, equalling the accuracy of a 6 dan human player. When the trained convolutional network was used directly to play games of Go, without any search, it beat the traditional-search program GnuGo in 97% of games, and matched the performance of a state-of-the-art Monte-Carlo tree search that simulates two million positions per move.

Question: How to evaluate a position in go?


Convolutional Neural Networks for Sentence Classification. We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

Question: What is a convolutional neural network?


Towards Linked Research Data: an Institutional Approach. For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and efficient way. Following a Linked Data approach we integrate this data into a unified linked data store and interlink it with additional data sources from inside the university and outside sources like DBpedia. Based on the existing platform, a concrete case study from the domain of biology is implemented that releases optical motion tracking data of stick insect locomotion. We investigate the cost and usefulness of such a detailed, domain-specific semantic enrichment in order to evaluate whether this approach might be considered for large-scale deployment.

Question: What is the cost of semantic enrichment?


A Game Theoretic Framework for Analyzing Re-Identification Risk. Given the potential wealth of insights in personal data the big databases can provide, many organizations aim to share data while protecting privacy by sharing de-identified data, but are concerned because various demonstrations show such data can be re-identified. Yet these investigations focus on how attacks can be perpetrated, not the likelihood they will be realized. This paper introduces a game theoretic framework that enables a publisher to balance re-identification risk with the value of sharing data, leveraging a natural assumption that a recipient only attempts re-identification if its potential gains outweigh the costs. We apply the framework to a real case study, where the value of the data to the publisher is the actual grant funding dollar amounts from a national sponsor and the re-identification gain of the recipient is the fine paid to a regulator for violation of federal privacy rules. There are three notable findings: 1) it is possible to achieve zero risk, in that the recipient never gains from re-identification, while sharing almost as much data as the optimal solution that allows for a small amount of risk; 2) the zero-risk solution enables sharing much more data than a commonly invoked de-identification policy of the U.S. Health Insurance Portability and Accountability Act (HIPAA); and 3) a sensitivity analysis demonstrates these findings are robust to order-of-magnitude changes in player losses and gains. In combination, these findings provide support that such a framework can enable pragmatic policy decisions about de-identified data sharing.

Question: What is a game theoretic framework for analyzing re-identification risk?


Jointless structure and under-actuation mechanism for compact hand exoskeleton. It is important for a wearable robot to be compact and sufficiently light for use as an assistive device. Since human fingers are arranged in a row in dense space, the concept of traditional wearable robots using a rigid frame and a pin joint result in size and complexity problems. A structure without a conventional pin joint, called a jointless structure, has the potential to be used as a wearable robotic hand because the human skeleton and joint can replace the robot's conventional structure. Another way to reduce the weight of the system is to use under-actuation. Under-actuation enables adaptive grasping with less number of actuators for robotic hands. Differential mechanisms are widely used for multi-finger under-actuation; however, they require additional working space. We propose a design with a jointless structure and a novel under-actuation mechanism to reduce the size and weight of a hand exoskeleton. Using these concepts, we developed a prototype that weighs only 80 grams. To evaluate the prototype, fingertip force and blocked force are measured. Fingertip force is the force that can be applied by the finger of the hand exoskeleton on the object surface. The fingertip force is about 18 N when actuated by a tension force of 35 N from the motor. 18 N is sufficient for simple pinch motion in daily activities. Another factor related to performance of the under-actuation mechanism is blocked force, which is a force required to stop one finger while the other finger keeps on moving. It is measured to be 0.5 N, which is sufficiently small. With these experiments, the feasibility of the new hand exoskeleton has been shown.

Question: What is a jointless structure?


Evaluation of the Patient with Nasal Obstruction.. Nasal obstruction is often multifactorial and knowledge of the contributing factors is critical to appropriate evaluation, diagnosis, and execution of a treatment plan. Recognizing and appropriately managing all components of nasal obstruction will increase the likelihood of symptomatic improvement and patient satisfaction.

Question: What is the best treatment for nasal obstruction?


Implicit and Explicit Stereotyping of Adolescents. Although adolescents are commonly assumed to be rebellious, risky and moody, two experiments demonstrate for the first time that these beliefs operate both explicitly and implicitly as stereotypes. In Experiment 1, participants (a) explicitly endorsed adolescent stereotypes and (b) implicitly associated adolescent stereotyped words more rapidly with the adolescent than the adult social category. Individual differences in the explicit endorsement of adolescent stereotypes predicted explicit perceptions of the rebelliousness of a 17-year-old but not a 71-year-old, although individual differences in implicit stereotyping did not. Identification with adults was associated with greater implicit stereotyping but not explicit stereotyping. In Experiment 2, subliminal exposure to adolescent stereotyped words increased subsequent perceptions of the rebelliousness of a 17-year-old but not a 71-year-old. Although individual differences in implicit adolescent stereotyping did not predict explicit evaluations of adolescents, stereotypes of adolescents nevertheless influenced explicit evaluations unconsciously and unintentionally.

Question: What is the difference between implicit and explicit stereotyping?


Question: What is the difference between the CDO and the CIO?


Safety Verification of Deep Neural Networks. Deep neural networks have achieved impressive experimental results in image classification, but can surprisingly be unstable with respect to adversarial perturbations, that is, minimal changes to the input image that cause the network to misclassify it. With potential applications including perception modules and end-to-end controllers for self-driving cars, this raises concerns about their safety. We develop a novel automated verification framework for feed-forward multi-layer neural networks based on Satisfiability Modulo Theory (SMT). We focus on image manipulations, such as scratches or changes to camera angle or lighting conditions, and define safety for an image classification decision in terms of invariance of the classification with respect to manipulations of the original image within a region of images that are close to it. We enable exhaustive search of the region by employing discretisation, and propagate the analysis layer by layer. Our method works directly with the network code and, in contrast to existing methods, can guarantee that adversarial examples, if they exist, are found for the given region and family of manipulations. If found, adversarial examples can be shown to human testers and/or used to fine-tune the network. We implement the techniques using Z3 and evaluate them on state-of-the-art networks, including regularised and deep learning networks. We also compare against existing techniques to search for adversarial examples and estimate network robustness.

Question: What is the difference between a deep neural network and a feed-forward multi-layer neural network?


Generalized Haar Filter based Deep Networks for Real-Time Object Detection in Traffic Scene. Vision-based object detection is one of the fundamental functions in numerous traffic scene applications such as self-driving vehicle systems and advance driver assistance systems (ADAS). However, it is also a challenging task due to the diversity of traffic scene and the storage, power and computing source limitations of the platforms for traffic scene applications. This paper presents a generalized Haar filter based deep network which is suitable for the object detection tasks in traffic scene. In this approach, we first decompose a object detection task into several easier local regression tasks. Then, we handle the local regression tasks by using several tiny deep networks which simultaneously output the bounding boxes, categories and confidence scores of detected objects. To reduce the consumption of storage and computing resources, the weights of the deep networks are constrained to the form of generalized Haar filter in training phase. Additionally, we introduce the strategy of sparse windows generation to improve the efficiency of the algorithm. Finally, we perform several experiments to validate the performance of our proposed approach. Experimental results demonstrate that the proposed approach is both efficient and effective in traffic scene compared with the state-of-the-art.

Question: What is the size of the canadian military?


CLASSIFICATION OF DEFECTS IN SOFTWARE USING DECISION TREE ALGORITHM. Software defects due to coding errors continue to plague the industry with disastrous impact, especially in the enterprise application software category. Identifying how much of these defects are specifically due to coding errors is a challenging problem. Defect prevention is the most vivid but usually neglected aspect of software quality assurance in any project. If functional at all stages of software development, it can condense the time, overheads and wherewithal entailed to engineer a high quality product. In order to reduce the time and cost, we will focus on finding the total number of defects if the test case shows that the software process not executing properly. That has occurred in the software development process. The proposed system classifying various defects using decision tree based defect classification technique, which is used to group the defects after identification. The classification can be done by employing algorithms such as ID3 or C4.5 etc. After the classification the defect patterns will be measured by employing pattern mining technique. Finally the quality will be assured by using various quality metrics such as defect density, etc. The proposed system will be implemented in JAVA.

Question: How to classify defects using decision tree based defect classification technique?


Sentiment analysis using Telugu SentiWordNet. In recent times, sentiment analysis in low resourced languages and regional languages has become emerging areas in natural language processing. Researchers have shown greater interest towards analyzing sentiment in Indian languages such as Hindi, Telugu, Tamil, Bengali, Malayalam, etc. In best of our knowledge, microscopic work has been reported till date towards Indian languages due to lack of annotated data set. In this paper, we proposed a two-phase sentiment analysis for Telugu news sentences using Telugu SentiWordNet. Initially, it identifies subjectivity classification where sentences are classified as subjective or objective. Objective sentences are treated as neutral sentiment as they don't carry any sentiment value. Next, Sentiment Classification has been done where the subjective sentences are further classified into positive and negative sentences. With the existing Telugu SentiWordNet, our proposed system attains an accuracy of 74% and 81% for subjectivity and sentiment classification respectively.

Question: What is the accuracy of the proposed system?


WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications. Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.

Question: What is the cost of cardiovascular disease in China?


SmartDroid: an automatic system for revealing UI-based trigger conditions in android applications. User interface (UI) interactions are essential to Android applications, as many Activities require UI interactions to be triggered. This kind of UI interactions could also help malicious apps to hide their sensitive behaviors (e.g., sending SMS or getting the user's device ID) from being detected by dynamic analysis tools such as TaintDroid, because simply running the app, but without proper UI interactions, will not lead to the exposure of sensitive behaviors. In this paper we focus on the challenging task of triggering a certain behavior through automated UI interactions. In particular, we propose a hybrid static and dynamic analysis method to reveal UI-based trigger conditions in Android applications. Our method first uses static analysis to extract expected activity switch paths by analyzing both Activity and Function Call Graphs, and then uses dynamic analysis to traverse each UI elements and explore the UI interaction paths towards the sensitive APIs. We implement a prototype system SmartDroid and show that it can automatically and efficiently detect the UI-based trigger conditions required to expose the sensitive behavior of several Android malwares, which otherwise cannot be detected with existing techniques such as TaintDroid.

Question: What is the canadian military?


Towards cloud based mobile ad hoc network simulation. Simulations are considered as the most convenient approach for validating any research proposal. A simulator attempts to model a real-world setting in order to analyze the behavior of any algorithm. A large number of simulation software for ad hoc network has been proposed in literature. These simulators are based on execution of simulation on standalone computers. This paper introduces the notion of `simulation as a service' for mobile ad hoc network's simulation based on cloud computing. Cloud computing is an emerging discipline that enables outsourcing computing task remotely based on pay-as-you-go model. The paper provides a generalized architecture to perform ad hoc network simulations on cloud platforms. Similar to different service models provided by cloud, some service models have been introduced for cloud based simulation. The proposed approach provides different advantages such as dynamic service provisioning to support large scale simulation, sharing of simulation models and reduced cost for simulation etc. The proposed tool has been implemented on Heroku platform that demonstrates the scalability of proposed approach.

Question: What is the difference between simulation and simulation as a service?


Ceramic interconnect bridge for heterogeneous integration and multiple chip packaging. In this paper, we describe the architecture and performance of a fine pitch multiple chip heterogeneous integration solution using ceramic interconnect bridge on organic substrate package. We present the increased IO density and improvement of electrical high-speed performance on signal integrity are achievable through this novel integration scheme, where dense copper routings on small ceramic elements are served as interconnect bridges. The cost and signal attenuation of using ceramic bridge is far better than that of silicon bridge or wafer interposer on substrate.

Question: What is the cost of using ceramic interconnect bridge?


Explicit Solution for Constrained Stochastic Linear-Quadratic Control with Multiplicative Noise. We study in this paper a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piece-wise affine function of the state and can be computed off-line efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems.

Question: What is the analytical control policy for a class of constrained LQ problems?


Digital receivers and transmitters using polyphase filter banks for wireless communications. This paper provides a tutorial overview of multichannel wireless digital receivers and the relationships between channel bandwidth, channel separation, and channel sample rate. The overview makes liberal use of figures to support the underlying mathematics. A multichannel digital receiver simultaneously down-convert a set of frequency-division-multiplexed (FDM) channels residing in a single sampled data signal stream. In a similar way, a multichannel digital transmitter simultaneously up-converts a number of baseband signals to assemble a set of FDM channels in a single sampled data signal stream. The polyphase filter bank has become the architecture of choice to efficiently accomplish these tasks. This architecture uses three interacting processes to assemble or to disassemble the channelized signal set. In a receiver, these processes are an input commutator to effect spectral folding or aliasing due to a reduction in sample rate, a polyphase -path filter to time align the partitioned and resampled time series in each path, and a discrete Fourier transform to phase align and separate the multiple baseband aliases. In a transmitter, these same processes operate in a related manner to alias baseband signals to high order Nyquist zones while increasing the sample rate with the output commutator. This paper presents a sequence of simple modifications to sampled data structures based on analog prototype systems to obtain the basic polyphase structure. We further discuss ways to incorporate small modifications in the operation of the polyphase system to accommodate secondary performance requirements. MATLAB simulations of a 10-, 40-, and 50-channel resampling receiver are included in the electronic version of this paper. An animated version of the ten-channel resampling receiver illustrates the time and frequency response of the filter bank when driven by a slowly varying linear

Question: What is a polyphase filter bank?


Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction. Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements (p < 0.01) over the state-of-the-art in both a human observer study and the semantic interpretability score.

Question: How to combine adversarial and perceptual loss functions with MSE loss function?


Tight bounds for rumor spreading in graphs of a given conductance. We study the connection between the rate at which a rumor spreads throughout a graph and the conductance of the graph—a standard measure of a graph's expansion properties. We show that for any n-node graph with conductance φ, the classical PUSH-PULL algorithm distributes a rumor to all nodes of the graph in O(φ log n) rounds with high probability (w.h.p.). This bound improves a recent result of Chierichetti, Lattanzi, and Panconesi [6], and it is tight in the sense that there exist graphs where Ω(φ log n) rounds of the PUSH-PULL algorithm are required to distribute a rumor w.h.p. We also explore the PUSH and the PULL algorithms, and derive conditions that are both necessary and sufficient for the above upper bound to hold for those algorithms as well. An interesting finding is that every graph contains a node such that the PULL algorithm takes O(φ log n) rounds w.h.p. to distribute a rumor started at that node. In contrast, there are graphs where the PUSH algorithm requires significantly more rounds for any start node. 1998 ACM Subject Classification G.3 [Mathematics of Computing]: Probability and Statistics

Question: What is the conductance of a graph?


The art of scenarios and strategic planning : tools and pitfalls. The term strategy has been misused and even abused. Worse, the word scenario is often confused with strategy to the point that clarification is needed if we are to understand one another. As a prolongation of the work done by the Rand Corporation in the 1960s, strategic planning, management and prospective approaches have been developed to help organizations master change. Over the past 25 years, we have contributed by creating or further developing various methodologies and procedures such as the Mactor and MICMAC methods for use in scenario building. These tools are doubly powerful in that they stimulate the imagination, reduce collective biases, and promote appropriation. One of the main functions of the strategic futures exercise is to eliminate two errors that we usually describe as the "hammer's risk" and the "nail's dream." In other words, we forget what a hammer's function is when staring at a nail (the nail's dream) or we know how to use a hammer and imagine that every problem is like a nail (the hammer's risk). In our case, we strive to give simple tools that may be appropriated. However, these simple tools are inspired by intellectual rigor that enables one to ask the right questions. Of course, these tools do not come with a guarantee. The natural talent, common sense, and intuition of the futurist also count!  2000 Elsevier Science Inc. Introduction Anticipation is not widely practiced by decision makers because when things are going well, they can manage without it, and when things are going badly, it is too late to see beyond the ends of their noses. Fast action is already urgently required! Yet reaction is not an end in itself. Although desirable in the short term, it leads nowhere if not directed towards the firm's long-term objectives. As Seneca said, "there is no favourable wind for the man who knows not where he is going." Action becomes meaningless without a goal, and only antici

Question: What is the difference between a scenario and a strategy?


Fuzzy-controlled Li-ion battery charge system with active state-of-charge controller. A fuzzy-controlled active state-of-charge controller (FC-ASCC) for improving the charging behavior of a lithium–ion (Li–ion) battery is proposed. The proposed FC-ASCC is designed to replace the general constant-voltage charging mode by two kinds of modes: sense and charge. A fuzzy-controlled algorithm is built with the predicted charger performance to program the charging trajectory faster and to remain the charge operation in a proposed safe-charge area (SCA). A modeling work is conducted for analyzing and describing the Li–ion battery in charging process. A three-dimensional Y-mesh diagram for describing the charging trajectories of the proposed FC charger is simulated. A prototype of a Li–ion battery charger with FC-ASCC is simulated and realized to assess the predicted charging performance. Experiment shows that the charging speed of the proposed FC charger compared with the general one increases about 23% and the charger can safely work in the SCA.

Question: How does a fuzzy-controlled Li-ion battery charge system work?


Proving the TLS Handshake Secure (As It Is). The TLS Internet Standard features a mixed bag of cryptographic algorithms and constructions, letting clients and servers negotiate their use for each run of the handshake. Although many ciphersuites are now well-understood in isolation, their composition remains problematic, and yet it is critical to obtain practical security guarantees for TLS. We experimentally confirm that all mainstream implementations of TLS share key materials between different algorithms, some of them of dubious strength. We outline attacks in their handling of resumption and renegotiation, stressing the need to model multiple related instances of the handshake. We study the provable security of the TLS handshake, as it is implemented and deployed. To capture the details of the standard and its main extensions, we rely on miTLS, a verified reference implementation of the protocol. miTLS inter-operates with mainstream browsers and servers for many protocol versions, configurations, and ciphersuites; and it provides application-level, provable security for some. We propose new agile security definitions and assumptions for the signatures, key encapsulation mechanisms (KEM), and key derivation algorithms used by the TLS handshake. By necessity, our definitions are stronger than those expected with simple modern protocols. To validate our model of key encapsulation, we prove that both RSA and Diffie-Hellman ciphersuites satisfy our definition for the KEM. In particular, we formalize the use of PKCS#1v1.5 encryption in TLS, including recommended countermeasures against Bleichenbacher attacks, and build a 3,000-line EasyCrypt proof of the security of the resulting master secret KEM against replayable chosen-ciphertext attacks under the assumption that ciphertexts are hard to re-randomize. Based on our new agile definitions, we construct a modular proof of security for the miTLS reference implementation of the handshake, including ciphersuite negotiation

Question: What is the security of the TLS handshake?


An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text. We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of "naturally" occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.

Question: What is the size of the canadian military?


Estimation and Removal of Clock Skew from Network Delay Measurements. Packet delay and loss traces are frequently used by network engineers, as well as network applications, to analyze netw ork performance. The clocks on the end-systems used to measure the dela ys, however, are not always synchronized, and this lack of synchron ization reduces the accuracy of these measurements. Therefore, estimating and removing relative skews and offsets from delay measurements between sender and receiver clocks are critical to the accurate assessment and analysis of network performance. In this paper we introduce a linear programming-based algorithm to estimate the clock skew in network delay measur ements and compare it with three other algorithms. We show that our algorithm has time complexity of O(N), leaves the delay after the skew removal positive, and is robust in the sense that the error margin of the skew est imate is independent of the magnitude of the skew. We use traces of real Int ernet delay measurements to assess the algorithm, and compare its perfo rmance to that of three other algorithms. Furthermore, we show through simulation that our algorithm is unbiased, and that the sample variance of the skew estimate is better (smaller) than existing algorithms. Keywords— clock skew, clock ratio, end-to-end delay, delay measurement.

Question: How to estimate clock skew in network delay measurements?


Result Diversification in Automatic Citation Recommendation. The increase in the number of published papers each year makes manual literature search inefficient and furthermore insufficient. Hence, automatized reference/citation recommendation have been of interest in the last 3-4 decades. Unfortunately, some of the developed approaches, such as keyword-based ones, are prone to ambiguity and synonymy. On the other hand, using the citation information does not suffer from the same problems since they do not consider textual similarity. Today, obtaining the desired information is as hard as looking for a needle in a haystack. And sometimes, we want that small haystack, e.g., a small result set containing only a few recommendations, cover all the important and relevant parts of the literature. That is, the set should be diversified enough. Here, we investigate the problem of result diversification in automatic citation recommendation. We enhance existing techniques, which were designed to recommend a set of citations with satisfactory quality and diversity, with direction-awareness to allow the users to reach either old, well-cited, well-known research papers or recent, less-known ones. We also propose some novel techniques for a better result diversification. Experimental results show that our techniques are very useful in automatic citation recommendation.

Question: What is the result diversification in automatic citation recommendation?


The effects of caffeine on heart rate variability in newborns with apnea of prematurity. Objective:Apnea of prematurity is a common complication in premature newborns and caffeine is a widespread medication used to treat this complication. Caffeine may have adverse effects on the cardiovascular and central nervous system, yet its effects on the autonomic nervous system modulation of heart rate have not been studied in premature newborns, which was the objective of our study.Study Design:We prospectively studied 21 premature newborns who were treated with caffeine. We analyzed heart rate variability by power spectral density and by dynamic nonlinear analyses methods.Result:There were no changes in heart rate, blood pressure or the autonomic nervous system tone following administration of caffeine, nor were the nonlinear dynamical properties of the system altered by caffeine.Conclusion:Caffeine does not have detrimental effects on heart rate variablility, heart rate or blood pressure in conventional doses given to premature newborns.

Question: What is the objective of this study?


Simultaneous Detection and Segmentation. We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS). Unlike classical bounding box detection, SDS requires a segmentation and not just a box. Unlike classical semantic segmentation, we require individual object instances. We build on recent work that uses convolutional neural networks to classify category-independent region proposals (R-CNN [16]), introducing a novel architecture tailored for SDS. We then use category-specific, topdown figure-ground predictions to refine our bottom-up proposals. We show a 7 point boost (16% relative) over our baselines on SDS, a 5 point boost (10% relative) over state-of-the-art on semantic segmentation, and state-of-the-art performance in object detection. Finally, we provide diagnostic tools that unpack performance and provide directions for future work.

Question: How do we detect and segment objects in an image?


Broken Promises : An Experiment. We test whether promises per se are effective in enhancing cooperative behavior in a form of trust game. In a new treatment, rather than permitting free-form messages, we instead allow only a bare promise-only message to be sent (or not). We find that bare promises are much less effective in achieving good social outcomes than free-form messages; in fact, bare promise-only messages lead to behavior that is much the same as when no messages are feasible. Our design also permits us to test the predictions of guilt aversion against the predictions of lying aversion. Our experimental results provide evidence that mainly supports the guilt-aversion predictions, but we also find some support for the presence of lying aversion.

Question: What is the difference between a promise and a promise-only message?


High Performance Single-Chip FPGA Rijndael Algorithm Implementations. This paper describes high performance single-chip FPGA implementations of the new Advanced Encryption Standard (AES) algorithm, Rijndael. The designs are implemented on the Virtex-E FPGA family of devices. FPGAs have proven to be very effective in implementing encryption algorithms. They provide more flexibility than ASIC implementations and produce higher data-rates than equivalent software implementations. A novel, generic, parameterisable Rijndael encryptor core capable of supporting varying key sizes is presented. The 192-bit key and 256-bit key designs run at data rates of 5.8 Gbits/sec and 5.1 Gbits/sec respectively. The 128-bit key encryptor core has a throughput of 7 Gbits/sec which is 3.5 times faster than similar existing hardware designs and 21 times faster than known software implementations, making it the fastest single-chip FPGA Rijndael encryptor core reported to date. A fully pipelined single-chip 128-bit key Rijndael encryptor/decryptor core is also presented. This design runs at a data rate of 3.2 Gbits/sec on a Xilinx Virtex-E XCV3200E-8-CG1156 FPGA device. There are no known singlechip FPGA implementations of an encryptor/decryptor Rijndael design.

Question: What is the highest data rate of a single-chip FPGA Rijndael encryptor/decryptor?


S-Sensors: Integrating physical world inputs with social networks using wireless sensor networks. Real-world monitoring and surveillance applications are rapidly increasing with the growth in the demand for awareness of the environment and surrounding. Sensor-to-web integration models are envisaged to improve the utilisation of sensory infrastructures and data via advanced coordination and collaboration of sensor nodes and web applications. This paper proposes to employ micro-blogging to publish and share sensory data and resources. S-Sensors provides a framework to globally share locally measured sensory readings. Short messages depicting the status of the environment are used to convey sensory data of the physical world. Moreover, data accessibility and resource manageability are facilitated by using social networks paradigms to form communities of sensor networks.

Question: What is the framework for integrating physical world inputs with social networks?


A Metadata Catalog Service for Data Intensive Applications. Advances in computational, storage and network technologies as well as middle ware such as the Globus Toolkit allow scientists to expand the sophistication and scope of data-intensive applications. These applications produce and analyze terabytes and petabytes of data that are distributed in millions of files or objects. To manage these large data sets efficiently, metadata or descriptive information about the data needs to be managed. There are various types of metadata, and it is likely that a range of metadata services will exist in Grid environments that are specialized for particular types of metadata cataloguing and discovery. In this paper, we present the design of a Metadata Catalog Service (MCS) that provides a mechanism for storing and accessing descriptive metadata and allows users to query for data items based on desired attributes. We describe our experience in using the MCS with several applications and present a scalability study of the service.

Question: What is a metadata catalog service?


Computer vision technology for food quality assurance Sundaram Gunasekaran. Quality assurance is one of the most important goals of any industry. The ability to manufacture high-quality products consistently is the basis for success in the highly competitive food industry. It encourages loyalty in customers and results in an expanding market share. The quality assurance methods used in the food industry have traditionally involved human visual inspection. Such methods are tedious, laborious, time-consuming and inconsistent. As plant throughput increased and quality tolerance tightened, it became necessary to employ automatic methods for quality assurance and quality control. During the past decade, computers have been introduced widely for automation of the quality assurance task. The food industry has been traditionally slow in adopting this new technology and moving towards programmable automation'. Renard? cited the following barriers to automating food processing plants: diverging opinions of what to measure because manufacturing processes themselves are not well understood; lack of appropriate standards against which properties can be compared; and the age of plants and equipment. However, the speed, reliability and cost savings inherent to process automation have provided sufficient impetus to the food industry to pursue aggressively the automatic, on-line control of all plant operations. Innovation of new technology often undergoes four phases3. The first is a research or discovery phase, in which new knowledge results in advances in the technology. Phase two is the early commercialization phase, in which a small market for the technology develops. Phase three represents the emergence of niche-specific applications of the technology. The last phase is that of widespread proliferation. A key characteristic of phase four is that the technology is transparent to users, that is,

Question: What is the food industry?