Content-based multimedia information retrieval: State of the art and challenges. Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.

Question: What is the role of content-based multimedia information retrieval in current research directions?


A multi-dimensional meter-adaptive method for automatic segmentation of music. Music structure appears on a wide variety of temporal levels (notes, bars, phrases, etc). Its highest-level expression is therefore dependent on music's lower-level organization, especially beats and bars. We propose a method for automatic structure segmentation that uses musically meaningful information and is content-adaptive. It relies on a meter-adaptive signal representation that prevents from the use of empirical parameters. Moreover, our method is designed to combine multiple signal features to account for various musical dimensions. Finally, it also combines multiple structural principles that yield complementary results. The resulting algorithm proves to already outperform state-of-the-art methods, especially within small tolerance windows, and yet offers several encouraging improvement directions.

Question: What is a multi-dimensional meter-adaptive method for automatic segmentation of music?


Ambiguity as a resource for design. Ambiguity is usually considered anathema in Human Computer Interaction. We argue, in contrast, that it is a resource for design that can be used to encourage close personal engagement with systems. We illustrate this with examples from contemporary arts and design practice, and distinguish three broad classes of ambiguity according to where uncertainty is located in the interpretative relationship linking person and artefact. Ambiguity of information finds its source in the artefact itself, ambiguity of context in the sociocultural discourses that are used to interpret it, and ambiguity of relationship in the interpretative and evaluative stance of the individual. For each of these categories, we describe tactics for emphasising ambiguity that may help designers and other practitioners understand and craft its use.

Question: What is the difference between ambiguity and uncertainty?


Detection of copy-move image forgery based on discrete cosine transform. Since powerful editing software is easily accessible, manipulation on images is expedient and easy without leaving any noticeable evidences. Hence, it turns out to be a challenging chore to authenticate the genuineness of images as it is impossible for human's naked eye to distinguish between the tampered image and actual image. Among the most common methods extensively used to copy and paste regions within the same image in tampering image is the copy-move method. Discrete Cosine Transform (DCT) has the ability to detect tampered regions accurately. Nevertheless, in terms of precision (FP) and recall (FN), the block size of overlapping block influenced the performance. In this paper, the researchers implemented the copy-move image forgery detection using DCT coefficient. Firstly, by using the standard image conversion technique, RGB image is transformed into grayscale image. Consequently, grayscale image is segregated into overlying blocks of m × m pixels, m = 4.8. 2D DCT coefficients are calculated and reposition into a feature vector using zig-zag scanning in every block. Eventually, lexicographic sort is used to sort the feature vectors. Finally, the duplicated block is located by the Euclidean Distance. In order to gauge the performance of the copy-move detection techniques with various block sizes with respect to accuracy and storage, threshold D_similar = 0.1 and distance threshold (N)_d = 100 are used to implement the 10 input images in order. Consequently, 4 × 4 overlying block size had high false positive thus decreased the accuracy of forged detection in terms of accuracy. However, 8 × 8 overlying block accomplished more accurately for forged detection in terms of precision and recall as compared to 4 × 4 overlying block. In a nutshell, the result of the accuracy performance of different overlying block size are influenced by the diverse size of forged area, distance between two forg

Question: How to detect copy-move image forgery based on discrete cosine transform?


WhyCon : An Efficent , Marker-based Localization System. We present an open-source marker-based localization system intended as a low-cost easy-to-deploy solution for aerial and swarm robotics. The main advantage of the presented method is its high computational efficiency, which allows its deployment on small robots with limited computational resources. Even on low-end computers, the core component of the system can detect and estimate 3D positions of hundreds of black and white markers at the maximum frame-rate of standard cameras. The method is robust to changing lighting conditions and achieves accuracy in the order of millimeters to centimeters. Due to its reliability, simplicity of use and availability as an open-source ROS module (http://purl.org/robotics/whycon), the system is now used in a number of aerial robotics projects where fast and precise relative localization is required.

Question: What is the name of the open-source localization system?


A review of soft computing applications in supply chain management. It is broadly recognised by global companies that supply chain management is one of the major core competencies for an organisation to compete in the marketplace. Organisational strategies are mainly concentrated on improvement of customer service levels as well as reduction of operational costs in order to maintain profit margins. Therefore supply chain performance has attracted researchers' attention. A variety of soft computing techniques including fuzzy logic and genetic algorithms have been employed to improve effectiveness and efficiency in various aspects of supply chain management. Meanwhile, an increasing number of papers have been published to address related issues. The aim of this paper is to summarise the findings by a systematic review of existing research papers concerning the application of soft computing techniques to supply chain management. Some areas in supply chain management that have rarely been exposed in existing papers, such as customer relationship management and reverse logistics, are therefore suggested for future research. 2009 Elsevier B.V. All rights reserved.

Question: What is a systematic review?


Evaluating Color Descriptors for Object and Scene Recognition. Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.

Question: What is the difference between the color descriptors in this paper and the color descriptors in this paper?


Context dependent recurrent neural network language model. Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.

Question: How does a recurrent neural network language model work?


Zero-Knowledge Proof of Decryption for FHE Ciphertexts. Zero-knowledge proofs of knowledge and fully-homomorphic encryption are two areas that have seen considerable advances in recent years, and these two techniques are used in conjunction in the context of verifiable decryption. Existing solutions for verifiable decryption are aimed at the batch setting, however there are many applications in which there will only be one ciphertext that requires a proof of decryption. The purpose of this paper is to provide a zero-knowledge proof of correct decryption on an FHE ciphertext, which for instance could hold the result of a cryptographic election. We give two main contributions. Firstly, we present a bootstrapping-like protocol to switch from one FHE scheme to another. The first scheme has efficient homomorphic capabilities; the second admits a simple zero-knowledge protocol. To illustrate this, we use the Brakerski et al. (ITCS, 2012) scheme for the former, and Gentry's original scheme (STOC, 2009) for the latter. Secondly, we present a simple one-shot zero-knowledge protocol for verifiable decryption using Gentry's original FHE scheme.

Question: What is a zero-knowledge proof of decryption?


The Pendubot: a mechatronic system for control research and education. 1 In this paper we describe the Pendubot, a mechatronic device for use in control engineering education and for research in nonlinear control and robotics. This device is a two{link planar robot with an actuator at the shoulder but no actuator at the elbow. With this system , a number of fundamental concepts in nonlinear dynamics and control theory may be illustrated. The pendubot complements previous mechatronic systems, such as the Acrobot 3] and the inverted pendulum of Furuta 4].

Question: What is a mechatronic system for control research and education?


M4: A Visualization-Oriented Time Series Data Aggregation. Visual analysis of high-volume time series data is ubiquitous in many industries, including finance, banking, and discrete manufacturing. Contemporary, RDBMS-based systems for visualization of high-volume time series data have difficulty to cope with the hard latency requirements and high ingestion rates of interactive visualizations. Existing solutions for lowering the volume of time series data disregard the semantics of visualizations and result in visualization errors. In this work, we introduce M4, an aggregation-based time series dimensionality reduction technique that provides errorfree visualizations at high data reduction rates. Focusing on line charts, as the predominant form of time series visualization, we explain in detail the drawbacks of existing data reduction techniques and how our approach outperforms state of the art, by respecting the process of line rasterization. We describe how to incorporate aggregation-based dimensionality reduction at the query level in a visualizationdriven query rewriting system. Our approach is generic and applicable to any visualization system that uses an RDBMS as data source. Using real world data sets from high tech manufacturing, stock markets, and sports analytics domains we demonstrate that our visualization-oriented data aggregation can reduce data volumes by up to two orders of magnitude, while preserving perfect visualizations.

Question: What is the volume of time series data?


Developing a bioinspired pole climbing robot. This paper proposes a pole climbing robot that has an ability to climb pipes. The novelty of this design is that it uses no motors to climb on the pole. Till now many robots have been fabricated with the ability to climb pipes but most of them solely depend on DC motors. The use of DC motor induces the risk of loosening the grip of the robot in case of power failure that may lead to disastrous situations if robot is working on high altitudes. This could be easily avoidable by the use of electro-pneumatics and by using self-locking circuits. We used two pairs of pneumatic cylinders as linear actuators for gripping the pole i.e. one pair for one claw. Besides this two more cylinders are used for climbing purpose. Unlike other mechanisms, installation is extremely easy. It is like controlling a radio controlled car. This mechanism can be easily modified for different payload capacities by simply using the appropriate diameter of the pneumatic cylinders. The robot can be controlled in tele-operated mode as well as autonomous mode which is a closed loop system. It has a high accuracy with 4mm error per cycle that has a cycle time period of 5 seconds. This robot can be very useful in oil industries for inspection of pipes and power poles in electric industries. It can serve of great help in cases of emergency situation caused during fire accidents.

Question: How to design a pole climbing robot?


Assessing and Quantifying Network Effects in an Online Dating Market. We empirically examine and quantify network effects on a large online dating platform in Brazil. We exploit a natural experiment, wherein a focal platform acquired its Brazilian competitor and subsequently imported the competitor's base of 150,000+ users over a 3-day period; a large exogenous shock to the composition of the purchasing platform. Our study context and the natural experiment provide two unique sources of identification: i) accounts purchased from the competitor were almost exclusively heterosexual users, even though the purchasing platform also played host to homosexual users, and ii) the treatment varied across cities, in that the "value" of new users to the existing user base differed. because purchased users differed from existing users in terms of their average characteristics (e.g., location within the city). We leverage the former to estimate a difference-in-differences specification, treating homosexual enrollment and exit rates as a plausible control for those of the heterosexual population, whereas the latter provides us with an opportunity to explore the importance of local market structure in the manifestation of network effects. We find that the treatment increased both rates of enrollment and rates of exit, amongst both genders, with a net positive effect that translated to a 17% increase in short-term revenue for the platform. We find clear evidence of local network effects; in cities where the average spatial distance between new users and existing users was larger, the treatment effect was significantly weaker. We discuss the implications for the literature and practice, and we suggest a number of avenues for future work in this space. ∗Assistant Professor, Information Systems Department, Carlson School of Management, University of Minnesota. gburtch@umn.edu. †Associate Professor, Information Systems Department, Desautels Faculty of Management, McGill University. jui.

Question: How does network effects affect the revenue of an online dating platform?


Beam selection for performance-complexity optimization in high-dimensional MIMO systems. Millimeter-wave (mm-wave) communications systems offer a promising solution to meeting the increasing data demands on wireless networks. Not only do mm-wave systems allow orders of magnitude larger bandwidths, they also create a high-dimensional spatial signal space due to the small wavelengths, which can be exploited for beamforming and multiplexing gains. However, the complexity of digitally processing the entire high-dimensional signal is prohibitive. By exploiting the inherent channel sparsity in beamspace due to highly directional propagation at mm-wave, it is possible to design near-optimal transceivers with dramatically lower complexity. In such beamspace MIMO systems, it is first necessary to determine the set of beams which define the low-dimensional communication subspace. In this paper, we address this beam selection problem and introduce a simple power-based classifier for determining the beamspace sparsity pattern that characterizes the communication subspace. We first introduce a physical model for a small cell which will serve as the setting for our analysis. We then develop a classifier for the physical model, and show its optimality for a class of ideal signals. Finally, we present illustrative numerical results and show the feasibility of the classifier in mobile settings.

Question: What is the physical model for a small cell?


CENTURION: Incentivizing multi-requester mobile crowd sensing. The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.

Question: What is the incentive mechanism for multi-requester mobile crowd sensing?


Evaluating User Privacy in Bitcoin. Bitcoin is quickly emerging as a popular digital payment system. However, in spite of its reliance on pseudonyms, Bitcoin raises a number of privacy concerns due to the fact that all of the transactions that take place are publicly announced in the system. In this paper, we investigate the privacy provisions in Bitcoin when it is used as a primary currency to support the daily transactions of individuals in a university setting. More specifically, we evaluate the privacy that is provided by Bitcoin (i) by analyzing the genuine Bitcoin system and (ii) through a simulator that faithfully mimics the use of Bitcoin within a university. In this setting, our results show that the profiles of almost 40% of the users can be, to a large extent, recovered even when users adopt privacy measures recommended by Bitcoin. To the best of our knowledge, this is the first work that comprehensively analyzes, and evaluates the privacy implications of Bitcoin.

Question: What is the privacy of bitcoin?


Uses and Gratification Theory – Why Adolescents Use Facebook ?. Due to a dynamic development of the Web 2.0 and new trends in the social media field that change on a daily basis, contemporary media research is shifting its focus to a greater extent on media users, their motivation and behavior in using social network sites in order to explain the extreme popularity of Facebook, Twitter, WhatsApp and other similar SNSs and mobile chat applications among the young. In this paper we wanted to explore the benefits of Facebook use among adolescents as well as which of their needs are gratified thereat. As the theoretical background we used the uses and gratification theory due to its user oriented approach. Furthermore, we wanted to test whether the uses and gratification concept is adequate for analyzing the motivation and behavior of SNSs users as suggested by some previous research. The survey comprising 431 adolescent Facebook users was conducted from October to December 2013 in the City of Zagreb. The results have shown that most adolescents use Facebook for socializing and communicating with their friends, discussing school activities, setting up meetings and dates with friends as well as

Question: What is the uses and gratification theory?


No-reference perceptual quality assessment of JPEG compressed images. Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signalto-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance. A Matlab implementation of the proposed method is available at http://anchovy.ece.ute xas.edu/ ̃zwang/research/nr jpeg quality/index.html .

Question: How to measure the quality of a JPEG image?


3D deeply supervised network for automated segmentation of volumetric medical images. While deep convolutional neural networks (CNNs) have achieved remarkable success in 2D medical image segmentation, it is still a difficult task for CNNs to segment important organs or structures from 3D medical images owing to several mutually affected challenges, including the complicated anatomical environments in volumetric images, optimization difficulties of 3D networks and inadequacy of training samples. In this paper, we present a novel and efficient 3D fully convolutional network equipped with a 3D deep supervision mechanism to comprehensively address these challenges; we call it 3D DSN. Our proposed 3D DSN is capable of conducting volume-to-volume learning and inference, which can eliminate redundant computations and alleviate the risk of over-fitting on limited training data. More importantly, the 3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model, accelerating the convergence speed and simultaneously improving the discrimination capability. Such a mechanism is developed by deriving an objective function that directly guides the training of both lower and upper layers in the network, so that the adverse effects of unstable gradient changes can be counteracted during the training procedure. We also employ a fully connected conditional random field model as a post-processing step to refine the segmentation results. We have extensively validated the proposed 3D DSN on two typical yet challenging volumetric medical image segmentation tasks: (i) liver segmentation from 3D CT scans and (ii) whole heart and great vessels segmentation from 3D MR images, by participating two grand challenges held in conjunction with MICCAI. We have achieved competitive segmentation results to state-of-the-art approaches in both challenges with a much faster speed, corroborating the effectiveness of our

Question: What is a volumetric medical image?


Using parallel stiffness to achieve improved locomotive efficiency with the Sandia STEPPR robot. In this paper we introduce STEPPR (Sandia Transmission-Efficient Prototype Promoting Research), a bipedal robot designed to explore efficient bipedal walking. The initial iteration of this robot achieves efficient motions through powerful electromagnetic actuators and highly back-drivable synthetic rope transmissions. We show how the addition of parallel elastic elements at select joints is predicted to provide substantial energetic benefits: reducing cost of transport by 30 to 50 percent. Two joints in particular, hip roll and ankle pitch, reduce dissipated power over three very different gait types: human walking, human-like robot walking, and crouched robot walking. Joint springs based on this analysis are tested and validated experimentally. Finally, this paper concludes with the design of two unique parallel spring mechanisms to be added to the current STEPPR robot in order to provide improved locomotive efficiency.

Question: How does the addition of parallel elastic elements at select joints improve locomotive efficiency?


Semi-Supervised and Unsupervised Extreme Learning Machines. Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

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


Wearable Ultrasonic Sensor Using Double-Layer PVDF Films for Monitoring Tissue Motion. Monitoring of the physical properties of the tissues provides valuable information for the clinical diagnosis and evaluation. However, one of the challenges of an ultrasonic method for continuous monitoring of a tissue motion using a conventional clinical ultrasonic image system could be motion artifacts due to the weight and size of its handheld ultrasonic probe employed. A wearable ultrasonic sensor, made of a polyvinylidene fluoride (PVDF) polymer piezoelectric film, may be able to reduce the motion artifacts due to its lightweight and flexible properties. However, the PVDF ultrasonic sensor has a relatively weak transmitting acoustic signal strength which causes poor signal-to-noise ratio of the ultrasonic signal acquired in pulse-echo measurements, particularly for the signal reflected from deeper tissue. This paper investigated an improvement of the ultrasonic performance of the WUS using double-layer PVDF films. The sensor was constructed using two 52-μm thick PVDF films. The developed double-layer WUS showed the 1.7 times greater ultrasonic signal amplitude compared to the WUS made of a single-layer PVDF having the equivalent PVDF film thickness. Thus, the developed double-layer PVDF WUS improved the depth of the ultrasonic penetration into the tissue. The developed WUS successfully demonstrated to monitor the contractions of biceps muscles in an upper arm. In addition, a cardiac tissue motion was clearly observed in the M-mode measurement corresponding with the cardiac cycles obtained from the ECG measurement.

Question: What is the difference between a single-layer and a double-layer PVDF film?


0-knowledge fuzzing. Nowadays fuzzing is a pretty common technique used both by attackers and software developers. Currently known techniques usually involve knowing the protocol/format that needs to be fuzzed and having a basic understanding of how the user input is processed inside the binary. In the past since fuzzing was little-used obtaining good results with a small amount of effort was possible. Today finding bugs requires digging a lot inside the code and the user-input as common vulnerabilies are already identified and fixed by developers. This paper will present an idea on how to effectively fuzz with no knowledge of the userinput and the binary. Specifically the paper will demonstrate how techniques like code coverage, data tainting and inmemory fuzzing allow to build a smart fuzzer with no need to instrument it.

Question: How to fuzz without knowing the protocol/format?


A privacy-preserving approach to policy-based content dissemination. We propose a novel scheme for selective distribution of content, encoded as documents, that preserves the privacy of the users to whom the documents are delivered and is based on an efficient and novel group key management scheme. Our document broadcasting approach is based on access control policies specifying which users can access which documents, or subdocuments. Based on such policies, a broadcast document is segmented into multiple subdocuments, each encrypted with a different key. In line with modern attribute-based access control, policies are specified against identity attributes of users. However our broadcasting approach is privacy-preserving in that users are granted access to a specific document, or subdocument, according to the policies without the need of providing in clear information about their identity attributes to the document publisher. Under our approach, not only does the document publisher not learn the values of the identity attributes of users, but it also does not learn which policy conditions are verified by which users, thus inferences about the values of identity attributes are prevented. Moreover, our key management scheme on which the proposed broadcasting approach is based is efficient in that it does not require to send the decryption keys to the users along with the encrypted document. Users are able to reconstruct the keys to decrypt the authorized portions of a document based on subscription information they have received from the document publisher. The scheme also efficiently handles new subscription of users and revocation of subscriptions.

Question: What is a privacy-preserving approach to policy-based content dissemination?


The max-min hill-climbing Bayesian network structure learning algorithm. We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

Question: What is the max-min hill-climbing Bayesian network structure learning algorithm?


Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search. This paper presents a supervised deep hashing approach that constructs binary hash codes from labeled data for large-scale image search. We assume that semantic labels are governed by a set of latent attributes in which each attribute can be on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semanticspreserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network in which binary codes are learned by the optimization of an objective function defined over classification error and other desirable properties of hash codes. With this design, SSDH has a nice property that classification and retrieval are unified in a single learning model, and the learned binary codes not only preserve the semantic similarity between images but also are efficient for image search. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a pointwised manner and thus is naturally scalable to large-scale datasets. SSDH is simple and can be easily realized by a slight modification of an existing deep architecture for classification; yet it is effective and outperforms other unsupervised and supervised hashing approaches on several benchmarks and one large dataset comprising more than 1 million images.

Question: What is the objective function of the deep network?


Lexical input as related to children's vocabulary acquisition: effects of sophisticated exposure and support for meaning.. A corpus of nearly 150,000 maternal word-tokens used by 53 low-income mothers in 263 mother-child conversations in 5 settings (e.g., play, mealtime, and book readings) was studied. Ninety-nine percent of maternal lexical input consisted of the 3,000 most frequent words. Children's vocabulary performance in kindergarten and later in 2nd grade related more to the occurrence of sophisticated lexical items than to quantity of lexical input overall. Density of sophisticated words heard and the density with which such words were embedded in helpful or instructive interactions, at age 5 at home, independently predicted over a third of the variance in children's vocabulary performance in both kindergarten and 2nd grade. These two variables, with controls for maternal education, child nonverbal IQ, and amount of child's talk produced during the interactive settings, at age 5, predicted 50% of the variance in children's 2nd-grade vocabulary.

Question: What is the effect of sophisticated lexical input on children's vocabulary acquisition?


Deep predictive policy training using reinforcement learning. Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small subnetwork with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.

Question: What is a PR2 robot?


COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGATION. OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper, we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA in the programming time, the execution time and the analysis of independence of OpenACC model in high performance problems. Our results show that, for our programs and our subject pool, this claim is true. We created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom environment and instrumented the WebCode website developed by ourselves to record details of students' coding process. Three hypotheses were supported by the statistical data: for the same parallelizable problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by previous CUDA experience

Question: What is the difference between OpenACC and CUDA?


Rotor Failures in Squirrel Cage Induction Motors. During the past two decades, tremendous improvement has been made in the design and manufacture of stator windings. This has been accomplished primarily through the development of improved insulation materials and treatment processes. As a result, the expected life from a thermal, dielectrical, mechanical, and environmental standpoint has been significantly increased. The rotor design and manufacturing remains basically unchanged. A survey of warranty data and service facilities suggests that rotor failures now account for a larger percentage of the total induction motor failures. The majority of these failures are caused by various stresses which act on the rotor assembly. These various stresses and how they affect the life of the motor and contribute to premature failure are discussed.

Question: What is the squirrel cage induction motor?


A Big Data system supporting Bosch Braga Industry 4.0 strategy. People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).

Question: What is Industry 4.0?


Using neural network ensembles for bankruptcy prediction and credit scoring. Bankruptcy prediction and credit scoring have long been regarded as critical topics and have been studied extensively in the accounting and finance literature. Artificial intelligence and machine learning techniques have been used to solve these financial decision-making problems. The multilayer perceptron (MLP) network trained by the back-propagation learning algorithm is the mostly used technique for financial decision-making problems. In addition, it is usually superior to other traditional statistical models. Recent studies suggest combining multiple classifiers (or classifier ensembles) should be better than single classifiers. However, the performance of multiple classifiers in bankruptcy prediction and credit scoring is not fully understood. In this paper, we investigate the performance of a single classifier as the baseline classifier to compare with multiple classifiers and diversified multiple classifiers by using neural networks based on three datasets. By comparing with the single classifier as the benchmark in terms of average prediction accuracy, the multiple classifiers only perform better in one of the three datasets. The diversified multiple classifiers trained by not only different classifier parameters but also different sets of training data perform worse in all datasets. However, for the Type I and Type II errors, there is no exact winner. We suggest that it is better to consider these three classifier architectures to make the optimal financial decision. 2007 Elsevier Ltd. All rights reserved.

Question: What is the best classifier for bankruptcy prediction and credit scoring?


Evidence for a common representation of decision values for dissimilar goods in human ventromedial prefrontal cortex.. To make economic choices between goods, the brain needs to compute representations of their values. A great deal of research has been performed to determine the neural correlates of value representations in the human brain. However, it is still unknown whether there exists a region of the brain that commonly encodes decision values for different types of goods, or if, in contrast, the values of different types of goods are represented in distinct brain regions. We addressed this question by scanning subjects with functional magnetic resonance imaging while they made real purchasing decisions among different categories of goods (food, nonfood consumables, and monetary gambles). We found activity in a key brain region previously implicated in encoding goal-values: the ventromedial prefrontal cortex (vmPFC) was correlated with the subjects' value for each category of good. Moreover, we found a single area in vmPFC to be correlated with the subjects' valuations for all categories of goods. Our results provide evidence that the brain encodes a "common currency" that allows for a shared valuation for different categories of goods.

Question: What is the ventromedial prefrontal cortex?


UTILIZING A GAME THEORETICAL APPROACH TO PREVENT COLLUSION AND INCENTIVIZE COOPERATION IN. Author: Arash Golchubian Title: Utilizing a Game Theoretical Approach to Prevent Collusion and Incentivize Cooperation in Cybersecurity Contexts Institution: Florida Atlantic University Thesis Advisor: Dr. Mehrdad Nojoumian Degree: Master of Science Year: 2017 In this research, a new reputation-based model is utilized to disincentivize collusion of defenders and attackers in Software Defined Networks (SDN), and also, to disincentivize dishonest mining strategies in Blockchain. In the context of SDN, the model uses the reputation values assigned to each entity to disincentivize collusion with an attacker. Our analysis shows that not-colluding actions become Nash Equilibrium using the reputationbased model within a repeated game setting. In the context of Blockchain and mining, we illustrate that by using the same socio-rational model, miners not only are incentivized to conduct honest mining but also disincentivized to commit to any malicious activities against other mining pools. We therefore show that honest mining strategies become Nash Equilibrium in our setting. This thesis is laid out in the following manner. In chapter 2 an introduction to game theory is provided followed by a survey of previous works in game theoretic network security, in chapter 3 a new reputation-based model is introduced to be used within the context of a Software Defined Network (SDN), in chapter 4 a reputation-based solution concept is introduced to force cooperation by each mining entity in Blockchain, and finally, in chapter 5, the concluding remarks and future works are presented. vii To: My loving wife, Sanaz. You inspire me to better myself. and My mother, Mina. You showed me that nothing is impossible. UTILIZING A GAME THEORETICAL APPROACH TO PREVENT COLLUSION AND INCENTIVIZE COOPERATION IN CYBERSECURITY CONTEXTS List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Question: How large is the canadian military?


Maximum Principle Based Algorithms for Deep Learning. The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin's maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate periteration, provided Hamiltonian maximization can be efficiently carried out a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

Question: What is the maximum principle based algorithm for deep learning?


Super High Gain Substrate Integrated Clamped-Mode Printed Log-Periodic Dipole Array Antenna. A compact, low profile, super high antenna gain, and broadband antenna, based on the printed log-periodic dipole array (PLPDA) antenna and the substrate integrated waveguide (SIW) technologies, has been proposed. It is so called substrate integrated waveguide clamped-mode printed log-periodic dipole array (SIW clamped-mode PLPDA) antenna. The arrangement of the dipole elements and feed lines in the SIW clamped-mode PLPDA antenna is designed and optimized. The proposed SIW clamped-mode PLPDA antenna was designed, fabricated, and tested, while a single SIW PLPDA antenna and a 2-element SIW PLPDA array were designed for comparison. The impedance bandwidth and the far-field radiation patterns in both E-plane and H-plane were measured. The measured results show that the performance of the SIW clamped-mode PLPDA antenna is similar to that of the 2-element SIW PLPDA array over the working frequency range 25 GHz-40 GHz, while the proposed antenna has only half the number of dipole elements and feed lines.

Question: What is the difference between a single SIW PLPDA antenna and a 2-element SIW PLPDA array?


An Unsupervised User Behavior Prediction Algorithm Based on Machine Learning and Neural Network For Smart Home. The user operates the smart home devices year in year out, have produced mass operation data, but these data do not be utilized well in past. Nowadays, these data can be used to predict user's behavior custom with the development of big data and machine learning technologies, and then the prediction results can be employed to enhance the intelligence of a smart home system. In view of this, this paper proposes a novel unsupervised user behavior prediction (UUBP) algorithm, which employs an artificial neural network and proposes a forgetting factor to overcome the shortcomings of the previous prediction algorithm. This algorithm has a high-level of autonomous and self-organizing learning ability while does not require too much human intervention. Furthermore, the algorithm can better avoid the influence of user's infrequent and out-of-date operation records, because of the forgetting factor. Finally, the use of real end user's operation records to demonstrate that UUBP algorithm has a better level of performance than other algorithms from effectiveness.

Question: What is the difference between a neural network and an artificial neural network?


Completely Derandomized Self-Adaptation in Evolution Strategies. This paper puts forward two useful methods for self-adaptation of the mutation distribution - the concepts of derandomization and cumulation. Principle shortcomings of the concept of mutative strategy parameter control and two levels of derandomization are reviewed. Basic demands on the self-adaptation of arbitrary (normal) mutation distributions are developed. Applying arbitrary, normal mutation distributions is equiv-alent to applying a general, linear problem encoding. The underlying objective of mutative strategy parameter control is roughly to favor previously selected mutation steps in the future. If this objective is pursued rigor-ously, a completely derandomized self-adaptation scheme results, which adapts arbitrary normal mutation distributions. This scheme, called covariance matrix adaptation (CMA), meets the previously stated demands. It can still be considerably improved by cumulation - utilizing an evolution path rather than single search steps. Simulations on various test functions reveal local and global search properties of the evolution strategy with and without covariance matrix adaptation. Their performances are comparable only on perfectly scaled functions. On badly scaled, non-separable functions usually a speed up factor of several orders of magnitude is ob-served. On moderately mis-scaled functions a speed up factor of three to ten can be expected.

Question: What is the difference between a normal mutation distribution and a general, linear problem encoding?


Wideband printed rectangular monopole antenna for circularly polarization. In this paper, a printed monopole antenna for circular polarization has been proposed. The relationship between the geometry of the antenna and the axial ratio is investigated. A wideband circularly polarized antenna is designed according to the parametric studies. The simulated bandwidth of 3dB-axial ratio is from 2.06GHz to 6.04GHz (98%).

Question: What is the relationship between the geometry of the antenna and the axial ratio?


Improving RDF Data Through Association Rule Mining. Linked Open Data comprises very many and often large public data sets, which are mostly presented in the Rdf triple structure of subject, predicate, and object. However, the heterogeneity of available open data requires significant integration steps before it can be used in applications. A promising and novel technique to explore such data is the use of association rule mining. We introduce "mining configurations", which allow us to mine Rdf data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. We present rule-based approaches for predicate suggestion, data enrichment, ontology improvement, and query relaxation. On the one hand we prevent inconsistencies in the data through predicate suggestion, enrichment with missing facts, and alignment of the corresponding ontology. On the other hand we support users to handle inconsistencies during query formulation through predicate expansion techniques. Based on these approaches, we show that association rule mining benefits the integration and usability of Rdf data.

Question: What is the difference between a data set and a data set?


Intent aware adaptive admittance control for physical Human-Robot Interaction. Effective physical Human-Robot Interaction (pHRI) needs to account for variable human dynamics and also predict human intent. Recently, there has been a lot of progress in adaptive impedance and admittance control for human-robot interaction. Not as many contributions have been reported on online adaptation schemes that can accommodate users with varying physical strength and skill level during interaction with a robot. The goal of this paper is to present and evaluate a novel adaptive admittance controller that can incorporate human intent, nominal task models, as well as variations in the robot dynamics. An outer-loop controller is developed using an ARMA model which is tuned using an adaptive inverse control technique. An inner-loop neuroadaptive controller linearizes the robot dynamics. Working in conjunction and online, this two-loop technique offers an elegant way to decouple the pHRI problem. Experimental results are presented comparing the performance of different types of admittance controllers. The results show that efficient online adaptation of the robot admittance model for different human subjects can be achieved. Specifically, the adaptive admittance controller reduces jerk which results in a smooth human-robot interaction.

Question: What is the difference between adaptive impedance and admittance control?


Learning Graphical State Transitions. Graph-structured data is important in modeling relationships between multiple entities, and can be used to represent states of the world as well as many data structures. Li et al. (2016) describe a model known as a Gated Graph Sequence Neural Network (GGS-NN) that produces sequences from graph-structured input. In this work I introduce the Gated Graph Transformer Neural Network (GGTNN), an extension of GGS-NNs that uses graph-structured data as an intermediate representation. The model can learn to construct and modify graphs in sophisticated ways based on textual input, and also to use the graphs to produce a variety of outputs. For example, the model successfully learns to solve almost all of the bAbI tasks (Weston et al., 2016), and also discovers the rules governing graphical formulations of a simple cellular automaton and a family of Turing machines.

Question: What is a Turing machine?


Exploiting 2D Floorplan for Building-Scale Panorama RGBD Alignment. This paper presents a novel algorithm that utilizes a 2D floorplan to align panorama RGBD scans. While effective panorama RGBD alignment techniques exist, such a system requires extremely dense RGBD image sampling. Our approach can significantly reduce the number of necessary scans with the aid of a floorplan image. We formulate a novel Markov Random Field inference problem as a scan placement over the floorplan, as opposed to the conventional scan-to-scan alignment. The technical contributions lie in multi-modal image correspondence cues (between scans and schematic floorplan) as well as a novel coverage potential avoiding an inherent stacking bias. The proposed approach has been evaluated on five challenging large indoor spaces. To the best of our knowledge, we present the first effective system that utilizes a 2D floorplan image for building-scale 3D pointcloud alignment. The source code and the data are shared with the community to further enhance indoor mapping research.

Question: How large is the canadian military?


One-against-all multi-class SVM classification using reliability measures. Support vector machines (SVM) is originally designed for binary classification. To extend it to multi-class scenario, a typical conventional way is to decompose an M-class problem into a series of two-class problems, for which one-against-all is the earliest and one of the most widely used implementations. However, certain theoretical analysis reveals a drawback, i.e., the competence of each classifier is totally neglected when the results of classification from the multiple classifiers are combined for the final decision. To overcome this limitation, this paper introduces reliability measures into the multi-class framework. Two measures are designed: static reliability measure (SRM) and dynamic reliability measure (DRM). SRM works on a collective basis and yields a constant value regardless of the location of the test sample. DRM, on the other hand, accounts for the spatial variation of the classifier's performance. Based on these two reliability measures, a new decision strategy for the one-against-all method is proposed, which is tested on benchmark data sets and demonstrates its effectiveness.

Question: What is the one-against-all multi-class SVM classification?


Multiple-event direct to histogram TDC in 65nm FPGA technology. A novel multiple-event Time to Digital Converter (TDC) with direct to histogram output is implemented in a 65nm Xilinx Virtex 5 FPGA. The delay-line based architecture achieves 16.3 ps temporal accuracy over a 2.86ns dynamic range. The measured maximum conversion rate of 6.17 Gsamples/s and the sampling rate of 61.7 Gsamples/s are the highest published in the literature. The system achieves a linearity of -0.9/+3 LSB DNL and -1.5/+5 LSB INL. The TDC is demonstrated in a direct time of flight optical ranging application with 12mm error over a 350mm range.

Question: What is the highest sampling rate achieved in the literature?


Gender differences in personality: a meta-analysis.. Four meta-analyses were conducted to examine gender differences in personality in the literature (1958-1992) and in normative data for well-known personality inventories (1940-1992). Males were found to be more assertive and had slightly higher self-esteem than females. Females were higher than males in extraversion, anxiety, trust, and, especially, tender-mindedness (e.g., nurturance). There were no noteworthy sex differences in social anxiety, impulsiveness, activity, ideas (e.g., reflectiveness), locus of control, and orderliness. Gender differences in personality traits were generally constant across ages, years of data collection, educational levels, and nations.

Question: What are the gender differences in personality?


Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora. We introduce (1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs, and (2) the concept of bilingual parsing with a variety of parallel corpus analysis applications. Aside from the bilingual orientation, three major features distinguish the formalism from the finite-state transducers more traditionally found in computational linguistics: it skips directly to a context-free rather than finite-state base, it permits a minimal extra degree of ordering flexibility, and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm. A convenient normal form is shown to exist. Analysis of the formalism's expressiveness suggests that it is particularly well suited to modeling ordering shifts between languages, balancing needed flexibility against complexity constraints. We discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks such as segmentation, bracketing, phrasal alignment, and parsing.

Question: What is the difference between a stochastic inversion transduction grammar and a finite-state transducer?


Accept or appropriate ? A design-oriented critique on technology acceptance models. Technology acceptance models are tools for predicting users' reception of technology by measuring how they rate statements on a questionnaire scale. It has been claimed that these tools help to assess the social acceptance of a final IT product when its development is still underway. However, their use is not without problems. This paper highlights some of the underlying shortcomings that arise particularly from a simplistic conception of "acceptance" that does not recognize the possibility that users can invent new uses for (i.e., appropriate) technology in many situations. This lack of recognition can easily lead one to assume that users are passive absorbers of technological products, so that every user would adopt the same usages irrespective of the context of use, the differences in work tasks, or the characteristics of interpersonal cooperation. In light of recent research on appropriation, technology use must actually be understood in a more heterogeneous way, as a process through which different users find the product useful in different ways. This paper maintains that if, in fact, a single technology can be used for multiple purposes, then subscribing to the thinking arising from technology acceptance model research may actually lead one into suboptimal design solutions and thus also compromise user acceptance. Therefore, this paper also presents some starting points for designing specifically for easier technology appropriation.

Question: What is the difference between acceptance and appropriation?


Design and Analysis of Slotless Brushless DC Motor. This paper presents a design method for a small-sized slotless brushless dc motor. Distributed hexagonal windings are employed for achieving a high torque density and for ease of manufacture. Numerical approaches with an analytic model are used to analyze the magnetic and output characteristics of the motor. The proposed motor is manufactured, and the empirical results are compared with the results of the simulation.

Question: What is a slotless brushless dc motor?


Relation Networks for Object Detection. Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector.

Question: What is the relation between object detection and object recognition?


Information quality in information fusion. Designing fusion systems for decision support in complex dynamic situations calls for an integrated human-machine information environment, in which some processes are best executed automatically while for others the judgment and guidance of human experts and end-users are critical. Thus decision making in such environment requires constant information exchange between human and automated agents that utilize operational data, data obtained from sensors, intelligence reports, and open source information. The quality of decision making strongly depends on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing the methods of representing and incorporating information quality into this environment is a relatively new and a rather difficult problem. The paper discusses major challenges and suggests some approaches to address this problem.

Question: What is information quality in information fusion?


A 3.9-ps RMS Precision Time-to-Digital Converter Using Ones-Counter Encoding Scheme in a Kintex-7 FPGA. A 3.9-ps time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter (TDC) is implemented in a Xilinx Kintex-7 field programmable gate array (FPGA). Unlike previous work, the TDC is achieved with a multichain tapped-delay line (TDL) followed by an ones-counter encoder. The four normal TDLs merged together make the TDC bins very small, so that the time precision can be significantly improved. The ones-counter encoder naturally applies global bubble error correction to the output of TDL, thus the TDC design is relatively simple even when using FPGAs made with current advanced process technology. The TDC implementation is a generally applicable method that can simultaneously achieve high time precision and high measurement throughput.

Question: What is a time-interval rms precision and 277-M events/second measurement throughput time-to-digital converter?


Emotion Detection from Speech to Enrich Multimedia Content. The paper describes an experimental study on the detection of emotion from speech. As computer based characters such as avatars and virtual chat faces become more common, the use of emotion to drive the expression of the virtual characters become more important. The study utilizes a corpus containing emotional speech with 721 short utterances expressing four emotions: anger, happiness, sadness, and the neutral (unemotional) state, which were captured manually from movies and teleplays. We introduce a new concept to evaluate emotions in speech. Emotions are so complex that most speech sentences cannot be precisely assigned into a particular emotion category; however, most emotional states nevertheless can be described as a mixture of multiple emotions. Based on this concept we have trained SVMs (support vector machines) to recognize utterances within these four categories and developed an agent that can recognize and express emotions.

Question: How to detect emotions from speech?


Object Detection in High-Resolution Remote Sensing Images Using Rotation Invariant Parts Based Model. In this letter, we propose a rotation invariant parts-based model to detect objects with complex shape in high-resolution remote sensing images. Specifically, the geospatial objects with complex shape are firstly divided into several main parts, and the structure information among parts is described and regulated in polar coordinates to achieve the rotation invariance on configuration. Meanwhile, the pose variance of each part relative to the object is also defined in our model. In encoding the features of the rotated parts and objects, a new rotation invariant feature is proposed by extending histogram oriented gradients. During the final detection step, a clustering method is introduced to locate the parts in objects, and that method can also be used to fuse the detection results. By this way, an efficient detection model is constructed and the experimental results demonstrate the robustness and precision of our proposed detection model.

Question: What is the rotation invariant parts based model?


Application-specific slicing for MVNO and traffic characterization [Invited]. In this paper, we apply the concept of a software-defined data plane to defining new services for mobile virtual network operators (MVNOs) to empower them with the capability of tailoring fine-grained subscription plans that can meet users' demands. For this purpose, we have recently proposed the concept of application-and/ or device-specific slicing that classifies application-and/or device-specific traffic into slices and introduced various applications of our proposed system [Optical Fiber Communication Conf. (OFC), 2016, paper Th3I.4]. This paper reports the prototype implementation of the proposal into a real MVNO connecting customized smartphones so we can identify applications/devices from the given traffic with 100% accuracy. We first characterize the traffic patterns of popular applications to build a detailed understanding of how network resources are utilized for real users and applications and what their demands on different network resources are. Then, we classify traffic according to the devices and find that the flow characteristics of LTE RAN slices are very similar to those of 3G. We discover that most of the bandwidth is consumed by those flows with the duration of several hundreds of seconds, and the flow size is larger than 5 Mbytes in both LTE and 3G MVNO networks.

Question: What is the difference between LTE and 3G?


Coordinating Hundreds of Cooperative, Autonomous Vehicles in Warehouses. The Kiva warehouse management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.

Question: What is the first commercially available, large-scale autonomous robot system?


Prospects of encoding Java source code in XML. Currently, the only standard format for representing Java source code is plain text-based. This paper explores the prospects of using Extensible Markup Language (XML) for this purpose. XML enables the leverage of tools and standards more powerful than those available for plain-text formats, while retaining broad accessibility. The paper outlines the potential benefits of future XML grammars that would allow for improved code structure and querying possibilities; code extensions, construction, and formatting; and referencing parts of code. It also introduces the concept of grammar levels and argues for the inclusion of several grammar levels into a common framework. It discusses conversions between grammars and some practical grammar design issues. Keywords—XML, Java, source code, parsing, code formatting

Question: What is the difference between Java and XML?


Iterative multi - view plane fitting. We present a method for the reconstruction of 3D planes from calibrated 2D images. Given a set of pixels Ω in a reference image, our method computes a plane which best approximates that part of the scene which has been projected to Ω by exploiting additional views. Based on classical image alignment techniques we derive linear matching equations minimally parameterized by the three parameters of an object-space plane. The resulting iterative algorithm is highly robust because it is able to integrate over large image regions due to the correct object-space approximation and hence is not limited to comparing small image patches. Our method can be applied to a pair of stereo images but is also able to take advantage of the additional information provided by an arbitrary number of input images. A thorough experimental validation shows that these properties enable robust convergence especially under the influence of image sensor noise and camera calibration errors.

Question: What is the name of the algorithm?


High-accuracy differential tracking of low-cost GPS receivers. In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This paper presents an approach that uses GPS to derive relative location information for multiple receivers. Nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. The system has been implemented using a network of Android phones equipped with a custom Bluetooth headset and integrated GPS chip to provide raw measurement data. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to the presence of uncorrelated measurement errors.

Question: How accurate is the tracking of low-cost GPS receivers?


FastQRE: Fast Query Reverse Engineering. We study the problem of Query Reverse Engineering (QRE), where given a database and an output table, the task is to find a simple project-join SQL query that generates that table when applied on the database. This problem is known for its efficiency challenge due to mainly two reasons. First, the problem has a very large search space and its various variants are known to be NP-hard. Second, executing even a single candidate SQL query can be very computationally expensive. In this work we propose a novel approach for solving the QRE problem efficiently. Our solution outperforms the existing state of the art by 2-3 orders of magnitude for complex queries, resolving those queries in seconds rather than days, thus making our approach more practical in real-life settings.

Question: What is the fastqre?


Gland Segmentation and Computerized Gleason Grading of Prostate Histology by Integrating Low-, High-level and Domain Specific Information. In this paper we present a method of automatically detecting and segmenting glands in digitized images of prostate histology and to use features derived from gland morphology to distinguish between intermediate Gleason grades. Gleason grading is a method of describing prostate cancer malignancy on a numerical scale from grade 1 (early stage cancer) through grade 5 (highly infiltrative cancer). Studies have shown that gland morphology plays a significant role in discriminating Gleason grades. We present a method of automated detection and segmentation of prostate gland regions. A Bayesian classifier is used to detect candidate gland regions by utilizing low-level image features to find the lumen, epithelial cell cytoplasm, and epithelial nuclei of the tissue. False positive regions identified as glands are eliminated via use of domain-specific knowledge constraints. Following candidate region detection via low-level and empirical domain information, the lumen area is used to initialize a level-set curve, which is evolved to lie at the interior boundary of the nuclei surrounding the gland structure. Features are calculated from the boundaries that characterize the morphology of the lumen and the gland regions, including area overlap ratio, distance ratio, standard deviation and variance of distance, perimeter ratio, compactness, smoothness, and area. The feature space is reduced using a manifold learning scheme (Graph Embedding) that is used to embed objects that are adjacent to each other in the high dimensional feature space into a lower dimensional embedding space. Objects embedded in this low dimensional embedding space are then classified via a support vector machine (SVM) classifier as belonging to Gleason grade 3, grade 4 cancer, or benign epithelium. We evaluate the efficacy of the automated segmentation algorithm by compar

Question: What is the difference between a prostate biopsy and a prostatectomy?


A parallel and efficient approach to large scale clone detection. Over the past few years, researchers have implemented various algorithms to improve the scalability of clone detection. Most of these algorithms focus on scaling vertically on a single machine, and require complex intermediate data structures (e.g., suffix tree, etc.). However, several new use-cases of clone detection have emerged, which are beyond the computational capacity of a single machine. Moreover, for some of these use-cases it may be expensive to invest upfront in the cost of building these data structures.  In this paper, we propose a technique to horizontally scale clone detection across multiple machines using the popular MapReduce framework. The technique does not require building any complex intermediate data structures. Moreover, in order to increase the efficiency, the technique uses a filtering heuristic to prune the number of code block comparisons. The filtering heuristic is independent of our approach and it can be used in conjunction with other approaches to increase their efficiency. In our experiments, we found that: (i) the computation time to detect clones decreases by almost half every time we double the number of nodes; and (ii) the scaleup is linear, with a decline of not more than 70% compared to the ideal case, on a cluster of 2-32 nodes for 150-2800 projects.

Question: How to scale clone detection horizontally?


BLE-Based Accurate Indoor Location Tracking for Home and Office. Nowadays the use of smart mobile devices and the accompanying needs for emerging services relying on indoor location-based services (LBS) for mobile devices are rapidly increasing. For more accurate location tracking using Bluetooth Low Energy (BLE), this paper proposes a novel trilateration-based algorithm and presents experimental results that demonstrate its effectiveness.

Question: How accurate is indoor location tracking using BLE?


Pattern classification with missing data: a review. Pattern classification has been successfully applied in many problem domains, such as biometric recognition, document classification or medical diagnosis. Missing or unknown data are a common drawback that pattern recognition techniques need to deal with when solving real-life classification tasks. Machine learning approaches and methods imported from statistical learning theory have been most intensively studied and used in this subject. The aim of this work is to analyze the missing data problem in pattern classification tasks, and to summarize and compare some of the well-known methods used for handling missing values.

Question: What is the aim of this work?


Data quality issues in implementing an ERP. Data quality is a critical issue during the implementation of an enterprise resource planning (ERP) system. Data quality problems can have a significant impact on an organisation's information system. Therefore, it is essential to understand data quality issues to ensure success in implementing ERP systems. This paper uses SAP as an example of an ERP system and describes a study, which explores data quality problems with existing systems, and identifies critical success factors that impact data quality. The study resulted in the development of a framework for understanding data quality issues in implementing an ERP, and application of this framework in a case study in two large Australian organisations. The findings of the study suggest that the importance of data quality needs to be widely understood in implementing an ERP, as well as providing recommendations that may be useful to practitioners. Computerised databases continue to proliferate and as organisations become increasingly dependent upon their databases to support business process and decision making, the number of errors in stored data and the organisational impact of these errors are likely to increase (Klein, 1998). Information research has demonstrated that inaccurate and incomplete data may adversely affect the competitive success of an organisation (Redman, 1992). Poor quality information can have significant social and business impacts (Strong et al., 1997). For example, NBC News reported, `̀ dead people still eat!'' It was found that, because of outdated information in government databases, food stamps continued to be sent to recipients long after they died. Fraud from food stamps cost US taxpayers billions of dollars. Business and industry often have similar DQ problems. For example, a financial company absorbed a net loss totalling more than $250 million when interest rates changed dramatically, all because the company database was lacking in quality a

Question: What is the impact of poor data quality on an organisation?


The Effects of Hyperparameters on SGD Training of Neural Networks. The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth. A number of attempts have been made to explore these parameters in the literature, and at times, to develop methods for optimizing them. However, exploration of parameter spaces has often been limited. In this note, I report the results of large scale experiments exploring these different parameters and their interactions. 1 Datasets and Libraries All experiments reported here were carried out using the Torch library [1] and CUDA (some of the experiments have been reproduced on a smaller scale with other libraries). The dataset for all the experiments is MNIST [3, 2]. Characters were deskewed prior to all experiments. Deskewing significantly reduces error rates in nearest neighbor classifiers. Skew corresponds to a simple one-parameter family of linear transformations in feature space and causes decision regions to become highly anisotropic. Without deskewing, differences in performance between different architectures might primarily reduce to their ability to "learn deskewing". With deskewing, MNIST character classification become more of an instance of a typical classification problem. Prior results on classifying deskewed MNIST data both with neural networks and with other methods are shown in the table below. 2 Logistic vs Softmax Outputs Multi-Layer Perceptrons (MLPs) used for classification usually attempt to approximate posterior probabilities and use those as their discriminant function. Two common approaches to this are the use of least square regression with logistic output units trained with a least square error measure ("logistic outputs") 1 ar X iv :1 50 8. 02 78 8v 1 [ cs .N E ] 1 2 A ug 2 01 5 Method Test Error Preprocessing Reference Reduced Set SVM deg 5 polynomial 1 deskewing LeCun et al. 1998 SVM deg 4 polynomial 1.1 deskewing LeCun et al. 1998

Question: What is the difference between logistic and softmax outputs?


A 6 Vpp, 52 dB, 30-dB Dynamic Range, 43 Gb/s InP DHBT Differential Limiting Amplifier. We report the design, fabrication and measurement of a 43 Gb/s differential limiting amplifier integrating a transimpedance amplifier (TIA) as input stage and a distributed amplifier (Driver) as output stage, realised in a 1.5-µm indium phosphide (InP) double heterojunction bipolar transistor (DHBT) technology. With a differential gain higher than 52 dB, the TIA-Driver provides a constant 6 Vpp differential output amplitude for an input amplitude ranging from 12.7 mVpp to 405 mVpp, achieving more than 30 dB of electrical dynamic range, for a power consumption of 2.7 W. Designed to be used between a photodiode and an electro-optical modulator, this circuit is well suited for optical-to-electrical-to-optical (O-E-O) wavelength converters at up to 43 Gb/s.

Question: What is the maximum input voltage of the amplifier?


Air Traffic Security : Aircraft Classification Using ADS-B Message ' s Phase-Pattern. Automatic Dependent Surveillance-Broadcast (ADS-B) is a surveillance system used in Air Traffic Control. With this system, the aircraft transmits their own information (identity, position, velocity, etc.) to any equipped listener for surveillance scope. The ADS-B is based on a very simple protocol and does not provide any kind of authentication and encryption, making it vulnerable to many types of cyber-attacks. In the paper, the use of the airplane/transmitter carrier phase is proposed as a feature to perform a classification of the aircraft and, therefore, distinguish legitimate messages from fake ones. The feature extraction process is described and a classification method is selected. Finally, a complete intruder detection algorithm is proposed and evaluated with real data.

Question: How to classify an aircraft using the phase of the ADS-B message?


Incremental multi-step Q-learning. This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic-programming based reinforcement learning method, with the TD(λ) return estimation process, which is typically used in actor-critic learning, another well-known dynamic-programming based reinforcement learning method. The parameter λ is used to distribute credit throughout sequences of actions, leading to faster learning and also helping to alleviate the non-Markovian effect of coarse state-space quatization. The resulting algorithm.Q(λ)-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. The behavior of this algorithm has been demonstrated through computer simulations.

Question: What is the incremental multi-step Q-learning?


A fuzzy logic-based text classification method for social media. A FUZZY LOGIC-BASED TEXT CLASSIFICATION METHOD FOR SOCIAL MEDIA

Question: What is a fuzzy logic-based text classification method for social media?


Stillbirths: economic and psychosocial consequences. Despite the frequency of stillbirths, the subsequent implications are overlooked and underappreciated. We present findings from comprehensive, systematic literature reviews, and new analyses of published and unpublished data, to establish the effect of stillbirth on parents, families, health-care providers, and societies worldwide. Data for direct costs of this event are sparse but suggest that a stillbirth needs more resources than a livebirth, both in the perinatal period and in additional surveillance during subsequent pregnancies. Indirect and intangible costs of stillbirth are extensive and are usually met by families alone. This issue is particularly onerous for those with few resources. Negative effects, particularly on parental mental health, might be moderated by empathic attitudes of care providers and tailored interventions. The value of the baby, as well as the associated costs for parents, families, care providers, communities, and society, should be considered to prevent stillbirths and reduce associated morbidity.

Question: What are the economic and psychosocial consequences of stillbirth?


Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images. This paper presents a novel classification-based optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. First, the green plane of each fundus image is resized and morphologically reconstructed using a circular structuring element. Bright regions are then extracted from the morphologically reconstructed image that lie in close vicinity of the major blood vessels. Next, the bright regions are classified as bright probable OD regions and non-OD regions using six region-based features and a Gaussian mixture model classifier. The classified bright probable OD region with maximum Vessel-Sum and Solidity is detected as the best candidate region for the OD. Other bright probable OD regions within 1-disc diameter from the centroid of the best candidate OD region are then detected as remaining candidate regions for the OD. A convex hull containing all the candidate OD regions is then estimated, and a best-fit ellipse across the convex hull becomes the segmented OD boundary. Finally, the centroid of major blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm has low computation time complexity and it is robust to variations in image illumination, imaging angles, and retinal abnormalities. This algorithm achieves 98.8%-100% OD segmentation success and OD segmentation overlap score in the range of 72%-84% on images from the six public datasets of DRIVE, DIARETDB1, DIARETDB0, CHASE_DB1, MESSIDOR, and STARE in less than 2.14 s per image. Thus, the proposed algorithm can be used for automated detection of retinal pathologies, such as glaucoma, diabetic retinopathy, and maculopathy.

Question: What is the optic disc boundary?


Joint wavelet-based spectrum sensing and FBMC modulation for cognitive mmWave small cell networks. Millimetre-wave (mmWave) 5G communications is an emerging technology to enhance the capacity of existing systems by thousand-fold improvement. Heterogeneous networks employing densely distributed small cells can optimise the available coverage and throughput of 5G systems. Efficiently utilising the spectrum bands by small cells is one of the approaches that will considerably increase the available data rate and capacity of the heterogeneous networks. This challenging task can be achieved by spectrum sensing capability of cognitive radios and new modulation techniques for data transmission. In this study, a wavelet-based filter bank is proposed for spectrum sensing and modulation in 5G heterogeneous networks. The proposed technique can mitigate the spectral leakage and interference by adapting the subcarriers according to cognitive information provided by wavelet packet based spectrum sensing (WPSS) and lowering sidelobes using wavelet-based filter bank multicarrier modulation. The performance improvement of WPSS compared with Fourier-based spectrum sensing is verified in terms of power spectral density comparison and probabilities of detection and false alarm. Meanwhile, the bit error rate performance demonstrates the superiority of the proposed wavelet-based system compared with its Fourier-based counterpart over the 60 GHz mmWave channel.

Question: What is the difference between the Fourier-based spectrum sensing and the wavelet-based spectrum sensing?


Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems. Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete-time systems with unknown functions and disturbances. In the design procedure, two networks are provided where one is an action network to generate an optimal control signal and the other is a critic network to approximate the cost function. An optimal control signal and adaptation laws can be generated based on two NNs. In the previous approaches, the weights of critic and action networks are updated based on the gradient descent rule and the estimations of optimal weight vectors are directly adjusted in the design. Consequently, compared with the existing results, the main contributions of this paper are: (1) only two parameters are needed to be adjusted, and thus the number of the adaptation laws is smaller than the previous results and (2) the updating parameters do not depend on the number of the subsystems for MIMO systems and the tuning rules are replaced by adjusting the norms on optimal weight vectors in both action and critic networks. It is proven that the tracking errors, the adaptation laws, and the control inputs are uniformly bounded using Lyapunov analysis method. The simulation examples are employed to illustrate the effectiveness of the proposed algorithm.

Question: Reinforcement Learning Design-Based Adaptive Tracking Control With Less Learning Parameters for Nonlinear Discrete-Time MIMO Systems. Based on the neural network (NN) approximator, an online reinforcement learning algorithm is proposed for a class of affine multiple input and multiple output (MIMO) nonlinear discrete


Knowledge-based processing of complex stock market events. Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event stream and background knowledge about companies which is stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the SPARQL query language for querying the external knowledge base and combine it with event data stream. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.

Question: What is the background knowledge about companies?


Provably secure ciphertext policy ABE. In ciphertext policy attribute-based encryption (CP-ABE), every secret key is associated with a set of attributes, and every ciphertext is associated with an access structure on attributes. Decryption is enabled if and only if the user's attribute set satisfies the ciphertext access structure. This provides fine-grained access control on shared data in many practical settings, e.g., secure database and IP multicast.  In this paper, we study CP-ABE schemes in which access structures are AND gates on positive and negative attributes. Our basic scheme is proven to be chosen plaintext (CPA) secure under the decisional bilinear Diffie-Hellman (DBDH) assumption. We then apply the Canetti-Halevi-Katz technique to obtain a chosen ciphertext (CCA) secure extension using one-time signatures. The security proof is a reduction to the DBDH assumption and the strong existential unforgeability of the signature primitive.  In addition, we introduce hierarchical attributes to optimize our basic scheme - reducing both ciphertext size and encryption/decryption time while maintaining CPA security. We conclude with a discussion of practical applications of CP-ABE.

Question: What is the security of the ciphertext policy ABE?


MDig : Multi-digit Recognition using Convolutional Nerual Network on Mobile. Multi-character recognition in arbitrary photographs on mobile platform is difficult, in terms of both accuracy and real-time performance. In this paper, we focus on the domain of hand-written multi-digit recognition. Convolutional neural network (CNN) is the state-of-the-art solution for object recognition, and presents a workload that is both compute and data intensive. To reduce the workload, we train a shallow CNN offline, achieving 99.07% top-1 accuracy. And we utilize preprocessing and segmentation to reduce input image size fed into CNN. For CNN implementation on the mobile platform, we adopt and modify DeepBeliefSDK to support batching fully-connected layers. On NVIDIA SHIELD tablet, the application processes a frame and extracts 32 digits in approximately 60ms, and batching the fully-connected layers reduces the CNN runtime by another 12%.

Question: How to implement a convolutional neural network on a mobile platform?


Self-organized computation with unreliable , memristive nanodevices. Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network—a self-organized spatial filter array—that demonstrate its performance as a function of defects and device variation. (Some figures in this article are in colour only in the electronic version) 1. Nanoelectronics and computing paradigms Nanodevices are crummy1. High defect rates, high device variability, device ageing, and limitations on device complexity (e.g., two-terminal devices are much easier to build) are to be expected if we intend to mass produce nanoelectronic systems economically. Not only that, it is almost axiomatic among many researchers that such systems will be built from simple structures, such as crossbars, composed of nanodevices that must be configured to implement the desired functionality (Heath et al 1998, Williams and Kuekes 2000, Kuekes and Williams 2002, DeHon 2003, Stan et al 2003,

Question: What is the difference between a nanodevice and a nanodevices?


Evaluation Techniques for Storage Hierarchies. This paper introduces an efficient technique called "stack processing" that can be used in the cost-performance evaluation of a large class of storage hierarchies. The technique depends on a classification of page replacement algorithms as "stack algorithms" for which various properties are derived. These properties may be of use in the general areas of program modeling and system analysis, as well as in the evaluation of storage hierarchies. For a better understanding of storage hierarchies, we briefly review some basic concepts of their design.

Question: What is a storage hierarchy?