Performance analysis of latent fingerprint enhancement techniques. Fingerprint Enhancement is an important step used to improve the accuracy and rate of automated detection of fingerprints. Enhancing the latent fingerprints facilitates the extraction of minutiae from the fingerprint image. In this paper, two methods of latent fingerprint enhancement are carried out. The first uses Histogram Equalization for enhancing the latent fingerprint. The second method uses a novel approach of Binarization followed by Wiener filtering and an additional Median filter. The improvements in the image are then analyzed based on their entropy, contrast and moment.

Question: What is the difference between the two methods?


Trajectory clustering: a partition-and-group framework. Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.

Question: How to cluster trajectories?


CONCEPTUAL REQUIREMENTS FOR THE AUTOMATIC RECONSTRUCTION OF BUILDING INFORMATION MODELS FROM UNINTERPRETED 3 D MODELS. A multitude of new applications is quickly emerging in the field of Building Information Models (BIM). BIM models describe buildings with respect to their spatial and especially semantic and thematic characteristics. Since BIM models are manually created during the planning and construction phase, they are only available for newly planned or recently constructed buildings. In order to apply the new applications to already existing buildings, methods for the acquisition of BIM models for built-up sites are required. Primary data source are 3D geometry models obtained from surveying, CAD, or computer graphics. Automation of this process is highly desirable, but faces a range of specific problems setting the bar very high for a reconstruction process. This paper discusses these problems and identifies consequential requirements on reconstruction methods. Above, a two-step strategy for BIM model reconstruction is proposed which incorporates CityGML as an intermediate layer between 3D graphics models and IFC/BIM models.

Question: How can I reconstruct a building model from a 3D model?


Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications. We describe a generalization of the PAC learning model that is based on statistical decision theory. In this model the learner receives randomly drawn examples, each example consisting of an instance x E X and an outcome J E Y, and tries to find a decision rule h: X + A, where h E X, that specifies the appropriate action a E A to take for each instance x in order to minimize the expectation of a loss I( y, a). Here X, Y, and A are arbitrary sets. I is a real-valued function, and examples are generated according to an arbitrary joint distribution on Xx Y. Special cases include the problem of learning a function from X into Y, the problem of learning the conditional probability distribution on Y given X (regression), and the problem of learning a distribution on X (density estimation). We give theorems on the uniform convergence of empirical loss estimates to true expected loss rates for certain decision rule spaces 2, and show how this implies learnability with bounded sample size, disregarding computational complexity. As an application, we give distribution-independent upper bounds on the sample size needed for learning with feedforward neural networks, Our theorems use a generalized notion of VC dimension that applies to classes of real-valued functions, adapted from Vapnik and Pollard's work, and a notion of capacity and metric dimension for classes of functions that map into a bounded metric space. '('1 1992 Academic Press, Inc.

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


The application of virtual reality to (chemical engineering) education. Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This poster presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual chemical plants, virtual laboratory accidents, and a virtual UIC campus. The first area provides guided exploration of domains otherwise inaccessible, such as the interior of operating reactors and microscopic reaction mechanisms. The second promotes safety by demonstrating the consequences of not following proper lab safety procedures. And the third provides valuable guidance for (foreign) visitors. All programs developed are available on the Web, for free download to any interested parties.

Question: What is the application of virtual reality to chemical engineering education?


BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory. Storing a database (rows and indexes) entirely in non-volatile memory (NVM) potentially enables both high performance and fast recovery. To fully exploit parallelism on modern CPUs, modern main-memory databases use latch-free (lock-free) index structures, e.g. Bw-tree or skip lists. To achieve high performance NVMresident indexes also need to be latch-free. This paper describes the design of the BzTree, a latch-free B-tree index designed for NVM. The BzTree uses a persistent multi-word compare-and-swap operation (PMwCAS) as a core building block, enabling an index design that has several important advantages compared with competing index structures such as the Bw-tree. First, the BzTree is latch-free yet simple to implement. Second, the BzTree is fast showing up to 2x higher throughput than the Bw-tree in our experiments. Third, the BzTree does not require any special-purpose recovery code. Recovery is near-instantaneous and only involves rolling back (or forward) any PMwCAS operations that were in-flight during failure. Our end-to-end recovery experiments of BzTree report an average recovery time of 145 μs. Finally, the same BzTree implementation runs seamlessly on both volatile RAM and NVM, which greatly reduces the cost of code maintenance. PVLDB Reference Format: Joy Arulraj, Justin Levandoski, Umar Farooq Minhas,Per-Ake Larson. BzTree: A High-Performance Latch-free Range Index for Non-Volatile Memory. PVLDB, 11(4): xxxx-yyyy, 2018. DOI: https://doi.org/10.1145/3164135.3164147

Question: What is the bztree?


Gratitude and depressive symptoms: the role of positive reframing and positive emotion.. Eight studies (N=2,973) tested the theory that gratitude is related to fewer depressive symptoms through positive reframing and positive emotion. Study 1 found a direct path between gratitude and depressive symptoms. Studies 2-5 demonstrated that positive reframing mediated the relationship between gratitude and depressive symptoms. Studies 6-7 showed that positive emotion mediated the relationship between gratitude and depressive symptoms. Study 8 found that positive reframing and positive emotion simultaneously mediated the relationship between gratitude and depressive symptoms. In sum, these eight studies demonstrate that gratitude is related to fewer depressive symptoms, with positive reframing and positive emotion serving as mechanisms that account for this relationship.

Question: What is the relationship between gratitude and depressive symptoms?


A construction of cryptography system based on quantum neural network. Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs).The purpose of this paper is to introduce an application of QNNs in construction of cryptography system in which two networks exchange their outputs (in qubits) and the key to be synchronized between two communicating parties. This system is based on multilayer qubit QNNs trained with back-propagation algorithm.

Question: What is a quantum neural network?


Smart wheelchairs: A literature review.. Several studies have shown that both children and adults benefit substantially from access to a means of independent mobility. While the needs of many individuals with disabilities can be satisfied with traditional manual or powered wheelchairs, a segment of the disabled community finds it difficult or impossible to use wheelchairs independently. To accommodate this population, researchers have used technologies originally developed for mobile robots to create "smart wheelchairs." Smart wheelchairs have been the subject of research since the early 1980s and have been developed on four continents. This article presents a summary of the current state of the art and directions for future research.

Question: What is a smart wheelchair?


A Neural Attention Model for Abstractive Sentence Summarization. Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.

Question: How does the model work?


Advocating an ethical memory model for artificial companions from a human-centred perspective. This paper considers the ethical implications of applying three major ethical theories to the memory structure of an artificial companion that might have different embodiments such as a physical robot or a graphical character on a hand-held device. We start by proposing an ethical memory model and then make use of an action-centric framework to evaluate its ethical implications. The case that we discuss is that of digital artefacts that autonomously record and store user data, where this data are used as a resource for future interaction with users.

Question: What is an ethical memory model?


An IoT based reference architecture for smart water management processes. Water is a vital resource for life, and its management is a key issue nowadays. Information and communications technology systems for water control are currently facing interoperability problems due to the lack of support of standardization in monitory and control equipment. This problem affects various processes in water management, such as water consumption, distribution, system identification and equipment maintenance. OPC UA (Object Linking and Embedding for Process Control Unified Architecture) is a platform independent service-oriented architecture for the control of processes in the logistic and manufacturing sectors. Based on this standard we propose a smart water management model combining Internet of Things technologies with business processes coordination and decision support systems. We provide an architecture for sub-system interaction and a detailed description of the physical scenario in which we will test our implementation, allowing specific vendor equipment to be manageable and interoperable in the specific context of water management processes.

Question: What is the difference between IoT and OPC UA?


A SECURE ONLINE PAYMENT SYSTEM USING STEGANOGRAPHY AND VISUAL CRYPTOGRAPHY. In This paper we presents a new methodology which provides limited information only that is important in transferring money while shopping online provides security for customers data and increasing in confidence of customer and preventing identity stealing. Here we used cryptography technique for image encryption. Data is encrypted in images shares which are meaningless images. These shares are then transferred over an untrusted communication channel. By combining only these shares give the original secret image. This images may contain ATM card details, credit card information etc. At merchants side merchant have to pay some amount per transaction so it's merchant's responsibility to protect customers information from being misused. So here is solution for such operations.

Question: How to encrypt images?


Bad to the bone: facial structure predicts unethical behaviour.. Researchers spanning many scientific domains, including primatology, evolutionary biology and psychology, have sought to establish an evolutionary basis for morality. While researchers have identified social and cognitive adaptations that support ethical behaviour, a consensus has emerged that genetically determined physical traits are not reliable signals of unethical intentions or actions. Challenging this view, we show that genetically determined physical traits can serve as reliable predictors of unethical behaviour if they are also associated with positive signals in intersex and intrasex selection. Specifically, we identify a key physical attribute, the facial width-to-height ratio, which predicts unethical behaviour in men. Across two studies, we demonstrate that men with wider faces (relative to facial height) are more likely to explicitly deceive their counterparts in a negotiation, and are more willing to cheat in order to increase their financial gain. Importantly, we provide evidence that the link between facial metrics and unethical behaviour is mediated by a psychological sense of power. Our results demonstrate that static physical attributes can indeed serve as reliable cues of immoral action, and provide additional support for the view that evolutionary forces shape ethical judgement and behaviour.

Question: What is the facial width-to-height ratio?


Question: How to find the number of documents in a database?


CryptoKnight: Generating and Modelling Compiled Cryptographic Primitives. Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware application.

Question: What is a cryptographic primitive?


Dynamic analysis and control of lego mindstorms NXT bicycle. The paper is inspired from a challenging experiment conducted with undergraduate students for bachelor project within Automatic Control and Computer Engineering Faculty, Iaşi. A bicycle type system was implemented, analyzed and some control strategies were implemented and tested in order to self-stabilize the system. The bicycle study is enjoyable both to undergraduate students and researchers because it uses a "hi-tech" relevant concrete example. This work encompasses and uses a full range of system-theoretic aspects which covers knowledge from analytical, computational and experimental point of view. The dynamic description of the bicycle is done using a set of dynamic equations that describes the location and orientation of the body, considering four rigid subsystems that form the bicycle. The control issues were conducted in order to follow a desired forward speed using classical PI controller, followed by the LQR approach for steering angle.

Question: What is the bicycle?


A first simulation on optimizing EDS for cabin baggage screening regarding throughput. Airport security screening is vital for secure air transportation. Screening of cabin baggage heavily relies on human operators reviewing X-ray images. Explosive detection systems (EDS) developed for cabin baggage screening can be a very valuable addition security-wise. Depending on the EDS machine and settings, false alarm rates increase, which could reduce throughput. A discrete event simulation was used to investigate how different machine settings of EDS, different groups of X-ray screeners, and different durations of alarm resolution with explosives trace detection (ETD) influence throughput of a specific cabin baggage screening process. For the modelling of screening behavior in the context of EDS and for the estimation of model parameters, data was borrowed from a human-machine interaction experiment and a work analysis. In a second step, certain adaptations were tested for their potential to reduce the impact of EDS on throughput. The results imply that moderate increases in the false alarm rate by EDS can be buffered by employing more experienced and trained X-ray screeners. Larger increases of the false alarm rate require a fast alarm resolution and additional resources for the manual search task.

Question: How can we improve the throughput of a cabin baggage screening process?


Recognition of Pollen-Bearing Bees from Video Using Convolutional Neural Network. In this paper, the recognition of pollen bearing honey bees from videos of the entrance of the hive is presented. This computer vision task is a key component for the automatic monitoring of honeybees in order to obtain large scale data of their foraging behavior and task specialization. Several approaches are considered for this task, including baseline classifiers, shallow Convolutional Neural Networks, and deeper networks from the literature. The experimental comparison is based on a new dataset of images of honeybees that was manually annotated for the presence of pollen. The proposed approach, based on Convolutional Neural Networks is shown to outperform the other approaches in terms of accuracy. Detailed analysis of the results and the influence of the architectural parameters, such as the impact of dedicated color based data augmentation, provide insights into how to apply the approach to the target application.

Question: What is the impact of architectural parameters on the performance of a convolutional neural network?


Visual-Interactive Preprocessing of Time Series Data. Time series data is an important data type in many different application scenarios. Consequently, there are a great variety of approaches for analyzing time series data. Within these approaches different strategies for cleaning, segmenting, representing, normalizing, comparing, and aggregating time series data can be found. When combining these operations, the time series analysis preprocessing workflow has many degrees of freedom. To define an appropriate preprocessing pipeline, the knowledge of experts coming from the application domain has to be included into the design process. Unfortunately, these experts often cannot estimate the effects of the chosen preprocessing algorithms and their parameterizations on the time series. We introduce a system for the visual-interactive exploitation of the preprocessing parameter space. In contrast to 'black box'-driven approaches designed by computer scientists based on the requirements of domain experts, our system allows these experts to visual-interactively compose time series preprocessing pipelines by themselves. Visual support is provided to choose the right order and parameterization of the preprocessing steps. We demonstrate the usability of our approach with a case study from the digital library domain, in which time-oriented scientific research data has to be preprocessed to realize a visual search and analysis application.

Question: What is the preprocessing workflow for time series data?


Generalization Properties and Implicit Regularization for Multiple Passes SGM. We study the generalization properties of stochastic gradient methods for learning with convex loss functions and linearly parameterized functions. We show that, in the absence of penalizations or constraints, the stability and approximation properties of the algorithm can be controlled by tuning either the step-size or the number of passes over the data. In this view, these parameters can be seen to control a form of implicit regularization. Numerical results complement the theoretical findings.

Question: How to control the generalization properties of SGMs?


From cyber-physical systems to Industry 4.0: make future manufacturing become possible. Recently, 'Industry 4.0' has become the supporting force in the manufacturing field. From CPS to Industry 4.0, they are leading a new round of industrial revolution. To meet the fourth industrial revolution, USA, European countries, Japan, South Korea and China have presented different versions of the so-called 'Industry 4.0' strategies respectively, which are different from each other in the content, but their common goal is to achieve manufacturing structural adjustment and restructuring and upgrading, to seize a place for future manufacturing, to provide users with better service. What is more, we summarised the five major trends of the future manufacturing. Then, we introduce some enabling technologies about Industry 4.0, among them, CPS plays a key role in the Industry 4.0 factory. Based on the customer to business (C2B) mode, we regard everything as a service, and propose a structure of Industry 4.0. Finally, the automotive industry as an example, we make a brief introduction about an Industry 4.0 case. [Received 15 December 2015; Revised 30 May 2016; Accepted 30 May 2016]

Question: What is the difference between Industry 4.0 and CPS?


Adversarial Reinforcement Learning in a Cyber Security Simulation. This paper focuses on cyber-security simulations in networks modeled as a Markov game with incomplete information and stochastic elements. The resulting game is an adversarial sequential decision making problem played with two agents, the attacker and defender. The two agents pit one reinforcement learning technique, like neural networks, Monte Carlo learning and Q-learning, against each other and examine their effectiveness against learning opponents. The results showed that Monte Carlo learning with the Softmax exploration strategy is most effective in performing the defender role and also for learning attacking strategies.

Question: What is reinforcement learning?


Impact of reducing STI-induced stress on layout dependence of MOSFET characteristics. Active-area layout dependence of MOSFET parametric characteristics and its reduction by reducing shallow trench isolation (STI)-induced mechanical stress were investigated. Threshold voltages (V/sub th/) and saturation drain currents (I/sub ds/) become sensitive to the active-area layout of MOSFET in scaled-down technology. This phenomenon is the effect of mechanical stress from STI edge, which reduces impurity diffusion in channel region and enhances carrier mobility. To reduce the STI-induced stress, we examined STI-wall-oxide nitridation and STI gap-fill-oxide densifying in pure N/sub 2/ ambient. These processes reduced the reoxidation of the STI wall oxide, therefore reduced the STI-induced stress. According to the new STI process, the active-area layout dependence of V/sub th/ and I/sub ds/ were reduced successfully.

Question: How to reduce the STI-induced stress?


VBaaS: VNF Benchmark-as-a-Service. When rolling out Network Function Virtualization (NFV) services, resource monitoring becomes a critical task subject to different cost-accuracy tradeoffs depending on whether continuous monitoring or more static infrastructure resource views are taken. In this context, we propose Virtualized Network Functions (VNF) Benchmark-as-a-Service (VBaaS) to enable not only run-time resource evaluation but also test-before-deploy opportunities for VNFs and NFV Infrastructures. We describe the motivation behind VBaaS and its main value proposition for a number of use cases around the orchestration tasks of VNF Forwarding Graphs We present the main components of VBaaS along their system interactions and interfaces, discussing the main benefits of adopting VBaaS and open research issues. Addressing the identified challenges and finalizing our proof of concept VBaaS are our main ongoing work activities.

Question: What is the motivation behind VBaaS?


I feel your pain: emotional closeness modulates neural responses to empathically experienced rejection.. Empathy is generally thought of as the ability to share the emotional experiences of others. In scientific terms, this is usually operationalized as an ability to vicariously feel others' mental and emotional experiences. Supporting this account, research demonstrates that watching others experience physical pain activates similar brain regions to the actual experience of pain itself. First-hand experience of social rejection also activates this network. The current work extends these findings by examining whether the "pain" network is similarly implicated in witnessing rejection, and whether emotional closeness modulates this response. We provide evidence for each of these suppositions, demonstrating: (a) that the pain network is activated when watching a friend suffer social rejection, and (b) that interpersonal closeness with that friend modulates this response. Further, we found that the inferior frontal gyrus, critical for representing others' mental and emotional states, mediates the relationship between emotional closeness and neural responses to watching the rejection of a friend.

Question: How does emotional closeness modulate neural responses to empathically experienced rejection?


Principles of micro-RNA production and maturation. Micro-RNAs (miRNAs) are a class of approximately 22-nucleotide non-coding RNAs expressed in multicellular organisms. They are first transcribed in a similar manner to pre-mRNAs. The transcripts then go through a series of processing steps, including endonucleolytic cleavage, nuclear export and a strand selection procedure, to yield the single-stranded mature miRNA products. The transcription and processing of miRNAs determines the abundance and the sequence of mature miRNAs and has important implications for the function of miRNAs.

Question: What is the function of miRNAs?


Low-Cost Printed Chipless RFID Humidity Sensor Tag for Intelligent Packaging. This paper presents a fully-printed chipless radio frequency identification sensor tag for short-range item identification and humidity monitoring applications. The tag consists of two planar inductor-capacitor resonators operating wirelessly through inductive coupling. One resonator is used to encode ID data based on frequency spectrum signature, and another one works as a humidity sensor, utilizing a paper substrate as a sensing material. The sensing performances of three paper substrates, including commercial packaging paper, are investigated. The use of paper provides excellent sensitivity and reasonable response time to humidity. The cheap and robust packaging paper, particularly, exhibits the largest sensitivity over the relative humidity range from 20% to 70%, which offers the possibility of directly printing the sensor tag on traditional packages to make the package intelligent at ultralow cost.

Question: What is the humidity range of the paper?


Deep Convolution Neural Networks for Twitter Sentiment Analysis. Twitter sentiment analysis technology provides the methods to survey public emotion about the events or products related to them. Most of the current researches are focusing on obtaining sentiment features by analyzing lexical and syntactic features. These features are expressed explicitly through sentiment words, emoticons, exclamation marks, and so on. In this paper, we introduce a word embeddings method obtained by unsupervised learning based on large twitter corpora, this method using latent contextual semantic relationships and co-occurrence statistical characteristics between words in tweets. These word embeddings are combined with n-grams features and word sentiment polarity score features to form a sentiment feature set of tweets. The feature set is integrated into a deep convolution neural network for training and predicting sentiment classification labels. We experimentally compare the performance of our model with the baseline model that is a word n-grams model on five Twitter data sets, the results indicate that our model performs better on the accuracy and F1-measure for twitter sentiment classification.

Question: What is the difference between a deep convolution neural network and a word n-grams model?


Cultural Differences in Collaborative Authoring of Wikipedia. This article explores the relationship between national culture and computer-mediated communication (CMC) in Wikipedia. The articles on the topic game from the French, German, Japanese, and Dutch Wikipedia websites were studied using content analysis methods. Correlations were investigated between patterns of contributions and the four dimensions of cultural influences proposed by Hofstede (Power Distance, Collectivism versus Individualism, Femininity versus Masculinity, and Uncertainty Avoidance). The analysis revealed cultural differences in the style of contributions across the cultures investigated, some of which are correlated with the dimensions identified by Hofstede. These findings suggest that cultural differences that are observed in the physical world also exist in the virtual world. doi:10.1111/j.1083-6101.2006.00316.x

Question: What is the relationship between cultural differences and computer mediated communication?


Classification of Alzheimer's disease subjects from MRI using hippocampal visual features. Indexing and classification tools for Content Based Visual Information Retrieval (CBVIR) have been penetrating the universe of medical image analysis. They have been recently investigated for Alzheimer's disease (AD) diagnosis. This is a normal "knowledge diffusion" process, when methodologies developed for multimedia mining penetrate a new application area. The latter brings its own specificities requiring an adjustment of methodologies on the basis of domain knowledge. In this paper, we develop an automatic classification framework for AD recognition in structural Magnetic Resonance Images (MRI). The main contribution of this work consists in considering visual features from the most involved region in AD (hippocampal area) and in using a late fusion to increase precision results. Our approach has been first evaluated on the baseline MR images of 218 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and then tested on a 3T weighted contrast MRI obtained from a subsample of a large French epidemiological study: "Bordeaux dataset". The experimental results show that our classification of patients with AD versus NC (Normal Control) subjects achieves the accuracies of 87 % and 85 % for ADNI subset and "Bordeaux dataset" respectively. For the most challenging group of subjects with the Mild Cognitive Impairment (MCI), we reach accuracies of 78.22 % and 72.23 % for MCI versus NC and MCI versus AD respectively on ADNI. The late fusion scheme improves classification results by 9 % in average for these three categories. Results demonstrate very promising classification performance and simplicity compared to the state-of-the-art volumetric AD diagnosis methods.

Question: What is the classification of Alzheimer's disease subjects from MRI using hippocampal visual features?


Improving optical character recognition through efficient multiple system alignment. Individual optical character recognition (OCR) engines vary in the types of errors they commit in recognizing text, particularly poor quality text. By aligning the output of multiple OCR engines and taking advantage of the differences between them, the error rate based on the aligned lattice of recognized words is significantly lower than the individual OCR word error rates. This lattice error rate constitutes a lower bound among aligned alternatives from the OCR output. Results from a collection of poor quality mid-twentieth century typewritten documents demonstrate an average reduction of 55.0% in the error rate of the lattice of alternatives and a realized word error rate (WER) reduction of 35.8% in a dictionary-based selection process. As an important precursor, an innovative admissible heuristic for the A* algorithm is developed, which results in a significant reduction in state space exploration to identify all optimal alignments of the OCR text output, a necessary step toward the construction of the word hypothesis lattice. On average 0.0079% of the state space is explored to identify all optimal alignments of the documents.

Question: How to improve OCR?


Policy Optimization with Demonstrations. Exploration remains a significant challenge to reinforcement learning methods, especially in environments where reward signals are sparse. Recent methods of learning from demonstrations have shown to be promising in overcoming exploration difficulties but typically require considerable highquality demonstrations that are difficult to collect. We propose to effectively leverage available demonstrations to guide exploration through enforcing occupancy measure matching between the learned policy and current demonstrations, and develop a novel Policy Optimization from Demonstration (POfD) method. We show that POfD induces implicit dynamic reward shaping and brings provable benefits for policy improvement. Furthermore, it can be combined with policy gradient methods to produce state-of-the-art results, as demonstrated experimentally on a range of popular benchmark sparse-reward tasks, even when the demonstrations are few and imperfect.

Question: What is policy optimization from demonstrations?


Visualizing Topic Models. Managing large collections of documents is an important problem for many areas of science, industry, and culture. Probabilistic topic modeling offers a promising solution. Topic modeling is an unsupervised machine learning method that learns the underlying themes in a large collection of otherwise unorganized documents. This discovered structure summarizes and organizes the documents. However, topic models are high-level statistical tools—a user must scrutinize numerical distributions to understand and explore their results. In this paper, we present a method for visualizing topic models. Our method creates a navigator of the documents, allowing users to explore the hidden structure that a topic model discovers. These browsing interfaces reveal meaningful patterns in a collection, helping end-users explore and understand its contents in new ways. We provide open source software of our method. Understanding and navigating large collections of documents has become an important activity in many spheres. However, many document collections are not coherently organized and organizing them by hand is impractical. We need automated ways to discover and visualize the structure of a collection in order to more easily explore its contents. Probabilistic topic modeling is a set of machine learning tools that may provide a solution (Blei and Lafferty 2009). Topic modeling algorithms discover a hidden thematic structure in a collection of documents; they find salient themes and represent each document as a combination of themes. However, topic models are high-level statistical tools. A user must scrutinize numerical distributions to understand and explore their results; the raw output of the model is not enough to create an easily explored corpus. We propose a method for using a fitted topic model to organize, summarize, visualize, and interact with a corpus. With our method, users can explore the corpus, moving between high level discovered summaries (t

Question: How large is the canadian military?


Dragonfly wing nodus: A one-way hinge contributing to the asymmetric wing deformation.. Dragonfly wings are highly specialized locomotor systems, which are formed by a combination of several structural components. The wing components, also known as structural elements, are responsible for the various aspects of the wing functionality. Considering the complex interactions between the wing components, modelling of the wings as a whole is only possible with inevitable huge oversimplifications. In order to overcome this difficulty, we have recently proposed a new approach to model individual components of complex wings comparatively. Here, we use this approach to study nodus, a structural element of dragonfly wings which has been less studied to date. Using a combination of several imaging techniques including scanning electron microscopy (SEM), wide-field fluorescence microscopy (WFM), confocal laser scanning microscopy (CLSM) and micro-computed tomography (micro-CT) scanning, we aim to characterize the spatial morphology and material composition of fore- and hindwing nodi of the dragonfly Brachythemis contaminata. The microscopy results show the presence of resilin in the nodi, which is expected to help the deformability of the wings. The computational results based on three-dimensional (3D) structural data suggest that the specific geometry of the nodus restrains its displacements when subjected to pressure on the ventral side. This effect, resulting from an interlocking mechanism, is expected to contribute to the dorso-ventral asymmetry of wing deformation and to provide a higher resistance to aerodynamic forces during the downstroke. Our results provide an important step towards better understanding of the structure-property-function relationship in dragonfly wings.   STATEMENT OF SIGNIFICANCE In this study, we investigate the wing nodus, a specialized wing component in dragonflies. Using a combination of modern imaging techniques, we demonstrate the presence of re

Question: What is the significance of this study?


Social Internet of Things: The Potential of the Internet of Things for Defining Human Behaviours. The potential of the artificial intelligence, data mining and the so-called Big Data to build solutions based on the understanding of human behaviours is being extended with the capabilities of the Future Internet and the Internet of Things (IoT). The described potential of data and networks have been analyzed by the complex network theory, offering a specialized branch, the defined as Human Dynamics. The goal with the Internet of Things in the social area is to describe in real-time the human behaviours and activities. These goals are starting to be feasible through the quantity of data provided by the personal devices such as smart phone, and the smart environments such as Smart cities that makes more intelligent the environment and offer a smart space that sense our movements, actions, and the evolution of the ecosystem. This work analyses the ecosystem defined through the triangle formed by Big Data, Smart Cities and Personal/Wearable Computing to determinate human behaviours and human dynamics. For this purpose, the life-cycle of human dynamics determinations in Smart Cities have been analysed in order to determinate the current status of the technology, challenges, and opportunities.

Question: What is the potential of the Internet of Things for defining human behaviours?


MQTT-S — A publish/subscribe protocol for Wireless Sensor Networks. Wireless sensor networks (WSNs) pose novel challenges compared with traditional networks. To answer such challenges a new communication paradigm, data-centric communication, is emerging. One form of data-centric communication is the publish/subscribe messaging system. Compared with other data-centric variants, publish/subscribe systems are common and wide-spread in distributed computing. Thus, extending publish/subscribe systems intoWSNs will simplify the integration of sensor applications with other distributed applications. This paper describes MQTT-S [1], an extension of the open publish/subscribe protocol message queuing telemetry transport (MQTT) [2] to WSNs. MQTT-S is designed in such a way that it can be run on low-end and battery-operated sensor/actuator devices and operate over bandwidth-constraint WSNs such as ZigBee-based networks. Various protocol design points are discussed and compared. MQTT-S has been implemented and is currently being tested on the IBM wireless sensor networking testbed [3]. Implementation aspects, open challenges and future work are also presented.

Question: What is the difference between MQTT and MQTT-S?


Efficient semantic image segmentation with superpixel pooling. In this work, we evaluate the use of superpixel pooling layers in deep network architectures for semantic segmentation. Superpixel pooling is a flexible and efficient replacement for other pooling strategies that incorporates spatial prior information. We propose a simple and efficient GPU-implementation of the layer and explore several designs for the integration of the layer into existing network architectures. We provide experimental results on the IBSR and Cityscapes dataset, demonstrating that superpixel pooling can be leveraged to consistently increase network accuracy with minimal computational overhead. Source code is available at https://github.com/bermanmaxim/superpixPool.

Question: What is superpixel pooling?


Secure LSB steganography for colored images using character-color mapping. Steganography is the science of embedding the secret messages inside other medium files in a way that hides the existence of the secret message at all. Steganography can be applied to text, audio, image, and video file types. In this study, we propose a new steganography approach for digital images in which the RGB coloring model was used. The efficiency of the proposed approach has been tested and evaluated. The experimental results show that the proposed approach produce high-quality stego images that resist against visual and statistical attacks.

Question: How to secure LSB steganography for colored images using character-color mapping?


Cortical mechanism for the visual guidance of hand grasping movements in the monkey: A reversible inactivation study.. Picking up an object requires two basic motor operations: reaching and grasping. Neurophysiological studies in monkeys have suggested that the visuomotor transformations necessary for these two operations are carried out by separate parietofrontal circuits and that, for grasping, a key role is played by a specific sector of the ventral premotor cortex: area F5. The aim of the present study was to test the validity of this hypothesis by reversibly inactivating area F5 in monkeys trained to grasp objects of different shape, size and orientation. In separate sessions, the hand field of the primary motor cortex (area F1 or area 4) was also reversibly inactivated. The results showed that after inactivation of area F5 buried in the bank of the arcuate sulcus (the F5 sector where visuomotor neurones responding to object presentation are located), the hand shaping preceding grasping was markedly impaired and the hand posture was not appropriate for the object size and shape. The monkeys were eventually able to grasp the objects, but only after a series of corrections made under tactile control. With small inactivations the deficits concerned the contralesional hand, with larger inactivations the ipsilateral hand as well. In addition, there were signs of peripersonal neglect in the hemispace contralateral to the inactivation site. Following inactivation of area F5 lying on the cortical convexity (the F5 sector where visuomotor neurones responding to action observation, 'mirror neurones', are found) only a motor slowing was observed, the hand shaping being preserved. The inactivation of the hand field of area F1 produced a severe paralysis of contralateral finger movements with hypotonia. The results of this study indicate the crucial role of the ventral premotor cortex in visuomotor transformations for grasping movements. More generally, they provide strong

Question: What is the role of the ventral premotor cortex in visuomotor transformations for grasping movements?


An Architecture for Fault-Tolerant Computation with Stochastic Logic. Mounting concerns over variability, defects, and noise motivate a new approach for digital circuitry: stochastic logic, that is to say, logic that operates on probabilistic signals and so can cope with errors and uncertainty. Techniques for probabilistic analysis of circuits and systems are well established. We advocate a strategy for synthesis. In prior work, we described a methodology for synthesizing stochastic logic, that is to say logic that operates on probabilistic bit streams. In this paper, we apply the concept of stochastic logic to a reconfigurable architecture that implements processing operations on a datapath. We analyze cost as well as the sources of error: approximation, quantization, and random fluctuations. We study the effectiveness of the architecture on a collection of benchmarks for image processing. The stochastic architecture requires less area than conventional hardware implementations. Moreover, it is much more tolerant of soft errors (bit flips) than these deterministic implementations. This fault tolerance scales gracefully to very large numbers of errors.

Question: What is a stochastic logic?


Photonic Crystal-Structures for THz Vacuum Electron Devices. The technology of photonic crystals (PhCs) is investigated here to improve the performance of THz vacuum electron devices. Compared with conventional metallic waveguides, the PhC arrangement alleviates typical issues in THz vacuum electron tubes, i.e. difficult vacuum pumping process and assembling, and improves the input/output coupling. A slow-wave structure (SWS) based on a corrugated waveguide assisted by PhC lateral walls and the efficient design of a PhC coupler for sheet-beam interaction devices are demonstrated. Based on the proposed technology, a backward-wave oscillator (BWO) is designed in this paper. Cold parameters of the novel PhC SWS as well as 3-D particle-in-cell simulations of the overall BWO are investigated, obtaining more than 70-mW-peak output power at 0.650 THz for beam voltage of 11 kV and beam current of 6 mA.

Question: What is a backward-wave oscillator?


Unsupervised Modeling of Twitter Conversations. We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available. This huge amount of data, available only because Twitter blurs the line between chatting and publishing, highlights the need to be able to adapt quickly to a new medium.

Question: How to model dialogue acts in an open domain?


Weather classification with deep convolutional neural networks. In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.

Question: What is the weather classification task?


Integrated path planning and dynamic steering control for autonomous vehicles. A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data'. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals'. Simulation results are presented to demonstrate the control scheme.

Question: How to combine two algorithms into one?


LTE in the sky: trading off propagation benefits with interference costs for aerial nodes. The popularity of unmanned aerial vehicles has exploded over the last few years, urgently demanding solutions to transfer large amounts of data from the UAV to the ground. Conversely, a control channel to the UAV is desired, in order to safely operate these vehicles remotely. This article analyzes the use of LTE for realizing this downlink data and uplink control. By means of measurements and simulations, we study the impact of interference and path loss when transmitting data to and from the UAV. Two scenarios are considered in which UAVs act as either base stations transmitting in downlink or UEs transmitting in uplink, and their impact on the respective downlink and uplink performance of an LTE ground network is analyzed. Both measurements and simulations are used to quantify such impact for a range of scenarios with varying altitude, distance from the base station, or UAV density. The measurement sets show that signal-to-interference ratio decreases up to 7 dB for UAVs at 150 m compared to ground users. Simulation results show that a UAV density of 10/km2 gives an average degradation of the signal-to-interference ratio of more than 6 dB. It is concluded that interference is going to be a major limiting factor when LTE enabled UAVs are introduced, and that strong technical solutions will have to be found.

Question: How does LTE affect UAVs?


Estimating 3D Hand Pose from a Cluttered Image. A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclidean space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this cluttertolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.

Question: How to estimate 3D hand pose from a cluttered image?


Influence and correlation in social networks. In many online social systems, social ties between users play an important role in dictating their behavior. One of the ways this can happen is through social influence, the phenomenon that the actions of a user can induce his/her friends to behave in a similar way. In systems where social influence exists, ideas, modes of behavior, or new technologies can diffuse through the network like an epidemic. Therefore, identifying and understanding social influence is of tremendous interest from both analysis and design points of view.  This is a difficult task in general, since there are factors such as homophily or unobserved confounding variables that can induce statistical correlation between the actions of friends in a social network. Distinguishing influence from these is essentially the problem of distinguishing correlation from causality, a notoriously hard statistical problem.  In this paper we study this problem systematically. We define fairly general models that replicate the aforementioned sources of social correlation. We then propose two simple tests that can identify influence as a source of social correlation when the time series of user actions is available.  We give a theoretical justification of one of the tests by proving that with high probability it succeeds in ruling out influence in a rather general model of social correlation. We also simulate our tests on a number of examples designed by randomly generating actions of nodes on a real social network (from Flickr) according to one of several models. Simulation results confirm that our test performs well on these data. Finally, we apply them to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.

Question: What is the difference between correlation and causality?


Swing-up and stabilization of a cart-pendulum system under restricted cart track length. This paper describes the swing-up and stabilization of a cart–pendulum system with a restricted cart track length and restricted control force using generalized energy control methods. Starting from a pendant position, the pendulum is swung up to the upright unstable equilibrium con5guration using energy control principles. An "energy well" is built within the cart track to prevent the cart from going outside the limited length. When su9cient energy is acquired by the pendulum, it goes into a "cruise" mode when the acquired energy is maintained. Finally, when the pendulum is close to the upright con5guration, a stabilizing controller is activated around a linear zone about the upright con5guration. The proposed scheme has worked well both in simulation and a practical setup and the conditions for stability have been derived using the multiple Lyapunov functions approach. c © 2002 Elsevier Science B.V. All rights reserved.

Question: How to stabilize a cart-pendulum system?


The Impact of Clinical Empathy on Patients and Clinicians : Understanding Empathy ' s Side Effects. Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

Question: What is the impact of clinical empathy on patients and clinicians?


CORONET: Fault tolerance for Software Defined Networks. Software Defined Networking, or SDN, based networks are being deployed not only in testbed networks, but also in production networks. Although fault-tolerance is one of the most desirable properties in production networks, there are not much study in providing fault-tolerance to SDN-based networks. The goal of this work is to develop a fault tolerant SDN architecture that can rapidly recover from faults and scale to large network sizes. This paper presents CORONET, a SDN fault-tolerant system that recovers from multiple link failures in the data plane. We describe a prototype implementation based on NOX that demonstrates fault recovery for emulated topologies using Mininet. We also discuss possible extensions to handle control plane and controller faults.

Question: What is a fault tolerant SDN?


Fast Fourier Transforms for Nonequispaced Data. Fast Fourier Transforms for Nonequispaced Data Alok Dutt Yale University 1993 Two groups of algorithms are presented generalizing the fast Fourier transform (FFT) to the case of non-integer frequencies and nonequispaced nodes on the interval [-7r, 7r]. These schemes are based on combinations of certain analytical considerations with the classical fast Fourier transform, and generalize both the forward and backward FFTs. The number of arithmetic operations required by each of the algorithms is proportional to Nlog N + Nlog(I/e), where 6 is the desired precision of computations and N is the number of nodes. Several related algorithms are also presented, each of which utilizes a similar set of techniques from analysis and linear algebra. These include an efficient version of the Fast Multipole Method in one dimension and fast algorithms for the evaluation, integration and differentiation of Lagrange polynomial interpolants. Several numerical examples are used to illustrate the efficiency of the approach, and to compare the performances of the two sets of nonuniform FFT algorithms. The work of this author was supported in part by the Office of Naval Research under Grant N00014-89-J-1527 and in part by the National Science Foundation under Grant DMS9012751. Approved for public release: distribution is unlimited.

Question: What is the number of arithmetic operations required by each of the algorithms?


Mimir: a Market-based Real-time Question and Answer Service. Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.  In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.

Question: What is a market-based real-time Q&A system?


Determination of differential leakage factors in electrical machines with non-symmetrical full and dead-coil windings. In this paper Görges polygons are used in conjunction with masses geometry to find an easy and affordable way to compute the differential leakage factor of non symmetrical full and dead coil winding. By following the traditional way, the use of the Ossanna's infinite series which has to be obviously truncated under the bound of a predetermined accuracy is mandatory. In the presented method no infinite series is instead required. An example is then shown and discussed to demonstrate practically the effectiveness of the proposed method.

Question: How to find the differential leakage factor of non symmetrical full and dead coil winding?


Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations. Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera setup and the 3D structure of the human body to probabilistically combine per view 2D ConvNet predictions into a globally optimal 3D pose. This 3D pose is used as the basis for harvesting annotations. The benefit of the annotations produced automatically with our approach is demonstrated in two challenging settings: (i) fine-tuning a generic ConvNet-based 2D pose predictor to capture the discriminative aspects of a subjects appearance (i.e.,personalization), and (ii) training a ConvNet from scratch for single view 3D human pose prediction without leveraging 3D pose groundtruth. The proposed multi-view pose estimator achieves state-of-the-art results on standard benchmarks, demonstrating the effectiveness of our method in exploiting the available multi-view information.

Question: How to harvest multiple views for marker-less 3D human pose annotations?


A Self-Organizing Spatial Vocabulary. Language is a shared set of conventions for mapping meanings to utterances. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with an expansion of the set of meanings.

Question: What is a self-organizing spatial vocabulary?


Detecting Curve Text in the Wild: New Dataset and New Solution. Scene text detection has been made great progress in recent years. The detection manners are evolving from axisaligned rectangle to rotated rectangle and further to quadrangle. However, current datasets contain very little curve text, which can be widely observed in scene images such as signboard, product name and so on. To raise the concerns of reading curve text in the wild, in this paper, we construct a curve text dataset named CTW1500, which includes over 10k text annotations in 1,500 images (1000 for training and 500 for testing). Based on this dataset, we pioneering propose a polygon based curve text detector (CTD) which can directly detect curve text without empirical combination. Moreover, by seamlessly integrating the recurrent transverse and longitudinal offset connection (TLOC), the proposed method can be end-to-end trainable to learn the inherent connection among the position offsets. This allows the CTD to explore context information instead of predicting points independently, resulting in more smooth and accurate detection. We also propose two simple but effective post-processing methods named nonpolygon suppress (NPS) and polygonal non-maximum suppression (PNMS) to further improve the detection accuracy. Furthermore, the proposed approach in this paper is designed in an universal manner, which can also be trained with rectangular or quadrilateral bounding boxes without extra efforts. Experimental results on CTW-1500 demonstrate our method with only a light backbone can outperform state-of-the-art methods with a large margin. By evaluating only in the curve or non-curve subset, the CTD + TLOC can still achieve the best results. Code is available at https://github.com/Yuliang-Liu/Curve-Text-Detector.

Question: How to detect curve text in the wild?


Batch Mode Active Learning for Regression With Expected Model Change. While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that result in the maximum change of the current model once labeled. The model change is quantified as the difference between the current model parameters and the updated parameters after the inclusion of the newly selected examples. In light of the stochastic gradient descent learning rule, we approximate the change as the gradient of the loss function with respect to each single candidate instance. Under the EMCM framework, we propose novel AL algorithms for the linear and nonlinear regression models. In addition, by simulating the behavior of the sequential AL policy when applied for <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> iterations, we further extend the algorithms to batch mode AL to simultaneously choose a set of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> most informative instances at each query time. Extensive experimental results on both UCI and StatLib benchmark data sets have demonstrated that the proposed algorithms are highly effective and efficient.

Question: What is the difference between active learning and batch mode active learning?


New Compact CMOS Li-Ion Battery Charger Using Charge-Pump Technique for Portable Applications. This paper presents a new compact CMOS Li-Ion battery charger for portable applications that uses a charge-pump technique. The proposed charger features a small chip size and a simple circuit structure. Additionally, it provides basic functions with voltage/current detection, end-of-charge detection, and charging speed control. The charger operates in dual-mode and is supported in the trickle/large constant-current mode to constant-voltage mode with different charging rates. This charger is implemented using a TSMC 0.35-mum CMOS process with a 5-V power supply. The output voltage is almost 4.2 V, and the maximum charging current reaches 700 mA. It has 67.89% power efficiency, 837-mW chip power dissipation, and only 1.455times1.348 mm2 in chip area including pads

Question: What is the size of the chip?


Open production: scientific foundation for co-creative product realization. Globalization and the use of technology call for an adaptation of value creation strategies. As the potential for rationalization and achieving flexibility within companies is to the greatest possible extent exhausted, approaches to the corporate reorganization of value creation are becoming increasingly important. In this process, the spread and further development of information and communication technology often provide the basis for a reorganization of cross-company value nets and lead to a redistribution of roles and tasks between the actors involved in value creation. While cooperative, decentralized and self-organizing value creation processes are in fact being promoted, the associated potential for development and production engineering is being underestimated and hence not implemented sufficiently. This contribution will introduce a value creation taxonomy and then, using its notion and structure, describe the emerging transformations in value creation on the basis of case studies. Finally an adequate framework for analysing and configuring value creation will be presented.

Question: What is the value creation taxonomy?


An ontology for representing financial headline news. This paper presents the development of an ontology to represent financial headline news. This ontology is developed using the On-To-Knowledge methodology where the focus is on the design steps of the Knowledge Meta Process. This development is part of an ongoing project which aims to design a virtual stock market simulator based on multi-agent systems. The proposed ontology has 31 concepts and includes 201 attributes. The testing results conducted on reliable headline news show that 99% of these headline news can be properly represented by the attributes of the right category in the ontology. Unreliable headline news characterized by news having uncertainties, incompleteness, ill-definition, or imprecision cannot be represented by the proposed ontology. Approaches for representing these unreliable headline

Question: What is the ontology for representing financial headline news?


ECOFEN: An End-to-end energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks. Wired networks are increasing in size and their power consumption is becoming a matter of concern. Evaluating the end-to-end electrical cost of new network architectures and protocols is difficult due to the lack of monitored realistic infrastructures. We propose an End-to-End energy Cost mOdel and simulator For Evaluating power consumption in large-scale Networks (ECOFEN) whose user's entries are the network topology and traffic. Based on configurable measurement of different network components (routers, switches, NICs, etc.), it provides the power consumption of the overall network including the end-hosts as well as the power consumption of each equipment over time.

Question: What is the end-to-end energy cost of a network?


Streaming Hierarchical Video Segmentation. The use of video segmentation as an early processing step in video analysis lags behind the use of image segmentation for image analysis, despite many available video segmentation methods. A major reason for this lag is simply that videos are an order of magnitude bigger than images; yet most methods require all voxels in the video to be loaded into memory, which is clearly prohibitive for even medium length videos. We address this limitation by proposing an approximation framework for streaming hierarchical video segmentation motivated by data stream algorithms: each video frame is processed only once and does not change the segmentation of previous frames. We implement the graph-based hierarchical segmentation method within our streaming framework; our method is the first streaming hierarchical video segmentation method proposed. We perform thorough experimental analysis on a benchmark video data set and longer videos. Our results indicate the graph-based streaming hierarchical method outperforms other streaming video segmentation methods and performs nearly as well as the full-video hierarchical graph-based method.

Question: Streaming Hierarchical Video Segmentation


On the Generation of Skeletons from Discrete Euclidean Distance Maps. The skeleton is an important representation for shape analysis. A common approach for generating discrete skeletons takes three steps: 1) computing the distance map, 2) detecting maximal disks from the distance map, and 3) linking the centers of maximal disks (CMDs) into a connected skeleton. Algorithms using approximate distance metrics are abundant and their theory has been well established. However, the resulting skeletons may be inaccurate and sensitive to rotation. In this paper, we study methods for generating skeletons based on the exact Euclidean metric. We first show that no previous algorithms identifies the exact set of discrete maximal disks under the Euclidean metric. We then propose new algorithms and show that they are correct. To link CMDs into connected skeletons, we examine two prevalent approaches: connected thinning and steepest ascent. We point out that the connected thinning approach does not work properly for Euclidean distance maps. Only the steepest ascent algorithm produces skeletons that are truly medially placed. The resulting skeletons have all the desirable properties: they have the same simple connectivity as the figure, they are well-centered, they are insensitive to rotation, and they allow exact reconstruction. The effectiveness of our algorithms is demonstrated with numerous examples.

Question: What is the difference between the discrete Euclidean distance map and the discrete Euclidean distance matrix?


Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder. Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model that lets the attention depend on the source-side syntax. Experiments on Chinese-English translation demonstrate that our proposed models outperform the sequential attentional model as well as a stronger baseline with a bottom-up tree encoder and word coverage.1

Question: What is the source-side syntax?


Audience Involvement in Advertising : Four Levels. The effectiveness of advertising messages is widely believed to be moderated by audience involvement. In this paper, psychological theories of attention and levels of processing are used to establish a framework that can accommodate the major consumer behavior theories of audience involvement. Four levels of involvement are identified (in order from low to high) as preattention, focal attention, comprehension, and elaboration. These levels allocate increasing attentional capacity to a message source, as needed for analysis of the message by increasingly abstract-and qualitatively distinct-representational systems. Lower levels use relatively little capacity and extract information needed to determine whether higher levels will be invoked. The higher levels require greater capacity and result in increasingly durable cognitive and attitudinal effects.

Question: What is the difference between preattention and focal attention?


Using Quality Models in Software Package Selection. 0 7 4 0 7 4 5 9 / 0 3 / $ 1 9 . 0 0 © 2 0 0 3 I E E E All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages' capabilities.3–5 There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback. (See the "Related Work" sidebar for information about other approaches.) A structured quality model for a given package domain provides a taxonomy of software quality features, and metrics for computing their value. Our approach relies on the International Organization for Standardization and International Electrotechnical Commission 9126-1 quality standard,6 which we selected for the following reasons:

Question: What is the quality standard for software?


Implicit Quantile Networks for Distributional Reinforcement Learning. For the merging function m, the simplest choice would be a simple vector concatenation of ψ(x) and φ(τ). Note however, that the MLP f which takes in the output of m and outputs the action-value quantiles, only has a single hidden layer in the DQN network. Therefore, to force a sufficiently early interaction between the two representations, we also considered a multiplicative function m(ψ, φ) = ψ φ, where denotes the element-wise (Hadamard) product of two vectors, as well as a 'residual' function m(ψ, φ) = ψ (1 + φ).

Question: What is the difference between the multiplicative and residual functions?


On the impact of knowledge discovery and data mining. Knowledge Discovery and Data Mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. The rapid dissemination of these technologies calls for an urgent examination of their social impact. This paper identifies social issues arising from Knowledge Discovery (KD) and Data Mining (DM). An overview of these technologies is presented, followed by a detailed discussion of each issue. The paper's intention is to primarily illustrate the cultural context of each issue and, secondly, to describe the impact of KD and DM in each case. Existing solutions specific to each issue are identified and examined for feasibility and effectiveness, and a solution that provides a suitably contextually sensitive means for gathering and analysing sensitive data is proposed and briefly outlined. The paper concludes with a discussion of topics for further consideration.

Question: What is the impact of knowledge discovery and data mining?


You are what you include: large-scale evaluation of remote javascript inclusions. JavaScript is used by web developers to enhance the interactivity of their sites, offload work to the users' browsers and improve their sites' responsiveness and user-friendliness, making web pages feel and behave like traditional desktop applications. An important feature of JavaScript, is the ability to combine multiple libraries from local and remote sources into the same page, under the same namespace. While this enables the creation of more advanced web applications, it also allows for a malicious JavaScript provider to steal data from other scripts and from the page itself. Today, when developers include remote JavaScript libraries, they trust that the remote providers will not abuse the power bestowed upon them.  In this paper, we report on a large-scale crawl of more than three million pages of the top 10,000 Alexa sites, and identify the trust relationships of these sites with their library providers. We show the evolution of JavaScript inclusions over time and develop a set of metrics in order to assess the maintenance-quality of each JavaScript provider, showing that in some cases, top Internet sites trust remote providers that could be successfully compromised by determined attackers and subsequently serve malicious JavaScript. In this process, we identify four, previously unknown, types of vulnerabilities that attackers could use to attack popular web sites. Lastly, we review some proposed ways of protecting a web application from malicious remote scripts and show that some of them may not be as effective as previously thought.

Question: What is the largest number of remote javascript libraries?


Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning. We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or local structure of the training data, our method, called linear regression analysis (LRA), applies least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intra-class facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and ∗Corresponding author. Tel:+86 10 62283059 Fax: +86 10 62285019 Email address: whdeng@bupt.edu.cn (Weihong Deng) Preprint submitted to Elsevier March 28, 2014

Question: What is the difference between the generic learning and incremental learning?


Simulating Urban Growth Using the SLEUTH Model in a Coastal Peri-Urban District in China. China's southeast coastal areas have witnessed rapid growth in the last two decades, owing mostly to their economic and social attractions. In this paper, we chose Jimei, a coastal peri-urban district of Xiamen city on the southeast coast of China, as a study area to explore the district's growth dynamics, to predict future sprawl during the next decade and to provide a basis for urban planning. The SLEUTH urban growth model was calibrated against historical data derived from a series of Landsat TM 5 satellite images taken between 1992 and 2007. A Lee-Sallee value of 0.48 was calculated for the district, which is a satisfactory result compared with related studies. Five coefficients of urban growth, diffusion, spread, breed, slope resistance and road gravity had values of 25, 68, 86, 24 and 23, respectively, in 2007. The growth coefficients (i.e., urban character) can capture urban growth characteristics in Jimei district. The urban DNA revealed that, over the study period, urban growth in the district was dominated both by urbanization through establishment of new urban centers, and by expansion outward from existing urban centers. In contrast to interior cities, in which expansions are dramatically shaped by actual road patterns, urban expansion in the district was likely constrained by the nearby coastline. Future urban growth patterns were predicted to 2020 assuming three different development scenarios. The first scenario simulated a continuation of historical urban growth without changing current conditions. The second scenario projected managed growth in which OPEN ACCESS Sustainability 2014, 6 3900 urban growth is limited by a layer with areas excluded from urbanization, which is the future development plan for Jimei district and Xiamen city. The third scenario depicted a growth with maximum protection in which growth was allowed to continue, similar to the second scena

Question: What is the future of Jimei?


Improving predictive inference under covariate shift by weighting the log-likelihood function. A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function. This approach is e ective in cases such as sample surveys or design of experiments, where the observed covariate follows a di erent distribution than that in the whole population. Under misspeci cation of the parametric model, the optimal choice of the weight function is asymptotically shown to be the ratio of the density function of the covariate in the population to that in the observations. This is the pseudo-maximum likelihood estimation of sample surveys. The optimality is de ned by the expected Kullback–Leibler loss, and the optimal weight is obtained by considering the importance sampling identity. Under correct speci cation of the model, however, the ordinary maximum likelihood estimate (i.e. the uniform weight) is shown to be optimal asymptotically. For moderate sample size, the situation is in between the two extreme cases, and the weight function is selected by minimizing a variant of the information criterion derived as an estimate of the expected loss. The method is also applied to a weighted version of the Bayesian predictive density. Numerical examples as well as Monte-Carlo simulations are shown for polynomial regression. A connection with the robust parametric estimation is discussed. c © 2000 Elsevier Science B.V. All rights reserved.

Question: What is the difference between the maximum likelihood and the pseudo-maximum likelihood estimation?


Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces. We propose a novel and fast multiscale feature detection and description approach that exploits the benefits of nonlinear scale spaces. Previous attempts to detect and describe features in nonlinear scale spaces are highly time consuming due to the computational burden of creating the nonlinear scale space. In this paper we propose to use recent numerical schemes called Fast Explicit Diffusion (FED) embedded in a pyramidal framework to dramatically speed-up feature detection in nonlinear scale spaces. In addition, we introduce a Modified-Local Difference Binary (M-LDB) descriptor that is highly efficient, exploits gradient information from the nonlinear scale space, is scale and rotation invariant and has low storage requirements. We present an extensive evaluation that shows the excellent compromise between speed and performance of our approach compared to state-of-the-art methods such as BRISK, ORB, SURF, SIFT and KAZE.

Question: What is the difference between the fast explicit diffusion and the fast explicit diffusion?


Quasi-online reinforcement learning for robots. This paper describes quasi-online reinforcement learning: while a robot is exploring its environment, in the background a probabilistic model of the environment is built on the fly as new experiences arrive; the policy is trained concurrently based on this model using an anytime algorithm. Prioritized sweeping, directed exploration, and transformed reward functions provide additional speed-ups. The robot quickly learns goal-directed policies from scratch, requiring few interactions with the environment and making efficient use of available computation time. From an outside perspective it learns the behavior online and in real time. We describe comparisons with standard methods and show the individual utility of each of the proposed techniques

Question: What is quasi-online reinforcement learning?


Cross-domain video concept detection using adaptive svms. Many multimedia applications can benefit from techniques for adapting existing classifiers to data with different distributions. One example is cross-domain video concept detection which aims to adapt concept classifiers across various video domains. In this paper, we explore two key problems for classifier adaptation: (1) how to transform existing classifier(s) into an effective classifier for a new dataset that only has a limited number of labeled examples, and (2) how to select the best existing classifier(s) for adaptation. For the first problem, we propose Adaptive Support Vector Machines (A-SVMs) as a general method to adapt one or more existing classifiers of any type to the new dataset. It aims to learn the "delta function" between the original and adapted classifier using an objective function similar to SVMs. For the second problem, we estimate the performance of each existing classifier on the sparsely-labeled new dataset by analyzing its score distribution and other meta features, and select the classifiers with the best estimated performance. The proposed method outperforms several baseline and competing methods in terms of classification accuracy and efficiency in cross-domain concept detection in the TRECVID corpus.

Question: How to adapt existing classifiers to a new dataset?


Miniaturized NFC Antenna Design for a Tablet PC With a Narrow Border and Metal Back-Cover. A novel structure of the near-field communication (NFC) antenna design for a tablet PC is proposed. This tablet PC has a narrow border and full metallic back-cover. A miniaturized loop antenna design is achieved by attaching ferrite sheets on both sides of the loop antenna. The ferrite sheets may reduce eddy currents induced on the adjacent metallic back-cover by the loop antenna to improve the communication range of the NFC. Only the edge of the tablet PC allows the antenna to radiate due to the full metallic back-cover. Thus, the NFC antenna needs to be narrow to be installed on the edge of the tablet PC. Therefore, we propose a miniaturized NFC antenna with the dimensions of 41.5 (L) × 7.5 (W) × 0.45 (T) mm3 only. Simulated magnetic field distributions are consistent with measured voltage distributions. This design has a good communication range more than 6 cm in front of the touchscreen panel and reaches 2 cm over the other side above the metal back-cover.

Question: How to design a miniaturized NFC antenna?


ARLearn: Augmented Reality Meets Augmented Virtuality. This article deals with educational opportunities for mixed reality games and related scenarios for learning. It discusses several issues and educational challenges to be tackled when linking augmented reality and augmented virtuality. Second, the paper describes the architecture of the ARLearn system which offers highly flexible support for different educational settings. Three prototypical use cases implemented based on the underlying ARLearn framework are discussed, which are a field trip system, an augmented Google StreetView client called StreetLearn, and a real time crisis intervention game. ARLearn combines real time notification and mixed reality games across Mobile Augmented Reality and Virtual Reality and the authors aim to use the underlying (open source) framework for further case studies and mixed reality applications for learning support.

Question: What is the ARLearn framework?


Information leakage from optical emanations. A previously unknown form of compromising emanations has been discovered. LED status indicators on data communication equipment, under certain conditions, are shown to carry a modulated optical signal that is significantly correlated with information being processed by the device. Physical access is not required; the attacker gains access to all data going through the device, including plaintext in the case of data encryption systems. Experiments show that it is possible to intercept data under realistic conditions at a considerable distance. Many different sorts of devices, including modems and Internet Protocol routers, were found to be vulnerable. A taxonomy of compromising optical emanations is developed, and design changes are described that will successfully block this kind of "Optical Tempest" attack.

Question: What is the taxonomy of compromising optical emanations?


High resolution stationary digital breast tomosynthesis using distributed carbon nanotube x-ray source array.. PURPOSE The purpose of this study is to investigate the feasibility of increasing the system spatial resolution and scanning speed of Hologic Selenia Dimensions digital breast tomosynthesis (DBT) scanner by replacing the rotating mammography x-ray tube with a specially designed carbon nanotube (CNT) x-ray source array, which generates all the projection images needed for tomosynthesis reconstruction by electronically activating individual x-ray sources without any mechanical motion. The stationary digital breast tomosynthesis (s-DBT) design aims to (i) increase the system spatial resolution by eliminating image blurring due to x-ray tube motion and (ii) reduce the scanning time. Low spatial resolution and long scanning time are the two main technical limitations of current DBT technology.   METHODS A CNT x-ray source array was designed and evaluated against a set of targeted system performance parameters. Simulations were performed to determine the maximum anode heat load at the desired focal spot size and to design the electron focusing optics. Field emission current from CNT cathode was measured for an extended period of time to determine the stable life time of CNT cathode for an expected clinical operation scenario. The source array was manufactured, tested, and integrated with a Selenia scanner. An electronic control unit was developed to interface the source array with the detection system and to scan and regulate x-ray beams. The performance of the s-DBT system was evaluated using physical phantoms.   RESULTS The spatially distributed CNT x-ray source array comprised 31 individually addressable x-ray sources covering a 30 angular span with 1 pitch and an isotropic focal spot size of 0.6 mm at full width at half-maximum. Stable operation at 28 kV(peak) anode voltage and 38 mA tube current was demonstrated with extended lifetime and good source-to-sour

Question: What is the purpose of this study?


Neural Machine Translation Advised by Statistical Machine Translation. Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces fluent but inadequate translations (Tu et al. 2016; He et al. 2016). This is in contrast to conventional Statistical Machine Translation (SMT), which usually yields adequate but non-fluent translations. It is natural, therefore, to leverage the advantages of both models for better translations, and in this work we propose to incorporate SMT model into NMT framework. More specifically, at each decoding step, SMT offers additional recommendations of generated words based on the decoding information from NMT (e.g., the generated partial translation and attention history). Then we employ an auxiliary classifier to score the SMT recommendations and a gating function to combine the SMT recommendations with NMT generations, both of which are jointly trained within the NMT architecture in an end-to-end manner. Experimental results on Chinese-English translation show that the proposed approach achieves significant and consistent improvements over state-of-the-art NMT and SMT systems on multiple NIST test sets.

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


Learning deep semantic attributes for user video summarization. This paper presents a Semantic Attribute assisted video SUMmarization framework (SASUM). Compared with traditional methods, SASUM has several innovative features. Firstly, we use a natural language processing tool to discover a set of keywords from an image and text corpora to form the semantic attributes of visual contents. Secondly, we train a deep convolution neural network to extract visual features as well as predict the semantic attributes of video segments which enables us to represent video contents with visual and semantic features simultaneously. Thirdly, we construct a temporally constrained video segment affinity matrix and use a partially near duplicate image discovery technique to cluster visually and semantically consistent video frames together. These frame clusters can then be condensed to form an informative and compact summary of the video. We will present experimental results to show the effectiveness of the semantic attributes in assisting the visual features in video summarization and our new technique achieves state-of-the-art performance.

Question: What is the difference between a deep convolution neural network and a convolution neural network?


System-level performance evaluation of downlink non-orthogonal multiple access (NOMA). As a promising downlink multiple access scheme for further LTE enhancement and future radio access (FRA), this paper investigates the system-level performance of non-orthogonal multiple access (NOMA) with a successive interference canceller (SIC) on the receiver side. The goal is to clarify the potential gains of NOMA over orthogonal multiple access (OMA) such as OFDMA, taking into account key link adaptation functionalities of the LTE radio interface such as adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ), time/frequency-domain scheduling, and outer loop link adaptation (OLLA), in addition to NOMA specific functionalities such as dynamic multi-user power allocation. Based on computer simulations, we show under multiple configurations that the system-level performance achieved by NOMA is superior to that for OMA.

Question: What is the system-level performance of downlink NOMA?


Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition. The mathematical properties of high-dimensional spaces seem remarkably suited for describing behaviors produces by brains. Brain-inspired hyperdimensional computing (HDC) explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. Hypervectors are high-dimensional, holographic, and (pseudo)random with independent and identically distributed (i.i.d.) components. These features provide an opportunity for energy-efficient computing applied to cyberbiological and cybernetic systems. We describe the use of HDC in a smart prosthetic application, namely hand gesture recognition from a stream of Electromyography (EMG) signals. Our algorithm encodes a stream of analog EMG signals that are simultaneously generated from four channels to a single hypervector. The proposed encoding effectively captures spatial and temporal relations across and within the channels to represent a gesture. This HDC encoder achieves a high level of classification accuracy (97.8%) with only 1/3 the training data required by state-of-the-art SVM on the same task. HDC exhibits fast and accurate learning explicitly allowing online and continuous learning. We further enhance the encoder to adaptively mitigate the effect of gesture-timing uncertainties across different subjects endogenously; further, the encoder inherently maintains the same accuracy when there is up to 30% overlapping between two consecutive gestures in a classification window.

Question: What is the difference between a hypervector and a vector?


Smart home research. This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.

Question: What is smart home research?