Understanding Human Motion and Gestures for Underwater Human-Robot Collaboration. In this paper, we present a number of robust methodologies for an underwater robot to visually detect, follow, and interact with a diver for collaborative task execution. We design and develop two autonomous diver-following algorithms, the first of which utilizes both spatialand frequency-domain features pertaining to human swimming patterns in order to visually track a diver. The second algorithm uses a convolutional neural network-based model for robust tracking-by-detection. In addition, we propose a hand gesture-based human-robot communication framework that is syntactically simpler and computationally more efficient than the existing grammar-based frameworks. In the proposed interaction framework, deep visual detectors are used to provide accurate hand gesture recognition; subsequently, a finite-state machine performs robust and efficient gesture-to-instruction mapping. The distinguishing feature of this framework is that it can be easily adopted by divers for communicating with underwater robots without using artificial markers or requiring memorization of complex language rules. Furthermore, we validate the performance and effectiveness of the proposed methodologies through extensive field experiments in closedand open-water environments. Finally, we perform a user interaction study to demonstrate the usability benefits of our proposed interaction framework compared to existing methods.

Question: What is the difference between a human and a robot?


Connecting modalities: Semi-supervised segmentation and annotation of images using unaligned text corpora. We propose a semi-supervised model which segments and annotates images using very few labeled images and a large unaligned text corpus to relate image regions to text labels. Given photos of a sports event, all that is necessary to provide a pixel-level labeling of objects and background is a set of newspaper articles about this sport and one to five labeled images. Our model is motivated by the observation that words in text corpora share certain context and feature similarities with visual objects. We describe images using visual words, a new region-based representation. The proposed model is based on kernelized canonical correlation analysis which finds a mapping between visual and textual words by projecting them into a latent meaning space. Kernels are derived from context and adjective features inside the respective visual and textual domains. We apply our method to a challenging dataset and rely on articles of the New York Times for textual features. Our model outperforms the state-of-the-art in annotation. In segmentation it compares favorably with other methods that use significantly more labeled training data.

Question: How does the model work?


Complex thermoelectric materials.. Thermoelectric materials, which can generate electricity from waste heat or be used as solid-state Peltier coolers, could play an important role in a global sustainable energy solution. Such a development is contingent on identifying materials with higher thermoelectric efficiency than available at present, which is a challenge owing to the conflicting combination of material traits that are required. Nevertheless, because of modern synthesis and characterization techniques, particularly for nanoscale materials, a new era of complex thermoelectric materials is approaching. We review recent advances in the field, highlighting the strategies used to improve the thermopower and reduce the thermal conductivity.

Question: What is the thermoelectric efficiency of a thermoelectric material?


A compact dual-band half-mode SIW based semi-circular antenna. In this paper, a half mode SIW based semi-circular antenna with co-axial feed has been designed for dual band operation. The fundamental resonating frequency is found to operate at 4.66 GHz while the next higher mode is at 8.3 GHz. The antenna performance is optimized through parametric studies and the dual band antenna is designed using Arlon AD270 substrate. Simulation studies have been carried out to study the various antenna parameters.

Question: What is a half-mode SIW based semi-circular antenna?


PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

Question: What is a participatory sensing application?


Freebase: A Shared Database of Structured General Human Knowledge. Freebase is a practical, scalable, graph-shaped database of structured general human knowledge, inspired by Semantic Web research and collaborative data communities such as the Wikipedia. Freebase allows public read and write access through an HTTP-based graph-query API for research, the creation and maintenance of structured data, and application building. Access is free and all data in Freebase has a very open (e.g. Creative Commons, GFDL) license.

Question: What is the freebase?


Applying Genre-Based Ontologies to Enterprise Architecture. This paper elaborates the approach of using ontologies as a conceptual base for enterprise architecture (EA) descriptions. The method focuses on recognising and modelling business critical information concepts, their content, and semantics used to operate the business. Communication genres and open and semi-structured information need interviews are used as a domain analysis method. Ontologies aim to explicate the results of domain analysis and to provide a common reference model for Business Information Architecture (BIA) descriptions. The results are generalised to model further aspects of EA.

Question: What is the ontology of enterprise architecture?


Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning. Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics.

Question: How can we create situation awareness of a vehicle?


Adversarial machine learning. In this paper (expanded from an invited talk at AISEC 2010), we discuss an emerging field of study: adversarial machine learning---the study of effective machine learning techniques against an adversarial opponent. In this paper, we: give a taxonomy for classifying attacks against online machine learning algorithms; discuss application-specific factors that limit an adversary's capabilities; introduce two models for modeling an adversary's capabilities; explore the limits of an adversary's knowledge about the algorithm, feature space, training, and input data; explore vulnerabilities in machine learning algorithms; discuss countermeasures against attacks; introduce the evasion challenge; and discuss privacy-preserving learning techniques.

Question: What is adversarial machine learning?


Children ' s Eye Movements during Listening : Developmental Evidence for a Constraint-Based Theory of Sentence Processing. Many comprehension studies of grammatical development have focused on the ultimate interpretation that children assign to sentences and phrases, yielding somewhat static snapshots of children's emerging grammatical knowledge. Studies of the dynamic processes underlying children's language comprehension have to date been rare, owing in part to the lack of online sentence processing techniques suitable for use with children. In this chapter, we describe recent work from our research group, which examines the moment-by-moment interpretation decisions of children (age 4 to 6 years) while they listen to spoken sentences. These real-time measures were obtained by recording the children's eye movements as they visually interrogated and manipulated objects in response to spoken instructions. The first of these studies established some striking developmental differences in processing ability, with the youngest children showing an inability to use relevant properties of the referential scene to resolve temporary grammatical ambiguities (Trueswell, Sekerina, Hill, & Logrip, 1999). This finding could be interpreted as support for an early encapsulated syntactic processor that has difficulty using non-syntactic information to revise parsing commitments. However, we will review evidence from a series of follow-up experiments which suggest that this pattern arises from a developing interactive parsing system. Under this account, adult and child sentence comprehension is a " perceptual guessing game " in which multiple statistical cues are used to recover detailed linguistic structure. These cues, which include lexical-distribution evidence, verb semantic biases, and referential scene information, come " online " (become automated) at different points in the course of development. The developmental timing of these effects is related to their differential reliab

Question: What is the developmental timing of the effects of lexical-distribution evidence, verb semantic biases, and referential scene information?


A Proposal for Statistical Outlier Detection in Relational Structures. This paper extends unsupervised statistical outlier detection to the case of relational data. For nonrelational data, where each individual is characterized by a feature vector, a common approach starts with learning a generative statistical model for the population. The model assigns a likelihood measure for the feature vector that characterizes the individual; the lower the feature vector likelihood, the more anomalous the individual. A difference between relational and nonrelational data is that an individual is characterized not only by a list of attributes, but also by its links and by attributes of the individuals linked to it. We refer to a relational structure that specifies this information for a specific individual as the individual's database. Our proposal is to use the likelihood assigned by a generative model to the individual's database as the anomaly score for the individual; the lower the model likelihood, the more anomalous the individual. As a novel validation method, we compare the model likelihood with metrics of individual success. An empirical evaluation reveals a surprising finding in soccer and movie data: We observe in the data a strong correlation between the likelihood and success metrics.

Question: What is the difference between a generative model and a discriminative model?


Design of auxetic structures via mathematical optimization.. The design of auxetic structures is traditionally based on engineering experience, see, for instance, refs. [1,2]. The goal of this article is to demonstrate how an existing auxetic structure can be improved by structural optimization techniques and manufactured by a selective electron beam melting (SEBM) system. Auxetic materials possess negative Poisson's ratios ν which is in contrast to almost all engineering materials (e. g. metals ν ≈ 0.3). The negative Poisson's ratio translates into the unusual geometric effect that these materials expand when stretched and contract when compressed. This behavior is of interest for example for fastener systems and fi lters. [ 2 , 3 ] It also results in interesting mechanical behavior like increased penetration resistance for large negative Poisson's ratios [ 4 ] and in the case of isotropy for higher shear strength relative to the Young's modulus [ 2 ] of the structure. The auxetic behavior can always be traced back to complex mesoor microscopic geometric mechanisms, e. g. unfolding of inverted elements or rotation of star shaped rigid units relative to each other. [ 5 – 7 ] These geometries can be realized on vastly different scales from specifi cally designed polymers to reactor cores. [ 2 , 8 ]

Question: What is the auxetic behavior of the structure?


Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation. Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. One of the key difficulties is insufficient exploration, resulting in an agent being unable to learn robust policies. Intrinsically motivated agents can explore new behavior for their own sake rather than to directly solve external goals. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchicalDQN (h-DQN), a framework to integrate hierarchical action-value functions, operating at different temporal scales, with goal-driven intrinsically motivated deep reinforcement learning. A top-level q-value function learns a policy over intrinsic goals, while a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse and delayed feedback: (1) a complex discrete stochastic decision process with stochastic transitions, and (2) the classic ATARI game – 'Montezuma's Revenge'.

Question: What is the name of the game?


Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images. In this paper, we study the challenging problem of predicting the dynamics of objects in static images. Given a query object in an image, our goal is to provide a physical understanding of the object in terms of the forces acting upon it and its long term motion as response to those forces. Direct and explicit estimation of the forces and the motion of objects from a single image is extremely challenging. We define intermediate physical abstractions called Newtonian scenarios and introduce Newtonian Neural Network (N3) that learns to map a single image to a state in a Newtonian scenario. Our evaluations show that our method can reliably predict dynamics of a query object from a single image. In addition, our approach can provide physical reasoning that supports the predicted dynamics in terms of velocity and force vectors. To spur research in this direction we compiled Visual Newtonian Dynamics (VIND) dataset that includes more than 6000 videos aligned with Newtonian scenarios represented using game engines, and more than 4500 still images with their ground truth dynamics.

Question: What is the dynamics of an object in a static image?


A Condensation Approach to Privacy Preserving Data Mining. In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and flexible approach for privacy preserving data mining which does not require new problem-specific algorithms, since it maps the original data set into a new anonymized data set. This anonymized data closely matches the characteristics of the original data including the correlations among the different dimensions. We present empirical results illustrating the effectiveness of the method.

Question: What is a condensation approach to privacy preserving data mining?


Walking on Water : Biolocomotion at the Interface. We consider the hydrodynamics of creatures capable of sustaining themselves on the water surface by means other than flotation. Particular attention is given to classifying water walkers according to their principal means of weight support and lateral propulsion. The various propulsion mechanisms are rationalized through consideration of energetics, hydrodynamic forces applied, or momentum transferred by the driving stroke. We review previous research in this area and suggest directions for future work. Special attention is given to introductory discussions of problems not previously treated in the fluid mechanics literature, with hopes of attracting physicists, applied mathematicians, and engineers to this relatively unexplored area of fluid mechanics. 339 A nn u. R ev . F lu id . M ec h. 2 00 6. 38 :3 39 -3 69 . D ow nl oa de d fr om a rj ou rn al s. an nu al re vi ew s. or g by Y al e U ni ve rs ity S O C IA L S C IE N C E L IB R A R Y o n 12 /1 7/ 05 . F or p er so na l u se o nl y. AR266-FL38-13 ARI 11 November 2005 20:7

Question: What is the difference between a fluid and a fluid?


Acute Sexual Assault in the Pediatric and Adolescent Population.. Children and adolescents are at high risk for sexual assault. Early medical and mental health evaluation by professionals with advanced training in sexual victimization is imperative to assure appropriate assessment, forensic evidence collection, and follow-up. Moreover, continued research and outreach programs are needed for the development of preventative strategies that focus on this vulnerable population. In this review we highlight key concepts for assessment and include a discussion of risk factors, disclosure, sequelae, follow-up, and prevention.

Question: What is the risk of sexual assault in children and adolescents?


Multiple SVM-RFE for gene selection in cancer classification with expression data. This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.

Question: What is the difference between the SVM-RFE and the proposed method?


User-oriented Bayesian identification and its configuration. Identification of tracked objects is a key capability of automated surveillance and information systems for air, surface and subsurface (maritime), and ground environments, improving situational awareness and offering decision support to operational users. The Bayesian-based identification data combining process (IDCP) provides an effective instrument for fusion of uncertain identity indications from various sources. A user-oriented approach to configuration of the process is introduced, which enables operators to adapt the IDCP to changing identification needs in varying operational scenarios. Application of results from cognitive psychology and decision theory provides good access to retrieval of Bayesian data and makes configuration easily feasible to operational experts.

Question: How to configure a bayesian identification system?


Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition. Long short-term memory (LSTM) based acoustic modeling methods have recently been shown to give state-of-the-art performance on some speech recognition tasks. To achieve a further performance improvement, in this research, deep extensions on LSTM are investigated considering that deep hierarchical model has turned out to be more efficient than a shallow one. Motivated by previous research on constructing deep recurrent neural networks (RNNs), alternative deep LSTM architectures are proposed and empirically evaluated on a large vocabulary conversational telephone speech recognition task. Meanwhile, regarding to multi-GPU devices, the training process for LSTM networks is introduced and discussed. Experimental results demonstrate that the deep LSTM networks benefit from the depth and yield the state-of-the-art performance on this task.

Question: How to construct deep recurrent neural networks for large vocabulary speech recognition?


FACTORS AFFECTING CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE IN EGYPT. The study makes a significant contribution to the corporate social responsibility (CSR) disclosure literature by offering the first study of its type undertaken in Egypt as an example of a developing country that examines the determinants of individual and aggregated types of CSR information. Using a sample of 111 Egyptian listed companies for the period of 2005–2010, we find that 66% of the Egyptian listed companies disclose on average 10–50 CSR statements. In addition, we find that product/customer information is used extensively by Egyptian listed companies compared with other types of CSR information. Finally we find that profitability is the main determinant for the aggregated and most of individual CSR information in Egypt.

Question: What is the main determinant for the aggregated and most of individual CSR information in Egypt?


Predicting defects using network analysis on dependency graphs. In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics.

Question: What is the difference between a dependency graph and a network graph?


Vitamin D: An overview of vitamin D status and intake in Europe. In recent years, there have been reports suggesting a high prevalence of low vitamin D intakes and vitamin D deficiency or inadequate vitamin D status in Europe. Coupled with growing concern about the health risks associated with low vitamin D status, this has resulted in increased interest in the topic of vitamin D from healthcare professionals, the media and the public. Adequate vitamin D status has a key role in skeletal health. Prevention of the well-described vitamin D deficiency disorders of rickets and osteomalacia are clearly important, but there may also be an implication of low vitamin D status in bone loss, muscle weakness and falls and fragility fractures in older people, and these are highly significant public health issues in terms of morbidity, quality of life and costs to health services in Europe. Although there is no agreement on optimal plasma levels of vitamin D, it is apparent that blood 25-hydroxyvitamin D [25(OH)D] levels are often below recommended ranges for the general population and are particularly low in some subgroups of the population, such as those in institutions or who are housebound and non-Western immigrants. Reported estimates of vitamin D status within different European countries show large variation. However, comparison of studies across Europe is limited by their use of different methodologies. The prevalence of vitamin D deficiency [often defined as plasma 25(OH)D <25 nmol/l] may be more common in populations with a higher proportion of at-risk groups, and/or that have low consumption of foods rich in vitamin D (naturally rich or fortified) and low use of vitamin D supplements. The definition of an adequate or optimal vitamin D status is key in determining recommendations for a vitamin D intake that will enable satisfactory status to be maintained all year round, including the winter months. In most European countries, there seems to be a shortfall in achieving

Question: What is the prevalence of vitamin D deficiency in Europe?


Medial temporal lobe activations in fMRI and PET studies of episodic encoding and retrieval.. Early neuroimaging studies often failed to obtain evidence of medial temporal lobe (MTL) activation during episodic encoding or retrieval, but a growing number of studies using functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) have provided such evidence. We review data from fMRI studies that converge on the conclusion that posterior MTL is associated with episodic encoding; too few fMRI studies of retrieval have reported MTL activations to allow firm conclusions about their exact locations. We then turn to a recent meta-analysis of PET studies (Lepage et al., Hippocampus 1998;8:313-322) that appears to contradict the fMRI encoding data. Based on their analysis of the rostrocaudal distribution of activations reported during episodic encoding or retrieval, Lepage et al. (1998) concluded that anterior MTL is strongly associated with episodic encoding, whereas posterior MTL is strongly associated with episodic retrieval. After considering the evidence reviewed by Lepage et al. (1998) along with additional studies, we conclude that PET studies of encoding reveal both anterior and posterior MTL activations. These observations indicate that the contradiction between fMRI and PET studies of encoding was more apparent than real. However, PET studies have reported anterior MTL encoding activations more frequently than have fMRI studies. We consider possible sources of these differences.

Question: What is the difference between fMRI and PET studies of episodic encoding?


Quadtree Convolutional Neural Networks. This paper presents a Quadtree Convolutional Neural Network (QCNN) for efficiently learning from image datasets representing sparse data such as handwriting, pen strokes, freehand sketches, etc. Instead of storing the sparse sketches in regular dense tensors, our method decomposes and represents the image as a linear quadtree that is only refined in the non-empty portions of the image. The actual image data corresponding to non-zero pixels is stored in the finest nodes of the quadtree. Convolution and pooling operations are restricted to the sparse pixels, leading to better efficiency in computation time as well as memory usage. Specifically, the computational and memory costs in QCNN grow linearly in the number of non-zero pixels, as opposed to traditional CNNs where the costs are quadratic in the number of pixels. This enables QCNN to learn from sparse images much faster and process high resolution images without the memory constraints faced by traditional CNNs. We study QCNN on four sparse image datasets for sketch classification and simplification tasks. The results show that QCNN can obtain comparable accuracy with large reduction in computational and memory costs.

Question: What is a quadtree convolutional neural network?


A multimodal feature learning approach for sentiment analysis of social network multimedia. In this paper we investigate the use of a multimodal feature learning approach, using neural network based models such as Skip-gram and Denoising Autoencoders, to address sentiment analysis of micro-blogging content, such as Twitter short messages, that are composed by a short text and, possibly, an image. The approach used in this work is motivated by the recent advances in: i) training language models based on neural networks that have proved to be extremely efficient when dealing with web-scale text corpora, and have shown very good performances when dealing with syntactic and semantic word similarities; ii) unsupervised learning, with neural networks, of robust visual features, that are recoverable from partial observations that may be due to occlusions or noisy and heavily modified images. We propose a novel architecture that incorporates these neural networks, testing it on several standard Twitter datasets, and showing that the approach is efficient and obtains good classification results.

Question: What is a multimodal feature learning approach?


Increased frontal and paralimbic activation following ayahuasca, the pan-amazonian inebriant. Ayahuasca is a South American psychoactive plant tea which contains the serotonergic psychedelic N,N-dimethyltryptamine (DMT) and monoamine-oxidase inhibitors that render DMT orally active. Previous investigations with ayahuasca have highlighted a psychotropic effect profile characterized by enhanced introspective attention, with individuals reporting altered somatic perceptions and intense emotional modifications, frequently accompanied by visual imagery. Despite recent advances in the study of ayahuasca pharmacology, the neural correlates of acute ayahuasca intoxication remain largely unknown. To investigate the effects of ayahuasca administration on regional cerebral blood flow. Fifteen male volunteers with prior experience in the use of psychedelics received a single oral dose of encapsulated freeze-dried ayahuasca equivalent to 1.0 mg DMT/kg body weight and a placebo in a randomized double-blind clinical trial. Regional cerebral blood flow was measured 100–110 min after drug administration by means of single photon emission tomography (SPECT). Ayahuasca administration led to significant activation of frontal and paralimbic brain regions. Increased blood perfusion was observed bilaterally in the anterior insula, with greater intensity in the right hemisphere, and in the anterior cingulate/frontomedial cortex of the right hemisphere, areas previously implicated in somatic awareness, subjective feeling states, and emotional arousal. Additional increases were observed in the left amygdala/parahippocampal gyrus, a structure also involved in emotional arousal. The present results suggest that ayahuasca interacts with neural systems that are central to interoception and emotional processing and point to a modulatory role of serotonergic neurotransmission in these processes.

Question: What is the role of serotonergic neurotransmission in emotional processing?


Blockchain-Free Cryptocurrencies : A Framework for Truly Decentralised Fast Transactions. The "blockchain" distributed ledger pioneered by Bitcoin is effective at preventing double-spending, but inherently attracts (1) "user cartels" and (2) incompressible delays, as a result of linear verification and a winner-takes-all incentive lottery. We propose to forgo the "blocks" and "chain" entirely, and build a truly distributed ledger system based on a lean graph of cross-verifying transactions, which now become the main and only objects in the system. A fully distributed consensus mechanism, based on progressive proofs of work with predictable incentives, ensures rapid convergence even across a large network of unequal participants, who all get rewards working at their own pace. Graph-based affirmation fosters snappy response through automatic scaling, while application-agnostic design supports all modern cryptocurrency features such as multiple denominations, swaps, securitisation, scripting, smart contracts, etc. We prove theoretically, and experimentally verify, our proposal to show it achieves a crucial "convergence" property — meaning that any valid transaction entering the system will quickly become enshrined into the ancestry upon which all future transactions will rest.

Question: What is a blockchain?


Dealing with the new user cold-start problem in recommender systems: A comparative review. The Recommender System (RS) is an efficient tool for decision makers that assists in the selection of appropriate items according to their preferences and interests. This system has been applied to various domains to personalize applications by recommending items such as books, movies, songs, restaurants, news articles and jokes, among others. An important issue problem, which occurs when there is a new user that has been registered to the system and no prior rating of this user is found in the rating table. In this paper, we first present a classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format. Next, some typical algorithms of these groups, such as MIPFGWC-CS, NHSM, FARAMS and HU–FCF, are described. Finally, these algorithms are implemented and validated on some benchmark RS datasets under various settings of the new user cold start. The experimental results indicate that NHSM achieves better accuracy and computational time than the relevant methods. & 2014 Elsevier Ltd. All rights reserved.

Question: What is the recommender system?


CFO: Conditional Focused Neural Question Answering with Large-scale Knowledge Bases. How can we enable computers to automatically answer questions like "Who created the character Harry Potter"? Carefully built knowledge bases provide rich sources of facts. However, it remains a challenge to answer factoid questions raised in natural language due to numerous expressions of one question. In particular, we focus on the most common questions — ones that can be answered with a single fact in the knowledge base. We propose CFO, a Conditional Focused neuralnetwork-based approach to answering factoid questions with knowledge bases. Our approach first zooms in a question to find more probable candidate subject mentions, and infers the final answers with a unified conditional probabilistic framework. Powered by deep recurrent neural networks and neural embeddings, our proposed CFO achieves an accuracy of 75.7% on a dataset of 108k questions – the largest public one to date. It outperforms the current state of the art by an absolute margin of 11.8%.

Question: How can we enable computers to automatically answer questions like "Who created the character Harry Potter"?


Tamper Hiding: Defeating Image Forensics. This paper introduces novel hiding techniques to counter the detection of image manipulations through forensic analyses. The presented techniques allow to resize and rotate (parts of) bitmap images without leaving a periodic pattern in the local linear predictor coefficients, which has been exploited by prior art to detect traces of manipulation. A quantitative evaluation on a batch of test images proves the proposed method's efficacy, while controlling for key parameters and for the retained image quality compared to conventional linear interpolation.

Question: How to hide a picture?


An Empirical Evaluation of Deep Learning for Network Anomaly Detection. Deep learning has been given a great deal of attention with its success story in many areas such as image analysis and speech recognition. In particular, deep learning is good at dealing with high-dimensional data exhibiting non-linearity. Our preliminary study reveals a very high degree of non-linearity from network traffic data, which explains why it is hard to improve the detection accuracy by using conventional machine learning techniques (e.g., SVM, Random Forest, Ad-aboosting). In this study, we empirically evaluate deep learning to see its feasibility for network anomaly detection. We examine a set of deep learning models constructed based on the Fully Connected Network (FCN), Variational AutoEncoder (VAE), and Long Short-Term Memory with Sequence to Sequence (LSTM Seq2Seq) structures, with two public traffic data sets that have distinctive properties with respect to the distribution of normal and attack populations. Our experimental results confirm the potential of deep learning models for network anomaly detection, and the model based on the LSTM Seq2Seq structure shows a highly promising performance, yielding 99% of binary classification accuracy on the public data sets.

Question: What is the difference between deep learning and machine learning?


Loss Max-Pooling for Semantic Image Segmentation. We introduce a novel loss max-pooling concept for handling imbalanced training data distributions, applicable as alternative loss layer in the context of deep neural networks for semantic image segmentation. Most real-world semantic segmentation datasets exhibit long tail distributions with few object categories comprising the majority of data and consequently biasing the classifiers towards them. Our method adaptively re-weights the contributions of each pixel based on their observed losses, targeting under-performing classification results as often encountered for under-represented object classes. Our approach goes beyond conventional cost-sensitive learning attempts through adaptive considerations that allow us to indirectly address both, inter- and intra-class imbalances. We provide a theoretical justification of our approach, complementary to experimental analyses on benchmark datasets. In our experiments on the Cityscapes and Pascal VOC 2012 segmentation datasets we find consistently improved results, demonstrating the efficacy of our approach.

Question: What is the loss max-pooling concept?


Falling for Fake News: Investigating the Consumption of News via Social Media. In the so called 'post-truth' era, characterized by a loss of public trust in various institutions, and the rise of 'fake news' disseminated via the internet and social media, individuals may face uncertainty about the veracity of information available, whether it be satire or malicious hoax. We investigate attitudes to news delivered by social media, and subsequent verification strategies applied, or not applied, by individuals. A survey reveals that two thirds of respondents regularly consumed news via Facebook, and that one third had at some point come across fake news that they initially believed to be true. An analysis task involving news presented via Facebook reveals a diverse range of judgement forming strategies, with participants relying on personal judgements as to plausibility and scepticism around sources and journalistic style. This reflects a shift away from traditional methods of accessing the news, and highlights the difficulties in combating the spread of fake news.

Question: How do people verify news?


Towards time-optimal race car driving using nonlinear MPC in real-time. This paper addresses the real-time control of autonomous vehicles under a minimum traveling time objective. Control inputs for the vehicle are computed from a nonlinear model predictive control (MPC) scheme. The time-optimal objective is reformulated such that it can be tackled by existing efficient algorithms for real-time nonlinear MPC that build on the generalized Gauss-Newton method. We numerically validate our approach in simulations and present a real-world hardware setup of miniature race cars that is used for an experimental comparison of different approaches.

Question: What is a nonlinear MPC?


Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging.. The relationship between brain structure and complex behavior is governed by large-scale neurocognitive networks. The availability of a noninvasive technique that can visualize the neuronal projections connecting the functional centers should therefore provide new keys to the understanding of brain function. By using high-resolution three-dimensional diffusion magnetic resonance imaging and a newly designed tracking approach, we show that neuronal pathways in the rat brain can be probed in situ. The results are validated through comparison with known anatomical locations of such fibers.

Question: What is the relationship between brain structure and complex behavior?


A fast transient LDO based on dual loop FVF with high PSRR. In this work, the design of a dual loop flipped voltage follower (FVF) based low-dropout regulator (LDO) to achieve fast transient response is proposed for all-digital phase locked loop (ADPLL) in the RFID application. With the help of an additional reference generation loop in FVF LDO and the cascaded structure, high power supply rejection ratio (PSRR) is achievable. This design is fabricated in GF 40nm CMOS technology. The FVF LDO core only occupies small area of 0.036 mm2. This area also includes 80pF on chip capacitor. Without large off-chip capacitor, this LDO is suitable for system-on-chip (SoC) requirement. Post layout simulation shows that fast response of 45ns and high PSRR of −42dB through up to 10GHz frequency range.

Question: What is the area of the LDO?


ETCF: An Ensemble Model for CTR Prediction. Online advertising has attracted lots of attention in both academic and industrial domains. Among many realworld advertising systems, click through rate (CTR) prediction plays a central role. With a large volume of user history log and given its various features, it is quite a challenge to fully extract the meaningful information inside that amount of data. What's more, for many machine learning models, in order to achieve the best performance of the CTR prediction, a lot of hyper-parameters need to be tuned, which often costs plenty of time. In this paper, we propose an ensemble model named ETCF, which cascades GBDT with gcForest to tackle the practical problems of CTR prediction and do not need much hyper-parameter tuning work to realize its best performance. Experimental results validate the effective prediction power of ETCF against classical baseline models, and the superiority of GBDT transformed features.

Question: What is the ensemble model for CTR prediction?


Reducibility Among Combinatorial Problems. A large class of computational problems involve the determination of properties of graphs, digraphs, integers, arrays of integers, finite families of finite sets, boolean formulas and elements of other countable domains. Through simple encodings from such domains into the set of words over a finite alphabet these problems can be converted into language recognition problems, and we can inquire into their computational complexity. It is reasonable to consider such a problem satisfactorily solved when an algorithm for its solution is found which terminates within a number of steps bounded by a polynomial in the length of the input. Many problems with wide applicability – e.g., set cover, knapsack, hitting set, max cut, and satisfiability – lack a polynomial algorithm for solving them, but also lack a proof that no such polynomial algorithm exists. Hence, they remain "open problems." This paper references the recent work, "On the Reducibility of Combinatorial Problems" [1]. BODY A large class of open problems are mutually convertible via poly-time reductions. Hence, either all can be solved in poly-time, or none can. REFERENCES [1] R. Karp. Reducibility Among Combinatorial Problems. In Complexity of Computer Computations, 1972. ∗With apologies to Professor Richard Karp. Volume X of Tiny Transactions on Computer Science This content is released under the Creative Commons Attribution-NonCommercial ShareAlike License. Permission to make digital or hard copies of all or part of this work is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. CC BY-NC-SA 3.0: http://creativecommons.org/licenses/by-nc-sa/3.0/.

Question: What is the computational complexity of combinatorial problems?


Learning Discriminative Affine Regions via. We present an accurate method for estimation of the affine shape of local features. The method is trained in a novel way, exploiting the recently proposed HardNet triplet loss. The loss function is driven by patch descriptor differences, avoiding problems with symmetries. Moreover, such training process does not require precisely geometrically aligned patches. The affine shape is represented in a way amenable to learning by stochastic gradient descent. When plugged into a state-of-the-art wide baseline matching algorithm, the performance on standard datasets improves in both the number of challenging pairs matched and the number of inliers. Finally, AffNet with combination of Hessian detector and HardNet descriptor improves bag-of-visual-words based state of the art on Oxford5k and Paris6k by large margin, 4.5 and 4.2 mAP points respectively. The source code and trained networks are available at https://github.com/ducha-aiki/affnet

Question: How accurate is the affine shape of local features?


Ranking outlier nodes in subspaces of attributed graphs. Outlier analysis is an important data mining task that aims to detect unexpected, rare, and suspicious objects. Outlier ranking enables enhanced outlier exploration, which assists the user-driven outlier analysis. It overcomes the binary detection of outliers vs. regular objects, which is not adequate for many applications. Traditional outlier ranking techniques focus on either vector data or on graph structures. However, many of today's databases store both, multi dimensional numeric information and relations between objects in attributed graphs. An open challenge is how outlier ranking should cope with these different data types in a unified fashion. In this work, we propose a first approach for outlier ranking in subspaces of attributed graphs. We rank graph nodes according to their degree of deviation in both graph and attribute properties. We describe novel challenges induced by this combination of data types and propose subspace analysis as important method for outlier ranking on attributed graphs. Subspace clustering provides a selected subset of nodes and its relevant attributes in which deviation of nodes can be observed. Our graph outlier ranking (GOutRank) introduces scoring functions based on these selected subgraphs and subspaces. In addition to this technical contribution, we provide an attributed graph extracted from the Amazon marketplace. It includes a ground truth of real outliers labeled in a user experiment. In order to enable sustainable and comparable research results, we publish this database on our website1 as benchmark for future publications. Our experiments on this graph demonstrate the potential and the capabilities of outlier ranking in subspaces of attributed graphs.

Question: How to rank outlier nodes in subspaces of attributed graphs?


Students' self-presentation on Facebook: An examination of personality and self-construal factors. The present research seeks to extend existing theory on self-disclosure to the online arena in higher educational institutions and contribute to the knowledge base and understanding about the use of a popular social networking site (SNS), Facebook, by college students. We conducted a non-experimental study to investigate how university students (N = 463) use Facebook, and examined the roles that personality and culture play in disclosure of information in online SNS-based environments. Results showed that individuals do disclose differently online vs. in-person, and that both culture and personality matter. Specifically, it was found that collectivistic individuals low on extraversion and interacting in an online environment disclosed the least honest and the most audience-relevant information, as compared to others. Exploratory analyses also indicate that students use sites such as Facebook primarily to maintain existing personal relationships and selectively used privacy settings to control their self-presentation on SNSs. The findings of this study offer insight into understanding college students' self-disclosure on SNS, add to the literature on personality and self-disclosure, and shape future directions for research and practice on online self-presentation. Published by Elsevier Ltd.

Question: What is the difference between self-presentation and self-disclosure?


Adaptive Deep Brain Stimulation In Advanced Parkinson Disease. OBJECTIVE Brain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.   METHODS We tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.   RESULTS Motor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.   INTERPRETATION BCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.

Question: How does adaptive deep brain stimulation work?


Dynamic Programming for Linear-Time Incremental Parsing. Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedyand only explores a tiny fraction of the whole space (even with beam search) as opposed to dynamic programming. We show that, surprisingly, dynamic programming is in fact possible for many shift-reduce parsers, by merging "equivalent" stacks based on feature values. Empirically, our algorithm yields up to a five-fold speedup over a state-of-the-art shift-reduce dependency parser with no loss in accuracy. Better search also leads to better learning, and our final parser outperforms all previously reported dependency parsers for English and Chinese, yet is much faster.

Question: What is a dependency parser?


Classification of teeth in cone-beam CT using deep convolutional neural network. Dental records play an important role in forensic identification. To this end, postmortem dental findings and teeth conditions are recorded in a dental chart and compared with those of antemortem records. However, most dentists are inexperienced at recording the dental chart for corpses, and it is a physically and mentally laborious task, especially in large scale disasters. Our goal is to automate the dental filing process by using dental x-ray images. In this study, we investigated the application of a deep convolutional neural network (DCNN) for classifying tooth types on dental cone-beam computed tomography (CT) images. Regions of interest (ROIs) including single teeth were extracted from CT slices. Fifty two CT volumes were randomly divided into 42 training and 10 test cases, and the ROIs obtained from the training cases were used for training the DCNN. For examining the sampling effect, random sampling was performed 3 times, and training and testing were repeated. We used the AlexNet network architecture provided in the Caffe framework, which consists of 5 convolution layers, 3 pooling layers, and 2 full connection layers. For reducing the overtraining effect, we augmented the data by image rotation and intensity transformation. The test ROIs were classified into 7 tooth types by the trained network. The average classification accuracy using the augmented training data by image rotation and intensity transformation was 88.8%. Compared with the result without data augmentation, data augmentation resulted in an approximately 5% improvement in classification accuracy. This indicates that the further improvement can be expected by expanding the CT dataset. Unlike the conventional methods, the proposed method is advantageous in obtaining high classification accuracy without the need for precise tooth segmentation. The proposed tooth classification method can be useful in automatic fili

Question: What is the classification accuracy of the proposed method?


A Comparative Study of Data Clustering Algorithms. Data clustering is a process of partitioning data points into meaningful clusters such that a cluster holds similar data and different clusters hold dissimilar data. It is an unsupervised approach to classify data into different patterns. In general, the clustering algorithms can be classified into the following two categories: firstly, hard clustering, where a data object can belong to a single and distinct cluster and secondly, soft clustering, where a data object can belong to different clusters. In this report we have made a comparative study of three major data clustering algorithms highlighting their merits and demerits. These algorithms are: k-means, fuzzy c-means and K-NN clustering algorithm. Choosing an appropriate clustering algorithm for grouping the data takes various factors into account for illustration one is the size of data to be partitioned.

Question: What is a comparative study of data clustering algorithms?


Effect of Video Game Practice on Spatial Skills in Girls and Boys. A study of the effect of video game practice on spatial abilities in girls ond boys wos carried out. Spatial performance, measured using two subtests of a computerized spatial skills battery, was significantly better in boys than in girls during pretest assessment. Subjects then practiced on an action video game, Marble Madness, or a computerized word game, Conjecture. Video game practice wos significantly more effective than the word game in improving spatiol performance on the posttest assessment; there was no significant interaction of gender with experimental treotment. However, video game practice was more effective for children who started out with relatively poor spatial skills. The pattern of results suggests that video games may be useful in equalizing individual differences in spatial skill performance, including those associated with gender.

Question: What is the effect of video game practice on spatial skills in girls and boys?


Additively Manufactured Scaffolds for Bone Tissue Engineering and the Prediction of their Mechanical Behavior: A Review. Additive manufacturing (AM), nowadays commonly known as 3D printing, is a revolutionary materials processing technology, particularly suitable for the production of low-volume parts with high shape complexities and often with multiple functions. As such, it holds great promise for the fabrication of patient-specific implants. In recent years, remarkable progress has been made in implementing AM in the bio-fabrication field. This paper presents an overview on the state-of-the-art AM technology for bone tissue engineering (BTE) scaffolds, with a particular focus on the AM scaffolds made of metallic biomaterials. It starts with a brief description of architecture design strategies to meet the biological and mechanical property requirements of scaffolds. Then, it summarizes the working principles, advantages and limitations of each of AM methods suitable for creating porous structures and manufacturing scaffolds from powdered materials. It elaborates on the finite-element (FE) analysis applied to predict the mechanical behavior of AM scaffolds, as well as the effect of the architectural design of porous structure on its mechanical properties. The review ends up with the authors' view on the current challenges and further research directions.

Question: What is additive manufacturing?


Learning Simpler Language Models with the Delta Recurrent Neural Network Framework. Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks like language modeling. Existing architectures that address the issue are often complex and costly to train. The Delta Recurrent Neural Network (Delta-RNN) framework is a simple and highperforming design that unifies previously proposed gated neural models. The DeltaRNN models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. This requires hardly any more parameters than a classical simple recurrent network. In language modeling at the word and character levels, the models outperform popular complex architectures, such as the Long Short Term Memory (LSTM) and the Gated Recurrent Unit (GRU), and, when regularized, perform comparably to several state-of-the-art baselines. At the subword level, we find the Delta-RNN's performance to be comparable to that of complex gated architectures.

Question: What is the delta recurrent neural network?


SCMA Codebook Design. Multicarrier CDMA is a multiple access scheme in which modulated QAM symbols are spread over OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequences allowing us to take advantage of a near optimal message passing algorithm (MPA) receiver with practically feasible complexity. Sparse code multiple access (SCMA) is a multi-dimensional codebook-based non-orthogonal spreading technique. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to multi-dimensional codewords of SCMA codebook sets. Each layer has its dedicated codebook. Shaping gain of a multi-dimensional constellation is one of the main sources of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. Meanwhile, like LDS, SCMA enjoys the low complexity reception techniques due to the sparsity of SCMA codewords. In this paper a systematic approach is proposed to design SCMA codebooks mainly based on the design principles of lattice constellations. Simulation results are presented to show the performance gain of SCMA compared to LDS and OFDMA.

Question: What is the difference between OFDMA and SCMA?


Transfer learning using computational intelligence: A survey. 26 27 28 29 30 31 32 33 34 35 36 37 38 Article history: Received 3 December 2014 Received in revised form 7 January 2015 Accepted 17 January 2015 Available online xxxx

Question: What is the history of this article?


SIFT Flow: Dense Correspondence across Scenes and Its Applications. While image alignment has been studied in different areas of computer vision for decades, aligning images depicting different scenes remains a challenging problem. Analogous to optical flow, where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes. The SIFT flow algorithm consists of matching densely sampled, pixelwise SIFT features between two images while preserving spatial discontinuities. The SIFT features allow robust matching across different scene/object appearances, whereas the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach robustly aligns complex scene pairs containing significant spatial differences. Based on SIFT flow, we propose an alignment-based large database framework for image analysis and synthesis, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence. This framework is demonstrated through concrete applications such as motion field prediction from a single image, motion synthesis via object transfer, satellite image registration, and face recognition.

Question: How does SIFT flow work?


Conformal printing of electrically small antennas on three-dimensional surfaces.. J. J. Adams ,[+] Prof. J. T. Bernhard Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801, USA E-mail: jbernhar@illinois.edu Dr. E. B. Duoss ,[+,++] T. F. Malkowski ,[+] Dr. B. Y. Ahn , Prof. J. A. Lewis Department of Materials Science and Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 USA E-mail: jalewis@illinois.edu Dr. M. J. Motala , Prof. R. G. Nuzzo Department of Chemistry University of Illinois at Urbana-Champaign Urbana, Illinois 61801, USA [+] These authors contributed equally to this work. [++] Presently at Lawrence Livermore National Laboratory, Center for Microand NanoTechnology, Livermore, CA 94550 USA

Question: What is the difference between the three authors?


KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition. We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The technique developed in this paper adapts the model conservatively by forcing the senone distribution estimated from the adapted model to be close to that from the unadapted model. This constraint is realized by adding Kullback-Leibler divergence (KLD) regularization to the adaptation criterion. We show that applying this regularization is equivalent to changing the target distribution in the conventional backpropagation algorithm. Experiments on Xbox voice search, short message dictation, and Switchboard and lecture speech transcription tasks demonstrate that the proposed adaptation technique can provide 2%-30% relative error reduction against the already very strong speaker independent CD-DNN-HMM systems using different adaptation sets under both supervised and unsupervised adaptation setups.

Question: How to adapt a neural network for speech recognition?


Making TCP/IP Viable for Wireless Sensor Networks. The TCP/IP protocol suite, which has proven itself highly successful in wired networks, is often claimed to be unsuited for wireless micro-sensor networks. In this work, we question this conventional wisdom and present a number of mechanisms that are intended to enable the use of TCP/IP for wireless sensor networks: spatial IP address assignment, shared context header compression, application overlay routing, and distributed TCP caching (DTC). Sensor networks based on TCP/IP have the advantage of being able to directly communicate with an infrastructure consisting either of a wired IP network or of IP-based wireless technology such as GPRS. We have implemented parts of our mechanisms both in a simulator environment and on actual sensor nodes. Our preliminary results are promising.

Question: How to make TCP/IP viable for wireless sensor networks?


A vehicle license plate recognition system based on analysis of maximally stable extremal regions. Vehicle License Plate Recognition (VLPR) system is a core module in Intelligent Transportation Systems (ITS). In this paper, a VLPR system is proposed. Considering that license plate localization is the most important and difficult part in VLPR system, we present an effective license plate localization method based on analysis of Maximally Stable Extremal Region (MSER) features. Firstly, MSER detector is utilized to extract candidate character regions. Secondly, the exact locations of license plates are inferred according to the arrangement of characters in standard license plates. The advantage of this license plate localization method is that less assumption of environmental illumination, weather and other conditions is made. After license plate localization, we continue to recognize the license plate characters and color to complete the whole VLPR system. Finally, the proposed VLPR system is tested on our own collected dataset. The experimental results show the availability and effectiveness of our VLPR system in locating and recognizing all the explicit license plates in an image.

Question: What is a maximally stable extremal region?


The successive mean quantization transform. This paper presents the successive mean quantization transform (SMQT). The transform reveals the organization or structure of the data and removes properties such as gain and bias. The transform is described and applied in speech processing and image processing. The SMQT is considered as an extra processing step for the mel frequency cepstral coefficients commonly used in speech recognition. In image processing the transform is applied in automatic image enhancement and dynamic range compression.

Question: What is the successive mean quantization transform?


Practical Signal-Dependent Noise Parameter Estimation From a Single Noisy Image. The additive white Gaussian noise is widely assumed in many image processing algorithms. However, in the real world, the noise from actual cameras is better modeled as signal-dependent noise (SDN). In this paper, we focus on the SDN model and propose an algorithm to automatically estimate its parameters from a single noisy image. The proposed algorithm identifies the noise level function of signal-dependent noise assuming the generalized signal-dependent noise model and is also applicable to the Poisson-Gaussian noise model. The accuracy is achieved by improved estimation of local mean and local noise variance from the selected low-rank patches. We evaluate the proposed algorithm with both synthetic and real noisy images. Experiments demonstrate that the proposed estimation algorithm outperforms the state-of-the-art methods.

Question: What is the noise model of the camera?


IOT based crop-field monitoring and irrigation automation. Internet Of Things (IoT)is a shared network of objects or things which can interact with each other provided the Internet connection. IoT plays an important role in agriculture industry which can feed 9.6 billion people on the Earth by 2050. Smart Agriculture helps to reduce wastage, effective usage of fertilizer and thereby increase the crop yield. In this work, a system is developed to monitor crop-field using sensors (soil moisture, temperature, humidity, Light) and automate the irrigation system. The data from sensors are sent to Web server database using wireless transmission. In server database the data are encoded in JSON format. The irrigation is automated if the moisture and temperature of the field falls below the brink. In greenhouses light intensity control can also be automated in addition to irrigation. The notifications are sent to farmers' mobile periodically. The farmers' can able to monitor the field conditions from anywhere. This system will be more useful in areas where water is in scarce. This system is 92% more efficient than the conventional approach.

Question: What is IoT?


Deep Learning for Efficient Discriminative Parsing. We propose a new fast purely discriminative algorithm for natural language parsing, based on a "deep" recurrent convolutional graph transformer network (GTN). Assuming a decomposition of a parse tree into a stack of "levels", the network predicts a level of the tree taking into account predictions of previous levels. Using only few basic text features which leverage word representations from Collobert and Weston (2008), we show similar performance (in F1 score) to existing pure discriminative parsers and existing "benchmark" parsers (like Collins parser, probabilistic context-free grammars based), with a huge speed advantage.

Question: What is a "purely discriminative parser"?


Bullying, cyberbullying, and mental health in young people.. OBJECTIVE To investigate the factors associated with exposure to in-real-life (IRL) bullying, cyberbullying, and both IRL and cyberbullying and to explore the relationship between these types of bullying and mental health among 13-16-year-old Swedish boys and girls.   METHODS Data was derived from a cross-sectional web-based study of 13-16-year-old students in northern Sweden (n=1214, response rate 81.9%).   RESULTS The combination of IRL- and cyberbullying was the most common type of bullying. A non-supportive school environment and poor body image were related to exposure to bullying for both genders but the relationship was more distinct in girls. All types of bullying were associated with depressive symptoms in both boys and girls and all forms of bullying increased the likelihood of psychosomatic problems in girls.   CONCLUSIONS Cyberbullying can be seen as an extension of IRL bullying. A combination of IRL- and cyberbullying seems to be particularly negative for mental health. Interventions should focus on improved school environment and body image as well as anti-violence programmes. Gender aspects of bullying need to be acknowledged.

Question: What is the relationship between bullying and mental health?


ERP components on reaction errors and their functional significance: a tutorial. Some years ago we described a negative (Ne) and a later positive (Pe) deflection in the event-related brain potentials (ERPs) of incorrect choice reactions [Falkenstein, M., Hohnsbein, J., Hoormann, J., Blanke, L., 1990. In: Brunia, C.H.M., Gaillard, A.W.K., Kok, A. (Eds.), Psychophysiological Brain Research. Tilburg Univesity Press, Tilburg, pp. 192-195. Falkenstein, M., Hohnsbein, J., Hoormann, J., 1991. Electroencephalography and Clinical Neurophysiology, 78, 447-455]. Originally we assumed the Ne to represent a correlate of error detection in the sense of a mismatch signal when representations of the actual response and the required response are compared. This hypothesis was supported by the results of a variety of experiments from our own laboratory and that of Coles [Gehring, W. J., Goss, B., Coles, M.G.H., Meyer, D.E., Donchin, E., 1993. Psychological Science 4, 385-390. Bernstein, P.S., Scheffers, M.K., Coles, M.G.H., 1995. Journal of Experimental Psychology: Human Perception and Performance 21, 1312-1322. Scheffers, M.K., Coles, M. G.H., Bernstein, P., Gehring, W.J., Donchin, E., 1996. Psychophysiology 33, 42-54]. However, new data from our laboratory and that of Vidal et al. [Vidal, F., Hasbroucq, T., Bonnet, M., 1999. Biological Psychology, 2000] revealed a small negativity similar to the Ne also after correct responses. Since the above mentioned comparison process is also required after correct responses it is conceivable that the Ne reflects this comparison process itself rather than its outcome. As to the Pe, our results suggest that this is a further error-specific component, which is independent of the Ne, and hence associated with a later aspect of error processing or post-error processing. Our new results with different age groups argue against the hypotheses that the Pe reflects conscious error processing or the post-error adjustment of response strategies. Further re

Question: What is the difference between the Ne and the Pe?


Co-Clustering Structural Temporal Data with Applications to Semiconductor Manufacturing. Recent years have witnessed data explosion in semiconductor manufacturing due to advances in instrumentation and storage techniques. The large amount of data associated with process variables monitored over time form a rich reservoir of information, which can be used for a variety of purposes, such as anomaly detection, quality control, and fault diagnostics. In particular, following the same recipe for a certain Integrated Circuit device, multiple tools and chambers can be deployed for the production of this device, during which multiple time series can be collected, such as temperature, impedance, gas flow, electric bias, etc. These time series naturally fit into a two-dimensional array (matrix), i.e., each element in this array corresponds to a time series for one process variable from one chamber. To leverage the rich structural information in such temporal data, in this article, we propose a novel framework named C-Struts to simultaneously cluster on the two dimensions of this array. In this framework, we interpret the structural information as a set of constraints on the cluster membership, introduce an auxiliary probability distribution accordingly, and design an iterative algorithm to assign each time series to a certain cluster on each dimension. Furthermore, we establish the equivalence between C-Struts and a generic optimization problem, which is able to accommodate various distance functions. Extensive experiments on synthetic, benchmark, as well as manufacturing datasets demonstrate the effectiveness of the proposed method.

Question: What is a two-dimensional array?


INFTY: an integrated OCR system for mathematical documents. An integrated OCR system for mathematical documents, called INFTY, is presented. INFTY consists of four procedures, i.e., layout analysis, character recognition, structure analysis of mathematical expressions, and manual error correction. In those procedures, several novel techniques are utilized for better recognition performance. Experimental results on about 500 pages of mathematical documents showed high character recognition rates on both mathematical expressions and ordinary texts, and sufficient performance on the structure analysis of the mathematical expressions.

Question: What is an integrated OCR system for mathematical documents?


DroidLink: Automated Generation of Deep Links for Android Apps. The mobile application (app) has become the main entrance to access the Internet on handheld devices. Unlike the Web where each webpage has a global URL to reach directly, a specific "content page" of an app can be opened only by exploring the app with several operations from the landing page. The interoperability between apps is quite fixed and thus limits the value-added "linked data" between apps. Recently, deep link has been proposed to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier (URI). However, implementing deep link for mobile apps requires a lot of manual efforts by app developers, which can be very error-prone and time-consuming. In this paper, we propose DroidLink to automatically generating deep links for existing Android apps. We design a deep link model suitable for automatic generation. Then we explore the transition of pages and build a navigation graph based on static and dynamic analysis of Android apps. Next, we realize an updating mechanism that keeps on revisiting the target app and discover new pages, and thus generates deep links for every single page of the app. Finally, we repackage the app with deep link supports, but requires no additional deployment requirements. We generate deep links for some popular apps and demonstrate the feasibility of DroidLink.

Question: How to generate deep links for Android apps?


Privacy is dead, long live privacy. Protecting social norms as confidentiality wanes.

Question: What is the difference between privacy and confidentiality?


The Myth of the "Biphasic" Hyaluronic Acid Filler.. BACKGROUND The terms "biphasic" and "monophasic" have been used frequently as a means of differentiating hyaluronic acid (HA) fillers. This type of categorization is based on misinterpretations of the term "phase" and provides no help to the practitioner when selecting the most appropriate product for each indication, patient, and injection technique.   OBJECTIVE The purpose of this study was to analyze the properties of 2 HA filler families; Juvederm (JUV) (Allergan), often stated to be monophasic and Restylane (RES) (Galderma), often stated to be biphasic, and discuss what properties may have led to the use of the terms monophasic and biphasic.   MATERIALS AND METHODS Three different methods were used for JUV and RES: determination of extractable HA; determination of water uptake; and microscopy.   RESULTS The analyzed products were shown to contain both observable gel particles and extractable HA and have the ability to absorb added water.   CONCLUSION The categorization of HA fillers as biphasic or monophasic was shown to be scientifically incorrect and should therefore be avoided. Further analytical measurement of the properties leading to this misinterpretation can provide information to discriminate and categorize HA fillers on a sounder scientific basis.

Question: What is the difference between monophasic and biphasic hyaluronic acid fillers?


A Lender-Based Theory of Collateral∗. We consider an imperfectly competitive loan market in which a local relationship lender has an information advantage vis-à-vis distant transaction lenders. Competitive pressure from the transaction lenders prevents the local lender from extracting the full surplus from projects, so that she inefficiently rejects marginally profitable projects. Collateral mitigates the inefficiency by increasing the local lender's payoff from precisely those marginal projects that she inefficiently rejects. The model predicts that, controlling for observable borrower risk, collateralized loans are more likely to default ex post, which is consistent with the empirical evidence. The model also predicts that borrowers for whom local lenders have a relatively smaller information advantage face higher collateral requirements, and that technological innovations that narrow the information advantage of local lenders, such as small business credit scoring, lead to a greater use of collateral in lending relationships. JEL classification: D82; G21

Question: What is the lender-based theory of collateral?


Embedded Trench Redistribution Layers (RDL) by Excimer Laser Ablation and Surface Planer Processes. This paper reports the demonstration of 2-5 µm embedded trench formation in dry film polymer dielectrics such as Ajinomoto build-up film (ABF) and Polyimide without using chemical mechanical polishing (CMP) process. The trenches in these dielectrics were formed by excimer laser ablation, followed by metallization of trenches by copper plating processes and overburden removal with surface planer tool. The materials and processes integrated in this work are scalable to large panel fabrication at much higher throughput, for interposers and high density fan-out packaging at lower cost and higher performance than silicon interposers.

Question: What is the cost of a silicon interposer?


The dynamics of organizational identity. While many organizational researchers make reference to Mead's theory of social identity, none have explored how Mead's ideas about the relationship between the "I" and the "me" might be extended to identity processes at the organizational level of analysis. In this paper we define organizational analogues for Mead's "I" and "me" and explain how these two phases of organizational identity are related. In doing so we bring together existing theory concerning the links between organizational identities and images, with new theory concerning how reflection embeds identity in organizational culture and how identity expresses cultural understandings through symbols. We offer a model of organizational identity dynamics built on four processes linking organizational identity to culture and image. While the processes linking identity and image (mirroring and impressing) have been described in the literature before, the contribution of this paper lies in articulation of the processes linking identity and culture (reflecting and expressing), and of the interaction of all four processes working dynamically together to create, maintain and change organizational identity. We discuss the implications of our model in terms of two dysfunctions of organizational identity dynamics: narcissism and loss of culture.

Question: How does organizational identity relate to organizational culture?


Online Footsteps to Purchase: Exploring Consumer Behaviors on Online Shopping Sites. As an important part of the Internet economy, online markets have gained much interest in research community as well as industry. Researchers have studied various aspects of online markets including motivations of consumer behaviors on online markets. However, due to the lack of log data of consumers' online behaviors including their purchase, it has not been thoroughly investigated or validated on what drives consumers to purchase products on online markets. Our research moves forward from prior studies by analyzing consumers' actual online behaviors that lead to actual purchases, and using datasets from multiple online shopping sites that can provide comparisons across different types of online shopping sites. We analyzed consumers' buying process and constructed consumers' behavior trajectory to gain deeper understanding of consumer behaviors on online markets. We find that a substantial portion (24%) of consumers in a general-purpose marketplace (like eBay) discover items from external sources (e.g., price comparison sites), while most (>95%) of consumers in a special-purpose shopping site directly access items from the site itself. We also reveal that item browsing patterns and cart usage patterns are the important predictors of the actual purchases. Using behavioral features identified by our analysis, we developed a prediction model to infer whether a consumer purchases item(s). Our prediction model of purchases achieved over 80% accuracy across four different online shopping sites.

Question: What is the difference between a general purpose and a special purpose online shopping site?


Flying Swarm of Drones Over Circulant Digraph. Drones are critical to military collaborative engagement, surveillance, and monitoring of areas of interest. They also play an increasingly influential role in commercial applications. We describe a new innovative method for collisionless, survivable, and persistent flying of given <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> drones over <inline-formula><tex-math notation="LaTeX">$2n$</tex-math></inline-formula> waypoints that results in dispersed coverage of a given area of interest, where <inline-formula><tex-math notation="LaTeX">$n > k \geq 3$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$k$</tex-math> </inline-formula> divides <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>. Our method is fundamentally different than any known to date since it does not need sensors to avoid collisions between drones. In addition, the flight of drones does not have to be coordinated.

Question: How many drones are needed to cover a given area?


Learning a non-linear knowledge transfer model for cross-view action recognition. This paper concerns action recognition from unseen and unknown views. We propose unsupervised learning of a non-linear model that transfers knowledge from multiple views to a canonical view. The proposed Non-linear Knowledge Transfer Model (NKTM) is a deep network, with weight decay and sparsity constraints, which finds a shared high-level virtual path from videos captured from different unknown viewpoints to the same canonical view. The strength of our technique is that we learn a single NKTM for all actions and all camera viewing directions. Thus, NKTM does not require action labels during learning and knowledge of the camera viewpoints during training or testing. NKTM is learned once only from dense trajectories of synthetic points fitted to mocap data and then applied to real video data. Trajectories are coded with a general codebook learned from the same mocap data. NKTM is scalable to new action classes and training data as it does not require re-learning. Experiments on the IXMAS and N-UCLA datasets show that NKTM outperforms existing state-of-the-art methods for cross-view action recognition.

Question: How to learn a non-linear knowledge transfer model for cross-view action recognition?


Generalized Methods for the Design of Quasi-Ideal Symmetric and Asymmetric Coupled-Line Sections and Directional Couplers. Generalized methods for the design of quasi-ideal symmetric and asymmetric coupled-line sections have been proposed. Two cases of coupled-line sections, in which the inductive coupling coefficient is either greater or smaller than the capacitive coupling coefficient, have been separately considered. To compensate for the coupling coefficients' inequality, a combination of lumped capacitors and short coupled-line sections has been introduced into the coupled-line section. The proposed methods allow for designing quasi-ideal coupled-line sections in nearly arbitrarily chosen dielectric media and for arbitrarily chosen coupling. The theoretical analyses have been verified by the design and measurement of three compensated coupled-line directional couplers illustrating application of the proposed methods in different dielectric structures.

Question: How to design a quasi-ideal coupled-line section?


Connective tissue graft to correct peri-implant soft tissue margin: A clinical report.. This clinical report describes the use of a subepithelial connective tissue graft to recontour a soft tissue margin discrepancy for a single-implant crown in the anterior maxilla. This procedure demonstrates that the use of soft tissue grafts to correct an esthetic deficiency may be a feasible approach to establish new and stable peri-implant soft tissue contours. The patient presented was followed for 18 months.

Question: How does a connective tissue graft work?


Discovery-driven graph summarization. Large graph datasets are ubiquitous in many domains, including social networking and biology. Graph summarization techniques are crucial in such domains as they can assist in uncovering useful insights about the patterns hidden in the underlying data. One important type of graph summarization is to produce small and informative summaries based on user-selected node attributes and relationships, and allowing users to interactively drill-down or roll-up to navigate through summaries with different resolutions. However, two key components are missing from the previous work in this area that limit the use of this method in practice. First, the previous work only deals with categorical node attributes. Consequently, users have to manually bucketize numerical attributes based on domain knowledge, which is not always possible. Moreover, users often have to manually iterate through many resolutions of summaries to identify the most interesting ones. This paper addresses both these key issues to make the interactive graph summarization approach more useful in practice. We first present a method to automatically categorize numerical attributes values by exploiting the domain knowledge hidden inside the node attributes values and graph link structures. Furthermore, we propose an interestingness measure for graph summaries to point users to the potentially most insightful summaries. Using two real datasets, we demonstrate the effectiveness and efficiency of our techniques.

Question: What is the difference between a graph and a graph database?


Self-regulation strategies improve self-discipline in adolescents : benefits of mental contrasting and implementation intentions. Self-regulation strategies improve self-discipline in adolescents: benefits of mental contrasting and implementation intentions Angela Lee Duckwortha; Heidi Grantb; Benjamin Loewa; Gabriele Oettingenac; Peter M. Gollwitzerad a Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA b Department of Psychology, Lehigh University, Bethlehem, PA, USA c Department of Psychology, University of Hamburg, Hamburg, Germany d Department of Psychology, University of Konstanz, Konstanz, Germany

Question: What is the benefits of mental contrasting?


1 Basic model for Speech-to-Speech Translation. This paper reviews the technology used in Speech-to-Speech Translation that is the phrases spoken in one language are immediately spoken in another language by the device. Speech-to-Speech Translation is a three step software process which includes Automatic speech Recognition, Machine Translation and voice synthesis. This paper includes the major speech translation projects using different approaches for speech recognition, translation and text to speech synthesis highlighting the major pros and cons for the approach being used.

Question: What is the pros and cons of speech-to-speech translation?


Speaker-listener neural coupling underlies successful communication.. Verbal communication is a joint activity; however, speech production and comprehension have primarily been analyzed as independent processes within the boundaries of individual brains. Here, we applied fMRI to record brain activity from both speakers and listeners during natural verbal communication. We used the speaker's spatiotemporal brain activity to model listeners' brain activity and found that the speaker's activity is spatially and temporally coupled with the listener's activity. This coupling vanishes when participants fail to communicate. Moreover, though on average the listener's brain activity mirrors the speaker's activity with a delay, we also find areas that exhibit predictive anticipatory responses. We connected the extent of neural coupling to a quantitative measure of story comprehension and find that the greater the anticipatory speaker-listener coupling, the greater the understanding. We argue that the observed alignment of production- and comprehension-based processes serves as a mechanism by which brains convey information.

Question: How does the speaker's brain activity influence the listener's brain activity?


AppFusion: Interactive Appearance Acquisition Using a Kinect Sensor. We present an interactive material acquisition system for average users to capture the spatially-varying appearance of daily objects. While an object is being scanned, our system computes its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect, a mirror ball and printed markers. We exploit the Kinect infra-red emitter/receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially-varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture

Question: What is the name of the system?


Sparse Dueling Bandits. The dueling bandit problem is a variation of the classical multi-armed bandit in which the allowable actions are noisy comparisons between pairs of arms. This paper focuses on a new approach for finding the "best" arm according to the Borda criterion using noisy comparisons. We prove that in the absence of structural assumptions, the sample complexity of this problem is proportional to the sum of the inverse squared gaps between the Borda scores of each suboptimal arm and the best arm. We explore this dependence further and consider structural constraints on the pairwise comparison matrix (a particular form of sparsity natural to this problem) that can significantly reduce the sample complexity. This motivates a new algorithm called Successive Elimination with Comparison Sparsity (SECS) that exploits sparsity to find the Borda winner using fewer samples than standard algorithms. We also evaluate the new algorithm experimentally with synthetic and real data. The results show that the sparsity model and the new algorithm can provide significant improvements over standard approaches.

Question: What is the borda winner?


GSM Based Home Automation System UsingApp-Inventor for Android Mobile Phone. Nowadays, the remote Home Automation turns out to be more and more significant and appealing. It improves the value of our lives by automating various electrical appliances or instruments. This paper describes GSM (Global System Messaging) based secured device control system using App Inventor for Android mobile phones. App Inventor is a latest visual programming platform for developing mobile applications for Android-based smart phones. The Android Mobile Phone Platform becomes more and more popular among software developers, because of its powerful capabilities and open architecture. It is a fantastic platform for the real world interface control, as it offers an ample of resources and already incorporates a lot of sensors. No need to write programming codes to develop apps in the App Inventor, instead it provides visual design interface as the way the apps looks and use blocks of interlocking components to control the app's behaviour. The App Inventor aims to make programming enjoyable and accessible to novices.

Question: What is the difference between App Inventor and App Inventor for Android?


Test Case Selection and Prioritization Using Multiple Criteria. Regression testing activities such as test case selection and test case prioritization are ordinarily based on the criteria which focused around code coverage, code modifications and test execution costs. The approach mainly based on the multiple criteria of code coverage which performs efficient selection of test case. The method mainly aims to maximize the coverage size by executing the test cases effectively. The selected test cases are then prioritized based on the priority which depends on the code coverage to achieve the desired results. Keywords—Test case selection, Test case prioritization, Cod coverage, Jaccard distance, Greedy algorithm.

Question: How to select test cases?


A compact branch-line coupler using folded microstrip lines. This paper presents a compact 3 dB Quadrature branch-line coupler using folded microstrip line geometry. Commercially available IE3D software has been used to design and simulate the structure. The proposed structure is simple and takes lesser optimization time. It has been shown that the structure provides about 43.63% compactness compared to the conventional branch line coupler. The structure, therefore, can be incorporated in the microwave circuit design where medium level compactness and faster design are preferred.

Question: What is a compact branch-line coupler?


R-ADMAD: high reliability provision for large-scale de-duplication archival storage systems. Data de-duplication has become a commodity component in data-intensive systems and it is required that these systems provide high reliability comparable to others. Unfortunately, by storing duplicate data chunks just once, de-duped system improves storage utilization at cost of error resilience or reliability. In this paper, R-ADMAD, a high reliability provision mechanism is proposed. It packs variable-length data chunks into fixed sized objects, and exploits ECC codes to encode the objects and distributes them among the storage nodes in a redundancy group, which is dynamically generated according to current status and actual failure domains. Upon failures, R-ADMAD proposes a distributed and dynamic recovery process. Experimental results show that R-ADMAD can provide the same storage utilization as RAID-like schemes, but comparable reliability to replication based schemes with much more redundancy. The average recovery time of R-ADMAD based configurations is about 2-6 times less than RAID-like schemes. Moreover, R-ADMAD can provide dynamic load balancing even without the involvement of the overloaded storage nodes.

Question: What is a de-duplication archival storage system?


IRON: Functional Encryption using Intel SGX. Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.

Question: What is functional encryption?