NativeGuard: protecting android applications from third-party native libraries. Android applications often include third-party libraries written in native code. However, current native components are not well managed by Android's security architecture. We present NativeGuard, a security framework that isolates native libraries from other components in Android applications. Leveraging the process-based protection in Android, NativeGuard isolates native libraries of an Android application into a second application where unnecessary privileges are eliminated. NativeGuard requires neither modifications to Android nor access to the source code of an application. It addresses multiple technical issues to support various interfaces that Android provides to the native world. Experimental results demonstrate that our framework works well with a set of real-world applications, and incurs only modest overhead on benchmark programs.

Question: What is native code?


Deep Reinforcement Learning for Robotic Manipulation. Reinforcement learning holds the promise of enabling autonomous robots to learn large repertoires of behavioral skills with minimal human intervention. However, robotic applications of reinforcement learning often compromise the autonomy of the learning process in favor of achieving training times that are practical for real physical systems. This typically involves introducing hand-engineered policy representations and human-supplied demonstrations. Deep reinforcement learning alleviates this limitation by training general-purpose neural network policies, but applications of direct deep reinforcement learning algorithms have so far been restricted to simulated settings and relatively simple tasks, due to their apparent high sample complexity. In this paper, we demonstrate that a recent deep reinforcement learning algorithm based on offpolicy training of deep Q-functions can scale to complex 3D manipulation tasks and can learn deep neural network policies efficiently enough to train on real physical robots. We demonstrate that the training times can be further reduced by parallelizing the algorithm across multiple robots which pool their policy updates asynchronously. Our experimental evaluation shows that our method can learn a variety of 3D manipulation skills in simulation and a complex door opening skill on real robots without any prior demonstrations or manually designed representations.

Question: What is the difference between reinforcement learning and supervised learning?


Participatory networking: an API for application control of SDNs. We present the design, implementation, and evaluation of an API for applications to control a software-defined network (SDN). Our API is implemented by an OpenFlow controller that delegates read and write authority from the network's administrators to end users, or applications and devices acting on their behalf. Users can then work with the network, rather than around it, to achieve better performance, security, or predictable behavior. Our API serves well as the next layer atop current SDN stacks. Our design addresses the two key challenges: how to safely decompose control and visibility of the network, and how to resolve conflicts between untrusted users and across requests, while maintaining baseline levels of fairness and security. Using a real OpenFlow testbed, we demonstrate our API's feasibility through microbenchmarks, and its usefulness by experiments with four real applications modified to take advantage of it.

Question: What is an API for application control of SDNs?


Implementing linearizability at large scale and low latency. Linearizability is the strongest form of consistency for concurrent systems, but most large-scale storage systems settle for weaker forms of consistency. RIFL provides a general-purpose mechanism for converting at-least-once RPC semantics to exactly-once semantics, thereby making it easy to turn non-linearizable operations into linearizable ones. RIFL is designed for large-scale systems and is lightweight enough to be used in low-latency environments. RIFL handles data migration by associating linearizability metadata with objects in the underlying store and migrating metadata with the corresponding objects. It uses a lease mechanism to implement garbage collection for metadata. We have implemented RIFL in the RAMCloud storage system and used it to make basic operations such as writes and atomic increments linearizable; RIFL adds only 530 ns to the 13.5 μs base latency for durable writes. We also used RIFL to construct a new multi-object transaction mechanism in RAMCloud; RIFL's facilities significantly simplified the transaction implementation. The transaction mechanism can commit simple distributed transactions in about 20 μs and it outperforms the H-Store main-memory database system for the TPC-C benchmark.

Question: What is the difference between linearizability and consistency?


Municipal solid waste source-separated collection in China: A comparative analysis.. A pilot program focusing on municipal solid waste (MSW) source-separated collection was launched in eight major cities throughout China in 2000. Detailed investigations were carried out and a comprehensive system was constructed to evaluate the effects of the eight-year implementation in those cities. This paper provides an overview of different methods of collection, transportation, and treatment of MSW in the eight cities; as well as making a comparative analysis of MSW source-separated collection in China. Information about the quantity and composition of MSW shows that the characteristics of MSW are similar, which are low calorific value, high moisture content and high proportion of organisms. Differences which exist among the eight cities in municipal solid waste management (MSWM) are presented in this paper. Only Beijing and Shanghai demonstrated a relatively effective result in the implementation of MSW source-separated collection. While the six remaining cities result in poor performance. Considering the current status of MSWM, source-separated collection should be a key priority. Thus, a wider range of cities should participate in this program instead of merely the eight pilot cities. It is evident that an integrated MSWM system is urgently needed. Kitchen waste and recyclables are encouraged to be separated at the source. Stakeholders involved play an important role in MSWM, thus their responsibilities should be clearly identified. Improvement in legislation, coordination mechanisms and public education are problematic issues that need to be addressed.

Question: What is the difference between the eight cities?


A real-time method for depth enhanced visual odometry. Visual odometry can be augmented by depth information such as provided by RGB-D cameras, or from lidars associated with cameras. However, such depth information can be limited by the sensors, leaving large areas in the visual images where depth is unavailable. Here, we propose a method to utilize the depth, even if sparsely available, in recovery of camera motion. In addition, the method utilizes depth by structure from motion using the previously estimated motion, and salient visual features for which depth is unavailable. Therefore, the method is able to extend RGBD visual odometry to large scale, open environments where depth often cannot be sufficiently acquired. The core of our method is a bundle adjustment step that refines the motion estimates in parallel by processing a sequence of images, in a batch optimization. We have evaluated our method in three sensor setups, one using an RGB-D camera, and two using combinations of a camera and a 3D lidar. Our method is rated #4 on the KITTI odometry benchmark irrespective of sensing modality—compared to stereo visual odometry methods which retrieve depth by triangulation. The resulting average position error is 1.14% of the distance traveled.

Question: How does the method work?


Making Smart Contracts Smarter. Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins.  In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.

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


Practical Private Set Intersection Protocols with Linear Complexity. The constantly increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection (if one exists), while neither should learn anything about other set elements. Although prior work has yielded a number of effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.

Question: What is the state-of-the-art in private set intersection protocols?


Control of microglial neurotoxicity by the fractalkine receptor. Microglia, the resident inflammatory cells of the CNS, are the only CNS cells that express the fractalkine receptor (CX3CR1). Using three different in vivo models, we show that CX3CR1 deficiency dysregulates microglial responses, resulting in neurotoxicity. Following peripheral lipopolysaccharide injections, Cx3cr1−/− mice showed cell-autonomous microglial neurotoxicity. In a toxic model of Parkinson disease and a transgenic model of amyotrophic lateral sclerosis, Cx3cr1−/− mice showed more extensive neuronal cell loss than Cx3cr1+ littermate controls. Augmenting CX3CR1 signaling may protect against microglial neurotoxicity, whereas CNS penetration by pharmaceutical CX3CR1 antagonists could increase neuronal vulnerability.

Question: What is the fractalkine receptor?


A simplified extension of the Area under the ROC to the multiclass domain. The Receiver Operator Characteristic (ROC) plot allows a classifier to be evaluated and optimised over all possible operating points. The Area Under the ROC (AUC) has become a standard performance evaluation criterion in two-class pattern recognition problems, used to compare different classification algorithms independently of operating points, priors, and costs. Extending the AUC to the multiclass case is considered in this paper, called the volume under the ROC hypersurface (VUS). A simplified VUS measure is derived that ignores specific intraclass dimensions, and regards inter-class performances only. It is shown that the VUS measure generalises from the 2-class case, but the bounds between random and perfect classification differ, with the lower bound tending towards zero as the dimensionality increases. A number of experiments with known distributions are used to verify the bounds, and to investigate a numerical integration approach to estimating the VUS. Experiments on real data compare several competing classifiers in terms of both error-rate and VUS. It was found that some classifiers compete in terms of error-rate, but have significantly different VUS scores, illustrating the importance of the VUS approach.

Question: What is the volume under the ROC hypersurface?


Geometric mechanics of curved crease origami.. Folding a sheet of paper along a curve can lead to structures seen in decorative art and utilitarian packing boxes. Here we present a theory for the simplest such structure: an annular circular strip that is folded along a central circular curve to form a three-dimensional buckled structure driven by geometrical frustration. We quantify this shape in terms of the radius of the circle, the dihedral angle of the fold, and the mechanical properties of the sheet of paper and the fold itself. When the sheet is isometrically deformed everywhere except along the fold itself, stiff folds result in creases with constant curvature and oscillatory torsion. However, relatively softer folds inherit the broken symmetry of the buckled shape with oscillatory curvature and torsion. Our asymptotic analysis of the isometrically deformed state is corroborated by numerical simulations that allow us to generalize our analysis to study structures with multiple curved creases.

Question: What is the simplest structure that can be folded from a sheet of paper?


Usability, quality, value and e-learning continuance decisions. Previous research suggests that an eventual information technology (IT) success depend on both its initial adoption (acceptance) and subsequent continued usage (continuance). Expectancy disconfirmation theory (EDT) has been successfully used to predict users intention to continue using information technologies. This study proposed a decomposed EDT model to examine cognitive beliefs and affect that influence users continuance decision in the context of e-learning service. The proposed model extended EDT by decomposing the perceived performance component into usability, quality, and value. Research hypotheses derived from this model are empirically validated using the responses to a survey on e-learning usage among 183 users. The results suggest that users continuance intention is determined by satisfaction, which in turn is jointly determined by perceived usability, perceived quality, perceived value, and usability disconfirmation. 2004 Elsevier Ltd. All rights reserved.

Question: How can I make my e-learning service more usable?


Bayesian Optimization with Empirical Constraints ( PhD Proposal ). This work is motivated by the experimental design problem of optimizing the power output of nano-enhanced microbial fuel cells. Microbial fuel cells (MFCs) (Bond and Lovley, 2003; Fan et al., 2007; Park and Zeikus, 2003; Reguera, 2005) use micro-organisms to break down organic matter and generate electricity. For a particular MFC design, it is critical to optimize the biological energetics and the microbial/electrode interface of the system, which research has shown to depend strongly on the surface properties of the anodes (Park and Zeikus, 2003; Reguera, 2005). This motivates the design of nano-enhanced anodes, where nano-structures (e.g. carbon nano-wire) are grown on the anode surface to improve the MFC's power output. Unfortunately, there is little understanding of the interaction between various possible nano-enhancements and MFC capabilities for different micro-organisms. Thus, optimizing anode design for a particular application is largely guess work. Our goal is to develop algorithms to aid this process. Bayesian optimization (Jones, 2001a; Brochu et al., 2009) has been widely used for experimental design problems where the goal is to optimize an unknown function f(·), that is costly to evaluate. In general, we are interested in finding the point x∗ ∈ X d ⊂ Rd such that: x∗ = argmax x∈X d f(x), (1)

Question: What is the goal of the research?


Segmented handwritten text recognition with recurrent neural network classifiers. Recognition of handwritten text is a useful technique that can be applied in different applications, such as signature recognition, bank check recognition, etc. However, the off-line handwritten text recognition in an unconstrained situation is still a very challenging task due to the high complexity of text strokes and image background. This paper presents a novel segmented handwritten text recognition technique that ensembles recurrent neural network (RNN) classifiers. Two RNN models are first trained that take advantage of the widely used geometrical feature and the Histogram of Oriented Gradient (HOG) feature, respectively. Given a handwritten word image, the optimal recognition result is then obtained by integrating the two trained RNN models together with a lexicon. Experiments on public datasets show the superior performance of our proposed technique.

Question: What is the difference between a geometrical feature and a histogram of oriented gradient?


A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Traditional rough set theory is mainly used to extract rules from and reduce attributes in databases in which attributes are characterized by partitions, while the covering rough set theory, a generalization of traditional rough set theory, does the same yet characterizes attributes by covers. In this paper, we propose a way to reduce the attributes of covering decision systems, which are databases characterized by covers. First, we define consistent and inconsistent covering decision systems and their attribute reductions. Then, we state the sufficient and the necessary conditions for reduction. Finally, we use a discernibility matrix to design algorithms that compute all the reducts of consistent and inconsistent covering decision systems. Numerical tests on four public data sets show that the proposed attribute reductions of covering decision systems accomplish better classification performance than those of traditional rough sets. 2007 Elsevier Inc. All rights reserved.

Question: What is a discernibility matrix?


The SIMON and SPECK lightweight block ciphers. The Simon and Speck families of block ciphers were designed specifically to offer security on constrained devices, where simplicity of design is crucial. However, the intended use cases are diverse and demand flexibility in implementation. Simplicity, security, and flexibility are ever-present yet conflicting goals in cryptographic design. This paper outlines how these goals were balanced in the design of Simon and Speck.

Question: What is a block cipher?


Selective Encoding for Abstractive Sentence Summarization. We propose a selective encoding model to extend the sequence-to-sequence framework for abstractive sentence summarization. It consists of a sentence encoder, a selective gate network, and an attention equipped decoder. The sentence encoder and decoder are built with recurrent neural networks. The selective gate network constructs a second level sentence representation by controlling the information flow from encoder to decoder. The second level representation is tailored for sentence summarization task, which leads to better performance. We evaluate our model on the English Gigaword, DUC 2004 and MSR abstractive sentence summarization datasets. The experimental results show that the proposed selective encoding model outperforms the state-ofthe-art baseline models.

Question: What is the selective encoding model?


Affect Analysis Model: novel rule-based approach to affect sensing from text. In this paper, we address the tasks of recognition and interpretation of affect communicated through text messaging in online communication environments. Specifically, we focus on Instant Messaging (IM) or blogs, where people use an informal or garbled style of writing. We introduced a novel rule-based linguistic approach for affect recognition from text. Our Affect Analysis Model (AAM) was designed to deal with not only grammatically and syntactically correct textual input, but also informal messages written in an abbreviated or expressive manner. The proposed rule-based approach processes each sentence in stages, including symbolic cue processing, detection and transformation of abbreviations, sentence parsing and word/phrase/sentence-level analyses. Our method is capable of processing sentences of different complexity, including simple, compound, complex (with complement and relative clauses) and complex–compound sentences. Affect in text is classified into nine emotion categories (or neutral). The strength of the resulting emotional state depends on vectors of emotional words, relations among them, tense of the analysed sentence and availability of first person pronouns. The evaluation of the Affect Analysis Model algorithm showed promising results regarding its capability to accurately recognize fine-grained emotions reflected in sentences from diary-like blog posts (averaged accuracy is up to 77 per cent), fairy tales (averaged accuracy is up to 70.2 per cent) and news headlines (our algorithm outperformed eight other systems on several measures).

Question: What is the affect analysis model?


FeatureAnalytics: An approach to derive relevant attributes for analyzing Android Malware. Ever increasing number of Android malware, has always been a concern for cybersecurity professionals. Even though plenty of anti-malware solutions exist, a rational and pragmatic approach for the same is rare and has to be inspected further. In this paper, we propose a novel two-set feature selection approach based on Rough Set and Statistical Test named as RSST to extract relevant system calls. To address the problem of higher dimensional attribute set, we derived suboptimal system call space by applying the proposed feature selection method to maximize the separability between malware and benign samples. Comprehensive experiments conducted on a dataset consisting of 3500 samples with 30 RSST derived essential system calls resulted in an accuracy of 99.9%, Area Under Curve (AUC) of 1.0, with 1% False Positive Rate (FPR). However, other feature selectors (Information Gain, CFsSubsetEval, ChiSquare, FreqSel and Symmetric Uncertainty) used in the domain of malware analysis resulted in the accuracy of 95.5% with 8.5% FPR. Besides, empirical analysis of RSST derived system calls outperform other attributes such as permissions, opcodes, API, methods, call graphs, Droidbox attributes and network traces.

Question: What is the accuracy of the proposed approach?


Emotional intelligence: the most potent factor in the success equation.. Star performers can be differentiated from average ones by emotional intelligence. For jobs of all kinds, emotional intelligence is twice as important as a person's intelligence quotient and technical skills combined. Excellent performance by top-level managers adds directly to a company's "hard" results, such as increased profitability, lower costs, and improved customer retention. Those with high emotional intelligence enhance "softer" results by contributing to increased morale and motivation, greater cooperation, and lower turnover. The author discusses the five components of emotional intelligence, its role in facilitating organizational change, and ways to increase an organization's emotional intelligence.

Question: What is emotional intelligence?


Genital trauma and vaginal bleeding: is it a lapse of time issue? A case report of a prepubertal girl and review of the literature. Child victims of sexual abuse may present with physical findings whose interpretation requires the most exhaustive evaluation and an accurate collection of a detailed history. Genital bleeding is usually considered as an acute sign, related to a trauma that occurred shortly before its appearance. We report a case of a 34-month-old child who was referred to the emergency room with a significant vaginal hemorrhage, originating from a wide laceration of the posterior fourchette, and a negative history for accidental trauma. The characteristics of the lesion, compared to the temporal evolution of the healing process, and the witnesses' depositions led us to assume that the time elapsed between the abusive event and the physical examination was longer in respect to what had appeared at the first sight. The judicial reconstruction of the events confirmed our assumption, allowing the charge of the right abuse perpetrator. As the literature regarding this eventuality is very poor, we report this case to stress the importance for physicians to consider that an active bleeding may be the manifestation of a trauma that occurred very long before.

Question: What is the time elapsed between the abusive event and the physical examination?


Mycorrhizosphere interactions to improve plant fitness and soil quality. Arbuscular mycorrhizal fungi are key components of soil microbiota and obviously interact with other microorganisms in the rhizosphere, i.e. the zone of influence of plant roots on microbial populations and other soil constituents. Mycorrhiza formation changes several aspects of plant physiology and some nutritional and physical properties of the rhizospheric soil. These effects modify the colonization patterns of the root or mycorrhizas (mycorrhizosphere) by soil microorganisms. The rhizosphere of mycorrhizal plants, in practice a mycorrhizosphere, harbors a great array of microbial activities responsible for several key ecosystem processes. This paper summarizes the main conceptual principles and accepted statements on the microbial interactions between mycorrhizal fungi and other members of rhizosphere microbiota and discusses current developments and future trends concerning the following topics: (i) effect of soil microorganisms on mycorrhiza formation; (ii) mycorrhizosphere establishment; (iii) interactions involved in nutrient cycling and plant growth; (iv) interactions involved in the biological control of plant pathogens; and (v) interactions to improve soil quality. The main conclusion is that microbial interactions in the rhizosphere of mycorrhizal plants improve plant fitness and soil quality, critical issues for a sustainable agricultural development and ecosystem functioning.

Question: What is the mycorrhizosphere?


Social-driven Internet of Connected Objects. Internet evolution has been recently related with some aspect of user empowerment, mostly in terms of content distribution, and this has been ultimately accelerated by the fast-paced introduction and expansion of wireless technologies. Hence, the Internet should start to be seen as a communications infrastructure able to support the integration of a myriad of embedded and personal wireless objects. This way a future Internet will support the interaction between users' social, physical and virtual sphere. This position paper aims to raise some discussion about the technology required to ensure an efficient interaction between the physical, social and virtual worlds by extending the Internet by means of interconnected objects. Namely, it is argued that an efficient interaction between the physical, social and virtual worlds requires the development of a data-centric architecture based on IP-driven opportunisitc networking able to make useful data available to people when and where they really need it, augmenting their social and environmental awareness.

Question: What is the future of the internet?


Action Design Research and Visualization Design. In applied visualization research, artifacts are shaped by a series of small design decisions, many of which are evaluated quickly and informally via methods that often go unreported and unverified. Such design decisions are influenced not only by visualization theory, but also by the people and context of the research. While existing applied visualization models support a level of reliability throughout the design process, they fail to explicitly account for the influence of the research context in shaping the resulting design artifacts. In this work, we look to action design research (ADR) for insight into addressing this issue. In particular, ADR offers a framework along with a set of guiding principles for navigating and capitalizing on the disruptive, subjective, human-centered nature of applied design work, while aiming to ensure reliability of the process and design, and emphasizing opportunities for conducting research. We explore the utility of ADR in increasing the reliability of applied visualization design research by: describing ADR in the language and constructs developed within the visualization community; comparing ADR to existing visualization methodologies; and analyzing a recent design study retrospectively through the lens of ADR's framework and principles.

Question: What is action design research?


Nonword repetition and sentence repetition as clinical markers of specific language impairment: the case of Cantonese.. PURPOSE Recent research suggests that nonword repetition (NWR) and sentence repetition (SR) tasks can be used to discriminate between children with SLI and their typically developing age-matched (TDAM) and younger (TDY) peers.   METHOD Fourteen Cantonese-speaking children with SLI and 30 of their TDAM and TDY peers were compared on NWR and SR tasks. NWR of IN nonwords (CV combinations attested in the language) and OUT nonwords (CV combinations unattested in the language) were compared. SR performance was compared using 4 different scoring methods.   RESULTS The SLI group did not score significantly lower than the TDAM group on the test of NWR (overall results were TDAM = SLI > TDY). There were nonsignificant group differences on IN syllables but not on OUT syllables. The results do not suggest a limitation in phonological working memory in Cantonese-speaking children with SLI. The SR task discriminated between children and their TDAM peers but not between children with SLI and their TDY peers matched for mean length of utterance.   CONCLUSIONS SR but not NWR discriminates between children with SLI and their TDAM peers. Poorer NWR for English-speaking children with SLI might be attributable to weaker use of the redintegration strategy in word repetition. Further cross-linguistic investigations of processing strategies are required.

Question: What is the difference between the redintegration and the chunking strategies?


Perspectives on usability guidelines for smartphone applications: An empirical investigation and systematic literature review. Context: Several usability guidelines have been proposed to improve the usability of smartphone apps. These guidelines can be classified into three disjoint sets: platform specific guidelines, genre specific guidelines, and generic guidelines. However, smartphone applications are usually developed for multiple platforms and targeted for a variety of users. Hence the usefulness of existing guidelines is severally limited. Objective: This study aims to develop a comprehensive list of usability guidelines suitable for multiple platforms and genres of smartphone applications.

Question: What is a usability guideline?


The apnea-ECG database. Sleep apnea is a sleep disorder with a high prevalence in the adult male population. Sleep apnea is regarded as an independent risk factor,for cardiovascular sequelae such as ischemic heart attacks and stroke. The diagnosis of sleep apnea requires polysomnographic studies in sleep laboratories with expensive equipment and attending personnel. Sleep apnea can be treated effectively using nasal ventilation therapy (nCPAP). Early recognition and selection of patients with sleep related breathing disorders is an important task. Although it has been suggested that this can be done on the basis of the ECG, careful quantitative studies of the accuracy of such techniques are needed. An annotated database with 70 nighttime ECG recordings has been created to support such studies. The annotations were based on visual scoring of disordered breathing during sleep.

Question: What is the apnea-ECG database?


Supervised Learning Based Algorithm Selection for Deep Neural Networks. Many recent deep learning platforms rely on thirdparty libraries (such as cuBLAS) to utilize the computing power of modern hardware accelerators (such as GPUs). However, we observe that they may achieve suboptimal performance because the library functions are not used appropriately. In this paper, we target at optimizing the operations of multiplying a matrix with the transpose of another matrix (referred to as NT operation hereafter), which contribute half of the training time of fully connected deep neural networks. Rather than directly calling the library function, we propose a supervised learning based algorithm selection approach named MTNN, which uses a gradient boosted decision tree to select one from two alternative NT implementations intelligently: (1) calling the cuBLAS library function; (2) calling our proposed algorithm TNN that uses an efficient out-of-place matrix transpose. We evaluate the performance of MTNN on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve 96% of prediction accuracy with very low computational overhead, which results in an average of 54% performance improvement on a range of NT operations. To further evaluate the impact of MTNN on the training process of deep neural networks, we have integrated MTNN into a popular deep learning platform Caffe. Our experimental results show that the revised Caffe can outperform the original one by an average of 28%. Both MTNN and the revised Caffe are open-source.

Question: What is the impact of MTNN on the training process of deep neural networks?


Miniaturized dual-band dipole antenna loaded with metamaterial based structure. In this paper, a new miniaturized double-sided printed dipole antenna loaded with balanced Capacitively Loaded Loops (CLLs) as Metamaterial structure is presented. CLLs placed close to the edge of the printed antenna cause the antenna to radiate at two different frequencies, one of which is lower than self-resonant frequency of dipole antenna. In the other words, the loaded dipole antenna can perform at low frequency as compared with natural resonance frequency of unload half wavelength dipole. Finally, the CLL element is integrated with chip capacitor to provide a larger capacitance which in turn allows the resulting CLL element to resonate at a lower frequency. It is demonstrated that the proposed loaded dipole antenna is a dual band radiator with sufficient gain suitable for applications such as mobile communication and Industrial, Scientific and Medical (ISM) system. Prototype of miniaturized double resonant dipole antenna is fabricated and tested. The measured results are in good agreement with those obtained from simulation.

Question: What is a dual band radiator?


A Low-Noise Amplifier With Tunable Interference Rejection for 3.1- to 10.6-GHz UWB Systems. An ultrawideband common-gate low noise amplifier with tunable interference rejection is presented. The proposed LNA embeds a tunable active notch filter to eliminate interferer at 5-GHz WLAN and employs a common-gate input stage and dual-resonant loads for wideband implementation. This LNA has been fabricated in a 0.18-¿m CMOS process. The measured maximum power gain is 13.2 dB and noise figure is 4.5-6.2 dB with bandwidth of 3.1-10.6 GHz. The interferer rejection is 8.2 dB compared to the maximum gain and 7.6 dB noise figure at 5.2 GHz , respectively. The measured input P1dB is -11 dBm at 10.3 GHz. It consumes 12.8 mA from 1.8-V supply voltage.

Question: What is the maximum power gain?


Exploiting semantic web knowledge graphs in data mining. Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. The tasks performed in that field are knowledge intensive and can often benefit from using additional knowledge from various sources. Therefore, many approaches have been proposed in this area that combine Semantic Web data with the data mining and knowledge discovery process. Semantic Web knowledge graphs are a backbone of many information systems that require access to structured knowledge. Such knowledge graphs contain factual knowledge about real word entities and the relations between them, which can be utilized in various natural language processing, information retrieval, and any data mining applications. Following the principles of the Semantic Web, Semantic Web knowledge graphs are publicly available as Linked Open Data. Linked Open Data is an open, interlinked collection of datasets in machine-interpretable form, covering most of the real world domains. In this thesis, we investigate the hypothesis if Semantic Web knowledge graphs can be exploited as background knowledge in different steps of the knowledge discovery process, and different data mining tasks. More precisely, we aim to show that Semantic Web knowledge graphs can be utilized for generating valuable data mining features that can be used in various data mining tasks. Identifying, collecting and integrating useful background knowledge for a given data mining application can be a tedious and time consuming task. Furthermore, most data mining tools require features in propositional form, i.e., binary, nominal or numerical features associated with an instance, while Linked Open Data sources are usually graphs by nature. Therefore, in Part I, we evaluate unsupervised feature generation strategies from types and relations in knowledge graphs, which are used in different data mining tasks, i.e., classification, regre

Question: What is the difference between a knowledge graph and a semantic web knowledge graph?


Estimating the Tensor of Curvature of a Surface from a Polyhedral Approximation. Estimating principal curvatures and principal directions of a surface from a polyhedral approximation with a large number of small faces, such as those produced by iso-surface construction algorithms, has become a basic step in many computer vision algorithms. Particularly in those targeted at medical applications. In this paper we describe a method to estimate the tensor of curvature of a surface at the vertices of a polyhedral approximation. Principal curvatures and principal directions are obtained by computing in closed form the eigenvalues and eigenvectors of certain 3 3 symmetric matrices defined by integral formulas, and closely related to the matrix representation of the tensor of curvature. The resulting algorithm is linear, both in time and in space, as a function of the number of vertices and faces of the polyhedral surface.

Question: How to estimate the tensor of curvature of a surface from a polyhedral approximation?


Understanding the Dynamic Interplay of Social Buzz and Contribution Behavior within and between Online Platforms - Evidence from Crowdfunding. Motivated by the growing interconnection between online platforms, we examine the dynamic interplay between social buzz and contribution behavior in the crowdfunding context. Since the utility of crowdfunding projects is usually difficult to ascertain, prospective backers draw on quality signals, such as social buzz and prior-contribution behavior, to make their funding decisions. We employ the panel vector autoregression (PVAR) methodology to investigate both intraand cross-platform effects based on data collected from three platforms: Indiegogo, one of the largest crowdfunding platforms on the web, Twitter and Facebook. Our results show a positive influence of social buzz on project backing, but a negative relationship in the reverse direction. Furthermore, we observe strong positive feedback cycles within each platform. Our results are supplemented by split-sample analyses for project orientation (Social, Cause and Entrepreneurial) and project success (Winners vs. Losers), in which Facebook shares were identified as a critical success factor.

Question: What is the dynamic interplay between social buzz and contribution behavior within and between online platforms?


Question Answering on Freebase via Relation Extraction and Textual Evidence. Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extraction method with additional evidence from Wikipedia. We first present a neural network based relation extractor to retrieve the candidate answers from Freebase, and then infer over Wikipedia to validate these answers. Experiments on the WebQuestions question answering dataset show that our method achieves an F1 of 53.3%, a substantial improvement over the state-of-the-art.

Question: What is the relation between the Canadian Armed Forces and the Canadian Navy?


"The Internet is a Mask": High School Students' Suggestions for Preventing Cyberbullying. INTRODUCTION Interactions through technology have an important impact on today's youth. While some of these interactions are positive, there are concerns regarding students engaging in negative interactions like cyberbullying behaviors and the negative impact these behaviors have on others. The purpose of the current study was to explore participant suggestions for both students and adults for preventing cyberbullying incidents.   METHODS Forty high school students participated in individual, semi-structured interviews. Participant experiences and perceptions were coded using constant comparative methods to illustrate ways in which students and adults may prevent cyberbullying from occurring within their school and community.   RESULTS Students reported that peers would benefit from increasing online security, as well as becoming more aware of their cyber-surroundings. Regarding adult-provided prevention services, participants often discussed that there is little adults can do to reduce cyberbullying. Reasons included the difficulties in restricting online behaviors or providing effective consequences. However, some students did discuss the use of in-school curricula while suggesting that adults blame people rather than technology as potential ways to prevent cyberbullying.   CONCLUSION Findings from the current study indicate some potential ways to improve adult efforts to prevent cyberbullying. These strategies include parent/teacher training in technology and cyberbullying, interventions focused more on student behavior than technology restriction, and helping students increase their online safety and awareness.

Question: What are some ways to prevent cyberbullying?


Correcting the Document Layout: A Machine Learning Approach. In this paper, a machine learning approach to support the user during the correction of the layout analysis is proposed. Layout analysis is the process of extracting a hierarchical structure describing the layout of a page. In our approach, the layout analysis is performed in two steps: firstly, the global analysis determines possible areas containing paragraphs, sections, columns, figures and tables, and secondly, the local analysis groups together blocks that possibly fall within the same area. The result of the local analysis process strongly depends on the quality of the results of the first step. We investigate the possibility of supporting the user during the correction of the results of the global analysis. This is done by allowing the user to correct the results of the global analysis and then by learning rules for layout correction from the sequence of user actions. Experimental results on a set of multi-page documents are reported and commented. 1. Background and motivation Strategies for the extraction of layout analysis have been traditionally classified as top-down or bottom-up [10]. In top-down methods, the document image is repeatedly decomposed into smaller and smaller components, while in bottom-up methods, basic layout components are extracted from bitmaps and then grouped together into larger blocks on the basis of their characteristics. In WISDOM++, a document image analysis system that can transform paper documents into XML format [1], the applied page decomposition method is hybrid, since it combines a top-down approach to segment the document image, and a bottom-up layout analysis method to assemble basic blocks into frames. Some attempts to learn the layout structure from a set of training examples have also been reported in the literature [2,3,4,7,11]. They are based on ad-hoc learning algorithms, which learn particular data structures, such as geometric trees and tree grammars. Re

Question: What is a hybrid approach?


Enhanced parallel cat swarm optimization based on the Taguchi method. In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the Liu, Chen, and Chou method (2009). The optimal solutions can be found by the proposed EPCSO method in a very short time. 2011 Elsevier Ltd. All rights reserved.

Question: What is the aircraft schedule recovery problem?


Depeche Mood: a Lexicon for Emotion Analysis from Crowd Annotated News. While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting – in a totally automated way – a highcoverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a naı̈ve approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.

Question: What is the size of the depeche mood lexicon?


Knowledge Begets Knowledge: Steps towards Assisted Knowledge Acquisition in Cyc. The Cyc project is predicated on the idea that, in order to be effective and flexible, computer software must have an understanding of the context in which its tasks are performed. We believe this context is what is known informally as "common sense." Over the last twenty years, sufficient common sense knowledge has been entered into Cyc to allow it to more effectively and flexibly support an important task: increasing its own store of world knowledge. In this paper, we describe the Cyc knowledge base and inference system, enumerate the means that it provides for knowledge elicitation, including some means suitable for use by untrained or lightly trained volunteers, review some ways in which we expect to have Cyc assist in verifying and validating collected knowledge, and describe how we expect the knowledge acquisition process to accelerate in the future.

Question: What is the cyc project?


Food vs Non-Food Classification. Automatic understanding of food is an important research challenge. Food recognition engines can provide a valid aid for automatically monitoring the patient's diet and food-intake habits directly from images acquired using mobile or wearable cameras. One of the first challenges in the field is the discrimination between images containing food versus the others. Existing approaches for food vs non-food classification have used both shallow and deep representations, in combination with multi-class or one-class classification approaches. However, they have been generally evaluated using different methodologies and data, making a real comparison of the performances of existing methods unfeasible. In this paper, we consider the most recent classification approaches employed for food vs non-food classification, and compare them on a publicly available dataset. Different deep-learning based representations and classification methods are considered and evaluated.

Question: What is the most recent classification approach employed for food vs non-food classification?


Automatic linkage of vital records.. The term record linkage has been used to indicate the bringing together of two or more separately re?orded pieces of information concerning a particular individual or family (I). Defined in this broad manner, it includes almost any use of a file of records to determine what has subsequently happened to people about whom one has some prior information, The various facts concerning an individual which in any modern society are recorded routinely would, if brought together, form an extensively documented history of his 1ife. In theory at least, an understanding might be derived from such collective histories concerning many of the factors which operate to influence the welfare of human populations, factors about which we are at present almost entirely in ignorance. Of course, much of the recorded information is in a relatively inaccessible form; but, even when circumstances have been most favorable, as in the registrations of births, deaths, and marriages, and in the census, there has been little recognition of the special value of the records as a source of statistics when they are brought together so as to relate the successive events in the lives of particular individuals and families. The chief reason for this lies in the high cost of searching manually for large numbers of single documents among vast accumulations of files. It is obvious that the searching could be mechanized, but as yet there has been no clear demonstration that machines can carry out the record linkages rapidly enough, cheaply enough, and with sufficient accuracy to make this practicable. The need for various follow-up studies such as might be carried out with the aid of record linkage have been discussed in detail elsewhere (I, 2), and there are numerous examples o{ important surveys which could be greatly extended in scope if existing record files were more readily linkable (3). Our special interest in the techniques of record linkage relates to their possi

Question: What is the cost of searching manually for large numbers of single documents among vast accumulations of files?


Predicting Airfare Prices. Airlines implement dynamic pricing for their tickets, and base their pricing decisions on demand estimation models. The reason for such a complicated system is that each flight only has a set number of seats to sell, so airlines have to regulate demand. In the case where demand is expected to exceed capacity, the airline may increase prices, to decrease the rate at which seats fill. On the other hand, a seat that goes unsold represents a loss of revenue, and selling that seat for any price above the service cost for a single passenger would have been a more preferable scenario.

Question: How do airlines predict airfare prices?


Boosting The Benefits of Hybrid SDN. The upgrade of a legacy network to a full software-defined networking (SDN) deployment is usually an incremental process, during which SDN switches and legacy switches coexist in the hybrid network. However, with inappropriate deployment of SDN switches and design of hybrid control, the advantages of SDN control could not exert, and it even results in performance degradation or inconsistency (e.g., loops, black-holes). Therefore, the hybrid SDN requires considerable coordination of the centralized control and distributed routing. In this paper, we propose a solution to handle the heterogeneity caused by distinct forwarding characteristics of SDN and legacy switches, therefore boosting the benefits of hybrid SDN. We plan SDN placement to enhance the SDN controllability over the hybrid network, and conduct traffic engineering considering both the forwarding characteristics of SDN and legacy switches. The experiments with various topologies show that the SDN placement planning and hybrid forwarding yield better network performance especially in the early 70% SDN deployment.

Question: How to boost the benefits of hybrid SDN?


A review and critique of emotional intelligence measures. Emotional intelligence measures vary widely in both their content and in their method of assessment. In particular, emotional intelligence measures tend to use either a self-report personality-based approach, an informant approach, or an ability-based assessment procedure. In this paper, the measurement and psychometric properties of four of the major emotional intelligence measures (Emotional Competence Inventory, Emotional Quotient Inventory, Multifactor Emotional Intelligence Scale, Mayer–Salovey–Caruso Emotional Intelligence Test) are reviewed, the comparability of these measures is examined, and some conclusions and suggestions for future research on emotional intelligence measures are provided. Copyright # 2005 John Wiley & Sons, Ltd.

Question: What is the difference between self-report and informant based measures?


Effective Clipart Image Vectorization through Direct Optimization of Bezigons. Bezigons, i.e., closed paths composed of Bézier curves, have been widely employed to describe shapes in image vectorization results. However, most existing vectorization techniques infer the bezigons by simply approximating an intermediate vector representation (such as polygons). Consequently, the resultant bezigons are sometimes imperfect due to accumulated errors, fitting ambiguities, and a lack of curve priors, especially for low-resolution images. In this paper, we describe a novel method for vectorizing clipart images. In contrast to previous methods, we directly optimize the bezigons rather than using other intermediate representations; therefore, the resultant bezigons are not only of higher fidelity compared with the original raster image but also more reasonable because they were traced by a proficient expert. To enable such optimization, we have overcome several challenges and have devised a differentiable data energy as well as several curve-based prior terms. To improve the efficiency of the optimization, we also take advantage of the local control property of bezigons and adopt an overlapped piecewise optimization strategy. The experimental results show that our method outperforms both the current state-of-the-art method and commonly used commercial software in terms of bezigon quality.

Question: How to vectorize clipart images?


Bi-Modal DRAM Cache: Improving Hit Rate, Hit Latency and Bandwidth. In this paper, we present Bi-Modal Cache - a flexible stacked DRAM cache organization which simultaneously achieves several objectives: (i) improved cache hit ratio, (ii) moving the tag storage overhead to DRAM, (iii) lower cache hit latency than tags-in-SRAM, and (iv) reduction in off-chip bandwidth wastage. The Bi-Modal Cache addresses the miss rate versus off-chip bandwidth dilemma by organizing the data in a bi-modal fashion - blocks with high spatial locality are organized as large blocks and those with little spatial locality as small blocks. By adaptively selecting the right granularity of storage for individual blocks at run-time, the proposed DRAM cache organization is able to make judicious use of the available DRAM cache capacity as well as reduce the off-chip memory bandwidth consumption. The Bi-Modal Cache improves cache hit latency despite moving the metadata to DRAM by means of a small SRAM based Way Locator. Further by leveraging the tremendous internal bandwidth and capacity that stacked DRAM organizations provide, the Bi-Modal Cache enables efficient concurrent accesses to tags and data to reduce hit time. Through detailed simulations, we demonstrate that the Bi-Modal Cache achieves overall performance improvement (in terms of Average Normalized Turnaround Time (ANTT)) of 10.8%, 13.8% and 14.0% in 4-core, 8-core and 16-core workloads respectively.

Question: How does the bi-modal cache work?


Adversarial Learning for Neural Dialogue Generation. In this paper, drawing intuition from the Turing test, we propose using adversarial training for open-domain dialogue generation: the system is trained to produce sequences that are indistinguishable from human-generated dialogue utterances. We cast the task as a reinforcement learning (RL) problem where we jointly train two systems, a generative model to produce response sequences, and a discriminator—analagous to the human evaluator in the Turing test— to distinguish between the human-generated dialogues and the machine-generated ones. The outputs from the discriminator are then used as rewards for the generative model, pushing the system to generate dialogues that mostly resemble human dialogues. In addition to adversarial training we describe a model for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls. Experimental results on several metrics, including adversarial evaluation, demonstrate that the adversarially-trained system generates higher-quality responses than previous baselines.

Question: What is adversarial learning?


Question: What is informal social communication?


Spherical CNNs. Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.

Question: What is the spherical cross-correlation?


Foundations of RDF⋆ and SPARQL⋆ (An Alternative Approach to Statement-Level Metadata in RDF). The standard approach to annotate statements in RDF with metadata has a number of shortcomings including data size blow-up and unnecessarily complicated queries. We propose an alternative approach that is based on nesting of RDF triples and of query patterns. The approach allows for a more compact representation of data and queries, and it is backwards compatible with the standard. In this paper we present the formal foundations of our proposal and of different approaches to implement it. More specifically, we formally capture the necessary extensions of the RDF data model and its query language SPARQL, and we define mappings based on which our extended notions can be converted back to ordinary RDF and SPARQL. Additionally, for such type of mappings we define two desirable properties, information preservation and query result equivalence, and we show that the introduced mappings possess these properties.

Question: What is the alternative approach to statement-level metadata in RDF?


JMASM Algorithms and Code Pseudo-Random Number Generators for Vector Processors and Multicore Processors. There is a lack of good pseudo-random number generators capable of utilizing the vector processing capabilities and multiprocessing capabilities of modern computers. A suitable generator must have a feedback path long enough to fit the vector length or permit multiple instances with different parameter sets. The risk that multiple random streams from identical generators have overlapping subsequences can be eliminated by combining two different generators. Combining two generators can also increase randomness by remedying weaknesses caused by the need for mathematical tractability. Larger applications need higher precision. The article discusses hitherto ignored imprecisions caused by quantization errors in the application of generators with prime modulus and when generating uniformly distributed integers with an arbitrary interval length. A C++ software package that overcomes all these problems is offered. The RANVEC1 code combines a Mersenne Twister variant and a multiply-with-carry generator to produce vector output. It is suitable for processors with vector sizes up to 512 bits. Some theorists have argued that there is no theoretical proof that the streams from different generators are statistically independent. The article contends that the request for such a proof misunderstands the nature of the problem, and that the mathematical tractability that would allow such a proof would also defeat it. This calls for a more fundamental philosophical discussion of the status of proofs in relation to deterministic pseudo-random sequences.

Question: What is the status of the proof that the streams from different generators are statistically independent?


Traffic sign recognition based on color, shape, and pictogram classification using support vector machines. Traffic sign recognition is the second part of traffic sign detection and recognition systems. It plays a crucial role in driver assistance systems and provides drivers with crucial safety and precaution information. In this study, the recognition of the TS is performed based on its border color, shape, and pictogram information. This technique breaks down the recognition system into small parts, which makes it efficient and accurate. Moreover, this makes it easy to understand TS components. The proposed technique is composed of three independent stages. The first stage involves extracting the border colors using an adaptive image segmentation technique that is based on learning vector quantization. Then, the shape of the TS is detected using a fast and simple matching technique based on the logical exclusive OR operator. Finally, the pictogram is extracted and classified using a support vector machines classifier model. The proposed technique is applied on the German traffic sign recognition benchmark and achieves an overall recognition rate of 98.23%, with an average computational speed of 30 ms.

Question: What is the average computational speed of the proposed technique?


A3P: adaptive policy prediction for shared images over popular content sharing sites. More and more people go online today and share their personal images using popular web services like Picasa. While enjoying the convenience brought by advanced technology, people also become aware of the privacy issues of data being shared. Recent studies have highlighted that people expect more tools to allow them to regain control over their privacy. In this work, we propose an Adaptive Privacy Policy Prediction (A3P) system to help users compose privacy settings for their images. In particular, we examine the role of image content and metadata as possible indicators of users' privacy preferences. We propose a two-level image classification framework to obtain image categories which may be associated with similar policies. Then, we develop a policy prediction algorithm to automatically generate a policy for each newly uploaded image. Most importantly, the generated policy will follow the trend of the user's privacy concerns evolved with time. We have conducted an extensive user study and the results demonstrate effectiveness of our system with the prediction accuracy around 90%.

Question: What is the role of image content and metadata as possible indicators of users' privacy preferences?


Biometric cryptosystems based fuzzy commitment scheme: a security evaluation. Biometric systems are developed in order to replace traditional authentication. However, protecting the stored templates is considered as one of the critical steps in designing a secure biometric system. When biometric data is compromised, unlike passwords, it can't be revoked. One methodology for biometric template protection is 'Biometric Cryptosystem'. Biometric cryptosystems benefit from both fields of cryptography and biometrics where the biometrics exclude the need to remember passwords and the cryptography provides high security levels for data. In order to, develop these systems, Fuzzy Commitment Scheme (FCS) is considered as well known approach proposed in the literature to protect the user's data and has been used in several applications. However, these biometric cryptosystems are hampered by the lack of formal security analysis to prove their security strength and effectiveness. Hence, in this paper we present several metrics to analyze the security and evaluate the weaknesses of biometric cryptosystems based on fuzzy commitment scheme.

Question: What is the difference between biometric cryptosystems and biometric systems?


A high-accuracy method for fine registration of overlapping point clouds. This paper presents a high-accuracy method for fine registration of two partially overlapping point clouds that have been coarsely registered. The proposed algorithm, which is named dual interpolating point-tosurface method, is principally a modified variant of point-to-surface Iterative Closest Point (ICP) algorithm. The original correspondences are established by adopting a dual surface fitting approach using B-spline interpolation. A novel auxiliary pair constraint based on the surface fitting approach, together with surface curvature information, is employed to remove unreliable point matches. The combined constraint directly utilizes global rigid motion consistency in conjunction with local geometric invariant to reject false correspondences precisely and efficiently. The experimental results involving a number of realistic point clouds demonstrate that the new method can obtain accurate and robust fine registration for pairwise 3D point clouds. This method addresses highest accuracy alignment with less focus on recovery from poor coarse registrations. 2009 Elsevier B.V. All rights reserved.

Question: How to register two point clouds?


A dynamic bayesian network click model for web search ranking. As with any application of machine learning, web search ranking requires labeled data. The labels usually come in the form of relevance assessments made by editors. Click logs can also provide an important source of implicit feedback and can be used as a cheap proxy for editorial labels. The main difficulty however comes from the so called position bias - urls appearing in lower positions are less likely to be clicked even if they are relevant. In this paper, we propose a Dynamic Bayesian Network which aims at providing us with unbiased estimation of the relevance from the click logs. Experiments show that the proposed click model outperforms other existing click models in predicting both click-through rate and relevance.

Question: What is a dynamic bayesian network?


SEMI-AUTOMATIC EXTRACTION OF RIBBON ROADS FORM HIGH RESOLUTION REMOTELY SENSED IMAGERY BY COOPERATION BETWEEN ANGULAR TEXTURE SIGNATURE AND TEMPLATE MATCHING. Road tracking is a promising technique to increase the efficiency of road mapping. In this paper an improved road tracker, based on cooperation between angular texture signature and template matching, is presented. Our tracker uses parabola to model the road trajectory and to predict the position of next road centreline point. It employs angular texture signature to get the exact moving direction of current road centreline point, and moves forward one predefined step along the direction to reach a new position, and then uses curvature change to verify the new added road point whether right enough. We also build compactness of angular texture signature polygon to check whether the angular texture signature is suitable to be used to go on tracking. When angular texture signature fails, least squares template matching is then employed instead. Cooperation between angular texture signature and template matching can reliably extract continuous and homogenous ribbon roads on high resolution remotely sensed imagery. ∗ Corresponding author

Question: How to extract road from remotely sensed imagery?


Neuronal Circuits for Fear Expression and Recovery: Recent Advances and Potential Therapeutic Strategies. Recent technological developments, such as single unit recordings coupled to optogenetic approaches, have provided unprecedented knowledge about the precise neuronal circuits contributing to the expression and recovery of conditioned fear behavior. These data have provided an understanding of the contributions of distinct brain regions such as the amygdala, prefrontal cortex, hippocampus, and periaqueductal gray matter to the control of conditioned fear behavior. Notably, the precise manipulation and identification of specific cell types by optogenetic techniques have provided novel avenues to establish causal links between changes in neuronal activity that develop in dedicated neuronal structures and the short and long-lasting expression of conditioned fear memories. In this review, we provide an update on the key neuronal circuits and cell types mediating conditioned fear expression and recovery and how these new discoveries might refine therapeutic approaches for psychiatric conditions such as anxiety disorders and posttraumatic stress disorder.

Question: What is the difference between the amygdala and the hippocampus?


Modelling and Simulation of Electric Vehicle Fast Charging Stations Driven by High Speed Railway Systems. The aim of this investigation is the analysis of the opportunity introduced by the use of railway infrastructures for the power supply of fast charging stations located in highways. Actually, long highways are often located far from urban areas and electrical infrastructure, therefore the installations of high power charging areas can be difficult. Specifically, the aim of this investigation is the analysis of the opportunity introduced by the use of railway infrastructures for the power supply of fast charging stations located in highways. Specifically, this work concentrates on fast-charging electric cars in motorway service areas by using high-speed lines for supplying the required power. Economic, security, safety and environmental pressures are motivating and pushing countries around the globe to electrify transportation, which currently accounts for a significant amount, above 70 percent of total oil demand. Electric cars require fast-charging station networks to allowing owners to rapidly charge their batteries when they drive relatively long routes. In other words, this means about the infrastructure towards building charging stations in motorway service areas and addressing the problem of finding solutions for suitable electric power sources. A possible and promising solution is proposed in the study that involves using the high-speed railway line, because it allows not only powering a high load but also it can be located relatively near the motorway itself. This paper presents a detailed investigation on the modelling and simulation of a 2 × 25 kV system to feed the railway. A model has been developed and implemented using the SimPower systems tool in MATLAB/Simulink to simulate the railway itself. Then, the model has been applied to simulate the battery charger and the system as a whole in two successive steps. The results showed that the concept coul

Question: What is the difference between a model and a simulation?


An iterative maximum-likelihood polychromatic algorithm for CT. A new iterative maximum-likelihood reconstruction algorithm for X-ray computed tomography is presented. The algorithm prevents beam hardening artifacts by incorporating a polychromatic acquisition model. The continuous spectrum of the X-ray tube is modeled as a number of discrete energies. The energy dependence of the attenuation is taken into account by decomposing the linear attenuation coefficient into a photoelectric component and a Compton scatter component. The relative weight of these components is constrained based on prior material assumptions. Excellent results are obtained for simulations and for phantom measurements. Beam-hardening artifacts are effectively eliminated. The relation with existing algorithms is discussed. The results confirm that improving the acquisition model assumed by the reconstruction algorithm results in reduced artifacts. Preliminary results indicate that metal artifact reduction is a very promising application for this new algorithm.

Question: What is the relation between the iterative maximum-likelihood polychromatic algorithm for CT and the iterative maximum-likelihood algorithm for CT?


Maintaining excellence: deliberate practice and elite performance in young and older pianists.. Two studies investigated the role of deliberate practice in the maintenance of cognitive-motor skills in expert and accomplished amateur pianists. Older expert and amateur pianists showed the normal pattern of large age-related reductions in standard measures of general processing speed. Performance on music-related tasks showed similar age-graded decline for amateur pianists but not for expert pianists, whose average performance level was only slightly below that of young expert pianists. The degree of maintenance of relevant pianistic skills for older expert pianists was predicted by the amount of deliberate practice during later adulthood. The role of deliberate practice in the active maintenance of superior domain-specific performance in spite of general age-related decline is discussed.

Question: What is the role of deliberate practice in the maintenance of cognitive-motor skills in expert and accomplished amateur pianists?


High voltage direct current transmission - A Review, Part II - Converter technologies. This paper is the 2nd part of the survey titled "High Voltage Direct Current Transmission - A Review, Part I". The main converter technologies and HVDC systems' components will be discussed in this complementary paper.

Question: What is the 2nd part of the survey titled "High Voltage Direct Current Transmission - A Review, Part I"?


Impulsive corporal punishment by mothers and antisocial behavior and impulsiveness of children.. This study tested the hypothesis that corporal punishment (CP), such as spanking or slapping a child for purposes of correcting misbehavior, is associated with antisocial behavior (ASB) and impulsiveness by the child. The data were obtained through interviews with a probability sample of 933 mothers of children age 2-14 in two small American cities. Analyses of variance found that the more CP experienced by the child, the greater the tendency for the child to engage in ASB and to act impulsively. These relationships hold even after controlling for family socioeconomic status, the age and sex of the child, nurturance by the mother, and the level of noncorporal interventions by the mother. There were also significant interaction effects of CP with impulsiveness by the mother. When CP was carried out impulsively, it was most strongly related to child impulsiveness and ASB; when CP was done when the mother was under control, the relationship to child behavior problems was reduced but still present. In view of the fact that there is a high risk of losing control when engaged in CP, even by parents who are not usually impulsive, and the fact that impulsive CP is so strongly associated with child behavior problems, the results of this study suggest that CP is an important risk factor for children developing a pattern of impulsive and antisocial behavior which, in turn, may contribute to the level of violence and other crime in society.

Question: What is the relationship between corporal punishment and antisocial behavior?


State-of-the-Art Predictive Maintenance Techniques*. This paper discusses the limitations of time-based equipment maintenance methods and the advantages of predictive or online maintenance techniques in identifying the onset of equipment failure. The three major predictive maintenance techniques, defined in terms of their source of data, are described as follows: 1) the existing sensor-based technique; 2) the test-sensor-based technique (including wireless sensors); and 3) the test-signal-based technique (including the loop current step response method, the time-domain reflectrometry test, and the inductance-capacitance-resistance test). Examples of detecting blockages in pressure sensing lines using existing sensor-based techniques and of verifying calibration using existing-sensor direct current output are given. Three Department of Energy (DOE)-sponsored projects, whose aim is to develop online and wireless hardware and software systems for performing predictive maintenance on critical equipment in nuclear power plants, DOE research reactors, and general industrial applications, are described.

Question: What is the difference between the three predictive maintenance techniques?


Computational models of trust and reputation: agents, evolutionary games, and social networks. Many recent studies of trust and reputation are made in the context of commercial reputation or rating systems for online communities. Most of these systems have been constructed without a formal rating model or much regard for our sociological understanding of these concepts. We first provide a critical overview of the state of research on trust and reputation. We then propose a formal quantitative model for the rating process. Based on this model, we formulate two personalized rating schemes and demonstrate their effectiveness at inferring trust experimentally using a simulated dataset and a real world movie-rating dataset. Our experiments show that the popular global rating scheme widely used in commercial electronic communities is inferior to our personalized rating schemes when sufficient ratings among members are available. The level of sufficiency is then discussed. In comparison with other models of reputation, we quantitatively show that our framework provides significantly better estimations of reputation. "Better" is discussed with respect to a rating process and specific games as defined in this work. Secondly, we propose a mathematical framework for modeling trust and reputation that is rooted in findings from the social sciences. In particular, our framework makes explicit the importance of social information (i.e., indirect channels of inference) in aiding members of a social network choose whom they want to partner with or to avoid. Rating systems that make use of such indirect channels of inference are necessarily personalized in nature, catering to the individual context of the rater. Finally, we have extended our trust and reputation framework toward addressing a fundamental problem for social science and biology: evolution of cooperation. We show that by providing an indirect inference mechanism for the propagation of trust and reputation, cooperation a

Question: What is the difference between a rating system and a reputation system?


The Effects of Interaction Goals on Negotiation Tactics and Outcomes: A Dyad-Level Analysis Across Two Cultures. This study investigates how negotiators' interaction goals influence their own and their counterparts' negotiation tactics and outcomes across two cultures using a simulated employment contract negotiation. Results show that when negotiators placed greater importance on competitive goals, they used more distributive persuasion and fewer priority information exchange tactics, which reduced their counterparts' profit; negotiators' competitive goals also caused their counterparts to use fewer priority information exchange tactics, which in turn hurt their own profit. Dyad members' competitive goals have an indirect, negative impact on joint profit. In addition, Chinese negotiators placed greater importance on competitive goals and used more distributive and fewer integrative tactics than Americans, but the associations between goals and tactics did not differ across cultures. Nevertheless, members of the two cultures took different paths to improve joint profit; as a result, Chinese dyads achieved no less joint profit than American dyads. The study sheds light on culture's effect on the interactive processes by which goals impact negotiation performance.

Question: How does culture affect negotiation?


The rises and falls of disconnection syndromes.. In a brain composed of localized but connected specialized areas, disconnection leads to dysfunction. This simple formulation underlay a range of 19th century neurological disorders, referred to collectively as disconnection syndromes. Although disconnectionism fell out of favour with the move against localized brain theories in the early 20th century, in 1965, an American neurologist brought disconnection to the fore once more in a paper entitled, 'Disconnexion syndromes in animals and man'. In what was to become the manifesto of behavioural neurology, Norman Geschwind outlined a pure disconnectionist framework which revolutionized both clinical neurology and the neurosciences in general. For him, disconnection syndromes were higher function deficits that resulted from white matter lesions or lesions of the association cortices, the latter acting as relay stations between primary motor, sensory and limbic areas. From a clinical perspective, the work reawakened interest in single case studies by providing a useful framework for correlating lesion locations with clinical deficits. In the neurosciences, it helped develop contemporary distributed network and connectionist theories of brain function. Geschwind's general disconnectionist paradigm ruled clinical neurology for 20 years but in the late 1980s, with the re-emergence of specialized functional roles for association cortex, the orbit of its remit began to diminish and it became incorporated into more general models of higher dysfunction. By the 1990s, textbooks of neurology were devoting only a few pages to classical disconnection theory. Today, new techniques to study connections in the living human brain allow us, for the first time, to test the classical formulation directly and broaden it beyond disconnections to include disorders of hyperconnectivity. In this review, on the 40th anniversary of Geschwind's publication, we describe the changing fortunes of disc

Question: What is the orbit of the remit of disconnection syndromes?


From centrality to temporary fame: Dynamic centrality in complex networks. We develop a new approach to the study of the dynamics of link utilization in complex networks using records of communication in a large social network. Counter to the perspective that nodes have particular roles, we find roles change dramatically from day to day. "Local hubs" have a power law degree distribution over time, with no characteristic degree value. Our results imply a significant reinterpretation of the concept of node centrality in complex networks, and among other conclusions suggest that interventions targeting hubs will have significantly less effect than previously thought. © 2006 Wiley Periodicals, Inc. Complexity 12: 000 – 000, 2006

Question: What is the difference between a local hub and a global hub?


R: A language and environment for statistical computing.. Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.

Question: How to install R on a Mac?


Music Feature Maps with Convolutional Neural Networks for Music Genre Classification. Nowadays, deep learning is more and more used for Music Genre Classification: particularly Convolutional Neural Networks (CNN) taking as entry a spectrogram considered as an image on which are sought different types of structure.  But, facing the criticism relating to the difficulty in understanding the underlying relationships that neural networks learn in presence of a spectrogram, we propose to use, as entries of a CNN, a small set of eight music features chosen along three main music dimensions: dynamics, timbre and tonality. With CNNs trained in such a way that filter dimensions are interpretable in time and frequency, results show that only eight music features are more efficient than 513 frequency bins of a spectrogram and that late score fusion between systems based on both feature types reaches 91% accuracy on the GTZAN database.

Question: What is the GTZAN database?


ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization. a r t i c l e i n f o

Question: What is the name of the algorithm?


An integrated GPU power and performance model. GPU architectures are increasingly important in the multi-core era due to their high number of parallel processors. Performance optimization for multi-core processors has been a challenge for programmers. Furthermore, optimizing for power consumption is even more difficult. Unfortunately, as a result of the high number of processors, the power consumption of many-core processors such as GPUs has increased significantly.  Hence, in this paper, we propose an integrated power and performance (IPP) prediction model for a GPU architecture to predict the optimal number of active processors for a given application. The basic intuition is that when an application reaches the peak memory bandwidth, using more cores does not result in performance improvement.  We develop an empirical power model for the GPU. Unlike most previous models, which require measured execution times, hardware performance counters, or architectural simulations, IPP predicts execution times to calculate dynamic power events. We then use the outcome of IPP to control the number of running cores. We also model the increases in power consumption that resulted from the increases in temperature.  With the predicted optimal number of active cores, we show that we can save up to 22.09%of runtime GPU energy consumption and on average 10.99% of that for the five memory bandwidth-limited benchmarks.

Question: What is the optimal number of active cores for a GPU?


Disruption of Large-Scale Brain Systems in Advanced Aging. Cognitive decline is commonly observed in advanced aging even in the absence of disease. Here we explore the possibility that normal aging is accompanied by disruptive alterations in the coordination of large-scale brain systems that support high-level cognition. In 93 adults aged 18 to 93, we demonstrate that aging is characterized by marked reductions in normally present functional correlations within two higher-order brain systems. Anterior to posterior components within the default network were most severely disrupted with age. Furthermore, correlation reductions were severe in older adults free from Alzheimer's disease (AD) pathology as determined by amyloid imaging, suggesting that functional disruptions were not the result of AD. Instead, reduced correlations were associated with disruptions in white matter integrity and poor cognitive performance across a range of domains. These results suggest that cognitive decline in normal aging arises from functional disruption in the coordination of large-scale brain systems that support cognition.

Question: What is the default network?


A Dual-Band Dual-Circular-Polarization Antenna for Ka-Band Satellite Communications. A novel Ka-band dual-band dual-circularly-polarized antenna array is presented in this letter. A dual-band antenna with left-hand circular polarization for the Ka-band downlink frequencies and right-hand circular polarization for the Ka-band uplink frequencies is realized with compact annular ring slots. By applying the sequential rotation technique, a 2 × 2 subarray with good performance is obtained. This letter describes the design process and presents simulation and measurement results.

Question: What is the dual-band dual-circularly-polarized antenna array?


Data Mining: A Preprocessing Engine. This study is emphasized on different types of normalization. Each of which was tested against the ID3 methodology using the HSV data set. Number of leaf nodes, accuracy and tree growing time are three factors that were taken into account. Comparisons between different learning methods were accomplished as they were applied to each normalization method. A new matrix was designed to check for the best normalization method based on the factors and their priorities. Recommendations were concluded.

Question: What is the best normalization method?


Learning from Ambiguously Labeled Face Images. Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled faces. We further extend MCar to incorporate the labeling constraints among instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvements on several ambiguously labeled datasets.

Question: How to learn a classifier from ambiguously labeled images?


Gated Recurrent Unit (GRU) for Emotion Classification from Noisy Speech. Despite the enormous interest in emotion classification from speech, the impact of noise on emotion classification is not well understood. This is important because, due to the tremendous advancement of the smartphone technology, it can be a powerful medium for speech emotion recognition in the outside laboratory natural environment, which is likely to incorporate background noise in the speech. We capitalize on the current breakthrough of Recurrent Neural Network (RNN) and seek to investigate its performance for emotion classification from noisy speech. We particularly focus on the recently proposed Gated Recurrent Unit (GRU), which is yet to be explored for emotion recognition from speech. Experiments conducted with speech compounded with eight different types of noises reveal that GRU incurs an 18.16% smaller run-time while performing quite comparably to the Long Short-Term Memory (LSTM), which is the most popular Recurrent Neural Network proposed to date. This result is promising for any embedded platform in general and will initiate further studies to utilize GRU to its full potential for emotion recognition on smartphones.

Question: What is the impact of noise on emotion classification?


Hierarchical Convolutional Features for Visual Tracking. Visual object tracking is challenging as target objects often undergo significant appearance changes caused by deformation, abrupt motion, background clutter and occlusion. In this paper, we exploit features extracted from deep convolutional neural networks trained on object recognition datasets to improve tracking accuracy and robustness. The outputs of the last convolutional layers encode the semantic information of targets and such representations are robust to significant appearance variations. However, their spatial resolution is too coarse to precisely localize targets. In contrast, earlier convolutional layers provide more precise localization but are less invariant to appearance changes. We interpret the hierarchies of convolutional layers as a nonlinear counterpart of an image pyramid representation and exploit these multiple levels of abstraction for visual tracking. Specifically, we adaptively learn correlation filters on each convolutional layer to encode the target appearance. We hierarchically infer the maximum response of each layer to locate targets. Extensive experimental results on a largescale benchmark dataset show that the proposed algorithm performs favorably against state-of-the-art methods.

Question: What is the largest convolutional neural network?


Edge-Oriented Computing Paradigms: A Survey on Architecture Design and System Management. While cloud computing has brought paradigm shifts to computing services, researchers and developers have also found some problems inherent to its nature such as bandwidth bottleneck, communication overhead, and location blindness. The concept of fog/edge computing is therefore coined to extend the services from the core in cloud data centers to the edge of the network. In recent years, many systems are proposed to better serve ubiquitous smart devices closer to the user. This article provides a complete and up-to-date review of edge-oriented computing systems by encapsulating relevant proposals on their architecture features, management approaches, and design objectives.

Question: What is edge computing?


Investigations on Knowledge Base Embedding for Relation Prediction and Extraction. We report an evaluation of the effectiveness of the existing knowledge base embedding models for relation prediction and for relation extraction on a wide range of benchmarks. We also describe a new benchmark, which is much larger and complex than previous ones, which we introduce to help validate the effectiveness of both tasks. The results demonstrate that knowledge base embedding models are generally effective for relation prediction but unable to give improvements for the state-of-art neural relation extraction model with the existing strategies, while pointing limitations of existing methods.

Question: What is the state-of-art neural relation extraction model?


A review on memristor applications. This article presents a review on the main applications of the fourth fundamental circuit element, named "memristor", which had been proposed for the first time by Leon Chua and has recently been developed by a team at HP Laboratories led by Stanley Williams. In particular, after a brief analysis of memristor theory with a description of the first memristor, manufactured at HP Laboratories, we present its main applications in the circuit design and computer technology, together with future developments.

Question: What is a memristor?


Design and control of underactuated tendon-driven mechanisms. Many robotic hands or prosthetic hands have been developed in the last several decades, and many use tendon-driven mechanisms for their transmissions. Robotic hands are now built with underactuated mechanisms, which have fewer actuators than degrees of freedom, to reduce mechanical complexity or to realize a biomimetic motion such as flexion of an index finger. The design is heuristic and it is useful to develop design methods for the underactuated mechanisms. This paper classifies mechanisms driven by tendons into three classes, and proposes a design method for them. The two classes are related to underactuated tendon-driven mechanisms, and these have been used without distinction so far. An index finger robot, which has four active tendons and two passive tendons, is developed and controlled with the proposed method.

Question: What is an underactuated tendon-driven mechanism?


Neural Responding Machine for Short-Text Conversation. We propose Neural Responding Machine (NRM), a neural networ k-based response generator for Short-Text Conversation. NRM takes the general encoder-de coder framework: it formalizes the generation of response as a decoding process based on the lat ent representation of the input text, while both encoding and decoding are realized with recurren t n ural networks (RNN). The NRM is trained with a large amount of one-round conversation dat a collected from a microblogging service. Empirical study shows that NRM can generate gramma tically correct and content-wise appropriate responses to over 75% of the input text, outperf orming state-of-the-arts in the same setting, including retrieval-based and SMT-based models.

Question: What is a neural networ k?


Challenges during the transition to Agile Methodologies : A Holistic Overview.

Question: What is a holistic overview?