Automatic Chinese Factual Question Generation

Question generation is an emerging research area of artificial intelligence in education. Question authoring tools are important in educational technologies, e.g., intelligent tutoring systems, as well as in dialogue systems. Approaches to generate factual questions, i.e., questions that have concrete answers, mainly make use of the syntactical and semantic information in a declarative sentence, which is then transformed into questions. Recently, some research has been conducted to investigate Chinese factual question generation with some limited success. Reported performance is poor due to unavoidable errors (e.g., sentence parsing, name entity recognition, and rule-based question transformation errors) and the complexity of long Chinese sentences. This article introduces a novel Chinese question generation system based on three stages, sentence simplification, question generation and ranking, to address the challenge of automatically generating factual questions in Chinese. The proposed approach and system have been evaluated on sentences from the New Practical Chinese Reader corpus. Experimental results show that ranking improves more than 20 percentage of questions rated as acceptable by annotators, from 65 percent of all questions to 87 percent of the top ranked 25 percent questions.


The art of scenarios and strategic planning : tools and pitfalls

The term strategy has been misused and even abused. Worse, the word scenario is often confused with strategy to the point that clarification is needed if we are to understand one another. As a prolongation of the work done by the Rand Corporation in the 1960s, strategic planning, management and prospective approaches have been developed to help organizations master change. Over the past 25 years, we have contributed by creating or further developing various methodologies and procedures such as the Mactor and MICMAC methods for use in scenario building. These tools are doubly powerful in that they stimulate the imagination, reduce collective biases, and promote appropriation. One of the main functions of the strategic futures exercise is to eliminate two errors that we usually describe as the “hammer’s risk” and the “nail’s dream.” In other words, we forget what a hammer’s function is when staring at a nail (the nail’s dream) or we know how to use a hammer and imagine that every problem is like a nail (the hammer’s risk). In our case, we strive to give simple tools that may be appropriated. However, these simple tools are inspired by intellectual rigor that enables one to ask the right questions. Of course, these tools do not come with a guarantee. The natural talent, common sense, and intuition of the futurist also count!  2000 Elsevier Science Inc. Introduction Anticipation is not widely practiced by decision makers because when things are going well, they can manage without it, and when things are going badly, it is too late to see beyond the ends of their noses. Fast action is already urgently required! Yet reaction is not an end in itself. Although desirable in the short term, it leads nowhere if not directed towards the firm’s long-term objectives. As Seneca said, “there is no favourable wind for the man who knows not where he is going.” Action becomes meaningless without a goal, and only anticipation points the way to action and gives it both meaning and direction. MICHEL GODET is Professor and holder of the Chaire de prospective industrielle du Conservatoire national des arts et métiers, Paris, France, Director of the Laboratory of Investigation in Prospective and Strategy (LIPS). See: lips@cnam.fr, http://www.cnam.fr/lips/, where there is an on-line working paper Scenarios and Strategies: A Toolbox for Scenario Planning. Address correspondence to Michel Godet, LIPS, CNAM, 2 rue Conte, 75141 Paris, Cedex 03, France. Tel.: 33-01-4027 2530; Fax: 33-01-40 27 27 43; e-mail: lips@cnam.fr. Technological Forecasting and Social Change 65, 3–22 (2000)  2000 Elsevier Science Inc. All rights reserved. 0040-1625/00/$–see front matter 655 Avenue of the Americas, New York, NY 10010 PII S0040-1625(99)00120-1


A 90–100-GHz 4  $\times$ 4 SiGe BiCMOS Polarimetric Transmit/Receive Phased Array With Simultaneous Receive-Beams Capabilities

This paper presents a 4 × 4 transmit/receive (T/R) SiGe BiCMOS phased-array chip at 90-100 GHz with vertical and horizontal polarization capabilities, 3-bit gain control (9 dB), and 4-bit phase control. The 4 × 4 phased array fits into a 1.6×1.5 mm2 grid, which is required at 94 GHz for wide scan-angle designs. The chip has simultaneous receive (Rx) beam capabilities (V and H) and this is accomplished using dual-nested 16:1 Wilkinson combiners/divider with high isolation. The phase shifter is based on a vector modulator with optimized design between circuit level and electromagnetic simulation and results in 1 dB and gain and phase error, respectively, at 85-110 GHz. The behavior of the vector modulator phase distortion versus input power level is investigated and measured, and design guidelines are given for proper operation in a transmit (Tx) chain. The V and H Rx paths result in a gain of 22 and 25 dB, respectively, a noise figure of 9-9.5 (max. gain), and 11 dB (min. gain) measured without the T/R switch, and an input P1 dB of -31 to -26 dBm over the gain control range. The measured output Psat is ~ -5 dBm per channel, limited by the T/R switch loss. Measurements show ±0.6- and ±0.75-dB variation between the 4 × 4 array elements in the Tx mode (Psat) and Rx mode, respectively, and 40-dB coupling between the different channels on the chip. The chip consumes 1100 mA from a 2-V supply in both the Tx and Rx modes. The design can be scaled to >10 000 elements using polyimide redistribution layers on top of the chip and the application areas are in W-band radars for landing systems.


Invited Paper: Enhanced Architectures, Design Methodologies and CAD Tools for Dynamic Reconfiguration of Xilinx FPGAs

The paper describes architectural enhancements to Xilinx FPGAs that provide better support for the creation of dynamically reconfigurable designs. These are augmented by a new design methodology that uses pre-routed IP cores for communication between static and dynamic modules and permits static designs to route through regions otherwise reserved for dynamic modules. A new CAD tool flow to automate the methodology is also presented. The new tools initially target the Virtex-II, Virtex-II Pro and Virtex-4 families and are derived from Xilinx's commercial CAD tools


TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing CAKIR, EMRE: Multilabel Sound Event Classification with Neural Networks Master of Science Thesis, 63 pages October 2014 Major: Signal Processing Examiner: Tuomas Virtanen, Heikki Huttunen


CG_Hadoop: computational geometry in MapReduce

Hadoop, employing the MapReduce programming paradigm, has been widely accepted as the standard framework for analyzing big data in distributed environments. Unfortunately, this rich framework was not truly exploited towards processing large-scale computational geometry operations. This paper introduces CG_Hadoop; a suite of scalable and efficient MapReduce algorithms for various fundamental computational geometry problems, namely, polygon union, skyline, convex hull, farthest pair, and closest pair, which present a set of key components for other geometric algorithms. For each computational geometry operation, CG_Hadoop has two versions, one for the Apache Hadoop system and one for the SpatialHadoop system; a Hadoop-based system that is more suited for spatial operations. These proposed algorithms form a nucleus of a comprehensive MapReduce library of computational geometry operations. Extensive experimental results on a cluster of 25 machines of datasets up to 128GB show that CG_Hadoop achieves up to 29x and 260x better performance than traditional algorithms when using Hadoop and SpatialHadoop systems, respectively.


Visual Analytics of Patterns in High-Dimensional Data

Due to the technological progress over the last decades, today’s scientific and commercial applications are capable of generating, storing, and processing, massive amounts of data sets. This influences the type of data generated, which in turn means that with each data entry di erent aspects are combined and stored into one common database. Often the describing attributes are numeric; we name data with more than a handful attributes (dimensions) high-dimensional. Having to make use of these types of data archives provides new challenges to analysis techniques. The work of this thesis centers around the question of finding interesting patterns (meaningful information) in high-dimensional data sets. This task is highly challenging because of the so called curse of dimensionality, expressing that when dimensionality increases the data becomes sparse. This phenomena disturbs standard analysis techniques. Automatic techniques have to deal with the data complexity not only increasing their runtime, but also vitiating their computation functions (like distance functions). Moreover, exploring these data sets visually is hindered by the high number of dimensions that have to be displayed on the two dimensional screen space. This thesis is motivated by the idea that searching for interesting patterns in this kind of data can be done through a mixed approach of automation, visualization, and interaction. The amount of patterns a visualization contains can be measured by so called quality metrics. These automated functions can then filter the high number of highdimensional visualizations and present to the user a pre-filtered good subset for further investigation. We propose quality metrics for scatterplots and parallel coordinates focusing on di erent user tasks like identifying clusters and correlations. We also evaluate these measures with regard to (1) their ability to identify clusters in a variety of real and synthetic datasets; (2) their correlation with human perception of clusters in scatterplots. A thorough discussion of results follows reflecting the impact on directions for future research. As quality metrics were developed for a large number of di erent high-dimensional visualization techniques, we present our reflections on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a comprehensive literature review. In high-dimensional data, patterns exist often only in a subset of the dimensions. Subspace clustering techniques aim at finding these subspaces where clusters exist and which might otherwise be hidden if a traditional clustering algorithm is applied. While subspace clustering approaches tackle the sparsity problem in high-dimensional data well, designing e ective visualization to help analyzing the clustering result is not trivial. In addition to the cluster membership information, the relevant sets of dimensions and the overlaps of memberships and dimensions need to also be considered. Although, a number of techniques (for example, scatterplots, heat maps, dendrograms, hierarchical parallel coordinates) exist for visualizing traditional clustering results, little research has been done for visualizing subspace clustering results. Moreover, while extensive research has been carried out with regard to designing subspace clustering algorithms, surprisingly little attention has been paid to the developing of e ective visualization tools analyzing the viii clustering result. Appropriate visualization techniques will not only help in monitoring the clustering process but, with special mining techniques, they could also enable the domain expert to guide and even to steer the subspace clustering process to reveal the patterns of interest. To this goal, we envision a concept that combines subspace clustering algorithms and interactive scalable visual exploration techniques. This work includes the task of comparative visualization and feedback guided computation of alternative clusterings. Zusammenfassung Bedingt durch den technologischen Fortschritt der letzten Jahrzehnte sind heutige kommerzielle Applikationen in der Lage, riesige Datenmengen zu erzeugen, zu speichern und zu verarbeiten. Diese Entwicklung beeinflusst auch die Natur der erzeugten Daten, d.h. dass für jeden Dateneintrag unterschiedliche Aspekte in der gleichen Datenbank gespeichert werden. Oft sind die beschreibenden Attribute numerisch. Datensätze, die mehr als fünf solcher Attribute (Dimensionen) beinhalten, nenne ich hochdimensional. Der wertbringende Gebrauch solcher Datenarchive bringt neue Herausforderungen an Analysetechniken mit sich. Die vorliegende Dissertation bearbeitet die Fragestellung, wie interessante Muster (bedeutende Information) in hochdimensionalen Räumen gefunden werden können. Diese Aufgabenstellung ist durch das Problem des Fluches der Dimensionalität äußerst herausfordernd. Dieses Problem besagt, dass Daten im hochdimensionalen Raum spärlich vorkommen. Herkömmliche Analysetechniken werden dadurch beeinträchtigt. Automatische Methoden müssen die Datenkomplexität nicht nur ihre Laufzeit, sondern auch ihre Berechnungsfunktionen (z.B. Distanzfunktionen) betre end, einbeziehen. Außerdem wird die visuelle Exploration dieser Daten durch die Zweidimensionalität der Darstellungen beeinträchtigt. Diese Dissertation stützt sich auf das Konzept, dass die Suche nach interessanten Mustern in hochdimensionalen Datenmengen mit einem kombinierten Ansatz von automatischen, visuellen und interaktiven Methoden durchgeführt werden kann. Die Ausprägung der Muster einer Visualisierung kann durch sogenannte Qualitätsmaße gemessen werden. Durch diese automatischen Funktionen kann die große Menge an hochdimensionalen Visualisierungen eingegrenzt und dem Benutzer eine ausgewählte Menge zur weiteren Untersuchung zur Verfügung gestellt werden. Ich schlage Qualitätsmaße für Scatterplots und Parallele Koordinaten vor, die sich auf unterschiedliche Aufgaben, wie die Identifikation von Gruppen oder Korrelationen, konzentrieren. Zusätzlich werden diese Techniken bezüglich (1) ihrer Fähigkeit Cluster in unterschiedlichen realen und synthetischen Datensätzen und (2) ihrer Korrelation mit der menschlichen Wahrnehmung untersucht. Der ausführlichen Diskussion dieser Resultate folgen Überlegungen für die zukünftige Forschung. Da viele verschiedene Qualitätsmaße für eine Reihe weiterer hochdimensionaler Visualisierungen entwickelt wurden, werde ich Vorschläge für deren Vernetzung und Weiterentwicklung vorstellen. Hierfür wird eine Übersicht über die verschiedenen Ansätze erstellt, welcher eine Systematisierung zugrunde liegt, die aufgrund einer umfassenden Literaturauswertung zustande kam. Im hochdimensionalen Raum existieren manche Muster nur in verschiedenen Unterräumen des Datenraumes. Subspace Clustering Algorithmen wurden entwickelt, um Unterräume zu finden in denen Cluster existieren, die durch traditionelle Clustering Algorithmen nicht gefunden werden würden. Obwohl diese Algorithmen spärlich mit Daten besetzte, hochdimensionale Räume gut explorieren können, ist das Entwickeln von e ektiven Visualisierungstechniken, um diese Clusteringresultate zu analysieren, nicht trivial. Zusätzlich zu der Clusterzugehörigkeit von Elementen müssen die relevanten Attributmengen eines Clusters und die Objektund Dimensionsüberlappungen von Subspaceclusx tern dargestellt werden. Auch wenn eine Reihe von Techniken für die Visualisierung von traditionellen Clustering Resultaten existiert (z.B. Scatterplots, Heatmaps, Dendrogramme, hierarchische Parallele Koordinaten) gibt es nur wenige Ansätze, um das Resultat von Subspace Clustering Algorithmen zu visualisieren. Außerdem wurden bisher erstaunlich wenige Ansätze vorgestellt, die eine visuelle Analyse der Subspace Clustering Ergebnisse unterstützen können, obwohl im Bereich der Subspace Clustering Algorithmen viel Forschung betrieben wurde. Angemessene Visualisierungstechniken, die von speziellen Methoden zur Extraktion von Informationen unterstützt werden, würden nicht nur die Nachverfolgung der Clustering Ergebnisse ermöglichen, sondern auch Fachleuten dabei helfen, den Subspace Clustering Prozess so zu steuern, dass relevante Muster zum Vorschein kommen. Dieses Ziel vor Augen stelle ich ein Konzept vor, das Subspace Clustering Algorithmen mit interaktiven skalierbaren Visualisierungen kombiniert. Meine Ansätze widmen sich deshalb der Aufgabe der Visualisierung zum Vergleich von alternativen Clustergruppen, die durch Nutzerfeedback gesteuert werden.


3D Printed Reversible Shape Changing Components with Stimuli Responsive Materials.

The creation of reversibly-actuating components that alter their shapes in a controllable manner in response to environmental stimuli is a grand challenge in active materials, structures, and robotics. Here we demonstrate a new reversible shape-changing component design concept enabled by 3D printing two stimuli responsive polymers-shape memory polymers and hydrogels-in prescribed 3D architectures. This approach uses the swelling of a hydrogel as the driving force for the shape change, and the temperature-dependent modulus of a shape memory polymer to regulate the time of such shape change. Controlling the temperature and aqueous environment allows switching between two stable configurations - the structures are relatively stiff and can carry load in each - without any mechanical loading and unloading. Specific shape changing scenarios, e.g., based on bending, or twisting in prescribed directions, are enabled via the controlled interplay between the active materials and the 3D printed architectures. The physical phenomena are complex and nonintuitive, and so to help understand the interplay of geometric, material, and environmental stimuli parameters we develop 3D nonlinear finite element models. Finally, we create several 2D and 3D shape changing components that demonstrate the role of key parameters and illustrate the broad application potential of the proposed approach.


A comparative study of data mining techniques for credit scoring in banking

Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.


A Stroke Order Verification Method for On-Line Handwritten Chinese Characters Based on Tempo-spatial Consistency Analysis

This paper proposes a method to recognize stroke orders of on-line handwritten Chinese characters based on analyzing both spatial and temporal information. A novel control-point-based analysis method is presented for spatial information analysis to match strokes with various shapes and styles. Its computation complexity is much lower than image correlation method and is suitable for applications on mobile devices. For temporal information analysis, Hidden Markov Model is adopted and a proposed rectification method is integrated to find the optimal pair-wise matching result of stroke sequences. Experimental results proved the effectiveness of the proposed method. The verification rate is 99.6% on the test set.


Position and Force Control Based on Mathematical Models of Pneumatic Artificial Muscles Reinforced by Straight Glass Fibers

This paper reports on the position and force control of pneumatic artificial muscles reinforced by straight glass fibers. This type of artificial muscle has a greater contraction ratio and power and a longer lifetime than conventional McKibben types. However, these muscles are highly non-linear; hence, it is difficult to use them in a mechanical system. Furthermore, this actuator has a high compliance characteristic. Though this characteristic is useful for human interactions, the position and force of this actuator cannot be easily controlled. In this paper, a mathematical model of this type of artificial muscle is proposed, and the relationship between design parameters and control specifications is realized. In addition, the position and force based on the mathematical model are determined and applied to artificial muscle linearization.


Efficient set joins on similarity predicates

In this paper we present an efficient, scalable and general algorithm for performing set joins on predicates involving various similarity measures like intersect size, Jaccard-coefficient, cosine similarity, and edit-distance. This expands the existing suite of algorithms for set joins on simpler predicates such as, set containment, equality and non-zero overlap. We start with a basic inverted index based probing method and add a sequence of optimizations that result in one to two orders of magnitude improvement in running time. The algorithm folds in a data partitioning strategy that can work efficiently with an index compressed to fit in any available amount of main memory. The optimizations used in our algorithm generalize to several weighted and unweighted measures of partial word overlap between sets.


Size dependency of nanocrystalline TiO 2 on its optical property and photocatalytic reactivity exemplified by 2-chlorophenol

Anatase TiO2 nanocrystallines (17–29 nm) were successfully synthesized by the metal–organic chemical vapor deposition method (MOCVD). Moderate manipulation of system parameters of MOCVD can control the particle size. The electro-optical and photocatalytic properties of the synthesized TiO2 nanoparticles were studied along with several commercially available ultra-fine TiO2 particles (e.g., 3.8–5.7 nm). The band gap of the TiO2 crystallines was determined using the transformed diffuse reflectance technique according to the Kubelka–Munk theory. Results showed that the band gap of TiO2 monotonically decreased from 3.239 to 3.173 eV when the particle size decreased from 29 to 17 nm and then increased from 3.173 to 3.289 eV as the particle size decreased from 17 to 3.8 nm. The results of band gap change as a function of particle size agreed well with what was predicted by the Brus’ equation, i.e., the effective mass model (EMM). However, results of the photocatalytic oxidation of 2-chlorophenol (2-CP), showed that the smaller the particle size, the faster the degradation rate. This is attributed in part to the combined effect of band gap change relative to the spectrum of the light source and the specific surface area (or particle size) of the photocatalysts. The change of band gap due to particle size represents only a small optical absorption window with respect to the total spectrum of the light source, i.e., from 380 to 400 nm versus>280 nm. Consequently, the gain in optical property of the larger particles was severely compromised by their decrease in specific surface area. Our results clearly indicated the importance of specific surface area in controlling the photocatalytic reactivity of photocatalysts. Results also showed that the secondary particle size grew with time due mainly to particle aggregation. The photocatalytic rate constants decreased exponentially with increase in primary particle size. Primary particle size alone is able to predict the photocatalytic rate as it is closely related to the electro-optical properties of photocatalysts. # 2006 Published by Elsevier B.V.


Structural Insights into the Niemann-Pick C1 (NPC1)-Mediated Cholesterol Transfer and Ebola Infection

Niemann-Pick disease type C (NPC) is associated with mutations in NPC1 and NPC2, whose gene products are key players in the endosomal/lysosomal egress of low-density lipoprotein-derived cholesterol. NPC1 is also the intracellular receptor for Ebola virus (EBOV). Here, we present a 4.4 Å structure of full-length human NPC1 and a low-resolution reconstruction of NPC1 in complex with the cleaved glycoprotein (GPcl) of EBOV, both determined by single-particle electron cryomicroscopy. NPC1 contains 13 transmembrane segments (TMs) and three distinct lumenal domains A (also designated NTD), C, and I. TMs 2-13 exhibit a typical resistance-nodulation-cell division fold, among which TMs 3-7 constitute the sterol-sensing domain conserved in several proteins involved in cholesterol metabolism and signaling. A trimeric EBOV-GPcl binds to one NPC1 monomer through the domain C. Our structural and biochemical characterizations provide an important framework for mechanistic understanding of NPC1-mediated intracellular cholesterol trafficking and Ebola virus infection.


Development of a Danish Language Version of the Manchester Foot Pain and Disability Index: Reproducibility and Construct Validity Testing

Introduction. The Manchester Foot Pain and Disability Index (MFPDI) is a 19-item questionnaire for the assessment of disability caused by foot pain. The aim was to develop a Danish language version of the MFPDI (MFPDI-DK) and evaluate its reproducibility and construct validity. Methods. A Danish version was created, following a forward-backward translation procedure. A sample of 84 adult patients with foot pain was recruited. Participants completed two copies of the MFPDI-DK within a 24- to 48-hour interval, along with the Medical Outcomes Study Short Form 36 (SF-36), and a pain Visual Analog Scale (VAS). Reproducibility was assessed using the intraclass correlation coefficient (ICC) and 95% limits of agreement (Bland-Altman plot). Construct validity was evaluated with Pearson's Rho, using a priori hypothesized correlations with SF-36 subscales and VASmean. Results. The MFPDI-DK showed very good reliability with an ICC of 0.92 (0.88-0.95). The 95% limits of agreement ranged from -6.03 to 6.03 points. Construct validity was supported by moderate to very strong correlations with the SF-36 physical subscales and VASmean. Conclusion. The MFPDI-DK appears to be a valid and reproducible instrument in evaluating foot-pain-related disability in Danish adult patients in cross-sectional samples. Further research is needed to test the responsiveness of the MFPDI-DK.


A retrospective study of SPECT/CT scans using SUV measurement of the normal pelvis with Tc-99m methylene diphosphonate.

OBJECTIVE
To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner.

MATERIAL AND METHODS
This retrospective study was performed on 31 patients with cancer undergoing bone SPECT/CT scans with 99mTc-MDP. SUVmax and SUVmean of the normal pelvis were calculated based on the body weight. SUVmax and SUVmean of the bilateral anterior superior iliac spine, posterior superior iliac spine, facies auricularis ossis ilii, ischial tuberosity, and sacrum were also calculated. Furthermore, the correlation of SUVmax and SUVmean of all parts of pelvis with weight, height, and CT was assessed.

RESULTS
The data for 31 patients (20 women and 11 men; mean age 58.97±9.12 years; age range 37-87 years) were collected. SUVmax and SUVmean changed from 1.65±0.40 to 3.8±1.0 and from 1.15±0.25 to 2.07±0.58, respectively. The coefficient of variation of SUVmax and SUVmean ranged from 0.22 to 0.31. SUVmax and SUVmean had no statistically significant difference between men and women. SUVmax and SUVmean also showed no significant correlation with weight and height. However, part of SUVmax and SUVmean showed a significant correlation with CT. In addition, SUVmax and SUVmean of the bilateral ischial tuberosity showed a significant correlation with CT values.

CONCLUSIONS
Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.


Challenges in automotive software engineering

The amount of software in cars grows exponentially. Driving forces of this development are cheaper and more powerful hardware and the demand for innovations by new functions. The rapid increase of software and software based functionality brings various challenges (see [21], [23], [25], [26]) for the automotive industries, for their organization, key competencies, processes, methods, tools, models, product structures, division of work, logistics, maintenance, and long term strategies. From a software engineering perspective, the automotive industry is an ideal and fascinating application domain for advanced techniques. Although the automotive industry may adopt general results and solutions from the software engineering body of knowledge gained in other domains, the specific constraints and domain specific requirements in the automotive industry ask for individual solutions and bring various challenges for automotive software engineering. In cars we find literally all interesting problems and challenging issues of software and systems engineering.


Proofs of Work for Blockchain Protocols

One of the most impactful applications of proofs of work (POW) currently is in the design of blockchain protocols such as Bitcoin. Yet, despite the wide recognition of POWs as the fundamental cryptographic tool in this context, there is no known cryptographic formulation that implies the security of the Bitcoin blockchain protocol. Indeed, all previous works formally arguing the security of the Bitcoin protocol relied on direct proofs in the random oracle model, thus circumventing the di culty of isolating the required properties of the core POW primitive. In this work we ll this gap by providing a formulation of the POW primitive that implies the security of the Bitcoin blockchain protocol in the standard model. Our primitive entails a number of properties that parallel an e cient non-interactive proof system: completeness and fast veri cation, security against malicious provers (termed hardness against tampering and chosen message attacks ) and security for honest provers (termed uniquely successful under chosen key and message attacks ). Interestingly, our formulation is incomparable with previous formulations of POWs that applied the primitive to contexts other than the blockchain. Our result paves the way for proving the security of blockchain protocols in the standard model assuming our primitive can be realized from computational assumptions.


Some Theoretical Considerations in Off-the-Shelf Text Analysis Software

This paper is concerned with theoretical considerations of commercial content analysis software, namely Linguistic Inquiry and Word Count (LIWC), developed by social psychologists at the University of Texas. LIWC is widely cited and forms the basis of many research papers from a range of disciplines. Here, LIWC is taken as an example of a context-independent, word-counting approach to text analysis, and the strengths and potential pitfalls of such a methodology are discussed. It is shown that text analysis software is constrained not only by its functions, but also by its underlying theoretical assumptions. The paper offers recommendations for good practice in software commercialisation and application, stressing the importance of transparency and acknowledgement of biases.


Mutation Spectrum and Phenotypic Features in Noonan Syndrome with PTPN11 Mutations: Definition of Two Novel Mutations.

OBJECTIVES
To evaluate the spectrum of PTPN11 gene mutations in Noonan syndrome patients and to study the genotype-phenotype associations.

METHODS
In this study, twenty Noonan syndrome patients with PTPN11 mutations were included. The patients underwent a detailed clinical and physical evaluation. To identify inherited cases, parents of all mutation positive patients were analyzed.

RESULTS
Thirteen different PTPN11 mutations, two of them being novel, were detected in the study group. These mutations included eleven missense mutations: p.G60A, p.D61N, p.Y62D, p.Y63C, p.E69Q, p.Q79R, p.Y279C,p.N308D, p.N308S, p.M504V, p.Q510R and two novel missense mutations: p.I56V and p.I282M. The frequency of cardiac abnormalities and short stature were found to be 80 % and 80 %, respectively. Mental retardation was not observed in patients having exon 8 mutations. No significant correlations were detected between other phenotypic features and genotypes.

CONCLUSIONS
By identifying genotype-phenotype correlations, this study provides information on phenotypes observed in NS patients with different PTPN11 mutations.


Recurrent Neural Network Regularization

We present a simple regularization technique for Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. Dropout, the most successful technique for regularizing neural networks, does not work well with RNNs and LSTMs. In this paper, we show how to correctly apply dropout to LSTMs, and show that it substantially reduces overfitting on a variety of tasks. These tasks include language modeling, speech recognition, and machine translation.


Recurrent Nets that Time and Count


Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription

We investigate the problem of modeling symbolic sequences of polyphonic music in a completely general piano-roll representation. We introduce a probabilistic model based on distribution estimators conditioned on a recurrent neural network that is able to discover temporal dependencies in high-dimensional sequences. Our approach outperforms many traditional models of polyphonic music on a variety of realistic datasets. We show how our musical language model can serve as a symbolic prior to improve the accuracy of polyphonic transcription.


Efficient Genome-Wide, Privacy-Preserving Similar Patient Query based on Private Edit Distance

Edit distance has been proven to be an important and frequently-used metric in many human genomic research, with Similar Patient Query (SPQ) being a particularly promising and attractive example. However, due to the widespread privacy concerns on revealing personal genomic data, the scope and scale of many novel use of genome edit distance are substantially limited. While the problem of private genomic edit distance has been studied by the research community for over a decade [6], the state-of-the-art solution [31] is far from even close to be applicable to real genome sequences. In this paper, we propose several private edit distance protocols that feature unprecedentedly high efficiency and precision. Our construction is a combination of a novel genomic edit distance ap- proximation algorithm and new construction of private set difference size protocols. With the private edit distance based secure SPQ primitive, we propose GENSETS, a genome-wide, privacy- preserving similar patient query system. It is able to support search- ing large-scale, distributed genome databases across the nation. We have implemented a prototype of GENSETS. The experimental results show that, with 100 Mbps network connection, it would take GENSETS less than 200 minutes to search through 1 million breast cancer patients (distributed nation-wide in 250 hospitals, each having 4000 patients), based on edit distances between their genomes of lengths about 75 million nucleotides each.


IBM Mega Traffic Simulator

IBM Mega Traffic Simulator (Megaffic) is an agent-based simulator of traffic flows with two unique features. First, Megaffic can build its model of simulation by directly estimating some of the parameters of the model from probe-car data. This capability is in contrast to existing agent-based simulators of traffic flows, where the values of their parameters are calibrated with iterative simulation. Second, Megaffic can run on massively parallel computers and simulate the microscopic traffic flows in the scale of an arbitrary city or even the whole Japan. This manuscript gives an overview of the design and capability of Megaffic. ∗5-6-52 Toyosu, Koto-ku, Tokyo 135-8511, Japan


Multilinear Sparse Principal Component Analysis

In this brief, multilinear sparse principal component analysis (MSPCA) is proposed for feature extraction from the tensor data. MSPCA can be viewed as a further extension of the classical principal component analysis (PCA), sparse PCA (SPCA) and the recently proposed multilinear PCA (MPCA). The key operation of MSPCA is to rewrite the MPCA into multilinear regression forms and relax it for sparse regression. Differing from the recently proposed MPCA, MSPCA inherits the sparsity from the SPCA and iteratively learns a series of sparse projections that capture most of the variation of the tensor data. Each nonzero element in the sparse projections is selected from the most important variables/factors using the elastic net. Extensive experiments on Yale, Face Recognition Technology face databases, and COIL-20 object database encoded the object images as second-order tensors, and Weizmann action database as third-order tensors demonstrate that the proposed MSPCA algorithm has the potential to outperform the existing PCA-based subspace learning algorithms.


CorMet: A Computational, Corpus-Based Conventional Metaphor Extraction System

CorMet is a corpus-based system for discovering metaphorical mappings between concepts. It does this by finding systematic variations in domain-specific selectional preferences, which are inferred from large, dynamically mined Internet corpora. Metaphors transfer structure from a source domain to a target domain, making some concepts in the target domain metaphorically equivalent to concepts in the source domain. The verbs that select for a concept in the source domain tend to select for its metaphorical equivalent in the target domain. This regularity, detectable with a shallow linguistic analysis, is used to find the metaphorical interconcept mappings, which can then be used to infer the existence of higher-level conventional metaphors. Most other computational metaphor systems use small, hand-coded semantic knowledge bases and work on a few examples. Although Cor Met's only knowledge base is Word Net (Fellbaum 1998) it can find the mappings constituting many conventional metaphors and in some cases recognize sentences instantiating those mappings. CorMet is tested on its ability to find a subset of the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991).


Metagenes and molecular pattern discovery using matrix factorization.

We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of genes or initial conditions and able to detect alternative or context-dependent patterns of gene expression in complex biological systems. This ability, similar to semantic polysemy in text, provides a general method for robust molecular pattern discovery.


Discriminative Training of Deep Fully Connected Continuous CRFs With Task-Specific Loss

Recent works on deep conditional random fields (CRFs) have set new records on many vision tasks involving structured predictions. Here, we propose a fully connected deep continuous CRF model with task-specific losses for both discrete and continuous labeling problems. We exemplify the usefulness of the proposed model on multi-class semantic labeling (discrete) and the robust depth estimation (continuous) problems. In our framework, we model both the unary and the pairwise potential functions as deep convolutional neural networks (CNNs), which are jointly learned in an end-to-end fashion. The proposed method possesses the main advantage of continuously valued CRFs, which is a closed-form solution for the maximum a posteriori (MAP) inference. To better take into account the quality of the predicted estimates during the cause of learning, instead of using the commonly employed maximum likelihood CRF parameter learning protocol, we propose task-specific loss functions for learning the CRF parameters. It enables direct optimization of the quality of the MAP estimates during the learning process. Specifically, we optimize the multi-class classification loss for the semantic labeling task and the Tukey’s biweight loss for the robust depth estimation problem. Experimental results on the semantic labeling and robust depth estimation tasks demonstrate that the proposed method compare favorably against both baseline and state-of-the-art methods. In particular, we show that although the proposed deep CRF model is continuously valued, with the equipment of task-specific loss, it achieves impressive results even on discrete labeling tasks.


C V ] 2 1 N ov 2 01 7 Emotion Classification with Data Augmentation Using Generative Adversarial Networks

It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework with a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN’s performance. Empirical results show that we can obtain 5%∼10% increase in the classification accuracy after employing the GAN-based data augmentation techniques.


AMOEBA: HIERARCHICAL CLUSTERING BASED ON SPATIAL PROXIMITY USING DELAUNATY DIAGRAM

Exploratory data analysis is increasingly more necessary as larger spatial data is managed in electro-magnetic media. We propose an exploratory method that reveals a robust clustering hierarchy. Our approach uses the Delaunay diagram to incorporate spatial proximity. It does not require any prior knowledge about the data set, nor does it require parameters from the user. Multi-level clusters are successfully discovered by this new method in only O(nlogn) time, where n is the size of the data set. The efficiency of our method allows us to construct and display a new type of tree graph that facilitates understanding of the complex hierarchy of clusters. We show that clustering methods that adopt a raster-like or vector-like representation of proximity are not appropriate for spatial clustering. We illustrate the robustness of our method with an experimental evaluation with synthetic data sets as well as real data sets.


Service-oriented middleware for the Future Internet: state of the art and research directions

Service-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last 10 years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today’s service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore, investigating service description, discovery, access, and composition in the Future Internet of services.


Analysis of the Co-purchase Network of Products to Predict Amazon Sales-Rank

Amazon sales-rank gives a relative estimate of a product item’s popularity among other items in the same category. An early prediction of the Amazon sales-rank of a product would imply an early guess of its sales-popularity relative to the other products on Amazon, which is one of the largest e-commerce hub across the globe. Traditional methods suggest use of product review related features, e.g., volume of reviews, text content of the reviews etc. for the purpose of prediction. In contrast, we propose in this paper for the first time a network-assisted approach to construct suitable features for prediction. In particular, we build a co-purchase network treating the individual products as nodes, with edges in between if two products are bought with one another. The way a product is positioned in this network (e.g., its centrality, clustering coefficient etc.) turns out to be a strong indicator of its sales-rank. This network-assisted approach has two distinct advantages over the traditional baseline method based on review analysis – (i) it works even if the product has no reviews (relevant especially in the early stages of the product launch) and (ii) it is notably more discriminative in classifying a popular (i.e., low sales-rank) product from an unpopular (i.e., high sales-rank) one. Based on this observation, we build a supervised model to early classify a popular product from an unpopular one. We report our results on two different product categories (CDs and cell phones) and obtain remarkably better classification accuracy compared to the baseline scheme. When the top 100 (700) products based on sales-rank are labelled as popular and the bottom 100 (700) are labelled as unpopular, the classification accuracy of our method is 89.85% (82.1%) for CDs and 84.11% (84.8%) for cell phones compared to 46.37% (68.75%) and 83.17% (71.95%) respectively from the baseline method.


IMPROVING WEB SERVICE CLUSTERING THROUGH POST FILTERING TO BOOTSTRAP THE SERVICE DISCOVERY

Web service clustering is one of a very efficient approach to discover Web services efficiently. Current approaches use similarity-distance measurement methods such as string-based, corpus-based, knowledge-based and hybrid methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information, shortage of high-quality ontologies and encoding fine-grained information. Thus, the approaches couldn’t identify the correct clusters for some services and placed them in wrong clusters. As a result of this, cluster performance is reduced. This paper proposes post-filtering approach to increase the performance of clusters by rearranging services incorrectly clustered. Our approach uses context aware similarity method that learns domain context by machine learning to produce models of context for terms retrieved from the Web in the filtering process to calculate the service similarity. We applied post filtering approach to hybrid term similarity based clustering approach that we proposed in our previous work. Experimental results show that our post-filtering approach works efficiently.


A difference in hypothalamic structure between heterosexual and homosexual men.

The anterior hypothalamus of the brain participates in the regulation of male-typical sexual behavior. The volumes of four cell groups in this region [interstitial nuclei of the anterior hypothalamus (INAH) 1, 2, 3, and 4] were measured in postmortem tissue from three subject groups: women, men who were presumed to be heterosexual, and homosexual men. No differences were found between the groups in the volumes of INAH 1, 2, or 4. As has been reported previously, INAH 3 was more than twice as large in the heterosexual men as in the women. It was also, however, more than twice as large in the heterosexual men as in the homosexual men. This finding indicates that INAH is dimorphic with sexual orientation, at least in men, and suggests that sexual orientation has a biological substrate.


Paxos made live: an engineering perspective

We describe our experience in building a fault-tolerant data-base using the Paxos consensus algorithm. Despite the existing literature in the field, building such a database proved to be non-trivial. We describe selected algorithmic and engineering problems encountered, and the solutions we found for them. Our measurements indicate that we have built a competitive system.


Adaptive configuration of lora networks for dense IoT deployments

Large-scale Internet of Things (IoT) deployments demand long-range wireless communications, especially in urban and metropolitan areas. LoRa is one of the most promising technologies in this context due to its simplicity and flexibility. Indeed, deploying LoRa networks in dense IoT scenarios must achieve two main goals: efficient communications among a large number of devices and resilience against dynamic channel conditions due to demanding environmental settings (e.g., the presence of many buildings). This work investigates adaptive mechanisms to configure the communication parameters of LoRa networks in dense IoT scenarios. To this end, we develop FLoRa, an open-source framework for end-to-end LoRa simulations in OMNeT++. We then implement and evaluate the Adaptive Data Rate (ADR) mechanism built into LoRa to dynamically manage link parameters for scalable and efficient network operations. Extensive simulations show that ADR is effective in increasing the network delivery ratio under stable channel conditions, while keeping the energy consumption low. Our results also show that the performance of ADR is severely affected by a highly-varying wireless channel. We thereby propose an improved version of the original ADR mechanism to cope with variable channel conditions. Our proposed solution significantly increases both the reliability and the energy efficiency of communications over a noisy channel, almost irrespective of the network size. Finally, we show that the delivery ratio of very dense networks can be further improved by using a network-aware approach, wherein the link parameters are configured based on the global knowledge of the network.


Generative Deep Neural Networks for Dialogue: A Short Review

Researchers have recently started investigating deep neural networks for dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq) models have shown promising results for unstructured tasks, such as word-level dialogue response generation. The hope is that such models will be able to leverage massive amounts of data to learn meaningful natural language representations and response generation strategies, while requiring a minimum amount of domain knowledge and hand-crafting. An important challenge is to develop models that can effectively incorporate dialogue context and generate meaningful and diverse responses. In support of this goal, we review recently proposed models based on generative encoder-decoder neural network architectures, and show that these models have better ability to incorporate long-term dialogue history, to model uncertainty and ambiguity in dialogue, and to generate responses with high-level compositional structure.


Neural Ordinary Differential Equations

We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a blackbox differential equation solver. These continuous-depth models have constant memory cost, adapt their evaluation strategy to each input, and can explicitly trade numerical precision for speed. We demonstrate these properties in continuous-depth residual networks and continuous-time latent variable models. We also construct continuous normalizing flows, a generative model that can train by maximum likelihood, without partitioning or ordering the data dimensions. For training, we show how to scalably backpropagate through any ODE solver, without access to its internal operations. This allows end-to-end training of ODEs within larger models.


Sentence Selection and Evaluation Metrics

Human-quality text summarization systems are di cult to design, and even more di cult to evaluate, in part because documents can di er along several dimensions, such as length, writing style and lexical usage. Nevertheless, certain cues can often help suggest the selection of sentences for inclusion in a summary. This paper presents our analysis of news-article summaries generated by sentence selection. Sentences are ranked for potential inclusion in the summary using a weighted combination of statistical and linguistic features. The statistical features were adapted from standard IR methods. The potential linguistic ones were derived from an analysis of news-wire summaries. To evaluate these features we use a normalized version of precision-recall curves, with a baseline of random sentence selection, as well as analyze the properties of such a baseline. We illustrate our discussions with empirical results showing the importance of corpus-dependent baseline summarization standards, compression ratios and carefully crafted long queries.


It's worth the hassle!: the added value of evaluating the usability of mobile systems in the field

The distinction between field and laboratory is classical in research methodology. In human-computer interaction, and in usability evaluation in particular, it has been a controversial topic for several years. The advent of mobile devices has revived this topic. Empirical studies that compare evaluations in the two settings are beginning to appear, but they provide very different results. This paper presents results from an experimental comparison of a field-based and a lab-based usability evaluation of a mobile system. The two evaluations were conducted in exactly the same way. The conclusion is that it is definitely worth the hassle to conduct usability evaluations in the field. In the field-based evaluation we identified significantly more usability problems and this setting revealed problems with interaction style and cognitive load that were not identified in the laboratory.


Generic Battery model covering self-discharge and internal resistance variation

Design and analysis of battery powered systems depend on effective battery model. Hardware-in-the-loop testing of Battery Management System (BMS) extensively uses battery models. Simulation of Hybrid Electric Vehicles highly rely on the battery models which provides actual characteristics. This paper proposes a performance enhanced battery model which can be included in electric circuit design and analysis. Combined electrical and thermal model replicates the real world performance of battery. Self-discharge is the characteristic of a battery which reduces the stored energy without external connection. Effect of self-discharge on State of Charge (SOC) of battery is considered in the paper. Internal resistance variation with SOC of a battery included in the model further enhances its exactness. Proposed model is validated for Sony US 18650 Li-ion battery in MATLAB Simulink.


Handling outliers in non-blind image deconvolution

Non-blind deconvolution is a key component in image deblurring systems. Previous deconvolution methods assume a linear blur model where the blurred image is generated by a linear convolution of the latent image and the blur kernel. This assumption often does not hold in practice due to various types of outliers in the imaging process. Without proper outlier handling, previous methods may generate results with severe ringing artifacts even when the kernel is estimated accurately. In this paper we analyze a few common types of outliers that cause previous methods to fail, such as pixel saturation and non-Gaussian noise. We propose a novel blur model that explicitly takes these outliers into account, and build a robust non-blind deconvolution method upon it, which can effectively reduce the visual artifacts caused by outliers. The effectiveness of our method is demonstrated by experimental results on both synthetic and real-world examples.


Radiometric calibration from a single image

Photometric methods in computer vision require calibration of the camera's radiometric response, and previous works have addressed this problem using multiple registered images captured under different camera exposure settings. In many instances, such an image set is not available, so we propose a method that performs radiometric calibration from only a single image, based on measured RGB distributions at color edges. This technique automatically selects appropriate edge information for processing, and employs a Bayesian approach to compute the calibration. Extensive experimentation has shown that accurate calibration results can be obtained using only a single input image.


Robust Radiometric Calibration and Vignetting Correction

In many computer vision systems, it is assumed that the image brightness of a point directly reflects the scene radiance of the point. However, the assumption does not hold in most cases due to nonlinear camera response function, exposure changes, and vignetting. The effects of these factors are most visible in image mosaics and textures of 3D models where colors look inconsistent and notable boundaries exist. In this paper, we propose a full radiometric calibration algorithm that includes robust estimation of the radiometric response function, exposures, and vignetting. By decoupling the effect of vignetting from the response function estimation, we approach each process in a manner that is robust to noise and outliers. We verify our algorithm with both synthetic and real data, which shows significant improvement compared to existing methods. We apply our estimation results to radiometrically align images for seamless mosaics and 3D model textures. We also use our method to create high dynamic range (HDR) mosaics that are more representative of the scene than normal mosaics.


Fast Image Deconvolution using Hyper-Laplacian Priors

This document is supplementary material to our NIPS 2009 pap er [1] of the same name. While we choose to solve the w sub-problem (Eqn. 5) in [1] using a LUT or analytically (for s ome specific values ofα), a number of numerical alternatives exist. The simplest an d f stest approach is to use Newton-Raphson (NR) to find the roots of the polynomials in Eq . 10 and Eqn 11 of [1]. As shown in Table 1,4 iterations of NR take a similar time to our analytic solution . However, the numerical algorithm has poor stability, particularly when the polynomi als skim the x-axis. In practice, we found it gave spurious solutions around 1-2% of the time, an unacce ptable rate given the iterative nature of our overall algorithm. Applying NR directly to Eqn. 5 of [1] i s similarly unreliable. Although techniques with greater stability than NR exist, they are comput ationally more expensive. For example, we also compare to Matlab’s roots command, based on solving an eigenvalue problem, but this was several orders of magnitude slower than our analytic app ro ch.


Motion-aware noise filtering for deblurring of noisy and blurry images

Image noise can present a serious problem in motion deblurring. While most state-of-the-art motion deblurring algorithms can deal with small levels of noise, in many cases such as low-light imaging, the noise is large enough in the blurred image that it cannot be handled effectively by these algorithms. In this paper, we propose a technique for jointly denoising and deblurring such images that elevates the performance of existing motion deblurring algorithms. Our method takes advantage of estimated motion blur kernels to improve denoising, by constraining the denoised image to be consistent with the estimated camera motion (i.e., no high frequency noise features that do not match the motion blur). This improved denoising then leads to higher quality blur kernel estimation and deblurring performance. The two operations are iterated in this manner to obtain results superior to suppressing noise effects through regularization in deblurring or by applying denoising as a preprocess. This is demonstrated in experiments both quantitatively and qualitatively using various image examples.


NFLB dropout: Improve generalization ability by dropping out the best -A biologically inspired adaptive dropout method for unsupervised learning

Generalization ability is widely acknowledged as one of the most important criteria to evaluate the quality of unsupervised models. The objective of our research is to find a better dropout method to improve the generalization ability of convolutional deep belief network (CDBN), an unsupervised learning model for vision tasks. In this paper, the phenomenon of low feature diversity during the training process is investigated. The attention mechanism of human visual system is more focused on rare events and depresses well-known facts. Inspired by this mechanism, No Feature Left Behind Dropout (NFLB Dropout), an adaptive dropout method is firstly proposed to automatically adjust the dropout rate feature-wisely. In the proposed method, the algorithm drops well-trained features and keeps poorly-trained ones with a high probability during training iterations. In addition, we apply two approximations of the quality of features, which are inspired by theory of saliency and optimization. Compared with the model trained by standard dropout, experiment results show that our NFLB Dropout method improves not only the accuracy but the convergence speed as well.


A Unified Framework for Multimodal Domain Adaptation

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


Metamaterial electromagnetic wave absorbers.

The advent of negative index materials has spawned extensive research into metamaterials over the past decade. Metamaterials are attractive not only for their exotic electromagnetic properties, but also their promise for applications. A particular branch-the metamaterial perfect absorber (MPA)-has garnered interest due to the fact that it can achieve unity absorptivity of electromagnetic waves. Since its first experimental demonstration in 2008, the MPA has progressed significantly with designs shown across the electromagnetic spectrum, from microwave to optical. In this Progress Report we give an overview of the field and discuss a selection of examples and related applications. The ability of the MPA to exhibit extreme performance flexibility will be discussed and the theory underlying their operation and limitations will be established. Insight is given into what we can expect from this rapidly expanding field and future challenges will be addressed.


Agricultural sustainability: concepts, principles and evidence.

Concerns about sustainability in agricultural systems centre on the need to develop technologies and practices that do not have adverse effects on environmental goods and services, are accessible to and effective for farmers, and lead to improvements in food productivity. Despite great progress in agricultural productivity in the past half-century, with crop and livestock productivity strongly driven by increased use of fertilizers, irrigation water, agricultural machinery, pesticides and land, it would be over-optimistic to assume that these relationships will remain linear in the future. New approaches are needed that will integrate biological and ecological processes into food production, minimize the use of those non-renewable inputs that cause harm to the environment or to the health of farmers and consumers, make productive use of the knowledge and skills of farmers, so substituting human capital for costly external inputs, and make productive use of people's collective capacities to work together to solve common agricultural and natural resource problems, such as for pest, watershed, irrigation, forest and credit management. These principles help to build important capital assets for agricultural systems: natural; social; human; physical; and financial capital. Improving natural capital is a central aim, and dividends can come from making the best use of the genotypes of crops and animals and the ecological conditions under which they are grown or raised. Agricultural sustainability suggests a focus on both genotype improvements through the full range of modern biological approaches and improved understanding of the benefits of ecological and agronomic management, manipulation and redesign. The ecological management of agroecosystems that addresses energy flows, nutrient cycling, population-regulating mechanisms and system resilience can lead to the redesign of agriculture at a landscape scale. Sustainable agriculture outcomes can be positive for food productivity, reduced pesticide use and carbon balances. Significant challenges, however, remain to develop national and international policies to support the wider emergence of more sustainable forms of agricultural production across both industrialized and developing countries.


A hybrid CAD-based construction site layout planning system using genetic algorithms

The efficient layout planning of a construction site is a fundamental task to any project undertaking. In an attempt to enhance the general practice of layout planning of construction sites, the paper introduces a novel approach for producing the sought layouts. This approach integrates the highly sophisticated graphical capabilities of computer-aided design (CAD) platforms with the robust search and optimization capabilities of genetic algorithms (GAs). In this context, GAs are utilized from within the CAD environment to optimize the location of temporary facilities on site. The functional interaction between GAs and CAD and the details of the GA-based layout optimization procedure are presented. A fully automated computer system is further developed to demonstrate the practicality of the chosen approach. In order to evaluate the system’s performance, a local construction project with a 24,000m site is used. The automated system produced highly satisfactory results and showed notable flexibility through its CAD-based input/output media. D 2003 Elsevier B.V. All rights reserved.


A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China

Rapid socio-economic development and urbanization have resulted in serious deterioration in air-quality in many world cities, including Beijing, China. This preliminary study is the first attempt to examine the effectiveness of air pollution control regulations implemented in Beijing during 2013 – 2017 through a data-driven regulatory intervention analysis. Our proposed machine-learning model utilizes proxy data including Aerosol Optical Depth (AOD) and meteorology; it can explain 80% of the PM2.5 variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM2.5 pollution in Beijing by 23% on average.


Visual Analytics for Explainable Deep Learning

Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.


Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation

Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.


Towards unobtrusive emotion recognition for affective social communication

Awareness of the emotion of those who communicate with others is a fundamental challenge in building affective intelligent systems. Emotion is a complex state of the mind influenced by external events, physiological changes, or relationships with others. Because emotions can represent a user's internal context or intention, researchers suggested various methods to measure the user's emotions from analysis of physiological signals, facial expressions, or voice. However, existing methods have practical limitations to be used with consumer devices, such as smartphones; they may cause inconvenience to users and require special equipment such as a skin conductance sensor. Our approach is to recognize emotions of the user by inconspicuously collecting and analyzing user-generated data from different types of sensors on the smartphone. To achieve this, we adopted a machine learning approach to gather, analyze and classify device usage patterns, and developed a social network service client for Android smartphones which unobtrusively find various behavioral patterns and the current context of users. Also, we conducted a pilot study to gather real-world data which imply various behaviors and situations of a participant in her/his everyday life. From these data, we extracted 10 features and applied them to build a Bayesian Network classifier for emotion recognition. Experimental results show that our system can classify user emotions into 7 classes such as happiness, surprise, anger, disgust, sadness, fear, and neutral with a surprisingly high accuracy. The proposed system applied to a smartphone demonstrated the feasibility of an unobtrusive emotion recognition approach and a user scenario for emotion-oriented social communication between users.


Jet lag: trends and coping strategies

The number of travellers undertaking long-distance flights has continued to increase. Such flights are associated with travel fatigue and jet lag, the symptoms of which are considered here, along with their similarities, differences, and causes. Difficulties with jet lag because of sleep loss and decreased performance are emphasised. Since jet lag is caused mainly by inappropriate timing of the body clock in the new time zone, the pertinent properties of the body clock are outlined, with a description of how the body clock can be adjusted. The methods, both pharmacological and behavioural, that have been used to alleviate the negative results of time-zone transitions, are reviewed. The results form the rationale for advice to travellers flying in different directions and crossing several time zones. Finally, there is an account of the main problems that remain unresolved.


MiningZinc: A declarative framework for constraint-based mining

We introduce MiningZinc, a declarative framework for constraint-based data mining. MiningZinc consists of two key components: a language component and an execution mechanism. First, the MiningZinc language allows for high-level and natural modeling of mining problems, so that MiningZinc models are similar to the mathematical definitions used in the literature. It is inspired by the Zinc family of languages and systems and supports user-defined constraints and functions. Secondly, the MiningZinc execution mechanism specifies how to compute solutions for the models. It is solver independent and supports both standard constraint solvers and specialized data mining systems. The high-level problem specification is first translated into a normalized constraint language (FlatZinc). Rewrite rules are then used to add redundant constraints or solve subproblems using specialized data mining algorithms or generic constraint programming solvers. Given a model, different execution strategies are automatically extracted that correspond to different sequences of algorithms to run. Optimized data mining algorithms, specialized processing routines and generic solvers can all be automatically combined. Thus, the MiningZinc language allows one to model constraint-based itemset mining problems in a solver independent way, and its execution mechanism can automatically chain different algorithms and solvers. This leads to a unique combination of declarative modeling with high-performance solving.


Neural measures of the causal role of observers’ facial mimicry on visual working memory for facial expressions

Simulation models of facial expressions propose that sensorimotor regions may increase the clarity of facial expressions representations in extrastriate areas. We monitored the event-related potential marker of visual working memory (VWM) representations, namely the sustained posterior contralateral negativity (SPCN), also termed contralateral delay activity, while participants performed a change detection task including to-be-memorized faces with different intensities of anger. In one condition participants could freely use their facial mimicry during the encoding/VWM maintenance of the faces while in a different condition participants had their facial mimicry blocked by a gel. Notably, SPCN amplitude was reduced for faces in the blocked mimicry condition when compared to the free mimicry condition. This modulation interacted with the empathy levels of participants such that only participants with medium-high empathy scores showed such reduction of the SPCN amplitude when their mimicry was blocked. The SPCN amplitude was larger for full expressions when compared to neutral and subtle expressions, while subtle expressions elicited lower SPCN amplitudes than neutral faces. These findings provide evidence of a functional link between mimicry and VWM for faces and further shed light on how this memory system may receive feedbacks from sensorimotor regions during the processing of facial expressions.


A Practical Message Falsification Attack on WPA

In 2008, Beck and Tews have proposed a practical attack on WPA. Their attack (called the Beck-Tews attack) can recover plaintext from an encrypted short packet, and can falsify it. The execution time of the Beck-Tews attack is about 12-15 minutes. However, the attack has the limitation, namely, the targets are only WPA implementations those support IEEE802.11e QoS features. In this paper, we propose a practical message falsification attack on any WPA implementation. In order to ease targets of limitation of wireless LAN products, we apply the Beck-Tews attack to the man-in-the-middle attack. In the man-inthe-middle attack, the user’s communication is intercepted by an attacker until the attack ends. It means that the users may detect our attack when the execution time of the attack is large. Therefore, we give methods for reducing the execution time of the attack. As a result, the execution time of our attack becomes about one minute in the best case.


The social and ethical issues of post-genomic human biobanks

Biobanking — the organized collection of biological samples and associated data — ranges in scope from small collections of samples in academic or hospital settings to large-scale national repositories. Biobanks raise many ethical concerns, to which authorities are responding by introducing specific regulations. Genomics research, which thrives on the sharing of samples and information, is affected by two prominent ethical questions: do ethical principles prevent or promote the sharing of stored biological resources? How does the advent of large-scale biobanking alter the way in which ethical issues are addressed?


A Functional Dynamic Boltzmann Machine

Dynamic Boltzmann machines (DyBMs) are recently developed generative models of a time series. They are designed to learn a time series by efficient online learning algorithms, whilst taking long-term dependencies into account with help of eligibility traces, recursively updatable memory units storing descriptive statistics of all the past data. The current DyBMs assume a finitedimensional time series and cannot be applied to a functional time series, in which the dimension goes to infinity (e.g., spatiotemporal data on a continuous space). In this paper, we present a functional dynamic Boltzmann machine (F-DyBM) as a generative model of a functional time series. A technical challenge is to devise an online learning algorithm with which F-DyBM, consisting of functions and integrals, can learn a functional time series using only finite observations of it. We rise to the above challenge by combining a kernel-based function approximation method along with a statistical interpolation method and finally derive closed-form update rules. We design numerical experiments to empirically confirm the effectiveness of our solutions. The experimental results demonstrate consistent error reductions as compared to baseline methods, from which we conclude the effectiveness of F-DyBM for functional time series prediction.


A Game of Things: Strategic Allocation of Security Resources for IoT

In many Internet of Thing (IoT) application domains security is a critical requirement, because malicious parties can undermine the effectiveness of IoT-based systems by compromising single components and/or communication channels. Thus, a security infrastructure is needed to ensure the proper functioning of such systems even under attack. However, it is also critical that security be at a reasonable resource and energy cost, as many IoT devices may not have sufficient resources to host expensive security tools. In this paper, we focus on the problem of efficiently and effectively securing IoT networks by carefully allocating security tools. We model our problem according to game theory, and provide a Pareto-optimal solution, in which the cost of the security infrastructure, its energy consumption, and the probability of a successful attack, are minimized. Our experimental evaluation shows that our technique improves the system robustness in terms of packet delivery rate for different network topologies.


The Effects of Violent Video Game Habits on Adolescent Aggressive Attitudes and Behaviors

Video games have become one of the favorite activities of children in America. A growing body of research links violent video game play to aggressive cognitions, attitudes, and behaviors. This study tested the predictions that exposure to violent video game content is (1) positively correlated with hostile attribution bias, (2) positively correlated with arguments with teachers and physical fights, and negatively correlated with school performance, and (3) positively correlated with hostility. 607 8and 9-grade students from four schools participated. Each prediction was supported. Youth who expose themselves to greater amounts of video game violence see the world as a more hostile place, are more hostile themselves, get into arguments with teachers more frequently, are more likely to be involved in physical fights, and perform more poorly in school. Video game violence exposure is a significant predictor of physical fights even when respondent sex, hostility level, and weekly amount of game play are statistically controlled. It is suggested that video game violence is a risk factor for aggressive behavior. The results also suggest that parental involvement in video game play may act as a protective factor for youth. Results are interpreted within and support the framework of the General Aggression Model. 1 Address correspondence to: Douglas A. Gentile, Ph.D., National Institute on Media and the Family, 606 24th Avenue South, Suite 606, Minneapolis, MN 55454. Phone: 612/672-5437; Fax: 612/672-4113; E-mail: dgentile@mediafamily.org Violent Video Games 2 The Popularity of Video Games Video games have become one of the favorite activities of children in America (Dewitt,1994). Sales have grown consistently with the entire electronic entertainment category taking in between $7 billion and $7.5 billion in 1999, surpassing theatrical box office revenues for the first time (“Come in and Play,” 2000). Worldwide video game sales are now at $20 billion (Cohen, 2000). Over 100 million Gameboys and 75 million PlayStations have been sold (Kent, 2000). The average American child between the ages of 2 and 17 plays video games for 7 hours a week (Gentile & Walsh, under review). A study by Buchman and Funk (1996) highlighted the differences between boys and girls, reporting that fourth through eighth grade boys played video games for 5 to 10 hours a week while girls played for 3 to 6 hours a week. Using industry polls, Provenzo (1991) studied the most popular Nintendo video games in America and found that 40 of the 47 had violence as their main theme. In another study (Buchman & Funk, 1996) in which video games were split into six categories, human and fantasy violence accounted for about 50% of children’s favorite games, with sports violence contributing another 1620% for boys and 6-15% for girls. Research On Video Games and Aggression Many observant parents agree that the effects of violent video games are probably deleterious to children; however, they generally believe that their own children will be unaffected. This may just be bias on their part, or they may be correct. Research has shown that not all children are affected in the same way by violent video games (Anderson & Dill, 2000; Lynch, 1994; Lynch, 1999). While the literature connecting video game violence and aggression is growing, much of the research that has been done on video games to date has not taken into consideration the effect of pre-existing hostility or aggression. Several correlational studies (e.g., Anderson & Dill, 2000; Colwell & Payne, 2000; Dominick, 1984; Lin & Lepper, 1987; Fling, Smith, Rodriguez, Thornton, Atkins, & Nixon, 1992) have investigated the effects of video game habits and found a positive correlation between video game habits and an increase in aggressive behavior. However, few studies have differentiated between violent and non-violent video games. Fewer still have looked at differences in the subjects' preexisting hostility or aggression. A growing number of experimental studies (e.g., Cooper & Mackie, 1986; Silvern & Williamson, 1987; Schuttte, Malouff, Post-Gorden, & Rodasta, 1988; Irwin & Gross, 1995; Anderson & Dill, 2000) have shown support for the hypothesis that violent video games lead to an increase in laboratory aggression. A meta-analytic study (Anderson & Bushman, in press-a) found that, across 54 independent tests of the relation between video game violence and aggression, involving 4,262 participants, the average effect size was both positive and significant. The General Aggression Model The General Aggression Model (GAM) and its relation to violent video games has been described by Anderson and Dill (2000). The GAM seeks to explain aggressive behavior in children after playing violent video games. This model describes a “multi-stage process by which personological (e.g., aggressive personality) and situational (e.g., video game play and provocation) input variables lead to aggressive behavior. They do so by influencing several related internal states and the outcomes of automatic and controlled appraisal (or decision) processes” (Anderson & Dill, 2000, p. 773). The GAM is relevant to the study of violent video games for several reasons. One reason is that it differentiates between short and long term effects of video game violence on the game-player. With regard to the short-term effects of violent video games, the GAM predicts that both kinds of Violent Video Games 3 input variables, person and situation, can influence the present internal state of the person. The GAM further describes the internal state of a person with cognitive, affective, and arousal variables. Summarizing the GAM’s predictions for the effects of violent video games on children’s behavior, Anderson and Dill drew the following conclusions: “Short-term violent video game increases in aggression are expected by [the model] whenever exposure to violent media primes aggressive thoughts, increases hostile feeling, or increases arousal” (Anderson & Dill, 2000, p. 774). The GAM describes the long term effects of violent video games as a result of the development, over-learning, and reinforcement of aggression-related knowledge structures. These knowledge structures include vigilance for enemies (i.e., hostile attribution bias), aggressive action against others, expectations that others will behave aggressively, positive attitudes towards the use of violence, and the belief that violent solutions are effective and appropriate. Repeated exposure to graphic scenes of violence is also postulated to be desensitizing. Furthermore, it is predicted that long term game-players become more aggressive in outlook, perceptual biases, attitudes, beliefs, and behavior than they were before the repeated exposure. Two studies were conducted to test the efficacy of the GAM in predicting aggression from violent video game play (Anderson & Dill, 2000). In the first study, it was found that real-life video game play was positively related to aggressive behavior and delinquency (long-term effects). The relationship was stronger for individuals who were characteristically aggressive. In addition, amount of video game play was negatively related to school performance. In the second study, laboratory exposure to a graphically violent video game increased aggressive thoughts and behavior (short-term effects), although there was no moderating effect of hostility (i.e., aggressive personality). Both of these studies were consistent with the main hypotheses regarding the GAM and video game violence. Lynch’s research on the physiological effects of violent video games (Lynch, 1994; Lynch, 1999) lends further credibility to the GAM. Lynch's results are consistent with a recent metaanalysis of seven independent tests showing that blood pressure and heart rate increase with exposure to violent video games (Anderson & Bushman, in press-a). This research demonstrates that hostility in adolescence is directly related to physiological reactivity to violent video games. It also demonstrates the efficacy of the GAM for predicting arousal measures, one of the three internal states described by the GAM that may lead to aggression. The GAM also predicts that long-term effects of violent video games will appear in a number of other areas, including hostile attribution bias, desensitization, and aggressive behaviors (such as physical fights). Children who tend to interpret ambiguous social cues as being of hostile intent (i.e., have a hostile attribution bias) are hypothesized to be more aggressive. This hypothesized relationship has been confirmed consistently across a wide range of samples ranging from early childhood through adulthood, and across a number of studies (e.g., Crick & Dodge, 1994; Dill, Anderson, Anderson, & Deuser, 1997). Furthermore, there is a robust relationship between hostile attribution bias and children’s social maladjustment, such as depression, negative selfperceptions, and peer rejection (Crick, 1995). Based on the GAM, we predict that long-term exposure to violent video games (or other violent media) may create a predisposition to interpret others’ actions as having malignant intent. Following this logic, if children come to have a greater hostile attribution bias from repeated, extended exposure to violent video games over time, it is also likely that they would become engaged in more aggressive behaviors such as arguments and physical fights. The current research is designed to test four hypotheses. First, video game violence exposure is positively correlated with seeing the world as a more hostile place (hostile attribution bias). Second, video game violence exposure is positively correlated with arguments with teachers and physical fights, and is negatively correlated with academic performance. Third, trait hostility will Violent Video Games 4 be positively correlated with video game violence exposure. Fourth, limiting the amount of violent video game play, either


BioWatch: A Noninvasive Wrist-Based Blood Pressure Monitor That Incorporates Training Techniques for Posture and Subject Variability

Noninvasive continuous blood pressure (BP) monitoring is not yet practically available for daily use. Challenges include making the system easily wearable, reducing noise level and improving accuracy. Variations in each person's physical characteristics, as well as the possibility of different postures, increase the complexity of continuous BP monitoring, especially outside the hospital. This study attempts to provide an easily wearable solution and proposes training to specific posture and individual for further improving accuracy. The wrist watch-based system we developed can measure electrocardiogram and photoplethysmogram. From these two signals, we measure pulse transit time through which we can obtain systolic and diastolic blood pressure through regression techniques. In this study, we investigate various functions to perform the training to obtain blood pressure. We validate measurements on different postures and subjects, and show the value of training the device to each posture and each subject. We observed that the average RMSE between the measured actual systolic BP and calculated systolic BP is between 7.83 to 9.37 mmHg across 11 subjects. The corresponding range of error for diastolic BP is 5.77 to 6.90 mmHg. The system can also automatically detect the arm position of the user using an accelerometer with an average accuracy of 98%, to make sure that the sensor is kept at the proper height. This system, called BioWatch, can potentially be a unified solution for heart rate, SPO2 and continuous BP monitoring.


Agent and Cyber-Physical System Based Self-Organizing and Self-Adaptive Intelligent Shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities.


Image description with features that summarize

We present a new method for describing images for the purposes of matching and registration. We take the point of view that large, coherent regions in the image provide a concise and stable basis for image description. We develop a new algorithm for feature detection that operates on several projections (feature spaces) of the image using kernel-based optimization techniques to locate local extrema of a continuous scale-space of image regions. Descriptors of these image regions and their relative geometry then form the basis of an image description. The emphasis of the work is on features that summarize image content and are highly robust to viewpoint changes and occlusion yet remain discriminative for matching and registration. We present experimental results of these methods applied to the problem of image retrieval. We find that our method performs comparably to two published techniques: Blobworld and SIFT features. However, compared to these techniques two significant advantages of our method are its (1) stability under large changes in the images and (2) its representational efficiency. 2008 Elsevier Inc. All rights reserved.


Feature recognition and shape design in sneakers

Article history: Available online xxxx


Deep Speech: Scaling up end-to-end speech recognition

We present a state-of-the-art speech recognition system developed using end-toend deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a “phoneme.” Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5’00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.


Understanding Loneliness in Social Awareness Streams: Expressions and Responses

We studied the experience of loneliness as communicated by thousands of people on Twitter. Using a data set of public Twitter posts containing explicit expressions of loneliness, we qualitatively developed a categorization scheme for these expressions, showing how the context of loneliness expressed on Twitter relates to existing theories about loneliness. A quantitative analysis of the data exposed categories and patterns in communication practices around loneliness. For example, users expressing more severe, enduring loneliness are more likely to be female, and less likely to include requests for social interaction in their tweets. Further, we studied the responses to expressions of loneliness in Twitter’s social settings. Deriving from the same dataset, we examined factors that correlate with the existence and type of response, showing, for example, that men were more likely to receive responses to lonely tweets, and expressions of enduring loneliness are critically less likely to receive responses.


From Node Embedding To Community Embedding

Most of the existing graph embedding methods focus on nodes, which aim to output a vector representation for each node in the graph such that two nodes being “close” on the graph are close too in the low-dimensional space. Despite the success of embedding individual nodes for graph analytics, we notice that an important concept of embedding communities (i.e., groups of nodes) is missing. Embedding communities is useful, not only for supporting various community-level applications, but also to help preserve community structure in graph embedding. In fact, we see community embedding as providing a higher-order proximity to define the node closeness, whereas most of the popular graph embedding methods focus on first-order and/or second-order proximities. To learn the community embedding, we hinge upon the insight that community embedding and node embedding reinforce with each other. As a result, we propose ComEmbed, the first community embedding method, which jointly optimizes the community embedding and node embedding together. We evaluate ComEmbed on real-world data sets. We show it outperforms the state-of-the-art baselines in both tasks of node classification and community prediction.