General Sales Forecast Models for Automobile Markets and their Analysis

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.


Quality expectations of machine translation

Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. There should, therefore, be no doubt as to the utility of MT. However, not everyone is convinced that MT can be useful, especially as a productivity enhancer for human translators. In this chapter, I address this issue, describing how MT is currently deployed, how its output is evaluated and how this could be enhanced, especially as MT quality itself improves. Central to these issues is the acceptance that there is no longer a single ‘gold standard’ measure of quality, such that the situation in which MT is deployed needs to be borne in mind, especially with respect to the expected ‘shelf-life’ of the translation itself. 1 Machine Translation Today Machine Translation (MT) is being deployed for a range of use-cases by millions of people on a daily basis. I will examine the reasons for this later in this chapter, but one inference is very clear: those people using MT in those use-cases must already be satisfied with the level of quality emanating from the MT systems they are deploying, otherwise they would stop using them. That is not the same thing at all as saying that MT quality is perfect, far from it. The many companies and academic researchers who develop and deploy MT engines today continue to strive to improve the quality of the translations produced. This too is an implicit acceptance of the fact that the level of quality is sub-optimal – for some use-cases at least – and can be improved. If MT system output is good enough for some areas of application, yet at the same time system developers are trying hard to improve the level of translations produced by their engines, then translation quality – whether produced by a machine or by a human – needs to be measurable. Note that this applies also to translators who complain that MT quality is too poor to be used in their workflows; in order to decide that with some certainty – rather than rejecting MT out-of-hand merely as a knee-jerk reaction to the onset of this new technology – the impact of MT on translators’ work needs to be measurable. In Way (2013), I appealed to two concepts, which are revisited here, namely:


Big data reduction framework for value creation in sustainable enterprises


Fuzzy Queries over NoSQL Graph Databases: Perspectives for Extending the Cypher Language

When querying databases, users often wish to express vague concepts, as for instance asking for the cheap hotels. This has been extensively studied in the case of relational databases. In this paper, we propose to study how such useful techniques can be adapted to NoSQL graph databases where the role of fuzziness is crucial. Such databases are indeed among the fastest-growing models for dealing with big data, especially when dealing with network data (e.g., social networks). We consider the Cypher declarative query language proposed for Neo4j which is the current leader on this market, and we present how to express fuzzy queries.


Are image quality metrics adequate to evaluate the quality of geometric objects?

Geometric objects are often represented by many millions of triangles or polygons, which limits the ease with which they can be transmitted and displayed electronically. This has led to the development of many algorithms for simplifying geometric models, and to the recognition that metrics are required to evaluate their success. The goal is to create computer graphic renderings of the object that do not appear to be degraded to a human observer. The perceptual evaluation of simplified objects is a new topic. One approach has been to use image-based metrics to predict the perceived degradation of simplified 3-D models 1 Since that 2-D images of 3-D objects can have significantly different perceived quality, depending on the direction of the illumination, 2 2-D measures of image quality may not adequately capture the perceived quality of 3-D objects. To address this question, we conducted experiments in which we explicitly compared the perceived quality of animated 3-D objects and their corresponding 2-D still image projections. Our results suggest that 2-D judgements do not provide a good predictor of 3-D image quality, and identify a need to develop “object quality metrics.”


Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis

Bearings are critical components in induction motors and brushless direct current motors. Bearing failure is the most common failure mode in these motors. By implementing health monitoring and fault diagnosis of bearings, unscheduled maintenance and economic losses caused by bearing failures can be avoided. This paper introduces trace ratio linear discriminant analysis (TR-LDA) to deal with high-dimensional non-Gaussian fault data for dimension reduction and fault classification. Motor bearing data with single-point faults and generalized-roughness faults are used to validate the effectiveness of the proposed method for fault diagnosis. Comparisons with other conventional methods, such as principal component analysis, local preserving projection, canonical correction analysis, maximum margin criterion, LDA, and marginal Fisher analysis, show the superiority of TR-LDA in fault diagnosis.


GPGPU Enabled Ray Directed Adaptive Volume Visualization for High Density Scans

This paper presents an open source implementation of a volume visualizer capable of rendering large scale simulated and tomographic volume datasets on everyday commodity hardware. The ability to visualize this data enables a wide range of analysis in many scientific disciplines, such as medicine, biology, engineering, and physics. The implementation presented takes advantage of a hierarchical space filling curve to efficiently query only a subset of the data required for accurate visualization. A major advantage of this implementation is that it is open source, and works on a wide range of visualization devices by using the Unity game engine, a cross platform engine capable of targeting over 25 platforms [24]. This work's unique contribution is utilizing a space filling curve for multiple bricks in the volume to permit adaptive streaming at different resolutions.


A real-time grading method of apples based on features extracted from defects

This paper presents a hierarchical grading method applied to Jonagold apples. Several images covering the whole surface of the fruits were acquired thanks to a prototype grading machine. These images were then segmented and the features of the defects were extracted. During a learning procedure, the objects were classified into clusters by k-mean clustering. The classification probabilities of the objects were summarised and on this basis the fruits were graded using quadratic discriminant analysis. The fruits were correctly graded with a rate of 73 %. The errors were found having origins in the segmentation of the defects or for a particular wound, in a confusion with the calyx end.


Immuno-inspired autonomic system for cyber defense

The biological immune system is an autonomic system for self-protection, which has evolved over millions of years probably through extensive redesigning, testing, tuning and optimization process. The powerful information processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors for its artificial counterpart. Our study focuses on building an autonomic defense system, using some immunological metaphors for information gathering, analyzing, decision making and launching threat and attack responses. This on-going research effort is not to mimic the nature but to explore and learn valuable lessons useful for self-adaptive cyber defense systems.


Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation

Many statistical models can be simulated forwards but have intractable likelihoods. Approximate Bayesian Computation (ABC) methods are used to infer properties of these models from data. Traditionally these methods approximate the posterior over parameters by conditioning on data being inside an -ball around the observed data, which is only correct in the limit →0. Monte Carlo methods can then draw samples from the approximate posterior to approximate predictions or error bars on parameters. These algorithms critically slow down as →0, and in practice draw samples from a broader distribution than the posterior. We propose a new approach to likelihood-free inference based on Bayesian conditional density estimation. Preliminary inferences based on limited simulation data are used to guide later simulations. In some cases, learning an accurate parametric representation of the entire true posterior distribution requires fewer model simulations than Monte Carlo ABC methods need to produce a single sample from an approximate posterior.


Gradient Descent Quantizes ReLU Network Features

Deep neural networks are often trained in the over-parametrized regime (i.e. with far more parameters than training examples), and understanding why the training converges to solutions that generalize remains an open problem Zhang et al. [2017]. Several studies have highlighted the fact that the training procedure, i.e. mini-batch Stochastic Gradient Descent (SGD) leads to solutions that have specific properties in the loss landscape. However, even with plain Gradient Descent (GD) the solutions found in the over-parametrized regime are pretty good and this phenomenon is poorly understood. We propose an analysis of this behavior for feedforward networks with a ReLU activation function under the assumption of small initialization and learning rate and uncover a quantization effect: The weight vectors tend to concentrate at a small number of directions determined by the input data. As a consequence, we show that for given input data there are only finitely many, “simple” functions that can be obtained, independent of the network size. This puts these functions in analogy to linear interpolations (for given input data there are finitely many triangulations, which each determine a function by linear interpolation). We ask whether this analogy extends to the generalization properties while the usual distribution-independent generalization property does not hold, it could be that for e.g. smooth functions with bounded second derivative an approximation property holds which could “explain” generalization of networks (of unbounded size) to unseen inputs.


An Intrusion Detection System Based on KDD-99 Data using Data Mining Techniques and Feature Selection

Internet and internet users are increasing day by day. Also due to rapid development of internet technology , security is becoming big issue. Intruders are monitoring comput er network continuously for attacks. A sophisticated firewall with efficient intrusion detection system (IDS) is required to pre vent computer network from attacks. A comprehensive study of litera tures proves that data mining techniques are more powerful techni que to develop IDS as a classifier. Performance of classif ier is a crucial issue in terms of its efficiency, also number of fe ature to be scanned by the IDS should also be optimized. In thi s paper two techniques C5.0 and artificial neural network (ANN) ar e utilized with feature selection. Feature selection techniques will discard some irrelevant features while C5.0 and ANN acts as a classifier to classify the data in either normal type or one of t he five types of attack.KDD99 data set is used to train and test the models ,C5.0 model with numbers of features is producing better r sults with all most 100% accuracy. Performances were also verified in terms of data partition size.


Reward, Motivation, and Reinforcement Learning

There is substantial evidence that dopamine is involved in reward learning and appetitive conditioning. However, the major reinforcement learning-based theoretical models of classical conditioning (crudely, prediction learning) are actually based on rules designed to explain instrumental conditioning (action learning). Extensive anatomical, pharmacological, and psychological data, particularly concerning the impact of motivational manipulations, show that these models are unreasonable. We review the data and consider the involvement of a rich collection of different neural systems in various aspects of these forms of conditioning. Dopamine plays a pivotal, but complicated, role.


Misconceptions about real-time computing: a serious problem for next-generation systems

The author defines real-time computing and states and dispels the most common misconceptions about it. He discusses the fundamental technical issues of real-time computing. He examines specification and verification, scheduling theory, operating systems, programming languages and design methodology, distributed databases, artificial intelligence, fault tolerance, architectures, and communication.<<ETX>>


The dark side of the sharing economy ... and how to lighten it

Improving the sharing economy will require addressing myriad problems.


On the momentum term in gradient descent learning algorithms

A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.


Real time contextual collective anomaly detection over multiple data streams

Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more di cult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and e ciency of our framework, we empirically validate it on two real world applications.


Evaluating Induced CCG Parsers on Grounded Semantic Parsing

We compare the effectiveness of four different syntactic CCG parsers for a semantic slotfilling task to explore how much syntactic supervision is required for downstream semantic analysis. This extrinsic, task-based evaluation provides a unique window to explore the strengths and weaknesses of semantics captured by unsupervised grammar induction systems. We release a new Freebase semantic parsing dataset called SPADES (Semantic PArsing of DEclarative Sentences) containing 93K cloze-style questions paired with answers. We evaluate all our models on this dataset. Our code and data are available at https://github.com/ sivareddyg/graph-parser.


Analysis of stairs-climbing ability for a tracked reconfigurable modular robot

Stairs-climbing ability is the crucial performance of mobile robot for urban environment mission such as urban search and rescue or urban reconnaissance. The track type mobile mechanism has been widely applied for its advantages such as high stability, easy to control, low terrain pressure, and continuous drive. Stairs-climbing is a complicated process for a tracked mobile robot under kinematics and dynamics constraints. In this paper, the stairs-climbing process has been divided into riser climbing, riser crossing, and nose line climbing. During each climbing process, robot's mobility has been analyzed for its kinematics and dynamics factor. The track velocity and acceleration's influences on riser climbing have been analyzed. And the semiempirical design method of the track grouser and the module length has been provided in riser crossing and nose line climbing correspondingly. Finally, stairs-climbing experiments have been made on the two-module robot in line type, and three-module robot in line type and in triangle type respectively.


An Overview of the Theory and Applications of Metasurfaces: The Two-Dimensional Equivalents of Metamaterials

Metamaterials are typically engineered by arranging a set of small scatterers or apertures in a regular array throughout a region of space, thus obtaining some desirable bulk electromagnetic behavior. The desired property is often one that is not normally found naturally (negative refractive index, near-zero index, etc.). Over the past ten years, metamaterials have moved from being simply a theoretical concept to a field with developed and marketed applications. Three-dimensional metamaterials can be extended by arranging electrically small scatterers or holes into a two-dimensional pattern at a surface or interface. This surface version of a metamaterial has been given the name metasurface (the term metafilm has also been employed for certain structures). For many applications, metasurfaces can be used in place of metamaterials. Metasurfaces have the advantage of taking up less physical space than do full three-dimensional metamaterial structures; consequently, metasurfaces offer the possibility of less-lossy structures. In this overview paper, we discuss the theoretical basis by which metasurfaces should be characterized, and discuss their various applications. We will see how metasurfaces are distinguished from conventional frequency-selective surfaces. Metasurfaces have a wide range of potential applications in electromagnetics (ranging from low microwave to optical frequencies), including: (1) controllable “smart” surfaces, (2) miniaturized cavity resonators, (3) novel wave-guiding structures, (4) angular-independent surfaces, (5) absorbers, (6) biomedical devices, (7) terahertz switches, and (8) fluid-tunable frequency-agile materials, to name only a few. In this review, we will see that the development in recent years of such materials and/or surfaces is bringing us closer to realizing the exciting speculations made over one hundred years ago by the work of Lamb, Schuster, and Pocklington, and later by Mandel'shtam and Veselago.


Millimeter-Wave Radar Sensor for Snow Height Measurements

A small and lightweight frequency-modulated continuous-wave (FMCW) radar system is used for the determination of snow height by measuring the distance to the snow surface from a platform. The measurements have been performed at the Centre des Études de la Neige (Col de Porte), which is located near Grenoble in the French Alps. It is shown that the FMCW radar at millimeter-wave frequencies is an extremely promising approach for distance measurements to snow surfaces, e.g., in the mountains or in an Arctic environment. The characteristics of the radar sensor are described in detail. The relevant accuracy to measure the distance to a snow layer is shown at different heights and over an extended time duration. A dedicated laser snow telemeter is used as reference. In addition, the reflection from different types of snow is shown.


Real-time multi-feature based fire flame detection in video

In this paper, we present a new approach to detect fire flame by processing and analyzing the stationary camera videos. For a fire detection system, it is desired to be sensitive and reliable. The proposed method improves not only the sensitivity but also the reliability through reducing the susceptibility to false alarms. The proposed approach based on multi-feature, i.e., chromatic features, dynamic features, texture features, and contour features, can both improve the sensitivity and reliability in fire detection. In our approach, we adopt a novel algorithm to extract the moving region and analyze the frequency of flickers. Experimental results show that the proposed method can run in real-time and performs favorably against the state-of-art methods with higher Page 1 of 22 IET Review Copy Only IET Image Processing This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication in an issue of the journal. To cite the paper please use the doi provided on the Digital Library page.


Melody Generation for Pop Music via Word Representation of Musical Properties

Automatic melody generation for pop music has been a long-time aspiration for both AI researchers and musicians. However, learning to generate euphonious melody has turned out to be highly challenging due to a number of factors. Representation of multivariate property of notes has been one of the primary challenges. It is also difficult to remain in the permissible spectrum of musical variety, outside of which would be perceived as a plain random play without auditory pleasantness. Observing the conventional structure of pop music poses further challenges. In this paper, we propose to represent each note and its properties as a unique ‘word,’ thus lessening the prospect of misalignments between the properties, as well as reducing the complexity of learning. We also enforce regularization policies on the range of notes, thus encouraging the generated melody to stay close to what humans would find easy to follow. Furthermore, we generate melody conditioned on song part information, thus replicating the overall structure of a full song. Experimental results demonstrate that our model can generate auditorily pleasant songs that are more indistinguishable from human-written ones than previous models. 1


PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing sensory data gathered by a smartphone to locate the phone’s owner, even when the user does not intend to share his location information, e.g., when the user has turned off the Global Positioning System (GPS) on the device. Previous research efforts rely on at least one of the two following fundamental requirements, which impose significant limitations on the adversary: (i) the attacker must accurately know either the user’s initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., device acceleration, for different potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user’s location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment’s air pressure and device’s timezone, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user’s location when all location services, e.g., GPS, are turned off. Unlike previously-proposed attacks, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. We demonstrate that PinMe can accurately estimate the user’s location during four activities (walking, traveling on a train, driving, and traveling on a plane). We also suggest several defenses against the proposed attack.


A Taxi Order Dispatch Model based On Combinatorial Optimization

Taxi-booking apps have been very popular all over the world as they provide convenience such as fast response time to the users. The key component of a taxi-booking app is the dispatch system which aims to provide optimal matches between drivers and riders. Traditional dispatch systems sequentially dispatch taxis to riders and aim to maximize the driver acceptance rate for each individual order. However, the traditional systems may lead to a low global success rate, which degrades the rider experience when using the app. In this paper, we propose a novel system that attempts to optimally dispatch taxis to serve multiple bookings. The proposed system aims to maximize the global success rate, thus it optimizes the overall travel efficiency, leading to enhanced user experience. To further enhance users' experience, we also propose a method to predict destinations of a user once the taxi-booking APP is started. The proposed method employs the Bayesian framework to model the distribution of a user's destination based on his/her travel histories.
 We use rigorous A/B tests to compare our new taxi dispatch method with state-of-the-art models using data collected in Beijing. Experimental results show that the proposed method is significantly better than other state-of-the art models in terms of global success rate (increased from 80% to 84%). Moreover, we have also achieved significant improvement on other metrics such as user's waiting-time and pick-up distance. For our destination prediction algorithm, we show that our proposed model is superior to the baseline model by improving the top-3 accuracy from 89% to 93%. The proposed taxi dispatch and destination prediction algorithms are both deployed in our online systems and serve tens of millions of users everyday.


LiveNet: Improving features generalization for face liveness detection using convolution neural networks

Performance of face liveness detection algorithms in cross-database face liveness detection tests is one of the key issues in face-biometric based systems. Recently, Convolution Neural Networks (CNN) classifiers have shown remarkable performance in intra-database face liveness detection tests. However, a little effort has been made to improve the generalization capability of CNN classifiers for cross-database and unconstrained face liveness detection tests. In this paper, we propose an efficient strategy for training deep CNN classifiers for face liveness detection task. We utilize continuous data-randomization (like bootstrapping) in the form of small mini-batches during training CNN classifiers on small scale face antispoofing database. Experimental results revealed that the proposed approach reduces the training time by 18.39%, while significantly lowering the HTER by 8.28% and 14.14% in cross-database tests on CASIAFASD and Replay-Attack database respectively as compared to state-of-the-art approaches. Additionally, the proposed approach achieves satisfactory results on intra-database and cross-database face liveness detection tests, claiming a good generality over other state-of-the-art face anti-spoofing approaches. © 2018 Elsevier Ltd. All rights reserved.


Character Recognition Using Convolutional Neural Networks

Pattern recognition is one of the traditional uses of neural networks. When trained with gradient-based learning methods, these networks can learn the classification of input data by example. An introduction to classifiers and gradient-based learning is given. It is shown how several perceptrons can be combined and trained gradient-based. Furthermore, an overview of convolutional neural networks, as well as a real-world example, are discussed.


Inverted Files Versus Signature Files for Text Indexing

Two well-known indexing methods are inverted files and signature files. We have undertaken a detailed comparison of these two approaches in the context of text indexing, paying particular attention to query evaluation speed and space requirements. We have examined their relative performance using both experimentation and a refined approach to modeling of signature files, and demonstrate that inverted files are distinctly superior to signature files. Not only can inverted files be used to evaluate typical queries in less time than can signature files, but inverted files require less space and provide greater functionality. Our results also show that a synthetic text database can provide a realistic indication of the behavior of an actual text database. The tools used to generate the synthetic database have been made publicly available


Fast Linear Model for Knowledge Graph Embeddings

This paper shows that a simple baseline based on a Bag-of-Words (BoW) representation learns surprisingly good knowledge graph embeddings. By casting knowledge base completion and question answering as supervised classification problems, we observe that modeling co-occurences of entities and relations leads to state-of-the-art performance with a training time of a few minutes using the open sourced library fastText1.


Color nonuniformity in projection-based displays: analysis and solutions

Large-area displays made up of several projectors show significant variation in color. Here, we identify different projector parameters that cause the color variation and study their effects on the luminance and chrominance characteristics of the display. This work leads to the realization that luminance varies significantly within and across projectors, while chrominance variation is relatively small, especially across projectors of same model. To address this situation, we present a method to achieve luminance matching across all pixels of a multiprojector display that results in photometrically uniform displays. We use a camera as a measurement device for this purpose. Our method comprises a one-time calibration step that generates a per channel per projector luminance attenuation map (LAM), which is then used to correct any image projected on the display at interactive rates on commodity graphics hardware. To the best of our knowledge, this is the first effort to match luminance across all the pixels of a multiprojector display.


Entity Synonyms for Structured Web Search

Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.


Rough Set Theory in analyzing the attributes of combination values for the insurance market

Based on Rough Set Theory, this research addresses the effect of attributes/features on the combination values of decisions that insurance companies make to satisfy customers’ needs. Attributes impact on combination values by yielding sets with fewer objects (such as one or two objects), which increases both the lower and upper approximations. It also increases the decision rules, and degrades the precision of decisions. Our approach redefines the value set of attributes through expert knowledge by reducing the independent data set and reclassifying it. This approach is based on an empirical study. The results demonstrate that the redefined combination values of attributes can contribute to the precision of decisions in insurance marketing. Following an empirical analysis, we use a hit test that incorporates 50 validated sample data into the decision rule so that the hit rate reaches 100%. The results of the empirical study indicate that the generated decision rules can cover all new data. Consequently, we believe that the effects of attributes on combination values can be fully applied in research into insurance marketing. 2005 Elsevier Ltd. All rights reserved.


Model-Driven Approach for Body Area Network Application Development

This paper introduces the sensor-networked IoT model as a prototype to support the design of Body Area Network (BAN) applications for healthcare. Using the model, we analyze the synergistic effect of the functional requirements (data collection from the human body and transferring it to the top level) and non-functional requirements (trade-offs between energy-security-environmental factors, treated as Quality-of-Service (QoS)). We use feature models to represent the requirements at the earliest stage for the analysis and describe a model-driven methodology to design the possible BAN applications. Firstly, we specify the requirements as the problem domain (PD) variability model for the BAN applications. Next, we introduce the generative technology (meta-programming as the solution domain (SD)) and the mapping procedure to map the PD feature-based variability model onto the SD feature model. Finally, we create an executable meta-specification that represents the BAN functionality to describe the variability of the problem domain though transformations. The meta-specification (along with the meta-language processor) is a software generator for multiple BAN-oriented applications. We validate the methodology with experiments and a case study to generate a family of programs for the BAN sensor controllers. This enables to obtain the adequate measure of QoS efficiently through the interactive adjustment of the meta-parameter values and re-generation process for the concrete BAN application.


Entropy-based algorithms for best basis selection

Adapted waveform analysis uses a library of orthonormal bases and an efficiency functional to match a basis to a given signal or family of signals. It permits efficient compression of a variety of signals such as sound and images. The predefined libraries of modulated waveforms include orthogonal wavelet-packets, and localized trigonometric functions, have reasonably well controlled time-frequency localization properties. The idea is to build out of the library functions an orthonormal basis relative to which the given signal or collection of signals has the lowest information cost. The method relies heavily on the remarkable orthogonality properties of the new libraries: all expansions in a given library conserve energy, hence are comparable. Several cost functionals are useful; one of the most attractive is Shannon entropy, which has a geometric interpretation in this context.


Non-invasive glucose monitoring in patients with diabetes: a novel system based on impedance spectroscopy.

The aim of this work was to evaluate the performance of a novel non-invasive continuous glucose-monitoring system based on impedance spectroscopy (IS) in patients with diabetes. Ten patients with type 1 diabetes (mean+/-S.D., age 28+/-8 years, BMI 24.2+/-3.2 kg/m(2) and HbA(1C) 7.3+/-1.6%) and five with type 2 diabetes (age 61+/-8 years, BMI 27.5+/-3.2 kg/m(2) and HbA(1C) 8.3+/-1.8%) took part in this study, which comprised a glucose clamp experiment followed by a 7-day outpatient evaluation. The measurements obtained by the NI-CGMD and the reference blood glucose-measuring techniques were evaluated using retrospective data evaluation procedures. Under less controlled outpatient conditions a correlation coefficient of r=0.640 and a standard error of prediction (SEP) of 45 mg dl(-1) with a total of 590 paired glucose measurements was found (versus r=0.926 and a SEP of 26 mg dl(-1) under controlled conditions). Clark error grid analyses (EGA) showed 56% of all values in zone A, 37% in B and 7% in C-E. In conclusion, these results indicate that IS in the used technical setting allows retrospective, continuous and truly non-invasive glucose monitoring under defined conditions for patients with diabetes. Technical advances and developments are needed to expand on this concept to bring the results from the outpatient study closer to those in the experimental section of the study. Further studies will not only help to evaluate the performance and limitations of using such a technique for non non-invasive glucose monitoring but also help to verify technical extensions towards a IS-based concept that offers improved performance under real life operating conditions.


Citation analysis as a tool in journal evaluation.

As a communications system, the network of journals that play a paramount role in the exchange of scientific and technical information is little understood. Periodically since 1927, when Gross and Gross published their study (1) of references in 1 year’s issues of the Journal of the American Chemical Socie/y, pieces of the network have been illuminated by the work of Bradford (2), Allen (3), Gross and Woodford (4), Hooker (5), Henkle (6), Fussier (7), Brown (8), and others (9). Nevertheless, there is still no map of the journal network as a whok. To date, studies of the network and of the interrelation of its components have been limited in the number of journak, the areas of scientific study, and the periods of time their authors were able to consider, Such shortcomings have not been due to any lack of purpose, insight, or energy on the part of investigators, but to the practical difficulty of compiling and manipulating manually the enormous amount of necessary data. A solution to this problem of data is available in the data base used to produce the Science Citation Index ( SCI ) (10). The coverage of the SCI is international and multidisciplinary; it has grown from 600 journals in 1964 to 2400 journals in 1972, and now includes the world’s most important scientific and technical journals in mow disciplines. The SCI is published quarterly and is cumulated annually and quinquennially, but the data base from which the volumes are compiled is maintained on magnetic tape and is updated weekly. At the end of 1971, this data base contained more than 27 mi[tion references to about 10 million different published items. These references appeared over the past decade in the footnotes and bibliographies of more than 2 million journal articles, communications, letters, and so on. The data base is, thus, not only multidisciplinary, it covers a substantial period of time and, being in machine-readable form, is amenable to extensive manipulation by computer. In 1971, the Institute for Scientfic Information (1S1) decided to undertake a systematic analysis of journal citation patterns across the whole of science and technology. It began by extracting from the data base all references pobIished during the last quarter of 1969 in the 2200 journals then covered by the SCL The resultant sample was about 1 million citations of journals, books, reports, theses, and so forth. To test whether this 3-month sample was representative of the year as a whole, it was matched against another sample made by selecting every 27th reference from the approximately 4 million references collected over the entire year. The two samples were similar enough in scope (number of diflerent items cited) and detail (relative frequency of their citation by different journals) to


Discrete Cross-Modal Hashing for Efficient Multimedia Retrieval

Hashing techniques have been widely adopted for cross-modal retrieval due to its low storage cost and fast query speed. Most existing cross-modal hashing methods aim to map heterogeneous data into the common low-dimensional hamming space and then threshold to obtain binary codes by relaxing the discrete constraint. However, this independent relaxation step also brings quantization errors, resulting in poor retrieval performances. Other cross-modal hashing methods try to directly optimize the challenging objective function with discrete binary constraints. Inspired by [1], we propose a novel supervised cross-modal hashing method called Discrete Cross-Modal Hashing (DCMH) to learn the discrete binary codes without relaxing them. DCMH is formulated through reconstructing the semantic similarity matrix and learning binary codes as ideal features for classification. Furthermore, DCMH alternately updates binary codes of each modality, and iteratively learns the discrete hashing codes bit by bit efficiently, which is quite promising for large-scale datasets. Extensive empirical results on three real-world datasets show that DCMH outperforms the baseline approaches significantly.


The Complementarity of Information Technology Infrastructure and E-Commerce Capability: A Resource-Based Assessment of Their Business Value

This study seeks to assess the business value of e-commerce capability and information technology (IT) infrastructure in the context of electronic business at the firm level. Grounded in the IT business-value literature and enhanced by the resource-based theory of the firm, we developed a research framework in which both the main effects and the interaction effects of e-commerce and IT on firm performance were tested. Within this theoretical framework, we formulated several hypotheses. We then developed a multidimensional e-commerce capability construct, and after establishing its validity and reliability, tested the hypotheses with empirical data from 114 companies in the retail industry. Controlling for variations of firm size and subindustry effects, our empirical analysis found a strong positive interaction effect between IT infrastructure and e-commerce capability. This suggests that their complementarity positively contributes to firm performance in terms of sales per employee, inventory turnover, and cost reduction. The results are consistent with the resource-based theory, and provide empirical evidence to the complementary synergy between front-end e-commerce capability and back-end IT infrastructure. Combined together, they become more effective in producing business value. Yet the value of this synergy has not been recognized in the IT payoff literature. The “productivity paradox” observed in various studies has been attributed to variation in methods and measures, yet we offer an additional explanation: ignoring complementarities in business value measurement implies that the impact of IT was seriously underestimated. Our results emphasized the integration of resources as a feasible path to e-commerce value—companies need to enhance the integration between front-end e-commerce capability and back-end IT infrastructure in order to reap the benefits of e-commerce investments.


Fast Kernels for String and Tree Matching

In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.


A Kernel Between Sets of Vectors

In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to a Hilbert space and fit a Gaussian distribution to the whole set using Kernel PCA. We define our kernel between examples as Bhattacharyya’s measure of affinity between such Gaussians. The resulting kernel is computable in closed form and enjoys many favorable properties, including graceful behavior under transformations, potentially justifying the vector set representation even in cases when more conventional representations also exist.


Convolution Kernels for Natural Language

We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional representations of these structures. We show how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and we give experimental results on the ATIS corpus of parse trees.


Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy

We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line learning. In contrast to gradient descent and EM, which estimate the mixture's covariance matrices, the proposed method estimates the inverses of the covariance matrices. Furthennore, the new parameter estimation procedure can be applied in both on-line and batch settings. We show experimentally that it is typically faster than EM, and usually requires about half as many iterations as EM.


Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI.

Electrophysiological and neuroimaging studies have shown that attention to visual motion can increase the responsiveness of the motion-selective cortical area V5 and the posterior parietal cortex (PP). Increased or decreased activation in a cortical area is often attributed to attentional modulation of the cortical projections to that area. This leads to the notion that attention is associated with changes in connectivity. We have addressed attentional modulation of effective connectivity using functional magnetic resonance imaging (fMRI). Three subjects were scanned under identical stimulus conditions (visual motion) while varying only the attentional component of the task. Haemodynamic responses defined an occipito-parieto-frontal network, including the, primary visual cortex (V1), V5 and PR A structural equation model of the interactions among these dorsal visual pathway areas revealed increased connectivity between V5 and PP related to attention. On the basis of our analysis and the neuroanatomical pattern of projections from the prefrontal cortex to PP we attributed the source of modulatory influences, on the posterior visual pathway, to the prefrontal cortex (PFC). To test this hypothesis we included the PFC in our model as a 'modulator' of the pathway between V5 and PP, using interaction terms in the structural equation model. This analysis revealed a significant modulatory effect of prefrontal regions on V5 afferents to posterior parietal cortex.


ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification

In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks [42] with learnable spatio-temporal feature aggregation [6]. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.


A practical model for subsurface light transport

This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.


Effects of lumbar stabilization exercise on functional disability and lumbar
lordosis angle in patients with chronic low back pain

[Purpose] This study examined the effects of lumbar stabilization exercises on the functional disability and lumbar lordosis angles in patients with chronic low back pain. [Subjects] The subjects were 30 patients with chronic low back pain divided into a lumbar stabilization exercise group (n = 15) and a conservative treatment group (n = 15). [Methods] The lumbar stabilization exercise and conservative treatment groups performed an exercise program and conservative physical treatment, respectively. Both programs were performed 3 times a week for 6 weeks. The degree of functional disability was assessed by the Oswestry disability index, and lumbar lordosis angles were measured by plain radiography. [Results] The Oswestry disability index decreased significantly in the both groups; however, it was significantly lower in the lumbar stabilization exercise group. The lumbar lordosis angle increased significantly in the lumbar stabilization exercise group after treatment and was also significantly greater than that in the conservative treatment group. [Conclusion] Lumbar stabilization exercise is more effective than conservative treatment for improving functional disability and lumbar lordosis angles.


Tetris game design based on the FPGA

Tetris game is a classic game of logic control. This article gives a programming design of Tetris game based on the FPGA using VHDL. Game players can move and rotate blocks with the PS/2 interface keyboard, and the game video is showed in a VGA monitor. The game realized the function of the movement and rotation of blocks, randomly generating next blocks, eliminating rows, getting scores and speeding up. It also contained the normal mode corresponding to 7 types of blocks and the expert mode corresponding to 11 types of blocks. The successful transplant of Tetris game provides a template for the development of other visual control systems in the FPGA.


Nonparametric Bayesian Models for Joint Analysis of Imagery and Text


The merits of sharing a ride

The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.


A Survey of Procedural Noise Functions

Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.


Pseudo-analysis approach to nonlinear partial differential equations

An overview of methods of pseudo-analysis in applications on important classes of nonlinear partial differential equations, occurring in different fields, is given. Hamilton-Jacobi equations, specially important in the control theory, are for important models usually with non-linear Hamiltonian H which is also not smooth, e.g., the absolute value, min or max operations, where it can not apply the classical mathematical analysis. Using the pseudo-analysis with generalized pseudo-convolution it is possible to obtain solutions which can be interpreted in the mentioned classical way. Another important classes of nonlinear equations, where there are applied the pseudo-analysis, are the Burgers type equations and Black and Shole equation in option pricing. Very recent applications of pseudo-analysis are obtained on equations which model fluid mechanics (Navier-Stokes equation) and image processing (Perona and Malik equation).


A deep learning method for classifying mammographic breast density categories.

PURPOSE
Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow.

METHODS
In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.

RESULTS
The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.

CONCLUSIONS
Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.


SOCIAL ENTREPRENEURSHIP LEADERSHIP THAT FACILITATES SOCIETAL TRANSFORMATION — AN EXPLORATORY STUDY 5

This study provides a comparative analysis of seven cases of social entrepre-neurship that have been widely recognized as successful. The paper suggests factors associated with successful social entrepreneurship, particularly with social entrepreneurship that leads to significant changes in the social, political and economic contexts for poor and marginalized groups. It generates hypotheses about core innovations, leadership, organization, and scaling up in successful social entrepreneurship. The paper concludes with a discussion of the implications for the practice of social entrepreneurship, for further research, and for the continued development of support technologies and institutions that will encourage future social entrepreneurship.


Plantar fasciopathy: revisiting the risk factors.

BACKGROUND
Plantar fasciopathy is the most common cause of acquired sub-calcaneal heel pain in adults. To-date, research of this condition has mainly focused on management rather than causal mechanisms. The aetiology of plantar fasciopathy is likely to be multifactorial, as both intrinsic and extrinsic risk factors have been reported. The purpose of this review is to critically reevaluate risk factors for plantar fasciopathy.

METHODS
A detailed literature review was undertaken using English language medical databases.

RESULTS
No clear consensus exists as to the relative strength of the risk factors reported.

CONCLUSIONS
To-date numerous studies have examined various intrinsic and extrinsic risk factors implicated in the aetiology of plantar fasciopathy. How these factors interact may provide useful data to establish an individuals' risk profile for plantar fasciopathy and their potential for response to treatment. Further research is indicated to rank the relative significance of these risk factors.


Why do Larks Perform Better at School than Owls ? The Mediating Effect of Conscientiousness

Article History: Received 26.06.2016 Received in revised form 25.10.2016 Accepted 26.10.2016 Available online 25.11.2016 Circadian preference refers to individuals’ preference for morning or evening activities. Its two dimensions (i.e., morningness and eveningness) are related to a number of academic outcomes. While morningness shows positive relations with academic achievement, eveningness shows negative relations. Further, morningness and eveningness show the same correlational pattern with conscientiousness (i.e., positive relations for morningness, negative relations for eveningness), which – in turn – predicts academic achievement. Therefore, the main aim of the present study was to investigate if the relation between circadian preference and academic achievement was mediated by conscientiousness. The sample comprised 422 students attending the 11th grade at a grammar school in Germany. Circadian preference (morningness and eveningness) and conscientiousness were assessed by self-report questionnaires; academic achievement was operationalized by school grades. Using confirmatory analyses and structural equation modelling, the results supported the assumption that conscientiousness mediates the relation between circadian preference and academic achievement. Implications for research into circadian preference and for education are discussed. © 2016 IOJES. All rights reserved


A Multiband Antenna for Satellite Communications on the Move

The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included


Porokeratosis with follicular involvement: report of three cases and review of literatures.

Porokeratosis is characterized clinically by annular plaques with a distinct peripheral keratotic ridge and histologically by the cornoid lamella. Porokeratosis with follicular involvement is rarely reported. To provide the basis of that follicular porokeratosis is a clinical variant or not. Biopsy was taken from three patients who were diagnosed porokeratosis. Routine stain was made and reviewed the literatures about well-documented cases of porokeratosis with follicular involvement. Porokeratosis with follicular involvement may have some clinical features: asymptomatic, erythematous, brownish or skin-color, less than 1 cm in the areas excluding palm and plantar, which commonly involved on middle-age. But there have still not enough proof as an independent clinical variant.


Interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system

An interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system is proposed. In a conventional single flyback inverter for the photovoltaic AC module system, this inverter has drawbacks of a conduction losses of each switch, transformer copper and high ripple current of capacitor. To overcome these problems, two flyback converter modules with interleaved pulse-width modulation (PWM) are connected in parallel at the input and output sides reducing the ripple current on the input and output capacitors and decrease the current stress on the transformer windings. Thus, the transformer copper losses and the conduction losses on the switches are reduced. Also, reliability and life of capacitor are increased due to reducing the ripple current of input and output capacitors. In order to verify these, theoretical analysis and simulation are performed.


Generative Local Metric Learning for Nearest Neighbor Classification

We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian.


Efficient Authentication and Signing of Multicast Streams over Lossy Channels

Multicast stream authentication and signing is an important and challenging problem. Applications include the continuous authentication of radio and TV Internet broadcasts, and authenticated data distribution by satellite. The main challenges are fourfold. First, authenticity must be guaranteed even when only the sender of the data is trusted. Second, the scheme needs to scale to potentially millions of receivers. Third, streamed media distribution can have high packet loss. Finally, the system needs to be efficient to support fast packet rates. We propose two efficient schemes, TESLA and EMSS, for secure lossy multicast streams. TESLA, short for Timed Efficient Stream Loss-tolerant Authentication, offers sender authentication, strong loss robustness, high scalability, and minimal overhead, at the cost of loose initial time synchronization and slightly delayed authentication. EMSS, short for Efficient Multi-chained Stream Signature, provides nonrepudiation of origin, high loss resistance, and low overhead, at the cost of slightly delayed verification. This work began in Summer 1999 when Adrian Perrig and Dawn Song were visiting the IBM T. J. Watson research lab. Initial research on stream authentication was done during Summer 1999 by Ran Canetti, Adrian Perrig, and Dawn Song at IBM. Additional improvements were suggested by J. D. Tygar in Fall 1999 at UC Berkeley. Implementation was done in Fall 1999 by Adrian Perrig at UC Berkeley. The work on stream signatures was done by J. D. Tygar, Adrian Perrig, and Dawn Song at UC Berkeley. Additional work was performed by Ran Canetti in Spring 2000. Ran Canetti is at IBM T. J. Watson Research Center, and Adrian Perrig, Dawn Song, and J. D. Tygar are at the Computer Science Division, UC Berkeley. This research was suported in part by the Defense Advanced Research Projects Agency under DARPA contract N6601-99-28913 (under supervision of the Space and Naval Warfare Systems Center San Diego), by the National Science foundation under grant FD99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C-3513. Views and conclusions contained in this document are those of the authors and do not necessarily represent the official opinion or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS, or IBM.


Task-Oriented Query Reformulation with Reinforcement Learning

Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upperbound performance of a model in a particular environment and verify that there is still large room for improvements.


Aggressive driving with model predictive path integral control

In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we approximate using an efficient importance sampling scheme. We experimentally verify the algorithm by implementing it on a Graphics Processing Unit (GPU) and apply it to the problem of controlling a fifth-scale Auto-Rally vehicle in an aggressive driving task.


A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems

We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an affine feedback control law, obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. Global convergence is guaranteed through a Levenberg-Marquardt method; convergence in the vicinity of a local minimum is quadratic. Performance is illustrated on a limited-torque inverted pendulum problem, as well as a complex biomechanical control problem involving a stochastic model of the human arm, with 10 state dimensions and 6 muscle actuators. A Matlab implementation of the new algorithm is availabe at www.cogsci.ucsd.edu//spl sim/todorov.


Policy Improvement Methods: Between Black-Box Optimization and Episodic Reinforcement Learning

Policy improvement methods seek to optimize the parameters of a policy with respect to a utility function. There are two main approaches to performing this optimization: reinforcement learning (RL) and black-box optimization (BBO). Whereas BBO algorithms are generic optimization methods that, due to there generality, may also be applied to optimizing policy parameters, RL algorithms are specifically tailored to leveraging the structure of policy improvement problems. In recent years, benchmark comparisons between RL and BBO have been made, and there has been several attempts to specify which approach works best for which types of problem classes. In this article, we make several contributions to this line of research: 1) We define four algorithmic properties that further clarify the relationship between RL and BBO: action-perturbation vs. parameter-perturbation, gradient estimation vs. rewardweighted averaging, use of only rewards vs. use of rewards and state information, actor-critic vs. direct policy search. 2) We show how the chronology of the derivation of ever more powerful algorithms displays a trend towards algorithms based on parameter-perturbation and reward-weighted averaging. A striking feature of this trend is that it has moved RL methods closer and closer to BBO. 3) We continue this trend by applying two modifications to the state-of-the-art “Policy Improvement with Path Integrals” (PI), which yields an algorithm we denote PI. We show that PI is a BBO algorithm, and, more specifically, that it is a special case of the “Covariance Matrix Adaptation – Evolutionary Strategy” algorithm. Our empirical evaluation demonstrates that the simpler PI outperforms PI on simple evaluation tasks in terms of convergence speed and final cost. 4) Although our evaluation implies that, for these five tasks, BBO outperforms RL, we do not hold this to be a general statement, and provide an analysis of why these tasks are particularly well-suited for BBO. Thus, rather than making the case for BBO or RL, one of the main contributions of this article is rather to provide an algorithmic framework in which such cases may be made, as PI and PI use identical perturbation and parameter update methods, and differ only in being BBO and RL approaches respectively.


A dynamically stable single-wheeled mobile robot with inverse mouse-ball drive

Multi-wheel statically-stable mobile robots tall enough to interact meaningfully with people must have low centers of gravity, wide bases of support, and low accelerations to avoid tipping over. These conditions present a number of performance limitations. Accordingly, we are developing an inverse of this type of mobile robot that is the height, width, and weight of a person, having a high center of gravity, that balances dynamically on a single spherical wheel. Unlike balancing 2-wheel platforms which must turn before driving in some direction, the single-wheel robot can move directly in any direction. We present the overall design, actuator mechanism based on an inverse mouse-ball drive, control system, and initial results including dynamic balancing, station keeping, and point-to-point motion


Evolutionary Function Approximation for Reinforcement Learning

Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods require a function approximator to represent the value function. However, using function approximators requires manually making crucial representational decisions. This thesis investigates evolutionary function approximation, a novel approach to automatically selecting function approximator representations that enable efficient individual learning. This method evolves individuals that are better able to learn. I present a fully implemented instantiation of evolutionary function approximation which combines NEAT, a neuroevolutionary optimization technique, with Q-learning, a popular TD method. The resulting NEAT+Q algorithm automatically discovers effective representations for neural network function approximators. This thesis also presents on-line evolutionary computation, which improves the on-line performance of evolutionary computation by borrowing selection mechanisms used in TD methods to choose individual actions and using them in evolutionary computation to select policies for evaluation. I evaluate these contributions with extended empirical studies in two domains: 1) the mountain car task, a standard reinforcement learning benchmark on which neural network function approximators have previously performed poorly and 2) server job scheduling, a large probabilistic domain drawn from the field of autonomic computing. The results demonstrate that evolutionary function approximation can significantly improve the performance of TD methods and on-line evolutionary computation can significantly improve evolutionary methods.


Loneliness, social contacts and Internet addiction: A cross-lagged panel study

This study aims to examine the causal priority in the observed empirical relationships between Internet addiction and other psychological problems. A cross-lagged panel survey of 361 college students in Hong Kong was conducted. Results show that excessive and unhealthy Internet use would increase feelings of loneliness over time. Although depression had a moderate and positive bivariate relationship with Internet addiction at each time point, such a relationship was not significant in the cross-lagged analyses. This study also found that online social contacts with friends and family were not an effective alternative for offline social interactions in reducing feelings of loneliness. Furthermore, while an increase in face-to-face contacts could help to reduce symptoms of Internet addiction, this effect may be neutralized by the increase in online social contacts as a result of excessive Internet use. Taken as a whole, findings from the study show a worrisome vicious cycle between loneliness and Internet addiction. 2013 Elsevier Ltd. All rights reserved.


Real-time Vision-based Hand Gesture Recognition Using Haar-like Features

This paper proposes a two level approach to solve the problem of real-time vision-based hand gesture classification. The lower level of the approach implements the posture recognition with Haar-like features and the AdaBoost learning algorithm. With this algorithm, real-time performance and high recognition accuracy can be obtained. The higher level implements the linguistic hand gesture recognition using a context-free grammar-based syntactic analysis. Given an input gesture, based on the extracted postures, the composite gestures can be parsed and recognized with a set of primitives and production rules.


DeepDefense: Training Deep Neural Networks with Improved Robustness

Despite the efficacy on a variety of computer vision tasks, deep neural networks (DNNs) are vulnerable to adversarial attacks, limiting their applications in securitycritical systems. Recent works have shown the possibility of generating imperceptibly perturbed image inputs (a.k.a., adversarial examples) to fool well-trained DNN classifiers into making arbitrary predictions. To address this problem, we propose a training recipe named “deep defense”. Our core idea is to integrate an adversarial perturbation-based regularizer into the classification objective, such that the obtained models learn to resist potential attacks, directly and precisely. The whole optimization problem is solved just like training a recursive network. Experimental results demonstrate that our method outperforms training with adversarial/Parseval regularizations by large margins on various datasets (including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code and models for reproducing our results will be made publicly available.


Ambiguity Resolution in a Cognitive Model of Language Comprehension

The Lucia comprehension system attempts to model human comprehension by using the Soar cognitive architecture, Embodied Construction Grammar (ECG), and an incremental, word-by-word approach to grounded processing. Traditional approaches use techniques such as parallel paths and global optimization to resolve ambiguities. Here we describe how Lucia deals with lexical, grammatical, structural, and semantic ambiguities by using knowledge from the surrounding linguistic and environmental context. It uses a local repair mechanism to maintain a single path, and shows a garden path effect when local repair breaks down. Data on adding new linguistic knowledge shows that the ECG grammar grows faster than the knowledge for handling context, and that lowlevel grammar items grow faster than more general ones.


Personalized Reliability Prediction of Web Services

Service Oriented Architecture (SOA) is a business-centric IT architectural approach for building distributed systems. Reliability of service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet connections. Designing efficient and effective reliability prediction approaches of Web services has become an important research issue. In this article, we propose two personalized reliability prediction approaches of Web services, that is, neighborhood-based approach and model-based approach. The neighborhood-based approach employs past failure data of similar neighbors (either service users or Web services) to predict the Web service reliability. On the other hand, the model-based approach fits a factor model based on the available Web service failure data and use this factor model to make further reliability prediction. Extensive experiments are conducted with our real-world Web service datasets, which include about 23 millions invocation results on more than 3,000 real-world Web services. The experimental results show that our proposed reliability prediction approaches obtain better reliability prediction accuracy than other competing approaches.


A Study on the Effective Permittivity of Carbon/PI Honeycomb Composites for Radar Absorbing Design

Radar absorbing honeycomb composites with different coating thicknesses are prepared by impregnation of aramid paper frame with solutions containing conductive carbon blacks (non-magnetic) and PI (polyimide). Expressions for the effective permittivity of the composites are studied and validated both in theory and experiment. It is found that a theoretical equivalent panel with given permittivity can be obtained to represent the honeycomb structure in the quasistatic approximation, which provides a feasible way to optimize the design of radar absorbing honeycomb structure by connecting the effective electromagnetic parameters with the unit cell dimensions. The effective permittivity is measured by a network analyzer system in the frequency range of 8-12 GHz and compared with the theoretical result.


SVO: Semidirect Visual Odometry for Monocular and Multicamera Systems

Direct methods for visual odometry (VO) have gained popularity for their capability to exploit information from all intensity gradients in the image. However, low computational speed as well as missing guarantees for optimality and consistency are limiting factors of direct methods, in which established feature-based methods succeed instead. Based on these considerations, we propose a semidirect VO (SVO) that uses direct methods to track and triangulate pixels that are characterized by high image gradients, but relies on proven feature-based methods for joint optimization of structure and motion. Together with a robust probabilistic depth estimation algorithm, this enables us to efficiently track pixels lying on weak corners and edges in environments with little or high-frequency texture. We further demonstrate that the algorithm can easily be extended to multiple cameras, to track edges, to include motion priors, and to enable the use of very large field of view cameras, such as fisheye and catadioptric ones. Experimental evaluation on benchmark datasets shows that the algorithm is significantly faster than the state of the art while achieving highly competitive accuracy.


Fuzzy memoization for floating-point multimedia applications

Instruction memoization is a promising technique to reduce the power consumption and increase the performance of future low-end/mobile multimedia systems. Power and performance efficiency can be improved by reusing instances of an already executed operation. Unfortunately, this technique may not always be worth the effort due to the power consumption and area impact of the tables required to leverage an adequate level of reuse. In this paper, we introduce and evaluate a novel way of understanding multimedia floating-point operations based on the fuzzy computation paradigm: performance and power consumption can be improved at the cost of small precision losses in computation. By exploiting this implicit characteristic of multimedia applications, we propose a new technique called tolerant memoization. This technique expands the capabilities of classic memoization by associating entries with similar inputs to the same output. We evaluate this new technique by measuring the effect of tolerant memoization for floating-point operations in a low-power multimedia processor and discuss the trade-offs between performance and quality of the media outputs. We report energy improvements of 12 percent for a set of key multimedia applications with small LUT of 6 Kbytes, compared to 3 percent obtained using previously proposed techniques.


Energy-Efficient Multiple-Precision Floating-Point Multiplier for Embedded Applications

Floating-point (FP) multipliers are the main energy consumers in many modern embedded digital signal processing (DSP) and multimedia systems. For lossy applications, minimizing the precision of FP multiplication operations under the acceptable accuracy loss is a well-known approach for reducing the energy consumption of FP multipliers. This paper proposes a multiple-precision FP multiplier to efficiently trade the energy consumption with the output quality. The proposed FP multiplier can perform lowprecision multiplication that generates 8−, 14−, 20−, or 26bit mantissa product through an iterative and truncated modified Booth multiplier. Energy saving for lowprecision multiplication is achieved by partially suppressing the computation of mantissa multiplier. In addition, the proposed multiplier allows the bitwidth of mantissa in the multiplicand, multiplier, and output product to be dynamically changed when it performs different FP multiplication operations to further reduce energy consumption. Experimental results show that the proposed multiplier can achieve 59 %, 71 %, 73 %, and 82 % energy saving under 0.1 %, 1 %, 5 %, and 11 % accuracy loss, respectively, for the RGB-to-YUV & YUV-to-RGB conversion when compared to the conventional IEEE single-precision multiplier. In addition, the results also exhibit that the proposed multiplier can obtain more energy reduction than previous multipleprecision iterative FP multipliers.


Bootstrapping a Neural Conversational Agent with Dialogue Self-Play, Crowdsourcing and On-Line Reinforcement Learning

End-to-end neural models show great promise towards building conversational agents that are trained from data and on-line experience using supervised and reinforcement learning. However, these models require a large corpus of dialogues to learn effectively. For goal-oriented dialogues, such datasets are expensive to collect and annotate, since each task involves a separate schema and database of entities. Further, the Wizard-of-Oz approach commonly used for dialogue collection does not provide sufficient coverage of salient dialogue flows, which is critical for guaranteeing an acceptable task completion rate in consumer-facing conversational agents. In this paper, we study a recently proposed approach for building an agent for arbitrary tasks by combining dialogue self-play and crowd-sourcing to generate fully-annotated dialogues with diverse and natural utterances. We discuss the advantages of this approach for industry applications of conversational agents, wherein an agent can be rapidly bootstrapped to deploy in front of users and further optimized via interactive learning from actual users of the system.


Characterization and Implementation of Dual-SiC MOSFET Modules for Future Use in Traction Converters

Silicon (Si) insulated-gate bipolar transistors are widely used in railway traction converters. In the near future, silicon carbide (SiC) technology will push the limits of switching devices in three directions: higher blocking voltage, higher operating temperature, and higher switching speeds. The first silicon carbide (SiC) MOSFET modules are available on the market and look promising. Although they are still limited in breakdown voltage, these wide-bandgap components should improve traction-chain efficiency. Particularly, a significant reduction in the switching losses is expected which should lead to improvements in power-weight ratios. Nevertheless, because of the high switching speed and the high current levels required by traction applications, the implementation of these new modules is critical. An original method is proposed to compare, in terms of stray inductance, several dc bus-bar designs. To evaluate the potential of these new devices, a first set of measurements, based on a single-pulse test-bench, was obtained. The switching behavior of SiC devices was well understood at turn-off and turn-on. To complete this work, the authors use an opposition method to compare Si-IGBT and SiC-MOSFET modules in voltage source inverter operation. For this purpose, a second test-bench, allowing electrical and thermal measurements, was developed. Experimental results confirm the theoretical loss-calculation of the single-pulse tests and the correct operation of up to three modules directly connected in parallel. This analysis provides guidelines for a full SiC inverter design, and prospects for developments in traction applications are presented.


A High-Speed Mesh of Tactile Sensors Fitting Arbitrary Surfaces

A tactile sensor is developed with the aim of covering a robot's entire structure, while reducing wiring requirement and ensuring high-speed response. The sensor detects the center point of load distribution on 2-D surfaces as well as the overall load. There are only four signal wires from the sensor. The sensor response time is nearly constant (within 1 ms) regardless of the number of detection elements, their placements or sensor areas. In this paper, the principles behind the operation of this sensor and the results of experiments using the sensor are described.


A PPG sensor for continuous cuffless blood pressure monitoring with self-adaptive signal processing

A new portable PPG-BP device designed for continuously measuring blood pressure (BP) without a cuff is proposed in this study. This continuous and long-time BP monitoring enabled herein by the proposed portable cuffless BP sensor. Towards the aforementioned goal, the sensor is designed capable of detecting in real time, non-invasively and continuously the temporal intravascular blood volume change based on the principle of photoplethysmograph (PPG) for estimating BP. The hardware of the sensor consists mainly of light emitting diodes (LEDs) in wavelengths of 660 nm, a photo detectors (PD), and also signal processing chips to read output signals of the PD. The PD readout circuit includes a PD pre-amplifier, a band-pass filter, a programmable gain amplifier (PGA), a microcontroller unit for calculation and a wireless module for communication. A laptop is also used to display continuous BPs and conducts statistical analysis and displaying results. 27 subjects participated in the experimental validation, in which the obtained BPs are calibrated by and then compared with the results from a commercial blood pressure monitor by OMRON. The resultant signal-to-noise ratio (SNR) is capable of rising more than 15%, correlation coefficient, R2, for systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 0.89 and 0.98, respectively.


How Visualization Layout Relates to Locus of Control and Other Personality Factors

Existing research suggests that individual personality differences are correlated with a user's speed and accuracy in solving problems with different types of complex visualization systems. We extend this research by isolating factors in personality traits as well as in the visualizations that could have contributed to the observed correlation. We focus on a personality trait known as "locus of control” (LOC), which represents a person's tendency to see themselves as controlled by or in control of external events. To isolate variables of the visualization design, we control extraneous factors such as color, interaction, and labeling. We conduct a user study with four visualizations that gradually shift from a list metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their locus of control and other personality factors. Our findings demonstrate that there is indeed a correlation between the two: participants with an internal locus of control perform more poorly with visualizations that employ a containment metaphor, while those with an external locus of control perform well with such visualizations. These results provide evidence for the externalization theory of visualization. Finally, we propose applications of these findings to adaptive visual analytics and visualization evaluation.


MVC architecture driven restructuring to achieve client-side web page composition

This paper presents a restructuring approach to relocating web page composition from servers to browsers for Java web applications. The objective is to reduce redundant manipulation and transfer of code/data that are shared by web pages. The reduction is carried out through a restructuring algorithm, effectively keeping consistency between source and target applications from the perspective of the model-view-controller (MVC) architecture, because the problem requires the target application to preserve the observable behavior of its source application. Case studies show that our restructuring tool can efficiently support the restructuring process.


High speed cascode flyback converter using multilayered coreless printed circuit board (PCB) step-down power transformer

In this paper, design and analysis of the high speed isolated cascode flyback converter using multilayered coreless PCB step down power transformer is presented. The converter is tested for the input voltage variation of 60–120V with a nominal DC input voltage of 90V. The designed converter was simulated and tested successfully up to the output power level of 30W within the switching frequency range of 2.6–3.7MHz. The cascode flyback converter is compared with the single switch flyback converter in terms of operating frequency, gate drive power consumption, conduction losses and stresses on MOSFETs. The maximum energy efficiency of the cascode converter is approximately 81% with a significant improvement of about 3–4% compared to single switch flyback converter. The gate drive power consumption which is more dominant compared to conduction losses of the cascode converter using GaN MOSFET is found to be negligible compared to single switch flyback converter.


Optimal integration of energy storage in distribution networks

Energy storage, traditionally well established in the form of large scale pumped-hydro systems, is finding increased attraction in medium and smaller scale systems. Such expansion is entirely complementary to the wider uptake of intermittent renewable resources and to distributed generation in general, which are likely to present a whole range of new business opportunities for storage systems and their suppliers. In the paper, by assuming that Distribution System Operator has got the ownership and operation of storage, a new software planning tool for distribution networks able to define the optimal placement, rating and control strategies of distributed storage systems that minimize the overall network cost is proposed. This tool will assist the System Operators in defining the better integration strategies of distributed storage systems in distribution networks and in assessing their potential as an option for a more efficient operation and development of future electricity distribution networks.


ACES: Automatic Compartments for Embedded Systems

Securing the rapidly expanding Internet of Things (IoT) is critical. Many of these “things” are vulnerable baremetal embedded systems where the application executes directly on hardware without an operating system. Unfortunately, the integrity of current systems may be compromised by a single vulnerability, as recently shown by Google’s P0 team against Broadcom’s WiFi SoC. We present ACES (Automatic Compartments for Embedded Systems), an LLVM-based compiler that automatically infers and enforces inter-component isolation on bare-metal systems, thus applying the principle of least privileges. ACES takes a developer-specified compartmentalization policy and then automatically creates an instrumented binary that isolates compartments at runtime, while handling the hardware limitations of baremetal embedded devices. We demonstrate ACES’ ability to implement arbitrary compartmentalization policies by implementing three policies and comparing the compartment isolation, runtime overhead, and memory overhead. Our results show that ACES’ compartments can have low runtime overheads (13% on our largest test application), while using 59% less Flash, and 84% less RAM than the Mbed μVisor—the current state-of-theart compartmentalization technique for bare-metal systems. ACES ‘ compartments protect the integrity of privileged data, provide control-flow integrity between compartments, and reduce exposure to ROP attacks by 94.3% compared to μVisor.


On the speedup of single-disk failure recovery in XOR-coded storage systems: Theory and practice

Modern storage systems stripe redundant data across multiple disks to provide availability guarantees against disk failures. One form of data redundancy is based on XOR-based erasure codes, which use only XOR operations for encoding and decoding. In addition to providing failure tolerance, a storage system must also provide fast failure recovery to avoid data unavailability. We consider the problem of speeding up the recovery of a single-disk failure for arbitrary XOR-based erasure codes. We address this problem from both theoretical and practical perspectives. We propose a replace recovery algorithm, which uses a hill-climbing technique to search for a fast recovery solution, such that the solution search can be completed within a short time period. We further implement our replace recovery algorithm atop a parallelized architecture to justify its practicality. We experiment our replace recovery algorithm and its parallelized implementation on a networked storage system testbed, and demonstrate that our replace recovery algorithm uses less recovery time than the conventional approach.


Internet of Cloud: Security and Privacy issues

The synergy between the cloud and the IoT has emerged largely due to the cloud having attributes which directly benefit the IoT and enable its continued growth. IoT adopting Cloud services has brought new security challenges. In this book chapter, we pursue two main goals: 1) to analyse the different components of Cloud computing and the IoT and 2) to present security and privacy problems that these systems face. We thoroughly investigate current security and privacy preservation solutions that exist in this area, with an eye on the Industrial Internet of Things, discuss open issues and propose future directions.