Kernel-Based Sensor Fusion With Application to Audio-Visual Voice Activity Detection

In this paper, we address the problem of multiple view data fusion in the presence of noise and interferences. Recent studies have approached this problem using kernel methods, by relying particularly on a product of kernels constructed separately for each view. From a graph theory point of view, we analyze this fusion approach in a discrete setting. More specifically, based on a statistical model for the connectivity between data points, we propose an algorithm for the selection of the kernel bandwidth, a parameter that, as we show, has important implications on the robustness of this fusion approach to interferences. Then, we consider the fusion of audio-visual speech signals measured by a single microphone and by a video camera pointed to the face of the speaker. Specifically, we address the task of voice activity detection, i.e., the detection of speech and nonspeech segments, in the presence of structured interferences such as keyboard taps and office noise. We propose an algorithm for voice activity detection based on the audio-visual signal. Simulation results show that the proposed algorithm outperforms competing fusion and voice activity detection approaches. In addition, we demonstrate that a proper selection of the kernel bandwidth indeed leads to improved performance.


Does Competition Destroy Ethical Behavior?

This paper shows that conduct described as unethical and blamed on “greed” is sometimes a consequence of market competition. I illustrate this observation using examples of five censured activities: employment of children, corruption, excessive executive pay, corporate earnings manipulation, and involvement of universities in commercial activities. In all these examples, I show how competitive pressures lead to the spread of the censured behavior. My point is not to excuse or condemn any or all of these practices, but merely to pinpoint the crucial role of competition, as opposed to greed, in their spread. I focus here on ethics, not efficiency. The relationship between the two is complex. In many cases, ethical norms evolve to sustain cooperative behavior and thus to promote successful functioning of social institutions. For example, ethical condemnation of corruption is based on the idea that officials should not selfishly abuse public trust, and that a society functions better when its government works fairly. Likewise, the ethical norm against the employment of children is driven in part by the more general concern with abuse of the weak by the strong. When ethics promote social cooperation, ethical behavior and efficient behavior typically go together. In other instances, there is a mismatch between ethics and efficiency, either because ethical standards which might have had efficiency justifications long ago no longer do or because the behavior that is ethical in some idealized society makes matters worse in the real world. For example, the ethical norm against debt or interest, which might have been justifiable a millennium ago, is clearly no longer efficient. And while child labor might be a bad idea in a world with good access to capital markets and educational opportunities, for many families in the developing world the alternative to child labor is malnutrition and disease. These examples of a mismatch suggest that behavior condemned as unethical is not always inefficient. In still other instances, the conduct that is advertised as ethical is the result of political indoctrination by parochial interests or of simple confusion. For example, the ethical exhortations to “Buy American!” or to pay the “living wage” are underwritten by labor unions serving their members, not the public. In various times and places, tribalism, racism, anti-Semitism, the hatred of the rich, and other unsavory sentiments reflected the dominant ethic. In these instances, what is considered ethical is very far from what economists would consider efficient. In some of the examples I discuss below, a credible case can be made that the conduct perceived to be unethical is also inefficient; in other examples, there is more ambiguity. My interest, however, is not to evaluate efficiency, but only to bring the crucial role of competition into the explanation of why activities morally sanctioned by the society spread. In four of the examples I discuss, censured behavior reduces costs (in the last one, it raises revenues). I assume that the proprietor of the firm values ethical behavior, but that such behavior is a normal good. When sanctioned behavior by competitors reduces their costs, it also reduces prices in the market, and as a result the proprietor’s income falls. When his income falls, so does his own demand for ethical behavior, leading to the spread of censured practices. The analysis I present is closely related to the ideas in Gary Becker’s (1957) classic study of discrimination and reveals a broad range of circumstances where competition promotes censured conduct.


The draft genome of the transgenic tropical fruit tree papaya (Carica papaya Linnaeus)

Papaya, a fruit crop cultivated in tropical and subtropical regions, is known for its nutritional benefits and medicinal applications. Here we report a 3× draft genome sequence of ‘SunUp’ papaya, the first commercial virus-resistant transgenic fruit tree to be sequenced. The papaya genome is three times the size of the Arabidopsis genome, but contains fewer genes, including significantly fewer disease-resistance gene analogues. Comparison of the five sequenced genomes suggests a minimal angiosperm gene set of 13,311. A lack of recent genome duplication, atypical of other angiosperm genomes sequenced so far, may account for the smaller papaya gene number in most functional groups. Nonetheless, striking amplifications in gene number within particular functional groups suggest roles in the evolution of tree-like habit, deposition and remobilization of starch reserves, attraction of seed dispersal agents, and adaptation to tropical daylengths. Transgenesis at three locations is closely associated with chloroplast insertions into the nuclear genome, and with topoisomerase I recognition sites. Papaya offers numerous advantages as a system for fruit-tree functional genomics, and this draft genome sequence provides the foundation for revealing the basis of Carica’s distinguishing morpho-physiological, medicinal and nutritional properties.


A Video Representation Using Temporal Superpixels

We develop a generative probabilistic model for temporally consistent super pixels in video sequences. In contrast to supermodel methods, object parts in different frames are tracked by the same temporal super pixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate super pixels in an online fashion. We consider four novel metrics to quantify performance of a temporal super pixel representation and demonstrate superior performance when compared to supermodel methods.


Multi-Modal Learning: Study on A Large-Scale Micro-Video Data Collection

Micro-video sharing social services, as a new phenomenon in social media, enable users to share micro-videos and thus gain increasing enthusiasm among people. One distinct characteristic of micro-videos is the multi-modality, as these videos always have visual signals, audio tracks, textual descriptions as well as social clues. Such multi-modality data makes it possible to obtain a comprehensive understanding of videos and hence provides new opportunities for researchers. However, limited efforts thus far have been dedicated to this new emerging user-generated contents (UGCs) due to the lack of large-scale benchmark dataset. Towards this end, in this paper, we construct a large-scale micro-video dataset, which can support many research domains, such as popularity prediction and venue estimation. Based upon this dataset, we conduct an initial study in popularity prediction of micro-videos. Finally, we identify our future work.


BLEU Evaluation of Machine-Translated English-Croatian Legislation

This paper presents work on the evaluation of online available machine translation (MT) service, i.e. Google Translate, for English-Croatian language pair in the domain of legislation. The total set of 200 sentences, for which three reference translations are provided, is divided into short and long sentences. Human evaluation is performed by native speakers, using the criteria of adequacy and fluency. For measuring the reliability of agreement among raters, Fleiss' kappa metric is used. Human evaluation is enriched by error analysis, in order to examine the influence of error types on fluency and adequacy, and to use it in further research. Translation errors are divided into several categories: non-translated words, word omissions, unnecessarily translated words, morphological errors, lexical errors, syntactic errors and incorrect punctuation. The automatic evaluation metric BLEU is calculated with regard to a single and multiple reference translations. System level Pearson’s correlation between BLEU scores based on a single and multiple reference translations is given, as well as correlation between short and long sentences BLEU scores, and correlation between the criteria of fluency and adequacy and each error category.


ITIL as common practice reference model for IT service management: formal assessment and implications for practice

Due to enhanced focus on the customer in the planning, development and delivery of information services, IT service management has become increasingly important. These days IT management is focussing particularly on the de facto standard ITIL (IT Infrastructure Library) for implementing IT service management. In doing this, deficits of the ITIL reference model are often being overlooked, the benefits are merely assumed and misunderstandings spread. This results in uncertainties, unfulfilled expectations and problems in the execution of ITIL transformation projects. Against this background the article critically assesses the ITIL reference model. A formal analysis of the model on the basis of established criteria according to the principles for proper modelling is undertaken and implications for IT management are deduced from this. Four case studies of ITIL transformation projects conducted for this purpose, as well as an analysis of the ITIL documents, serve as basis for the assessment.


Prevalence of kidney stones in the United States.

BACKGROUND
The last nationally representative assessment of kidney stone prevalence in the United States occurred in 1994. After a 13-yr hiatus, the National Health and Nutrition Examination Survey (NHANES) reinitiated data collection regarding kidney stone history.

OBJECTIVE
Describe the current prevalence of stone disease in the United States, and identify factors associated with a history of kidney stones.

DESIGN, SETTING, AND PARTICIPANTS
A cross-sectional analysis of responses to the 2007-2010 NHANES (n=12 110).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS
Self-reported history of kidney stones. Percent prevalence was calculated and multivariable models were used to identify factors associated with a history of kidney stones.

RESULTS AND LIMITATIONS
The prevalence of kidney stones was 8.8% (95% confidence interval [CI], 8.1-9.5). Among men, the prevalence of stones was 10.6% (95% CI, 9.4-11.9), compared with 7.1% (95% CI, 6.4-7.8) among women. Kidney stones were more common among obese than normal-weight individuals (11.2% [95% CI, 10.0-12.3] compared with 6.1% [95% CI, 4.8-7.4], respectively; p<0.001). Black, non-Hispanic and Hispanic individuals were less likely to report a history of stone disease than were white, non-Hispanic individuals (black, non-Hispanic: odds ratio [OR]: 0.37 [95% CI, 0.28-0.49], p<0.001; Hispanic: OR: 0.60 [95% CI, 0.49-0.73], p<0.001). Obesity and diabetes were strongly associated with a history of kidney stones in multivariable models. The cross-sectional survey design limits causal inference regarding potential risk factors for kidney stones.

CONCLUSIONS
Kidney stones affect approximately 1 in 11 people in the United States. These data represent a marked increase in stone disease compared with the NHANES III cohort, particularly in black, non-Hispanic and Hispanic individuals. Diet and lifestyle factors likely play an important role in the changing epidemiology of kidney stones.


End to End Automation on Cloud with Build Pipeline: The Case for DevOps in Insurance Industry, Continuous Integration, Continuous Testing, and Continuous Delivery

In modern environment, delivering innovative idea in a fast and reliable manner is extremely significant for any organizations. In the existing scenario, Insurance industry need to better respond to dynamic market requirements, faster time to market for new initiatives and services, and support innovative ways of customer interaction. In past few years, the transition to cloud platforms has given benefits such as agility, scalability, and lower capital costs but the application lifecycle management practices are slow with this disruptive change. DevOps culture extends the agile methodology to rapidly create applications and deliver them across environment in automated manner to improve performance and quality assurance. Continuous Integration (CI) and Continuous delivery (CD) has emerged as a boon for traditional application development and release management practices to provide the capability to release quality artifacts continuously to customers with continuously integrated feedback. The objective of the paper is to create a proof of concept for designing an effective framework for continuous integration, continuous testing, and continuous delivery to automate the source code compilation, code analysis, test execution, packaging, infrastructure provisioning, deployment, and notifications using build pipeline concept.


Improving Neural Knowledge Base Completion with Cross-Lingual Projections

In this paper we present a cross-lingual extension of a neural tensor network model for knowledge base completion. We exploit multilingual synsets from BabelNet to translate English triples to other languages and then augment the reference knowledge base with cross-lingual triples. We project monolingual embeddings of different languages to a shared multilingual space and use them for network initialization (i.e., as initial concept embeddings). We then train the network with triples from the cross-lingually augmented knowledge base. Results on WordNet link prediction show that leveraging cross-lingual information yields significant gains over exploiting only monolingual triples.


How does EMDR work ?

Eye movement desensitisation and reprocessing (EMDR) is an effective treatment for alleviating trauma symptoms, and the positive effects of this treatment have been scientifically confirmed under wellcontrolled conditions. This has provided an opportunity to explore how EMDR works. The present paper reports on the findings of a long series of experiments that disproved the hypothesis that eye movements or other ‘dual tasks’ are unnecessary. These experiments also disproved the idea that ‘bilateral stimulation’ is needed; moving the eyes up and down produces the same effect as horizontal eye movement, and so do tasks that require no eye movement at all. However, it is important that the dual task taxes working memory. Several predictions can be derived from the working memory explanation for eye movements in EMDR. These seem to hold up extremely well in critical experimental tests, and create a solid explanation on how eye movements work. This paper discusses the implications that this theory and the empirical findings may have for the EMDR technique. © Copyright 2012 Textrum Ltd. All rights reserved.


An Empirical Analysis of Bug Reports and Bug Fixing in Open Source Android Apps

Smartphone platforms and applications (apps) have gained tremendous popularity recently. Due to the novelty of the smartphone platform and tools, and the low barrier to entry for app distribution, apps are prone to errors, which affects user experience and requires frequent bug fixes. An essential step towards correcting this situation is understanding the nature of the bugs and bug-fixing processes associated with smartphone platforms and apps. However, prior empirical bug studies have focused mostly on desktop and server applications. Therefore, in this paper, we perform an in-depth empirical study on bugs in the Google Android smartphone platform and 24 widely-used open-source Android apps from diverse categories such as communication, tools, and media. Our analysis has three main thrusts. First, we define several metrics to understand the quality of bug reports and analyze the bug-fix process, including developer involvement. Second, we show how differences in bug life-cycles can affect the bug-fix process. Third, as Android devices carry significant amounts of security-sensitive information, we perform a study of Android security bugs. We found that, although contributor activity in these projects is generally high, developer involvement decreases in some projects, similarly, while bug-report quality is high, bug triaging is still a problem. Finally, we observe that in Android apps, security bug reports are of higher quality but get fixed slower than non-security bugs. We believe that the findings of our study could potentially benefit both developers and users of Android apps.


Dynamic Tangible User Interface Palettes

Graphics editors often suffer from a large number of tool palettes that compete with valuable document space. To address this problem and to bring back physical affordances similar to a painter’s palette, we propose to augment a digital tabletop with spatially tracked handheld displays. These displays are dynamically updated depending on their spatial location. We introduce the concept of spatial Work Zones that take up distinct 3D regions above the table surface and serve as physical containers for digital content that is organized as stacks of horizontal layers. Spatial Work Zones are represented either by physical objects or on-screen on the tabletop. Associated layers can be explored fluently by entering a spatial Work Zone with a handheld display. This provides quick access and seamless changes between tools and parts of the document that are instantly functional, i.e., ready to be used by a digital pen. We discuss several use cases illustrating our techniques and setting them into context with previous systems. Early user feedback indicates that combining dynamic GUI functionality with the physicality of spatially tracked handheld displays is promising and can be generalized beyond graphics editing.


Prevalence of Vitamin D Insufficiency in an Adult Normal Population

The vitamin D status of a general adult urban population was estimated between November and April in 1569 subjects selected from 20 French cities grouped in nine geographical regions (between latitude 43° and 51° N). Major differences in 25-hydroxyvitamin D (25(OH)D) concentration were found between regions, the lowest values being seen in the North and the greatest in the South, with a significant ‘sun’ effect (r = 0.72; p = 0.03) and latitude effect (r = -0.79; p = 0.01). In this healthy adult population, 14% of subjects exhibited 25(OH)D values ≤ 30 nmol/l (12 ng/ml), which represents the lower limit (< 2 SD) for a normal adult population measured in winter with the same method (RIA Incstar). A significant negative correlation was found between serum intact parathyroid hormone (iPTH) and serum 25(OH)D values (p < 0.01). Serum iPTH held a stable plateau level at 36 pg/ml as long as serum 25(OH)D values were higher than 78 nmol/l (31 ng/ml), but increased when the serum 25(OH)D value fell below this. When the 25(OH)D concentration became equal to or lower than 11.3 nmol/l (4.6 ng/ml), the PTH values reached the upper limit of normal values (55 pg/ml) found in vitamin D replete subjects. These results showed that in French normal adults living in an urban environment with a lack of direct exposure to sunshine, diet failed to provide an adequate amount of vitamin D. It is important to pay attention to this rather high prevalence of vitamin D insufficiency in the general adult population and to discuss the clinical utility of winter supplementation with low doses of vitamin D.


Marital processes predictive of later dissolution: behavior, physiology, and health.

Seventy-three married couples were studied in 1983 and 1987. To identify marital processes associated with dissolution, a balance theory of marriage was used to generate 1 variable for dividing couples into regulated and nonregulated groups. For studying the precursors of divorce, a "cascade" model of marital dissolution, which forms a Guttman-like scale, received preliminary support. Compared with regulated couples, nonregulated couples had (a) marital problems rated as more severe (Time 1); (b) lower marital satisfaction (Time 1 and Time 2); (c) poorer health (Time 2); (d) smaller finger pulse amplitudes (wives); (e) more negative ratings for interactions; (f) more negative emotional expression; (g) less positive emotional expression; (h) more stubbornness and withdrawal from interaction; (i) greater defensiveness; and (j) greater risk for marital dissolution (lower marital satisfaction and higher incidence of consideration of dissolution and of actual separation).


A Tutorial on Machine Learning and Data Science Tools with Python

In this tutorial, we will provide an introduction to the main Python software tools used for applying machine learning techniques to medical data. The focus will be on open-source software that is freely available and is cross platform. To aid the learning experience, a companion GitHub repository is available so that you can follow the examples contained in this paper interactively using Jupyter notebooks. The notebooks will be more exhaustive than what is contained in this chapter, and will focus on medical datasets and healthcare problems. Briefly, this tutorial will first introduce Python as a language, and then describe some of the lower level, general matrix and data structure packages that are popular in the machine learning and data science communities, such as NumPy and Pandas. From there, we will move to dedicated machine learning software, such as SciKit-Learn. Finally we will introduce the Keras deep learning and neural networks library. The emphasis of this paper is readability, with as little jargon used as possible. No previous experience with machine learning is assumed. We will use openly available medical datasets throughout.


High frequency ultrasound imaging of surface and subsurface structures of fingerprints

High frequency ultrasound is suitable for non-invasive evaluation of skin because it can obtain both morphological and biomechanical information. A specially developed acoustic microscope system with the central frequency of 100 MHz was developed. The system was capable of (1) conventional C-mode acoustic microscope imaging of thinly sliced tissue, (2) ultrasound impedance imaging of the surface of in vivo thick tissue and (3) 3D ultrasound imaging of inside of the in vivo tissue. In the present study, ultrasound impedance imaging and 3D ultrasound imaging of in vivo fingerprints were obtained. The impedance image showed pores of the sweat glands in the surface of fingerprint and 3D ultrasound imaging showed glands of the rear surface of fingerprint. Both findings were not visualized by normal optical imaging, thus the system can be applied to pathological diagnosis of skin lesions and assessment of aging of the skin in cosmetic point of view.


A privacy-preserving approach to policy-based content dissemination

We propose a novel scheme for selective distribution of content, encoded as documents, that preserves the privacy of the users to whom the documents are delivered and is based on an efficient and novel group key management scheme. Our document broadcasting approach is based on access control policies specifying which users can access which documents, or subdocuments. Based on such policies, a broadcast document is segmented into multiple subdocuments, each encrypted with a different key. In line with modern attribute-based access control, policies are specified against identity attributes of users. However our broadcasting approach is privacy-preserving in that users are granted access to a specific document, or subdocument, according to the policies without the need of providing in clear information about their identity attributes to the document publisher. Under our approach, not only does the document publisher not learn the values of the identity attributes of users, but it also does not learn which policy conditions are verified by which users, thus inferences about the values of identity attributes are prevented. Moreover, our key management scheme on which the proposed broadcasting approach is based is efficient in that it does not require to send the decryption keys to the users along with the encrypted document. Users are able to reconstruct the keys to decrypt the authorized portions of a document based on subscription information they have received from the document publisher. The scheme also efficiently handles new subscription of users and revocation of subscriptions.


Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems.


Data-Efficient Hierarchical Reinforcement Learning

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of current HRL methods require careful task-specific design and on-policy training, making them difficult to apply in real-world scenarios. In this paper, we study how we can develop HRL algorithms that are general, in that they do not make onerous additional assumptions beyond standard RL algorithms, and efficient, in the sense that they can be used with modest numbers of interaction samples, making them suitable for real-world problems such as robotic control. For generality, we develop a scheme where lower-level controllers are supervised with goals that are learned and proposed automatically by the higher-level controllers. To address efficiency, we propose to use off-policy experience for both higherand lower-level training. This poses a considerable challenge, since changes to the lower-level behaviors change the action space for the higher-level policy, and we introduce an off-policy correction to remedy this challenge. This allows us to take advantage of recent advances in off-policy model-free RL to learn both higherand lower-level policies using substantially fewer environment interactions than on-policy algorithms. We term the resulting HRL agent HIRO and find that it is generally applicable and highly sample-efficient. Our experiments show that HIRO can be used to learn highly complex behaviors for simulated robots, such as pushing objects and utilizing them to reach target locations,1 learning from only a few million samples, equivalent to a few days of real-time interaction. In comparisons with a number of prior HRL methods, we find that our approach substantially outperforms previous state-of-the-art techniques.2


Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models

Data-efficient reinforcement learning (RL) in continuous state-action spaces using very high-dimensional observations remains a key challenge in developing fully autonomous systems. We consider a particularly important instance of this challenge, the pixels-to-torques problem, where an RL agent learns a closed-loop control policy (“torques”) from pixel information only. We introduce a data-efficient, model-based reinforcement learning algorithm that learns such a closed-loop policy directly from pixel information. The key ingredient is a deep dynamical model for learning a low-dimensional feature embedding of images jointly with a predictive model in this low-dimensional feature space. Joint learning is crucial for longterm predictions, which lie at the core of the adaptive nonlinear model predictive control strategy that we use for closed-loop control. Compared to state-of-the-art RL methods for continuous states and actions, our approach learns quickly, scales to high-dimensional state spaces, is lightweight and an important step toward fully autonomous end-to-end learning from pixels to torques.


Optimal number of response categories in rating scales: reliability, validity, discriminating power, and respondent preferences.

Using a self-administered questionnaire, 149 respondents rated service elements associated with a recently visited store or restaurant on scales that differed only in the number of response categories (ranging from 2 to 11) and on a 101-point scale presented in a different format. On several indices of reliability, validity, and discriminating power, the two-point, three-point, and four-point scales performed relatively poorly, and indices were significantly higher for scales with more response categories, up to about 7. Internal consistency did not differ significantly between scales, but test-retest reliability tended to decrease for scales with more than 10 response categories. Respondent preferences were highest for the 10-point scale, closely followed by the seven-point and nine-point scales. Implications for research and practice are discussed.


FPGA vs. GPU for sparse matrix vector multiply

Sparse matrix-vector multiplication (SpMV) is a common operation in numerical linear algebra and is the computational kernel of many scientific applications. It is one of the original and perhaps most studied targets for FPGA acceleration. Despite this, GPUs, which have only recently gained both general-purpose programmability and native support for double precision floating-point arithmetic, are viewed by some as a more effective platform for SpMV and similar linear algebra computations. In this paper, we present an analysis comparing an existing GPU SpMV implementation to our own, novel FPGA implementation. In this analysis, we describe the challenges faced by any SpMV implementation, the unique approaches to these challenges taken by both FPGA and GPU implementations, and their relative performance for SpMV.


The ArchC Architecture Description Language and Tools

This paper presents an architecture description language (ADL) called ArchC, which is an open-source SystemC-based language that is specialized for processor architecture description. Its main goal is to provide enough information, at the right level of abstraction, in order to allow users to explore and verify new architectures, by automatically generating software tools like simulators and co-verification interfaces. ArchC’s key features are a storage-based co-verification mechanism that automatically checks the consistency of a refined ArchC model against a reference (functional) description, memory hierarchy modeling capability, the possibility of integration with other SystemC IPs and the automatic generation of high-level SystemC simulators and assemblers. We have used ArchC to synthesize both functional and cycle-based simulators for the MIPS and Intel 8051 processors, as well as functional models of architectures like SPARC V8, TMS320C62x, XScale and PowerPC.


Sign Language Recognition using Sequential Pattern Trees

This paper presents a novel, discriminative, multi-class classifier based on Sequential Pattern Trees. It is efficient to learn, compared to other Sequential Pattern methods, and scalable for use with large classifier banks. For these reasons it is well suited to Sign Language Recognition. Using deterministic robust features based on hand trajectories, sign level classifiers are built from sub-units. Results are presented both on a large lexicon single signer data set and a multi-signer Kinect™ data set. In both cases it is shown to out perform the non-discriminative Markov model approach and be equivalent to previous, more costly, Sequential Pattern (SP) techniques.


Hardware-In-the-Loop Simulation of Electric Vehicle Powertrain System

This paper presents a design procedure of HardwareIn-the-Loop (HIL) for Electric Vehicle powertrain system modeling and simulation. First, simulation is performed in MATLAB/Simulink platform. Then, an Electric Vehicle Powertrain Hardware-In-the-Loop system including BMS (Battery Manage System), MCU (Motor Control Unit) and VMS (vehicle management system) is built based on the dSPACE with MATLAB/Simulink, along with the RTW toolbox (real time workshop). Through this system, the Electric Vehicle Control strategy is simulated and validated, and the results of offline simulation & real time simulation with laboratory prototype are presented. Keywords-Hardware In the Loop; Electric Vehicle; Simulation; dSPACE; DSP


Introduction: Hybrid intelligent adaptive systems

This issue of International Journal of Intelligent Systems includes extended versions of selected papers from the 4th International Conference on Soft Computing, held in Iizuka, Japan, September 30]October 5, 1996. The topic of the special issue is ‘‘Hybrid Intelligent Adaptive Systems.’’ Research on hybrid systems is one of the key issues of developing intelligent systems and it can apply a wide range of tools, including artificial neural networks, fuzzy logic, knowledge-based systems, genetic algorithms, evolutionary computation, and chaos models. The papers in this issue have been carefully reviewed and modified to give the readers a comprehensive overview of theoretical aspects, design, and implementation issues of hybrid intelligent adaptive systems. In the first paper by Kasabov and Kozma, a general framework of developing hybrid, intelligent, and adaptive systems is given. This work develops multimodular, fuzzy neural network systems and applies it to phoneme-based speech recognition. The second paper by Miyata, Furuhashi, and Uchikawa proposes fuzzy abductive inference with degrees of manifestations. This method infers irredundant combinations of candidates with degrees of belief for the manifestations. It is also demonstrated that the results of the inference method are applicable to medical diagnosis and system fault detection. Cho adopts ideas of artificial life in his work to develop evolutionary neural networks. The introduced modular neural network can evole its structure autonomously using a structural genetic code. The effectiveness of the method is demonstrated on the example of handwritten digit recognition. Feuring and Lippe study theoretical aspects of fuzzy neural networks. They propose a training algorithm for fuzzy neural networks that satisfies a certain goodness criterion. The second part of the special issue contains articles related to time series analysis and systems control. Yamazaki, Kang, and Ochiai introduce a hierarchical neural network system for adaptive, intelligent control, based on the analogy with the human thinking process. The optimum parameter space of the neural network system is found by a self-controllable algorithm, which can lead to either equilibrium or to nonequilibrium, chaotic behavior. The results of this study are applied, e.g., to laser beam analysis, semiconductor design, and design of magnetic devices. The work by Kozma, Kasabov, Kim, and Cohen presents a chaotic neuro-fuzzy method for time series analysis and process control. The


A 6LoWPAN Accelerator for Internet of Things Endpoint Devices

The Internet of Things (IoT) is revolutionizing the Internet of the future and the way smart devices, people, and processes interact with each other. Challenges in endpoint devices (EDs) communication are due not only to the security and privacy-related requirements but also due to the ever-growing amount of data transferred over the network that needs to be handled, which can considerably reduce the system availability to perform other tasks. This paper presents an IPv6 over low power wireless personal area networks (6LoWPAN) accelerator for the IP-based IoT networks, which is able to process and filter IPv6 packets received by an IEEE 802.15.4 radio transceiver without interrupting the microcontroller. It offers nearly 13.24% of performance overhead reduction for the packet processing and the address filtering tasks, while guaranteeing full system availability when unwanted packets are received and discarded. The contribution is meant to be deployed on heterogeneous EDs at the network edge, which include on the same architecture, field-programmable gate array (FPGA) technology beside a microcontroller and an IEEE 802.15.4-compliant radio transceiver. Performed evaluations include the low FPGA hardware resources utilization and the energy consumption analysis when the processing and accepting/rejection of a received packet is performed by the 6LoWPAN accelerator.


Real-time Incremental Speech-to-Speech Translation of Dialogs

In a conventional telephone conversation between two speakers of the same language, the interaction is real-time and the speakers process the information stream incrementally. In this work, we address the problem of incremental speech-to-speech translation (S2S) that enables cross-lingual communication between two remote participants over a telephone. We investigate the problem in a novel real-time Session Initiation Protocol (SIP) based S2S framework. The speech translation is performed incrementally based on generation of partial hypotheses from speech recognition. We describe the statistical models comprising the S2S system and the SIP architecture for enabling real-time two-way cross-lingual dialog. We present dialog experiments performed in this framework and study the tradeoff in accuracy versus latency in incremental speech translation. Experimental results demonstrate that high quality translations can be generated with the incremental approach with approximately half the latency associated with nonincremental approach.


An Edge Driven Wavelet Frame Model for Image Restoration

Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images. In this paper, we introduce a new edge driven wavelet frame model for image restoration by approximating images as piecewise smooth functions. With an implicit representation of image singularities sets, the proposed model inflicts different strength of regularization on smooth and singular image regions and edges. The proposed edge driven model is robust to both image approximation and singularity estimation. The implicit formulation also enables an asymptotic analysis of the proposed models and a rigorous connection between the discrete model and a general continuous variational model. Finally, numerical results on image inpainting and deblurring show that the proposed model is compared favorably against several popular image restoration models.


Long-term stability of anterior open-bite treatment by intrusion of maxillary posterior teeth.

INTRODUCTION
Anterior open bite results from the combined influences of skeletal, dental, functional, and habitual factors. The long-term stability of anterior open bite corrected with absolute anchorage has not been thoroughly investigated. The purpose of this study was to examine the long-term stability of anterior open-bite correction with intrusion of the maxillary posterior teeth.

METHODS
Nine adults with anterior open bite were treated by intrusion of the maxillary posterior teeth. Lateral cephalographs were taken immediately before and after treatment, 1 year posttreatment, and 3 years posttreatment to evaluate the postintrusion stability of the maxillary posterior teeth.

RESULTS
On average, the maxillary first molars were intruded by 2.39 mm (P<0.01) during treatment and erupted by 0.45 mm (P<0.05) at the 3-year follow-up, for a relapse rate of 22.88%. Eighty percent of the total relapse of the intruded maxillary first molars occurred during the first year of retention. Incisal overbite increased by a mean of 5.56 mm (P<0.001) during treatment and decreased by a mean of 1.20 mm (P<0.05) by the end of the 3-year follow-up period, for a relapse rate of 17.00%. Incisal overbite significantly relapsed during the first year of retention (P<0.05) but did not exhibit significant recurrence between the 1-year and 3-year follow-ups.

CONCLUSIONS
Most relapse occurred during the first year of retention. Thus, it is reasonable to conclude that the application of an appropriate retention method during this period clearly enhances the long-term stability of the treatment.


The orbitofrontal cortex and reward.

The primate orbitofrontal cortex contains the secondary taste cortex, in which the reward value of taste is represented. It also contains the secondary and tertiary olfactory cortical areas, in which information about the identity and also about the reward value of odors is represented. The orbitofrontal cortex also receives information about the sight of objects and faces from the temporal lobe cortical visual areas, and neurons in it learn and reverse the visual stimulus to which they respond when the association of the visual stimulus with a primary reinforcing stimulus (such as a taste reward) is reversed. However, the orbitofrontal cortex is involved in representing negative reinforcers (punishers) too, such as aversive taste, and in rapid stimulus-reinforcement association learning for both positive and negative primary reinforcers. In complementary neuroimaging studies in humans it is being found that areas of the orbitofrontal cortex (and connected subgenual cingulate cortex) are activated by pleasant touch, by painful touch, by rewarding and aversive taste, and by odor. Damage to the orbitofrontal cortex in humans can impair the learning and reversal of stimulus- reinforcement associations, and thus the correction of behavioral responses when these are no longer appropriate because previous reinforcement contingencies change. This evidence thus shows that the orbitofrontal cortex is involved in decoding and representing some primary reinforcers such as taste and touch; in learning and reversing associations of visual and other stimuli to these primary reinforcers; and in controlling and correcting reward-related and punishment-related behavior, and thus in emotion.


Adverse Effects Associated with Protein Intake above the Recommended Dietary Allowance for Adults

Background. While high-protein consumption-above the current recommended dietary allowance for adults (RDA: 0.8 g protein/kg body weight/day)-is increasing in popularity, there is a lack of data on its potential adverse effects. Objective. To determine the potential disease risks due to high protein/high meat intake obtained from diet and/or nutritional supplements in humans. Design. Review. Subjects. Healthy adult male and female subjects. Method. In order to identify relevant studies, the electronic databases, Medline and Google Scholar, were searched using the terms:"high protein diet," "protein overconsumption," "protein overuse," and "high meat diet." Papers not in English were excluded. Further studies were identified by citations in retrieved papers. Results. 32 studies (21 experimental human studies and 11 reviews) were identified. The adverse effects associated with long-term high protein/high meat intake in humans were (a) disorders of bone and calcium homeostasis, (b) disorders of renal function, (c) increased cancer risk, (d) disorders of liver function, and (e) precipitated progression of coronary artery disease. Conclusions. The findings of the present study suggest that there is currently no reasonable scientific basis in the literature to recommend protein consumption above the current RDA (high protein diet) for healthy adults due to its potential disease risks. Further research needs to be carried out in this area, including large randomized controlled trials.


Automated Lip Reading Technique for Password Authentication

As technology is starting to conquer every strata of the society, the war for protecting confidential data from being intercepted is growing intense by the hour. Biometricsecurity stands out as the most secure form of authentication in high security zones such as defense, space missions and research head-quarters. Today, forms of password-protection range from facerecognition to retina -scan. Here, we develop a system for recognizing and converting lip movement of an individual into a recognized pattern which is set as a password for the system using image-processing. This system is also a break-through for providing people with motor-disabilities a robust and easy way of protecting their data. By capturing and tracing the successive movement of lips during speech, the corresponding word can be detected. The captured images are represented as points on a two-dimensional flat manifold that enables us to efficiently define the pronunciation of each word and thereby analyze or synthesize the motion of the lips. The motion of lips helps us track the word syllable-by-syllable. With multiple levels of image processing, it becomes possible to set the matching parameters to a very close value, hence not allowing any bruteforce or other infamous hacking techniques to break into the user’s system. This lip reading technique also serves applications in areas where communication via direct speech is not possible.


Efficient Deep Learning on Multi-Source Private Data

Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.


The crisis in antibiotic resistance.

The synthesis of large numbers of antibiotics over the past three decades has caused complacency about the threat of bacterial resistance. Bacteria have become resistant to antimicrobial agents as a result of chromosomal changes or the exchange of the exchange of genetic material via plasmids and transposons. Streptococcus pneumoniae, Streptococcus pyogenes, and staphylococci, organisms that cause respiratory and cutaneous infections, and members of the Enterobacteriaceae and Pseudomonas families, organisms that cause diarrhea, urinary infection, and sepsis, are now resistant to virtually all of the older antibiotics. The extensive use of antibiotics in the community and hospitals has fueled this crisis. Mechanisms such as antibiotic control programs, better hygiene, and synthesis of agents with improved antimicrobial activity need to be adopted in order to limit bacterial resistance.


A case for dynamic frequency tuning in on-chip networks

Performance and power are the first order design metrics for Network-on-Chips (NoCs) that have become the de-facto standard in providing scalable communication backbones for multicores/CMPs. However, NoCs can be plagued by higher power consumption and degraded throughput if the network and router are not designed properly. Towards this end, this paper proposes a novel router architecture, where we tune the frequency of a router in response to network load to manage both performance and power. We propose three dynamic frequency tuning techniques, FreqBoost, FreqThrtl and FreqTune, targeted at congestion and power management in NoCs. As enablers for these techniques, we exploit Dynamic Voltage and Frequency Scaling (DVFS) and the imbalance in a generic router pipeline through time stealing. Experiments using synthetic workloads on a 8x8 wormhole-switched mesh interconnect show that FreqBoost is a better choice for reducing average latency (maximum 40%) while, FreqThrtl provides the maximum benefits in terms of power saving and energy delay product (EDP). The FreqTune scheme is a better candidate for optimizing both performance and power, achieving on an average 36% reduction in latency, 13% savings in power (up to 24% at high load), and 40% savings (up to 70% at high load) in EDP. With application benchmarks, we observe IPC improvement up to 23% using our design. The performance and power benefits also scale for larger NoCs.


Deep CNNs With Spatially Weighted Pooling for Fine-Grained Car Recognition

Fine-grained car recognition aims to recognize the category information of a car, such as car make, car model, or even the year of manufacture. A number of recent studies have shown that a deep convolutional neural network (DCNN) trained on a large-scale data set can achieve impressive results at a range of generic object classification tasks. In this paper, we propose a spatially weighted pooling (SWP) strategy, which considerably improves the robustness and effectiveness of the feature representation of most dominant DCNNs. More specifically, the SWP is a novel pooling layer, which contains a predefined number of spatially weighted masks or pooling channels. The SWP pools the extracted features of DCNNs with the guidance of its learnt masks, which measures the importance of the spatial units in terms of discriminative power. As the existing methods that apply uniform grid pooling on the convolutional feature maps of DCNNs, the proposed method can extract the convolutional features and generate the pooling channels from a single DCNN. Thus minimal modification is needed in terms of implementation. Moreover, the parameters of the SWP layer can be learned in the end-to-end training process of the DCNN. By applying our method to several fine-grained car recognition data sets, we demonstrate that the proposed method can achieve better performance than recent approaches in the literature. We advance the state-of-the-art results by improving the accuracy from 92.6% to 93.1% on the Stanford Cars-196 data set and 91.2% to 97.6% on the recent CompCars data set. We have also tested the proposed method on two additional large-scale data sets with impressive results observed.


The Art of Multiple Sequence Alignment in R

Figure 1: The art of multiple sequence alignment. This document is intended to illustrate the art of multiple sequence alignment in R using DECIPHER. Even though its beauty is often concealed, multiple sequence alignment is a form of art in more ways than one. Take a look at Figure 1 for an illustration of what is happening behind the scenes during multiple sequence alignment. The practice of sequence alignment is one that requires a degree of skill, and it is that art which this vignette intends to convey. It is simply not enough to “plug” sequences into a multiple sequence aligner and blindly trust the result. An appreciation for the art as well a careful consideration of the results are required. What really is multiple sequence alignment, and is there a single correct alignment? Generally speaking, alignment seeks to perform the act of taking multiple divergent biological sequences of the same “type” and fitting them to a form that reflects some shared quality. That quality


Incorporating query-specific feedback into learning-to-rank models

Relevance feedback has been shown to improve retrieval for a broad range of retrieval models. It is the most common way of adapting a retrieval model for a specific query. In this work, we expand this common way by focusing on an approach that enables us to do query-specific modification of a retrieval model for learning-to-rank problems. Our approach is based on using feedback documents in two ways: 1) to improve the retrieval model directly and 2) to identify a subset of training queries that are more predictive than others. Experiments with the Gov2 collection show that this approach can obtain statistically significant improvements over two baselines; learning-to-rank (SVM-rank) with no feedback and learning-to-rank with standard relevance feedback.


EuroSense: Automatic Harvesting of Multilingual Sense Annotations from Parallel Text

Parallel corpora are widely used in a variety of Natural Language Processing tasks, from Machine Translation to cross-lingual Word Sense Disambiguation, where parallel sentences can be exploited to automatically generate high-quality sense annotations on a large scale. In this paper we present EUROSENSE, a multilingual sense-annotated resource based on the joint disambiguation of the Europarl parallel corpus, with almost 123 million sense annotations for over 155 thousand distinct concepts and entities from a languageindependent unified sense inventory. We evaluate the quality of our sense annotations intrinsically and extrinsically, showing their effectiveness as training data for Word Sense Disambiguation.


Gradient Descent Provably Optimizes Over-parameterized Neural Networks

One of the mysteries in the success of neural networks is randomly initialized first order methods like gradient descent can achieve zero training loss even though the objective function is non-convex and non-smooth. This paper demystifies this surprising phenomenon for two-layer fully connected ReLU activated neural networks. For anm hidden node shallow neural network with ReLU activation and n training data, we show as long asm is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss function. Our analysis relies on the following observation: over-parameterization and random initialization jointly restrict every weight vector to be close to its initialization for all iterations, which allows us to exploit a strong convexity-like property to show that gradient descent converges at a global linear rate to the global optimum. We believe these insights are also useful in analyzing deep models and other first order methods.


Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set

We address a cross-scenario clothing retrieval problem- given a daily human photo captured in general environment, e.g., on street, finding similar clothing in online shops, where the photos are captured more professionally and with clean background. There are large discrepancies between daily photo scenario and online shopping scenario. We first propose to alleviate the human pose discrepancy by locating 30 human parts detected by a well trained human detector. Then, founded on part features, we propose a two-step calculation to obtain more reliable one-to-many similarities between the query daily photo and online shopping photos: 1) the within-scenario one-to-many similarities between a query daily photo and an extra auxiliary set are derived by direct sparse reconstruction; 2) by a cross-scenario many-to-many similarity transfer matrix inferred offline from the auxiliary set and the online shopping set, the reliable cross-scenario one-to-many similarities between the query daily photo and all online shopping photos are obtained.


Robust Subspace Segmentation by Low-Rank Representation

We propose low-rank representation (LRR) to segment data drawn from a union of multiple linear (or affine) subspaces. Given a set of data vectors, LRR seeks the lowestrank representation among all the candidates that represent all vectors as the linear combination of the bases in a dictionary. Unlike the well-known sparse representation (SR), which computes the sparsest representation of each data vector individually, LRR aims at finding the lowest-rank representation of a collection of vectors jointly. LRR better captures the global structure of data, giving a more effective tool for robust subspace segmentation from corrupted data. Both theoretical and experimental results show that LRR is a promising tool for subspace segmentation.


Reducing the energy cost of human walking using an unpowered exoskeleton

With efficiencies derived from evolution, growth and learning, humans are very well-tuned for locomotion. Metabolic energy used during walking can be partly replaced by power input from an exoskeleton, but is it possible to reduce metabolic rate without providing an additional energy source? This would require an improvement in the efficiency of the human–machine system as a whole, and would be remarkable given the apparent optimality of human gait. Here we show that the metabolic rate of human walking can be reduced by an unpowered ankle exoskeleton. We built a lightweight elastic device that acts in parallel with the user's calf muscles, off-loading muscle force and thereby reducing the metabolic energy consumed in contractions. The device uses a mechanical clutch to hold a spring as it is stretched and relaxed by ankle movements when the foot is on the ground, helping to fulfil one function of the calf muscles and Achilles tendon. Unlike muscles, however, the clutch sustains force passively. The exoskeleton consumes no chemical or electrical energy and delivers no net positive mechanical work, yet reduces the metabolic cost of walking by 7.2 ± 2.6% for healthy human users under natural conditions, comparable to savings with powered devices. Improving upon walking economy in this way is analogous to altering the structure of the body such that it is more energy-effective at walking. While strong natural pressures have already shaped human locomotion, improvements in efficiency are still possible. Much remains to be learned about this seemingly simple behaviour.


A spelling correction model for end-to-end speech recognition

Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs. Thus, the language model component of the end-to-end model is only trained on transcribed audio-text pairs, which leads to performance degradation especially on rare words. While there have been a variety of work that look at incorporating an external LM trained on text-only data into the end-to-end framework, none of them have taken into account the characteristic error distribution made by the model. In this paper, we propose a novel approach to utilizing text-only data, by training a spelling correction (SC) model to explicitly correct those errors. On the LibriSpeech dataset, we demonstrate that the proposed model results in an 18.6% relative improvement in WER over the baseline model when directly correcting top ASR hypothesis, and a 29.0% relative improvement when further rescoring an expanded n-best list using an external LM.


Yield optimization using advanced statistical correlation methods

This work presents a novel yield optimization methodology based on establishing a strong correlation between a group of fails and an adjustable process parameter. The core of the methodology comprises three advanced statistical correlation methods. The first method performs multivariate correlation analysis to uncover linear correlation relationships between groups of fails and measurements of a process parameter. The second method partitions a dataset into multiple subsets and tries to maximize the average of the correlations each calculated based on one subset. The third method performs statistical independence test to evaluate the risk of adjusting a process parameter. The methodology was applied to an automotive product line to improve yield. Five process parameter changes were discovered which led to significant improvement of the yield and consequently significant reduction of the yield fluctuation.


Development trends for human monoclonal antibody therapeutics

Fully human monoclonal antibodies (mAbs) are a promising and rapidly growing category of targeted therapeutic agents. The first such agents were developed during the 1980s, but none achieved clinical or commercial success. Advances in technology to generate the molecules for study — in particular, transgenic mice and yeast or phage display — renewed interest in the development of human mAbs during the 1990s. In 2002, adalimumab became the first human mAb to be approved by the US Food and Drug Administration (FDA). Since then, an additional six human mAbs have received FDA approval: panitumumab, golimumab, canakinumab, ustekinumab, ofatumumab and denosumab. In addition, 3 candidates (raxibacumab, belimumab and ipilimumab) are currently under review by the FDA, 7 are in Phase III studies and 81 are in either Phase I or II studies. Here, we analyse data on 147 human mAbs that have entered clinical study to highlight trends in their development and approval, which may help inform future studies of this class of therapeutic agents.


Changes in structural and functional connectivity among resting-state networks across the human lifespan

At rest, the brain's sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks.


A Nested-Graph Model for the Representation and Manipulation of Complex Objects

Three recent trends in database research are object-oriented and deductive databases and graph-based user interfaces. We draw these trends together in a data model we call the Hypernode Model. The single data structure of this model is the hypernode, a graph whose nodes can themselves be graphs. Hypernodes are typed, and types, too, are nested graphs. We give the theoretical foundations of hypernodes and types, and we show that type checking is tractable. We show also how conventional type-forming operators can be simulated by our graph types, including cyclic types. The Hypernode Model comes equipped with a rule-based query language called Hyperlog, which is complete with respect to computation and update. We define the operational semantics of Hyperlog and show that the evaluation can be performed efficiently. We discuss also the use of Hyperlog for supporting database browsing, an essential feature of Hypertext databases. We compare our work with other graph-based data models—unlike previous graph-based models, the Hypernode Model provides inherent support for data abstraction via its nesting of graphs. Finally, we briefly discuss the implementation of a DBMS based on the Hypernode Model.


Fast Compact City Modeling for Navigation Pre-Visualization

Nowadays, GPS-based car navigation systems mainly use speech and aerial views of simplified road maps to guide drivers to their destination. However, drivers often experience difficulties in linking the simple 2D aerial map with the visual impression that they get from the real environment, which is inherently ground-level based. Therefore, supplying realistically textured 3D city models at ground-level proves very useful for pre-visualizing an upcoming traffic situation. Because this pre-visualization can be rendered from the expected future viewpoints of the driver, the latter will more easily understand the required maneuver. 3D city models can be reconstructed from the imagery recorded by surveying vehicles. The vastness of image material gathered by these vehicles, however, puts extreme demands on vision algorithms to ensure their practical usability. Algorithms need to be as fast as possible and should result in compact, memory efficient 3D city models for future ease of distribution and visualization. For the considered application, these are not contradictory demands. Simplified geometry assumptions can speed up vision algorithms while automatically guaranteeing compact geometry models. We present a novel city modeling framework which builds upon this philosophy to create 3D content at high speed which could allow for pre-visualization of any conceivable traffic situation by car navigation modules.


Design of impedance matching circuits for RF energy harvesting systems

This paper presents a systematic design methodology for impedance matching circuits of an RF energy harvester to maximize the harvested energy for a range of input power levels with known distribution. Design of input matching networks for RF rectifier differs from those for traditional RF circuits such as low-noise amplifier because the transistors in the RF rectifiers operate in different regions. In this work, it is shown that the input impedance of the rectifier estimated based on small signal simulation of transistors at the peak of input signal in the rectifier when the transistors are biased with stable DC operating voltage provides the largest harvested energy from a fixed input power. For variable input power levels, a matching network selection strategy is proposed to maximize expected harvested energy given the probability density distribution of input power levels. As an experimental sample, an RF energy harvester circuit is designed to maximize the harvested energy in the 902–928 MHz band employing an off-chip impedance matching circuit designed using the proposed methodology. The proposed RF energy harvester exhibits a measured maximum power conversion efficiency (PCE) of 32% at −15 dBm (32 μW) and delivers an output DC voltage of 3.2 V to a 1 MΩ load.


Fast and Accurate Calibration of a Kinect Sensor

The article describes a new algorithm for calibrating a Kinect sensor that achieves high accuracy using only 6 to 10 image-disparity pairs of a planar checkerboard pattern. The method estimates the projection parameters for both color and depth cameras, the relative pose between them, and the function that converts kinect disparity units (kdu) into metric depth. We build on the recent work of Herrera et. al [8] that uses a large number of input frames and multiple iterative minimization steps for obtaining very accurate calibration results. We propose several modifications to this estimation pipeline that dramatically improve stability, usability, and runtime. The modifications consist in: (i) initializing the relative pose using a new minimal, optimal solution for registering 3D planes across different reference frames, (ii) including a metric constraint during the iterative refinement to avoid a drift in the disparity to depth conversion, and (iii) estimating the parameters of the depth distortion model in an open-loop post-processing step. Comparative experiments show that our pipeline can achieve a calibration accuracy similar to [8] while using less than 1/6 of the input frames and running in 1/30 of the time.


Ring oscillators for CMOS process tuning and variability control

Test structures utilizing ring oscillators to monitor MOSFET ac characteristics for digital CMOS circuit applications are described. The measurements provide information on the average behavior of sets of a few hundred MOSFETs under high speed switching conditions. The design of the ring oscillators is specifically tailored for process centering and monitoring of variability in circuit performance in the manufacturing line as well as in the product. The delay sensitivity to key MOSFET parameter variations in a variety of ring oscillator designs is studied using a compact model for partially depleted silicon on insulator(PD-SOI) technology, but the analysis is equally valid for conventional bulk Si technology. Examples of hardware data illustrating the use of this methodology are taken primarily from experimental hardware in the 90-nm CMOS technology node in PD-SOI. The design and data analysis techniques described here allow very rapid investigation of the sources of variations in circuit delays.


The Power of Word Clusters for Text Classification

The recently introducedInformation Bottleneck method [21] provides an information theoretic framework, for extr acting features of one variable, that are relevant for the values of an ther variable. Several previous works already suggeste d applying this method for document clustering, gene expression data a nalysis, spectral analysis and more. In this work we present a novel implementation of this method for supervised text cla ssification. Specifically, we apply the information bottlen eck method to find word-clusters that preserve the information a bout document categories and use these clusters as features for classification. Previous work [1] used a similar clustering procedure to show that word-clusters can significantly redu c the feature space dimensionality, with only a minor change in cl assification accuracy. In this work we present similar resul ts and go further to show that when the training sample is small word clusters can yield significant improvement in classificatio n accuracy (up to18%) over the performance using the words directly.


PSSE: An Architecture For A Personalized Semantic Search Engine

Semantic technologies promise a next generation of semantic search engines. General search engines don’t take into consideration the semantic relationships between query terms and other concepts that might be significant to user. Thus, semantic web vision and its core ontologies are used to overcome this defect. The order in which these results are ranked is also substantial. Moreover, user preferences and interests must be taken into consideration so as to provide user a set of personalized results. In this paper we propose, an architecture for a Personalized Semantic Search Engine (PSSE). PSSE is a crawler-based search engine that makes use of multi-crawlers to collect resources from both semantic as well as traditional web resources. In order for the system to reduce processing time, web pages' graph is clustered, then clusters are annotated using document annotation agents that work in parallel. Annotation agents use methods of ontology matching to find resources of the semantic web as well as means of information extraction techniques in order to provide a well description of HTML documents. System ranks resources based on a final score that's calculated based on traditional link analysis, content analysis and a weighted user profile for more personalized results. We have a belief that the merge of these techniques together enhances search results.


On the Philosophy of Bitcoin/Blockchain Technology: Is it a Chaotic, Complex System?

The Philosophy of Blockchain Technology is concerned, among other things, with its ontology, how might it be characterised, how is it being created, implemented, and adopted, how does it operate in the world, and how does it evolve over time. Here, we concentrate on whether Bitcoin/blockchain can be considered a complex system and, if so, whether a chaotic one. Beyond mere academic curiosity, a positive response would raise concerns about the likelihood of Bitcoin/blockchain entering a 2010-FlashCrash-type of chaotic regime, with catastrophic consequences for financial systems based on it. This paper starts by enhancing the relevant details of Bitcoin/blockchain ecosystem formed by the blockchain itself, bitcoin end-users (payers and payees), capital gain seekers, miners, full nodes maintainers, and developers, and their interactions. Secondly, the Information Theory of Complex Systems is briefly discussed for later use. Finally, blockchain is investigated with the help of Crutchfield‟s Statistical Complexity measure. The low non-null statistical complexity value obtained suggests that blockchain may be considered algorithmically complicated, but hardly a complex system and unlikely to enter a chaotic regime.


WiStep: Device-free Step Counting with WiFi Signals

Inspired by the emerging WiFi-based applications, in this paper, we leverage ubiquitous WiFi signals and propose a device-free step counting system, called WiStep. Based on the multipath propagation model, when a person is walking, her torso and limbs move at different speeds, which modulates wireless signals to the propagation paths with different lengths and thus introduces different frequency components into the received Channel State Information (CSI). To count walking steps, we first utilize time-frequency analysis techniques to segment and recognize the walking movement, and then dynamically select the sensitive subcarriers with largest amplitude variances from multiple CSI streams. Wavelet decomposition is applied to extract the detail coefficients corresponding to the frequencies induced by feet or legs, and compress the data so as to improve computing speed. Short-time energy of the coefficients is then calculated as the metric for step counting. Finally, we combine the results derived from the selected subcarriers to produce a reliable step count estimation. In contrast to counting steps based on the torso frequency analysis, WiStep can count the steps of in-place walking even when the person's torso speed is null. We implement WiStep on commodity WiFi devices in two different indoor scenarios, and various influence factors are taken into consideration when evaluating the performance of WiStep. The experimental results demonstrate that WiStep can realize overall step counting accuracies of 90.2% and 87.59% respectively in these two scenarios, and it is resilient to the change of scenarios.


Word Attention for Sequence to Sequence Text Understanding

Attention mechanism has been a key component in Recurrent Neural Networks (RNNs) based sequence to sequence learning framework, which has been adopted in many text understanding tasks, such as neural machine translation and abstractive summarization. In these tasks, the attention mechanism models how important each part of the source sentence is to generate a target side word. To compute such importance scores, the attention mechanism summarizes the source side information in the encoder RNN hidden states (i.e., ht), and then builds a context vector for a target side word upon a subsequence representation of the source sentence, since ht actually summarizes the information of the subsequence containing the first t-th words in the source sentence. We in this paper, show that an additional attention mechanism called word attention, that builds itself upon word level representations, significantly enhances the performance of sequence to sequence learning. Our word attention can enrich the source side contextual representation by directly promoting the clean word level information in each step. Furthermore, we propose to use contextual gates to dynamically combine the subsequence level and word level contextual information. Experimental results on abstractive summarization and neural machine translation show that word attention significantly improve over strong baselines. In particular, we achieve the state-of-the-art result on WMT’14 English-French translation task with 12M training data.


Neither Snow Nor Rain Nor MITM...: An Empirical Analysis of Email Delivery Security

The SMTP protocol is responsible for carrying some of users' most intimate communication, but like other Internet protocols, authentication and confidentiality were added only as an afterthought. In this work, we present the first report on global adoption rates of SMTP security extensions, including: STARTTLS, SPF, DKIM, and DMARC. We present data from two perspectives: SMTP server configurations for the Alexa Top Million domains, and over a year of SMTP connections to and from Gmail. We find that the top mail providers (e.g., Gmail, Yahoo, and Outlook) all proactively encrypt and authenticate messages. However, these best practices have yet to reach widespread adoption in a long tail of over 700,000 SMTP servers, of which only 35% successfully configure encryption, and 1.1% specify a DMARC authentication policy. This security patchwork---paired with SMTP policies that favor failing open to allow gradual deployment---exposes users to attackers who downgrade TLS connections in favor of cleartext and who falsify MX records to reroute messages. We present evidence of such attacks in the wild, highlighting seven countries where more than 20% of inbound Gmail messages arrive in cleartext due to network attackers.


Fake News Headline Classification using Neural Networks with Attention

The Fake News Challenge is a competition to classify articles as agreeing, disagreeing, discussing, or having no relation to their headlines. The competition provides an annotated dataset (FNC-1) for training supervised models as well as a baseline model to compare against. We cast our fake news problem as a textual entailment task by using multiple Bidirectional LSTMs and an attention mechanism to predict the entailment of the articles to their paired headlines. To tune our model, we adjust a multilayer perceptron predictor layer’s width as well as the amount of dropout and regularization to control overfitting. We evaluate the effectiveness of our model against the baseline and compare to implementations of solutions to the same FNC-1 stance classification task.


A Fusion Approach for Efficient Human Skin Detection

A reliable human skin detection method that is adaptable to different human skin colors and illumination conditions is essential for better human skin segmentation. Even though different human skin-color detection solutions have been successfully applied, they are prone to false skin detection and are not able to cope with the variety of human skin colors across different ethnic. Moreover, existing methods require high computational cost. In this paper, we propose a novel human skin detection approach that combines a smoothed 2-D histogram and Gaussian model, for automatic human skin detection in color image(s). In our approach, an eye detector is used to refine the skin model for a specific person. The proposed approach reduces computational costs as no training is required, and it improves the accuracy of skin detection despite wide variation in ethnicity and illumination. To the best of our knowledge, this is the first method to employ fusion strategy for this purpose. Qualitative and quantitative results on three standard public datasets and a comparison with state-of-the-art methods have shown the effectiveness and robustness of the proposed approach.


Stemming Algorithms: A Comparative Study and their Analysis

Stemming is an approach used to reduce a word to its stem or root form and is used widely in information retrieval tasks to increase the recall rate and give us most relevant results. There are number of ways to perform stemming ranging from manual to automatic methods, from language specific to language independent each having its own advantage over the other. This paper represents a comparative study of various available stemming alternatives widely used to enhance the effectiveness and efficiency of information retrieval.


Simulation and analysis of DQ frame and P+Resonant controls for voltage source inverter to distributed generation

An inverter control design in DQ reference frame takes 3-phase sinusoidal signals and converts them to the DQ-frame to make a 2-phase based DC variable control. The error is calculated by subtracting the feedback from reference and then feedding it to PI controllers. The outputs of PI controllers are of the voltage form and transferred back to the ABC frame and fed to SPWM modules. P+Resonant controllers suggest establishing the control strategy in stationary (alpha-beta) frame. In this frame the axis are fix and three phase variables are mapped in a rotating vector with the same angular speed of the original signals. The P+Resonant controller has zero steady state error at the resonant frequency. This paper explains how to apply DQ-frame and P+Resonant for inverter controller design for grid connected single-phase Voltage Source Inverters (VSI). To evaluate the effectiveness of both control techniques, it is also presented simulated results with application of these controllers.


The effect of Bikram yoga on arterial stiffness in young and older adults.

BACKGROUND AND OBJECTIVE
Bikram yoga is the most popular form of hot yoga, despite the limited information available on its cardiovascular benefits. This study sought to determine the effect of Bikram yoga on arterial stiffness and insulin resistance in young and older adults.

METHODS
Twenty-four young (mean age±standard deviation, 30±1 years) and 18 middle-aged and older (mean age, 53±2 years) adults completed an 8-week Bikram yoga intervention. Bikram yoga classes were performed for 90 minutes per session, three times per week, in a room heated to 40.5°C with 40%--60% relative humidity.

RESULTS
Body mass, body fat percentage, blood pressure, and fasting blood glucose and triglyceride concentrations did not significantly change as a result of the intervention in either the young or the older group. Trunk flexibility, as measured by the sit-and-reach test, increased in both groups (p<0.01). Total (p<0.05) and low-density lipoprotein cholesterol (p<0.05) levels, plasma insulin concentrations (p<0.01), and scores on the homeostatic model of the assessment of insulin resistance (p<0.01) decreased in older adults, whereas total and high-density lipoprotein cholesterol concentrations were reduced in young adults (all p<0.05). Carotid artery compliance (p<0.05) was increased and β-stiffness index decreased in young (p<0.05) but not in older adults. Carotid pulse pressure did not significantly change in either group.

CONCLUSION
A relatively short-term Bikram yoga intervention improved arterial stiffness in young but not older adults and significantly reduced insulin resistance index in older but not young adults.


Spectral Hashing

Semantic hashing[1] seeks compact binary codes of data-points so that the Hamming distance between codewords correlates with semantic similarity. In this paper, we show that the problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard. By relaxing the original problem, we obtain a spectral method whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian. By utilizing recent results on convergence of graph Laplacian eigenvectors to the Laplace-Beltrami eigenfunctions of manifolds, we show how to efficiently calculate the code of a novel datapoint. Taken together, both learning the code and applying it to a novel point are extremely simple. Our experiments show that our codes outperform the state-of-the art.


Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.


Anterior prefrontal function and the limits of human decision-making.

The frontopolar cortex (FPC), the most anterior part of the frontal lobes, forms the apex of the executive system underlying decision-making. Here, we review empirical evidence showing that the FPC function enables contingent interposition of two concurrent behavioral plans or mental tasks according to respective reward expectations, overcoming the serial constraint that bears upon the control of task execution in the prefrontal cortex. This function is mechanistically explained by interactions between FPC and neighboring prefrontal regions. However, its capacity appears highly limited, which suggests that the FPC is efficient for protecting the execution of long-term mental plans from immediate environmental demands and for generating new, possibly more rewarding, behavioral or cognitive sequences, rather than for complex decision-making and reasoning.


Non linear hybrid watermarking for High Dynamic Range images

The present work explores the use of a specific method, called non linear hybrid watermarking to watermark High Dynamic Range (HDR) images. The non linear hybrid technique combines both additive and multiplicative watermark embedding and is based on a square root embedding equation. We evaluate the robustness and objective quality of HDR tone mapped watermarked images on two different HDR databases. The experimentations show that the watermark is imperceptible and successfully survives tone mapping attack.


Autonomous Intersection Management: A Heuristic Approach

Self-driving cars do not sound like a part of science fiction movies anymore. With most of the automobile giants committed to launching their autonomous cars as soon as possible, it is expected that self-driving cars will hit the road in a very short time. Increasing the penetration factor of autonomous vehicles will need algorithms to control them in an efficient way in different scenarios. One such scenario is intersection management (IM). We propose an intuitive heuristic to resolve space–time conflicts in vehicle’s trip, enabling vehicles to cross the intersection safely, and with minimum delay. The intersection is modeled as a group of conflict points where intersecting internal links cross each other. For safe and efficient scheduling of vehicles, heuristics are proposed separately for: 1) entering in approach lane; 2) safe traversal in the approach lane; 3) safe and efficient crossing of intersection; and (4) safe departure along depart lane. Vehicle scheduling is done using a three-leveled heuristic in which each level serves a distinct purpose and together guarantee collision-free passing of vehicles. Performance evaluation of the proposed scheme is done in Simulation of Urban MObility simulator and a comparative analysis is done with three other IM schemes. Experiments show that the proposed scheme gives a smaller average trip delay of vehicles compared with other IM schemes.


A Hybrid Document Feature Extraction Method Using Latent Dirichlet Allocation and Word2Vec

Latent Dirichlet Allocation (LDA) is a probabilistic topic model to discover latent topics from documents and describe each document with a probability distribution over the discovered topics. It defines a global hierarchical relationship from words to a topic and then from topics to a document. Word2Vec is a word-embedding model to predict a target word from its surrounding contextual words. In this paper, we propose a hybrid approach to extract features from documents with bag-of-distances in a semantic space. By using both Word2Vec and LDA, our hybrid method not only generates the relationships between documents and topics, but also integrates the contextual relationships among words. Experimental results indicate that document features generated by our hybrid method are useful to improve classification performance by consolidating both global and local relationships.


Exploitation of spectral redundancy in cyclostationary signals

It is shown that the cyclostationarity attribute, as it is reflected in the periodicities of (second-order) moments of the signal, can be interpreted in terms of the property that allows generation of spectral lines from the signal by putting it through a (quadratic) nonlinear transformation. The fundamental link between the spectral-line generation property and the statistical property called spectral correlation, which corresponds to the correlation that exists between the random fluctuations of components of the signal residing in distinct spectral bands, is explained. The effects on the spectral-correlation characteristics of some basic signal processing operations, such as filtering, product modulation, and time sampling, are examined. It is shown how to use these results to derive the spectral-correlation characteristics for various types of man-made signals. Some ways of exploiting the inherent spectral redundancy associated with spectral correlation to perform various signal processing tasks involving detection and estimation of highly corrupted man-made signals are described.<<ETX>>


Decentralized Reactive Power Sharing and Frequency Restoration in Islanded Microgrid

P-f and Q-V droop methods are the most common decentralized control methods in islanded microgrid. Although with the P-f droop method, an accurate active power sharing can be achieved among distributed energy resources (DERs), by Q-V droop, the reactive power sharing among DERs often deteriorates due to its highly dependence on the power line impedances and the local load. Variation of frequency and voltage by load changes is another challenge in droop control method. In this paper, a new autonomous control method is proposed to share reactive power among DERs accurately and restore frequency of a microgrid. The proposed method does not require any communication link and so maintains reliability and simplicity of the network. The synchronizing among DERs is obtained by the load change detection, which is accomplished by wavelet transform. The method operation principle is explained and analyzed. Simulation results are presented to validate the effectiveness of the proposed method.


Classification of Alzheimer’s disease subjects from MRI using hippocampal visual features

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


Knowledge Representation for Robots through Human-Robot Interaction

The representation of the knowledge needed by a robot to perform complex tasks is restricted by the limitations of perception. One possible way of overcoming this situation and designing “knowledgeable” robots is to rely on the interaction with the user. We propose a multi-modal interaction framework that allows to effectively acquire knowledge about the environment where the robot operates. In particular, in this paper we present a rich representation framework that can be automatically built from the metric map annotated with the indications provided by the user. Such a representation, allows then the robot to ground complex referential expressions for motion commands and to devise topological navigation plans to achieve the target locations.


A DUAL-BAND CIRCULARLY POLARIZED STUB LOADED MICROSTRIP PATCH ANTENNA FOR GPS APPLICATIONS

A single-feed low-profile and easy to fabricate circularly polarized microstrip patch antenna has been developed for GPS applications. For dual frequency operation, four slots are etched near edges of the patch and a crossed slot etched in the center for generating circular polarization. In order to reducing the frequency ratio of two frequency bands of the antenna, the patch is loaded by four short circuit microstrip stubs. The paper reports several simulation results that confirm the desired characteristics of the antenna. Using stub loading, the frequency ratio of two bands of the antenna can be, even, reduced to 1.1.


Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks

Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through controlled experiments/trials but controlled experiments are expensive. Hence it is important to choose the best policies on the basis of observational data. This presents two difficult challenges: (i) missing counterfactuals, and (ii) selection bias. This paper presents theoretical bounds on estimation errors of counterfactuals from observational data by making connections to domain adaptation theory. It also presents a principled way of choosing optimal policies using domain adversarial neural networks. This illustrates the effectiveness of domain adversarial training together with various features of our algorithm on a semi-synthetic breast cancer dataset.


Examining the psychometric properties of a sport-related concussion survey: a Rasch measurement approach

Awareness of sport-related concussion (SRC) is an essential step in increasing the number of athletes or parents who report on SRC. This awareness is important, as there is no established data on medical care at youth-level sports and may be limited to individuals with only first aid training. In this circumstance, aside from the coach, it is the players and their parents who need to be aware of possible signs and symptoms. The aim of this study was to examine the psychometric properties of a parent and player concussion survey intended for use before and after an education campaign regarding SRC. 1441 questionnaires were received from parents and 284 questionnaires from players. The responses to the sixteen-item section of the questionnaire’s ‘recognition of signs and symptoms’ were submitted to psychometric analysis using the dichotomous and polytomous Rasch model via the Rasch Unidimensional Measurement Model software RUMM2030. The Rasch model of Modern Test Theory can be considered a refinement of, or advance on, traditional analyses of an instrument’s psychometric properties. The main finding is that these sixteen items measure two factors: items that are symptoms of concussion and items that are not symptoms of concussion. Parents and athletes were able to identify most or all of the symptoms, but were not as good at distinguishing symptoms that are not symptoms of concussion. Analyzing these responses revealed differential item functioning for parents and athletes on non-symptom items. When the DIF was resolved a significant difference was found between parents and athletes. The main finding is that the items measure two ‘dimensions’ in concussion symptom recognition. The first dimension consists of those items that are symptoms of concussion and the second dimension of those items that are not symptoms of concussion. Parents and players were able to identify most or all of the symptoms of concussion, so one would not expect to pick up any positive change on these items after an education campaign. Parents and players were not as good at distinguishing symptoms that are not symptoms of concussion. It is on these items that one may possibly expect improvement to manifest, so to evaluate the effectiveness of an education campaign it would pay to look for improvement in distinguishing symptoms that are not symptoms of concussion.