title: Incentivize crowd labeling under budget constraint

abstract: Crowdsourcing systems allocate tasks to a group of workers over the Internet, which have become an effective paradigm for human-powered problem solving such as image classification, optical character recognition and proofreading. In this paper, we focus on incentivizing crowd workers to label a set of binary tasks under strict budget constraint. We properly profile the tasks' difficulty levels and workers' quality in crowdsourcing systems, where the collected labels are aggregated with sequential Bayesian approach. To stimulate workers to undertake crowd labeling tasks, the interaction between workers and the platform is modeled as a reverse auction. We reveal that the platform utility maximization could be intractable, for which an incentive mechanism that determines the winning bid and payments with polynomial-time computation complexity is developed. Moreover, we theoretically prove that our mechanism is truthful, individually rational and budget feasible. Through extensive simulations, we demonstrate that our mechanism utilizes budget efficiently to achieve high platform utility with polynomial computation complexity.


title: Montessori education and its scientific basis

abstract: Oxford University Press, Oxford (2005) ISBN 0-19-516868-2, 224 pp., $35 (cloth).Italians have always revered the arts and promoted the infusion of beauty and ﬁ ne design into the quality of everyday life. Perhaps it is this distinctive cultural ﬂ air, revealed in ﬁ elds such as architecture, painting, inte-rior design, graphics, fashion, and cuisine, that has inﬂ uenced Italy also to become a giant in the ﬁ eld of early childhood education. In the 20th century, Italy has produced two of the world’s most innovative and inﬂ uential leaders: Maria Montessori (1870–1952) from Rome, in southern Italy; and Loris Malagazzi (1920–1994) from Reggio Emilia, in the northern part of the country.Montessori education is the subject of Angeline Lillard’s book. Montessori, a brilliant ﬁ gure who was Ita-ly’s ﬁ rst woman physician, created an approach that reﬂ ected a late 19th century vision of mental development and theoretical kinship with the great European progressive educational philosophers, Jean-Jacque Rousseau, Johann Pestalozzi and Fredrich Froebel (Edwards, 2002 and Edwards, 2003). Th e many parallels between her ideas and those of the American progressive, John Dewey, her contemporary, are due to the fact that their ideas grew out of shared theoretical roots and were responsive to the social and cultural transformations engendered by the industrial revolution. Montessori is the only woman regularly listed as one of the very great ﬁ gures in the history and philosophy of education, and up until 2002 when the European Union issued the Euro as common currency, her country’s deep regard was indicated by her face on the Italian 1000 Lira bill.As Lillard’s book explains, Montessori’s vision anticipated many of the twentieth century’s developments in child psychology and education. Montessori was convinced that children’s natural intelligence involved, from the start, rational, empirical, and spiritual aspects. After drawing on Edouard Seguin’s and Jean Itard’s work to innovate a methodology for working with children with disabilities, she started her Casa dei Bambini (Chil-dren’s House) in 1907 for children aged 4–7 in a housing project in the poor slums of Rome. Her education-al movement (including her highly original concepts for curriculum materials, child-sized furniture, classroom layout, mixed age grouping of children, and teaching strategies) spread to other countries, especially once Mus-solini’s Fascist regime denounced her methods and Montessori left Italy to live the rest of her life abroad.In the United States, there was strong but brief interest in the Montessori method from 1910 to 1920, but then it fell out of favor (Torrence & Chattin-McNichols, 2000), though during that time the movement be-gan to ﬂ ourish in Europe and India. In the second half of the 20th century, Montessori education ﬁ nally be-came a world force in education. In the 1950s, American educator Nancy Rambush led a movement of renewal, and Montessori education spread as an independent (private) school movement (Loeﬄ er, 1992). Today, there are probably 5000 or more schools in the US calling themselves “Montessori.” Of these, about 20% are aﬃ li-ated with two major accrediting organizations. Th e larger organization within this country, the American Mon-tessori Society (AMS), supports Montessori education in the context of contemporary American culture, and has been more inﬂ uential in outreach to the American public school system as well as in promoting a network of teacher-based classroom research. Association Montessori Internationale (AMI), founded by Montessori in 1929, promotes the study, application and propagation of (original) Montessori education worldwide, and is the branch of Montessori to which Angeline Lillard holds allegiance. To prepare teachers for North American Montessori schools, there are many training institutes, some adapting more to the cultural contexts of contem-porary times and others staying close to Montessori’s original methods. For example, in the United States, over 70 training programs are aﬃ liated with AMS, and 16 with AMI. In the 1960s, American parents began to ad-vocate for Montessori education in public schools, leading to hundreds of programs (often magnet programs) at the pre-K and elementary levels, and now increasingly the middle and high school levels. Montessori infant–toddler programs are also becoming numerous.


title: Real-time myoelectric control of a multi-fingered hand prosthesis using principal components analysis

abstract: BackgroundIn spite of the advances made in the design of dexterous anthropomorphic hand prostheses, these sophisticated devices still lack adequate control interfaces which could allow amputees to operate them in an intuitive and close-to-natural way. In this study, an anthropomorphic five-fingered robotic hand, actuated by six motors, was used as a prosthetic hand emulator to assess the feasibility of a control approach based on Principal Components Analysis (PCA), specifically conceived to address this problem. Since it was demonstrated elsewhere that the first two principal components (PCs) can describe the whole hand configuration space sufficiently well, the controller here employed reverted the PCA algorithm and allowed to drive a multi-DoF hand by combining a two-differential channels EMG input with these two PCs. Hence, the novelty of this approach stood in the PCA application for solving the challenging problem of best mapping the EMG inputs into the degrees of freedom (DoFs) of the prosthesis.MethodsA clinically viable two DoFs myoelectric controller, exploiting two differential channels, was developed and twelve able-bodied participants, divided in two groups, volunteered to control the hand in simple grasp trials, using forearm myoelectric signals. Task completion rates and times were measured. The first objective (assessed through one group of subjects) was to understand the effectiveness of the approach; i.e., whether it is possible to drive the hand in real-time, with reasonable performance, in different grasps, also taking advantage of the direct visual feedback of the moving hand. The second objective (assessed through a different group) was to investigate the intuitiveness, and therefore to assess statistical differences in the performance throughout three consecutive days.ResultsSubjects performed several grasp, transport and release trials with differently shaped objects, by operating the hand with the myoelectric PCA-based controller. Experimental trials showed that the simultaneous use of the two differential channels paradigm was successful.ConclusionsThis work demonstrates that the proposed two-DoFs myoelectric controller based on PCA allows to drive in real-time a prosthetic hand emulator into different prehensile patterns with excellent performance. These results open up promising possibilities for the development of intuitive, effective myoelectric hand controllers.


title: Beyond Fitts' law: models for trajectory-based HCI tasks

abstract: As a developing discipline, research results in the field of human computer interaction (HCI) tends to be "soft". Many workers in the field have argued that the advancement of HCI lies in "hardening" the field with quantitative and robust models. In reality, few theoretical, quantitative tools are available in user interface research and development. A rare exception to this is Fitts' law. Extending information theory to human perceptual-motor system, Paul Fitts (1954) found a logarithmic relationship that models speed accuracy tradeoffs in aimed movements. A great number of studies have verified and / or applied Fitts' law to HCI problems, such as pointing performance on a screen, making Fitts' law one of the most intensively studied topic in the HCI literature.


title: Supplier Selection using Multi-criteria Decision Making Approaches: A Current Review

abstract: Selection of supplier is one of the most important decisions in purchasing. An inappropriate decision on supplier may affect the whole supply chain management. Decision making can be a stressful process as the performance of potential suppliers is evaluated based on multi-criteria. Various multicriteria decision making approaches have been proposed to solve supplier selection problem. The purpose of this paper is to provide a review of supplier selection using multi-criteria decision making approaches from 2009 to 2013. The present paper extends the previous reviews by gathering related articles appearing in the international journals and conferences proceedings. Two classifications of individual approaches and integrated approaches were made in this paper. This review aids the researchers and decision makers in applying the approaches effectively. From the observation, it was noticed that six papers or 19 percent were using individual approaches against 25 papers or 81 percent of integrated approaches. About 48 percent of approaches in the category of integrated approaches were decision making method based on fuzzy set theory. The reviews also notice that 25 out of 31 papers (81%) considered quality as the most important criterion in supplier selection process. This paper offers useful information for other researchers since it provides the latest evidence of decision making approaches in supplier selection.


title: A 10-bit 50-MS/s SAR ADC With a Monotonic Capacitor Switching Procedure

abstract: This paper presents a low-power 10-bit 50-MS/s successive approximation register (SAR) analog-to-digital converter (ADC) that uses a monotonic capacitor switching procedure. Compared to converters that use the conventional procedure, the average switching energy and total capacitance are reduced by about 81% and 50%, respectively. In the switching procedure, the input common-mode voltage gradually converges to ground. An improved comparator diminishes the signal-dependent offset caused by the input common-mode voltage variation. The prototype was fabricated using 0.13-μm 1P8M CMOS technology. At a 1.2-V supply and 50 MS/s, the ADC achieves an SNDR of 57.0 dB and consumes 0.826 mW, resulting in a figure of merit (FOM) of 29 fJ/conversion-step. The ADC core occupies an active area of only 195 × 265 μm 2 .


title: The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation

abstract: IntroductionThe choice of criteria in determining optimal cut-off value is a matter of concern in quantitative diagnostic tests. Several indexes such as Youden’s index, Euclidean index, product of ...


title: A Chaotic Map-based Authentication Scheme for Telecare Medicine Information Systems

abstract: With the development of Internet, patients could enjoy health-care delivery services through telecare medicine information systems (TMIS) in their home. To control the access to remote medical servers' resources, many authentication schemes using smart cards have been proposed. However, the performance of these schemes is not satisfactory since modular exponential operations are used in these schemes. In the paper, we propose a chaotic map-based authentication scheme for telecare medicine information systems. The security and performance analysis shows our scheme is more suitable for TMIS.


title: Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks

abstract: Deep learning methods such as convolutional neural networks (CNNs) can deliver highly accurate classification results when provided with large enough data sets and respective labels. However, using CNNs along with limited labeled data can be problematic, as this leads to extensive overfitting. In this letter, we propose a novel method by considering a pretrained CNN designed for tackling an entirely different classification problem, namely, the ImageNet challenge, and exploit it to extract an initial set of representations. The derived representations are then transferred into a supervised CNN classifier, along with their class labels, effectively training the system. Through this two-stage framework, we successfully deal with the limited-data problem in an end-to-end processing scheme. Comparative results over the UC Merced Land Use benchmark prove that our method significantly outperforms the previously best stated results, improving the overall accuracy from 83.1% up to 92.4%. Apart from statistical improvements, our method introduces a novel feature fusion algorithm that effectively tackles the large data dimensionality by using a simple and computationally efficient approach.


title: A packing algorithm for non-manhattan hexagon/triangle placement design by using an adaptive o-tree representation

abstract: A non-Manhattan Hexagon/Triangle Placement (HTP for short) paradigm is proposed in the present paper. Main feature of this paradigm lies in adapting to the Y- architecture which is one of the promising non-Manhattan VLSI circuit layout architectures. Aim of the HTP is to place a set of equilateral triangles with given size onto a hexagonal chip with maximal chip area usage. Based on the O-tree representation, some adaptive packing rules are adopted to develop an effective placement algorithm for solving the HTP problem in BBL mode. Two examples with benchmark data transformed from the Manhattan BBL mode placement (ami33/49) are presented to justify the feasibility and effectiveness of our algorithms. Experiment results demonstrate that the chip area usage of 94% is achieved through simulated annealing optimization.


title: Exploring Multivariate Event Sequences Using Rules, Aggregations, and Selections

abstract: Multivariate event sequences are ubiquitous: travel history, telecommunication conversations, and server logs are some examples. Besides standard properties such as type and timestamp, events often have other associated multivariate data. Current exploration and analysis methods either focus on the temporal analysis of a single attribute or the structural analysis of the multivariate data only. We present an approach where users can explore event sequences at multivariate and sequential level simultaneously by interactively defining a set of rewrite rules using multivariate regular expressions. Users can store resulting patterns as new types of events or attributes to interactively enrich or simplify event sequences for further investigation. In Eventpad we provide a bottom-up glyph-oriented approach for multivariate event sequence analysis by searching, clustering, and aligning them according to newly defined domain specific properties. We illustrate the effectiveness of our approach with real-world data sets including telecommunication traffic and hospital treatments.


title: Resolution errors associated with gridded precipitation fields

abstract: Spatial-resolution errors are inherent in gridded precipitation (P ) fields – such as those produced by climate models and from satellite observations – and they can be sizeable when P is averaged spatially onto a coarse grid. They can also vary dramatically over space and time. In this paper, we illustrate the importance of evaluating resolution errors associated with gridded P fields by investigating the relationships between grid resolution and resolution error for monthly P within the Amazon Basin. Spatial-resolution errors within gridded-monthly and average-monthly P fields over the Amazon Basin are evaluated for grid resolutions ranging from 0.1° to 5.0°. A resolution error occurs when P is estimated for a location of interest within a grid-cell from the unbiased, grid-cell average P . Graphs of January, July and annual resolution errors versus resolution show that, at the higher resolutions (<3°), aggregation quickly increases resolution error. Resolution error then begins to level off as the grid becomes coarser. Within the Amazon Basin, the largest resolution errors occur during January (summer), but the largest percentage errors appear in July (winter). In January of 1980, e.g., resolution errors of 29, 52 and 65 mm – or 11, 19 and 24% of the grid-cell means – were estimated at resolutions of 1.0° ,3 .0 ° and 5.0° .I n July of 1980, however, the percentage errors at these three resolutions were considerably larger, that is, 15%, 27% and 33% of the grid-cell means. Copyright  2005 Royal Meteorological Society.


EUCAST definitive document EDef 7.1: method for the determination of broth dilution MICs of antifungal agents for fermentative yeasts.


title: A basic need theory approach to problematic Internet use and the mediating effect of psychological distress

abstract: The Internet provides an easily accessible way to meet certain needs. Over-reliance on it leads to problematic use, which studies show can be predicted by psychological distress. Self-determination theory proposes that we all have the basic need for autonomy, competency, and relatedness. This has been shown to explain the motivations behind problematic Internet use. This study hypothesizes that individuals who are psychologically disturbed because their basic needs are not being met are more vulnerable to becoming reliant on the Internet when they seek such needs satisfaction from online activities, and tests a model in which basic needs predict problematic Internet use, fully mediated by psychological distress. Problematic Internet use, psychological distress, and basic needs satisfaction were psychometrically measured in a sample of 229 Hong Kong University students and structural equation modeling was used to test the hypothesized model. All indices showed the model has a good fit. Further, statistical testing supported a mediation effect for psychological distress between needs satisfaction and problematic Internet use. The results extend our understanding of the development and prevention of problematic Internet use based on the framework of self-determination theory. Psychological distress could be used as an early predictor, while preventing and treating problematic Internet use should emphasize the fulfillment of unmet needs.


title: Low cost phased array for mobile Ku-band satellite terminal

abstract: This article presents the design of a low cost fully active phased array antenna with specific emphasis on the realization of an elementary radiating cell. The phased array antenna is designed for mobile satellite services and dedicated for automotive applications. Details on the radiating element design as well as its implementation in a multilayer's build-up are presented and discussed. Results of the measurements and characterization of the elementary radiating cell are also presented and discussed. An outlook of the next steps in the antenna realization concludes this paper.


title: Innovation in Teaching and Learning through Problem Posing Tasks and Metacognitive Strategies

abstract: Problem posing and metacognitive activities in teaching-learning materials can be incorporated in student-centred teaching methods. In the other word , these tasks involve learners in specific learning process by shifting of responsibility generating new problem and reformulating given problem from teachers to students through "Inquiry - based -learning " environment. In this respect, teachers as facilitators of learning must be able to create tasks with suitable situations which engage their students in problem posing activities and improve them in this challenge via the scaffolding such as metacognitive approaches. Therefore, this paper discusses some initial findings of our research related to classification of problem posing and metacognitive activities. These effectiveness instructions may encourage educators for embedding pupils in problem posing and metacognition activities during classroom interactions.


title: A Through-Dielectric Radar Imaging System

abstract: Through-lossy-slab radar imaging will be shown at stand-off ranges using a low-power, ultrawideband (UWB), frequency modulated continuous wave (FMCW) radar system. FMCW is desirable for through-slab applications because of the signal gain resulting from pulse compression of long transmit pulses (1.926-4.069 GHz chirp in 10 ms). The difficulty in utilizing FMCW radar for this application is that the air-slab boundary dominates the scattered return from the target scene and limits the upper bound of the receiver dynamic range, reducing sensitivity for targets behind the slab. A method of range-gating out the air-slab boundary by significant band-limiting of the IF stages facilitates imaging of low radar cross section (RCS) targets behind the slab. This sensor is combined with a 1D linear rail and utilized as a rail synthetic aperture radar (SAR) imaging system. A 2D model of a slab and cylinder shows that image blurring due to the slab is negligible when the SAR is located at a stand-off range of 6 m or greater, and thus, the two-way attenuation due to wave propagation through the slab is the greatest challenge at stand-off ranges when the air-slab boundary is range-gated out of the scattered return. Measurements agree with the model, and also show that this radar is capable of imaging target scenes of cylinders and rods 15.24 cm in height and 0.95 cm in diameter behind a 10 cm thick lossy dielectric slab. Further, this system is capable of imaging free-space target scenes with transmit power as low as 5 pW, providing capability for RCS measurement.


title: Exploring simultaneous keyword and key sentence extraction: improve graph-based ranking using wikipedia

abstract: Summarization and Keyword Selection are two important tasks in NLP community. Although both aim to summarize the source articles, they are usually treated separately by using sentences or words. In this paper, we propose a two-level graph based ranking algorithm to generate summarization and extract keywords at the same time. Previous works have reached a consensus that important sentence is composed by important keywords. In this paper, we further study the mutual impact between them through context analysis. We use Wikipedia to build a two-level concept-based graph, instead of traditional term-based graph, to express their homogenous relationship and heterogeneous relationship. We run PageRank and HITS rank on the graph to adjust both homogenous and heterogeneous relationships. A more reasonable relatedness value will be got for key sentence selection and keyword selection. We evaluate our algorithm on TAC 2011 data set. Traditional term-based approach achieves a score of 0.255 in ROUGE-1 and a score of 0.037 and ROUGE-2 and our approach can improve them to 0.323 and 0.048 separately.


title: Cross-cultural differences in cognitive development: Attention to relations and objects

abstract: Growing evidence indicates a suite of generalized differences in the attentional and cognitive processing of adults from Eastern and Western cultures. Cognition in Eastern adults is often more relational and in Western adults is more object focused. Three experiments examined whether these differences characterize the cognition of preschool children in the two cultures. In Experiment 1, 4-year-olds from the two cultures (N = 64) participated in a relational match-to-standard task in two conditions, with simple or richly detailed objects, in which a focus on individual objects may hurt performance. Rich objects, consistent with past research, strongly limited the performance of U.S. children but not Japanese children. In Experiment 2, U.S. and Japanese 4-year-olds (N = 72) participated in a visual search task that required them to find a specific object in a cluttered, but organized as a scene, visual field in which object-centric attention might be expected to aid performance and relational attentional pattern may hinder the performance because of relational structure that was poised by the scene. U.S. children outperformed Japanese children. In Experiment 3, 4-year-olds from both cultures (N = 36) participated in a visual search task that was similar to Experiment 2 but with randomly placed objects, where there should not be a difference between the performance of two cultures because the relational structure that may be posed by the scene is eliminated. This double-dissociation is discussed in terms of implications for different developmental trajectories, with different developmental subtasks in the two cultures.


title: Analysis of user keyword similarity in online social networks

abstract: How do two people become friends? What role does homophily play in bringing two people closer to help them forge friendship? Is the similarity between two friends different from the similarity between any two people? How does the similarity between a friend of a friend compare to similarity between direct friends? In this work, our goal is to answer these questions. We study the relationship between semantic similarity of user profile entries and the social network topology. A user profile in an on-line social network is characterized by its profile entries. The entries are termed as user keywords. We develop a model to relate keywords based on their semantic relationship and define similarity functions to quantify the similarity between a pair of users. First, we present a ‘forest model’ to categorize keywords across multiple categorization trees and define the notion of distance between keywords. Second, we use the keyword distance to define similarity functions between a pair of users. Third, we analyze a set of Facebook data according to the model to determine the effect of homophily in on-line social networks. Based on our evaluations, we conclude that direct friends are more similar than any other user pair. However, the more striking observation is that except for direct friends, similarities between users are approximately equal, irrespective of the topological distance between them.


title: Alternative strategies of categorization

abstract: Abstract Psychological studies of categorization often assume that all concepts are of the same general kind, and are operated on by the same kind of categorization process. In this paper, we argue against this unitary view, and for the existence of qualitatively different categorization processes. In particular, we focus on the distinction between categorizing an item by: (a) applying a category-defining rule to the item vs. (b) determining the similarity of that item to remembered exemplars of a category. We begin by characterizing rule application and similarity computations as strategies of categorization. Next, we review experimental studies that have used artificial categories and shown that differences in instructions or time pressure can lead to either rule-based categorization or similarity-based categorization. Then we consider studies that have used natural concepts and again demonstrated that categorization can be done by either rule application or similarity calculations. Lastly, we take up evidence from cognitive neuroscience relevant to the rule vs. similarity issue. There is some indirect evidence from brain-damaged patients for neurological differences between categorization based on rules vs. that based on similarity (with the former involving frontal regions, and the latter relying more on posterior areas). For more direct evidence, we present the results of a recent neuroimaging experiment, which indicates that different neural circuits are involved when people categorize items on the basis of a rule as compared with when they categorize the same items on the basis of similarity.


title: The Second Dialog State Tracking Challenge

abstract: A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step. This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system’s actions. The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking techniques, providing common testbeds and evaluation suites. This paper presents a second challenge, which continues this tradition and introduces some additional features ‐ a new domain, changing user goals and a richer dialog state. The challenge received 31 entries from 9 research groups. The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions. An investigation into ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers.


title: r Human Brain Mapping 00:000–000 (2012) r Prediction of Alzheimer’s Disease and Mild Cognitive Impairment Using Cortical Morphological Patterns

abstract: This paper describes a novel approach to extract cortical morphological abnormality patterns from structural magnetic resonance imaging (MRI) data to improve the prediction accuracy of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). Conventional approaches extract cortical morphological information, such as regional mean cortical thickness and regional cortical volumes, independently at different regions of interest (ROIs) without considering the relationship between these regions. Our approach involves constructing a similarity map where every element in thte map represents the correlation of regional mean cortical thickness between a pair of ROIs. We will demonstrate in this paper that this correlative morphological information gives significant improvement in classification performance when compared to ROI-based morphological information. Classification performance is further improved by integrating the correlative information with ROI-based information via multi-kernel support vector machines. This integrated framework achieves an accuracy of 92.35% for AD classification with an area of 0.9744 under the receiver operating characteristic (ROC) curve, and an accuracy of 83.75% for MCI classification with an area of 0.9233. In differentiating MCI subjects who converted to AD within 36 months from non-converters, an accuracy of 75.05% with an area of 0.8426 under ROC curve was achieved, indicating excellent diagnostic power and generalizability. The current work provides an alternative approach to extraction of high-order cortical information from structural MRI data for prediction of neurodegerative diseases such as AD.


Evaluation of the Dynamics of a Distillation Column with Two Liquid Phases


title: Multi-level Fusion Based 3D Object Detection from Monocular Images

abstract: In this paper, we present an end-to-end multi-level fusion based framework for 3D object detection from a single monocular image. The whole network is composed of two parts: one for 2D region proposal generation and another for simultaneously predictions of objects' 2D locations, orientations, dimensions, and 3D locations. With the help of a stand-alone module to estimate the disparity and compute the 3D point cloud, we introduce the multi-level fusion scheme. First, we encode the disparity information with a front view feature representation and fuse it with the RGB image to enhance the input. Second, features extracted from the original input and the point cloud are combined to boost the object detection. For 3D localization, we introduce an extra stream to predict the location information from point cloud directly and add it to the aforementioned location prediction. The proposed algorithm can directly output both 2D and 3D object detection results in an end-to-end fashion with only a single RGB image as the input. The experimental results on the challenging KITTI benchmark demonstrate that our algorithm significantly outperforms monocular state-of-the-art methods.


title: Safety assessment of driving behavior in multi-lane traffic for autonomous vehicles

abstract: An approach for the safety assessment of planned trajectories of autonomous vehicles is presented. Due to the unsafe nature of road traffic, the proposed safety assessment is performed in a probabilistic setting, where the probability distributions of possible future positions of traffic participants are computed based on dynamic models. The underlying algorithms have to be efficient as they need to provide a safety level of the currently planned path while the vehicle is in operation. In order to achieve the necessary efficiency of computation, the dynamics of traffic participants is abstracted into Markov-chains. The approach considers vehicle dynamics, interaction between traffic participants, and lane changes in multi-lane traffic.


title: Context-based machine translation

abstract: Context-Based Machine Translation™ (CBMT) is a new paradigm for corpus- based translation that requires no parallel text. Instead, CBMT relies on a light- weight translation model utilizing a full- form bilingual dictionary and a sophisti- cated decoder using long-range context via long n-grams and cascaded overlap- ping. The translation process is enhanced via in-language substitution of tokens and phrases, both for source and target, when top candidates cannot be confirmed or re- solved in decoding. Substitution utilizes a synonym and near-synonym generator im- plemented as a corpus-based unsupervised learning process. Decoding requires a very large target-language-only corpus, and while substitution in target can be per- formed using that same corpus, substitu- tion in source requires a separate (and smaller) source monolingual corpus. Spanish-to-English CBMT was tested on Spanish newswire text, achieving a BLEU score of 0.6462 in June 2006, the highest BLEU reported for any language pair. Further testing also shows that quality in- creases above the reported score as the target corpus size increases and as diction- ary coverage of source words and phrases becomes more complete. 1


title: A Multimodal Embedded Sensor System for Scalable Robotic and Prosthetic Fingers

abstract: The development of dexterous and robust anthropomorphic hands with rich sensor feedback remains a challenging task for both humanoid robotics as well as prosthetics as of today. The design of hands that are scalable in size and equipped with integrated multimodal sensor systems is a key requirement for advanced control schemes and reactive behaviour. In this paper, we present the design of a scalable and low cost robotic finger with a soft fingertip and position, temperature as well as normal and shear force sensors. All cables and sensors are completely enclosed inside the finger to ensure an anthropometric appearance. The finger is modelled based on a 50th percentile male little finger and can be easily adapted to other dimensions in terms of size and sensor system configuration. We describe the design of the sensor system, provide an experimental analysis for the characterization of the different sensor types in terms of sensor range, resolution, creep, spatial response as well as temperature flux.


title: Automated Breast Lesion Segmentation in Ultrasound Images

abstract: The main objective of this project is to segment different breast ultrasound images to find out lesion area by discarding the low contrast regions as well as the inherent speckle noise. The proposed method consists of three stages (removing noise, segmentation, classification) in order to extract the correct lesion. We used normalized cuts approach to segment ultrasound images into regions of interest where we can possibly finds the lesion, and then K-means classifier is applied to decide finally the location of the lesion. For every original image, an annotated ground-truth image is given to perform comparison with the obtained experimental results, providing accurate evaluation measures.


title: Cryptographic randomness on a CC2538: A case study

abstract: Smart metering, smart parking, health, environment monitoring, and other applications drive the deployment of the so-called Internet of Things (IoT). Whilst cost and energy efficiency are the main factors that contribute to the popularity of commercial devices in the IoT domain, security features are increasingly desired. Security features typically guarantee authenticity of devices and/or data, as well as confidentiality of data in transit. Our study finds that whilst cryptographic algorithms for confidentiality and authenticity are supported in hardware on a popular class of devices, there is no adequate support for random number generation available. We show how to non-invasive manipulate the on-board source for randomness, and thereby we can completely undermine the security provided by (otherwise) strong cryptographic algorithms, with devastating results.


title: Understanding the Internet: Model, Metaphor, and Analogy.

abstract: THEEFFECTIVE USE, BY STUDENTS AND OTHER USERS, of online and Internet resources depends crucially upon a clear understanding of the form and content of complex electronic networks. Because these networks, and related electronic systems, are often initially unfamiliar even to sophisticated users, it is important that adequate models and analogies be available to support learning and teaching of, and with, these resources. This article discusses some of the obstacles to effective learning inherent in the nature of these systems, and in the ad hoc conceptual tools that many users bring to their understanding of these systems. Particular attention is given to the nature of metaphorical explanation and comprehension in other disciplines, and the ways in which these patterns of understanding can be applied to our interaction with the Internet. Finally, a modest suggestion concerning one kind of metaphor for the Internet is proposed and described for use in classroom instruction.


title: Sequence to Logic with Copy and Cache

abstract: Generating logical form equivalents of human language is a fresh way to employ neural architectures where long short-term memory effectively captures dependencies in both encoder and decoder units. The logical form of the sequence usually preserves information from the natural language side in the form of similar tokens, and recently a copying mechanism has been proposed which increases the probability of outputting tokens from the source input through decoding. In this paper we propose a caching mechanism as a more general form of the copying mechanism which also weighs all the words from the source vocabulary according to their relation to the current decoding context. Our results confirm that the proposed method achieves improvements in sequence/token-level accuracy on sequence to logical form tasks. Further experiments on cross-domain adversarial attacks show substantial improvements when using the most influential examples of other domains for training.


title: Documentation Redux: Prolegomenon to (Another) Philosophy of Information

abstract: A philosophy of information is grounded in a philosophy of documentation. Nunberg’s conception of the phenomenon of information heralds a shift of attention away from the question “What is information?” toward a critical investigation of the sources and legitimation of the question itself. Analogies between Wittgenstein’s deconstruction of philosophical accounts of meaning and a corresponding deconstruction of philosophical accounts of information suggest that because the informativeness of a document depends on certain kinds of practices with it, and because information emerges as an effect of such practices, documentary practices are ontologically primary to information. The informativeness of documents therefore refers us to the properties of documentary practices. These fall into four broad categories: their materiality; their institutional sites; the ways in which they are socially disciplined; and their historical contingency. Two examples from early modern science, which contrast the scholastic documentary practices of continental natural philosophers to those of their peers in Restoration England, illustrate the richness of the factors that must be taken into account to understand how documents become informing.


title: A New Design of In-Memory File System Based on File Virtual Address Framework

abstract: The emerging technologies of persistent memory, such as PCM, MRAM, provide opportunities for preserving files in memory. Traditional file system structures may need to be re-studied. Even though there are several file systems proposed for memory, most of them have limited performance without fully utilizing the hardware at the processor side. This paper presents a framework based on a new concept, “File Virtual Address Space”. A file system, Sustainable In-Memory File System (SIMFS), is designed and implemented, which fully utilizes the memory mapping hardware at the file access path. First, SIMFS embeds the address space of an open file into the process’ address space. Then, file accesses are handled by the memory mapping hardware. Several optimization approaches are also presented for the proposed SIMFS. Extensive experiments are conducted. The experimental results show that the throughput of SIMFS achieves significant performance improvement over the state-of-the-art in-memory file systems.


title: Non-edible vegetable oils: A critical evaluation of oil extraction, fatty acid compositions, biodiesel production, characteristics, engine performance and emissions production

abstract: World energy demand is expected to increase due to the expanding urbanization, better living standards and increasing population. At a time when society is becoming increasingly aware of the declining reserves of fossil fuels beside the environmental concerns, it has become apparent that biodiesel is destined to make a substantial contribution to the future energy demands of the domestic and industrial economies. There are different potential feedstocks for biodiesel production. Non-edible vegetable oils which are known as the second generation feedstocks can be considered as promising substitutions for traditional edible food crops for the production of biodiesel. The use of non-edible plant oils is very significant because of the tremendous demand for edible oils as food source. Moreover, edible oils’ feedstock costs are far expensive to be used as fuel. Therefore, production of biodiesel from non-edible oils is an effective way to overcome all the associated problems with edible oils. However, the potential of converting non-edible oil into biodiesel must be well examined. This is because physical and chemical properties of biodiesel produced from any feedstock must comply with the limits of ASTM and DIN EN specifications for biodiesel fuels. This paper introduces non-edible vegetable oils to be used as biodiesel feedstocks. Several aspects related to these feedstocks have been reviewed from various recent publications. These aspects include overview of non-edible oil resources, advantages of non-edible oils, problems in exploitation of non-edible oils, fatty acid composition profiles (FAC) of various non-edible oils, oil extraction techniques, technologies of biodiesel production from non-edible oils, biodiesel standards and characterization, properties and characteristic of non-edible biodiesel and engine performance and emission production. As a conclusion, it has been found that there is a huge chance to produce biodiesel from non-edible oil sources and therefore it can boost the future production of biodiesel.


title: Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features

abstract: Purpose ::: Detection and segmentation of a brain tumor such as glioblastoma multiforme (GBM) in magnetic resonance (MR) images are often challenging due to its intrinsically heterogeneous signal characteristics. A robust segmentation method for brain tumor MRI scans was developed and tested.


TITLE : Physical Therapy Treatments for Chronic Non-Cancer Pain : A Review of Guidelines


title: Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks

abstract: Recently, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is a powerful machine learning algorithm in classification while extracting low to high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified structural MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 98.84%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.


title: Reclaiming modernity: Indigenous cosmopolitanism and the coming of the second revolution in Bolivia

abstract: In this article I explore the emergence of complicated new forms of indigeneity in Bolivia over the last 15 years. I argue that although what I describe as a second revolution is under way in contemporary Bolivia, there is a danger that this revolution will be misread by scholars, political commentators, and others because of the prevailing tendency to interpret social and moral movements in Bolivia (and elsewhere) in rigidly neopolitical–economic terms. I offer an alternative theoretical framework for understanding current developments in Bolivia, which I describe as “indigenous cosmopolitanism”: the ability of national political leaders, youth rappers in El Alto, rural indigenous activists, and others to bring together apparently disparate discursive frameworks as a way of reimagining categories of belonging in Bolivia, and, by extension, the meanings of modernity itself.


title: iSoft: A Customizable Soft Sensor with Real-time Continuous Contact and Stretching Sensing

abstract: We present iSoft, a single volume soft sensor capable of sensing real-time continuous contact and unidirectional stretching. We propose a low-cost and an easy way to fabricate such piezoresistive elastomer-based soft sensors for instant interactions. We employ an electrical impedance tomography (EIT) technique to estimate changes of resistance distribution on the sensor caused by fingertip contact. To compensate for the rebound elasticity of the elastomer and achieve real-time continuous contact sensing, we apply a dynamic baseline update for EIT. The baseline updates are triggered by fingertip contact and movement detections. Further, we support unidirectional stretching sensing using a model-based approach which works separately with continuous contact sensing. We also provide a software toolkit for users to design and deploy personalized interfaces with customized sensors. Through a series of experiments and evaluations, we validate the performance of contact and stretching sensing. Through example applications, we show the variety of examples enabled by iSoft.


title: Aggregation and Pricing in Internet Commerce

abstract: The Internet continues to grow as a medium to support commerce. Economic analysis of Internet commerce is still in a nascent stage while Internet technology and use has rapidly advanced. The result is an Internet marketplace brimming with entrepreneurs and major corporations experimenting with business strategies and technology advances even though the economics of Internet commerce is not well understood. This thesis responds to this need by exploring how the Internet reduces the market friction common in physical commerce. The intermediaries who help reduce market friction in physical markets may be eliminated, when suppliers and consumers increasingly rely on the Internet as a transaction medium. An intermediary in any market may reduce transaction costs by performing four roles: aggregation, pricing, search, and trust. The intermediary roles of aggregation and pricing may provide little or no value as the Internet becomes the medium for commerce because the technology, not the intermediary, reduces transaction costs. This thesis examines the possible elimination of aggregation and pricing intermediaries in Internet commerce. It does so by extending the theory of the economics of intermediation and electronic markets; developing a methodology for the analysis of Internet price competition; analyzing exploratory empirical data of the book, compact disc, and software markets to test a subset of this theory; and exploring the public policy implications of Internet price discrimination. The approach is interdisciplinary because this thesis integrates the technology, policy, and economics that underpin the role of aggregation and pricing intermediaries in Internet commerce. The thesis shows that Internet commerce may not reduce market friction because prices are higher when consumers buy homogeneous products on the Internet, and price dispersion for homogenous products among Internet retailers is greater than the price dispersion among physical retailers. Internet retailers—even those selling homogenous goods—can develop pricing strategies to differentiate themselves from their competitors and to price discriminate. The ability for the Internet to become a medium for price discrimination is an area that requires the attention of public policy makers. While self-regulation of Internet price discrimination may be the most appropriate policy, monitoring by the United States government through the Federal Trade Commission and international organizations such as the World Trade Organization helps establish a trusted transaction environment for future Internet commerce growth. Thesis Committee: Lee W. McKnight Lecturer, Technology and Policy Program Erik Brynjolfsson Associate Professor, MIT Sloan School of Management David D. Clark Senior Research Scientist, Laboratory for Computer Science


title: A semi-quantitative approach to GMO risk-benefit analysis

abstract: In many countries there are increasing calls for the benefits of genetically modified organisms (GMOs) to be considered as well as the risks, and for a risk-benefit analysis to form an integral part of GMO regulatory frameworks. This trend represents a shift away from the strict emphasis on risks, which is encapsulated in the Precautionary Principle that forms the basis for the Cartagena Protocol on Biosafety, and which is reflected in the national legislation of many countries. The introduction of risk-benefit analysis of GMOs would be facilitated if clear methodologies were available to support the analysis. Up to now, methodologies for risk-benefit analysis that would be applicable to the introduction of GMOs have not been well defined. This paper describes a relatively simple semi-quantitative methodology that could be easily applied as a decision support tool, giving particular consideration to the needs of regulators in developing countries where there are limited resources and experience. The application of the methodology is demonstrated using the release of an insect resistant maize variety in South Africa as a case study. The applicability of the method in the South African regulatory system is also discussed, as an example of what might be involved in introducing changes into an existing regulatory process.


title: Glans Necrosis Following Penile Prosthesis Implantation: Prevention and Treatment Suggestions.

abstract: OBJECTIVE ::: To examine possible etiology and treatment outcomes in 21 patients with glans necrosis following penile prosthesis implantation. ::: ::: ::: METHODS ::: Glans necrosis typically presented with a dusky glans on the first postoperative day following prosthesis implantation. ::: ::: ::: RESULTS ::: The blood supply to the glans penis consists of the dorsal arteries and the terminal branches of the spongiosal arteries. Using the cohort in our study, we compiled preoperative comorbidities and adjunctive surgical maneuvers that might compromise glans vascularity, leading to glans necrosis. Preoperative risk factors were arteriosclerotic cardiovascular disease (90%), diabetes mellitus (81%), smoking (81%), previous prosthesis explantation (57%), and previous radiation therapy (48%). The most prevalent intraoperative and postoperative factor was subcoronal incision for reasons as simple as coincident circumcision or as complex as for penile degloving (86%). Other factors detected were penile wrapping with an occlusive elastic bandage (62%), use of a sliding technique for penile lengthening (33%), and coincident distal urethral injury repair (29%). Seventeen patients (81%) managed expectantly with preservation of implanted prosthesis sustained significant glandular loss. Four patients managed with immediate prosthesis removal healed without sequelae. ::: ::: ::: CONCLUSION ::: Patients with preoperative risk factors undergoing penile prosthesis implantation should avoid high-risk adjunctive surgical maneuvers. Upon development of signs of glans necrosis postoperatively, in the setting of these high-risk factors, immediate implant removal may prevent subsequent glans necrosis.


title: Performance impact of data compression on virtual private network transactions

abstract: Virtual private networks (VPNs) allow two or more parties to communicate securely over a public network. Using cryptographic algorithms and protocols, VPNs provide security services such as confidentiality, host authentication and data integrity. The computation required to provide adequate security, however, can significantly degrade the performance. We characterize the extent to which data compression can alleviate this performance problem in a VPN implemented with the IP Security Protocol (IPsec). We use a system model for IPsec transactions to derive an inequality that specifies the conditions required for data compression to improve performance. We generate performance results for many combinations of network types, data types, packet sizes, and encryption, authentication and compression algorithms. We find that compression usually improves the performance when using 10 Mbps or slower networks, but compression only improves the performance in systems with 100 Mbps or 1 Gbps networks when using computationally intensive encryption algorithms.


title: PI voltage control technique for 3 phase induction motor using Simulink and Arduino

abstract: Induction motor is the most used in industry because of its high robustness, reliability, low cost, high efficiency and good self starting capability. However is also has its own limitation which can be divided into three categories which is nonlinearity, complex computation and uncertainty system. The development of this project is to control the 3 phase induction motor by using Arduino Uno controller and also using Simulink in MATLAB. Arduino Uno also is a part from series of controller where it is connected to the PWM inverter and also connected to motor driver as to turn the 3 phase induction motor on. Voltage sensor is used as to detect any changes that are supplied from the motor driver to the induction motor as to ensure that the voltage that is supplied to a constant voltage value to the induction motor. This method is known as voltage control. The measured voltage is then compared with the desired reference voltage. The error that is produced is than corrected as to minimize the existing error by using a controller is design using Simulink in Matlab and than is interfaced into the Arduino Uno. Arduino Uno will create PWM signal that is sent to the PWM inverter as to improve the induction motor performance. Based on the result obtain shows that the error from reference voltage and measured voltage is minimized and the PWM that is produced also has differences in term of width.


title: Performance of Image Registration Tools on High-Resolution 3D Brain Images

abstract: Recent progress in tissue clearing has allowed for the imaging of entire organs at single-cell resolution. These methods produce very large 3D images (several gigabytes for a whole mouse brain). A necessary step in analysing these images is registration across samples. Existing methods of registration were developed for lower resolution image modalities (e.g. MRI) and it is unclear whether their performance and accuracy is satisfactory at this larger scale. In this study, we used data from different mouse brains cleared with the CUBIC protocol to evaluate five freely available image registration tools. We used several performance metrics to assess accuracy, and completion time as a measure of efficiency. The results of this evaluation suggest that the ANTS registration tool provides the best registration accuracy while Elastix has the highest computational efficiency among the methods with an acceptable accuracy. The results also highlight the need to develop new registration methods optimised for these high-resolution 3D images.


title: Credit Card Fraud Detection using Big Data Analytics: Use of PSOAANN based One-Class Classification

abstract: Banking and financial industries are facing severe challenges in the form of fraudulent transactions. Credit card fraud is one example of them. In order to detect credit card fraud, we employed one-class classification approach in big data paradigm. We implemented a hybrid architecture of Particle Swarm Optimization and Auto-Associative Neural Network for one-class classification in Spark computational framework. In this paper, we implemented parallelization of the auto-associative neural network in the hybrid architecture.


title: Structural and functional associations of the rostral anterior cingulate cortex with subjective happiness

abstract: Abstract Happiness is one of the most fundamental human goals, which has led researchers to examine the source of individual happiness. Happiness has usually been discussed regarding two aspects (a temporary positive emotion and a trait-like long-term sense of being happy) that are interrelated; for example, individuals with a high level of trait-like subjective happiness tend to rate events as more pleasant. In this study, we hypothesized that the interaction between the two aspects of happiness could be explained by the interaction between structure and function in certain brain regions. Thus, we first assessed the association between gray matter density (GMD) of healthy participants and trait-like subjective happiness using voxel-based morphometry (VBM). Further, to assess the association between the GMD and brain function, we conducted functional magnetic resonance imaging (MRI) using the task of positive emotion induction (imagination of several emotional life events). VBM indicated that the subjective happiness was positively correlated with the GMD of the rostral anterior cingulate cortex (rACC). Functional MRI demonstrated that experimentally induced temporal happy feelings were positively correlated with subjective happiness level and rACC activity. The rACC response to positive events was also positively correlated with its GMD. These results provide convergent structural and functional evidence that the rACC is related to happiness and suggest that the interaction between structure and function in the rACC may explain the trait–state interaction in happiness.


title: A Highly Practical Approach toward Achieving Minimum Data Sets Storage Cost in the Cloud

abstract: Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.


title: Psychobiological allostasis: resistance, resilience and vulnerability

abstract: The brain and body need to adapt constantly to changing social and physical environments. A key mechanism for this adaptation is the ‘stress response’, which is necessary and not negative in and of itself. The term ‘stress’, however, is ambiguous and has acquired negative connotations. We argue that the concept of allostasis can be used instead to describe the mechanisms employed to achieve stability of homeostatic systems through active intervention (adaptive plasticity). In the context of allostasis, resilience denotes the ability of an organism to respond to stressors in the environment by means of the appropriate engagement and efficient termination of allostatic responses. In this review, we discuss the neurobiological and organismal factors that modulate resilience, such as growth factors, chaperone molecules and circadian rhythms, and highlight its consequences for cognition and behavior. Brain adaptation, resilience and vulnerability The brain and body constantly adapt. Indeed, the brain may be considered the primary organ allowing for adaptation to changing environments. The brain constantly sorts relevant from irrelevant environmental inputs and engages body systems to respond to these changes. However, it is only in the past few decades that the brain has been recognized as an adaptable and resilient organ not


title: npn-soft sets theory and their applications

abstract: In this paper, we flrstly deflned neutrosophic parameterized neutrosophic soft sets(npnisoft sets) which is combination of a neutro- sophic sets and a soft sets. Our npnisoft sets generalizes the concept of the other soft sets such as; fuzzy soft sets, intuitionistic fuzzy soft sets, neutrosophic soft sets, fuzzy parameterized soft sets, intuitionistic fuzzy parameterized soft sets, neutrosophic parameterized soft sets and so on. Then, we introduce some deflnitions and operations on npnisoft sets and some properties of the sets which are connected to operations have been established. Also, we have introduced the concept of npnisoft matrix and their operators which are more functional to make theoretical studies in the npnisoft set theory. Finally, we proposed the decision making method on the npnisoft set theory which can be applied to problems of many flelds that contain uncertainty and provided an example that demonstrated that this method can be successfully worked. 2010 AMS Classiflcation: 03E72, 08A72


title: A Survey of Content-Based Image Retrieval Systems using Scale-Invariant Feature Transform (SIFT)

abstract: Content-based image retrieval (CBIR) is a method for finding similar images from large image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. In recent years, local descriptors are used as image features to improve the performance of CBIR. The SIFT is one of the most local feature detector and descriptors used in many computer vision applications. This paper provides the surveys of CBIR systems that using SIFT algorithm to extract the local features of images.


Analysis and Mining of Educational Data for Predicting the Performance of Students


title: Charts of fetal size: limb bones

abstract: OBJECTIVE ::: To construct new size charts for all fetal limb bones. ::: ::: ::: DESIGN ::: A prospective, cross sectional study. ::: ::: ::: SETTING ::: Ultrasound department of a large hospital. ::: ::: ::: SAMPLE ::: 663 fetuses scanned once only for the purpose of the study at gestations between 12 and 42 weeks. ::: ::: ::: METHODS ::: Centiles were estimated by combining separate regression models fitted to the mean and standard deviation, assuming that the measurements have a normal distribution at each gestational age. ::: ::: ::: MAIN OUTCOME MEASURES ::: Determination of fetal limb lengths from 12 to 42 weeks of gestation. ::: ::: ::: RESULTS ::: Size charts for fetal bones (radius, ulna, humerus, tibia, fibula, femur and foot) are presented and compared with previously published data. ::: ::: ::: CONCLUSIONS ::: We present new size charts for fetal limb bones which take into consideration the increasing variability with gestational age. We have compared these charts with other published data; the differences seen may be largely due to methodological differences. As standards for fetal head and abdominal measurements have been published from the same population, we suggest that the use of the new charts may facilitate prenatal diagnosis of skeletal dysplasias.


title: Word sense disambiguation as a traveling salesman problem

abstract: Word sense disambiguation (WSD) is a difficult problem in Computational Linguistics, mostly because of the use of a fixed sense inventory and the deep level of granularity. This paper formulates WSD as a variant of the traveling salesman problem (TSP) to maximize the overall semantic relatedness of the context to be disambiguated. Ant colony optimization, a robust nature-inspired algorithm, was used in a reinforcement learning manner to solve the formulated TSP. We propose a novel measure based on the Lesk algorithm and Vector Space Model to calculate semantic relatedness. Our approach to WSD is comparable to state-of-the-art knowledge-based and unsupervised methods for benchmark datasets. In addition, we show that the combination of knowledge-based methods is superior to the most frequent sense heuristic and significantly reduces the difference between knowledge-based and supervised methods. The proposed approach could be customized for other lexical disambiguation tasks, such as Lexical Substitution or Word Domain Disambiguation.


title: Reduction of Mutual Coupling Between Closely Packed Patch Antennas Using Waveguided Metamaterials

abstract: The reduction of mutual coupling between closely spaced antenna elements is attractive in the electromagnetic and antenna community. An efficient approach to suppress the mutual coupling between microstrip patch antennas is proposed using waveguided metamaterials. The waveguided metamaterials are designed and realized by crossed-meander-line slits, which exhibit magnetic resonances and further the band-gap property. By inserting the waveguided metamaterials between two H-plane coupled rectangular patch antennas with the edge-to-edge distance less than λ0/8, about 6 dB reduction of mutual coupling throughout the -10-dB bandwidth has been achieved, which is verified by measurement results.


title: Fast and Robust Training of Recurrent Neural Networks for Offline Handwriting Recognition

abstract: In this paper we demonstrate a modified topology for long short-term memory recurrent neural networks that controls the shape of the squashing functions in gating units. We further propose an efficient training framework based on a mini-batch training on sequence level combined with a sequence chunking approach. The framework is evaluated on publicly available data sets containing English and French handwriting by utilizing a GPU based implementation. Speedups of more than 3x are achieved in training recurrent neural network models which outperform state of the art recognition results.


TROPE R HCRAESE R PAID I END-TO-END ACOUSTIC MODELING USING CONVOLUTIONAL NEURAL NETWORKS FOR AUTOMATIC SPEECH RECOGNITION


Some structural aspects of Soviet‐type investment policy∗


title: A Convolutional Neural Network for Leaves Recognition Using Data Augmentation

abstract: Recently, convolutional neural networks (ConvNets) have achieved marvellous results in different field of recognition, especially in computer vision. In this paper, a seven-layer ConvNet using data augmentation is proposed for leaves recognition. First, we implement multiform transformations (e.g., rotation and translation etc.) to enlarge the dataset without changing their labels. This novel technique recently makes tremendous contribution to the performance of ConvNets as it is able to reduce the over-fitting degree and enhance the generalization ability of the ConvNet. Moreover, in order to get the shapes of leaves, we sharpen all the images with a random parameter. This method is similar to the edge detection, which has been proved useful in the image classification. Then we train a deep convolutional neural network to classify the augmented leaves data with three groups of test set and finally find that the method is quite feasible and effective. The accuracy achieved by our algorithm outperforms other methods for supervised learning on the popular leaf dataset Flavia.


KEVM: A Complete Semantics of the Ethereum Virtual Machine


title: Joint radar alignment and odometry calibration

abstract: An unsupervised online procedure for the precise alignment of fully integrated Doppler radar sensors is proposed. Alignment is the precise determination of the angle between the principal beam direction of the radar sensor and the thrust axis of the vehicle. The method is based on the accurate determination of the sensor movement through the analysis of the Doppler distribution of stationary targets over the azimuth angle. The precise alignment facilitates estimating the ego motion of the vehicle. The approach is long-term stable and bias free and therefore predestined for the calibration of standard vehicle's odometry (gyroscope and wheel sensors). A hierarchical optimization strategy to obtain the systematic errors is presented and Maximum Likelihood estimators are derived for each step. A Monte-Carlo simulation is used to identify critical impact factors and determine them quantitatively. The performance of all approaches is finally evaluated with promising results using measurements obtained by a pre-series 77 GHz Doppler radar sensor. An alignment precision on the order of 0.01–0.05° is achieved.


title: A Frequency Adaptive Phase Shift Modulation Control Based LLC Series Resonant Converter for Wide Input Voltage Applications

abstract: This paper presents an isolated LLC series resonant DC/DC converter with novel frequency adaptive phase shift modulation control, which suitable for wide input voltage (200–400 V) applications. The proposed topology integrates two half-bridge in series on the primary side to reduce the switching stress to half of the input voltage. Unlike the conventional converter, this control strategy increases the voltage gain range with zero-voltage-switching (ZVS) to all switches under all operating voltage and load variations. Adaptive frequency control is used to secure ZVS in the primary bridge with regards to load change. To do so, the voltage gain becomes independent of the loaded quality factor. In addition, the phase shift control is used to regulate the output voltage as constant under all possible inputs. The control of these two variables also significantly minimizes the circulating current, especially from the low-voltage side, which increases the efficiency as compared to a conventional converter. Experimental results of a 1-Kw prototype converter with 200–400-V input and 48-V output are presented to verify all theoretical analysis and characteristics.


title: Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications

abstract: We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, it generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate the effectiveness of these algorithms in tackling computer vision tasks, we apply them to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy in the three above-mentioned applications.


title: Smart and secure IOT based child behaviour and health monitoring system using hadoop

abstract: — IoT is becoming very relevant and getting popular in the field of medical diagnosis nowadays. In this paper, we are bound to limelight in this field which focuses briefly over the child health monitoring. In this work, we implemented a smart and secure health care monitor application that personify the monitoring of total health and mind status of the children. As part of which we are adopting the use of wireless sensors that will keep the child to get monitored. Our system enables the child to get involved with some android games which will make him/her to think and act dynamically. The game scores and the sensor readings obtained from the child will be monitored and analyzed by the system and actions will be taken accordingly. Since the data generated by the system are humongous, we adopt Hadoop in the background to effectively map the data and to reduce it into elementary. As this data need confidentiality in transmission, Apache Ranger is being used for the secure transmission and for the classification of the child behavior, C4.5 decision algorithm is used in the system. While testing with the medical dataset, C4.5 proved to be more accurate than ID3 algorithm.


title: Discrete event-triggered sliding mode control with fast output sampling feedback

abstract: This paper deals with event-triggering based design of discrete-time sliding mode (DTSM) control for linear systems. Multirate output feedback based DTSM is designed with event-triggering strategy such that the system trajectories remain bounded in the vicinity of sliding manifold. An event-triggering rule is developed for DTSM which is evaluated only at periodic time intervals. The control is updated whenever the triggering instant is generated at these discrete time instants. It is shown that with this technique the system trajectories remain bounded. Also, in this triggering scheme there is always guaranteed Zeno free execution of triggering sequences as it is inherently discrete. Simulation results are shown to demonstrate the theoretical treatments of this paper.


title: Evolutionary Algorithms Applied to Cross Aperture Coupled Circularly Polarized Microstrip Antenna

abstract: Utilizing evolutionary algorithms to achieve a low axial ratio of a circularly polarized microstrip antenna over a wide bandwidth and optimal impedance matching is the main aim of this study. A circularly polarized cross aperture coupled microstrip antenna using a 50Ω microstrip feed line is designed. A software simulation program (CST microwave studio) is used to design and compare the performance of the antenna in terms of return loss and axial ratio. Two distinct evolutionary algorithms; Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been applied to optimize the fitness function to ensure optimal axial ratio and impedance matching. The optimal patch dimensions, the aperture length, and the microstrip feed line length are obtained from both methods and their performances are compared. Moreover, the antenna is fabricated and the return loss is measured using a network analyzer. A comparative analysis is carried out demonstrating a high agreement between measured and simulated return loss.


Distributed Representations of Words and Documents for Discriminating Similar Languages


title: Fake Face Detection Based on Skin Elasticity

abstract: Biometric system provides a way of automatic verification or identification a person. But nowadays due to lack of secrecy, there is lot of security threat due to spoofing. Spoofing with photograph or video is one of the most common manners to attack a face recognition system. Liveness detection is a technique that can be used for validating whether the data originate is from a valid user or not. Liveness detection can be hardware based or software based or a combination of both. In this paper, we present a non intrusive and real time method to address this problem, based on skin elasticity of human face. In this technique user is asked to do some movement like chewing and forehead movement simultaneously, so that a full movement to face skin can be given and then sequence of face images is captured with a gap of few milliseconds. Then by applying correlation coefficient between images and then discriminate analysis using some method, face skin is discriminate from the other materials like gelatin, rubber, cadaver, clay etc. In comparison to other face liveness detection, this method will be much user friendly. On the other hand, one of the images captured for liveness detection can be used for face recognition.


title: An ensemble of patch-based subspaces for makeup-robust face recognition

abstract: We propose a face-matching framework to address the problem of makeup.An ensemble of subspaces is used for face representation and matching.Each subspace pertains to a set of randomly selected patches.Multiple texture descriptors are used to describe a patch.Combination of sparse and collaborative classifiers is used in these subspaces.Large number of experiments are conducted to convey the efficacy of the scheme. Recent research has demonstrated the negative impact of makeup on automated face recognition. In this work, we introduce a patch-based ensemble learning method, which uses multiple subspaces generated by sampling patches from before-makeup and after-makeup face images, to address this problem. In the proposed scheme, each face image is tessellated into patches and each patch is represented by a set of feature descriptors, viz., Local Gradient Gabor Pattern (LGGP), Histogram of Gabor Ordinal Ratio Measures (HGORM) and Densely Sampled Local Binary Pattern (DS-LBP). Then, an improved Random Subspace Linear Discriminant Analysis (SRS-LDA) method is used to perform ensemble learning by sampling patches and constructing multiple common subspaces between before-makeup and after-makeup facial images. Finally, Collaborative-based and Sparse-based Representation Classifiers are used to compare feature vectors in this subspace and the resulting scores are combined via the sum-rule. The proposed face matching algorithm is evaluated on the YMU makeup dataset and is shown to achieve very good results. It outperforms other methods designed specifically for the makeup problem.


title: Robot self-initiative and personalization by learning through repeated interactions

abstract: We have developed a robotic system that interacts with the user, and through repeated interactions, adapts to the user so that the system becomes semi-autonomous and acts proactively. In this work we show how to design a system to meet a user's preferences, show how robot pro-activity can be learned and provide an integrated system using verbal instructions. All these behaviors are implemented in a real platform that achieves all these behaviors and is evaluated in terms of user acceptability and efficiency of interaction.


title: Managing Expressions as Data in Relational Database Systems

abstract: A wide-range of applications, including Publish/Subscribe, Workflow, and Web-site Personalization, require maintaining user’s interest in expected data as conditional expressions. This paper proposes to manage such expressions as data in Relational Database Systems (RDBMS). This is accomplished 1) by allowing expressions to be stored in a column of a database table and 2) by introducing a SQL EVALUATE operator to evaluate expressions for given data. Expressions when combined with predicates on other forms of data in a database, are just a flexible and powerful way of expressing interest in a data item. The ability to evaluate expressions (via EVALUATE operator) in SQL, enables applications to take advantage of the expressive power of SQL to support complex subscription models. The paper describes the key concepts, presents our approach of managing expressions in Oracle RDBMS, discusses a novel indexing scheme that allows efficient filtering of a large set of expressions, and outlines future directions.


title: Clio: Schema Mapping Creation and Data Exchange

abstract: The Clio project provides tools that vastly simplify information integration. Information integration requires data conversions to bring data in different representations into a common form. Key contributions of Clio are the definition of non-procedural schema mappings to describe the relationship between data in heterogeneous schemas, a new paradigm in which we view the mapping creation process as one of query discovery, and algorithms for automatically generating queries for data transformation from the mappings. Clio provides algorithms to address the needs of two major information integration problems, namely, data integration and data exchange . In this chapter, we present our algorithms for both schema mapping creation via query discovery, and for query generation for data exchange. These algorithms can be used in pure relational, pure XML, nested relational, or mixed relational and nested contexts.


title: Predicting Student Dropout in Higher Education

abstract: Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model student dropout using the largest known dataset on higher education attrition, which tracks over 32,500 students' demographics and transcript records at one of the nation's largest public universities. Our results highlight several early indicators of student attrition and show that dropout can be accurately predicted even when predictions are based on a single term of academic transcript data. These results highlight the potential for machine learning to have an impact on student retention and success while pointing to several promising directions for future work.


title: Sequence-to-Sequence Models Can Directly Translate Foreign Speech

abstract: We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language, nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.


title: MagicFuzzer: Scalable deadlock detection for large-scale applications

abstract: We present MagicFuzzer, a novel dynamic deadlock detection technique. Unlike existing techniques to locate potential deadlock cycles from an execution, it iteratively prunes lock dependencies that each has no incoming or outgoing edge. Combining with a novel thread-specific strategy, it dramatically shrinks the size of lock dependency set for cycle detection, improving the efficiency and scalability of such a detection significantly. In the real deadlock confirmation phase, it uses a new strategy to actively schedule threads of an execution against the whole set of potential deadlock cycles. We have implemented a prototype and evaluated it on large-scale C/C++ programs. The experimental results confirm that our technique is significantly more effective and efficient than existing techniques.


title: Modeling and Construction of a Furuta Pendulum Prototype

abstract: In order to provide a material that can facilitate the modeling and construction of a Furuta pendulum, this paper presents the deduction, step-by-step, of a Furuta pendulum mathematical model by using the Lagrange equations of motion. Later, a mechanical design of the Furuta pendulum is carried out via the software Solid Works and subsequently a prototype is built. Numerical simulations of the Furuta pendulum model are performed via Mat lab-Simulink. Furthermore, the Furuta pendulum prototype built is experimentally tested by using Mat lab-Simulink, Control Desk, and a DS1104 board from dSPACE.


title: The Neuropsychology of Memory Illusions: False Recall and Recognition in Amnesic Patients

abstract: Abstract Memory illusions have been explored extensively in cognitive studies of normal memory, but hardly at all in neuropsychological research with amnesic patients. The present experiment examined false recall and recognition of nonstudied words that are preceded by a list of strong associates. We used the Deese (1959) paradigm, recently revived by Roediger and McDermott (1995), in which people frequently claim that nonstudied words appeared on a presented list. Results showed that amnesic patients were less susceptible to false recognition than were matched controls and showed different patterns of false recall. To account for the observed differences between amnesics and controls, we suggest that false recognition of nonstudied words preceded by numerous associates depends on the same kinds of semantic and associative information about study list words that also supports accurate recognition. Amnesic patients do not retain such information, resulting in poor recollection of study list words and decreased susceptibility to false recognition.


title: Advances in Facial Rejuvenation: Botulinum Toxin Type A, Hyaluronic Acid Dermal Fillers, and Combination Therapies???-Consensus Recommendations

abstract: BACKGROUND ::: Facial aesthetics and rejuvenation are evolving rapidly due to changes in products, procedures, and patient demographics. Clinicians can benefit from ongoing guidance on products, tailoring treatments to individual patients, treating multiple facial areas, and using combinations of products and ways to optimize outcomes. ::: ::: ::: METHODS ::: A multidisciplinary group of aesthetic treatment experts convened to review the properties and uses of botulinum toxin type A (BoNTA) and hyaluronic acid fillers and to update consensus recommendations for facial rejuvenation using these two types of products. The group considered paradigm shifts in facial aesthetics; optimal techniques for using BoNTA and hyaluronic acid fillers alone and in combination; the influence of patient sex, ethnicity, cultural ideals, and skin color on treatment; general techniques; patient education and counseling; and emerging trends and needs in facial rejuvenation. ::: ::: ::: RESULTS ::: The group provided specific recommendations by facial area, focusing on relaxing musculature, restoring volume, and recontouring using BoNTA and hyaluronic acid fillers alone and in combination. For the upper face, BoNTA remains the cornerstone of treatment, with hyaluronic acid fillers used to augment results. These fillers are central to the midface because of the need to restore volume. BoNTA and hyaluronic acid in combination can improve outcomes in the lower face. ::: ::: ::: CONCLUSIONS ::: Optimal outcomes in facial aesthetics require in-depth knowledge of facial aging and anatomy, an appreciation that rejuvenation is a three-dimensional process involving muscle control, volume restoration, and recontouring, and thorough knowledge of properties and techniques specific to each product in the armamentarium.


title: Less may be more when choosing is difficult: Choice complexity and too much choice

abstract: Although consumers readily seek choice and abundance, the so-called too-much-choice effect suggests that having many alternatives to choose from eventually leads to negative consequences, such as decreased post-choice satisfaction. The present research extends this research by highlighting the role of choice complexity. It is argued that too-much-choice effects are associated with choice complexity, which is influenced not only by the number of alternatives, but also by other features of the choice set, such as the number of attributes that alternatives are differentiated upon. These other influences of choice complexity may propel or hinder the emergence of too-much-choice effects. Two experiments tested this hypothesis by orthogonally manipulating the number of alternatives and the number of attributes. Results revealed a too-much-choice effect when alternatives were differentiated on many attributes, but not when alternatives were differentiated on few attributes. Implications for theory and practice are discussed.


title: A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification

abstract: Natural phenomenon's and swarms behavior are the warm area of research among the researchers. A large number of algorithms have been developed on the account of natural phenomenon's and swarms behavior. These algorithms have been implemented on the various computational problems for the sake of solutions and provided significant results than conventional methods but there is no such algorithm which can be applied for all of the computational problems. In 2009, a new algorithm was developed on the behalf of theory of gravity and was named gravitational search algorithm (GSA) for continuous optimization problems. In short span of time, GSA algorithm gain popularity among researchers and has been applied to large number of problems such as clustering, classification, parameter identification etc. This paper presents the compendious survey on the GSA algorithm and its applications as well as enlightens the applicability of GSA in data clustering & classification.


title: Is Water-Scrum-Fall Reality? On the Use of Agile and Traditional Development Practices

abstract: For years, agile methods are considered the most promising route toward successful software development, and a considerable number of published studies the successful use of agile methods and reports on the benefits companies have from adopting agile methods. Yet, since the world is not black or white, the question for what happened to the traditional models arises. Are traditional models replaced by agile methods? How is the transformation toward Agile managed, and, moreover, where did it start? With this paper we close a gap in literature by studying the general process use over time to investigate how traditional and agile methods are used. Is there coexistence or do agile methods accelerate the traditional processes' extinction? The findings of our literature study comprise two major results: First, studies and reliable numbers on the general process model use are rare, i.e., we lack quantitative data on the actual process use and, thus, we often lack the ability to ground process-related research in practically relevant issues. Second, despite the assumed dominance of agile methods, our results clearly show that companies enact context-specific hybrid solutions in which traditional and agile development approaches are used in combination.


title: An Analysis of Generalization and Regularization in Nonlinear Learning Systems

abstract: We present an analysis of how the generalization performance (expected test set error) relates to the expected training set error for nonlinear learning systems, such as multilayer perceptrons and radial basis functions. The principal result is the following relationship (computed to second order) between the expected test set and training set errors: 〈etest(λ)〉ξξ′ ≈ 〈etrain(λ)〉ξ + 2σeff2 peff(λ)/n (1) Here, n is the size of the training sample ξ, σeff2 is the effective noise variance in the response variable(s), λ, is a regularization or weight decay parameter, and Peff(λ) is the effective number of parameters in the nonlinear model. The expectations 〈 〉 of training set and test set errors are taken over possible training sets ξ and training and test sets ξ′ respectively. The effective number of parameters peff(λ) usually differs from the true number of model parameters p for nonlinear or regularized models; this theoretical conclusion is supported by Monte Carlo experiments. In addition to the surprising result that peff(λ) ≠ p, we propose an estimate of (1) called the generalized prediction error (GPE) which generalizes well established estimates of prediction risk such as Akaike's F P E and AIC, Mallows Cp, and Barron's P S E to the nonlinear setting.


title: Adaptation of muscle size and myofascial force transmission: a review and some new experimental results

abstract: This paper considers the literature and some new experimental results important for adaptation of muscle fiber cross-sectional area and serial sarcomere number. Two major points emerge: (1) general rules for the regulation of adaptation (for in vivo immobilization, low gravity conditions, synergist ablation, tenotomy and retinaculum trans-section experiments) cannot be derived. As a consequence, paradoxes are reported in the literature. Some paradoxes are resolved by considering the interaction between different levels of organization (e.g. muscle geometrical effects), but others cannot. (2) An inventory of signal transduction pathways affecting rates of muscle protein synthesis and/or degradation reveals controversy concerning the pathways and their relative contributions. A major explanation for the above is not only the inherently limited control of the experimental conditions in vivo, but also of in situ experiments. Culturing of mature single Xenopus muscle fibers at high and low lengths (allowing longitudinal study of adaptation for periods up to 3 months) did not yield major changes in the fiber cross-sectional area or the serial sarcomere number. This is very different from substantial effects (within days) of immobilization in vivo. It is concluded that overall strain does not uniquely regulate muscle fiber size. Force transmission, via pathways other than the myotendinous junctions, may contribute to the discrepancies reported: because of substantial serial heterogeneity of sarcomere lengths within muscle fibers creating local variations in the mechanical stimuli for adaptation. For the single muscle fiber, mechanical signalling is quite different from the in vivo or in vitro condition. Removal of tensile and shear effects of neighboring tissues (even of antagonistic muscle) modifies or removes mechanical stimuli for adaptation. It is concluded that the study of adaptation of muscle size requires an integrative approach taking into account fundamental mechanisms of adaptation, as well as effects of higher levels of organization. More attention should be paid to adaptation of connective tissues within and surrounding the muscle and their effects on muscular properties.


A Semantic Approach to Person Profile Extraction from Farsi Documents


title: Single document keyphrase extraction using neighborhood knowledge

abstract: Existing methods for single document keyphrase extraction usually make use of only the information contained in the specified document. This paper proposes to use a small number of nearest neighbor documents to provide more knowledge to improve single document keyphrase extraction. A specified document is expanded to a small document set by adding a few neighbor documents close to the document, and the graph-based ranking algorithm is then applied on the expanded document set to make use of both the local information in the specified document and the global information in the neighbor documents. Experimental results demonstrate the good effectiveness and robustness of our proposed approach.