Automated Diagnosis of Glaucoma Using Digital Fundus Images

Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time. The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing, morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity of 100% and 80% respectively.


Conditional Generative Adversarial Networks for Speech Enhancement and Noise-Robust Speaker Verification

Improving speech system performance in noisy environments remains a challenging task, and speech enhancement (SE) is one of the effective techniques to solve the problem. Motivated by the promising results of generative adversarial networks (GANs) in a variety of image processing tasks, we explore the potential of conditional GANs (cGANs) for SE, and in particular, we make use of the image processing framework proposed by Isola et al. [1] to learn a mapping from the spectrogram of noisy speech to an enhanced counterpart. The SE cGAN consists of two networks, trained in an adversarial manner: a generator that tries to enhance the input noisy spectrogram, and a discriminator that tries to distinguish between enhanced spectrograms provided by the generator and clean ones from the database using the noisy spectrogram as a condition. We evaluate the performance of the cGAN method in terms of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and equal error rate (EER) of speaker verification (an example application). Experimental results show that the cGAN method overall outperforms the classical short-time spectral amplitude minimum mean square error (STSA-MMSE) SE algorithm, and is comparable to a deep neural network-based SE approach (DNN-SE).


Direct and Indirect Discrimination Prevention Methods

Along with privacy, discrimination is a very import ant issue when considering the legal and ethical aspects of data mini ng. It is more than obvious that most people do not want to be discriminated because of their gender, religion, nationality, age and so on, especially when those att ribu es are used for making decisions about them like giving them a job, loan, insu rance, etc. Discovering such potential biases and eliminating them from the traini ng data without harming their decision-making utility is therefore highly desirab le. For this reason, antidiscrimination techniques including discrimination discovery and prevention have been introduced in data mining. Discrimination prev ention consists of inducing patterns that do not lead to discriminatory decisio n even if the original training datasets are inherently biased. In this chapter, by focusing on the discrimination prevention, we present a taxonomy for classifying a d examining discrimination prevention methods. Then, we introduce a group of p re-processing discrimination prevention methods and specify the different featur es of each approach and how these approaches deal with direct or indirect discr imination. A presentation of metrics used to evaluate the performance of those app ro ches is also given. Finally, we conclude our study by enumerating interesting fu ture directions in this research body.


Markowitz Revisited: Mean-Variance Models in Financial Portfolio Analysis

Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We describe in detail the interplay between objective and constraints in a number of single-period variants, including semivariance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multiperiod models based on scenario trees. A key property is the possibility of removing surplus money in future decisions, yielding approximate downside risk minimization.


Federated Learning for Keyword Spotting

We propose a practical approach based on federated learning to solve out-of-domain issues with continuously running embedded speech-based models such as wake word detectors. We conduct an extensive empirical study of the federated averaging algorithm for the “Hey Snips” wake word based on a crowdsourced dataset that mimics a federation of wake word users. We empirically demonstrate that using an adaptive averaging strategy inspired from Adam in place of standard weighted model averaging highly reduces the number of communication rounds required to reach our target performance. The associated upstream communication costs per user are estimated at 8 MB, which is a reasonable in the context of smart home voice assistants. Additionally, the dataset used for these experiments is being open sourced with the aim of fostering further transparent research in the application of federated learning to speech data.


Sample-Based Tree Search with Fixed and Adaptive State Abstractions

Sample-based tree search (SBTS) is an approach to solving Markov decision problems based on constructing a lookahead search tree using random samples from a generative model of the MDP. It encompasses Monte Carlo tree search (MCTS) algorithms like UCT as well as algorithms such as sparse sampling. SBTS is well-suited to solving MDPs with large state spaces due to the relative insensitivity of SBTS algorithms to the size of the state space. The limiting factor in the performance of SBTS tends to be the exponential dependence of sample complexity on the depth of the search tree. The number of samples required to build a search tree is O((|A|B)), where |A| is the number of available actions, B is the number of possible random outcomes of taking an action, and d is the depth of the tree. State abstraction can be used to reduce B by aggregating random outcomes together into abstract states. Recent work has shown that abstract tree search often performs substantially better than tree search conducted in the ground state space. This paper presents a theoretical and empirical evaluation of tree search with both fixed and adaptive state abstractions. We derive a bound on regret due to state abstraction in tree search that decomposes abstraction error into three components arising from properties of the abstraction and the search algorithm. We describe versions of popular SBTS algorithms that use fixed state abstractions, and we introduce the Progressive Abstraction Refinement in Sparse Sampling (PARSS) algorithm, which adapts its abstraction during search. We evaluate PARSS as well as sparse sampling with fixed abstractions on 12 experimental problems, and find that PARSS outperforms search with a fixed abstraction and that search with even highly inaccurate fixed abstractions outperforms search without abstraction. These results establish progressive abstraction refinement as a promising basis for new tree search algorithms, and we propose directions for future work within the progressive refinement framework.


Query by Committee

We propose an algorithm called query by commitee, in which a committee of students is trained on the same data set. The next query is chosen according to the principle of maximal disagreement. The algorithm is studied for two toy models: the high-low game and perceptron learning of another perceptron. As the number of queries goes to infinity, the committee algorithm yields asymptotically finite information gain. This leads to generalization error that decreases exponentially with the number of examples. This in marked contrast to learning from randomly chosen inputs, for which the information gain approaches zero and the generalization error decreases with a relatively slow inverse power law. We suggest that asymptotically finite information gain may be an important characteristic of good query algorithms.


Adaptive Manifold Learning

Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.


Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning

Advances in hyperspectral sensing provide new capability for characterizing spectral signatures in a wide range of physical and biological systems, while inspiring new methods for extracting information from these data. HSI data often lie on sparse, nonlinear manifolds whose geometric and topological structures can be exploited via manifold-learning techniques. In this article, we focused on demonstrating the opportunities provided by manifold learning for classification of remotely sensed data. However, limitations and opportunities remain both for research and applications. Although these methods have been demonstrated to mitigate the impact of physical effects that affect electromagnetic energy traversing the atmosphere and reflecting from a target, nonlinearities are not always exhibited in the data, particularly at lower spatial resolutions, so users should always evaluate the inherent nonlinearity in the data. Manifold learning is data driven, and as such, results are strongly dependent on the characteristics of the data, and one method will not consistently provide the best results. Nonlinear manifold-learning methods require parameter tuning, although experimental results are typically stable over a range of values, and have higher computational overhead than linear methods, which is particularly relevant for large-scale remote sensing data sets. Opportunities for advancing manifold learning also exist for analysis of hyperspectral and multisource remotely sensed data. Manifolds are assumed to be inherently smooth, an assumption that some data sets may violate, and data often contain classes whose spectra are distinctly different, resulting in multiple manifolds or submanifolds that cannot be readily integrated with a single manifold representation. Developing appropriate characterizations that exploit the unique characteristics of these submanifolds for a particular data set is an open research problem for which hierarchical manifold structures appear to have merit. To date, most work in manifold learning has focused on feature extraction from single images, assuming stationarity across the scene. Research is also needed in joint exploitation of global and local embedding methods in dynamic, multitemporal environments and integration with semisupervised and active learning.


Stochastic Neighbor Embedding

We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to define a probability distribution over all the potential neighbors of the object. The aim of the embedding is to approximate this distribution as well as possible when the same operation is performed on the low-dimensional “images” of the objects. A natural cost function is a sum of Kullback-Leibler divergences, one per object, which leads to a simple gradient for adjusting the positions of the low-dimensional images. Unlike other dimensionality reduction methods, this probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images. This allows ambiguous objects, like the document count vector for the word “bank”, to have versions close to the images of both “river” and “finance” without forcing the images of outdoor concepts to be located close to those of corporate concepts.


Nonlinear Component Analysis as a Kernel Eigenvalue Problem

A new method for performing a nonlinear form of principal component analysis is proposed. By the use of integral operator kernel functions, one can efficiently compute principal components in high-dimensional feature spaces, related to input space by some nonlinear mapfor instance, the space of all possible five-pixel products in 16 16 images. We give the derivation of the method and present experimental results on polynomial feature extraction for pattern recognition.


Leveraging graph topology and semantic context for pharmacovigilance through twitter-streams

Adverse drug events (ADEs) constitute one of the leading causes of post-therapeutic death and their identification constitutes an important challenge of modern precision medicine. Unfortunately, the onset and effects of ADEs are often underreported complicating timely intervention. At over 500 million posts per day, Twitter is a commonly used social media platform. The ubiquity of day-to-day personal information exchange on Twitter makes it a promising target for data mining for ADE identification and intervention. Three technical challenges are central to this problem: (1) identification of salient medical keywords in (noisy) tweets, (2) mapping drug-effect relationships, and (3) classification of such relationships as adverse or non-adverse. We use a bipartite graph-theoretic representation called a drug-effect graph (DEG) for modeling drug and side effect relationships by representing the drugs and side effects as vertices. We construct individual DEGs on two data sources. The first DEG is constructed from the drug-effect relationships found in FDA package inserts as recorded in the SIDER database. The second DEG is constructed by mining the history of Twitter users. We use dictionary-based information extraction to identify medically-relevant concepts in tweets. Drugs, along with co-occurring symptoms are connected with edges weighted by temporal distance and frequency. Finally, information from the SIDER DEG is integrate with the Twitter DEG and edges are classified as either adverse or non-adverse using supervised machine learning. We examine both graph-theoretic and semantic features for the classification task. The proposed approach can identify adverse drug effects with high accuracy with precision exceeding 85 % and F1 exceeding 81 %. When compared with leading methods at the state-of-the-art, which employ un-enriched graph-theoretic analysis alone, our method leads to improvements ranging between 5 and 8 % in terms of the aforementioned measures. Additionally, we employ our method to discover several ADEs which, though present in medical literature and Twitter-streams, are not represented in the SIDER databases. We present a DEG integration model as a powerful formalism for the analysis of drug-effect relationships that is general enough to accommodate diverse data sources, yet rigorous enough to provide a strong mechanism for ADE identification.


IMPLICIT SHAPE MODELS FOR OBJECT DETECTION IN 3D POINT CLOUDS

We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m of urban area in total.


Multi-task Learning for Maritime Traffic Surveillance from AIS Data Streams

In a world of global trading, maritime safety, security and efficiency are crucial issues. We propose a multi-task deep learning framework for vessel monitoring using Automatic Identification System (AIS) data streams. We combine recurrent neural networks with latent variable modeling and an embedding of AIS messages to a new representation space to jointly address key issues to be dealt with when considering AIS data streams: massive amount of streaming data, noisy data and irregular timesampling. We demonstrate the relevance of the proposed deep learning framework on real AIS datasets for a three-task setting, namely trajectory reconstruction, anomaly detection and vessel type identification.


The Dysphagia Outcome and Severity Scale (DOSS) is a simple, easy-to-use, 7-point scale developed to systematically rate the functional severity of dysphagia based on objective assessment and make recommendations for diet level, independence level, and type of nutrition. Intra- and interjudge reliabilities of the DOSS was established by four clinicians on 135 consecutive patients who underwent a modified barium swallow procedure at a large teaching hospital. Patients were assigned a severity level, independence level, and nutritional level based on three areas most associated with final recommendations: oral stage bolus transfer, pharyngeal stage retention, and airway protection. Results indicate high interrater (90%) and intrarater (93%) agreement with this scale. Implications are suggested for use of the DOSS in documenting functional outcomes of swallowing and diet status based on objective assessment.


Depth Map Super-Resolution Considering View Synthesis Quality

Accurate and high-quality depth maps are required in lots of 3D applications, such as multi-view rendering, 3D reconstruction and 3DTV. However, the resolution of captured depth image is much lower than that of its corresponding color image, which affects its application performance. In this paper, we propose a novel depth map super-resolution (SR) method by taking view synthesis quality into account. The proposed approach mainly includes two technical contributions. First, since the captured low-resolution (LR) depth map may be corrupted by noise and occlusion, we propose a credibility based multi-view depth maps fusion strategy, which considers the view synthesis quality and interview correlation, to refine the LR depth map. Second, we propose a view synthesis quality based trilateral depth-map up-sampling method, which considers depth smoothness, texture similarity and view synthesis quality in the up-sampling filter. Experimental results demonstrate that the proposed method outperforms state-of-the-art depth SR methods for both super-resolved depth maps and synthesized views. Furthermore, the proposed method is robust to noise and achieves promising results under noise-corruption conditions.


AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems


Detection of exudates in fundus photographs using convolutional neural networks

Diabetic retinopathy is one of the leading causes of preventable blindness in the developed world. Early diagnosis of diabetic retinopathy enables timely treatment and in order to achieve it a major effort will have to be invested into screening programs and especially into automated screening programs. Detection of exudates is very important for early diagnosis of diabetic retinopathy. Deep neural networks have proven to be a very promising machine learning technique, and have shown excellent results in different compute vision problems. In this paper we show that convolutional neural networks can be effectively used in order to detect exudates in color fundus photographs.


Random Forests

Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, ***, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.


Multi-task Learning Of Deep Neural Networks For Audio Visual Automatic Speech Recognition

Multi-task learning (MTL) involves the simultaneous training of two or more related tasks over shared representations. In this work, we apply MTL to audio-visual automatic speech recognition(AV-ASR). Our primary task is to learn a mapping between audio-visual fused features and frame labels obtained from acoustic GMM/HMM model. This is combined with an auxiliary task which maps visual features to frame labels obtained from a separate visual GMM/HMM model. The MTL model is tested at various levels of babble noise and the results are compared with a base-line hybrid DNN-HMM AVASR model. Our results indicate that MTL is especially useful at higher level of noise. Compared to base-line, upto 7% relative improvement in WER is reported at -3 SNR dB1.


Ethnicity estimation with facial images

We have advanced an effort to develop vision based human understanding technologies for realizing human-friendly machine interfaces. Visual information, such as gender, age ethnicity, and facial expression play an important role in face-to-face communication. This paper addresses a novel approach for ethnicity classification with facial images. In this approach, the Gabor wavelets transformation and retina sampling are combined to extract key facial features, and support vector machines that are used for ethnicity classification. Our system, based on this approach, has achieved approximately 94% for ethnicity estimation under various lighting conditions.


Bullying and victimization in adolescence: concurrent and stable roles and psychological health symptoms.

From an initial sample of 1278 Italian students, the authors selected 537 on the basis of their responses to a self-report bully and victim questionnaire. Participants' ages ranged from 13 to 20 years (M = 15.12 years, SD = 1.08 years). The authors compared the concurrent psychological symptoms of 4 participant groups (bullies, victims, bully/victims [i.e., bullies who were also victims of bullying], and uninvolved students). Of participants, 157 were in the bullies group, 140 were in the victims group, 81 were in the bully/victims group, and 159 were in the uninvolved students group. The results show that bullies reported a higher level of externalizing problems, victims reported more internalizing symptoms, and bully/victims reported both a higher level of externalizing problems and more internalizing symptoms. The authors divided the sample into 8 groups on the basis of the students' recollection of their earlier school experiences and of their present role. The authors classified the participants as stable versus late bullies, victims, bully/victims, or uninvolved students. The authors compared each stable group with its corresponding late group and found that stable victims and stable bully/victims reported higher degrees of anxiety, depression, and withdrawal than did the other groups. The authors focus their discussion on the role of chronic peer difficulties in relation to adolescents' symptoms and well-being.


Side gate HiGT with low dv/dt noise and low loss

This paper presents a novel side gate HiGT (High-conductivity IGBT) that incorporates historical changes of gate structures for planar and trench gate IGBTs. Side gate HiGT has a side-wall gate, and the opposite side of channel region for side-wall gate is covered by a thick oxide layer to reduce Miller capacitance (Cres). In addition, side gate HiGT has no floating p-layer, which causes the excess Vge overshoot. The proposed side gate HiGT has 75% smaller Cres than the conventional trench gate IGBT. The excess Vge overshoot during turn-on is effectively suppressed, and Eon + Err can be reduced by 34% at the same diode's recovery dv/dt. Furthermore, side gate HiGT has sufficiently rugged RBSOA and SCSOA.


Incremental Dialogue System Faster than and Preferred to its Nonincremental Counterpart

Current dialogue systems generally operate in a pipelined, modular fashion on one complete utterance at a time. Evidence from human language understanding shows that human understanding operates incrementally and makes use of multiple sources of information during the parsing process, including traditionally “later” components such as pragmatics. In this paper we describe a spoken dialogue system that understands language incrementally, provides visual feedback on possible referents during the course of the user’s utterance, and allows for overlapping speech and actions. We further present findings from an empirical study showing that the resulting dialogue system is faster overall than its nonincremental counterpart. Furthermore, the incremental system is preferred to its nonincremental counterpart – beyond what is accounted for by factors such as speed and accuracy. These results indicate that successful incremental understanding systems will improve both performance and usability.


The balanced scorecard: a foundation for the strategic management of information systems

Ž . The balanced scorecard BSC has emerged as a decision support tool at the strategic management level. Many business leaders now evaluate corporate performance by supplementing financial accounting data with goal-related measures from the following perspectives: customer, internal business process, and learning and growth. It is argued that the BSC concept can be adapted to assist those managing business functions, organizational units and individual projects. This article develops a Ž . balanced scorecard for information systems IS that measures and evaluates IS activities from the following perspectives: business value, user orientation, internal process, and future readiness. Case study evidence suggests that a balanced IS scorecard can be the foundation for a strategic IS management system provided that certain development guidelines are followed, appropriate metrics are identified, and key implementation obstacles are overcome. q 1999 Elsevier Science B.V. All rights reserved.


Adversarial Machine Learning at Scale

Adversarial examples are malicious inputs designed to fool machine learning models. They often transfer from one model to another, allowing attackers to mount black box attacks without knowledge of the target model’s parameters. Adversarial training is the process of explicitly training a model on adversarial examples, in order to make it more robust to attack or to reduce its test error on clean inputs. So far, adversarial training has primarily been applied to small problems. In this research, we apply adversarial training to ImageNet (Russakovsky et al., 2014). Our contributions include: (1) recommendations for how to succesfully scale adversarial training to large models and datasets, (2) the observation that adversarial training confers robustness to single-step attack methods, (3) the finding that multi-step attack methods are somewhat less transferable than singlestep attack methods, so single-step attacks are the best for mounting black-box attacks, and (4) resolution of a “label leaking” effect that causes adversarially trained models to perform better on adversarial examples than on clean examples, because the adversarial example construction process uses the true label and the model can learn to exploit regularities in the construction process.


A Combined Model- and Learning-Based Framework for Interaction-Aware Maneuver Prediction

This paper presents a novel online-capable interaction-aware intention and maneuver prediction framework for dynamic environments. The main contribution is the combination of model-based interaction-aware intention estimation with maneuver-based motion prediction based on supervised learning. The advantages of this framework are twofold. On one hand, expert knowledge in the form of heuristics is integrated, which simplifies the modeling of the interaction. On the other hand, the difficulties associated with the scalability and data sparsity of the algorithm due to the so-called curse of dimensionality can be reduced, as a reduced feature space is sufficient for supervised learning. The proposed algorithm can be used for highly automated driving or as a prediction module for advanced driver assistance systems without the need of intervehicle communication. At the start of the algorithm, the motion intention of each driver in a traffic scene is predicted in an iterative manner using the game-theoretic idea of stochastic multiagent simulation. This approach provides an interpretation of what other drivers intend to do and how they interact with surrounding traffic. By incorporating this information into a Bayesian network classifier, the developed framework achieves a significant improvement in terms of reliable prediction time and precision compared with other state-of-the-art approaches. By means of experimental results in real traffic on highways, the validity of the proposed concept and its online capability is demonstrated. Furthermore, its performance is quantitatively evaluated using appropriate statistical measures.


Frequency Tracking Control for a Capacitor-Charging Parallel Resonant Converter with Phase-Locked Loop

This study investigates a phase-locked loop (PLL) controlled parallel resonant converter (PRC) for a pulse power capacitor charging application. The dynamic nature of the capacitor charging is such that it causes a shift in the resonant frequency of the PRC. Using the proposed control method, the PRC can be optimized to operate with its maximum power capability and guarantee ZVS operation, even when the input voltage and resonant tank parameters vary. The detailed implementation of the PLL controller, as well as the determination of dead-time and leading time, is presented in this paper. Simulation and experimental results verify the performance of the proposed control method.


A Novel Connectionist System for Unconstrained Handwriting Recognition

Recognizing lines of unconstrained handwritten text is a challenging task. The difficulty of segmenting cursive or overlapping characters, combined with the need to exploit surrounding context, has led to low recognition rates for even the best current recognizers. Most recent progress in the field has been made either through improved preprocessing or through advances in language modeling. Relatively little work has been done on the basic recognition algorithms. Indeed, most systems rely on the same hidden Markov models that have been used for decades in speech and handwriting recognition, despite their well-known shortcomings. This paper proposes an alternative approach based on a novel type of recurrent neural network, specifically designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. In experiments on two large unconstrained handwriting databases, our approach achieves word recognition accuracies of 79.7 percent on online data and 74.1 percent on offline data, significantly outperforming a state-of-the-art HMM-based system. In addition, we demonstrate the network's robustness to lexicon size, measure the individual influence of its hidden layers, and analyze its use of context. Last, we provide an in-depth discussion of the differences between the network and HMMs, suggesting reasons for the network's superior performance.


Enhancing computer vision to detect face spoofing attack utilizing a single frame from a replay video attack using deep learning

Recently, automatic face recognition has been applied in many web and mobile applications. Developers integrate and implement face recognition as an access control into these applications. However, face recognition authentication is vulnerable to several attacks especially when an attacker presents a 2-D printed image or recorded video frames in front of the face sensor system to gain access as a legitimate user. This paper introduces a non-intrusive method to detect face spoofing attacks that utilize a single frame of sequenced frames. We propose a specialized deep convolution neural network to extract complex and high features of the input diffused frame. We tested our method on the Replay Attack dataset which consists of 1200 short videos of both real-access and spoofing attacks. An extensive experimental analysis was conducted that demonstrated better results when compared to previous static algorithms results.


ISAR - radar imaging of targets with complicated motion

ISAR imaging is described for general motion of a radar target. ISAR imaging may be seen as a 3D to 2D projection, and the importance of the ISAR image projection plane is stated. For general motion, ISAR images are often smeared when using FFT processing. Time frequency methods are used to analyze such images, and to form sharp images. A given smeared image is shown to be the result of changes both in scale and in the projection plane orientation.


Closing the Loop: Evaluating a Measurement Instrument for Maturity Model Design

To support the systematic improvement of business intelligence (BI) in organizations, we have designed and refined a BI maturity model (BIMM) and a respective measurement instrument (MI) in prior research. In this study, we devise an evaluation strategy, and evaluate the validity of the designed measurement artifact. Through cluster analysis of maturity assessments of 92 organizations, we identify characteristic BI maturity scenarios and representative cases for the relevant scenarios. For evaluating the designed instrument, we compare its results with insights obtained from in-depth interviews in the respective companies. A close match between our model's quantitative maturity assessments and the maturity levels from the qualitative analyses indicates that the MI correctly assesses BI maturity. The applied evaluation approach has the potential to be re-used in other design research studies where the validity of utility claims is often hard to prove.


An architecture for scalable, universal speech recognition

This thesis describes MultiSphinx, a concurrent architecture for scalable, low-latency automatic speech recognition. We first consider the problem of constructing a universal “core” speech recognizer on top of which domain and task specific adaptation layers can be constructed. We then show that when this problem is restricted to that of expanding the search space from a “core” vocabulary to a superset of this vocabulary across multiple passes of search, it allows us to effectively “factor” a recognizer into components of roughly equal complexity. We present simple but effective algorithms for constructing the reduced vocabulary and associated statistical language model from an existing system. Finally, we describe the MultiSphinx decoder architecture, which allows multiple passes of recognition to operate concurrently and incrementally, either in multiple threads in the same process, or across multiple processes on separate machines, and which allows the best possible partial results, including confidence scores, to be obtained at any time during the recognition process.


' SMART ’ CANE FOR THE VISUALLY IMPAIRED : DESIGN AND CONTROLLED FIELD TESTING OF AN AFFORDABLE OBSTACLE DETECTION SYSTEM

(USA) respectively. Since graduation, they have been associated with this project in voluntary and individual capacity. Sandeep Singh Gujral, an occupational therapist was an intern at the IIT Delhi from August-November 2009 and conducted user training for the trials.


Forecasting Nike's sales using Facebook data

This paper tests whether accurate sales forecasts for Nike are possible from Facebook data and how events related to Nike affect the activity on Nike's Facebook pages. The paper draws from the AIDA sales framework (Awareness, Interest, Desire, and Action) from the domain of marketing and employs the method of social set analysis from the domain of computational social science to model sales from Big Social Data. The dataset consists of (a) selection of Nike's Facebook pages with the number of likes, comments, posts etc. that have been registered for each page per day and (b) business data in terms of quarterly global sales figures published in Nike's financial reports. An event study is also conducted using the Social Set Visualizer (SoSeVi). The findings suggest that Facebook data does have informational value. Some of the simple regression models have a high forecasting accuracy. The multiple regressions have a lower forecasting accuracy and cause analysis barriers due to data set characteristics such as perfect multicollinearity. The event study found abnormal activity around several Nike specific events but inferences about those activity spikes, whether they are purely event-related or coincidences, can only be determined after detailed case-by-case text analysis. Our findings help assess the informational value of Big Social Data for a company's marketing strategy, sales operations and supply chain.


Wiktionary-Based Word Embeddings

Vectorial representations of words have grown remarkably popular in natural language processing and machine translation. The recent surge in deep learning-inspired methods for producing distributed representations has been widely noted even outside these fields. Existing representations are typically trained on large monolingual corpora using context-based prediction models. In this paper, we propose extending pre-existing word representations by exploiting Wiktionary. This process results in a substantial extension of the original word vector representations, yielding a large multilingual dictionary of word embeddings. We believe that this resource can enable numerous monolingual and cross-lingual applications, as evidenced in a series of monolingual and cross-lingual semantic experiments that we have conducted.


Concept Based Query Expansion

Query expansion methods have been studied for a long time - with debatable success in many instances. In this paper we present a probabilistic query expansion model based on a similarity thesaurus which was constructed automatically. A similarity thesaurus reflects domain knowledge about the particular collection from which it is constructed. We address the two important issues with query expansion: the selection and the weighting of additional search terms. In contrast to earlier methods, our queries are expanded by adding those terms that are most similar to the concept of the query, rather than selecting terms that are similar to the query terms. Our experiments show that this kind of query expansion results in a notable improvement in the retrieval effectiveness when measured using both recall-precision and usefulness.


An Association Thesaurus for Information Retrieval

Although commonly used in both commercial and experimental information retrieval systems, thesauri have not demonstrated consistent beneets for retrieval performance, and it is diicult to construct a thesaurus automatically for large text databases. In this paper, an approach, called PhraseFinder, is proposed to construct collection-dependent association thesauri automatically using large full-text document collections. The association thesaurus can be accessed through natural language queries in INQUERY, an information retrieval system based on the probabilistic inference network. Experiments are conducted in IN-QUERY to evaluate diierent types of association thesauri, and thesauri constructed for a variety of collections.


Experiments in Automatic Statistical Thesaurus Construction

A well constructed thesaurus has long been recognized as a valuable tool in the effective operation of an information retrieval system. This paper reports the results of experiments designed to determine the validity of an approach to the automatic construction of global thesauri (described originally by Crouch in [1] and [2] based on a clustering of the document collection. The authors validate the approach by showing that the use of thesauri generated by this method results in substantial improvements in retrieval effectiveness in four test collections. The term discrimination value theory, used in the thesaurus generation algorithm to determine a term's membership in a particular thesaurus class, is found not to be useful in distinguishing a “good” from an “indifferent” or “poor” thesaurus class). In conclusion, the authors suggest an alternate approach to automatic thesaurus construction which greatly simplifies the work of producing viable thesaurus classes. Experimental results show that the alternate approach described herein in some cases produces thesauri which are comparable in retrieval effectiveness to those produced by the first method at much lower cost.


Term-Weighting Approaches in Automatic Text Retrieval

The experimental evidence accumulated over the past 20 years indicates that text indexing systems based on the assignment of appropriately weighted single terms produce retrieval results that are superior to those obtainable with other more elaborate text representations. These results depend crucially on the choice of effective termweighting systems. This article summarizes the insights gained in automatic term weighting, and provides baseline single-term-indexing models with which other more elaborate content analysis procedures can be compared. 1. AUTOMATIC TEXT ANALYSIS In the late 195Os, Luhn [l] first suggested that automatic text retrieval systems could be designed based on a comparison of content identifiers attached both to the stored texts and to the users’ information queries. Typically, certain words extracted from the texts of documents and queries would be used for content identification; alternatively, the content representations could be chosen manually by trained indexers familiar with the subject areas under consideration and with the contents of the document collections. In either case, the documents would be represented by term vectors of the form D= (ti,tj,...ytp) (1) where each tk identifies a content term assigned to some sample document D. Analogously, the information requests, or queries, would be represented either in vector form, or in the form of Boolean statements. Thus, a typical query Q might be formulated as Q = (qa,qbr.. . ,4r) (2)


Build-to-order supply chain management : a literature review and framework for development

The build-to-order supply chain management (BOSC) strategy has recently attracted the attention of both researchers and practitioners, given its successful implementation in many companies including Dell computers, Compaq, and BMW. The growing number of articles on BOSC in the literature is an indication of the importance of the strategy and of its role in improving the competitiveness of an organization. The objective of a BOSC strategy is to meet the requirements of individual customers by leveraging the advantages of outsourcing and information technology. There are not many research articles that provide an overview of BOSC, despite the fact that this strategy is being promoted as the operations paradigm of the future. The main objective of this research is to (i) review the concepts of BOSC, (ii) develop definitions of BOSC, (iii) classify the literature based on a suitable classification scheme, leading to some useful insights into BOSC and some future research directions, (iv) review the selected articles on BOSC for their contribution to the development and operations of BOSC, (v) develop a framework for BOSC, and (vi) suggest some future research directions. The literature has been reviewed based on the following four major areas of decision-making: organizational competitiveness, the development and implementation of BOSC, the operations of BOSC, and information technology in BOSC. Some of the important observations are: (a) there is a lack of adequate research on the design and control of BOSC, (b) there is a need for further research on the implementation of BOSC, (c) human resource issues in BOSC have been ignored, (d) issues of product commonality and modularity from the perspective of partnership or supplier development require further attention and (e) the trade-off between responsiveness and the cost of logistics needs further study. The paper ends with concluding remarks. # 2004 Elsevier B.V. All rights reserved.


Team MIT Urban Challenge Technical Report

This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new crossmodal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of wellproven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using purepursuit control and our local frame perception strategy, obstacle avoidance using kinodynamic RRT path planning, Uturns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. † Executive Summary This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross-modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real-time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well-proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure-pursuit control and our local frame perception strategy, obstacle avoidance using kino-dynamic RRT path planning, U-turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. DISCLAIMER: The information contained in this paper does not represent the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA) or the Department of Defense. DARPA does not guarantee the accuracy or reliability of the information in this paper. Additional support …


Platforms in healthcare innovation ecosystems: The lens of an innovation intermediary

Healthcare innovation has made progressive strides. Innovative solutions now tend to incorporate device integration, data collection and data analysis linked across a diverse range of actors building platform-centric healthcare ecosystems. The interconnectedness and inter-disciplinarity of the ecosystems bring with it a number of vital issues around how to strategically manage such a complex system. This paper highlights the importance of innovation intermediaries particularly in a platform-centric ecosystem such as the healthcare industry. It serves as a reminder of why it is important for healthcare technologists to consider proactive ways to contribute to the innovation ecosystem by creating devices with the platform perspective in mind.


Clustering Sensors in Wireless Ad Hoc Networks Operating in a Threat Environment

Sensors in a data fusion environment over hostile territory are geographically dispersed and change location with time. In order to collect and process data from these sensors an equally flexible network of fusion beds (i.e., clusterheads) is required. To account for the hostile environment, we allow communication links between sensors and clusterheads to be unreliable. We develop a mixed integer linear programming (MILP) model to determine the clusterhead location strategy that maximizes the expected data covered minus the clusterhead reassignments, over a time horizon. A column generation (CG) heuristic is developed for this problem. Computational results show that CG performs much faster than a standard commercial solver and the typical optimality gap for large problems is less than 5%. Improvements to the basic model in the areas of modeling link failure, consideration of bandwidth capacity, and clusterhead changeover cost estimation are also discussed.


This document is a complete draft of a chapter by Rajeev Gor e on \Tableau Methods for Modal and Temporal Logics" which is part of the \Handbook of Tableau Methods", edited


General Spectral Camera Lens Simulation

We present a camera lens simulation model capable of producing advanced photographic phenomena in a general spectral Monte Carlo image rendering system. Our approach incorporates insights from geometrical diffraction theory, from optical engineering and from glass science. We show how to efficiently simulate all five monochromatic aberrations, spherical and coma aberration, astigmatism, field curvature and distortion. We also consider chromatic aberration, lateral colour and aperture diffraction. The inclusion of Fresnel reflection generates correct lens flares and we present an optimized sampling method for path generation.


An optimal technology mapping algorithm for delay optimization in lookup-table based FPGA designs

In this paper we present a polynomial time technology mapping algorithm, called Flow-Map, that optimally solves the LUT-based FPGA technology mapping problem for depth minimization for general Boolean networks. This theoretical breakthrough makes a sharp contrast with the fact that conventional technology mapping problem in library-based designs is NP-hard. A key step in Flow-Map is to compute a minimum height K-feasible cut in a network, solved by network flow computation. Our algorithm also effectively minimizes the number of LUTs by maximizing the volume of each cut and by several postprocessing operations. We tested the Flow-Map algorithm on a set of benchmarks and achieved reductions on both the network depth and the number of LUTs in mapping solutions as compared with previous algorithms.


A gender-specific behavioral analysis of mobile device usage data

Mobile devices provide a continuous data stream of contextual behavioral information which can be leveraged for a variety of user services, such as in personalizing ads and customizing home screens. In this paper, we aim to better understand gender-related behavioral patterns found in application, Bluetooth, and Wi-Fi usage. Using a dataset which consists of approximately 19 months of data collected from 189 subjects, gender classification is performed using 1,000 features related to the frequency of events yielding up to 91.8% accuracy. Then, we present a behavioral analysis of application traffic using techniques commonly used for web browsing activity as an alternative data exploration approach. Finally, we conclude with a discussion on impersonation attacks, where we aim to determine if one gender is less vulnerable to unauthorized access on their mobile device.


From Databases to Big Data


Educational game design for online education

The use of educational games in learning environments is an increasingly relevant trend. The motivational and immersive traits of game-based learning have been deeply studied in the literature, but the systematic design and implementation of educational games remain an elusive topic. In this study some relevant requirements for the design of educational games in online education are analyzed, and a general game design method that includes adaptation and assessment features is proposed. Finally, a particular implementation of that design is described in light of its applicability to other implementations and environments. 2008 Elsevier Ltd. All rights reserved.


A review of the literature on the aging adult skull and face: implications for forensic science research and applications.

This paper is a summary of findings of adult age-related craniofacial morphological changes. Our aims are two-fold: (1) through a review of the literature we address the factors influencing craniofacial aging, and (2) the general ways in which a head and face age in adulthood. We present findings on environmental and innate influences on face aging, facial soft tissue age changes, and bony changes in the craniofacial and dentoalveolar skeleton. We then briefly address the relevance of this information to forensic science research and applications, such as the development of computer facial age-progression and face recognition technologies, and contributions to forensic sketch artistry.


Feature-based survey of model transformation approaches

Concrete Figure 5 Features of the body of a domain: (A) patterns and (B) logic Language Paradigm Value Specification Element Creation Implicit Explicit Logic Constraint Object-Oriented Functional Procedural Logic Value Binding Imperative Assignment IBM SYSTEMS JOURNAL, VOL 45, NO 3, 2006 CZARNECKI AND HELSEN 629 operating on one model from the parts operating on other models. For example, classical rewrite rules have an LHS operating on the source model and an RHS operating on the target model. In other approaches, such as a rule implemented as a Java program, there might not be any such syntactic distinction. Multidirectionality Multidirectionality refers to the ability to execute a rule in different directions (see Figure 4A). Rules supporting multidirectionality are usually defined over in/out-domains. Multidirectional rules are available in MTF and QVT Relations. Application condition Transformation rules in some approaches may have an application condition (see Figure 4A) that must be true in order for the rule to be executed. An example is the when-clause in QVT Relations (Example 1). Intermediate structure The execution of a rule may require the creation of some additional intermediate structures (see Figure 4A) which are not part of the models being transformed. These structures are often temporary and require their own metamodel. A particular example of intermediate structures are traceability links. In contrast to other intermediate structures, traceability links are usually persisted. Even if traceability links are not persisted, some approaches, such as AGG and VIATRA, rely on them to prevent multiple ‘‘firings’’ of a rule for the same input element. Parameterization The simplest kind of parameterization is the use of control parameters that allow passing values as control flags (Figure 7). Control parameters are useful for implementing policies. For example, a transformation from class models to relational schemas could have a control parameter specifying which of the alternative patterns of object-relational mapping should be used in a given execution. 7 Generics allow passing data types, including model element types, as parameters. Generics can help make transformation rules more reusable. Generic transformations have been described by Varró and Pataricza. 17 Finally, higher-order rules take other rules as parameters and may provide even higher levels of reuse and abstraction. Stratego 64 is an example of a term rewriting language for program transformation supporting higher-order rules. We are currently not aware of any model transformation approaches with a first class support for higherorder rules. Reflection and aspects Some authors advocate the support for reflection and aspects (Figure 4) in transformation languages. Reflection is supported in ATL by allowing reflective access to transformation rules during the execution of transformations. An aspect-oriented extension of MTL was proposed by Silaghi et al. 65 Reflection and aspects can be used to express concerns that crosscut several rules, such as custom traceability management policies. 66 Rule application control: Location determination A rule needs to be applied to a specific location within its source scope. As there may be more than one match for a rule within a given source scope, we need a strategy for determining the application locations (Figure 8A). The strategy could be deterministic, nondeterministic, or interactive. For example, a deterministic strategy could exploit some standard traversal strategy (such as depth first) over the containment hierarchy in the source. Stratego 64 is an example of a term rewriting language with a rich mechanism for expressing traversal in tree structures. Examples of nondeterministic strategies include one-point application, where a rule is applied to one nondeterministically selected location, and concurrent application, where one rule is Figure 6 Typing Untyped Syntactically Typed Semantically Typed Typing Figure 7 Parameterization Control Parameters Generics Higher-Order Rules Parameterization CZARNECKI AND HELSEN IBM SYSTEMS JOURNAL, VOL 45, NO 3, 2006 630 Figure 8 Model transformation approach features: (A) location determination, (B) rule scheduling, (C) rule organization, (D) source-target relationship, (E) incrementality, (F) directionality, and (G) tracing Concurrent One-Point Non-Deterministic Deterministic Interactive Rule Application Strategy A


Robotic versus Open Partial Nephrectomy: A Systematic Review and Meta-Analysis

OBJECTIVES
To critically review the currently available evidence of studies comparing robotic partial nephrectomy (RPN) and open partial nephrectomy (OPN).

MATERIALS AND METHODS
A comprehensive review of the literature from Pubmed, Web of Science and Scopus was performed in October 2013. All relevant studies comparing RPN with OPN were included for further screening. A cumulative meta-analysis of all comparative studies was performed and publication bias was assessed by a funnel plot.

RESULTS
Eight studies were included for the analysis, including a total of 3418 patients (757 patients in the robotic group and 2661 patients in the open group). Although RPN procedures had a longer operative time (weighted mean difference [WMD]: 40.89; 95% confidence interval [CI], 14.39-67.40; p = 0.002), patients in this group benefited from a lower perioperative complication rate (19.3% for RPN and 29.5% for OPN; odds ratio [OR]: 0.53; 95%CI, 0.42-0.67; p<0.00001), shorter hospital stay (WMD: -2.78; 95%CI, -3.36 to -1.92; p<0.00001), less estimated blood loss(WMD: -106.83; 95%CI, -176.4 to -37.27; p = 0.003). Transfusions, conversion to radical nephrectomy, ischemia time and estimated GFR change, margin status, and overall cost were comparable between the two techniques. The main limitation of the present meta-analysis is the non-randomization of all included studies.

CONCLUSIONS
RPN appears to be an efficient alternative to OPN with the advantages of a lower rate of perioperative complications, shorter length of hospital stay and less blood loss. Nevertheless, high quality prospective randomized studies with longer follow-up period are needed to confirm these findings.


Black-Box Calibration for ADCs With Hard Nonlinear Errors Using a Novel INL-Based Additive Code: A Pipeline ADC Case Study

This paper presents a digital nonlinearity calibration technique for ADCs with strong input–output discontinuities between adjacent codes, such as pipeline, algorithmic, and SAR ADCs with redundancy. In this kind of converter, the ADC transfer function often involves multivalued regions, where conventional integral-nonlinearity (INL)-based calibration methods tend to miscalibrate, negatively affecting the ADC’s performance. As a solution to this problem, this paper proposes a novel INL-based calibration which incorporates information from the ADC’s internal signals to provide a robust estimation of static nonlinear errors for multivalued ADCs. The method is fully generalizable and can be applied to any existing design as long as there is access to internal digital signals. In pipeline or subranging ADCs, this implies access to partial subcodes before digital correction; for algorithmic or SAR ADCs, conversion bit/bits per cycle are used. As a proof-of-concept demonstrator, the experimental results for a 1.2 V 23 mW 130 nm-CMOS pipeline ADC with a SINAD of 58.4 dBc (in nominal conditions without calibration) is considered. In a stressed situation with 0.95 V of supply, the ADC has SINAD values of 47.8 dBc and 56.1 dBc, respectively, before and after calibration (total power consumption, including the calibration logic, being 15.4 mW).


WebSelF: A Web Scraping Framework

We present WebSelF, a framework for web scraping which models the process of web scraping and decomposes it into four conceptually independent, reusable, and composable constituents. We have validated our framework through a full parameterized implementation that is flexible enough to capture previous work on web scraping. We conducted an experiment that evaluated several qualitatively different web scraping constituents (including previous work and combinations hereof) on about 11,000 HTML pages on daily versions of 17 web sites over a period of more than one year. Our framework solves three concrete problems with current web scraping and our experimental results indicate that composition of previous and our new techniques achieve a higher degree of accuracy, precision and specificity than existing tech-


KNN Model-Based Approach in Classification

The k-Nearest-Neighbours (kNN) is a simple but effective method for classification. The major drawbacks with respect to kNN are (1) its low efficiency being a lazy learning method prohibits it in many applications such as dynamic web mining for a large repository, and (2) its dependency on the selection of a “good value” for k. In this paper, we propose a novel kNN type method for classification that is aimed at overcoming these shortcomings. Our method constructs a kNN model for the data, which replaces the data to serve as the basis of classification. The value of k is automatically determined, is varied for different data, and is optimal in terms of classification accuracy. The construction of the model reduces the dependency on k and makes classification faster. Experiments were carried out on some public datasets collected from the UCI machine learning repository in order to test our method. The experimental results show that the kNN based model compares well with C5.0 and kNN in terms of classification accuracy, but is more efficient than the standard kNN.


A new retexturing method for virtual fitting room using Kinect 2 camera

This research work proposes a new method for garment retexturing using a single static image along with depth information obtained using the Microsoft Kinect 2 camera. First the garment is segmented out from the image and texture domain coordinates are computed for each pixel of the shirt using 3D information. After that shading is applied on the new colours from the texture image by applying linear stretching of the luminance of the segmented garment. The proposed method is colour and pattern invariant and results in to visually realistic retexturing. The proposed method has been tested on various images and it is shown that it generally performs better and produces more realistic results compared to the state-of-the-art methods. The proposed method can be an application for the virtual fitting room.


Classification of Various Neighborhood Operations for the Nurse Scheduling Problem

Since the nurse scheduling problem (NSP) is a problem of finding a feasible solution, the solution space must include infeasible solutions to solve it using a local search algorithm. However, the solution space consisting of all the solutions is so large that the search requires much CPU time. In the NSP, some constraints have higher priority. Thus, we can define the solution space to be the set of solutions satisfying some of the important constraints, which are called the elementary constraints. The connectivity of the solution space is also important for the performance. However, the connectivity is not obvious when the solution space consists only of solutions satisfying the elementary constraints and is composed of small neighborhoods. This paper gives theoretical support for using 4-opt-type neighborhood operations by discussing the connectivity of its solution space and the size of the neighborhood. Another interesting point in our model is a special case of the NSP corresponds to the bipartite transportation problem, and our result also applies to it.


Ecient Sparse Matrix-Vector Multiplication on CUDA

The massive parallelism of graphics processing units (GPUs) offers tremendous performance in many high-performance computing applications. While dense linear algebra readily maps to such platforms, harnessing this potential for sparse matrix computations presents additional challenges. Given its role in iterative methods for solving sparse linear systems and eigenvalue problems, sparse matrix-vector multiplication (SpMV) is of singular importance in sparse linear algebra. In this paper we discuss data structures and algorithms for SpMV that are efficiently implemented on the CUDA platform for the fine-grained parallel architecture of the GPU. Given the memory-bound nature of SpMV, we emphasize memory bandwidth efficiency and compact storage formats. We consider a broad spectrum of sparse matrices, from those that are well-structured and regular to highly irregular matrices with large imbalances in the distribution of nonzeros per matrix row. We develop methods to exploit several common forms of matrix structure while offering alternatives which accommodate greater irregularity. On structured, grid-based matrices we achieve performance of 36 GFLOP/s in single precision and 16 GFLOP/s in double precision on a GeForce GTX 280 GPU. For unstructured finite-element matrices, we observe performance in excess of 15 GFLOP/s and 10 GFLOP/s in single and double precision respectively. These results compare favorably to prior state-of-the-art studies of SpMV methods on conventional multicore processors. Our double precision SpMV performance is generally two and a half times that of a Cell BE with 8 SPEs and more than ten times greater than that of a quad-core Intel Clovertown system.


High-speed VLSI architectures for the AES algorithm

This paper presents novel high-speed architectures for the hardware implementation of the Advanced Encryption Standard (AES) algorithm. Unlike previous works which rely on look-up tables to implement the SubBytes and InvSubBytes transformations of the AES algorithm, the proposed design employs combinational logic only. As a direct consequence, the unbreakable delay incurred by look-up tables in the conventional approaches is eliminated, and the advantage of subpipelining can be further explored. Furthermore, composite field arithmetic is employed to reduce the area requirements, and different implementations for the inversion in subfield GF(2/sup 4/) are compared. In addition, an efficient key expansion architecture suitable for the subpipelined round units is also presented. Using the proposed architecture, a fully subpipelined encryptor with 7 substages in each round unit can achieve a throughput of 21.56 Gbps on a Xilinx XCV1000 e-8 bg560 device in non-feedback modes, which is faster and is 79% more efficient in terms of equivalent throughput/slice than the fastest previous FPGA implementation known to date.


Improving Real-Time Captioning Experiences for Deaf and Hard of Hearing Students

We take a qualitative approach to understanding deaf and hard of hearing (DHH) students' experiences with real-time captioning as an access technology in mainstream university classrooms. We consider both existing human-based captioning as well as new machine-based solutions that use automatic speech recognition (ASR). We employed a variety of qualitative research methods to gather data about students' captioning experiences including in-class observations, interviews, diary studies, and usability evaluations. We also conducted a co-design workshop with 8 stakeholders after our initial research findings. Our results show that accuracy and reliability of the technology are still the most important issues across captioning solutions. However, we additionally found that current captioning solutions tend to limit students' autonomy in the classroom and present a variety of user experience shortcomings, such as complex setups, poor feedback and limited control over caption presentation. Based on these findings, we propose design requirements and recommend features for real-time captioning in mainstream classrooms.


An Empirical Examination of the Effects of Web Personalization at Different Stages of Decision-Making

Personalization agents are incorporated in many websites to tailor content and interfaces for individual users. But in contrast to the proliferation of personalized web services worldwide, empirical research studying the effects of web personalization is scant. How does exposure to personalized offers affect subsequent product consideration and choice outcome? Drawing on the literature in HCI and consumer behavior, this research examines the effects of web personalization on users’ information processing and expectations through different decision-making stages. A study using a personalized ring-tone download website was conducted. Our findings provide empirical evidence of the effects of web personalization. In particular, when consumers are forming their consideration sets, the agents have the capacity to help them discover new products and generate demand for unfamiliar products. Once the users have arrived at their choice, however, the persuasive effects from the personalization agent diminish. These results establish that adaptive role of personalization agents at different stages of decision-making.


Pacific kids DASH for health (PacDASH) randomized, controlled trial with DASH eating plan plus physical activity improves fruit and vegetable intake and diastolic blood pressure in children.

BACKGROUND
Pacific Kids DASH for Health (PacDASH) aimed to improve child diet and physical activity (PA) level and prevent excess weight gain and elevation in blood pressure (BP) at 9 months.

METHODS
PacDASH was a two-arm, randomized, controlled trial (ClinicalTrials.gov: NCT00905411). Eighty-five 5- to 8-year-olds in the 50th-99th percentile for BMI were randomly assigned to treatment (n=41) or control (n=44) groups; 62 completed the 9-month trial. Sixty-two percent were female. Mean age was 7.1±0.95 years. Race/ethnicity was Asian (44%), Native Hawaiian or Other Pacific Islander (28%), white (21%), or other race/ethnicity (7%). Intervention was provided at baseline and 3, 6 and 9 months, with monthly supportive mailings between intervention visits, and a follow-up visit at 15 months to observe maintenance. Diet and PA were assessed by 2-day log. Body size, composition, and BP were measured. The intervention effect on diet and PA, body size and composition, and BP by the end of the intervention was tested using an F test from a mixed regression model, after adjustment for sex, age, and ethnic group.

RESULTS
Fruit and vegetable (FV) intake decreased less in the treatment than control group (p=0.04). Diastolic BP (DBP) was 12 percentile units lower in the treatment than control group after 9 months of intervention (p=0.01). There were no group differences in systolic BP (SBP) or body size/composition.

CONCLUSIONS
The PacDASH trial enhanced FV intake and DBP, but not SBP or body size/composition.


Efficient Time-Domain Image Formation with Precise Topography Accommodation for General Bistatic SAR Configurations

Due to the lack of an appropriate symmetry in the acquisition geometry, general bistatic synthetic aperture radar (SAR) cannot benefit from the two main properties of low-to-moderate resolution monostatic SAR: azimuth-invariance and topography-insensitivity. The precise accommodation of azimuth-variance and topography is a real challenge for efficent image formation algorithms working in the Fourier domain, but can be quite naturally handled by time-domain approaches. We present an efficient and practical implementation of a generalised bistatic SAR image formation algorithm with an accurate accommodation of these two effects. The algorithm has a common structure with the monostatic fast-factorised backprojection (FFBP), and is therefore based on subaperture processing. The images computed over the different subapertures are displayed in an advantageous elliptical coordinate system capable of incorporating the topographic information of the imaged scene in an analogous manner as topography-dependent monostatic SAR algorithms do. Analytical expressions for the Nyquist requirements using this coordinate system are derived. The overall discussion includes practical implementation hints and a realistic computational burden estimation. The algorithm is tested with both simulated and actual bistatic SAR data. The actual data correspond to the spaceborne-airborne experiment between TerraSAR-X and F-SAR performed in 2007 and to the DLR-ONERA airborne experiment carried out in 2003. The presented approach proves its suitability for the precise SAR focussing of the data acquired in general bistatic configurations.


Machine Comprehension Based on Learning to Rank

Machine comprehension plays an essential role in NLP and has been widely explored with dataset like MCTest. However, this dataset is too simple and too small for learning true reasoning abilities. (Hermann et al., 2015) therefore release a large scale news article dataset and propose a deep LSTM reader system for machine comprehension. However, the training process is expensive. We therefore try feature-engineered approach with semantics on the new dataset to see how traditional machine learning technique and semantics can help with machine comprehension. Meanwhile, our proposed L2R reader system achieves good performance with efficiency and less training data.


The challenges of building mobile underwater wireless networks for aquatic applications

The large-scale mobile underwater wireless sensor network (UWSN) is a novel networking paradigm to explore aqueous environments. However, the characteristics of mobile UWSNs, such as low communication bandwidth, large propagation delay, floating node mobility, and high error probability, are significantly different from ground-based wireless sensor networks. The novel networking paradigm poses interdisciplinary challenges that will require new technological solutions. In particular, in this article we adopt a top-down approach to explore the research challenges in mobile UWSN design. Along the layered protocol stack, we proceed roughly from the top application layer to the bottom physical layer. At each layer, a set of new design intricacies is studied. The conclusion is that building scalable mobile UWSNs is a challenge that must be answered by interdisciplinary efforts of acoustic communications, signal processing, and mobile acoustic network protocol design.


A transmission control scheme for media access in sensor networks

We study the problem of media access control in the novel regime of sensor networks, where unique application behavior and tight constraints in computation power, storage, energy resources, and radio technology have shaped this design space to be very different from that found in traditional mobile computing regime. Media access control in sensor networks must not only be energy efficient but should also allow fair bandwidth allocation to the infrastructure for all nodes in a multihop network. We propose an adaptive rate control mechanism aiming to support these two goals and find that such a scheme is most effective in achieving our fairness goal while being energy efficient for both low and high duty cycle of network traffic.


Challenges for efficient communication in underwater acoustic sensor networks

Ocean bottom sensor nodes can be used for oceanographic data collection, pollution monitoring, offshore exploration and tactical surveillance applications. Moreover, Unmanned or Autonomous Underwater Vehicles (UUVs, AUVs), equipped with sensors, will find application in exploration of natural undersea resources and gathering of scientific data in collaborative monitoring missions. Underwater acoustic networking is the enabling technology for these applications. Underwater Networks consist of a variable number of sensors and vehicles that are deployed to perform collaborative monitoring tasks over a given area.In this paper, several fundamental key aspects of underwater acoustic communications are investigated. Different architectures for two-dimensional and three-dimensional underwater sensor networks are discussed, and the underwater channel is characterized. The main challenges for the development of efficient networking solutions posed by the underwater environment are detailed at all layers of the protocol stack. Furthermore, open research issues are discussed and possible solution approaches are outlined.


The WHOI micro-modem: an acoustic communications and navigation system for multiple platforms

The micro-modem is a compact, low-power, underwater acoustic communications and navigation subsystem. It has the capability to perform low-rate frequency-hopping frequency-shift keying (FH-FSK), variable rate phase-coherent keying (PSK), and two different types of long base line navigation, narrow-band and broadband. The system can be configured to transmit in four different bands from 3 to 30 kHz, with a larger board required for the lowest frequency. The user interface is based on the NMEA standard, which is a serial port specification. The modem also includes a simple built-in networking capability which supports up to 16 units in a polled or random-access mode and has an acknowledgement capability which supports guaranteed delivery transactions. The paper contains a detailed system description and results from several tests are also presented


The state of the art in underwater acoustic telemetry

Progress in underwater acoustic telemetry since 1982 is reviewed within a framework of six current research areas: (1) underwater channel physics, channel simulations, and measurements; (2) receiver structures; (3) diversity exploitation; (4) error control coding; (5) networked systems; and (6) alternative modulation strategies. Advances in each of these areas as well as perspectives on the future challenges facing them are presented. A primary thesis of this paper is that increased integration of high-fidelity channel models into ongoing underwater telemetry research is needed if the performance envelope (defined in terms of range, rate, and channel complexity) of underwater modems is to expand.


Performance of Store Brands: A Cross-Country Analysis of Consumer Store Brand Preferences, Perceptions, and Risk

This paper empirically studies consumer choice behavior in regard to store brands in the US, UK and Spain. Store brand market shares differ by country and they are usually much higher in Europe than in the US. However, there is surprisingly little work in marketing that empirically studies the reasons that underlie higher market shares associated with store brands in Europe over the US. In this paper, we empirically study the notion that the differential success of store brands in the US versus in Europe is the higher brand equity that store brands command in Europe over the US. We use a framework based on previous work to conduct our analysis: consumer brand choice under uncertainty, and brands as signals of product positions. More specifically, we examine whether uncertainty about quality (or, the positioning of the brand in the product space), perceived quality of store brands versus national brands, consistency in store brand offerings over time, as well as consumer attitudes towards risk, quality and price underlie the differential success of store brands at least partially in the US versus Europe. We propose and estimate a model that explicitly incorporates the impact of uncertainty on consumer behavior. We compare 1) levels of uncertainty associated with store brands versus national brands, 2) consistency in product positions over time for both national and store brands, 3) relative quality levels of national versus store brands, and 4) consumer sensitivity to price, quality and risk across the three countries we study. The model is estimated on scanner panel data on detergent in the US, UK and Spanish markets, and on toilet paper and margarine in the US and Spain. We find that consumer learning and perceived risk (and the associated brand equity), as well as consumer attitude towards risk, quality and price, play an important role in consumers’ store versus national brand choices and contribute to the differences in relative success of store brands across the countries we study.


Approximate queuing analysis for IEEE 802.15.4 sensor network

Wireless sensor networks (WSNs) have attracted much attention in recent years for their unique characteristics and wide use in many different applications. Especially, in military networks, all sensor motes are deployed randomly and densely. How can we optimize the number of deployed nodes (sensor node and sink) with a QoS guarantee (minimal end-to-end delay and packet drop)? In this paper, using the M/M/1 queuing model we propose a deployment optimization model for non-beacon-mode 802.15.4 sensor networks. The simulation results show that the proposed model is a promising approach for deploying the sensor network.


A Baseline for General Music Object Detection with Deep Learning

Deep learning is bringing breakthroughs to many computer vision subfields including Optical Music Recognition (OMR), which has seen a series of improvements to musical symbol detection achieved by using generic deep learning models. However, so far, each such proposal has been based on a specific dataset and different evaluation criteria, which made it difficult to quantify the new deep learning-based state-of-the-art and assess the relative merits of these detection models on music scores. In this paper, a baseline for general detection of musical symbols with deep learning is presented. We consider three datasets of heterogeneous typology but with the same annotation format, three neural models of different nature, and establish their performance in terms of a common evaluation standard. The experimental results confirm that the direct music object detection with deep learning is indeed promising, but at the same time illustrates some of the domain-specific shortcomings of the general detectors. A qualitative comparison then suggests avenues for OMR improvement, based both on properties of the detection model and how the datasets are defined. To the best of our knowledge, this is the first time that competing music object detection systems from the machine learning paradigm are directly compared to each other. We hope that this work will serve as a reference to measure the progress of future developments of OMR in music object detection.


System for the Measurement of Cathodic Currents in Electrorefining Processes That Employ Multicircuital Technology

This paper presents a measurement system of cathodic currents for copper electrorefining processes using multicircuital technology with optibar intercell bars. The proposed system is based on current estimation using 55 magnetic field sensors per intercell bar. Current values are sampled and stored every 5 min for seven days in a compatible SQL database. The method does not affect the normal operation of the process and does not require any structural modifications. The system for online measurement of 40 cells involving 2090 sensors is in operation in an electrorefinery site.


An Inductive 2-D Position Detection IC With 99.8% Accuracy for Automotive EMR Gear Control System

In this paper, the analog front end (AFE) for an inductive position sensor in an automotive electromagnetic resonance gear control applications is presented. To improve the position detection accuracy, a coil driver with an automatic two-step impedance calibration is proposed which, despite the load variation, provides the desired driving capability by controlling the main driver size. Also, a time shared analog-to-digital converter (ADC) is proposed to convert eight-phase signals while reducing the current consumption and area to 1/8 of the conventional structure. A relaxation oscillator with temperature compensation is proposed to generate a constant clock frequency in vehicle temperature conditions. This chip is fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process and the die area is 2 mm <inline-formula> <tex-math notation="LaTeX">$\times 1.5$ </tex-math></inline-formula> mm. The power consumption of the AFE is 23.1 mW from the supply voltage of 3.3 V to drive one transmitter (Tx) coil and eight receiver (Rx) coils. The measured position detection accuracy is greater than 99.8 %. The measurement of the Tx shows a driving capability higher than 35 mA with respect to the load change.


Transcriptional regulatory networks in Saccharomyces cerevisiae.

We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.


Online crowdsourcing: Rating annotators and obtaining cost-effective labels

Labeling large datasets has become faster, cheaper, and easier with the advent of crowdsourcing services like Amazon Mechanical Turk. How can one trust the labels obtained from such services? We propose a model of the labeling process which includes label uncertainty, as well a multi-dimensional measure of the annotators' ability. From the model we derive an online algorithm that estimates the most likely value of the labels and the annotator abilities. It finds and prioritizes experts when requesting labels, and actively excludes unreliable annotators. Based on labels already obtained, it dynamically chooses which images will be labeled next, and how many labels to request in order to achieve a desired level of confidence. Our algorithm is general and can handle binary, multi-valued, and continuous annotations (e.g. bounding boxes). Experiments on a dataset containing more than 50,000 labels show that our algorithm reduces the number of labels required, and thus the total cost of labeling, by a large factor while keeping error rates low on a variety of datasets.


Neural Question Answering at BioASQ 5B

This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-ofthe-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.


Compact Planar Microstrip Branch-Line Couplers Using the Quasi-Lumped Elements Approach With Nonsymmetrical and Symmetrical T-Shaped Structure

A class of the novel compact-size branch-line couplers using the quasi-lumped elements approach with symmetrical or nonsymmetrical T-shaped structures is proposed in this paper. The design equations have been derived, and two circuits using the quasi-lumped elements approach were realized for physical measurements. This novel design occupies only 29% of the area of the conventional approach at 2.4 GHz. In addition, a third circuit was designed by using the same formula implementing a symmetrical T-shaped structure and occupied both the internal and external area of the coupler. This coupler achieved 500-MHz bandwidth while the phase difference between S21 and S31 is within 90degplusmn1deg. Thus, the bandwidth is not only 25% wider than that of the conventional coupler, but occupies only 70% of the circuit area compared to the conventional design. All three proposed couplers can be implemented by using the standard printed-circuit-board etching processes without any implementation of lumped elements, bonding wires, and via-holes, making it very useful for wireless communication systems


The Anatomy of Motivation: An Evolutionary-Ecological Approach

There have been few attempts to bring evolutionary theory to the study of human motivation. From this perspective motives can be considered psychological mechanisms to produce behavior that solves evolutionarily important tasks in the human niche. From the dimensions of the human niche we deduce eight human needs: optimize the number and survival of gene copies; maintain bodily integrity; avoid external threats; optimize sexual, environmental, and social capital; and acquire reproductive and survival skills. These needs then serve as the foundation for a necessary and sufficient list of 15 human motives, which we label: lust, hunger, comfort, fear, disgust, attract, love, nurture, create, hoard, affiliate, status, justice, curiosity, and play. We show that these motives are consistent with evidence from the current literature. This approach provides us with a precise vocabulary for talking about motivation, the lack of which has hampered progress in behavioral science. Developing testable theories about the structure and function of motives is essential to the project of understanding the organization of animal cognition and learning, as well as for the applied behavioral sciences.


Students’ opinions on blended learning and its implementation in terms of their learning styles

The purpose of this article is to examine students’ views on the blended learning method and its use in relation to the students’ individual learning style. The study was conducted with 31 senior students. Web based media together with face to face classroom settings were used in the blended learning framework. A scale of Students’ Views on Blended Learning and its implementation, Kolb’s Learning Style Inventory, Pre-Information Form and open ended questions were used to gather data. The majority of the students’ fell into assimilators, accommodators and convergers learning styles. Results revealed that students’ views on blended learning method and its use are quite positive.


A Method for Obtaining Digital Signatures and Public-Key Cryptosystems (Reprint)

An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences:Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intended recipient. Only he can decipher the message, since only he knows the corresponding decryption key.
A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret prime numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d = 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.


Blockchain Platform for Industrial Internet of Things

Internet of Things (IoT) are being adopted for industrial and manufacturing applications such as manufacturing automation, remote machine diagnostics, prognostic health management of industrial machines and supply chain management. CloudBased Manufacturing is a recent on-demand model of manufacturing that is leveraging IoT technologies. While Cloud-Based Manufacturing enables on-demand access to manufacturing resources, a trusted intermediary is required for transactions between the users who wish to avail manufacturing services. We present a decentralized, peer-to-peer platform called BPIIoT for Industrial Internet of Things based on the Block chain technology. With the use of Blockchain technology, the BPIIoT platform enables peers in a decentralized, trustless, peer-to-peer network to interact with each other without the need for a trusted intermediary.


Tree Edit Models for Recognizing Textual Entailments, Paraphrases, and Answers to Questions

This document provides additional details about the experiments described in (Heilman and Smith, 2010). Note that while this document provides information about the datasets and experimental methods, it does not provide further results. If you have any further questions, please feel free to contact the first author. The preprocessed datasets (i.e., tagged and parsed) will be made available for research purposes upon request.