Conditional High-Order Boltzmann Machines for Supervised Relation Learning

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.


Rectifying the Bound Document Image Captured by the Camera: A Model Based Approach

A model based approach for rectifying the camera image of the bound document has been developed, i.e., the surface of the document is represented by a general cylindrical surface. The principle of using the model to unwrap the image is discussed. Practically, the skeleton of each horizontal text line is extracted to help estimate the parameter of the model, and rectify the images. To use the model, only a few priori is required, and no more auxiliary device is necessary. Experiment results are given to demonstrate the feasibility and the stability of the method.


The SAGE project: a storage centric approach for exascale computing: invited paper

SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme Computing (BDEC) capable infrastructure with associated software stack. The SAGE system follows a storage centric approach as it is capable of storing and processing large data volumes at the Exascale regime.
 SAGE addresses the convergence of Big Data Analysis and HPC in an era of next-generation data centric computing. This convergence is driven by the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors where data needs to be processed, analyzed and integrated into simulations to derive scientific and innovative insights. A first prototype of the SAGE system has been been implemented and installed at the Jülich Supercomputing Center. The SAGE storage system consists of multiple types of storage device technologies in a multi-tier I/O hierarchy, including flash, disk, and non-volatile memory technologies. The main SAGE software component is the Seagate Mero Object Storage that is accessible via the Clovis API and higher level interfaces. The SAGE project also includes scientific applications for the validation of the SAGE concepts.
 The objective of this paper is to present the SAGE project concepts, the prototype of the SAGE platform and discuss the software architecture of the SAGE system.


Outlier Detection with Kernel Density Functions

Outlier detection has recently become an important problem in many industrial and financial applications. In this paper, a novel unsupervised algorithm for outlier detection with a solid statistical foundation is proposed. First we modify a nonparametric density estimate with a variable kernel to yield a robust local density estimation. Outliers are then detected by comparing the local density of each point to the local density of its neighbors. Our experiments performed on several simulated data sets have demonstrated that the proposed approach can outperform two widely used outlier detection algorithms (LOF and LOCI).


Episodic Training for Domain Generalization

Domain generalization (DG) is the challenging and topical problem of learning models that generalize to novel testing domain with different statistics than a set of known training domains. The simple approach of aggregating data from all source domains and training a single deep neural network end-to-end on all the data provides a surprisingly strong baseline that surpasses many prior published methods. In this paper we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime. Specifically, we decompose a deep network into feature extractor and classifier components, and then train each component by simulating it interacting with a partner who is badly tuned for the current domain. This makes both components more robust, ultimately leading to our networks producing state-of-the-art performance on three DG benchmarks. As a demonstration, we consider the pervasive workflow of using an ImageNet trained CNN as a fixed feature extractor for downstream recognition tasks. Using the Visual Decathlon benchmark, we demonstrate that our episodic-DG training improves the performance of such a general purpose feature extractor by explicitly training it for robustness to novel problems. This provides the largest-scale demonstration of heterogeneous DG to date.


Personality Based E-Recruitment System

Nodes in Mobile Ad Hoc Networks (MANETs) are limited battery powered. That’s why energy efficient routing has become an important optimization criterion in MANETs. The conventional routing protocols do not consider energy of the nodes while selecting routes which leads to early exhaustion of nodes and partitioning of the network. This paper attempts to provide an energy aware routing algorithm. The proposed algorithm finds the transmission energy between the nodes relative to the distance and the performance of the algorithm is analyzed between two metrics Total Transmission energy of a route and Maximum Number of Hops. The proposed algorithm shows efficient energy utilization and increased network lifetime with total transmission energy metric.


Translation of Natural Language Query Into Keyword Query Using a RNN Encoder-Decoder

The number of natural language queries submitted to search engines is increasing as search environments get diversified. However, legacy search engines are still optimized for short keyword queries. Thus, the use of natural language queries at legacy search engines degrades the retrieval performance of the engines. This paper proposes a novel method to translate a natural language query into a keyword query relevant to the natural language query for retrieving better search results without change of the engines. The proposed method formulates the translation as a generation task. That is, the method generates a keyword query from a natural language query by preserving the semantics of the natural language query. A recurrent neural network encoder-decoder architecture is adopted as a generator of keyword queries from natural language queries. In addition, an attention mechanism is also used to cope with long natural language queries.


Harnessing the power of FPGAs using altera's OpenCL compiler

In recent years, Field-Programmable Gate Arrays have become extremely powerful computational platforms that can efficiently solve many complex problems. The most modern FPGAs comprise effectively millions of programmable elements, signal processing elements and high-speed interfaces, all of which are necessary to deliver a complete solution. The power of FPGAs is unlocked via low-level programming languages such as VHDL and Verilog, which allow designers to explicitly specify the behavior of each programmable element. While these languages provide a means to create highly efficient logic circuits, they are akin to "assembly language" programming for modern processors. This is a serious limiting factor for both productivity and the adoption of FPGAs on a wider scale. In this talk, we use the OpenCL language to explore techniques that allow us to program FPGAs at a level of abstraction closer to traditional software-centric approaches. OpenCL is an industry standard parallel language based on 'C' that offers numerous advantages that enable designers to take full advantage of the capabilities offered by FPGAs, while providing a high-level design entry language that is familiar to a wide range of programmers.
 To demonstrate the advantages a high-level programming language can offer, we demonstrate how to use Altera's OpenCL Compiler on a set of case studies. The first application is single-precision general-element matrix multiplication (SGEMM). It is an example of a highly-parallel algorithm for which an efficient circuit structures are well known. We show how this application can be implemented in OpenCL and how the high-level description can be optimized to generate the most efficient circuit in hardware. The second application is a Fast Fourier Transform (FFT), which is a classical FPGA benchmark that is known to have a good implementation on FPGAs. We show how we can implement the FFT algorithm, while exploring the many different possible architectural choices that lead to an optimized implementation for a given FPGA. Finally, we discuss a Monte-Carlo Black-Scholes simulation, which demonstrates the computational power of FPGAs. We describe how a random number generator in conjunction with computationally intensive operations can be harnessed on an FPGA to generate a high-speed benchmark, which also consumes far less power than the same benchmark running on a comparable GPU. We conclude the tutorial with a set of live demonstrations.
 Through this tutorial we show the benefits high-level languages offer for system-level design and productivity. In particular, Altera's OpenCL compiler is shown to enable high-performance application design that fully utilizes capabilities of modern FPGAs.


A Portable SDR Non-Orthogonal Multiple Access Testbed for 5G Networks

Non-orthogonal multiple access (NOMA) is envisioned to be one of the promising radio access techniques for the fifth generation (5G) mobile networks. In this paper, a portable NOMA testbed based on software defined radio (SDR) is developed by transporting our NOMA system to mini personal computers (PCs). Moreover, the NOMA testbed has been enhanced from 5 MHz bandwidth to 10 MHz bandwidth. As the computation complexity grows higher with the increase of system bandwidth, the portable NOMA testbed is not competent to fully demonstrate the performance of our NOMA system due to the limitation of mini PC. For a better understanding of the NOMA testbed, the system architecture and scenario are introduced in brief. Then, the implementation of NOMA transceiver and the protocol stack are described separately. Finally, a series of experiments are carried out to evaluate its performance loss compared with the original NOMA system based on desktops. The experimental results indicate that the performance of processors may be a short slab for the development of portable SDR-based testbeds towards 5G networks.


Lower Bounds for Finding Stationary Points of Non-Convex , Smooth High-Dimensional Functions ∗

We establish lower bounds on the complexity of finding -stationary points of smooth, non-convex, high-dimensional functions. For functions with Lipschitz continuous pth derivative, we show that all algorithms—even randomized algorithms observing arbitrarily high-order derivatives—have worst-case iteration count Ω( −(p+1)/p). Our results imply that the O( −2) convergence rate of gradient descent is unimprovable without additional assumptions (e.g. Lipschitz Hessian), and that cubic regularization of Newton’s method and pth order regularization in general are similarly optimal. Additionally, we prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates better than O( −8/5), which is within −1/15 of the recently established Õ( −5/3) rate for accelerated gradient descent.


Bayesian Neural Word Embedding

Recently, several works in the domain of natural language processing presented successful methods for word embedding. Among them, the Skip-Gram with negative sampling, known also as word2vec, advanced the state-of-the-art of various linguistics tasks. In this paper, we propose a scalable Bayesian neural word embedding algorithm. The algorithm relies on a Variational Bayes solution for the SkipGram objective and a detailed step by step description is provided. We present experimental results that demonstrate the performance of the proposed algorithm for word analogy and similarity tasks on six different datasets and show it is competitive with the original Skip-Gram method.


Exploring the factors affecting MOOC retention: A survey study

Massive Open Online Courses (MOOCs) hold the potential to open up educational opportunities to a global audience. However, evidence suggests that only a small proportion of MOOC participants go on to complete their courses and relatively little is understood about the MOOC design and implementation factors that influence retention. This paper reports a survey study of 379 participants enrolled at university in Cairo who were encouraged to take a MOOC of their own choice as part of their development. 122 participants (32.2%) went onto to complete an entire course. There were no significant differences in completion rates by gender, level of study (undergraduate or postgraduate) or MOOC platform. A post-MOOC survey of students' perceptions found that MOOC Course Content was a significant predictor of MOOC retention, with the relationship mediated by the effect of content on the Perceived Effectiveness of the course. Interaction with the instructor of the MOOC was also found to be significant predictor of MOOC retention. Overall these constructs explained 79% of the variance in MOOC retention. © 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).


Corporate Sustainability : First Evidence on Materiality

An increasing number of companies make sustainability investments, and an increasing number of investors integrate sustainability performance data in their capital allocation decisions. To date however, the prior academic literature has not distinguished between investments in material versus immaterial sustainability issues. We develop a novel dataset by hand-mapping data on sustainability investments classified as material for each industry into firm-specific performance data on a variety of sustainability investments. This allows us to present new evidence on the value implications of sustainability investments. Using calendar-time portfolio stock return regressions we find that firms with good performance on material sustainability issues significantly outperform firms with poor performance on these issues, suggesting that investments in sustainability issues are shareholder-value enhancing. Further, firms with good performance on sustainability issues not classified as material do not underperform firms with poor performance on these same issues, suggesting investments in sustainability issues are at a minimum not value-destroying. Finally, firms with good performance on material issues and concurrently poor performance on immaterial issues perform the best. These results speak to the efficiency of firms’ sustainability investments, and also have implications for asset managers who have committed to the integration of sustainability factors in their capital allocation decisions.  Mozaffar Khan is a visiting Associate Professor at Harvard Business School. George Serafeim is the Jakurski Family Associate Professor of Business Administration at Harvard Business School. Aaron Yoon is a doctoral student at Harvard Business School. We are grateful for comments from seminar participants at National University of Singapore and Harvard Business School. We are grateful for financial support from the Division of Faculty Research and Development at Harvard Business School. George Serafeim has served on the Standards Council of SASB. Contact emails: mkhan@hbs.edu; gserafeim@hbs.edu; ayoon@hbs.edu.


VBaaS: VNF Benchmark-as-a-Service

When rolling out Network Function Virtualization (NFV) services, resource monitoring becomes a critical task subject to different cost-accuracy tradeoffs depending on whether continuous monitoring or more static infrastructure resource views are taken. In this context, we propose Virtualized Network Functions (VNF) Benchmark-as-a-Service (VBaaS) to enable not only run-time resource evaluation but also test-before-deploy opportunities for VNFs and NFV Infrastructures. We describe the motivation behind VBaaS and its main value proposition for a number of use cases around the orchestration tasks of VNF Forwarding Graphs We present the main components of VBaaS along their system interactions and interfaces, discussing the main benefits of adopting VBaaS and open research issues. Addressing the identified challenges and finalizing our proof of concept VBaaS are our main ongoing work activities.


AN ACCEPTANCE MODEL FOR USEFUL AND FUN INFORMATION SYSTEMS

Investigating the factors associated with user acceptance of new software systems has been an important research stream in the field of information systems for many years. The technology acceptance model has long been used to examine the acceptance of utilitarian systems. Recently, it has been used to examine recreational or pleasure-oriented systems. Many examples exist of software that, depending on the context of use, can be used for productive and pleasurable interaction. This paper examines the determinants of use of one such “dual” system. A survey of users of a dual system was conducted. Results show that perceived usefulness is more important in determining intention to use than perceived enjoyment, and that perceived ease of use has no direct impact on intention, but still has a strong indirect effect.


Interactive Neural Translation Assistance for Human Translators

We present the first user study on neural interactive translation prediction. Our neural translation aid was built into existing online translation software. Depending on the prefix already typed by the user the system suggests a list of continuations. We assessed the impact of the system on human translators in a user study with both professional and non-professional translators. The analysis was done with respect to translation speed and translation accuracy as assessed by human judges. We find that our neural translation aid enables relatively fast translations and does not compromise on quality.


Accurate and robust localization of duplicated region in copy–move image forgery

Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.


Using Information Content to Evaluate Semantic Similarity in a Taxonomy

1. Please explain how this manuscript advances this field of research and/or contributes something new to the literature. They tackle the problem of semantic similarity based on the information content the concepts share. They combine the taxonomic structure with the empirical problem estimates which provides better way of adapting knowledge to multiple context concepts. They also try to find a solution to the multiple inheritance problems.


Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy

This paper presents a new approach for measuring semantic similarity/distance between words and concepts. It combines a lexical taxonomy structure with corpus statistical information so that the semantic distance between nodes in the semantic space constructed by the taxonomy can be better quantified with the computational evidence derived from a distributional analysis of corpus data. Specifically, the proposed measure is a combined approach that inherits the edge-based approach of the edge counting scheme, which is then enhanced by the node-based approach of the information content calculation. When tested on a common data set of word pair similarity ratings, the proposed approach outperforms other computational models. It gives the highest correlation value (r = 0.828) with a benchmark based on human similarity judgements, whereas an upper bound (r = 0.885) is observed when human subjects replicate the same task.


Automatic Word Sense Discrimination

This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words.


Using WordNet to Disambiguate Word Senses for Text Retrieval

This paper describes an automatic indexing procedure that uses the “IS-A” relations contained within WordNet and the set of nouns contained in a text to select a sense for each plysemous noun in the text. The result of the indexing procedure is a vector in which some of the terms represent word senses instead of word stems. Retrieval experiments comparing the effectivenss of these sense-based vectors vs. stem-based vectors show the stem-based vectors to be superior overall, although the sense-based vectors do improve the performance of some queries. The overall degradation is due in large part to the difficulty of disambiguating senses in short query statements. An analysis of these results suggests two conclusions: the IS-A links define a generalization/specialization hierarchy that is not sufficient to reliably select the correct sense of a noun from the set of fine sense distinctions in WordNet; and missing correct matches because of incorrect sense resolution has a much more deleterious effect on retrieval performance than does making spurious matches.


Are Slice-Based Cohesion Metrics Actually Useful in Effort-Aware Post-Release Fault-Proneness Prediction? An Empirical Study

Background. Slice-based cohesion metrics leverage program slices with respect to the output variables of a module to quantify the strength of functional relatedness of the elements within the module. Although slice-based cohesion metrics have been proposed for many years, few empirical studies have been conducted to examine their actual usefulness in predicting fault-proneness. Objective. We aim to provide an in-depth understanding of the ability of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction, i.e. their effectiveness in helping practitioners find post-release faults when taking into account the effort needed to test or inspect the code. Method. We use the most commonly used code and process metrics, including size, structural complexity, Halstead's software science, and code churn metrics, as the baseline metrics. First, we employ principal component analysis to analyze the relationships between slice-based cohesion metrics and the baseline metrics. Then, we use univariate prediction models to investigate the correlations between slice-based cohesion metrics and post-release fault-proneness. Finally, we build multivariate prediction models to examine the effectiveness of slice-based cohesion metrics in effort-aware post-release fault-proneness prediction when used alone or used together with the baseline code and process metrics. Results. Based on open-source software systems, our results show that: 1) slice-based cohesion metrics are not redundant with respect to the baseline code and process metrics; 2) most slice-based cohesion metrics are significantly negatively related to post-release fault-proneness; 3) slice-based cohesion metrics in general do not outperform the baseline metrics when predicting post-release fault-proneness; and 4) when used with the baseline metrics together, however, slice-based cohesion metrics can produce a statistically significant and practically important improvement of the effectiveness in effort-aware post-release fault-proneness prediction. Conclusion. Slice-based cohesion metrics are complementary to the most commonly used code and process metrics and are of practical value in the context of effort-aware post-release fault-proneness prediction.


Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning

Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resourcescarce and do not have such parallel data due to low volume of queries and nonexistence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resourcescarce languages. We propose a sequenceto-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.


Spatial-Aware Object-Level Saliency Prediction by Learning Graphlet Hierarchies

To fill the semantic gap between the predictive power of computational saliency models and human behavior, this paper proposes to predict where people look at using spatial-aware object-level cues. While object-level saliency has been recently suggested by psychophysics experiments and shown effective with a few computational models, the spatial relationship between the objects has not yet been explored in this context. We in this work for the first time explicitly model such spatial relationship, as well as leveraging semantic information of an image to enhance object-level saliency modeling. The core computational module is a graphlet-based (i.e., graphlets are moderate-sized connected subgraphs) deep architecture, which hierarchically learns a saliency map from raw image pixels to object-level graphlets (oGLs) and further to spatial-level graphlets (sGLs). Eye tracking data are also used to leverage human experience in saliency prediction. Experimental results demonstrate that the proposed oGLs and sGLs well capture object-level and spatial-level cues relating to saliency, and the resulting saliency model performs competitively compared with the state-of-the-art.


An interleaving and load sharing method for multiphase LLC converters

Interleaving frequency-controlled LLC resonant converters will encounter load-sharing problem due to the tolerance of the resonant components. In this paper, full-wave and half-wave switch-controlled capacitors (SCCs) are used in LLC stages to solve this problem. By using resonant capacitance as a control variable, the output current can be modulated even when all the LLC stages are synchronized at the same switching frequency. A design procedure is developed. A 600W prototype is built to verify the feasibility.


Transforming Models with ATL 1

This paper presents ATL (ATLAS Transformation Language): a hybrid model transformation language that allows both declarative and imperative constructs to be used in transformation definitions. The paper describes the language syntax by using examples. Language semantics is described in pseudo-code and various optimizations of transformation executions are discussed. ATL is supported by a set of development tools such as an editor, a compiler, a virtual machine, and a debugger. A case study shows the applicability of the language constructs. Alternative ways for implementing the case study are outlined. ATL language features are classified according to a model that captures the commonalities and variabilities of the model transformations domain.


Assessing It/Business Alignment

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Development of next-generation system-on-package (SOP) technology based on silicon carriers with fine-pitch chip interconnection

of next-generation system-on-package (SOP) technology based on silicon carriers with fine-pitch chip interconnection J. U. Knickerbocker P. S. Andry L. P. Buchwalter A. Deutsch R. R. Horton K. A. Jenkins Y. H. Kwark G. McVicker C. S. Patel R. J. Polastre C. Schuster A. Sharma S. M. Sri-Jayantha C. W. Surovic C. K. Tsang B. C. Webb S. L. Wright S. R. McKnight E. J. Sprogis B. Dang System-on-Package (SOP) technology based on silicon carriers has the potential to provide modular design flexibility and highperformance integration of heterogeneous chip technologies and to support robust chip manufacturing with high-yield/low-cost chips for a wide range of twoand three-dimensional product applications. Key technology enablers include silicon through-vias, high-density wiring, high-I/O chip interconnection, and supporting test and assembly technologies. The silicon through-vias are a key feature permitting efficient area array signal, power, and ground interconnection through these thinned silicon packages. Highdensity wiring and high-density chip I/O interconnection can enable tight integration of heterogeneous chip technologies which approximate the performance of an integrated system-on-chip with a ‘‘virtual chip’’ using the silicon package for integration. Silicon carrier fabrication leverages existing manufacturing capability and mid-UV lithography to provide very dense package wiring following CMOS back-end-of-line design rules. Further, the thermal expansion of the silicon carrier package matches the chip, which helps maintain reliability even as the high-density chip microbump interconnections scale to smaller size. In addition to heterogeneous chip integration, SOP products may leverage the integration of passive components, active devices, and electrooptic structures to enhance system-level performance while also maintaining functional test capability and known good chips when needed. This paper describes the technical challenges and recent progress made in the development of silicon carrier technology for potential new applications.


Optical Character Recognition from Text Image

Optical Character Recognition (OCR) is a system that provides a full alphanumeric recognition of printed or handwritten characters by simply scanning the text image. OCR system interprets the printed or handwritten characters image and converts it into corresponding editable text document. The text image is divided into regions by isolating each line, then individual characters with spaces. After character extraction, the texture and topological features like corner points, features of different regions, ratio of character area and convex area of all characters of text image are calculated. Previously features of each uppercase and lowercase letter, digit, and symbols are stored as a template. Based on the texture and topological features, the system recognizes the exact character using feature matching between the extracted character and the template of all characters as a measure of similarity.


Pomegranate: fast and flexible probabilistic modeling in python

We present pomegranate, an open source machine learning package for probabilistic modeling in Python. Probabilistic modeling encompasses a wide range of methods that explicitly describe uncertainty using probability distributions. Three widely used probabilistic models implemented in pomegranate are general mixture models, hidden Markov models, and Bayesian networks. A primary focus of pomegranate is to abstract away the complexities of training models from their definition. This allows users to focus on specifying the correct model for their application instead of being limited by their understanding of the underlying algorithms. An aspect of this focus involves the collection of additive sufficient statistics from data sets as a strategy for training models. This approach trivially enables many useful learning strategies, such as out-of-core learning, minibatch learning, and semi-supervised learning, without requiring the user to consider how to partition data or modify the algorithms to handle these tasks themselves. pomegranate is written in Cython to speed up calculations and releases the global interpreter lock to allow for built-in multithreaded parallelism, making it competitive with—or outperform—other implementations of similar algorithms. This paper presents an overview of the design choices in pomegranate, and how they have enabled complex features to be supported by simple code. The code is available at https://github.com/jmschrei/pomegranate


Entropic Brain-computer Interfaces - Using fNIRS and EEG to Measure Attentional States in a Bayesian Framework

Implicit Brain-Computer Interfaces (BCI) adapt system settings subtly based on real time measures of brain activation without the user’s explicit awareness. For example, measures of the user’s cognitive workload might drive a system that alters the timing of notifications in order to minimize user interruption. Here, we consider new avenues for implicit BCI based on recent discoveries in cognitive neuroscience and conduct a series of experiments using BCI’s principal non-invasive brain sensors, fNIRS and EEG. We show how Bayesian and systems neuroscience formulations explain the difference in performance of machine learning algorithms trained on brain data in different conditions. These new formulations posit that the brain aims to minimize its long-term surprisal of sensory data and organizes its calculations on two anti-correlated networks. We consider how to use real-time input that portrays a user along these dimensions in designing Bidirectional BCIs, which are Implicit BCIs that aim to optimize the user’s state by modulating computer output based on feedback from a brain monitor. We introduce Entropic Brain-Computer Interfacing as a type of Bidirectional BCI which uses physiological measurements of information theoretical dimensions of the user’s state to evaluate the digital flow of information to the user’s brain, tweaking this output in a feedback loop to the user’s benefit.


3D face recognition using local binary patterns

It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. In this paper, we develop a novel 3D face recognition algorithm using Local Binary Pattern (LBP) under expression varieties, which is an extension of the LBP operator widely used in ordinary facial analysis. First, to depict the human face more accurately and reduce the effect of facial local distortion for face recognition, a special feature-based 3D face division scheme is proposed. Then, the LBP representation framework for 3D faces is described, and the facial depth and normal information are extracted and encoded by LBP, to reduce the expression effect. For each face region, the statistical histogram is utilized to summarize the facial details, and accordingly three matching strategies are presented to address the recognition task. Finally, the proposed 3D face recognition algorithm is tested on BJUT-3D and FRGC v2.0 databases, achieves promising results, and concludes that it is feasible and valid to apply the LBP representation framework on 3D face recognition. & 2012 Elsevier B.V. All rights reserved.


Server Siblings: Identifying Shared IPv4/IPv6 Infrastructure Via Active Fingerprinting

We present, validate, and apply an active measurement technique that ascertains whether candidate IPv4 and IPv6 server addresses are “siblings,” i.e., assigned to the same physical machine. In contrast to prior efforts limited to passive monitoring, opportunistic measurements, or end-client populations, we propose an active methodology that generalizes to all TCP-reachable devices, including servers. Our method extends prior device fingerprinting techniques to improve their feasibility in modern environments, and uses them to support measurement-based detection of sibling interfaces. We validate our technique against a diverse set of 65 web servers with known sibling addresses and find it to be over 97% accurate with 99% precision. Finally, we apply the technique to characterize the top ∼6,400 Alexa IPv6-capable web domains, and discover that a DNS name in common does not imply that the corresponding IPv4 and IPv6 addresses are on the same machine, network, or even autonomous system. Understanding sibling and non-sibling relationships gives insight not only into IPv6 deployment and evolution, but also helps characterize the potential for correlated failures and susceptibility to certain attacks.


Syntax Parsing: Implementation Using Grammar-Rules for English Language

Syntactic parsing deals with syntactic structure of a sentence. The word 'syntax' refers to the grammatical arrangement of words in a sentence and their relationship with each other. The objective of syntactic analysis is to find syntactic structure of a sentence which is usually depicted as a tree. Identifying the syntactic structure is useful in determining the meaning of a sentence. Natural language processing is an arena of computer science and linguistics, concerned with the dealings amongst computers and human languages. It processes the data through lexical analysis, Syntax analysis, Semantic analysis, Discourse processing, Pragmatic analysis. This paper gives various parsing methods. The algorithm in this paper splits the English sentences into parts using POS tagger, It identifies the type of sentence (Facts, active, passive etc.) and then parses these sentences using grammar rules of Natural language. The algorithm has been tested on real sentences of English and it accomplished an accuracy of 81%.


English Language Learners and Academic Achievement : Revisiting the Threshold Hypothesis

This nonexperimental study explored the predictive strength of English proficiency levels on academic achievement of middle school students in a sample of 17,470 native English-speaking (NES) students, 558 English language learners (current ELLs), and 500 redesignated fluent English proficient students (former ELLs). Results of multilevel analyses indicated that after controlling for relevant studentand school-level characteristics, former ELLs significantly outperformed current ELL and NES students in reading (effect sizes: 1.07 and 0.52) and mathematics (effect sizes: 0.86 and 0.42). The results support Cummins’s (1979, 2000) lower level threshold hypothesis predicting that upon reaching adequate proficiency in the language of schooling and testing, ELLs would no longer experience academic disadvantages. Refinements for the theory and directions for future research are discussed.


LSCitter: building social machines by augmenting existing social networks with interaction models

We present LSCitter, an implemented framework for supporting human interaction on social networks with formal models of interaction, designed as a generic tool for creating social machines on existing infrastructure. Interaction models can be used to choreograph distributed systems, providing points of coordination and communication between multiple interacting actors. While existing social networks specify how interactions happen---who messages go to and when, the effects of carrying out actions---these are typically implicit, opaque and non user-editable. Treating interaction models as first class objects allows the creation of electronic institutions, on which users can then choose the kinds of interaction they wish to engage in, with protocols which are explicit, visible and modifiable. However, there is typically a cost to users to engage with these institutions. In this paper we introduce the notion of "shadow institutions", where actions on existing social networks are mapped onto formal interaction protocols, allowing participants access to computational intelligence in a seamless, zero-cost manner to carry out computation and store information.


PPG-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks

Non invasive and continuous measurement of blood pressure can enable an effective 24/7 monitoring of hypertensive patients to timely prevent cardiovascular problems and precisely regulate anti-hypertension cures. Unfortunately, to date, blood pressure can be only measured through either cuff-based or invasive instruments that cannot be continuously used due to their characteristics. This paper proposes an overview of techniques and approaches based on the photoplethysmographic (PPG) signal for non invasive and continuous measurement of blood pressure. In particular, the PPG signal can be easily acquired from an optical sensor applied on the epidermis and used, alone or integrated with the ECG signal, to estimate the blood pressure. On the basis of such methods new instruments and sensor-based systems can be developed and integrated with computer-based health care systems that aim at supporting continuous and remote monitoring of assisted livings.


The See-Through System: From implementation to test-drive

Cooperative awareness in vehicular networks is probably the killer application for vehicle-to-vehicle (V2V) communications that cannot be matched by infrastructure-based alternatives even when disregarding communication costs. New and improved driver assistance systems can be introduced by extending their reach to sensors residing in neighboring vehicles, such as windshield-installed cameras. In previous work, we defined theoretical foundations for a driver assistance system that leverages on V2V communication and windshield-installed cameras to transform vision-obstructing vehicles into transparent tubular objects. We now present an implementation of the actual See-Through System (STS), where we combine the communication aspects with the control and augmented reality components of the system. We present a validation methodology and test the system with multiple vehicles on a closed road segment. This evaluation shows that the STS is able to increase the visibility of drivers intending to overtake, thus increasing the safety of such critical maneuvers. It also shows that Dedicated Short Range Communication (DSRC) provides the required latency for this delay-critical inter-vehicle communication, which could hardly be guaranteed with infrastructure-based communication technologies.


MediaPaaS: A Cloud-Based Media Processing Platform for Elastic Live Broadcasting

Mobility is changing the way of how people consume live media content. By staying always connected with the Internet from various mobile devices, people expect to have enhanced TV viewing experience from anywhere on any device. Therefore, live broadcasting needs to be widely accessible and customizable, instead of being passive content only on TV. In this paper we present a cloud-based media processing platform, called MediaPaaS, for enabling elastic live broadcasting in the cloud. As an ecosystem-oriented solution for content providers, we outsource complex media processing from both content providers and terminal devices to the cloud. A distributed media processing model is proposed to enable dynamic pipeline composition and cross-pipeline task sharing in the cloud for flexible live content processing. Also, a prediction-based task scheduling algorithm is presented to minimize cloud resource usage without affecting quality of streams. The MediaPaaS platform allows third-party application developers to extend its capability to enable certain customization for running live channels. To our knowledge, this paper is the first work to openly discuss the detailed design issues of a cloud-based platform for elastic live broadcasting.


The UJI librarian robot

This paper describes the UJI Librarian Robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.


Smart Supervisory Control for Optimized Power Management System of hybrid Micro-Grid

Micro grid have been accepted concept widely for the better interconnection of distributed generators (DGs). Corresponding to the conventional power system ac microgrid have been proposed, particularly increasing the use of renewable energy sources generate dc power which is need a dc link for the purpose of grid connection and as a result of increasing modern dc loads. Dc micro grid has been recently emerged for their benefits in terms of efficiency cost and no of conversion stages.. During the islanding operation of the hybrid ac/dc microgrid, the IC is intended to take the role of supplier to one microgrid and at the same time acts as a load to the other microgrid and the power management system should be able to share the power demand between the exiting ac and dc sources in both the microgrids. This paper considers the power flow control and management issues amongst multiple sources distributed throughout both ac and dc microgrids. The paper proposes a decentralized power sharing method in order to eliminate the need for communication between DGs or microgrids. The performance of the proposed power control strategy is validated for different operating conditions, using MATLAB/SIMULATION environment. Keyword: Mixed integer linear programming, hybrid ac/dc microgrid, interlinking ac/dc converter, power management. Introduction Nowadays, electrical grids are more distributed, intelligent and flexible. They are not only driven by the growing environmental concern and the energy security, but also by the ornamenting of the electricity market. Over 100 years the three phase AC power systems existing due to its different operating voltage levels and over long distance. Newly more renewable power conversion systems are connected in ac distribution systems due to environmental issues caused by fueled power plants. Nowadays, more DC loads like LED and Electric vehicles are connected to AC power systems to save energy and to reduce the pollution caused by the fossil fueled power plants. The rising rate of consumption of nuclear and fossil fuels, and the community demand for reducing pollutant emission in electricity generation field are the most significant reasons for worldwide attention to the renewable energy resources. In generally micro grids are defined as a cluster of loads, distributed energy sources, and storage devices. It is accepted that for excellent operation of the micro-grid, a PMS is essential to manage power flow in the micro-grid. It is noteworthy that the power flow means the determination of the output of the electricity generation facilities to meet the demanded power. There are two general approaches to develop PMSs: 1) rule based and 2) optimization-based. In contrast, the latter approach supervises power flow in a micro-grid by minimizing a cost function, which is derived based on performance expectations of the micro-grid, and considering some operational constraints. Recently, a robust PMS for a grid-connected system is presented in [1], in which uncertainty in the generation prediction is considered in the design procedure. On the other hand, hybrid ac/dc micro-grid is a new concept, which decouples dc sources with dc loads and ac sources with ac loads, while power is exchanged between both sides using a bidirectional converter/inverter [2], [3]. In [4]-[6], a droop-based controller is introduced to manage power sharing between the ac and dc micro-grids. In this paper, the ac and dc micro-grids are treated as two separate entities with individual droop representations, where the information from these two droop characteristics is merged to decide the amount of power to exchange between the microgrids. In [7], a hybrid ac/dc micro-grid consisting a WG as an ac source and a PV array as a dc source is International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 6 (2016) pp 3980-3986 © Research India Publications. http://www.ripublication.com 3981 presented, where a rule-based system is proposed to manage the power flow in the hybrid ac/dcmicro-grid. In [8] and [9], the amount of power which should be exchanged between the micro-grids is determined through arule-based management system with four predefined operating modes. Finally, a rulebased PMS for a hybrid ac/dc microgrid is presented in [10], where more distinct operation modes are considered. Hence, microgrids [12] are becoming a reality to cope with a new scenario in which renewable energy, distributed generation (DG) and distributed energy-storage systems have to be integrated together. This new concept makes the final user not to be a passive element in the grid, but an entity able to generate, storage, control and manage part of the energy that he/she will consume. Besides, a reduction in cost and an increment in reliability and transparency are achieved. The radial transformation of the electrical grid entails deeply challenges not only on the architecture of the power system, but also in the control system. There are many control techniques in the literature based on the droop control method whose aim is to avoid communication between DG units [13][18]. Although this method achieves good reliability and flexibility, it presents several drawbacks [19], [20]: 1) it is not suitable when parallel DG units share nonlinear loads, it must take into account harmonic currents; 2) the output impedance of DG units and the line impedances affect the power sharing accuracy; 3)it is dependent of the load frequency deviations, which implies a phase deviation between DG units and grid/load. To cope with this problem, two additional control loops have been defined in [21] and [22]: Secondary control, which restores the nominal values of the frequency and voltage in the MG; and Tertiary control, which sets the reference of the frequency and voltage in the MG. This paper is organized as follows. Section II gives a brief overview of the typical hybrid ac/dc micro grid modeling In section III gives the overview of renewable power source models and their corresponding converters, where the models of PV and DG as the sources of the ac micro-grid are given in Section III-A, the model of battery as the source of the dc micro-gridis given in Section III-B, and the model of Generator as the source of dc micro grid in section III-C. In Section IV, a PMS to coordinating between the ac and dc micro-grids is proposed. Section IV-Apresents, Grid Connected Mode. In Section IV-B, minimum and maximum charge/discharge power of the battery banks, and minimum allowable power exchange between the ac and dc micro-grids in isolated mode computation are presented. The simulation results obtained with the proposed PMS are also reported in Section V. Finally, Section VI summarizes the main outcome of this paper. Microgrid Modelling MicroGrid design begins by understanding the load profile needed to be served by the system. Often, the load is adjusted in the system design by applying energy efficiency measures such as demand response controls, equipment upgrades, and other system adjustments to reduce the overall generation needs. What does not typically happen, however, is a closer look at those loads to determine how much can be served natively by DC power sources. This is a fundamental flaw in optimizing the use of renewable energy resources that supply DC electricity-like photovoltaic, battery, and fuel cell technologies. Instead, the entire load is considered and the resulting power generation requirements are sized accordingly. With some additional thought and separation of the DC loads from the AC loads, the amount of renewable generation required can be dramatically reduced to only supply dc power for the dc equipment in the building. In other words, by designing separate DC and AC networks, a building MicroGrid could be developed that minimizes power losses due to transformation or conversion by simply supplying the equipment with the native electricity it requires. So, instead of using 50 rooftop solar panels to supply a building, you might only need 10, driving down system costs and increasing efficiencies by utilizing the best type of power for each piece of equipment being served. The conceptual architecture described in this article proposes a building MicroGrid using a hybrid approach of DC renewable generation resources for DC equipment and the utility grid for AC equipment. An “energy router” acts as the hub that manages electricity across the AC and DC buses and minimizes the need for lossy DC-AC and AC-DC electricity transformations. Figure 2.1: Architecture of Microgrid Figure 2. 1, above, shows the overall building level DC MicroGrid architecture. Because of the power losses incurred with DC power over long distances, this concept is best used in a building level MicroGrid with short runs for the circuits on the system. In reality, calling it a hybrid MicroGrid is probably more apropos, but for the purposes of this discussion, we’ll use the DC MicroGrid terminology to keep it short. The Green Lines represent the DC part of the system, while the Orange Lines represent the AC portion. In typical office buildings, the amount of DC power equipment ranges from 20-30% of the overall load, with those numbers steadily rising as LED lights, computers, more electronics, and electric International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 6 (2016) pp 3980-3986 © Research India Publications. http://www.ripublication.com 3982 vehicles enter the equation. The architectural rendering in Figure 1 may not be complete as some DC equipment will require DC-DC transformations from the DC network’s supply voltage, but these transformations are less lossy than AC-DC transformations and also produce much less heat. DC Network The building’s DC electricity generation needs can be met through renewable energy technologies:  Solar (Photovoltaic)


BDO-SD: An efficient scheme for big data outsourcing with secure deduplication

In the big data era, duplicated copies of data may be generated by various smart devices. Although convergent encryption has been extensively adopted for secure deduplication, it is still very challenging to efficiently and reliably manage the convergent keys and support keyword search over encrypted data. This paper addresses this problem by presenting an efficient scheme for big data outsourcing with secure deduplication, named BDO-SD. Specifically, with the convergent encryption technique, data owners can outsource their data to the cloud server with data deduplication; with the idea of public key encryption and keywords search, a data user can query encrypted data while preserving its privacy. Security analysis demonstrates that our proposed BDO-SD scheme can achieve the data confidentiality, query privacy and key security. Simulation results show that compared with other schemes, our scheme can achieve better efficiency than the existing data deduplication schemes.


Protocol Oblivious Forwarding (POF): Software-Defined Networking with Enhanced Programmability

Software-defined networking separates the control and forwarding planes of a network to make it more programmable and application- aware. As one of the initial implementations of SDN, OpenFlow abstracts a forwarding device as a flow table and realizes flow processing by applying the "match-and-act" principle. However, the protocol-dependent nature of OpenFlow still limits the programmability of the forwarding plane. Hence, in this article, we discuss how to leverage protocol-oblivious forwarding (POF) to further enhance the network programmability such that the forwarding plane becomes protocol-independent and can be dynamically reprogrammed to support new protocol stacks seamlessly. We first review the development of OpenFlow and explain the motivations for introducing POF. Then we explain the working principle of POF, discuss our efforts on realizing the POF development ecosystem, and show our implementation of POF-based source routing as a novel use case. Finally, we elaborate on the first WAN-based POF network testbed that includes POF switches located in two cities in China.


Digital Game-Based Learning in high school Computer Science education: Impact on educational effectiveness and student motivation

0360-1315/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.compedu.2008.06.004 * Tel.: +3


The 'digital natives' debate: A critical review of the evidence

The idea that a new generation of students is entering the education system has excited recent attention among educators and education commentators. Termed ‘digital natives’ or the ‘Net generation’, these young people are said to have been immersed in technology all their lives, imbuing them with sophisticated technical skills and learning preferences for which traditional education is unprepared. Grand claims are being made about the nature of this generational change and about the urgent necessity for educational reform in response. A sense of impending crisis pervades this debate. However, the actual situation is far from clear. In this paper, the authors draw on the fields of education and sociology to analyse the digital natives debate. The paper presents and questions the main claims made about digital natives and analyses the nature of the debate itself. We argue that rather than being empirically and theoretically informed, the debate can be likened to an academic form of a ‘moral panic’. We propose that a more measured and disinterested approach is now required to investigate ‘digital natives’ and their implications for education. The one thing that does not change is that at any and every time it appears that there have been ‘great changes’. Marcel Proust, Within a Budding Grove


Forecasting emerging technologies : Use of bibliometrics and patent analysis

It is rather difficult to forecast emerging technologies as there is no historical data available. In such cases, the use of bibliometrics and patent analysis have provided useful data. This paper presents the forecasts for three emerging technology areas by integrating the use of bibliometrics and patent analysis into well-known technology forecasting tools such as scenario planning, growth curves and analogies. System dynamics is also used to be able to model the dynamic ecosystem of the technologies and their diffusion. Technologies being forecasted are fuel cell, food safety and optical storage technologies. Results from these three applications help us to validate the proposed methods as appropriate tools to forecast emerging technologies. © 2006 Elsevier Inc. All rights reserved.


Food-related illness and death in the United States.

Letters Letters Letters Letters Letters correctly identified E. faecium and E. faecalis to the species level, most (4 of 5) did not correctly identify E. gallinarum (three misidentified it as E. casseliflavus and one as E. faecalis). The results of this study are consistent with those of previous studies in the United States (4,5), South America (6), Spain (7), and Mexico (8). Although in countries like Chile, disk diffusion is practical and reliable for most susceptibility testing, detecting low-level vanco-mycin resistance in enterocci is difficult without supplementary testing. In Chile, as in other countries, strategies should be implemented to improve detection of these strains, including improvement of phenotypical and genotypical methods for VRE detection and species identification. Documentation of proficiency in detecting VRE is important for improving laboratory performance, detecting clinical isolates, and accurate and reliable reporting to local, national, and international surveillance systems. A, Febre N, et al. Epidemiologic analysis for acquisition of vancomycin-resistant enterococcus (VRE) in an intensive care unit in Brazil. Altschuler M, et al. Study to determine the ability of clinical laboratories to detect antimicrobial-resistant To the Editor: Dr. Mead and colleagues should be commended for attempting to estimate the prevalence of foodborne disease in the United States (1). Their study provides more complete estimates than previous studies in terms of the number of foodborne pathogens included; for example, it includes the first realistic estimate of the number of cases of disease due to Norwalk-like caliciviruses. However, the publication of these estimates raises some important issues. Even though " accurate estimates of disease burden are the foundation of sound public health policy " (2), most of these estimates (in particular, the assumption that unknown agents are transmitted by food in the same proportion as known agents) were derived from assumptions rather than data. Known foodborne agents clearly cannot account for most gastrointestinal illnesses (1). However, illnesses from unknown agents may be as likely to have the transmission characteristics of rotavirus (1% foodborne) or Cryptosporidium (10% foodborne) as those of the Norwalk-like viruses (40% foodborne). Furthermore , it was assumed that detecting outbreaks or cases of toxin-mediated illnesses (e.g., due to Bacillus cereus, Staphylococcus aureus, or Clostridium perfringens) follows the model of Salmonella. In the authors' entire list of known foodborne agents, data are presented for cases identified both from outbreaks and active surveillance for only three agents: Salmonella, Shigella, and Campylobacter. Salmonella is clearly the most …


What makes things fun to learn? heuristics for designing instructional computer games

In this paper, I will describe my intuitions about what makes computer games fun. More detailed descriptions of the experiments and the theory on which this paper is based are given by Malone (1980a, 1980b). My primary goal here is to provide a set of heuristics or guidelines for designers of instructional computer games. I have articulated and organized common sense principles to spark the creativity of instructional designers (see Banet, 1979, for an unstructured list of similar principles). To demonstrate the usefulness of these principles, I have included several applications to actual or proposed instructional games. Throughout the paper I emphasize games with educational uses, but I focus on what makes the games fun, not on what makes them educational.
 Though I will not emphasize the point in this paper, these same ideas can be applied to other educational environments and life situations. In a sense, the categories I will describe constitute a general taxonomy of intrinsic motivation—of what makes an activity fun or rewarding for its own sake rather than for the sake of some external reward (See Lepper and Greene, 1979).
 I think the essential characteristics of good computer games and other intrinsically enjoyable situations can be organized into three categories: challenge, fantasy, and curiosity.


Code and tell: assessing young children's learning of computational thinking using peer video interviews with ScratchJr

In this paper, we present a novel technique for assessing the learning of computational thinking in the early childhood classroom. Students in three second grade classrooms learned foundational computational thinking concepts using ScratchJr and applied what they learned to creating animated collages, stories, and games. They then conducted artifact-based video interviews with each other in pairs using their iPad cameras. As discussed in the results, this technique can show a broad range of what young children learn about computational thinking in classroom interventions using ScratchJr than more traditional assessment techniques. It simultaneously provides a developmentally appropriate educational activity (i.e. peer interviews) for early childhood classrooms.


Tadalafil therapy for pulmonary arterial hypertension.

BACKGROUND
Treatment options for pulmonary arterial hypertension target the prostacyclin, endothelin, or nitric oxide pathways. Tadalafil, a phosphodiesterase type-5 inhibitor, increases cGMP, the final mediator in the nitric oxide pathway.

METHODS AND RESULTS
In this 16-week, double-blind, placebo-controlled study, 405 patients with pulmonary arterial hypertension (idiopathic or associated), either treatment-naive or on background therapy with the endothelin receptor antagonist bosentan, were randomized to placebo or tadalafil 2.5, 10, 20, or 40 mg orally once daily. The primary end point was the change from baseline to week 16 in the distance walked in 6 minutes. Changes in World Health Organization functional class, clinical worsening, and health-related quality of life were also assessed. Patients completing the 16-week study could enter a long-term extension study. Tadalafil increased the distance walked in 6 minutes in a dose-dependent manner; only the 40-mg dose met the prespecified level of statistical significance (P<0.01). Overall, the mean placebo-corrected treatment effect was 33 m (95% confidence interval, 15 to 50 m). In the bosentan-naive group, the treatment effect was 44 m (95% confidence interval, 20 to 69 m) compared with 23 m (95% confidence interval, -2 to 48 m) in patients on background bosentan therapy. Tadalafil 40 mg improved the time to clinical worsening (P=0.041), incidence of clinical worsening (68% relative risk reduction; P=0.038), and health-related quality of life. The changes in World Health Organization functional class were not statistically significant. The most common treatment-related adverse events reported with tadalafil were headache, myalgia, and flushing.

CONCLUSIONS
In patients with pulmonary arterial hypertension, tadalafil 40 mg was well tolerated and improved exercise capacity and quality of life measures and reduced clinical worsening.


Classification of rice grain varieties using two artificial neural networks (MLP and neuro-fuzzy).

Artificial neural networks (ANNs) have many applications in various scientific areas such as identification, prediction and image processing. This research was done at the Islamic Azad University, Shahr-e-Rey Branch, during 2011 for classification of 5 main rice grain varieties grown in different environments in Iran. Classification was made in terms of 24 color features, 11 morphological features and 4 shape factors that were extracted from color images of each grain of rice. The rice grains were then classified according to variety by multi layer perceptron (MLP) and neuro-fuzzy neural networks. The topological structure of the MLP model contained 39 neurons in the input layer, 5 neurons (Khazar, Gharib, Ghasrdashti, Gerdeh and Mohammadi) in the output layer and two hidden layers; neuro-fuzzy classifier applied the same structure in input and output layers with 60 rules. Average accuracy amounts for classification of rice grain varieties computed 99.46% and 99.73% by MLP and neuro-fuzzy classifiers alternatively. The accuracy of MLP and neuro-fuzzy networks changed after feature selections were 98.40% and 99.73 % alternatively.


Skin force sensor using piezoresistive PEDOT:PSS with arabitol on flexible PDMS

In this paper, a flexible conducting polymer based force sensor array has been developed. A conducting polymer, namely poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) with additive arabitol, was utilized as the piezoresistive layer on a flexible polydimethylsiloxane (PDMS) substrate owing to its high conductivity and stretchability. A simplified strain sensor has been developed to demonstrate the properties of the arabitol-PEDOT:PSS. Suitable curing conditions and arabitol concentration have been established with design of experiment. The arabitol-PEDOT:PSS can be stretched up to 20% and it shows a significant gauge factor of 11.5. A flexible interdigital force sensor array has been developed to demonstrate the effectiveness of arabitol-PEDOT:PSS on PDMS as a force sensor.


Electrostatic adhesive brakes for high spatial resolution refreshable 2.5D tactile shape displays

We investigate the mechanism, design, modeling and fabrication of a scalable high resolution, low cost and lightweight refreshable 2.5D tactile pin array controlled by electrostatic adhesive brakes. By replacing linear actuators in motorized shape displays with a high voltage solid-state circuit that can be fabricated with printable electronics techniques, we can decrease the cost and complexity of such devices. Electrostatic adhesive brakes, made by patterning interdigital electrodes on high dielectric constant thin films, are used to hold metal pins' positions and provide contact force to the user's fingertip. We present designs of two high resolution brake modules which are 1.7 mm pitch with 0.8 mm width pins and 4 mm pitch with 1.58 mm width pins with a maximum measured dynamic loading force of 76.3 gf and static loading force of 28 gf on an individual pin (for the later size). A small demonstration of 4 × 2 pin array with a 4 mm pitch size within a row and 2.5 mm pitch size between the rows, using 1.58 mm width pins, was created. We also characterized the refresh time to be 37.5 ms for each brake, which enables refreshable actuated pin displays.


Fuzzy Passive-Aggressive classification: A robust and efficient algorithm for online classification problems


Electric field effect in atomically thin carbon films.

We describe monocrystalline graphitic films, which are a few atoms thick but are nonetheless stable under ambient conditions, metallic, and of remarkably high quality. The films are found to be a two-dimensional semimetal with a tiny overlap between valence and conductance bands, and they exhibit a strong ambipolar electric field effect such that electrons and holes in concentrations up to 10(13) per square centimeter and with room-temperature mobilities of approximately 10,000 square centimeters per volt-second can be induced by applying gate voltage.


Quality Traits of “Cannabidiol Oils”: Cannabinoids Content, Terpene Fingerprint and Oxidation Stability of European Commercially Available Preparations

Cannabidiol (CBD)-based oil preparations are becoming extremely popular, as CBD has been shown to have beneficial effects on human health. CBD-based oil preparations are not unambiguously regulated under the European legislation, as CBD is not considered as a controlled substance. This means that companies can produce and distribute CBD products derived from non-psychoactive hemp varieties, providing an easy access to this extremely advantageous cannabinoid. This leaves consumers with no legal quality guarantees. The objective of this project was to assess the quality of 14 CBD oils commercially available in European countries. An in-depth chemical profiling of cannabinoids, terpenes and oxidation products was conducted by means of GC-MS and HPLC-Q-Exactive-Orbitrap-MS in order to improve knowledge regarding the characteristics of CBD oils. Nine out of the 14 samples studied had concentrations that differed notably from the declared amount, while the remaining five preserved CBD within optimal limits. Our results highlighted a wide variability in cannabinoids profile that justifies the need for strict and standardized regulations. In addition, the terpenes fingerprint may serve as an indicator of the quality of hemp varieties, while the lipid oxidation products profile could contribute in evaluation of the stability of the oil used as milieu for CBD rich extracts.


Hybrid BFOA-PSO algorithm for automatic generation control of linear and nonlinear interconnected power systems

In the Bacteria Foraging Optimization Algorithm (BFAO), the chemotactic process is randomly set, imposing that the bacteria swarm together and keep a safe distance from each other. In hybrid bacteria foraging optimization algorithm and particle swarm optimization (hBFOA-PSO) algorithm the principle of swarming is introduced in the framework of BFAO. The hBFOA-PSO algorithm is based on the adjustment of each bacterium position according to the neighborhood environment. In this paper, the effectiveness of the hBFOA-PSO algorithm has been tested for Automatic Generation Control (AGC) of an interconnected power system. A widely used linear model of two area non-reheat thermal system equipped with Proportional-Integral (PI) controller is considered initially for the design and analysis purpose. At first, a conventional Integral Time multiply Absolute Error (ITAE) based objective function is considered and the performance of hBFOA-PSO algorithm is compared with PSO, BFOA and GA. Further a modified objective function using ITAE, damping ratio of dominant eigenvalues and settling time with appropriate weight coefficients is proposed to increase the performance of the controller. Further, robustness analysis is carried out by varying the operating load condition and time constants of speed governor, turbine, tieline power in the range of +50% to -50% as well as size and position of step load perturbation to demonstrate the robustness of the proposed hBFOA-PSO optimized PI controller. The proposed approach is also extended to a nonlinear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of Craziness based Particle Swarm Optimization (CRAZYPSO) approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different PI coefficients and comparison between ANFIS and proposed approach has been provided.


miRNAs and apoptosis: RNAs to die for

MicroRNAs (miRNAs) are small non-coding RNAs of about 18–24 nucleotides in length that negatively regulate gene expression. Discovered only recently, it has become clear that they are involved in many biological processes such as developmental timing, differentiation and cell death. Data that connect miRNAs to various kinds of diseases, particularly cancer, are accumulating. miRNAs can influence cancer development in many ways, including the regulation of cell proliferation, cell transformation, and cell death. In this review, we focus on miRNAs that have been shown to play a role in the regulation of apoptosis. We first describe in detail how Drosophila has been utilized as a model organism to connect several miRNAs with the cell death machinery. We discuss the genetic approaches that led to the identification of those miRNAs and subsequent work that helped to establish their function. In the second part of the review article, we focus on the involvement of miRNAs in apoptosis regulation in mammals. Intriguingly, many of the miRNAs that regulate apoptosis have been shown to affect cancer development. In the end, we discuss a virally encoded miRNA that influences the cell death response in the mammalian host cell. In summary, the data gathered over the recent years clearly show the potential and important role of miRNAs to regulate apoptosis at various levels and in several organisms.


DOES THE BALANCED SCORECARD WORK : AN EMPIRICAL INVESTIGATION Professor

Commentators suggest that between 30 and 60% of large US firms have adopted the Balanced Scorecard, first described by Bob Kaplan and David Norton in their seminal Harvard Business Review paper of 1992 (Kaplan and Norton, 1992; Marr et al, 2004). Empirical evidence that explores the performance impact of the balanced scorecard, however, is extremely rare and much that is available is anecdotal at best. This paper reports a study that set out to explore the performance impact of the balanced scorecard by employing a quasi-experimental design. Up to three years worth of financial data were collected from two sister divisions of an electrical wholesale chain based in the UK, one of which had implemented the balanced scorecard and one of which had not. The relative performance improvements of geographically matched pairs of branches were compared to establish what, if any, performance differentials existed between the branches that had implemented the balanced scorecard and those that had not. The key findings of the study are that while the Electrical division – the division that implemented the balanced scorecard – sees improvements in sales and gross profit; similar performance improvements are also observed in the sister division. Hence the performance impact of the balanced scorecard has to be questioned. Clearly further work on this important topic is required in similar settings where natural experiments occur.


A flexible architecture integrating monitoring and analytics for managing large-scale data centers

To effectively manage large-scale data centers and utility clouds, operators must understand current system and application behaviors. This requires continuous, real-time monitoring along with on-line analysis of the data captured by the monitoring system, i.e., integrated monitoring and analytics -- Monalytics [28]. A key challenge with such integration is to balance the costs incurred and associated delays, against the benefits attained from identifying and reacting to, in a timely fashion, undesirable or non-performing system states. This paper presents a novel, flexible architecture for Monalytics in which such trade-offs are easily made by dynamically constructing software overlays called Distributed Computation Graphs (DCGs) to implement desired analytics functions. The prototype of Monalytics implementing this flexible architecture is evaluated with motivating use cases in small scale data center experiments, and a series of analytical models is used to understand the above trade-offs at large scales. Results show that the approach provides the flexibility needed to meet the demands of autonomic management at large scale with considerably better performance/cost than traditional and brute force solutions.


Learning Automata as a Basis for Multi Agent Reinforcement Learning

Learning Automata (LA) are adaptive decision making devices suited for operation in unknown environments [12]. Originally they were developed in the area of mathematical psychology and used for modeling observed behavior. In its current form, LA are closely related to Reinforcement Learning (RL) approaches and most popular in the area of engineering. LA combine fast and accurate convergence with low computational complexity, and have been applied to a broad range of modeling and control problems. However, the intuitive, yet analytically tractable concept of learning automata makes them also very suitable as a theoretical framework for Multi agent Reinforcement Learning (MARL). Reinforcement Learning (RL) is already an established and profound theoretical framework for learning in stand-alone or single-agent systems. Yet, extending RL to multi-agent systems (MAS) does not guarantee the same theoretical grounding. As long as the environment an agent is experiencing is Markov, and the agent can experiment sufficiently, RL guarantees convergence to the optimal strategy. In a MAS however, the reinforcement an agent receives, may depend on the actions taken by the other agents acting in the same environment. Hence, the Markov property no longer holds. And as such, guarantees of convergence are lost. In the light of the above problem it is important to fully understand the dynamics of multi-agent reinforcement learning. Although, they are not fully recognized as such, LA are valuable tools for current MARL research. LA are updated strictly on the basis of the response of the environment, and not on the basis of any knowledge regarding other automata, i.e. nor their strategies, nor their feedback. As such LA agents are very simple. Moreover, LA can be treated analytically. Convergence proofs do exist for a variety of settings ranging from a single automaton model acting in a simple stationary random environment to a distributed automata model interacting in a complex environment. In this paper we argue that LA are very interesting building blocks for learning in multi agent systems. The LA can be viewed as policy iterators, who update their action probabilities based on private information only. This is a very attractive property in applications where communication is expensive. LA are in particular appealing in games with stochastic payoffs. Then we move to collections of learning automata, that can independently converge to interesting solution concepts. We study the single stage setting, including the analytical results. Then we generalize to interconnected learning automata, that can deal with multi agent multi-stage problems. We also show how Ant Colony Optimization can be mapped to the interconnected Learning Automata setting. This extended abstract is a summary of an article published earlier [14].


A New Approach of copy move Forgery Detection using Rigorous Preprocessing and Feature Extraction

these days, advanced pictures are being used in an extensive variety of uses and for numerous reasons. They additionally assume an imperative part in the capacity and exchange of visual data, particularly the mystery ones. With this far reaching utilization of advanced pictures, notwithstanding the expanding number of devices and programming of computerized pictures altering, it has turned out to be anything but difficult to control and change the real data of the picture. In this way, it has turned out to be important to check the credibility and the respectability of the picture by utilizing present day and advanced methods, which add to examination and comprehension of the pictures’ substance, and after that ensure their trustworthiness. There are many sorts of picture imitation, the most critical and prominent sort is called duplicate glue fabrication, which utilizes a similar picture during the time spent falsification. This sort of fraud is utilized for one of two things, first to cover a protest or scene by replicating the region of the picture and gluing it on another zone of a similar picture. In this paper we have presented a new approach of copy move forgery detection. proposed scheme uses Oriented FAST and rotated BRIEF(ORB) alternative of scale invariant feature transform (SIFT) technique which is integrated with modified local contrast modification-contrast limited adaptive histogram equalization(LCM-CLAHE). Experimental results shows that proposed scheme is more promising in terms of false positive rate(FPR) and true positive rate(TPR) compare to state of the art techniques. Keywords— Image Processing, Image Enhancement, Histogram Equalization, SIFT, TPR, FPR, Copy move forgery, ORB


An Ontology-based Summarization System for Arabic Documents (OSSAD)

With the problem of increased web resources and the huge amount of information available, the necessity of having automatic summarization systems appeared. Since summarization is needed the most in the process of searching for information on the web, where the user aims at a certain domain of interest according to his query, domain-based summaries would serve the best. Despite the existence of plenty of research work in the domain-based summarization in English, there is lack of them in Arabic due to the shortage of existing knowledge bases. In this paper an Ontology-based Summarization System for Arabic Documents, OSSAD, is introduced. Domain knowledge is extracted from an Arabic corpus and represented by topic related concepts/keywords and the lexical relations among them. The user’s query is first expanded by using the Arabic WordNet and then by adding the domain-specific knowledge base to the expansion. For summarization, decision tree algorithm (C4.5) is used, which was trained by a set of features extracted from the original documents. For the testing dataset, Essex Arabic Summaries Corpus (EASC) was used. Recall Oriented Understudy for Gisting Evaluation (ROUGE) was used to compare OSSAD summaries with the human summaries along with other automatic summarization systems, showing that the proposed approach demonstrated promising results.


Adult hippocampal neurogenesis is involved in anxiety-related behaviors

Adult hippocampal neurogenesis is a unique example of structural plasticity, the functional role of which has been a matter of intense debate. New transgenic models have recently shown that neurogenesis participates in hippocampus-mediated learning. Here, we show that transgenic animals, in which adult hippocampal neurogenesis has been specifically impaired, exhibit a striking increase in anxiety-related behaviors. Our results indicate that neurogenesis plays an important role in the regulation of affective states and could be the target of new treatments for anxiety disorders.


A quasi fixed frequency constant on time controlled boost converter

Continuous conduction mode (CCM) boost converter is difficult to compensate due to the right half plane (RHP) zero. This paper presents a quasi fixed frequency constant on time (COT) controlled boost converter, it has no loop compensation requirement. With the proposed COT control, pulse frequency modulation (PFM) comes free at light load, which improves the light load efficiency compared with traditional PWM control. A compact on-time generator that controls the operation frequency is proposed. And a current sink based feedback circuit is used to improve the quality of feedback signal.The COT controller is designed with 1.5 mum BCD process. Simulation results demonstrate that the COT boost converter has fast load transient response and quasi fixed frequency operation.


Credit decision tool using mobile application data for microfinance in agriculture

Smallholder agriculture is vital for food security and poverty reduction. Poor, unbanked smallholder farmers face challenges in obtaining loans for agriculture due to missing financial credit history. Although credit decisions tools with alternatives such as big data from mobile phones currently exist, these are mainly targeted towards consumer credit solutions. There is need for research focused on applying such data to credit decisions in agriculture, considering the unique challenges of the field. This research proposes a credit decision tool using alternative data from a mobile phone application used for reporting agricultural activities. 213 users submitted 41613 reports of farm activity by 11336 farmers over a 5-year period. Users were classified groups based on personal information, association with farmers, and reports. Three methods are applied for analysis: multiple logistic regression, a support vector machine with a linear kernel, and a support vector machine with a radial basis function kernel. Three sets of features, selected with different methods, are used. The results are evaluated by accuracy, and Area Under the Receiver operating curve (AUC). The support vector machine with a radial basis function kernel shows the best performance on the original data set with an Area Under the Receiver operating curve (AUC) value of 0.983. Feasibility of applying each method in agricultural microfinance is considered. Finally, further development of the models using Geographic Information System analysis of geospatial data which affects agricultural productivity, such as soil type and water resources, is suggested to incorporate the ability of the borrower to repay loans from farm activity.


A robot's spatial perception communicated via human touch

A robot perceives space in order to navigate, map, search and investigate its surroundings. One application area where humans interact with robots is search and rescue. The robot may have unique capabilities such as seeing outside the visible spectrum and being able to navigate in tight and hazardous spaces. In such an operation, the human may also be an effective search agent. Thus, it would be beneficial if the robot’s spatial perception was conveyed to the human via a secondary peripheral modality. We have worked on developing a visual to tactile substitution.device for people who are visually impaired or blind. We propose to use this similar technique to the remote robot in order that it convey its spatial perception to the human operator or co-worker.


A Structured Vector Space Model for Word Meaning in Context

We address the task of computing vector space representations for the meaning of word occurrences, which can vary widely according to context. This task is a crucial step towards a robust, vector-based compositional account of sentence meaning. We argue that existing models for this task do not take syntactic structure sufficiently into account. We present a novel structured vector space model that addresses these issues by incorporating the selectional preferences for words’ argument positions. This makes it possible to integrate syntax into the computation of word meaning in context. In addition, the model performs at and above the state of the art for modeling the contextual adequacy of paraphrases.


Phrase-Based Statistical Machine Translation

This paper is based on the work carried out in the framework of the Verbmobil project, which is a limited-domain speech translation task (German-English). In the final evaluation, the statistical approach was found to perform best among five competing approaches. In this paper, we will further investigate the used statistical translation models. A shortcoming of the single-word based model is that it does not take contextual information into account for the translation decisions. We will present a translation model that is based on bilingual phrases to explicitly model the local context. We will show that this model performs better than the single-word based model. We will compare monotone and non-monotone search for this model and we will investigate the benefit of using the sum criterion instead of the maximum approximation.


Mining the Web for Bilingual Text

STRAND (Resnik, 1998) is a languageindependent system for automatic discovery of text in parallel translation on the World Wide Web. This paper extends the preliminary STRAND results by adding automatic language identification, scaling up by orders of magnitude, and formally evaluating performance. The most recent end-product is an automatically acquired parallel corpus comprising 2491 English-French document pairs, approximately 1.5 million words per language. 1 I n t r o d u c t i o n Text in parallel translation is a valuable resource in natural language processing. Statistical methods in machine translation (e.g. (Brown et al., 1990)) typically rely on large quantities of bilingual text aligned at the document or sentence level, and a number of approaches in the burgeoning field of crosslanguage information retrieval exploit parallel corpora either in place of or in addition to mappings between languages based on information from bilingual dictionaries (Davis and Dunning, 1995; Landauer and Littman, 1990; Hull and Oard, 1997; Oard, 1997). Despite the utility of such data, however, sources of bilingual text are subject to such limitations as licensing restrictions, usage fees, restricted domains or genres, and dated text (such as 1980's Canadian politics); or such sources simply may not exist for * This work was supported by Department of Defense contract MDA90496C1250, DARPA/ITO Contract N66001-97-C-8540, and a research grant from Sun Microsystems Laboratories. The author gratefully acknowledges the comments of the anonymous reviewers, helpful discussions with Dan Melamed and Doug Oard, and the assistance of Jeff Allen in the French-English experimental evaluation. language pairs of interest. Although the majority of Web content is in English, it also shows great promise as a source of multilingual content. Using figures from the Babel survey of multilinguality on the Web (h tZp : / /www. i s o c . o r g / ) , it is possible to estimate that as of June, 1997, there were on the order of 63000 primarily non-English Web servers, ranging over 14 languages. Moreover, a followup investigation of the non-English servers suggests that nearly a third contain some useful cross-language data, such as parallel English on the page or links to parallel English pages the follow-up also found pages in five languages not identified by the Babel study (Catalan, Chinese, Hungarian, Icelandic, and Arabic; Michael Littman, personal communication). Given the continued explosive increase in the size of the Web, the trend toward business organizations that cross national boundaries, and high levels of competition for consumers in a global marketplace, it seems impossible not to view multilingual content on the Web as an expanding resource. Moreover, it is a dynamic resource, changing in content as the world changes. For example, Diekema et al., in a presentation at the 1998 TREC-7 conference (Voorhees and Harman, 1998), observed that the performance of their cross-language information retrieval was hurt by lexical gaps such as Bosnia/Bosniethis illustrates a highly topical missing pair in their static lexical resource (which was based on WordNet 1.5). And Gey et al., also at TREC-7, observed that in doing cross-language retrieval using commercial machine translation systems, gaps in the lexicon (their example was acupuncture/Akupunktur) could make the difference between precision of 0.08 and precision of 0.83 on individual queries. ttesnik (1998) presented an algorithm called