Violent Crime Rate Studies in Philosophical Context : A Destructive Testing Approach to Heat and Southern Culture ofViolence Effects

The logic behind the translation of conceptual hypotheses into testable propositions was illustrated with the heat hypothesis, The destructive testing philosophy was introduced and applied, This consists of first showing that a predicted empirical relation exists, then attempting to break that relation. by adding competitor variables. The key question in destructive testing is "How difficult was it to break the relation?'~ This approach was used to analyze the heat effect on violent crime rates (Study I ) and on White violent crime arrest rates (Study 2) in U.S. cities. One competitor variable was the particular focus of analysis: southern culture of violence. The heat hypothesis was supported by highly significant correlations between the warmth of a city and its violence rate. This heat effect survived multiple destructive tests. Some support for the southern culture effect was also found, but this effect was more easily broken.


2D Color Barcodes for Mobile Phones

We propose a new high capacity color barcode, named HCC2D (High Capacity Colored 2-Dimensional), which use colors to increase the barcode data density. The introduction and recognition of colored modules poses some new and non-trivial computer vision challenges, such as handling the color distortions introduced by the hardware equipment that realizes the Print&Scan process. We developed a prototype for generating and reading the HCC2D code format, both on desktops (Linux and Windows platforms) and on mobile phones (Android platforms). We tested this prototype in many experiments considering different operating scenarios and data densities, and compared it to known 2-dimensional barcodes.


Fast Computation of Graph Kernels

Using extensions of linear algebra concepts to Reproducing Kernel Hilbert Spaces (RKHS), we define a unifying framework for random walk kernels on graphs. Reduction to a Sylvester equation allows us to compute many of these kernels in O(n) worst-case time. This includes kernels whose previous worst-case time complexity was O(n), such as the geometric kernels of Gärtner et al. [1] and the marginal graph kernels of Kashima et al. [2]. Our algebra in RKHS allow us to exploit sparsity in directed and undirected graphs more effectively than previous methods, yielding sub-cubic computational complexity when combined with conjugate gradient solvers or fixed-point iterations. Experiments on graphs from bioinformatics and other application domains show that our algorithms are often more than 1000 times faster than existing approaches.


Parallel and Distributed Data Pipelining with KNIME

In recent years a new category of data analysis applications have evolved, known as data pipelining tools, which enable even nonexperts to perform complex analysis tasks on potentially huge amounts of data. Due to the complex and computing intensive analysis processes and methods used, it is often neither sufficient nor possible to simply rely on the increase of performance of single processors. Promising solutions to this problem are parallel and distributed approaches that can accelerate the analysis process. In this paper we discuss the parallel and distribution potential of pipelining tools by demonstrating several parallel and distributed implementations in the open source pipelining platform KNIME. We verify the practical applicability in a number of real world experiments.


A Neural Representation of Sketch Drawings

We present sketch-rnn, a recurrent neural network (RNN) able to construct stroke-based drawings of common objects. The model is trained on thousands of crude human-drawn images representing hundreds of classes. We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.


Multi-Document Summarization with GIST EXTER

This paperpresentsthe architectureandthe multidocument summarizationtechniquesimplementedin the GISTEXTER system.The paperpresentsanalgorithmfor producing incrementalmulti-document summariesif extractiontemplatesof goodquality areavailable. An empirical methodof generatingad-hoctemplatesthat can be populated with information extractedfrom texts by automatically acquiredextractionpatternsis alsopresented . The resultsof GISTEXTER in the DUC-2001evaluations account for the advantagesof usingthetechniquespresentedin this paper .


Robust Matrix Elastic Net based Canonical Correlation Analysis: An Effective Algorithm for Multi-View Unsupervised Learning

This paper presents a robust matrix elastic net based canonical correlation analysis (RMEN-CCA) for multiple view unsupervised learning problems, which emphasizes the combination of CCA and the robust matrix elastic net (RMEN) used as coupled feature selection. The RMEN-CCA leverages the strength of the RMEN to distill naturally meaningful features without any prior assumption and to measure effectively correlations between different ’views’. We can further employ directly the kernel trick to extend the RMEN-CCA to the kernel scenario with theoretical guarantees, which takes advantage of the kernel trick for highly complicated nonlinear feature learning. Rather than simply incorporating existing regularization minimization terms into CCA, this paper provides a new learning paradigm for CCA and is the first to derive a coupled feature selection based CCA algorithm that guarantees convergence. More significantly, for CCA, the newly-derived RMEN-CCA bridges the gap between measurement of relevance and coupled feature selection. Moreover, it is nontrivial to tackle directly the RMEN-CCA by previous optimization approaches derived from its sophisticated model architecture. Therefore, this paper further offers a bridge between a new optimization problem and an existing efficient iterative approach. As a consequence, the RMEN-CCA can overcome the limitation of CCA and address large-scale and streaming data problems. Experimental results on four popular competing datasets illustrate that the RMEN-CCA performs more effectively and efficiently than do state-of-the-art approaches.


Mars Sample Return campaign status

The proposed Mars Sample Return (MSR) Campaign would be the next big step in the Mars Exploration Program (MEP). Identified by the Planetary Decadal Survey of 2011 as the highest science priority for large missions, the Mars Program has been working in partnership with the European Space Agency (ESA) to define a campaign to return a surface sample of martian surface material to Earth for analysis. As currently envisioned, the MSR campaign concept consists of a series of flight missions to collect and return the sample, and a ground based project to develop a sample receiving facility. The first mission in this proposed campaign would be baselined for launch in 2018, and would consist of a rover capable of scientifically selecting and caching the sample for retrieval by a later mission. Subsequent missions would retrieve the cache, launch it into orbit around Mars, where it would be captured by an orbiter and returned to the Earth. This paper discusses the current status of the MSR Campaign architecture definition and requirements development and near term plans for technical definition and review. Also discussed are selected design drivers and proposed key requirements which would have to be addressed in the definition of the campaign.1, 2


Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.


On-line cursive script recognition using time-delay neural networks and hidden Markov models

We present a writer-independent system for online handwriting recognition that can handle a variety of writing styles including cursive script and handprinting. The input to our system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. A time-delay neural network is used to estimate a posteriori probabilities for characters in a word. A hidden Markov model segments the word in a way that optimizes the global word score, using a dictionary in the process. A geometrical normalization scheme and a fast but efficient dictionary search are also presented. Trained on 20 k words from 59 writers, using a 25 k-word dictionary, our system reached recognition rates of 89% for characters and 80% for words on test data from a disjoint set of writers.


Force of Habit and Information Systems Usage: Theory and Initial Validation

Over the last two decades, information systems (IS) research has primarily focused on people’s conscious (intentional) behavior when trying to explain and predict IS usage. Consequently, almost no research has investigated the potential importance of subconscious (automatic) behaviors, also known as habits. This study represents a first step toward validating the idea that one can add explanatory power to a behavioral model such as Ajzen’s [1985] theory of planned behavior (TPB) by including the habit construct. We conducted a two-stage questionnaire-based survey involving two different groups of students who had access to a sophisticated internet-based communication tool (IBCT). These data were used to test a behavioral model integrating theoretical constructs of TPB and a relevant subset of Triandis’ [1980] behavioral framework. Our findings highlight the importance of considering both conscious (intentions) and subconscious (habits) factors in explaining usage behavior. Furthermore, we share our observations about antecedents of IBCT usage in the educational context. Implications for practice and research are discussed.


Interacting with in-vehicle systems: understanding, measuring, and evaluating attention

In-vehicle systems research is becoming a significant field as the market for in-vehicle systems continue to grow. As a consequence, researchers are increasingly concerned with opportunities and limitations of HCI in a moving vehicle. Especially aspects of attention constitute a challenge for in-vehicle systems development. This paper seeks to remedy this by defining and exemplifying attention understandings. 100 papers were classified in a two-fold perspective; under what settings are in-vehicle systems evaluated and how is driver attention measured in regard to in-vehicle systems HCI. A breakdown of the distribution of driving settings and measures is presented and the impact of driver attention is discussed. The classification revealed that most of the studies were conducted in driving simulators and real traffic driving, while lateral and longitudinal control and eye behaviour were the most used measures. Author


Chemi-net: a graph convolutional network for accurate drug property prediction

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.


Threatening faces and social anxiety: a literature review.

A threatening facial expression is a potent social sign of hostility or dominance. During the past 20 years, photographs of threatening faces have been increasingly included as stimuli in studies with socially anxious participants, based on the hypothesis that a threatening face is especially salient to people with fears of social interaction or negative evaluation. The purpose of this literature review is to systematically evaluate the accumulated research and suggest possible avenues for further research. The main conclusion is that photographs of threatening faces engage a broad range of perceptual processes in socially anxious participants, particularly when exposure times are very short.


A 1 . 5V , 10-bit , 14 . 3-MS / s CMOS Pipeline Analog-to-Digital Converter

A 1.5-V, 10-bit, 14.3-MS/s pipeline analog-to-digital converter was implemented in a 0.6m CMOS technology. Emphasis was placed on observing device reliability constraints at low voltage. MOS switches were implemented without lowthreshold devices by using a bootstrapping technique that does not subject the devices to large terminal voltages. The converter achieved a peak signal-to-noise-and-distortion ratio of 58.5 dB, maximum differential nonlinearity of 0.5 least significant bit (LSB), maximum integral nonlinearity of 0.7 LSB, and a power consumption of 36 mW.


Remote Sensing Image Classification Based on Stacked Denoising Autoencoder

Focused on the issue that conventional remote sensing image classification methods have run into the bottlenecks in accuracy, a new remote sensing image classification method inspired by deep learning is proposed, which is based on Stacked Denoising Autoencoder. First, the deep network model is built through the stacked layers of Denoising Autoencoder. Then, with noised input, the unsupervised Greedy layer-wise training algorithm is used to train each layer in turn for more robust expressing, characteristics are obtained in supervised learning by Back Propagation (BP) neural network, and the whole network is optimized by error back propagation. Finally, Gaofen-1 satellite (GF-1) remote sensing data are used for evaluation, and the total accuracy and kappa accuracy reach 95.7% and 0.955, respectively, which are higher than that of the Support Vector Machine and Back Propagation neural network. The experiment results show that the proposed method can effectively improve the accuracy of remote sensing image classification.


A wearable fingertip haptic device with 3 DoF asymmetric 3-RSR kinematics

A novel wearable haptic device for modulating skin stretch at the fingertip is presented. Rendering of skin stretch in 3 degrees of freedom (DoF), with contact - no contact capabilities, was implemented through rigid parallel kinematics. The novel asymmetrical three revolute-spherical-revolute (3-RSR) configuration allowed compact dimensions with minimum encumbrance of the hand workspace and minimum inter-finger interference. A differential method for solving the non-trivial inverse kinematics is proposed and implemented in real time for controlling the position of the skin tactor. Experiments involving the grasping of a virtual object were conducted using two devices (thumb and index fingers) in a group of 4 subjects: results showed that participants performed the grasping task more precisely and with grasping forces closer to the expected natural behavior when the proposed device provided haptic feedback.


Deformable Classifiers

Geometric variations of objects, which do not modify the object class, pose a major challenge for object recognition. These variations could be rigid as well as non-rigid transformations. In this paper, we design a framework for training deformable classifiers, where latent transformation variables are introduced, and a transformation of the object image to a reference instantiation is computed in terms of the classifier output, separately for each class. The classifier outputs for each class, after transformation, are compared to yield the final decision. As a by-product of the classification this yields a transformation of the input object to a reference pose, which can be used for downstream tasks such as the computation of object support. We apply a two-step training mechanism for our framework, which alternates between optimizing over the latent transformation variables and the classifier parameters to minimize the loss function. We show that multilayer perceptrons, also known as deep networks, are well suited for this approach and achieve state of the art results on the rotated MNIST and the Google Earth dataset, and produce competitive results on MNIST and CIFAR-10 when training on smaller subsets of training data.


Evolutionary Delivery versus the "waterfall model"

The conventional wisdom of planning software engineering projects, using the widely cited "waterfall model" is not the only useful software development process model. In fact, the "waterfall model" may be unrealistic, and dangerous to the primary objectives of any software project.The alternative model, which I choose to call "evolutionary delivery" is not widely taught or practiced yet. But there is already more than a decade of practical experience in using it. In various forms. It is quite clear from these experiences that evolutionary delivery is a powerful general tool for both software development and associated systems development.Almost all experienced software developers do make use of some of the ideas in evolutionary development at one time or another. But, this is often unplanned, informal and it is an incomplete exploitation of this powerful method. This paper will try to expose the theoretical and practical aspects of the method in a fuller perspective. We need to learn the theory fully, so that we can apply and learn it completely.


Deep Generative Models with Learnable Knowledge Constraints

The broad set of deep generative models (DGMs) has achieved remarkable advances. However, it is often difficult to incorporate rich structured domain knowledge with the end-to-end DGMs. Posterior regularization (PR) offers a principled framework to impose structured constraints on probabilistic models, but has limited applicability to the diverse DGMs that can lack a Bayesian formulation or even explicit density evaluation. PR also requires constraints to be fully specified a priori, which is impractical or suboptimal for complex knowledge with learnable uncertain parts. In this paper, we establish mathematical correspondence between PR and reinforcement learning (RL), and, based on the connection, expand PR to learn constraints as the extrinsic reward in RL. The resulting algorithm is modelagnostic to apply to any DGMs, and is flexible to adapt arbitrary constraints with the model jointly. Experiments on human image generation and templated sentence generation show models with learned knowledge constraints by our algorithm greatly improve over base generative models.


A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative – that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e., deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision – convolutional neural networks (CNNs). We start with “AlexNet” as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.


Glaucoma-Deep : Detection of Glaucoma Eye Disease on Retinal Fundus Images using Deep Learning Detection of Glaucoma by Abbas

Detection of glaucoma eye disease is still a challenging task for computer-aided diagnostics (CADx) systems. During eye screening process, the ophthalmologists measures the glaucoma by structure changes in optic disc (OD), loss of nerve fibres (LNF) and atrophy of the peripapillary region (APR). In retinal images, the automated CADx systems are developed to assess this eye disease through segmentation-based hand-crafted features. Therefore in this paper, the convolutional neural network (CNN) unsupervised architecture was used to extract the features through multilayer from raw pixel intensities. Afterwards, the deep-belief network (DBN) model was used to select the most discriminative deep features based on the annotated training dataset. At last, the final decision is performed by softmax linear classifier to differentiate between glaucoma and non-glaucoma retinal fundus image. This proposed system is known as Glaucoma-Deep and tested on 1200 retinal images obtained from publically and privately available datasets. To evaluate the performance of Glaucoma-Deep system, the sensitivity (SE), specificity (SP), accuracy (ACC), and precision (PRC) statistical measures were utilized. On average, the SE of 84.50%, SP of 98.01%, ACC of 99% and PRC of 84% values were achieved. Comparing to state-of-the-art systems, the Nodular-Deep system accomplished significant higher results. Consequently, the Glaucoma-Deep system can easily recognize the glaucoma eye disease to solve the problem of clinical experts during eye-screening process on large-scale environments. Keywords—Fundus imaging; glaucoma; diabetic retinopathy; deep learning; convolutional neural networks; deep belief network


On Laser Ranging Based on High-Speed/Energy Laser Diode Pulses and Single-Photon Detection Techniques

This paper discusses the construction principles and performance of a pulsed time-of-flight (TOF) laser radar based on high-speed (FWHM ~100 ps) and high-energy (~1 nJ) optical transmitter pulses produced with a specific laser diode working in an “enhanced gain-switching” regime and based on single-photon detection in the receiver. It is shown by analysis and experiments that single-shot precision at the level of 2W3 cm is achievable. The effective measurement rate can exceed 10 kHz to a noncooperative target (20% reflectivity) at a distance of > 50 m, with an effective receiver aperture size of 2.5 cm2. The effect of background illumination is analyzed. It is shown that the gating of the SPAD detector is an effective means to avoid the blocking of the receiver in a high-level background illumination case. A brief comparison with pulsed TOF laser radars employing linear detection techniques is also made.


Detecting Structural Similarities between XML Documents

In this paper we propose a technique for detecting the similarity in the structure of XML documents. The technique is based on the idea of representing the structure of an XML document as a time series in which each occurrence of a tag corresponds to a given impulse. By analyzing the frequencies of the corresponding Fourier transform, we can hence state the degree of similarity between documents. The efficiency and effectiveness of this approach are compelling when compared with traditional ones.


Translating Learning into Numbers: A Generic Framework for Learning Analytics

With the increase in available educational data, it is expected that Learning Analytics will become a powerful means to inform and support learners, teachers and their institutions in better understanding and predicting personal learning needs and performance. However, the processes and requirements behind the beneficial application of Learning and Knowledge Analytics as well as the consequences for learning and teaching are still far from being understood. In this paper, we explore the key dimensions of Learning Analytics (LA), the critical problem zones, and some potential dangers to the beneficial exploitation of educational data. We propose and discuss a generic design framework that can act as a useful guide for setting up Learning Analytics services in support of educational practice and learner guidance, in quality assurance, curriculum development, and in improving teacher effectiveness and efficiency. Furthermore, the presented article intends to inform about soft barriers and limitations of Learning Analytics. We identify the required skills and competences that make meaningful use of Learning Analytics data possible to overcome gaps in interpretation literacy among educational stakeholders. We also discuss privacy and ethical issues and suggest ways in which these issues can be addressed through policy guidelines and best practice examples.


Trust-aware crowdsourcing with domain knowledge

The rise of social network and crowdsourcing platforms makes it convenient to take advantage of the collective intelligence to estimate true labels of questions of interest. However, input from workers is often noisy and even malicious. Trust is used to model workers in order to better estimate true labels of questions. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. Finally, we demonstrate that our model is superior to state-of-the-art by testing it on multiple real-world datasets.


GORAM – Group ORAM for Privacy and Access Control in Outsourced Personal Records 1

Cloud storage has rapidly become a cornerstone of many IT infrastructures, constituting a seamless solution for the backup, synchronization, and sharing of large amounts of data. Putting user data in the direct control of cloud service providers, however, raises security and privacy concerns related to the integrity of outsourced data, the accidental or intentional leakage of sensitive information, the profiling of user activities and so on. Furthermore, even if the cloud provider is trusted, users having access to outsourced files might be malicious and misbehave. These concerns are particularly serious in sensitive applications like personal health records and credit score systems. To tackle this problem, we present GORAM, a cryptographic system that protects the secrecy and integrity of outsourced data with respect to both an untrusted server and malicious clients, guarantees the anonymity and unlinkability of accesses to such data, and allows the data owner to share outsourced data with other clients, selectively granting them read and write permissions. GORAM is the first system to achieve such a wide range of security and privacy properties for outsourced storage. In the process of designing an efficient construction, we developed two new, generally applicable cryptographic schemes, namely, batched zero-knowledge proofs of shuffle and an accountability technique based on chameleon signatures, which we consider of independent interest. We implemented GORAM in Amazon Elastic Compute Cloud (EC2) and ran a performance evaluation demonstrating the scalability and efficiency of our construction.


Position Control of a Permanent Magnet DC Motor by Model Reference Adaptive Control

Model reference adaptive control (MRAC) is one of the various techniques of solving the control problem when the parameters of the controlled process are poorly known or vary during normal operation. To understand the dynamic behavior of a dc motor it is required to know its parameters; armature inductance and resistance (La, Ra), inertia of the rotor (Jm), motor constant (Km), friction coefficient (Bm), etc. To identify these parameters, some experiments should be performed. However, motor parameters change under operation according to several conditions. Therefore, the performance of controller, which has been designed considering constant motor parameters becomes poorer. For this reason, a model reference adaptive control method is proposed to control the position of a dc motor without requiring fixed motor parameters. Experimental results show how well this method controls the position of a permanent magnet dc motor.


Solutions Strategies for Die Shift Problem in Wafer Level Compression Molding

Die shift problem that arises during the wafer molding process in embedded micro wafer level package fabrication was systematically analyzed and solution strategies were developed. A methodology to measure die shift was developed and applied to create maps of die shift on an 8 inch wafer. A total of 256 dies were embedded in an 8 inch mold compound wafer using compression molding. Thermal and cure shrinkages of mold compound are determined to be the primary reasons for die shift in wafer molding. Die shift value increases as the distance from the center of the wafer increases. Pre-compensation of die shift during pick and place is demonstrated as an effective method to control die shift. Applying pre-compensation method 99% of dies can be achieved to have die shift values of less than 40 μm. Usage of carrier wafer during wafer molding reduces the maximum die shift in a wafer from 633 μm to 79 μm. Die area/package area ratio has a strong influence on the die shift values. Die area/package area ratios of 0.81, 0.49, and 0.25 lead to maximum die shift values of 26, 76, and 97 μ.m, respectively. Wafer molding using low coefficient of thermal expansion (7 × 10-6/°C) and low cure shrinkage (0.094%) mold compounds is demonstrated to yield maximum die shift value of 28 μm over the whole 8 inch wafer area.


Gender differences in participation and reward on Stack Overflow

Programming is a valuable skill in the labor market, making the underrepresentation of women in computing an increasingly important issue. Online question and answer platforms serve a dual purpose in this field: they form a body of knowledge useful as a reference and learning tool, and they provide opportunities for individuals to demonstrate credible, verifiable expertise. Issues, such as male-oriented site design or overrepresentation of men among the site’s elite may therefore compound the issue of women’s underrepresentation in IT. In this paper we audit the differences in behavior and outcomes between men and women on Stack Overflow, the most popular of these Q&A sites. We observe significant differences in how men and women participate in the platform and how successful they are. For example, the average woman has roughly half of the reputation points, the primary measure of success on the site, of the average man. Using an Oaxaca-Blinder decomposition, an econometric technique commonly applied to analyze differences in wages between groups, we find that most of the gap in success between men and women can be explained by differences in their activity on the site and differences in how these activities are rewarded. Specifically, 1) men give more answers than women and 2) are rewarded more for their answers on average, even when controlling for possible confounders such as tenure or buy-in to the site. Women ask more questions and gain more reward per question. We conclude with a hypothetical redesign of the site’s scoring system based on these behavioral differences, cutting the reputation gap in half.


Named data networking

Named Data Networking (NDN) is one of five projects funded by the U.S. National Science Foundation under its Future Internet Architecture Program. NDN has its roots in an earlier project, Content-Centric Networking (CCN), which Van Jacobson first publicly presented in 2006. The NDN project investigates Jacobson's proposed evolution from today's host-centric network architecture (IP) to a data-centric network architecture (NDN). This conceptually simple shift has far-reaching implications for how we design, develop, deploy, and use networks and applications. We describe the motivation and vision of this new architecture, and its basic components and operations. We also provide a snapshot of its current design, development status, and research challenges. More information about the project, including prototype implementations, publications, and annual reports, is available on named-data.net.


A survey of information-centric networking

The information-centric networking (ICN) concept is a significant common approach of several future Internet research activities. The approach leverages in-network caching, multiparty communication through replication, and interaction models decoupling senders and receivers. The goal is to provide a network infrastructure service that is better suited to today¿s use (in particular. content distribution and mobility) and more resilient to disruptions and failures. The ICN approach is being explored by a number of research projects. We compare and discuss design choices and features of proposed ICN architectures, focusing on the following main components: named data objects, naming and security, API, routing and transport, and caching. We also discuss the advantages of the ICN approach in general.


Chronos : Serverless Multi-User Chat Over NDN

Multi-user applications are commonly implemented using a centralized server. This paper presents a new design for multi-user chat applications (Chronos) that works in a distributed, serverless fashion over Named Data Networking. In Chronos, all participants share their views by exchanging the cryptographic digests of the chat room data set. A newly generated message causes a change of the digest at the message originator, which leads to retrieving the new data by all other participants in an efficient way and resynchronization of chat room views. Chronos does not have a single point of failure and eliminates traffic concentration problem of server-based implementations. We use simulations to evaluate and compare Chronos with a traditional serverbased chat room implementation. Our results demonstrate Chronos’ robustness and efficiency in data dissemination. Chronos’ approach of replacing centralized servers by distributed data synchronization can be applied to a variety of distributed applications to simplify design and ease deployment.


NLSR: named-data link state routing protocol

This paper presents the design of the Named-data Link State Routing protocol (NLSR), a routing protocol for Named Data Networking (NDN). Since NDN uses names to identify and retrieve data, NLSR propagates reachability to name prefixes instead of IP prefixes. Moreover, NLSR differs from IP-based link-state routing protocols in two fundamental ways. First, NLSR uses Interest/Data packets to disseminate routing updates, directly benefiting from NDN's data authenticity. Second, NLSR produces a list of ranked forwarding options for each name prefix to facilitate NDN's adaptive forwarding strategies. In this paper we discuss NLSR's main design choices on (1) a hierarchical naming scheme for routers, keys, and routing updates, (2) a hierarchical trust model for routing within a single administrative domain, (3) a hop-by-hop synchronization protocol to replace the traditional network-wide flooding for routing update dissemination, and (4) a simple way to rank multiple forwarding options. Compared with IP-based link state routing, NLSR offers more efficient update dissemination, built-in update authentication, and native support of multipath forwarding.


Interest flooding attack and countermeasures in Named Data Networking

Distributed Denial of Service (DDoS) attacks are an ongoing problem in today's Internet, where packets from a large number of compromised hosts thwart the paths to the victim site and/or overload the victim machines. In a newly proposed future Internet architecture, Named Data Networking (NDN), end users request desired data by sending Interest packets, and the network delivers Data packets upon request only, effectively eliminating many existing DDoS attacks. However, an NDN network can be subject to a new type of DDoS attack, namely Interest packet flooding. In this paper we investigate effective solutions to mitigate Interest flooding. We show that NDN's inherent properties of storing per packet state on each router and maintaining flow balance (i.e., one Interest packet retrieves at most one Data packet) provides the basis for effective DDoS mitigation algorithms. Our evaluation through simulations shows that the solution can quickly and effectively respond and mitigate Interest flooding.


Interpolating and approximating implicit surfaces from polygon soup

This paper describes a method for building interpolating or approximating implicit surfaces from polygonal data. The user can choose to generate a surface that exactly interpolates the polygons, or a surface that approximates the input by smoothing away features smaller than some user-specified size. The implicit functions are represented using a moving least-squares formulation with constraints integrated over the polygons. The paper also presents an improved method for enforcing normal constraints and an iterative procedure for ensuring that the implicit surface tightly encloses the input vertices.


Adaptive Spatial-Scale-Aware Deep Convolutional Neural Network for High-Resolution Remote Sensing Imagery Scene Classification

High-resolution remote sensing (HRRS) scene classification plays an important role in numerous applications. During the past few decades, a lot of remarkable efforts have been made to develop various methods for HRRS scene classification. In this paper, focusing on the problems of complex context relationship and large differences of object scale in HRRS scene images, we propose a deep CNN-based scene classification method, which not only enables to enhance the ability of spatial representation, but adaptively recalibrates channel-wise feature responses to suppress useless feature channels. We evaluated the proposed method on a publicly large-scale dataset with several state-of-the-art convolutional neural network (CNN) models. The experimental results demonstrate that the proposed method is effective to extract high-level category features for HRRS scene classification.


Sentiment Analysis: From Opinion Mining to Human-Agent Interaction

The opinion mining and human-agent interaction communities are currently addressing sentiment analysis from different perspectives that comprise, on the one hand, disparate sentiment-related phenomena and computational representations, and on the other hand, different detection and dialog management methods. In this paper we identify and discuss the growing opportunities for cross-disciplinary work that may increase individual advances. Sentiment/opinion detection methods used in human-agent interaction are indeed rare and, when they are employed, they are not different from the ones used in opinion mining and consequently not designed for socio-affective interactions (timing constraint of the interaction, sentiment analysis as an input and an output of interaction strategies). To support our claims, we present a comparative state of the art which analyzes the sentiment-related phenomena and the sentiment detection methods used in both communities and makes an overview of the goals of socio-affective human-agent strategies. We propose then different possibilities for mutual benefit, specifying several research tracks and discussing the open questions and prospects. To show the feasibility of the general guidelines proposed we also approach them from a specific perspective by applying them to the case of the Greta embodied conversational agents platform and discuss the way they can be used to make a more significative sentiment analysis for human-agent interactions in two different use cases: job interviews and dialogs with museum visitors.


Floor Layout Planning Using Artificial Intelligence Technique

In the era of e-commerce while buying furniture online the customers obviously feel the need for visual representation of the arrangement of their furniture. Even when doing interiors of the house it's difficult to just rely on assumptions about best layouts possible and professional help may become quite expensive. In this project, we make use of Genetic Algorithm (GA) which is an Artificial Intelligence technique to display various optimal arrangements of furniture. The basic idea behind using GA is developing an evolutionary design model. This is done by generating chromosomes for each possible solution and then performing a crossover between them in each generation until an optimum fitness function is reached. Modification in chromosome representation may also be done for better results. The proposed system will generate different layout designs for the furniture keeping in consideration the structure of a master bedroom.


Generation of Panoramic View from 360 Degree Fisheye Images Based on Angular Fisheye Projection

this paper proposes a algorithm to generate the panoramic view from 360 ¡ãfisheye images based on angular fisheye projection model. Firstly, the image plane will be mapped to the view plane, the sphere plane, to obtain the coordinates of spherical points. Then use the angular fisheye projection model to get fisheye images' radius. Secondly, calculate the coordinates of fisheye images' points with the radius of fisheye images and the coordinates of spherical points. Finally, map the points of fisheye images to the target plane by the way of backward mapping. Experiments show that this algorithm is simple and fast, and can obtain the ideal images.


Accident prediction model for railway-highway interfaces.

Considerable past research has explored relationships between vehicle accidents and geometric design and operation of road sections, but relatively little research has examined factors that contribute to accidents at railway-highway crossings. Between 1998 and 2002 in Korea, about 95% of railway accidents occurred at highway-rail grade crossings, resulting in 402 accidents, of which about 20% resulted in fatalities. These statistics suggest that efforts to reduce crashes at these locations may significantly reduce crash costs. The objective of this paper is to examine factors associated with railroad crossing crashes. Various statistical models are used to examine the relationships between crossing accidents and features of crossings. The paper also compares accident models developed in the United States and the safety effects of crossing elements obtained using Korea data. Crashes were observed to increase with total traffic volume and average daily train volumes. The proximity of crossings to commercial areas and the distance of the train detector from crossings are associated with larger numbers of accidents, as is the time duration between the activation of warning signals and gates. The unique contributions of the paper are the application of the gamma probability model to deal with underdispersion and the insights obtained regarding railroad crossing related vehicle crashes.


Determination of spinal curvature from scoliosis X-ray images using K-means and curve fitting for early detection of scoliosis disease

One of the disease that require X-ray diagnosis is scoliosis. Early detection of scoliosis is important to do for anyone. From the early detection information, the doctor may take the firts step to further treatment quickly. Determination of spinal curvature is a first step method that used to measure how severe the degree of scoliosis. The severity degree of scoliosis can be assess by using Cobb angle. Therefore, by approximate the spinal curvature, we can approximate the cobb angle too. From previous work that interobserver measurement value may reach 11.8° and intraobserver measurement error is 6°. So, as far as the cobb angle measuring, the subjectivity aspect is the natural thing and can be tolerated until now. This research propose an algorithm how to define spinal curvature with the aid of a computer in digital X-ray image quickly but has a standard error that can be tolerated. The preprocessing has been done by canny edge detection. The k-means clustering algorithm can detect the centroid point after segmentation preprocessing of the spinal segment and polynomial curve fitting will be used in the process for determining the spinal curve. From the spinal curvature information, the scoliosis curve can be classified into 4 condition, normal, mild, moderate, and severe scoliosis.


A study of attention-based neural machine translation model on Indian languages

Neural machine translation (NMT) models have recently been shown to be very successful in machine translation (MT). The use of LSTMs in machine translation has significantly improved the translation performance for longer sentences by being able to capture the context and long range correlations of the sentences in their hidden layers. The attention model based NMT system has become state-of-the-art, performing equal or better than other statistical MT approaches. In this paper, we studied the performance of the attention-model based NMT system on the Indian language pair, Hindi and Bengali. We analysed the types of errors that occur in morphologically rich languages when there is a scarcity of large parallel training corpus. We then carried out certain post-processing heuristic steps to improve the quality of the translated statements and suggest further measures.


A Voltage Efficient PMOS Charge Pump Architecture

In this paper a charge pump circuit is presented: it is based on PMOS pass transistors with dynamic control of the gate and body voltages. By controlling the gate and the bulk of each PMOS pass transistor, the voltage loss due to the device threshold is removed and the charge is pumped from one stage to the other with negligible voltage drop. Furthermore, the overdrive voltage of the pass transistors grows progressively from the first to the last stage. Hence, a larger output voltage can be achieved compared to conventional charge pumps, still retaining a simple two-phase clocking scheme, along with the Dickson basic architecture. The prototype was fabricated using the STMicroelectronics T10 90nm CMOS process. Measurements results are also presented confirming the validity of the design


A novel planer transition structure from micro-strip line to waveguide

A planer micro-strip to waveguide transition structure is given in this paper. Coupled patch on the bottom of the substrate is excited by the micro-strip feed-line on the top, which realizes the transformation from the TE10 mode in the waveguide to the quasi-TEM mode on the micro-strip line. Modeling and simulation of the structure are conducted for the design by using electromagnetic simulation software HFSS. The measurement of the manufactured structure exhibits an insertion loss less than 0.5dB within 33GHz~37GHz. Compared to traditional waveguide E-plane probe or H-plane probe transition, this design has properties of low profile and easy integration.


Single Shot Text Detector with Regional Attention

We present a novel single-shot text detector that directly outputs word-level bounding boxes in a natural image. We propose an attention mechanism which roughly identifies text regions via an automatically learned attentional map. This substantially suppresses background interference in the convolutional features, which is the key to producing accurate inference of words, particularly at extremely small sizes. This results in a single model that essentially works in a coarse-to-fine manner. It departs from recent FCN-based text detectors which cascade multiple FCN models to achieve an accurate prediction. Furthermore, we develop a hierarchical inception module which efficiently aggregates multi-scale inception features. This enhances local details, and also encodes strong context information, allowing the detector to work reliably on multi-scale and multi-orientation text with single-scale images. Our text detector achieves an F-measure of 77% on the ICDAR 2015 benchmark, advancing the state-of-the-art results in [18, 28]. Demo is available at: http://sstd.whuang.org/.


A Novel Approach for Addressing Wandering Off Elderly Using Low Cost Passive RFID Tags

Wandering (e.g. elopement) by elderly persons at acute hospitals and nursing homes poses a significant problem to providing patient care as such incidents can lead to injury and even accidental morbidity. These problems are particularly serious given aging populations around the world. While various technologies exit (such as door alarms), they are expensive and the reliability of such systems have not been evaluated in the past. In this article we propose two novel methods for a very low cost solution to address the problem of wandering off patients using body worn low cost passive Radio Frequency Identification (RFID) tags using phase based measurements. Our approach requires no modification to the air interface protocols, firmware or hardware. Results from extensive experiments show that: i) the proposed algorithms can accurately identify whether a person is moving into or out of, for example, a room; and ii) it can be implemented in real-time to develop a low cost wandering off alarm.


Cybersecurity Games and Investments: A Decision Support Approach

In this paper we investigate how to optimally invest in cybersecurity controls. We are particularly interested in examining cases where the organization suffers from an underinvestment problem or inefficient spending on cybersecurity. To this end, we first model the cybersecurity environment of an organization. We then model non-cooperative cybersecurity control-games between the defender which abstracts all defense mechanisms of the organization and the attacker which can exploit different vulnerabilities at different network locations. To implement our methodology we use the SANS Top 20 Critical Security Controls and the 2011 CWE/SANS top 25 most dangerous software errors. Based on the profile of an organization, which forms its preferences in terms of indirect costs, its concerns about different kinds of threats and the importance of the assets given their associated risks we derive the Nash Equilibria of a series of control-games. These game solutions are then handled by optimization techniques, in particular multi-objective, multiple choice Knapsack to determine the optimal cybersecurity investment. Our methodology provides security effective and cost efficient solutions especially against commodity attacks. We believe our work can be used to advise security managers on how they should spend an available cybersecurity budget given their organization profile.


Parallel and distributed simulation

Research and development efforts in the parallel and distributed simulation field over the last 15 years has progressed, largely independently, in two separate camps: the largely academic high performance parallel and distributed (discrete event) simulation (PADS) community, and the DoD-centered distributed interactive simulation (DIS) community. This tutorial gives an overview and comparison of work in these two areas, emphasizing issues related to distributed execution where these fields have the most overlap. Differences in the fundamental assumptions routinely used within each community are contrasted, followed by overviews of work in each community.


Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior


Visual Exploration of Climate Variability Changes Using Wavelet Analysis

Due to its nonlinear nature, the climate system shows quite high natural variability on different time scales, including multiyear oscillations such as the El Nino southern oscillation phenomenon. Beside a shift of the mean states and of extreme values of climate variables, climate change may also change the frequency or the spatial patterns of these natural climate variations. Wavelet analysis is a well established tool to investigate variability in the frequency domain. However, due to the size and complexity of the analysis results, only few time series are commonly analyzed concurrently. In this paper we will explore different techniques to visually assist the user in the analysis of variability and variability changes to allow for a holistic analysis of a global climate model data set consisting of several variables and extending over 250 years. Our new framework and data from the IPCC AR4 simulations with the coupled climate model ECHAM5/MPI-OM are used to explore the temporal evolution of El Nino due to climate change.


Highly reliable M1X MLC NAND flash memory cell with novel active air-gap and p+ poly process integration technologies

Our Middle-1X nm MLC NAND (M1X) flash cell is intensively characterized with respect to reliability and manufacturability. For the first time, the novel active air-gap technology is applied to alleviate the drop of channel boosting potential of program inhibition mode, BL-BL interference is reduced to our 2y nm node level by this novel integration technology. Furthermore, it also relaxes the effect of process variation like EFH (Effective Field oxide Height) on cell Vt distribution. Better endurance and retention characteristics can be obtained by p+ doped poly gate. By optimization of active air-gap profile and poly doping level, M1X nm MLC NAND flash memory has been successfully implemented with superior manufacturability and acceptable reliability.


Non-sentential Question Resolution using Sequence to Sequence Learning

An interactive Question Answering (QA) system frequently encounters non-sentential (incomplete) questions. These non-sentential questions may not make sense to the system when a user asks them without the context of conversation. The system thus needs to take into account the conversation context to process the incomplete question. In this work, we present a recurrent neural network (RNN) based encoder decoder network that can generate a complete (intended) question, given an incomplete question and conversation context. RNN encoder decoder networks have been show to work well when trained on a parallel corpus with millions of sentences, however it is extremely hard to obtain conversation data of this magnitude. We therefore propose to decompose the original problem into two separate simplified problems where each problem focuses on an abstraction. Specifically, we train a semantic sequence model to learn semantic patterns, and a syntactic sequence model to learn linguistic patterns. We further combine syntactic and semantic sequence models to generate an ensemble model. Our model achieves a BLEU score of 30.15 as compared to 18.54 using a standard RNN encoder decoder model.


Development of an Omni-Directional Mobile Robot with 3 DOF Decoupling Drive Mechanism

A ne'ui driving mech,anism for holonomic ornwidirectional mobi le robots i s designzed, ,which enu.bles 3 DOF m o t i o n control by three correspondent actuators in, (I decozipled ma'nner ,with n o redundan,cy. A prototype of the omni-drrectional mobile robot w i th th,e dr iu ing mechan i sm is developed including a parallel link suspemion m,echanism. T h e k inemat ics of th.e omnidirectionsal mobile robot i s also analyzed, and simulat i o n fo,r velocity control of the robot is perform.ed by od f o r ,oelocity modula t ion wi th in,terpolation. to uchieve the given target pos i t ion and velocity.


Residential exposure to aircraft noise and hospital admissions for cardiovascular diseases: multi-airport retrospective study

OBJECTIVE
To investigate whether exposure to aircraft noise increases the risk of hospitalization for cardiovascular diseases in older people (≥ 65 years) residing near airports.

DESIGN
Multi-airport retrospective study of approximately 6 million older people residing near airports in the United States. We superimposed contours of aircraft noise levels (in decibels, dB) for 89 airports for 2009 provided by the US Federal Aviation Administration on census block resolution population data to construct two exposure metrics applicable to zip code resolution health insurance data: population weighted noise within each zip code, and 90th centile of noise among populated census blocks within each zip code.

SETTING
2218 zip codes surrounding 89 airports in the contiguous states.

PARTICIPANTS
6 027 363 people eligible to participate in the national medical insurance (Medicare) program (aged ≥ 65 years) residing near airports in 2009.

MAIN OUTCOME MEASURES
Percentage increase in the hospitalization admission rate for cardiovascular disease associated with a 10 dB increase in aircraft noise, for each airport and on average across airports adjusted by individual level characteristics (age, sex, race), zip code level socioeconomic status and demographics, zip code level air pollution (fine particulate matter and ozone), and roadway density.

RESULTS
Averaged across all airports and using the 90th centile noise exposure metric, a zip code with 10 dB higher noise exposure had a 3.5% higher (95% confidence interval 0.2% to 7.0%) cardiovascular hospital admission rate, after controlling for covariates.

CONCLUSIONS
Despite limitations related to potential misclassification of exposure, we found a statistically significant association between exposure to aircraft noise and risk of hospitalization for cardiovascular diseases among older people living near airports.


Continuous Requirements Engineering and Human-Centered Agile Software Development

The idea of Continuous Requirements Engineering in relation to a Human-Centered Agile Development Process is discussed. First, it is argued that Continuous Requirements Engineering has to cover design-time and runtime aspects. In this way maintenance is covered as well. Second, arguments are provided for integrating aspects of usability and user experience into requirements specifications. This has to be done continuously. Therefore, the term Continuous Human-Centered Development is introduced and discussed. Based on a process model for SCRUM some aspects of integrating HCD into the development process are discussed.


From Non-Functional Requirements to Design through Patterns

Design patterns aid in documenting and communicating proven design solutions to recurring problems. They describe not only how to solve design problems, but also why a solution is chosen over others and what trade-offs are made. Non-functional requirements (NFRs) are pervasive in descriptions of design patterns. They play a crucial role in understanding the problem being addressed, the trade-offs discussed, and the design solution proposed. However, since design patterns are mostly expressed as informal text, the structure of the design reasoning is not systematically organised. This paper proposes a systematic treatment of NFRs in descriptions of patterns and when applying patterns during design. The approach organises, analyses and refines non-functional requirements, and provides guidance and reasoning support when applying patterns during the design of a software system. Three design patterns taken from the literature are used to illustrate this approach.


Tools for ontology matching practical considerations

There exists a large body of scienti c literature devoted to ontology matching, aligning, mapping translating and merging. With it, comes a long list (90+) of tools that support various aspects of these operations. We have approached such tools from the perspective of the INTER-IoT project, in which one of the goals is to facilitate semantic interoperability of Internet of Things platforms. Thus, we had to answer a question: what is actually available when one needs to align/merge ontologies. Here, we summarize our ndings.


Speedy transactions in multicore in-memory databases

Silo is a new in-memory database that achieves excellent performance and scalability on modern multicore machines. Silo was designed from the ground up to use system memory and caches efficiently. For instance, it avoids all centralized contention points, including that of centralized transaction ID assignment. Silo's key contribution is a commit protocol based on optimistic concurrency control that provides serializability while avoiding all shared-memory writes for records that were only read. Though this might seem to complicate the enforcement of a serial order, correct logging and recovery is provided by linking periodically-updated epochs with the commit protocol. Silo provides the same guarantees as any serializable database without unnecessary scalability bottlenecks or much additional latency. Silo achieves almost 700,000 transactions per second on a standard TPC-C workload mix on a 32-core machine, as well as near-linear scalability. Considered per core, this is several times higher than previously reported results.


To Lock, Swap, or Elide: On the Interplay of Hardware Transactional Memory and Lock-Free Indexing

The release of hardware transactional memory (HTM) in commodity CPUs has major implications on the design and implementation of main-memory databases, especially on the architecture of highperformance lock-free indexing methods at the core of several of these systems. This paper studies the interplay of HTM and lockfree indexing methods. First, we evaluate whether HTM will obviate the need for crafty lock-free index designs by integrating it in a traditional B-tree architecture. HTM performs well for simple data sets with small fixed-length keys and payloads, but its benefits disappear for more complex scenarios (e.g., larger variable-length keys and payloads), making it unattractive as a general solution for achieving high performance. Second, we explore fundamental differences between HTM-based and lock-free B-tree designs. While lock-freedom entails design complexity and extra mechanism, it has performance advantages in several scenarios, especially high-contention cases where readers proceed uncontested (whereas HTM aborts readers). Finally, we explore the use of HTM as a method to simplify lock-free design. We find that using HTM to implement a multi-word compare-and-swap greatly reduces lockfree programming complexity at the cost of only a 10-15% performance degradation. Our study uses two state-of-the-art index implementations: a memory-optimized B-tree extended with HTM to provide multi-threaded concurrency and the Bw-tree lock-free B-tree used in several Microsoft production environments.


An effective hybrid transactional memory system with strong isolation guarantees

We propose signature-accelerated transactional memory (SigTM), ahybrid TM system that reduces the overhead of software transactions. SigTM uses hardware signatures to track the read-set and write-set forpending transactions and perform conflict detection between concurrent threads. All other transactional functionality, including dataversioning, is implemented in software. Unlike previously proposed hybrid TM systems, SigTM requires no modifications to the hardware caches, which reduces hardware cost and simplifies support for nested transactions and multithreaded processor cores. SigTM is also the first hybrid TM system to provide strong isolation guarantees between transactional blocks and non-transactional accesses without additional read and write barriers in non-transactional code.
 Using a set of parallel programs that make frequent use of coarse-grain transactions, we show that SigTM accelerates software transactions by 30% to 280%. For certain workloads, SigTM can match the performance of a full-featured hardware TM system, while for workloads with large read-sets it can be up to two times slower. Overall, we show that SigTM combines the performance characteristics and strong isolation guarantees of hardware TM implementations with the low cost and flexibility of software TM systems.


The End of an Architectural Era (It's Time for a Complete Rewrite)

In previous papers [SC05, SBC+07], some of us predicted the end of “one size fits all” as a commercial relational DBMS paradigm. These papers presented reasons and experimental evidence that showed that the major RDBMS vendors can be outperformed by 1-2 orders of magnitude by specialized engines in the data warehouse, stream processing, text, and scientific database markets. Assuming that specialized engines dominate these markets over time, the current relational DBMS code lines will be left with the business data processing (OLTP) market and hybrid markets where more than one kind of capability is required. In this paper we show that current RDBMSs can be beaten by nearly two orders of magnitude in the OLTP market as well. The experimental evidence comes from comparing a new OLTP prototype, H-Store, which we have built at M.I.T., to a popular RDBMS on the standard transactional benchmark, TPC-C. We conclude that the current RDBMS code lines, while attempting to be a “one size fits all” solution, in fact, excel at nothing. Hence, they are 25 year old legacy code lines that should be retired in favor of a collection of “from scratch” specialized engines. The DBMS vendors (and the research community) should start with a clean sheet of paper and design systems for tomorrow’s requirements, not continue to push code lines and architectures designed for yesterday’s needs.


Biped gait controller for large speed variations, combining reflexes and a central pattern generator in a neuromuscular model

Controllers based on neuromuscular models hold the promise of energy-efficient and human-like walkers. However, most of them rely on optimizations or cumbersome hand-tuning to find controller parameters which, in turn, are usually working for a specific gait or forward speed only. Consequently, designing neuromuscular controllers for a large variety of gaits is usually challenging and highly sensitive. In this contribution, we propose a neuromuscular controller combining reflexes and a central pattern generator able to generate gaits across a large range of speeds, within a single optimization. Applying this controller to the model of COMAN, a 95 cm tall humanoid robot, we were able to get energy-efficient gaits ranging from 0.4 m/s to 0.9 m/s. This covers normal human walking speeds once scaled to the robot height. In the proposed controller, the robot speed could be continuously commanded within this range by changing three high-level parameters as linear functions of the target speed. This allowed large speed transitions with no additional tuning. By combining reflexes and a central pattern generator, this approach can also predict when the next strike will occur and modulate the step length to step over a hole.


Using Argument Mining to Assess the Argumentation Quality of Essays

Argument mining aims to determine the argumentative structure of texts. Although it is said to be crucial for future applications such as writing support systems, the benefit of its output has rarely been evaluated. This paper puts the analysis of the output into the focus. In particular, we investigate to what extent the mined structure can be leveraged to assess the argumentation quality of persuasive essays. We find insightful statistical patterns in the structure of essays. From these, we derive novel features that we evaluate in four argumentation-related essay scoring tasks. Our results reveal the benefit of argument mining for assessing argumentation quality. Among others, we improve the state of the art in scoring an essay’s organization and its argument strength.


Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synsets) as defined in WordNet and represent a word token in a particular context by estimating a distribution over relevant semantic concepts. We use the new, context-sensitive embeddings in a model for predicting prepositional phrase (PP) attachments and jointly learn the concept embeddings and model parameters. We show that using context-sensitive embeddings improves the accuracy of the PP attachment model by 5.4% absolute points, which amounts to a 34.4% relative reduction in errors.


Developing an Isolated Word Recognition System in MATLAB

The Development Workflow There are two major stages within isolated word recognition: a training stage and a testing stage. Training involves “teaching” the system by building its dictionary, an acoustic model for each word that the system needs to recognize. In our example, the dictionary comprises the digits ‘zero’ to ‘nine’. In the testing stage we use acoustic models of these digits to recognize isolated words using a classification algorithm. The development workflow consists of three steps: Speech acquisition • Speech analysis • User interface development •


Ruminating Reader: Reasoning with Gated Multi-Hop Attention

To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BIDAF). We propose novel layer structures that construct a query aware context vector representation and fuse encoding representation with intermediate representation on top of BIDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BIDAF baseline by 2.1 F1 score and 2.7 EM score. Our analysis shows that different hops of the attention have different responsibilities in selecting answers.


An investigation of current models of second language speech perception: the case of Japanese adults' perception of English consonants.

This study reports the results of two experiments with native speakers of Japanese. In experiment 1, near-monolingual Japanese listeners participated in a cross-language mapping experiment in which they identified English and Japanese consonants in terms of a Japanese category, then rated the identifications for goodness-of-fit to that Japanese category. Experiment 2 used the same set of stimuli in a categorial discrimination test. Three groups of Japanese speakers varying in English-language experience, and one group of native English speakers participated. Contrast pairs composed of two English consonants, two Japanese consonants, and one English and one Japanese consonant were tested. The results indicated that the perceived phonetic distance of second language (L2) consonants from the closest first language (L1) consonant predicted the discrimination of L2 sounds. In addition, this study investigated the role of experience in learning sounds in a second language. Some of the consonant contrasts tested showed evidence of learning (i.e., significantly higher scores for the experienced than the relatively inexperienced Japanese groups). The perceived phonetic distance of L1 and L2 sounds was found to predict learning effects in discrimination of L1 and L2 sounds in some cases. The results are discussed in terms of models of cross-language speech perception and L2 phonetic learning.


Resource Sharing in Continuous Sliding-Window Aggregates

We consider the problem of resource sharing when processing large numbers of continuous queries. We specifically address sliding-window aggregates over data streams, an important class of continuous operators for which sharing has not been addressed. We present a suite of sharing techniques that cover a wide range of possible scenarios: different classes of aggregation functions (algebraic, distributive, holistic), different window types (time-based, tuple-based, suffix, historical), and different input models (single stream, multiple substreams). We provide precise theoretical performance guarantees for our techniques, and show their practical effectiveness through experimental study.


Design of Wireless Sensor System for Neonatal Monitoring

In this paper, we present the application of wireless sensor technology and the advantages it will inherently have for neonatal care and monitoring at Neonatal Intensive Care Units (NICU). An electrocardiography (ECG) readout board and a wireless transceiver module developed by IMEC at the Holst Centre in the Netherlands are embedded in the proposed wireless sensor systems in combination with the signal processing and software interface developed at the Department of Industrial Design, Eindhoven University of Technology (TU/e). Through development of this prototype system, we opt to ensure correct data transmission, detection and display. The wireless system is designed to be suitable for integration into non-invasive monitoring platforms such as a smart neonatal jacket developed at TU/e. Experiments at Maxima Medical Centre (MMC) in Veldhoven, the Netherlands demonstrate the wireless transmission of ECG data from the smart jacket integrated with textile electrodes.


Sentiment Analysis and Subjectivity

Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals or feelings toward entities, events and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on mining and retrieval of factual information, e.g., information retrieval, Web search, text classification, text clustering and many other text mining and natural language processing tasks. Little work had been done on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.


Comparison of Classifier Fusion Methods for Classification in Pattern Recognition Tasks

This work presents a comparison of current research in the use of voting ensembles of classifiers in order to improve the accuracy of single classifiers and make the performance more robust against the difficulties that each individual classifier may have. Also, a number of combination rules are proposed. Different voting schemes are discussed and compared in order to study the performance of the ensemble in each task. The ensembles have been trained on real data available for benchmarking and also applied to a case study related to statistical description models of melodies for music genre recognition.


Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless

Unmanned aerial vehicles (UAVs) are mainly used by military and government organizations, but with low-cost sensors, electronics, and airframes there is significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industries. Applications such as mapping, search and rescue, patrol, and surveillance require the UAV to autonomously follow a predefined path at a prescribed height. The most commonly used paths are straight lines and circular orbits. Path-following algorithms ensure that the UAV will follow a predefined path in three or two dimensions at constant height. A basic requirement for these path-following algorithms is that they must be accurate and robust to wind disturbances.


Erratum to: Eye–hand coordination in a sequential target contact task

Most object manipulation tasks involve a series of actions demarcated by mechanical contact events, and gaze is typically directed to the locations of these events as the task unfolds. Here, we examined the timing of gaze shifts relative to hand movements in a task in which participants used a handle to contact sequentially five virtual objects located in a horizontal plane. This task was performed both with and without visual feedback of the handle position. We were primarily interested in whether gaze shifts, which in our task shifted from a given object to the next about 100 ms after contact, were predictive or triggered by tactile feedback related to contact. To examine this issue, we included occasional catch contacts where forces simulating contact between the handle and object were removed. In most cases, removing force did not alter the timing of gaze shifts irrespective of whether or not vision of handle position was present. However, in about 30% of the catch contacts, gaze shifts were delayed. This percentage corresponded to the fraction of contacts with force feedback in which gaze shifted more than 130 ms after contact. We conclude that gaze shifts are predictively controlled but timed so that the hand actions around the time of contact are captured in central vision. Furthermore, a mismatch between the expected and actual tactile information related to the contact can lead to a reorganization of gaze behavior for gaze shifts executed greater than 130 ms after a contact event.


Recognition with Local Features: the Kernel Recipe

Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that it is suitable for many established local feature frameworks. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs with the proposed kernel perform better than standard matching techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.


Fast k-Means Based on k-NN Graph

In the big data era, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high when the data size and the cluster number are large. The processing bottleneck of k-means lies in the operation of seeking the closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore broken. The most interesting thing is that k-nearest neighbor graph is constructed by calling the fast k-means itself. Compared with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, it would take 3 years for traditional k-means to fulfill the same scale of clustering.


Malassezia Intra-Specific Diversity and Potentially New Species in the Skin Microbiota from Brazilian Healthy Subjects and Seborrheic Dermatitis Patients

Malassezia yeasts are part of the resident cutaneous microbiota, and are also associated with skin diseases such as seborrheic dermatitis (SD). The role these fungi play in skin diseases and why they are pathogenic for only some individuals remain unclear. This study aimed to characterize Malassezia microbiota from different body sites in healthy and SD subjects from Brazil. Scalp and forehead samples from healthy, mild SD and severe SD subjects were collected. Non-scalp lesions from severe SD patients were also sampled. 5.8S rDNA/ITS2 amplicons from Malassezia sp. were analyzed by RFLP and sequencing. Results indicate that Malassezia microbiota did not group according to health condition or body area. Phylogenetic analysis revealed that three groups of sequences did not cluster together with any formally described species, suggesting that they might belong to potential new species. One of them was found in high proportions in scalp samples. A large variety of Malassezia subtypes were detected, indicating intra-specific diversity. Higher M. globosa proportions were found in non-scalp lesions from severe SD subjects compared with other areas, suggesting closer association of this species with SD lesions from areas other than scalp. Our results show the first panorama of Malassezia microbiota in Brazilian subjects using molecular techniques and provide new perspectives for further studies to elucidate the association between Malassezia microbiota and skin diseases.


ECISER: Efficient Clip-art Image SEgmentation by Re-rasterization

a r t i c l e i n f o


An integrated theory of the mind.

Adaptive control of thought-rational (ACT-R; J. R. Anderson & C. Lebiere, 1998) has evolved into a theory that consists of multiple modules but also explains how these modules are integrated to produce coherent cognition. The perceptual-motor modules, the goal module, and the declarative memory module are presented as examples of specialized systems in ACT-R. These modules are associated with distinct cortical regions. These modules place chunks in buffers where they can be detected by a production system that responds to patterns of information in the buffers. At any point in time, a single production rule is selected to respond to the current pattern. Subsymbolic processes serve to guide the selection of rules to fire as well as the internal operations of some modules. Much of learning involves tuning of these subsymbolic processes. A number of simple and complex empirical examples are described to illustrate how these modules function singly and in concert.


Compact Broadband Microstrip Crossover With Isolation Improvement and Phase Compensation

In this letter, we present a broadband microstrip crossover structure in two different configurations. Both structures have compact size, i.e., less than 10 mm × 10 mm. Across the measured band from dc to 10 GHz, the structure 1 has less than 1 dB insertion loss, more than 20 dB return loss and at least 23.3 dB isolation. Within the same band, the structure 2 has less than 1 dB insertion loss, more than 22 dB return loss and at least 18.9 dB isolation. Phase difference between two paths in both structures is kept in less than 5 °.


Efficient Local Search Heuristics for Packing Irregular Shapes in Two-Dimensional Heterogeneous Bins

In this paper we proposed a local search heuristic and a genetic algorithm to solve the two-dimensional irregular multiple bin-size bin packing problem. The problem consists of placing a set of pieces represented as 2D polygons in rectangular bins with different dimensions such that the total area of bins used is minimized. Most packing algorithms available in the literature for 2D irregular bin packing consider single size bins only. However, for many industries the material can be supplied in a number of standard size sheets, for example, metal, foam, plastic and timber sheets. For this problem, the cut plans must decide the set of standard size stock sheets as well as which pieces to cut from each bin and how to arrange them in order to minimise waste material. Moreover, the literature constrains the orientation of pieces to a single or finite set of angles. This is often an artificial constraint that makes the solution space easier to navigate. In this paper we do not restrict the orientation of the pieces. We show that the local search heuristic and the genetic algorithm can address all of these decisions and obtain good solutions, with the local search performing better. We also discuss the affect of different groups of stock sheet sizes.


Why are some problems hard? Evidence from Tower of Hanoi

This paper analyzes the causes for large differences in difficulty of various isomorphic versions of the Tower of Hanoi problem. Some forms of the problem take 16 times as long to solve, on average, as other versions. Since isomorphism rules out size of task domain as a determinant of relative difficulty, these experiments seek and find causes for the differences in features of the problem representation. Analysis of verbal protocols and the temporal patterning of moves allows the problem-solving behavior to be divided into exploratory and final-path phases. Entry into the final-path phase depends on acquisition of the ability to plan pairs of moves, an achievement made difficult by the working memory load it entails. This memory load can be reduced by automating the rules governing moves, either through problem exploration or training. Once automation has occurred, the solution is obtained very rapidly. Memory load is also proposed as the locus of other differences in difficulty found between various problem rep-


Problem-solving methods in artificial intelligence

Well, someone can decide by themselves what they want to do and need to do but sometimes, that kind of person will need some problem solving methods in artificial intelligence references. People with open minded will always try to seek for the new things and information from many sources. On the contrary, people with closed mind will always think that they can do it by their principals. So, what kind of person are you?


Product Geometric Crossover for the Sudoku Puzzle

Geometric crossover is a representation-independent definition of crossover based on the distance of the search space interpreted as a metric space. It generalizes the traditional crossover for binary strings and other important recombination operators for the most used representations. Using a distance tailored to the problem at hand, the abstract definition of crossover can be used to design new problem specific crossovers that embed problem knowledge in the search. In recent work, we have introduced the important notion of product geometric crossover that enables the construction of new geometric crossovers combining preexisting geometric crossovers in a simple way. In this paper, we use it to design an evolutionary algorithm to solve the Sudoku puzzle. The different types of constraints make Sudoku an interesting study case for crossover design. We conducted extensive experimental testing and found that, on medium and hard problems, the new geometric crossovers perform significantly better than hill-climbers and mutations alone.


GramFuzz: Fuzzing testing of web browsers based on grammar analysis and structural mutation

Fuzz testing is an automated black-box testing technique providing random data as input to a software system in the hope to find vulnerability. In order to be effective, the fuzzed input must be common enough to pass elementary consistency checks. Web Browser accepts JavaScript, CSS files as well as the html as input, which must be considered in fuzzing testing, while traditional fuzzing technology generates test cases using simple mutation strategies, ignoring the grammar and code structure. In this article, vulnerability patterns are summarized and a new fuzzing testing method are proposed based on grammar analysis of input data and mutation of code structure. Combining methods of generation and mutation, test cases will be more effective in the fuzzing testing of web browsers. Applied on the Mozilla and IE web browsers, it discovered a total of 36 new severe vulnerabilities(and thus became one of the top security bug bounty collectors within this period).


Clinical and radiological pictures of two newborn babies with manifestations of chondrodysplasia punctata and review of available literature

BACKGROUND
Chondrodysplasia punctata (CDP) is a rare, heterogeneous congenital skeletal dysplasia, characterized by punctate or dot-like calcium deposits in cartilage observed on neonatal radiograms. A number of inborn metabolic diseases are associated with CDP, including peroxisomal and cholesterol biosynthesis dysfunction and other inborn errors of metabolism such as: mucolipidosis type II, mucopolysacharidosis type III, GM1 gangliosidosis. CDP is also related to disruption of vitamin K-dependent metabolism, causing secondary effects on the embryo, as well as fetal alcohol syndrome (FAS), chromosomal abnormalities that include trisomies 18 and 21, Turner syndrome.

CASE REPORT
This article presents clinical data and diagnostic imaging findings of two newborn babies with chondrodysplasia punctata. Children presented with skeletal and cartilage anomalies, dysmorphic facial feature, muscles tone abnormalities, skin changes and breathing difficulties. One of the patients demonstrated critical stenosis of spinal canal with anterior subluxation of C1 vertebra relative to C2. The aim of this article is to present cases and briefly describe current knowledge on etiopathogenesis as well as radiological and clinical symptoms of diseases coexisting with CDP.

CONCLUSIONS
Radiological diagnostic imaging allows for visualization of punctate focal mineralization in bone epiphyses during neonatal age and infancy. Determining the etiology of chondrodysplasia punctata requires performing various basic as well as additional examinations, including genetic studies.


Numerical Synthesis of Six-Bar Linkages for Mechanical Computation

This paper presents a design procedure for six-bar linkages that use eight accuracy points to approximate a specified input-output function. In the kinematic synthesis of linkages, this is known as the synthesis of a function generator to perform mechanical computation. Our formulation uses isotropic coordinates to define the loop equations of the Watt II, Stephenson II, and Stephenson III six-bar linkages. The result is 22 polynomial equations in 22 unknowns that are solved using the polynomial homotopy software Bertini. The bilinear structure of the system yields a polynomial degree of 705,432. Our first run of Bertini generated 92,736 nonsingular solutions, which were used as the basis of a parameter homotopy solution. The algorithm was tested on the design of the Watt II logarithmic function generator patented by Svoboda in 1944. Our algorithm yielded his linkage and 64 others, in 129 minutes of parallel computation on a Mac Pro with 12⇥2.93 GHz processors. Three additional examples are provided as well.


Skin Segmentation Using YCBCR and RGB Color Models

Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel. Keywords—Gaussian probability, YCrCb,RGB Model


Monte Carlo Filtering Using Kernel Embedding of Distributions

Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.


Should I do that? using relational reinforcement learning and declarative programming to discover domain axioms

Robots assisting humans in complex domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper focuses on the challenge of incrementally and interactively discovering previously unknown axioms governing domain dynamics, and describes an architecture that integrates declarative programming and relational reinforcement learning to address this challenge. Answer Set Prolog (ASP), a declarative programming paradigm, is used to represent and reason with incomplete domain knowledge for planning and diagnostics. For any given goal, unexplained failure of plans created by ASP-based inference is taken to indicate the existence of unknown domain axioms. The task of discovering these axioms is formulated as a reinforcement learning problem, and a relational representation is used to incrementally generalize from specific axioms identified over time. These generic axioms are then added to the ASP-based representation for subsequent inference. The architecture's capabilities are demonstrated and evaluated in two domains, Blocks World and Robot Butler.


The ONE simulator for DTN protocol evaluation

Delay-tolerant Networking (DTN) enables communication in sparse mobile ad-hoc networks and other challenged environments where traditional networking fails and new routing and application protocols are required. Past experience with DTN routing and application protocols has shown that their performance is highly dependent on the underlying mobility and node characteristics. Evaluating DTN protocols across many scenarios requires suitable simulation tools. This paper presents the Opportunistic Networking Environment (ONE) simulator specifically designed for evaluating DTN routing and application protocols. It allows users to create scenarios based upon different synthetic movement models and real-world traces and offers a framework for implementing routing and application protocols (already including six well-known routing protocols). Interactive visualization and post-processing tools support evaluating experiments and an emulation mode allows the ONE simulator to become part of a real-world DTN testbed. We show sample simulations to demonstrate the simulator’s flexible support for DTN protocol evaluation.


Scene text extraction using stroke width transform for tourist translator on android platform

Scene text extraction is recent research area in the field of Computer Vision. Text component in an image is of particular interest, as it is easy to understand by both humans as well as machines and can be used in variety of applications like content-based image retrieval, aids for visually impaired people, translator for tourists. Although there exists a lot of research activities in this field, it is still remained as a challenging problem, mainly due to two issues: different variety of text patterns like fonts, colors, sizes, orientations; and presence of background outliers similar to text characters, such as windows, bricks. In this paper, an android application named as TravelMate is developed, which is a user-friendly application to assist the tourist navigation, while they are roaming in foreign countries. Proposed application is able to extract text from an image, which is captured by an android mobile camera. Extraction is performed using stroke width transform (SWT) approach and connected component analysis. Extracted texts are recognized using Google's open source optical character recognition (OCR) engine ‘Tesseract’ and translated to a target language using Google's translation. The entire result is displayed back onto the screen of the mobile Phone.