Query: MapReduce-based deep learning with handwritten digit recognition case study

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MapReduce : Distributed Computing for Machine Learning <=SEP=> We use Hadoop, an open-source implementation of Google’s distributed file system and the MapReduce framework for distributed data processing, on modestly-sized compute clusters to evaluate its efficacy for standard machine learning tasks. We show benchmark performance on searching and sorting tasks to investigate the effects of various system configurations. We also distinguish classes of machine-learning problems that are reasonable to address within the MapReduce framework, and offer improvements to the Hadoop implementation. We conclude that MapReduce is a good choice for basic operations on large datasets, although there are complications to be addressed for more complex machine learning tasks.

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach <=SEP=> The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains.

Large-scale Artificial Neural Network: MapReduce-based Deep Learning <=SEP=> Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload. This project is mainly focused on the solution to the issues above, combining deep learning algorithm with cloud computing platform to deal with large-scale data. A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data. Careful discussion and experiment will be developed to illustrate how deep learning algorithm works to train handwritten digits data, how MapReduce is implemented on deep learning neural network, and why this combination accelerates computation. Besides performance, the scalability and robustness will be mentioned in this report as well. Our system comes with two demonstration software that visually illustrates our handwritten digit recognition/encoding application. 1

Large Scale Distributed Deep Networks <=SEP=> Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestlysized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm.

MapReduce: Simplified Data Processing on Large Clusters <=SEP=> MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

A novel modular compliant knee joint actuator for use in assistive and rehabilitation orthoses <=SEP=> Despite significant advancements in the field of wearable robots (WRs), commercial WRs still use traditional direct-drive actuation units to power their joints. On the other hand, in research prototypes compliant actuators are increasingly being used to more adequately address the issues of safety, robustness, control and overall system efficiency. The advantages of mechanical compliance are exploited in a novel modular actuator prototype designed for the knee joint. Due to its modularity, the actuator can be implemented in a knee joint of a standalone or a multi-joint lower-limbs orthosis, for use in gait rehabilitation and/or walking assistance. Differently from any other actuator used in orthotic research prototypes, it combines a Variable Stiffness Actuator (VSA) and a Parallel Elasticity Actuation (PEA) unit in a single modular system. Although independent, the units are designed to work together in order to fully mimic dynamic behavior of the human knee joint. In this paper, design aspects and functional evaluation of the new actuator are presented and a rationale for such a design in biomechanics of the human knee joint is given. The VSA subsystem is characterized in a quasi-static benchmarking environment and the results showing main performance indicators are presented.

How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. <=SEP=> This target article considers the relation of fluid cognitive functioning to general intelligence. A neurobiological model differentiating working memory/executive function cognitive processes of the prefrontal cortex from aspects of psychometrically defined general intelligence is presented. Work examining the rise in mean intelligence-test performance between normative cohorts, the neuropsychology and neuroscience of cognitive function in typically and atypically developing human populations, and stress, brain development, and corticolimbic connectivity in human and nonhuman animal models is reviewed and found to provide evidence of mechanisms through which early experience affects the development of an aspect of cognition closely related to, but distinct from, general intelligence. Particular emphasis is placed on the role of emotion in fluid cognition and on research indicating fluid cognitive deficits associated with early hippocampal pathology and with dysregulation of the hypothalamic-pituitary-adrenal axis stress-response system. Findings are seen to be consistent with the idea of an independent fluid cognitive construct and to assist with the interpretation of findings from the study of early compensatory education for children facing psychosocial adversity and from behavior genetic research on intelligence. It is concluded that ongoing development of neurobiologically grounded measures of fluid cognitive skills appropriate for young children will play a key role in understanding early mental development and the adaptive success to which it is related, particularly for young children facing social and economic disadvantage. Specifically, in the evaluation of the efficacy of compensatory education efforts such as Head Start and the readiness for school of children from diverse backgrounds, it is important to distinguish fluid cognition from psychometrically defined general intelligence.

Solution of the Generalized Noah's Ark Problem. <=SEP=> The phylogenetic diversity (PD) of a set of species is a measure of the evolutionary distance among the species in the collection, based on a phylogenetic tree. Such a tree is composed of a root, internal nodes, and leaves that correspond to the set of taxa under study. With each edge of the tree is associated a non-negative branch length (evolutionary distance). If a particular survival probability is associated with each taxon, the PD measure becomes the expected PD measure. In the Noah's Ark Problem (NAP) introduced by Weitzman (1998), these survival probabilities can be increased at some cost. The problem is to determine how best to allocate a limited amount of resources to maximize the expected PD of the considered species. It is easy to formulate the NAP as a (difficult) nonlinear 0-1 programming problem. The aim of this article is to show that a general version of the NAP (GNAP) can be solved simply and efficiently with any set of edge weights and any set of survival probabilities by using standard mixed-integer linear programming software. The crucial point to move from a nonlinear program in binary variables to a mixed-integer linear program, is to approximate the logarithmic function by the lower envelope of a set of tangents to the curve. Solving the obtained mixed-integer linear program provides not only a near-optimal solution but also an upper bound on the value of the optimal solution. We also applied this approach to a generalization of the nature reserve problem (GNRP) that consists of selecting a set of regions to be conserved so that the expected PD of the set of species present in these regions is maximized. In this case, the survival probabilities of different taxa are not independent of each other. Computational results are presented to illustrate potentialities of the approach. Near-optimal solutions with hypothetical phylogenetic trees comprising about 4000 taxa are obtained in a few seconds or minutes of computing time for the GNAP, and in about 30 min for the GNRP. In all the cases the average guarantee varies from 0% to 1.20%.

The Design and Implementation of a Log-Structured File System <=SEP=> This paper presents a new technique for disk storage management called a log-structured file system. A log-structured file system writes all modifications to disk sequentially in a log-like structure, thereby speeding up both file writing and crash recovery. The log is the only structure on disk; it contains indexing information so that files can be read back from the log efficiently. In order to maintain large free areas on disk for fast writing, we divide the log into segments and use a segment cleaner to compress the live information from heavily fragmented segments. We present a series of simulations that demonstrate the efficiency of a simple cleaning policy based on cost and benefit. We have implemented a prototype log-structured file system called Sprite LFS; it outperforms current Unix file systems by an order of magnitude for small-file writes while matching or exceeding Unix performance for reads and large writes. Even when the overhead for cleaning is included, Sprite LFS can use 70% of the disk bandwidth for writing, whereas Unix file systems typically can use only 5--10%.

Robot team learning enhancement using Human Advice <=SEP=> The paper discusses the augmentation of the Concurrent Individual and Social Learning (CISL) mechanism with a new Human Advice Layer (HAL). The new layer is characterized by a Gaussian Mixture Model (GMM), which is trained on human experience data. The CISL mechanism consists of the Individual Performance and Task Allocation Markov Decision Processes (MDP), and the HAL can provide preferred action selection policies to the individual agents. The data utilized for training the GMM is collected using a heterogeneous team foraging simulation. When leveraging human experience in the multi-agent learning process, the team performance is enhanced significantly.

UI on the Fly: Generating a Multimodal User Interface <=SEP=> UI on the Fly is a system that dynamically presents coordinated multimodal content through natural language and a small-screen graphical user interface. It adapts to the user’s preferences and situation. Multimodal Functional Unification Grammar (MUG) is a unification-based formalism that uses rules to generate content that is coordinated across several communication modes. Faithful variants are scored with a heuristic function.

NFC antenna research and a simple impedance matching method <=SEP=> Near Field Communication (NFC) technology evolved from a non-contact radio frequency identification (RFID) technology and intercommunication technology. Sensor reader, contactless card and peer to peer features integrate to a single chip. This chip could communicate with compatible devices by the identification and data exchange at a short distance. The technology was a simple merger of RFID technology and network technology at first, but it has evolved into a short-range wireless communications technology, which develops very quickly. This paper introduces the concept and characteristics of NFC technology first. Then, starting from the antenna coil used by NFC technology, this paper accomplished the theoretical analysis, Matlab simulation, HFSS and ADS2009 joint simulation respectively, and improved design an antenna impedance matching approach.

No Unbiased Estimator of the Variance of K-Fold Cross-Validation <=SEP=> Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the estimation of uncertainty around the K-fold cross-validation estimator. The main theorem shows that there exists no universal unbiased estimator of the variance of K-fold cross-validation. An analysis based on the eigendecomposition of the covariance matrix of errors helps to better understand the nature of the problem and shows that naive estimators may grossly underestimate variance, as con£rmed by numerical experiments.

Analysis of SDN Contributions for Cloud Computing Security <=SEP=> Cloud infrastructures are composed fundamentally of computing, storage, and networking resources. In regards to network, Software-Defined Networking (SDN) has become one of the most important architectures for the management of networks that require frequent re-policing or re-configurations. Considering the already known security issues of Cloud Computing, SDN helps to give fast answers to emerging threats, but also introduces new vulnerabilities related to its own architecture. In this paper, we analyze recent security proposals derived from the use of SDN, and elaborate on whether it helps to improve trust, security and privacy in Cloud Computing. Moreover, we discuss security concerns introduced by the SDN architecture and how they could compromise Cloud services. Finally, we explore future security perspectives with regard to leveraging SDN benefits and mitigating its security issues.

A Digital Twin-Based Approach for Designing and Multi-Objective Optimization of Hollow Glass Production Line <=SEP=> Various new national advanced manufacturing strategies, such as Industry 4.0, Industrial Internet, and Made in China 2025, are issued to achieve smart manufacturing, resulting in the increasing number of newly designed production lines in both developed and developing countries. Under the individualized designing demands, more realistic virtual models mirroring the real worlds of production lines are essential to bridge the gap between design and operation. This paper presents a digital twin-based approach for rapid individualized designing of the hollow glass production line. The digital twin merges physics-based system modeling and distributed real-time process data to generate an authoritative digital design of the system at pre-production phase. A digital twin-based analytical decoupling framework is also developed to provide engineering analysis capabilities and support the decision-making over the system designing and solution evaluation. Three key enabling techniques as well as a case study in hollow glass production line are addressed to validate the proposed approach.

A Focus on Recent Developments and Trends in Underwater Imaging <=SEP=> ■ Compact, efficient and easy to program digital signal processors execute algorithms once too computationally expensive for real-time applications. ■ Modeling and simulation programs more accurately predict the effects that physical ocean parameters have on the performance of imaging systems under different geometric configurations. ■ Image processing algorithms that handle data from multiple synchronous sources and that can extract and match feature points from each such source derive accurate 3-D scene information. ■ Digital compression schemes provide high-quality standardizations for increased data transfer rates (i.e. streaming video) and reduced storage requirements. This paper reports developments over the past three years in the following topics: ■ Image formation and image processing methods; ■ Extended range imaging techniques;

Velocity and acceleration estimation for optical incremental encoders q <=SEP=> Optical incremental encoders are extensively used for position measurements in motion systems. The position measurements suffer from quantization errors. Velocity and acceleration estimations obtained by numerical differentiation largely amplify the quantization errors. In this paper, the time stamping concept is used to obtain more accurate position, velocity and acceleration estimations. Time stamping makes use of stored events, consisting of the encoder counts and their time instants, captured at a high resolution clock. Encoder imperfections and the limited resolution of the capturing rate of the encoder events result in errors in the estimations. In this paper, we propose a method to extend the observation interval of the stored encoder events using a skip operation. Experiments on a motion system show that the velocity estimation is improved by 54% and the acceleration estimation by 92%. 2009 Elsevier Ltd. All rights reserved.

Phase spectrum prediction of audio signals <=SEP=> Modeling the phases of audio signals has received significantly less attention in comparison to the modeling of magnitudes. This paper proposes to use linear least squares and neural networks to predict phases from the neighboring points only in the phase spectrum. The simulation results show that there is a structure in the phase components which could be used in further analysis algorithms based on the phase spectrum.

About Face 3: the essentials of interaction design <=SEP=> Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd about face 3 the essentials of interaction design alan cooper as the choice of reading, you can find here.

From Distributional Semantics to Conceptual Spaces: A Novel Computational Method for Concept Creation <=SEP=> We investigate the relationship between lexical spaces and contextually-defined conceptual spaces, offering applications to creative concept discovery. We define a computational method for discovering members of concepts based on semantic spaces: starting with a standard distributional model derived from corpus co-occurrence statistics, we dynamically select characteristic dimensions associated with seed terms, and thus a subspace of terms defining the related concept. This approach performs as well as, and in some cases better than, leading distributional semantic models on a WordNet-based concept discovery task, while also providing a model of concepts as convex regions within a space with interpretable dimensions. In particular, it performs well on more specific, contextualized concepts; to investigate this we therefore move beyond WordNet to a set of human empirical studies, in which we compare output against human responses on a membership task for novel concepts. Finally, a separate panel of judges rate both model output and human responses, showing similar ratings in many cases, and some commonalities and divergences which reveal interesting issues for computational concept discovery.

Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective <=SEP=> The lithium-ion battery is an ideal candidate for a wide variety of applications due to its high energy/power density and operating voltage. Some limitations of existing lithium-ion battery technology include underutilization, stress-induced material damage, capacity fade, and the potential for thermal runaway. This paper reviews efforts in the modeling and simulation of lithium-ion batteries and their use in the design of better batteries. Likely future directions in battery modeling and design including promising research opportunities are outlined. © 2011 The Electrochemical Society. [DOI: 10.1149/2.018203jes] All rights reserved.

An analytical framework for evaluating e-commerce business models and strategies <=SEP=> Electronic commerce or business is more than just another way to sustain or enhance existing business practices. Rather, e-commerce is a paradigm shift. It is a `̀ disruptive’’ innovation that is radically changing the traditional way of doing business. The industry is moving so fast because it operates under totally different principles and work rules in the digital economy. A general rule in e-commerce is that there is no simple prescription and almost no such thing as an established business or revenue model for companies even within the same industry. Under such conditions, an analytical framework is needed to assist e-commerce planners and strategic managers in assessing the critical success factors when formulating e-commerce business models and strategies. This research develops an analytical framework based on the theories of transaction costs and switching costs. Both demand-side and supply-side economies of scale and scope are also applied to the development of this framework. In addition, e-commerce revenue models and strategies are also discussed. Based on the analytical framework developed by this research, this paper discusses the five essential steps for e-commerce success. They are: redefine the competitive advantage; rethink business strategy; re-examine traditional business and revenue models, re-engineer the corporation and Web site; and re-invent customer service. E-commerce planners and strategic managers will be able to use the framework to analyze and evaluate the critical successful factors for e-commerce success.

OpenPAWS: An open source PAWS and UHF TV White Space database implementation for India <=SEP=> TV white space (TVWS) geolocation database is being used for the protection of the terrestrial TV broadcast receivers, and the coexistence of secondary devices. To the best of our knowledge, though TV White Space calculations are available, an active online database does not exist for India. In this paper, the development of the first TVWS database for India is detailed and is released for public access. A standardized protocol to access the TVWS database on a readily available hardware platform is implemented. A hardware prototype, which is capable of querying the TVWS database and operating in the TV band without causing harmful interference to the TV receivers in UHF TV bands, is developed. The source code of our implementation has been released under the GNU general public license version 2.0.

Automatic Verb Classification Based on Statistical Distributions of Argument Structure <=SEP=> Automatic acquisition of lexical knowledge is critical to a wide range of natural language processing tasks. Especially important is knowledge about verbs, which are the primary source of relational information in a sentence-the predicate-argument structure that relates an action or state to its participants (i.e., who did what to whom). In this work, we report on supervised learning experiments to automatically classify three major types of English verbs, based on their argument structure-specifically, the thematic roles they assign to participants. We use linguistically-motivated statistical indicators extracted from large annotated corpora to train the classifier, achieving 69.8 accuracy for a task whose baseline is 34, and whose expert-based upper bound we calculate at 86.5. A detailed analysis of the performance of the algorithm and of its errors confirms that the proposed features capture properties related to the argument structure of the verbs. Our results validate our hypotheses that knowledge about thematic relations is crucial for verb classification, and that it can be gleaned from a corpus by automatic means. We thus demonstrate an effective combination of deeper linguistic knowledge with the robustness and scalability of statistical techniques.

A Review of Scar Scales and Scar Measuring Devices <=SEP=> OBJECTIVE
Pathologic scarring affects millions of people worldwide. Quantitative and qualitative measurement modalities are needed to effectively evaluate and monitor treatments.


METHODS
This article reviews the literature on available tools and existent assessment scales used to subjectively and objectively characterize scar.


RESULTS
We describe the attributes and deficiencies of each tool and scale and highlight areas where further development is critical.


CONCLUSION
An optimal, universal scar scoring system is needed in order to better characterize, understand and treat pathologic scarring.

Revealing Event Saliency in Unconstrained Video Collection <=SEP=> Recent progresses in multimedia event detection have enabled us to find videos about a predefined event from a large-scale video collection. Research towards more intrinsic unsupervised video understanding is an interesting but understudied field. Specifically, given a collection of videos sharing a common event of interest, the goal is to discover the salient fragments, i.e., the curt video fragments that can concisely portray the underlying event of interest, from each video. To explore this novel direction, this paper proposes an unsupervised event saliency revealing framework. It first extracts features from multiple modalities to represent each shot in the given video collection. Then, these shots are clustered to build the cluster-level event saliency revealing framework, which explores useful information cues (i.e., the intra-cluster prior, inter-cluster discriminability, and inter-cluster smoothness) by a concise optimization model. Compared with the existing methods, our approach could highlight the intrinsic stimulus of the unseen event within a video in an unsupervised fashion. Thus, it could potentially benefit to a wide range of multimedia tasks like video browsing, understanding, and search. To quantitatively verify the proposed method, we systematically compare the method to a number of baseline methods on the TRECVID benchmarks. Experimental results have demonstrated its effectiveness and efficiency.

RTL Hardware IP Protection Using Key-Based Control and Data Flow Obfuscation <=SEP=> Recent trends of hardware intellectual property (IP) piracy and reverse engineering pose major business and security concerns to an IP-based system-on-chip (SoC) design flow. In this paper, we propose a Register Transfer Level (RTL) hardware IP protection technique based on low-overhead key-based obfuscation of control and data flow. The basic idea is to transform the RTL core into control and data flow graph (CDFG) and then integrate a well-obfuscated finite state machine (FSM) of special structure, referred as “Mode-Control FSM”, into the CDFG in a manner that normal functional behavior is enabled only after application of a specific input sequence. We provide formal analysis of the effectiveness of the proposed approach and present a simple metric to quantify the level of obfuscation. We also present an integrated design flow that implements the proposed obfuscation at low computational overhead. Simulation results for two open-source IP cores show that high levels of security is achievable at nominal area and power overheads under delay constraint.

Micro bubble fluidics by EWOD and ultrasonic excitation for micro bubble tweezers <=SEP=> Recently, we envisioned so called micro bubble tweezers where EWOD (electrowetting-on-dielectric) actuated bubbles can manipulate micro objects such as biological cells by pushing or pulling them. Besides, oscillating (shrinking and expanding) bubbles in the presence of ultrasonic wave act as a to deliver drugs and molecules into the cells. In this paper, as a great stride in our quest for micro bubble tweezers, we present (1) full realization of two critical bubble operations (generating of bubbles in an on-chip and on-demand manner and splitting of single bubbles) and (2) two possible applications of mobile bubbles oscillating under acoustic excitation (a mobile vortex generator and micro particle carrier).

To ban or not to ban: Differences in mobile phone policies at elementary, middle, and high schools <=SEP=> The present study was to examine differences in mobile phone policies at elementary, middle and high schools. We surveyed 245 elementary, middle and high schools teachers in Shenzhen of China, using a specially designed 18-item questionnaire. Teachers’ responses indicate that, across elementary, middle and high schools, significant differences exist in (1) students’ percentages of using mobile phones among students, (2) students’ dependence of mobile phones, (3) the number of schools banning students’ mobile phone use, (4) oral and written forms used by schools to ban students’ mobile phone use, and (5) policy reinforcement strategies used by schools. However, no school-level differences was found in (1) students’ fondness of using mobile phones, (2) teachers’ assessment of low-level effectiveness of mobile phone policies, and (3) teachers’ policy improvement recommendations. Significance and implications of the findings are discussed. 2014 Elsevier Ltd. All rights reserved.

Energy-efficient deployment of Intelligent Mobile sensor networks <=SEP=> Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.