Improving deep neural network acoustic models using generalized maxout networks

Recently, maxout networks have brought significant improvements to various speech recognition and computer vision tasks. In this paper we introduce two new types of generalized maxout units, which we call p-norm and soft-maxout. We investigate their performance in Large Vocabulary Continuous Speech Recognition (LVCSR) tasks in various languages with 10 hours and 60 hours of data, and find that the p-norm generalization of maxout consistently performs well. Because, in our training setup, we sometimes see instability during training when training unbounded-output nonlinearities such as these, we also present a method to control that instability. This is the “normalization layer”, which is a nonlinearity that scales down all dimensions of its input in order to stop the average squared output from exceeding one. The performance of our proposed nonlinearities are compared with maxout, rectified linear units (ReLU), tanh units, and also with a discriminatively trained SGMM/HMM system, and our p-norm units with p equal to 2 are found to perform best.


Hypothesis spaces for minimum Bayes risk training in large vocabulary speech recognition

The Minimum Bayes Risk (MBR) framework has been a successful strategy for the training of hidden Markov models for large vocabulary speech recognition. Practical implementations of MBR must select an appropriate hypothesis space and loss function. The set of word sequences and a word-based Levenshtein distance may be assumed to be the optimal choice but use of phoneme-based criteria appears to be more successful. This paper compares the use of different hypothesis spaces and loss functions defined using the system constituents of word, phone, physical triphone, physical state and physical mixture component. For practical reasons the competing hypotheses are constrained by sampling. The impact of the sampling technique on the performance of MBR training is also examined.


Improving neural networks by preventing co-adaptation of feature detectors

When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random “dropout” gives big improvements on many benchmark tasks and sets new records for speech and object recognition. A feedforward, artificial neural network uses layers of non-linear hidden units between its inputs and its outputs. By adapting the weights on the incoming connections of these hidden units it learns feature detectors that enable it to predict the correct output when given an input vector [15]. If the relationship between the input and the correct output is complicated and the network has enough hidden units to model it accurately, there will typically be many different settings of the weights that can model the training set almost perfectly, especially if there is only a limited amount of labeled training data. Each of these weight vectors will make different predictions on held-out test data and almost all of them will do worse on the test data than on the training data because the feature detectors have been tuned to work well together on the training data but not on the test data. Overfitting can be reduced by using “dropout” to prevent complex co-adaptations on the training data. On each presentation of each training case, each hidden unit is randomly omitted from the network with a probability of 0.5, so a hidden unit cannot rely on other hidden units being present. Another way to view the dropout procedure is as a very efficient way of performing model averaging with neural networks. A good way to reduce the error on the test set is to average the predictions produced by a very large number of different networks. The standard way to do this is to train many separate networks and then to apply each of these networks to the test data, but this is computationally expensive during both training and testing. Random dropout makes it possible to train a huge number of different networks in a reasonable time. There is almost certainly a different network for each presentation of each training case but all of these networks share the same weights for the hidden units that are present. We use the standard, stochastic gradient descent procedure for training the dropout neural networks on mini-batches of training cases, but we modify the penalty term that is normally used to prevent the weights from growing too large. Instead of penalizing the squared length (L2 norm) of the whole weight vector, we set an upper bound on the L2 norm of the incoming weight vector for each individual hidden unit. If a weight-update violates this constraint, we renormalize the weights of the hidden unit by division. Using a constraint rather than a penalty prevents weights from growing very large no matter how large the proposed weight-update is. This makes it possible to start with a


LIMSI$@$WMT'16: Machine Translation of News

This paper describes LIMSI’s submissions to the shared WMT’16 task “Translation of News”. We report results for Romanian-English in both directions, for English to Russian, as well as preliminary experiments on reordering to translate from English into German. Our submissions use mainly NCODE and MOSES along with continuous space models in a post-processing step. The main novelties of this year’s participation are the following: for the translation into Russian and Romanian, we have attempted to extend the output of the decoder with morphological variations and to use a CRF model to rescore this new search space; as for the translation into German, we have been experimenting with source-side pre-ordering based on a dependency structure allowing permutations in order to reproduce the tar-


Expanded Semantic Graph Representation for Matching Related Information of Interest across Free Text Documents

This research proposes an expanded semantic graph definition that serves as a basis for an expanded semantic graph representation and graph matching approach. This representation separates the content and context and adds a number of semantic structures that encapsulate inferred information. The expanded semantic graph approach facilitates finding additional matches, identifying and eliminating poor matches, and prioritizing matches based on how much new knowledge is provided. By focusing on information of interest, doing pre-processing, and reducing processing requirements, the approach is applicable to problems where related information of interest is sought across a massive body of free text documents. Key aspects of the approach include (1) expanding the nodes and edges through inference using DL-Safe rules, abductive hypotheses, and syntactic patterns, (2) separating semantic content into nodes and semantic context into edges, and (3) applying relatedness measures on a node, edge, and sub graph basis. Results from tests using a ground-truthed subset of a large dataset of law enforcement investigator emails illustrate the benefits of these approaches.


Blind Deconvolution Using Alternating Maximum a Posteriori Estimation with Heavy-Tailed Priors

Single image blind deconvolution aims to estimate the unknown blur from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and typical approaches involve some heuristic or other steps without clear mathematical explanation to arrive at an acceptable solution. We show that a straightforward maximum a posteriory estimation combined with very sparse priors and an efficient numerical method can produce results, which compete with much more complicated state-of-the-art methods.


A 3D object detection and pose estimation pipeline using RGB-D images

3D object detection and pose estimation has been studied extensively in recent decades for its potential applications in robotics. However, there still remains challenges when we aim at detecting multiple objects while retaining low false positive rate in cluttered environments. This paper proposes a robust 3D object detection and pose estimation pipeline based on RGB-D images, which can detect multiple objects simultaneously while reducing false positives. Detection begins with template matching and yields a set of template matches. A clustering algorithm then groups templates of similar spatial location and produces multiple-object hypotheses. A scoring function evaluates the hypotheses using their associated templates and non-maximum suppression is adopted to remove duplicate results based on the scores. Finally, a combination of point cloud processing algorithms are used to compute objects' 3D poses. Existing object hypotheses are verified by computing the overlap between model and scene points. Experiments demonstrate that our approach provides competitive results comparable to the state-of-the-arts and can be applied to robot random bin-picking.


Mis problems and failures: a socio-technical perspective

Many of the problems arjd failures of Management Information Systems (MIS) and Management Science/Operations Research (MS/OR) projects have been attributed to organizational behavioral problems. The millions of dollars organizations spend on MIS and MS/OR development are of little benefit because systems continue to fail. Steps can be taken to understand and solve these behavioral probiems. This article argues that in most oases these behavioral problems are the result of inadequate designs. These bad designs are attributed to the way MIS systems designers view organizations, their members, and the function of an MIS within them. I.e., systems designers' frames of reference. These frames of reference cause faulty design choices and failures to perceive better design alternatives. Seven conditions are discussed which reflect current systems designers' points of view. The discussion of these conditions demonstrates the need to refrawe MIS design methodology within the Socio-Technicai Systems (STS) design approach and change systems designers' perspectives. The STS approach is introduced as a realistic view of organizations and a way to change them. This article is the first of two to appear in consecutive issues of the MIS Quarterly. The purpose of this first article is to demonstrate the need for the srs approaj:h. The second will present the basic concepts and principles of the STS methodology and how it can be utilized in the design of an MIS.


Dynamic Weighted Majority for Incremental Learning of Imbalanced Data Streams with Concept Drift

Concept drifts occurring in data streams will jeopardize the accuracy and stability of the online learning process. If the data stream is imbalanced, it will be even more challenging to detect and cure the concept drift. In the literature, these two problems have been intensively addressed separately, but have yet to be well studied when they occur together. In this paper, we propose a chunk-based incremental learning method called Dynamic Weighted Majority for Imbalance Learning (DWMIL) to deal with the data streams with concept drift and class imbalance problem. DWMIL utilizes an ensemble framework by dynamically weighting the base classifiers according to their performance on the current data chunk. Compared with the existing methods, its merits are four-fold: (1) it can keep stable for non-drifted streams and quickly adapt to the new concept; (2) it is totally incremental, i.e. no previous data needs to be stored; (3) it keeps a limited number of classifiers to ensure high efficiency; and (4) it is simple and needs only one thresholding parameter. Experiments on both synthetic and real data sets with concept drift show that DWMIL performs better than the state-of-the-art competitors, with less computational cost.


Android smartphone vulnerabilities: A survey

Round the globe mobile devices like Smartphone, PDAs & tablets are playing an essential role in every person day to day lives. Various operating systems such as android, ios, blackberry etc provides a platform for smart devices. Google's Android is a one of the most popular and user friendly open source software platform for mobile devices. Along with its convenience people are likely to download and install malicious applications developed to misguide the user which will create a security gap to penetrate for the attacker. Hackers are inclined to discover and exploit the new vulnerabilities which will bring forth with the latest version of Android. In this paper, we concentrate on examining and understanding the vulnerabilities exist in android operating system. We will also suggest a metadata model which gives the information of all the related terms required for vulnerability assessment. Here, analyzing data is extracted from Open Source Vulnerability Database (OSVDB) and National Vulnerability Database (NVD).


Kernel Region ( a ) Top Level ( b ) Compute Unit Level ( c ) Processing Element Level

OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks . In this paper, we first propose an analytical performance model and apply it to perform an in-depth analysis on the resource requirement of CNN classifier kernels and available resources on modern FPGAs. We identify that the key performance bottleneck is the onchip memory bandwidth. We propose a new kernel design to effectively address such bandwidth limitation and to provide an optimal balance between computation, on-chip, and off-chip memory access. As a case study, we further apply these techniques to design a CNN accelerator based on the VGG model. Finally, we evaluate the performance of our CNN accelerator using an Altera Arria 10 GX1150 board. We achieve 866 Gop/s floating point performance at 370MHz working frequency and 1.79 Top/s 16-bit fixed-point performance at 385MHz. To the best of our knowledge, our implementation achieves the best power efficiency and performance density compared to existing work.


Valgrind: a framework for heavyweight dynamic binary instrumentation

Dynamic binary instrumentation (DBI) frameworks make it easy to build dynamic binary analysis (DBA) tools such as checkers and profilers. Much of the focus on DBI frameworks has been on performance; little attention has been paid to their capabilities. As a result, we believe the potential of DBI has not been fully exploited.
 In this paper we describe Valgrind, a DBI framework designed for building heavyweight DBA tools. We focus on its unique support for shadow values-a powerful but previously little-studied and difficult-to-implement DBA technique, which requires a tool to shadow every register and memory value with another value that describes it. This support accounts for several crucial design features that distinguish Valgrind from other DBI frameworks. Because of these features, lightweight tools built with Valgrind run comparatively slowly, but Valgrind can be used to build more interesting, heavyweight tools that are difficult or impossible to build with other DBI frameworks such as Pin and DynamoRIO.


Estimation of land surface temperature over Delhi using Landsat-7 ETM +

Land surface temperature (LST) is important factor in global change studies, in estimating radiation budgets in heat balance studies and as a control for climate models. The knowledge of surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change, and human-environment interactions. In the study an attempt has been made to estimate surface temperature over Delhi area using Landsat-7 ETM+ satellite data. The variability of these retrieved LSTs has been investigated with respect to different land use / land cover (LU/LC) types determined from the Landsat visible and NIR channels. The classification uncertainties over different land use land cover were assessed and it has been observed that the classification uncertainties were found to be lowest using Minimum Noise Fraction (MNF) components. The emissivity per pixel is retrieved directly from satellite data and has been estimated as narrow band emissivity at the satellite sensor channel in order to have least error in the surface temperature estimation. Strong correlation is observed between surface temperature with Normalized Difference Vegetation Index (NDVI) over different LU/LC classes and the relationship is moderate with fractional vegetation cover (FVC). A regression relation between these parameters has also been estimated indicating that surface temperatures can be predicted if NDVI values are known. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban areas.


A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding

Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial neighborhood information into the standard FCM clustering algorithm. Two improved implementations of the k-nearest neighbor (k-NN) algorithm are introduced for calculating the weight in the SWFCM algorithm so as to improve the performance of image thresholding. To speed up the FCM algorithm, the iteration is carried out with the gray level histogram of image instead of the conventional whole data of image. The performance of the algorithm is compared with those of an existed fuzzy thresholding algorithm and widely applied between variance and entropy methods. Experimental results with synthetic and real images indicate the proposed approach is effective and efficient. In addition, due to the neighborhood model, the proposed method is more tolerant to noise.


MIMO-SAR: Opportunities and Pitfalls

This paper reviews advanced radar architectures that employ multiple transmit and multiple receive antennas to improve the performance of future synthetic aperture radar (SAR) systems. These advanced architectures have been dubbed multiple-input multiple-output SAR (MIMO-SAR) in analogy to MIMO communication systems. Considerable confusion arose, however, with regard to the selection of suitable waveforms for the simultaneous transmission via multiple channels. It is shown that the mere use of orthogonal waveforms is insufficient for the desired performance improvement in view of most SAR applications. As a solution to this fundamental MIMO-SAR challenge, a new class of short-term shift-orthogonal waveforms is introduced. The short-term shift orthogonality avoids mutual interferences from the radar echoes of closely spaced scatterers, while interferences from more distant scatterers are suppressed by digital beamforming on receive in elevation. Further insights can be gained by considering the data acquisition of a side-looking imaging radar in a 3-D information cube. It becomes evident that the suggested waveforms fill different subspaces that can be individually accessed by a multichannel receiver. For completeness, the new class of short-term shift-orthogonal waveforms is also compared to a recently proposed pair of orthogonal frequency-division multiplexing waveforms. It is shown that both sets of waveforms require essentially the same principle of range time to elevation angle conversion via a multichannel receiver in order to be applicable for MIMO-SAR imaging without interference.


Tools to support systematic reviews in software engineering: a cross-domain survey using semi-structured interviews

Background: A number of software tools are being developed to support systematic reviewers within the software engineering domain. However, at present, we are not sure which aspects of the review process can most usefully be supported by such tools or what characteristics of the tools are most important to reviewers. Aim: The aim of the study is to explore the scope and practice of tool support for systematic reviewers in other disciplines. Method: Researchers with experience of performing systematic reviews in Healthcare and the Social Sciences were surveyed. Qualitative data was collected through semi-structured interviews and data analysis followed an inductive approach. Results: 13 interviews were carried out. 21 software tools categorised into one of seven types were identified. Reference managers were the most commonly mentioned tools. Features considered particularly important by participants were support for multiple users, support for data extraction and support for tool maintenance. The features and importance levels identified by participants were compared with those proposed for tools to support systematic reviews in software engineering. Conclusions: Many problems faced by systematic reviewers in other disciplines are similar to those faced in software engineering. There is general consensus across domains that improved tools are needed.


DeepEmo: Learning and Enriching Pattern-Based Emotion Representations

We propose a graph-based mechanism to extract rich-emotion bearing patterns, which fosters a deeper analysis of online emotional expressions, from a corpus. The patterns are then enriched with word embeddings and evaluated through several emotion recognition tasks. Moreover, we conduct analysis on the emotion-oriented patterns to demonstrate its applicability and to explore its properties. Our experimental results demonstrate that the proposed techniques outperform most stateof-the-art emotion recognition techniques.


A low power and high speed CMOS Voltage-Controlled Ring Oscillator

In this paper a new Voltage Controlled Ring Oscillator is presented. The proposed VCO uses partial positive feedback in its delay cell, allowing the circuit to operate with single two stages, achieving high speed with reduced power consumption. The new VCO has a wide range of operation frequencies (0.2 at 2.1 GHz), good linearity between the output frequency and the control voltage, phase noise of -90 dBc/Hz at 100 kHz offset and it only consumes 7.01 mW in its central frequency of 1.2 GHz using a 3.3 V power supply. The circuit was fabricated in a 0.35 /spl mu/m CMOS-AMS process and occupies an area of 67.5/spl times/77.5 /spl mu/m/sup 2/.


Low correlation MIMO antenna for LTE 700MHz band

A key feature of upcoming 4G wireless communication standards is multiple-input-multiple-output (MIMO) technology. To make the best use of MIMO, the antenna correlation between adjacent antennas must be low (< 0.5). In this context, we propose a new correlation reduction technique suitable for closely spaced antennas (distance, d < λ/40). This technique reduces mutual coupling between antennas and concurrently uncorrelates the antennas' radiation characteristic by inducing negative group delay in the target frequency band. The validity of the technique is demonstrated with a USB dongle MIMO antenna designed for LTE 700MHz band. Measurement results show that the antenna correlation is reduced more than 37% using the proposed technique.


Design of fuzzy controller for induction heating using DSP

This paper presents the development of fuzzy controller for induction heating temperature control. Induction heating coil can be controlled by single phase sinusoidal pulse width modulation (SPWM) digital signal processor (DSP)-based inverter. DSP is used to store the required commands for generating the necessary waveforms to control the frequency of the inverter through proper design of switching pulses. The SPWM technique produces pure sinusoidal wave of voltage and current output. Fuzzy controller software has been developed in the C++ language. VisSim software is used to experiment with a DSP. The inverter is current-fed and SPWM is used for DC to AC conversion. SPWM normally cuts off high-order harmonic by low-pass filter to produce sine wave power. The LC low-pass filter with cutoff frequency at 300Hz is used. Since low frequency produces ripple to cause transformer and inductance magnetic saturation, the range of frequency is between 100 Hz and 300 Hz. The results of the experiment are presented and show the effectiveness and superiority of this temperature control system.


Trust in school : a pathway to inhibit teacher burnout ?

Purpose – The purpose of this paper is to consider trust as an important relational source in schools by exploring whether trust lowers teacher burnout. The authors examine how trust relationships with different school parties such as the principal relate to distinct dimensions of teacher burnout. The authors further analyze whether school-level trust additionally influences burnout. In doing this, the authors account for other teacher and school characteristics. Design/methodology/approach – The authors use quantitative data gathered during the 2008-2009 school year from 673 teachers across 58 elementary schools in Flanders (i.e. the northern Dutch-speaking region of Belgium). Because teacher and school characteristics are simultaneously related to burnout, multilevel modeling is applied. Findings – Trust can act as a buffer against teacher burnout. Teachers’ trust in students demonstrates the strongest association with burnout compared to trust in principals or colleagues. Exploring relationships of trust in distinct school parties with different burnout dimensions yield interesting additional insights such as the specific importance of teacher-principal trust for teachers’ emotional exhaustion. Burnout is further an individual teacher matter to which school-level factors are mainly unrelated. Research limitations/implications – Principals fulfill an important role in inhibiting emotional exhaustion among teachers. They are advised to create a school atmosphere that is conducive for different kinds of trust relationships to develop. Actions to strengthen trust and inhibit teacher burnout are necessary, although further qualitative and longitudinal research is desirable. Originality/value – This paper offers a unique contribution by examining trust in different school parties as a relational buffer against teacher burnout. It indicates that principals can affect teacher burnout and prevent emotional exhaustion by nurturing trusting relationships in school.


New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots

Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis.


Contactless Sleep Apnea Detection on Smartphones

We present a contactless solution for detecting sleep apnea events on smartphones. To achieve this, we introduce a novel system that monitors the minute chest and abdomen movements caused by breathing on smartphones. Our system works with the phone away from the subject and can simultaneously identify and track the fine-grained breathing movements from multiple subjects. We do this by transforming the phone into an active sonar system that emits frequency-modulated sound signals and listens to their reflections; our design monitors the minute changes to these reflections to extract the chest movements. Results from a home bedroom environment shows that our design operates efficiently at distances of up to a meter and works even with the subject under a blanket.
 Building on the above system, we develop algorithms that identify various sleep apnea events including obstructive apnea, central apnea, and hypopnea from the sonar reflections. We deploy our system at the UW Medicine Sleep Center at Harborview and perform a clinical study with 37 patients for a total of 296 hours. Our study demonstrates that the number of respiratory events identified by our system is highly correlated with the ground truth and has a correlation coefficient of 0.9957, 0.9860, and 0.9533 for central apnea, obstructive apnea and hypopnea respectively. Furthermore, the average error in computing of rate of apnea and hypopnea events is as low as 1.9 events/hr.


Doubly Stochastic Variational Inference for Deep Gaussian Processes

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to overfitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalizations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm that does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.


An experimental investigation of model-based parameter optimisation: SPO and beyond

This work experimentally investigates model-based approaches for optimising the performance of parameterised randomised algorithms. We restrict our attention to procedures based on Gaussian process models, the most widely-studied family of models for this problem. We evaluated two approaches from the literature, and found that sequential parameter optimisation (SPO) [4] offered the most robust performance. We then investigated key design decisions within the SPO paradigm, characterising the performance consequences of each. Based on these findings, we propose a new version of SPO, dubbed SPO+, which extends SPO with a novel intensification procedure and log-transformed response values. Finally, in a domain for which performance results for other (model-free) parameter optimisation approaches are available, we demonstrate that SPO+ achieves state-of-the-art performance.


Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain's complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks.


Automatic Gait Optimization with Gaussian Process Regression

Gait optimization is a basic yet challenging problem for both quadrupedal and bipedal robots. Although techniques for automating the process exist, most involve local function optimization procedures that suffer from three key drawbacks. Local optimization techniques are naturally plagued by local optima, make no use of the expensive gait evaluations once a local step is taken, and do not explicitly model noise in gait evaluation. These drawbacks increase the need for a large number of gait evaluations, making optimization slow, data inefficient, and manually intensive. We present a Bayesian approach based on Gaussian process regression that addresses all three drawbacks. It uses a global search strategy based on a posterior model inferred from all of the individual noisy evaluations. We demonstrate the technique on a quadruped robot, using it to optimize two different criteria: speed and smoothness. We show in both cases our technique requires dramatically fewer gait evaluations than state-of-the-art local gradient approaches.


Efficient Global Optimization of Expensive Black-Box Functions

In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.


Inter-satellite links for cubesats

Realizing inter-satellite links is a must for ensuring the success of cubesat swarm missions. Nevertheless, it has hardly been considered until now. The communication systems for cubesats have to deal with a few peculiar demands regarding consumed power, geometry and throughput. Depending on the type of application, required data rates can go up to tens of megabits per second, while power consumption and physical size are limited by the platform. The proposed communication scheme will combine power-efficient modulation and channel coding with multiple access and spread spectrum techniques, enabling the deployment of multiple satellites. Apart from this, the antenna system has to be designed such that links can be established and maintained independent of the satellites' orientation. An electrically steerable radiation pattern is achieved by placing antennas on each face of the cube. Conformal beamforming provides the system with 5 dBi gain for any desired direction of transmission, eliminating the need for attitude control. Furthermore, using planar antennas reduces the complexity of the mechanical part as they require no deployment.


Transparent, conductive graphene electrodes for dye-sensitized solar cells.

Transparent, conductive, and ultrathin graphene films, as an alternative to the ubiquitously employed metal oxides window electrodes for solid-state dye-sensitized solar cells, are demonstrated. These graphene films are fabricated from exfoliated graphite oxide, followed by thermal reduction. The obtained films exhibit a high conductivity of 550 S/cm and a transparency of more than 70% over 1000-3000 nm. Furthermore, they show high chemical and thermal stabilities as well as an ultrasmooth surface with tunable wettability.


BUILDING GENERALIZABLE AGENTS

Towards bridging the gap between machine and human intelligence, it is of utmost importance to introduce environments that are visually realistic and rich in content. In such environments, one can evaluate and improve a crucial property of practical intelligent systems, namely generalization. In this work, we build House3D, a rich, extensible and efficient environment that contains 45,622 human-designed 3D scenes of houses, ranging from single-room studios to multistoreyed houses, equipped with a diverse set of fully labeled 3D objects, textures and scene layouts, based on the SUNCG dataset (Song et al., 2017). With an emphasis on semantic-level generalization, we study the task of concept-driven navigation, RoomNav, using a subset of houses in House3D. In RoomNav, an agent navigates towards a target specified by a semantic concept. To succeed, the agent learns to comprehend the scene it lives in by developing perception, understand the concept by mapping it to the correct semantics, and navigate to the target by obeying the underlying physical rules. We train RL agents with both continuous and discrete action spaces and show their ability to generalize in new unseen environments. In particular, we observe that (1) training is substantially harder on large house sets but results in better generalization, (2) using semantic signals (e.g. segmentation mask) boosts the generalization performance, and (3) gated networks on semantic input signal lead to improved training performance and generalization. We hope House3D1, including the analysis of the RoomNav task, serves as a building block towards designing practical intelligent systems and we wish it to be broadly adopted by the community.


Inferring the meaning of chord sequences via lyrics

This paper discusses how meanings associated with chord sequences can be inferred from word associations based on lyrics. The approach works by analyzing in-line chord annotations of lyrics to maintain co-occurrence statistics for chords and lyrics. This is analogous to the way parallel corpora are analyzed in order to infer translation lexicons. The result can benefit musical discovery systems by modeling how the chord structure complements the lyrics.


Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO2 Memristive Spiking-Neuron

Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010–1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010–1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.


New circularly polarized slot radiator for substrate integrated waveguide (SIW) planar array

A circularly polarized slot radiator in millimetre band is presented. Low reflection and good polarization performance is obtained in comparison with state of art slot elements. A high gain array prototype has been implemented in substrate integrated waveguides (SIW) technology, in order to check the new element performance. A cosecant amplitude coverage in the elevation plane, as well as a 6° tilted monopulse pattern in the azimuth plane are obtained. A gain peak value of 28.6 dBi and 79% of efficiency at 36.7 GHz have been obtained in the specified operation band (36.7 – 37 GHz) for a manufactured antenna prototype. Likewise, the top measured axial ratio is 1.95 dB at 36.85 GHz.


Defects of the nose, lip, and cheek: rebuilding the composite defect.

BACKGROUND
The face can be divided into regions (units) with characteristic skin quality, border outline, and three-dimensional contour. A defect may lie entirely within a single major unit or encompass several adjacent units, creating unique problems for repair. Composite defects overlap two or more facial units. Nasal defects often extend into the adjacent lip and cheek. The simplest solution may appear to be to simply "fill the hole"--just replace the missing bulk. Poor contour, incorrect dimension, malposition, asymmetry, poor blending into adjacent normal tissues, and a patch-like repair often follow.

METHODS
The following principles of regional unit repair were applied to guide these complex reconstructions: (1) reconstruct units, not defects; (2) alter the wound in site, size, shape, and depth; (3) consider using separate grafts and flaps for each unit and subunit, if appropriate; (4) use "like" tissue for "like" tissue; (5) restore a stable platform; (6) build in stages; (7) use distant tissue for "invisible" needs and local skin for resurfacing; and (8) disregard old scars.

RESULTS
Clinical cases of increasing composite complexity were repaired with local, regional, and distant tissues. Excellent aesthetics and function were obtained.

CONCLUSIONS
Careful visual analysis of the normal face and the defect can direct the choice, timing, and technique of facial repair. Regional unit principles provide a coordinated approach to the vision, planning, and fabrication of these difficult wounds. The entire repair should be intellectually planned, designed step by step, and laid out in a series of coordinated steps, with general principles applied to successfully repair composite defects of the nose, lip, and cheek.


Nasal Duplication Combined with Cleft Lip and Palate: Surgical Correction and Long-Term Follow-Up

Background
Diprosopus dirrhinus, or nasal duplication, is a rare entity of partial craniofacial duplication.

Methods
The case we present is the first report of diprosopus dirrhinus associated with complete cleft lip and palate. The baby was born in Cambodia at full term by normal vaginal delivery with no significant perinatal and family history. Physical examination revealed significant facial deformity due to the duplicated nose and the left complete cleft lip/palate on the right subset.

Results
There were 4 nostrils; both medial apertures including the cleft site were found to be 10-15 mm deep cul-de-sac structures without communication to the nasopharynx. The upper third of the face was notable for hypertelorism with a duplication of the soft-tissue nasion and glabella. Between the 2 nasal dorsums, there was a small cutaneous depression with a lacrimal fistula in the midline. Surgical treatment included the first stage of primary lip and nose repair and the second stage of palatoplasty.

Conclusions
The patient was followed up at the age of 10 years showing satisfactory results for both aesthetic and functional aspects. Further management in the future will be required for the hypertelorism and nasal deformity.


Compositionality and the angular gyrus: A multi-voxel similarity analysis of the semantic composition of nouns and verbs

The cognitive and neural systems that enable conceptual processing must support the ability to combine (and recombine) concepts to form an infinite number of ideas. Two candidate neural systems for conceptual combination-the left anterior temporal lobe (ATL) and the left angular gyrus (AG)-have been characterized as "semantic hubs" due to both functional and anatomical properties; however, these two regions likely support different aspects of composition. Here we consider two hypotheses for the role of AG in conceptual combination, both of which differ from a putative role for the ATL in "feature-based" combinatorics (i.e., meaning derived by combining concepts' features). Firstly, we examine whether AG is more sensitive to function-argument relations of the sort that arise when a predicate is combined with its arguments. Secondly, we examine the non-mutually exclusive possibility that AG represents information carried on a verb in particular, whether this be information about event composition or about thematic relations denoted uniquely by verbs. We identified voxels that respond differentially to two-word versus one-word stimuli, and we measured the similarity of the patterns in these voxels evoked by (1) pairs of two-word phrases that shared a noun that was an argument, thus sharing function-argument composition (e.g. eats meat and with meat), in comparison with two-word phrases that shared only a noun, not an argument (e.g., eats meat and tasty meat); and (2) stimulus pairs that shared only an event (operationalized here as sharing a verb; e.g. eats meat and eats quickly), in comparison to both of the above. We found that activity patterns in left AG tracked information relating to the presence of an event-denoting verb in a pair of two-word phrases. We also found that the neural similarity in AG voxel patterns between two phrases sharing a verb correlated with subjects' ratings of how similar the meanings of those two verb phrases were. These findings indicate that AG represents information specific to verbs, perhaps event structure or thematic relations mediated by verbs, as opposed to argument structure in general.


A review of trisomy X (47,XXX)

Trisomy X is a sex chromosome anomaly with a variable phenotype caused by the presence of an extra X chromosome in females (47,XXX instead of 46,XX). It is the most common female chromosomal abnormality, occurring in approximately 1 in 1,000 female births. As some individuals are only mildly affected or asymptomatic, it is estimated that only 10% of individuals with trisomy X are actually diagnosed. The most common physical features include tall stature, epicanthal folds, hypotonia and clinodactyly. Seizures, renal and genitourinary abnormalities, and premature ovarian failure (POF) can also be associated findings. Children with trisomy X have higher rates of motor and speech delays, with an increased risk of cognitive deficits and learning disabilities in the school-age years. Psychological features including attention deficits, mood disorders (anxiety and depression), and other psychological disorders are also more common than in the general population. Trisomy X most commonly occurs as a result of nondisjunction during meiosis, although postzygotic nondisjunction occurs in approximately 20% of cases. The risk of trisomy X increases with advanced maternal age. The phenotype in trisomy X is hypothesized to result from overexpression of genes that escape X-inactivation, but genotype-phenotype relationships remain to be defined. Diagnosis during the prenatal period by amniocentesis or chorionic villi sampling is common. Indications for postnatal diagnoses most commonly include developmental delays or hypotonia, learning disabilities, emotional or behavioral difficulties, or POF. Differential diagnosis prior to definitive karyotype results includes fragile X, tetrasomy X, pentasomy X, and Turner syndrome mosaicism. Genetic counseling is recommended. Patients diagnosed in the prenatal period should be followed closely for developmental delays so that early intervention therapies can be implemented as needed. School-age children and adolescents benefit from a psychological evaluation with an emphasis on identifying and developing an intervention plan for problems in cognitive/academic skills, language, and/or social-emotional development. Adolescents and adult women presenting with late menarche, menstrual irregularities, or fertility problems should be evaluated for POF. Patients should be referred to support organizations to receive individual and family support. The prognosis is variable, depending on the severity of the manifestations and on the quality and timing of treatment.


Recognition and identification of the clothes in the photo or video using neural networks

We introduce our solution of the problem of clothing recognition and identification. The cause of taking convolutional neural network as a mathematic model for the further research was the result of an experimental survey of methods of keypoints detection and computing their descriptors. The convolutional neural network model was developed and trained on the Apparel Classification with Style clothing image dataset. The training image dataset was expanded with the help of augmentation methods. The computational experiment aimed at determination of neural network efficiency and stability proved its productivity. Moreover, we developed clothing color recognition, which let us perform clothing identification by 2 parameters: type and color.


A Survey: Smart agriculture IoT with cloud computing

IoT is a revolutionary technology that represents the future of computing and communications. Most of the people over all worlds depend on agriculture. Because of this reason smart IT technologies are needed to migrate with traditional agriculture methods. Using modern technologies can control the cost, maintenance and monitoring performance. Satellite and aerial imagery play a vital role in modern agriculture. Precision agriculture sensor monitoring network is used greatly to measure agri-related information like temperature, humidity, soil PH, soil nutrition levels, water level etc. so, with IoT farmers can remotely monitor their crop and equipment by phones and computers. In this paper, we surveyed some typical applications of Agriculture IoT Sensor Monitoring Network technologies using Cloud computing as the backbone. This survey is used to understand the different technologies and to build sustainable smart agriculture. Simple IoT agriculture model is addressed with a wireless network.


Efficiently indexing shortest paths by exploiting symmetry in graphs

Shortest path queries (SPQ) are essential in many graph analysis and mining tasks. However, answering shortest path queries on-the-fly on large graphs is costly. To online answer shortest path queries, we may materialize and index shortest paths. However, a straightforward index of all shortest paths in a graph of N vertices takes O(N2) space. In this paper, we tackle the problem of indexing shortest paths and online answering shortest path queries. As many large real graphs are shown richly symmetric, the central idea of our approach is to use graph symmetry to reduce the index size while retaining the correctness and the efficiency of shortest path query answering. Technically, we develop a framework to index a large graph at the orbit level instead of the vertex level so that the number of breadth-first search trees materialized is reduced from O(N) to O(|Δ|), where |Δ| ≤ N is the number of orbits in the graph. We explore orbit adjacency and local symmetry to obtain compact breadth-first-search trees (compact BFS-trees). An extensive empirical study using both synthetic data and real data shows that compact BFS-trees can be built efficiently and the space cost can be reduced substantially. Moreover, online shortest path query answering can be achieved using compact BFS-trees.


Trends in Obesity Among Adults in the United States, 2005 to 2014.

IMPORTANCE
Between 1980 and 2000, the prevalence of obesity increased significantly among adult men and women in the United States; further significant increases were observed through 2003-2004 for men but not women. Subsequent comparisons of data from 2003-2004 with data through 2011-2012 showed no significant increases for men or women.

OBJECTIVE
To examine obesity prevalence for 2013-2014 and trends over the decade from 2005 through 2014 adjusting for sex, age, race/Hispanic origin, smoking status, and education.

DESIGN, SETTING, AND PARTICIPANTS
Analysis of data obtained from the National Health and Nutrition Examination Survey (NHANES), a cross-sectional, nationally representative health examination survey of the US civilian noninstitutionalized population that includes measured weight and height.

EXPOSURES
Survey period.

MAIN OUTCOMES AND MEASURES
Prevalence of obesity (body mass index ≥30) and class 3 obesity (body mass index ≥40).

RESULTS
This report is based on data from 2638 adult men (mean age, 46.8 years) and 2817 women (mean age, 48.4 years) from the most recent 2 years (2013-2014) of NHANES and data from 21,013 participants in previous NHANES surveys from 2005 through 2012. For the years 2013-2014, the overall age-adjusted prevalence of obesity was 37.7% (95% CI, 35.8%-39.7%); among men, it was 35.0% (95% CI, 32.8%-37.3%); and among women, it was 40.4% (95% CI, 37.6%-43.3%). The corresponding prevalence of class 3 obesity overall was 7.7% (95% CI, 6.2%-9.3%); among men, it was 5.5% (95% CI, 4.0%-7.2%); and among women, it was 9.9% (95% CI, 7.5%-12.3%). Analyses of changes over the decade from 2005 through 2014, adjusted for age, race/Hispanic origin, smoking status, and education, showed significant increasing linear trends among women for overall obesity (P = .004) and for class 3 obesity (P = .01) but not among men (P = .30 for overall obesity; P = .14 for class 3 obesity).

CONCLUSIONS AND RELEVANCE
In this nationally representative survey of adults in the United States, the age-adjusted prevalence of obesity in 2013-2014 was 35.0% among men and 40.4% among women. The corresponding values for class 3 obesity were 5.5% for men and 9.9% for women. For women, the prevalence of overall obesity and of class 3 obesity showed significant linear trends for increase between 2005 and 2014; there were no significant trends for men. Other studies are needed to determine the reasons for these trends.


Chipless RFID: Bar Code of the Future

Radio-frequency identification (RFID) is a wireless data capturing technique that utilizes radio frequency (RF) waves for automatic identification of objects. RFID relies on RF waves for data transmission between the data carrying device, called the RFID tag, and the interrogator.


Electronically Reconfigurable Microwave Bandpass Filter

A new class of reconfigurable bandpass filter based on parallel-coupled switched delay line approach is presented in this paper. The approach allows both center frequency and passband bandwidth to be reconfigured electronically. In contrast to conventional approach, the effects of the switch losses are relatively insignificant and can be treated as an auxiliary effect and therefore the filter has low passband loss. Despite the loss performance, the filter provides excellent linearity performance with large power handling capability. Theoretical analysis is presented and the feasibility of the approach has been experimentally verified with microstrip circuit prototypes. The first filter prototype provides narrowband quasi-elliptic filter response with constant bandwidth (50-MHz) tuning across 45% tuning range. The minimum stopband rejection level is 45 dB. The second filter possesses passband-width 3:1 ratio tunability. The third filter provides both functionalities which allow center frequency and passband bandwidth to be reconfigured. All filters have a < 4-dB passband loss with an OIP3 of >20 dBm and are immune from power saturation effect. The measured results demonstrate improved performance in terms of linear and nonlinear characteristic compared with prior works.


Fraud : The Affinity of Classification Techniques to Insurance Fraud Detection

Quite a large number of data mining techniques employed in financial fraud detection (FFD) are seen to be classification techniques. In this paper, we developed an algorithm to find the features of classification techniques (or method) that so much place it (classification techniques) in the heart of researchers in their various efforts in the study of insurance frauds detection. We also got to know the characteristics of insurance frauds data that made data mining classification techniques so much attracted to it (insurance data).


Practical steganalysis of digital images: state of the art

Steganography is the art of hiding the very presence of communication by embedding secret messages into innocuous looking cover documents, such as digital images. Detection of steganography, estimation of message length, and its extraction belong to the field of steganalysis. Steganalysis has recently received a great deal of attention both from law enforcement and the media. In our paper, we classify and review current stego-detection algorithms that can be used to trace popular steganographic products. We recognize several qualitatively different approaches to practical steganalysis – visual detection, detection based on first order statistics (histogram analysis), dual statistics methods that use spatial correlations in images and higher-order statistics (RS steganalysis), universal blind detection schemes, and special cases, such as JPEG compatibility steganalysis. We also present some new results regarding our previously proposed detection of LSB embedding using sensitive dual statistics. The recent steganalytic methods indicate that the most common paradigm in image steganography – the bit-replacement or bit substitution – is inherently insecure with “safe capacities” far smaller than previously thought.


Topic-aware Social Influence Minimization

In this paper, we address the problem of minimizing the negative influence of undesirable things in a network by blocking a limited number of nodes from a topic modeling perspective. When undesirable thing such as a rumor or an infection emerges in a social network and part of users have already been infected, our goal is to minimize the size of ultimately infected users by blocking $k$ nodes outside the infected set. We first employ the HDP-LDA and KL divergence to analysis the influence and relevance from a topic modeling perspective. Then two topic-aware heuristics based on betweenness and out-degree for finding approximate solutions to this problem are proposed. Using two real networks, we demonstrate experimentally the high performance of the proposed models and learning schemes.


Value creation in web services: An integrative model

Web services are redefining the way organizations exchange business-critical information internally and externally with customers, suppliers, and other business associates. In this paper, we develop an integrative model for understanding value creation in web services from a provider’s perspective. The model integrates the static representation of conventional business values with the fluidity of the emergent IT services domain. It captures the complexity and contradictions facing Web services providers in their drive towards market leadership, strategic differentiation and revenue generation from web services. The model comprises twelve propositions to guide our understanding and future research and practice in this increasingly important segment in the IT field. 2005 Elsevier B.V. All rights reserved.


DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks

Our daily lives have been immersed in widespread location-based social networks (LBSNs). As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing users' decisions in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine-learning-based model for detection. Using real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.


Application of electrostatic separation to the recycling of plastic wastes: separation of PVC, PET, and ABS.

Plastics are widely used in everyday life as a useful material, and thus their consumption is growing at a rate of about 5% per year in Korea. However, the constant generation of plastic wastes and their disposal generates environmental problems along with economic loss. In particular, mixed waste plastics are difficult to recycle because of their inferior characteristics. A laboratory-scale triboelectrostatic separator unit has been designed and assembled for this study. On the basis of the control of electrostatic charge, the separation of three kinds of mixed plastics, polyvinyl chloride (PVC), poly(ethylene terephthalate) (PET), and acrylonitrile-butadiene-styrene (ABS), in a range of similar gravities has been performed through a two-stage separation process. Polypropylene (PP) and high-impact polystyrene (HIPS) were found to be the most effective materials for a tribo-charger in the separation of PVC, PET, and ABS. The charge-to-mass ratio (nC/g) of plastics increased with increasing air velocity in the tribo charger. In the first stage, using the PP cyclone charger, the separation efficiency of particles considerably depended on the air velocity (10 m/s), the relative humidity (< 30%), the electrode potential (> 20 kV), and the splitter position (+2 cm from the center) in the triboelelctrostatic separator unit. At this time, a PVC grade of 99.40% and a recovery of 98.10% have successfully been achieved. In the second stage, using the HIPS cyclone charger, a PET grade of 97.80% and a recovery of 95.12% could be obtained under conditions of 10 m/s, over 25 kV, a central splitter position, and less than 40% relative humidity. In order to obtain 99.9% PVC grade and 99.3% PET grade, their recoveries should be sacrificed by 20.9% and 27%, respectively, with moving the splitter from the center to a (+)6 cm position.


Reconsidering Research on Learning from Media

Recent meta-analyses and other studies of media's influence on learning are reviewed, Consistent evidence is found for the generalization that there are no learning benefits to be gained from employing any specific medium to deliver instruction. Research showing performance or timesaving gains from one or another medium are shown to be vulnerable to compelling rival hypotheses concerning the uncontrolled effects of instructional method and novelty. Problems with current media attribute and symbol system theories are described and suggestions made for more promising research directions. Studies of the influence of media on learning have ken a fixed feature of educational research since Thorndike (1912) recommended pictures as a laborsaving device in instruction, Most of this research is buttressed by the hope that learning will be enhanced with the proper mix of medium, student, subject matter content, and learning task. A typical study compares the relative achievement of groups who have received similar subject matter from different media. This research has led to so-called 'media selection" schemes or models 1982). These models generally promise to incorporate existing research and practice into procedures for selecting the best medium or mix of media to deliver instruction. Most of these models base many of their prescriptions on presumed learning benefits from media (Jamison, Suppes, & Welles, 1974). However, this article will argue that most current summaries and meta-analyses of media comparison studies clearly suggest that media do not influence learning under any conditions. Even in the few cases where dramatic changes in achievement or ability have followed the introduction of a medium, as was the case with television in El Salvador (Schramm, 1977), it was not the medium that caused the change but rather a curricular reform that accompanied the change. The best current evidence is that media are mere vehicles that deliver instruction but do not influence student achievement any more than the truck that delivers our groceries causes changes in our nutrition. Basically, the choice of vehicle might influence the cost or extent of distributing instruction, but only the content of the vehicle can influence achievement. While research often shows a slight learning advantage for newer media over more conventional instructional vehicles, this advantage will be __________________ The author wishes to acknowledge the substantive advice of Gavriel Salomon, William Winn, and anonymous reviewers without making them responsible for errors. ___________ p 445 shown to be vulnerable to compelling rival hypotheses. Among these rival explanations is evidence of artifact and confounding in existing studies and biased editorial decisions which may favor research showing larger effect sizes for newer media. After summarizing evidence from current meta-analyses of media research, I will discuss advantages and problems with current "media attribute" and 'symbol system" theories and will conclude by suggesting tentative solutions to past problems and future directions for research involving media. Media Comparison Studies In the 1960s, Lumsdaine (1963) and others (e.g,, Mielke, 1968) argued that gross media comparison and selection studies might not pay off. They implied that media, when viewed as collections of mechanical instruments, such as television and computers, were sample delivery devices. Nevertheless, earlier reviewers also held the door open to learning effects from media by attributing much of the lack of significance in prior research to poor design and lack of adequate models or theory. Lumsdaine (1963) dealt primarily with adequate studies that had used defensible methodology, and had found significant differences between treatments. With the benefit of hindsight it is not surprising that most of the studies he selected for review employed media as simple vehicles for instructional methods, such as text organization, size of step in programming, cueing, repeated exposures, and prompting. These studies compared the effects of. for example, different step size in programmed instruction via television. It was step size (and other methods), not television (or other media), which were the focus of thew studies. This is an example of what Salomon and Clark (1977) called research with media. In these studies media are mere conveyances for the treatments being examined and are not the focus of the study, though the results are often mistakenly interpreted as suggesting benefits for various media. An example of instructional research with media would be a study which contrasted a logically organized audiotutorial lesson on photosynthesis with a randomly sequenced presentation of the same frames (cf. Clark & Snow, 1975; Salomon & Clark, 1977, for a review of similar studies). Perhaps as a result of this confusion, Lumsdaine (1963) reached few conclusions beyond the suggestion that media might reduce the cost of instruction when many students are served because “the cost of perfecting it can be prorated in terms of a denominator representing thousands of students" (p. 670). A decade later, Glaser and Cooley (1973) and Levie and Dickie (1973) were cautious about media comparison studies, which apparently were still being conducted in large numbers. Glaser and Cooley (1973) recommended using acceptable medium as "a vehicle for making available to schools what psychologists have learned about learning" (p. 855). Levie and Dickie (1973) noted that most media comparison studies to that date had been fruitless and suggested that objectives can be attained through "instruction presented by any of a variety of different media" (p. 859). At that time televised education was still a lively topic and studies of computerized instruction were just beginning to appear. During the past decade, television research seems to have diminished considerably, but computer learning studies are now popular. This current h belongs to the familiar but generally fruitless media comparison approach or is concerned _____________ p 446 with different contents or methods being presented via different media (e.g., science teaching via computers). Generally, each new medium seems to attract its own set of advocates who make claims for improved learning and stimulate research questions which are similar to those asked about the previously popular medium. Most of the radio research approaches suggested in the 1950s (e.g., Hovland, Lumsdaine, & Sheffield, 1949) were very similar to those employed by the television movement of the 1960s (e.g., Schramm, 1977) and to the more recent reports of the computer-assisted instruction studies of the 1970s and 1980s (e.g., Dixon & Judd, 1977). It seems that similar research questions have resulted in similar and ambiguous data. Media comparison studies, regardless of the media employed, tend to result i n "no significant difference" conclusions (Mielke, 1968). These findings were incorrectly offered-as-evidence that different media were “equally effective” as conventional means in promoting learning. No significant difference results simply suggest that changes in the outcome scores (e.g., learning) did not result from any systematic differences in the treatments compared. Occasionally a study would find evidence for one or another medium. When this happens, Mielke (1968) has suggested that the active ingredient might be some uncontrolled aspect of the content or instructional strategy rather than the medium. When we investigate these positive studies, we find that the treatments are confounded. The evidence for this confounding may be found in the current meta-analyses of media comparison studies. The next section argues that it is the uncontrolled effects of novelty and instructional method which account for the existing evidence for the effects of various media on learning gains. Reviews and Meta-analyses of Media Research One of the most interesting trends in the past decade has been a significant increase in the number of excellent reviews and meta-analyses of research comparing the learning advantage of various media. The results of these overviews of past comparison studies seem to be reasonably unambiguous and unanimous. Taken together, they provide strong evidence that media comparison studies that find causal connections between media and achievement are confounded. Size of Effect of Media Treatments A recent series of meta-analyses of media comparison studies have been conducted by James Kulik and his colleagues at the University of Michigan (Cohen, Ebling & Kulik, 1981; C. Kulik, Kulik, & Cohen, 1980; J. Kulik, Bangert, & Williams, 1983; J. Kulik, Kulik, & Cohen, 1979). These reviews employ the relatively new technology of meta-analysis (Glass, 1976), which provides more precise estimates of the effect size of various media treatments than were possible a few years ago. Previous reviews dealing primarily with "box score" sums of significant findings for media versus conventional instructional delivery were sometimes misleading. Effect size estimates often were expressed in portions of standard score advantages for one or another type of treatment. This discussion will express effects in one of two ways: (a) the number of standard deviations separating experimental and control groups, and (b) as improvements in percent) scores on a final examination. ____________ p 447 Box Scores versus Effect Size An illustration of the advantage of meta-analytical effect size descriptions of past research over "box scores" is available in a recent review of Postlethwait's audiotutorial instruction studies (J. Kulik, Kulik, & Cohen,, 1979). The authors found 42 adequate studies, of which 29 favored audiotutorial instruction and only 13 favored conventional instruction. Of those 42, only 15 reported significant differences, but 11 of the 15 favored audiotutorial and only 4 favored conventional instruction. This type of box


Environmental Detectives--The Development of an Augmented Reality Platform for Environmental Simulations

This research was supported with a grant from Microsoft MIT iCampus as a part of the Games-to-Teach Project. The authors would like to thank the PIs of the Games-to-Teach Project, Randy Hinrichs at Microsoft Research and Henry Jenkins, Director MIT Comparative Media Studies for their support of this project, as well as Kodjo Hesse, Gunnar Harboe, and Walter Holland for their hard work in the development of Environmental Detectives.


Social constructivist perspectives on teaching and learning.

Social constructivist perspectives focus on the interdependence of social and individual processes in the co-construction of knowledge. After the impetus for understanding the influence of social and cultural factors on cognition is reviewed, mechanisms hypothesized to account for learning from this perspective are identified, drawing from Piagetian and Vygotskian accounts. The empirical research reviewed illustrates (a) the application of institutional analyses to investigate schooling as a cultural process, (b) the application of interpersonal analyses to examine how interactions promote cognition and learning, and (c) discursive analyses examining and manipulating the patterns and opportunities in instructional conversation. The review concludes with a discussion of the application of this perspective to selected contemporary issues, including: acquiring expertise across domains, assessment, educational equity, and educational reform.


What video games have to teach us about learning and literacy

Good computer and video games like System Shock 2, Deus Ex, Pikmin, Rise of Nations, Neverwinter Nights, and Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won't get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so to speak. But most gamers don't want short and easy games. Thus, designers face and largely solve an intriguing educational dilemma, one also faced by schools and workplaces: how to get people, often young people, to learn and master something that is long and challenging--and enjoy it, to boot.


Participatory Simulations: Building Collaborative Understanding Through Immersive Dynamic Modeling

The Participatory Simulations Project explores how a new kind of collaborative learning environment, which is supported by small, wearable computers, can facilitate collaborative theory-building and lead to a richer understanding of scientific experimentation. In a Participatory Simulation, participants become "agents" in a full-scale simulation. Unlike previous work, Participatory Simulations combines the notion of a microworld with the affordances of real world experience. By involving a large number of people (typically between 15 and 30) in a "life-sized" experience, the project brings a microworld off of the computer screen and into the physical world. In particular, this study explored the use of a series of Participatory Simulations in a high school Biology class. The students became "agents" in a simulation of a disease infecting their community. The results of this project show that Participatory Simulations fully engage participants in the simulation, facilitate collaborative problem solving, provide a substrate for collaboratively designing and running experiments, and support the definition of new vocabulary to discuss the underlying rules of the simulation. Thesis Advisor: Mitchel Resnick Associate Professor of Media Arts and Sciences Fukutake Career Development Professor of Research in Education This research was sponsored by the National Science Foundation (grant # CDA-9616444), the Lego Group, and the Things That Think and Digital Life consortia. Participatory Simulations: Building Collaborative Understanding through Immersive Dynamic Modeling


Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series

Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site—one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.


A spatio-temporal reference model of the aging brain

Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9-91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.


Autonomous website categorization with pre-defined dictionary

In this technology emerging era, the number of websites is increasing dramatically. The content and category of information are overflowing the Internet World. Finding the right information from almost a billion of websites is considerably hard, but finding the accurate and quality one is even harder. Hence, the need of website categorization's demand is increasing tremendously. Unfortunately, the website categorization techniques in previous works are still immature and not good enough to satisfy the need. Additionally, a training dataset is a limitation of supervised learning algorithm and unsupervised learning algorithm also have a complex algorithm. Regularly, they can categorize into only 1 category but the content usually contains various types. Therefore, in this paper, we propose the simple yet powerful algorithm for website categorization which can give a multi-category results with confidence level, distributed systems supported, and does not even need to be trained because the algorithm uses word frequency in the content of each website to match with the categories in a pre-defined dictionary. The result shows that the accuracy of our proposed algorithm is over 95% when tested with Reuters dataset. The comparison of our algorithm and another Text Analysis API shows that our algorithm has more accuracy with less computation time. The accuracy can also be increased by improving the pre-defined dictionary and filtering noise words.


FPGA Vendor Agnostic True Random Number Generator

This paper describes a solution for the generation of true random numbers in a purely digital fashion; making it suitable for any FPGA type, because no FPGA vendor specific features (e.g., like phase-locked loop) or external analog components are required. Our solution is based on a framework for a provable secure true random number generator recently proposed by Sunar, Martin and Stinson. It uses a large amount of ring oscillators with identical ring lengths as a fast noise source - but with some deterministic bits - and eliminates the non-random samples by appropriate post-processing based on resilient functions. This results in a slower bit stream with high entropy. Our FPGA implementation achieves a random bit throughput of more than 2 Mbps, remains fairly compact (needing minimally 110 ring oscillators of 3 inverters) and is highly portable


Algorithmic Bayesian persuasion

Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs of the various actions for both players. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff in expectation assuming that the receiver rationally acts to maximize his own payoff. When the payoffs of various actions follow a joint distribution (the common prior), the sender's problem is nontrivial, and its computational complexity depends on the representation of this prior. We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithmic solution, and a ``simple'' (1-1/e)-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. In doing so, we rule out a generalized Border's theorem, as defined by Gopalan et al, for this setting. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. Our FPTAS is based on Monte-Carlo sampling, and its analysis relies on the principle of deferred decisions. Moreover, we show that this result is the best possible in the black-box model for information-theoretic reasons.


Functional genomic analysis of the AUXIN RESPONSE FACTOR gene family members in Arabidopsis thaliana: unique and overlapping functions of ARF7 and ARF19.

The AUXIN RESPONSE FACTOR (ARF) gene family products, together with the AUXIN/INDOLE-3-ACETIC ACID proteins, regulate auxin-mediated transcriptional activation/repression. The biological function(s) of most ARFs is poorly understood. Here, we report the identification and characterization of T-DNA insertion lines for 18 of the 23 ARF gene family members in Arabidopsis thaliana. Most of the lines fail to show an obvious growth phenotype except of the previously identified arf2/hss, arf3/ett, arf5/mp, and arf7/nph4 mutants, suggesting that there are functional redundancies among the ARF proteins. Subsequently, we generated double mutants. arf7 arf19 has a strong auxin-related phenotype not observed in the arf7 and arf19 single mutants, including severely impaired lateral root formation and abnormal gravitropism in both hypocotyl and root. Global gene expression analysis revealed that auxin-induced gene expression is severely impaired in the arf7 single and arf7 arf19 double mutants. For example, the expression of several genes, such as those encoding members of LATERAL ORGAN BOUNDARIES domain proteins and AUXIN-REGULATED GENE INVOLVED IN ORGAN SIZE, are disrupted in the double mutant. The data suggest that the ARF7 and ARF19 proteins play essential roles in auxin-mediated plant development by regulating both unique and partially overlapping sets of target genes. These observations provide molecular insight into the unique and overlapping functions of ARF gene family members in Arabidopsis.


Design of 4-Bit Universal Shift Register Using Reversible Gates

Power dissipation is one of the important factor in digital circuit design. Landauer's principle states that logic computations which are not reversible necessarily generate KT*ln2 Joules of heat energy for every bit of information that is lost, where k is Boltzmann's constant and T the absolute temperature at which computation is performed. Reversibility is the property of digital circuits in which there is one-to-one mapping between the inputs and the output vectors that is for each input vector there is a unique output vector and vice-versa. Thus one of the primary motivations for adopting reversible logic lies in the fact that it can provide a logic design methodologies for designing low power dissipating circuits. Fredkin gate, Feynman Gate is popularly used reversible logic gate which are used to implement various sequential circuits which are basic for design of digital circuits.


In Defense of the Triplet Loss for Person Re-Identification

In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person reidentification subfield is no exception to this, thanks to the notable publication of the Market-1501 and MARS datasets and several strong deep learning approaches. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms any other published method by a large margin.


3D Simulation for Robot Arm Control with Deep Q-Learning

Intelligent control of robotic arms has huge potential over the coming years, but as of now will often fail to adapt when presented with new and unfamiliar environments. Recent trends to solve this problem have seen a shift to endto-end solutions using deep reinforcement learning to learn policies from visual input, rather than relying on a handcrafted, modular pipeline. Building upon the recent success of deep Q-networks, we present an approach which uses threedimensional simulations to train a 7-DOF robotic arm in a robot arm control task without any prior knowledge. Policies accept images of the environment as input and output motor actions. However, the high-dimensionality of the policies as well as the large state space makes policy search difficult. This is overcome by ensuring interesting states are explored via intermediate rewards that guide the policy towards higher reward states. Our results demonstrate that deep Q-networks can be used to learn policies for a task that involves locating a cube, grasping, and then finally lifting. The agent is able to learn to deal with a range of starting joint configurations and starting cube positions when tested in simulation. Moreover, we show that policies trained via simulation have the potential to be directly applied to real-world equivalents without any further training. We believe that robot simulations can decrease the dependency on physical robots and ultimately improve productivity of training robot control tasks.


SARL: A General-Purpose Agent-Oriented Programming Language

Complex software systems development require appropriate high-level features to better and easily tackle the new requirements in terms of interactions, concurrency and distribution. This requires a paradigm change in software engineering and corresponding programming languages. We are convinced that agent-oriented programming may be the support for this change by focusing on a small corpus of commonly accepted concepts and the corresponding programming language in line with the current developers' programming practices. This papers introduces SARL, a new general-purpose agent-oriented programming language undertaking this challenge. SARL comes with its full support in the Eclipse IDE for compilation and debugging, and a new version 2.0 of the Janus platform for execution purposes. The main perspective that guided the creation of SARL is the establishment of an open and easily extensible language. Our expectation is to provide the community with a common forum in terms of a first working test bed to study and compare various programming alternatives and associated metamodels.


On the Impossibility of Basing Identity Based Encryption on Trapdoor Permutations

We ask whether an identity based encryption (IBE) system can be built from simpler public-key primitives. We show that there is no black-box construction of IBE from trapdoor permutations (TDP) or even from chosen ciphertext secure public key encryption (CCA-PKE). These black-box separation results are based on an essential property of IBE, namely that an IBE system is able to compress exponentially many public-keys into a short public parameters string.


Discovery of convoys in trajectory databases

As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for reallife applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the wellknown filter-refinement framework. In the filter step, we apply linesimplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper’s proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.


Time scales in motor learning and development.

A theoretical framework based on the concepts and tools of nonlinear dynamical systems is advanced to account for both the persistent and transitory changes traditionally shown for the learning and development of motor skills. The multiple time scales of change in task outcome over time are interpreted as originating from the system's trajectory on an evolving attractor landscape. Different bifurcations between attractor organizations and transient phenomena can lead to exponential, power law, or S-shaped learning curves. This unified dynamical account of the functions and time scales in motor learning and development offers several new hypotheses for future research on the nature of change in learning theory.


Structured Time Series Analysis for Human Action Segmentation and Recognition

We address the problem of structure learning of human motion in order to recognize actions from a continuous monocular motion sequence of an arbitrary person from an arbitrary viewpoint. Human motion sequences are represented by multivariate time series in the joint-trajectories space. Under this structured time series framework, we first propose Kernelized Temporal Cut (KTC), an extension of previous works on change-point detection by incorporating Hilbert space embedding of distributions, to handle the nonparametric and high dimensionality issues of human motions. Experimental results demonstrate the effectiveness of our approach, which yields realtime segmentation, and produces high action segmentation accuracy. Second, a spatio-temporal manifold framework is proposed to model the latent structure of time series data. Then an efficient spatio-temporal alignment algorithm Dynamic Manifold Warping (DMW) is proposed for multivariate time series to calculate motion similarity between action sequences (segments). Furthermore, by combining the temporal segmentation algorithm and the alignment algorithm, online human action recognition can be performed by associating a few labeled examples from motion capture data. The results on human motion capture data and 3D depth sensor data demonstrate the effectiveness of the proposed approach in automatically segmenting and recognizing motion sequences, and its ability to handle noisy and partially occluded data, in the transfer learning module.


Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning

Predictive geometric models deliver excellent results for many Machine Learning use cases. Despite their undoubted performance, neural predictive algorithms can show unexpected degrees of instability and variance, particularly when applied to large datasets. We present an approach to measure changes in geometric models with respect to both output consistency and topological stability. Considering the example of a recommender system using word2vec, we analyze the influence of single data points, approximation methods and parameter settings. Our findings can help to stabilize models where needed and to detect differences in informational value of data points on a large scale.