Query: Real World BCI: Cross-Domain Learning and Practical Applications

Relevant passages:
EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces <=SEP=> OBJECTIVE
Brain-computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct characteristics of its expected EEG control signal, limiting its application to that specific signal. Convolutional neural networks (CNNs), which have been used in computer vision and speech recognition to perform automatic feature extraction and classification, have successfully been applied to EEG-based BCIs; however, they have mainly been applied to single BCI paradigms and thus it remains unclear how these architectures generalize to other paradigms. Here, we ask if we can design a single CNN architecture to accurately classify EEG signals from different BCI paradigms, while simultaneously being as compact as possible.


APPROACH
In this work we introduce EEGNet, a compact convolutional neural network for EEG-based BCIs. We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI. We compare EEGNet, both for within-subject and cross-subject classification, to current state-of-the-art approaches across four BCI paradigms: P300 visual-evoked potentials, error-related negativity responses (ERN), movement-related cortical potentials (MRCP), and sensory motor rhythms (SMR).


MAIN RESULTS
We show that EEGNet generalizes across paradigms better than, and achieves comparably high performance to, the reference algorithms when only limited training data is available across all tested paradigms. In addition, we demonstrate three different approaches to visualize the contents of a trained EEGNet model to enable interpretation of the learned features.


SIGNIFICANCE
Our results suggest that EEGNet is robust enough to learn a wide variety of interpretable features over a range of BCI tasks. Our models can be found at: https://github.com/vlawhern/arl-eegmodels.

Signal Processing Approaches to Minimize or Suppress Calibration Time in Oscillatory Activity-Based Brain–Computer Interfaces <=SEP=> One of the major limitations of brain-computer interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose three different methods to do so. We also propose a new, fast and simple approach to perform user-to-user transfer for BCI. Finally, we study and compare offline different approaches, both old and new ones, on the data of 50 users from three different BCI data sets. This enables us to identify guidelines about how to reduce or suppress calibration time for BCI.

Very Deep Convolutional Networks for Large-Scale Image Recognition <=SEP=> In this work we investigate the effect of the convolutional n etwork depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth, which shows that a significant improvement on the prior-art configurations can be achi eved by pushing the depth to 16–19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first a nd he second places in the localisation and classification tracks respec tively. We also show that our representations generalise well to other datasets, whe re t y achieve the stateof-the-art results. Importantly, we have made our two bestp rforming ConvNet models publicly available to facilitate further research o n the use of deep visual representations in computer vision.

Going deeper with convolutions <=SEP=> We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift <=SEP=> Training Deep Neural Networks is complicated by the fact that the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a stateof-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.

DESIGN OF AN ULTRA-WIDEBAND POWER DIVIDER VIA THE COARSE-GRAINED PARALLEL MICRO-GENETIC ALGORITHM <=SEP=> An ultra-wideband (UWB) power divider is designed in this paper. The UWB performance of this power divider is obtained by using a tapered microstrip line that consists of exponential and elliptic sections. The coarse grained parallel micro-genetic algorithm (PMGA) and CST Microwave Studio are combined to achieve an automated parallel design process. The method is applied to optimize the UWB power divider. The optimized power divider is fabricated and measured. The measured results show relatively low insertion loss, good return loss, and high isolation between the output ports across the whole UWB (3.1–10.6 GHz).

Visual Path Prediction in Complex Scenes with Crowded Moving Objects <=SEP=> This paper proposes a novel path prediction algorithm for progressing one step further than the existing works focusing on single target path prediction. In this paper, we consider moving dynamics of co-occurring objects for path prediction in a scene that includes crowded moving objects. To solve this problem, we first suggest a two-layered probabilistic model to find major movement patterns and their cooccurrence tendency. By utilizing the unsupervised learning results from the model, we present an algorithm to find the future location of any target object. Through extensive qualitative/quantitative experiments, we show that our algorithm can find a plausible future path in complex scenes with a large number of moving objects.

NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING <=SEP=> WHAT IS DATA MINING? WHY DATA MINING? NEED FOR HUMAN DIRECTION OF DATA MINING CROSS-INDUSTRY STANDARD PROCESS: CRISP–DM CASE STUDY 1: ANALYZING AUTOMOBILE WARRANTY CLAIMS: EXAMPLE OF THE CRISP–DM INDUSTRY STANDARD PROCESS IN ACTION FALLACIES OF DATA MINING WHAT TASKS CAN DATA MINING ACCOMPLISH? CASE STUDY 2: PREDICTING ABNORMAL STOCK MARKET RETURNS USING NEURAL NETWORKS CASE STUDY 3: MINING ASSOCIATION RULES FROM LEGAL DATABASES CASE STUDY 4: PREDICTING CORPORATE BANKRUPTCIES USING DECISION TREES CASE STUDY 5: PROFILING THE TOURISM MARKET USING k-MEANS CLUSTERING ANALYSIS

Design and implementation of server cluster dynamic load balancing based on OpenFlow <=SEP=> Nowadays, the Internet is flooded with huge traffic, many applications have millions users, a single server is difficult to bear a large number of clients' access, so many application providers will put several servers as a computing unit to provide support for a specific application, usually people will use distributed computing, load balancing technology to complete the work. A typical load balancing technique is to use a dedicated load balancer to forward the client requests to different servers, this technique requires dedicated hardware support, the hardware is expensive, lacks of flexibility and is easy to become a single point failure. There will be a new solution for load balancing with OpenFlow proposed., this paper mainly studies dynamic load balancing technology in the OpenFlow environment, the Controller collected the server running status through the SNMP protocol, and calculated the aggregated load of the severs according to dynamic load balancing scheduling algorithm, the OpenFlow switch will forward the client's request to the server whose aggregated load is smallest, thus minimize the response time of the web server. In the OpenFlow network environment, using this method can brings high flexibility without additional equipment.

Solving the 0-1 Knapsack Problem with Genetic Algorithms <=SEP=> This paper describes a research project on using Genetic Algorithms (GAs) to solve the 0-1 Knapsack Problem (KP). The Knapsack Problem is an example of a combinatorial optimization problem, which seeks to maximize the benefit of objects in a knapsack without exceeding its capacity. The paper contains three sections: brief description of the basic idea and elements of the GAs, definition of the Knapsack Problem, and implementation of the 0-1 Knapsack Problem using GAs. The main focus of the paper is on the implementation of the algorithm for solving the problem. In the program, we implemented two selection functions, roulette-wheel and group selection. The results from both of them differed depending on whether we used elitism or not. Elitism significantly improved the performance of the roulette-wheel function. Moreover, we tested the program with different crossover ratios and single and double crossover points but the results given were not that different.

Cosmetics as a Feature of the Extended Human Phenotype: Modulation of the Perception of Biologically Important Facial Signals <=SEP=> Research on the perception of faces has focused on the size, shape, and configuration of inherited features or the biological phenotype, and largely ignored the effects of adornment, or the extended phenotype. Research on the evolution of signaling has shown that animals frequently alter visual features, including color cues, to attract, intimidate or protect themselves from conspecifics. Humans engage in conscious manipulation of visual signals using cultural tools in real time rather than genetic changes over evolutionary time. Here, we investigate one tool, the use of color cosmetics. In two studies, we asked viewers to rate the same female faces with or without color cosmetics, and we varied the style of makeup from minimal (natural), to moderate (professional), to dramatic (glamorous). Each look provided increasing luminance contrast between the facial features and surrounding skin. Faces were shown for 250 ms or for unlimited inspection time, and subjects rated them for attractiveness, competence, likeability and trustworthiness. At 250 ms, cosmetics had significant positive effects on all outcomes. Length of inspection time did not change the effect for competence or attractiveness. However, with longer inspection time, the effect of cosmetics on likability and trust varied by specific makeup looks, indicating that cosmetics could impact automatic and deliberative judgments differently. The results suggest that cosmetics can create supernormal facial stimuli, and that one way they may do so is by exaggerating cues to sexual dimorphism. Our results provide evidence that judgments of facial trustworthiness and attractiveness are at least partially separable, that beauty has a significant positive effect on judgment of competence, a universal dimension of social cognition, but has a more nuanced effect on the other universal dimension of social warmth, and that the extended phenotype significantly influences perception of biologically important signals at first glance and at longer inspection.

Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana. <=SEP=> The Agrobacterium vacuum infiltration method has made it possible to transform Arabidopsis thaliana without plant tissue culture or regeneration. In the present study, this method was evaluated and a substantially modified transformation method was developed. The labor-intensive vacuum infiltration process was eliminated in favor of simple dipping of developing floral tissues into a solution containing Agrobacterium tumefaciens, 5% sucrose and 500 microliters per litre of surfactant Silwet L-77. Sucrose and surfactant were critical to the success of the floral dip method. Plants inoculated when numerous immature floral buds and few siliques were present produced transformed progeny at the highest rate. Plant tissue culture media, the hormone benzylamino purine and pH adjustment were unnecessary, and Agrobacterium could be applied to plants at a range of cell densities. Repeated application of Agrobacterium improved transformation rates and overall yield of transformants approximately twofold. Covering plants for 1 day to retain humidity after inoculation also raised transformation rates twofold. Multiple ecotypes were transformable by this method. The modified method should facilitate high-throughput transformation of Arabidopsis for efforts such as T-DNA gene tagging, positional cloning, or attempts at targeted gene replacement.

Device-to-device communications: The physical layer security advantage <=SEP=> In systems that allow device-to-device (D2D) communications, user pairs in close proximity communicate directly without using an access point (AP) as an intermediary. D2D communications leads to improved throughput, reduced power consumption and interference, and more flexible resource allocation. We show that the D2D paradigm also provides significantly improved security at the physical layer, by reducing exposure of the information to eavesdroppers from two relatively high-power transmissions to a single low-power hop. We derive the secrecy outage probability (SOP) for the D2D and cellular systems, and compare performance for D2D scenarios in the presence of a multi-antenna eavesdropper. The cellular approach is only seen to have an advantage in certain cases when the AP has a large number of antennas and perfect channel state information.

Efficient Self-Interpretation in Lambda Calculus <=SEP=> We start by giving a compact representation schema for λ-terms and show how this leads to an exceedingly small and elegant self-interpreter. We then define the notion of a self-reducer, and show how this too can be written as a small λ-term. Both the self-interpreter and the self-reducer are proved correct. We finally give a constructive proof for the second fixed point theorem for the representation schema. All the constructions have been implemented on a computer, and experiments verify their correctness. Timings show that the self-interpreter and self-reducer are quite efficient, being about 35 and 50 times slower than direct execution using a call-byneed reduction strategy.

A 10000 frames/s CMOS digital pixel sensor <=SEP=> A 352 288 pixel CMOS image sensor chip with perpixel single-slope ADC and dynamic memory in a standard digital 0.18m CMOS process is described. The chip performs “snapshot” image acquisition, parallel 8-bit A/D conversion, and digital readout at continuous rate of 10 000 frames/s or 1 Gpixels/s with power consumption of 50 mW. Each pixel consists of a photogate circuit, a three-stage comparator, and an 8-bit 3T dynamic memory comprising a total of 37 transistors in 9.4 9.4 m with a fill factor of 15%. The photogate quantum efficiency is 13.6%, and the sensor conversion gain is 13.1 V/e . At 1000 frames/s, measured integral nonlinearity is 0.22% over a 1-V range, rms temporal noise with digital CDS is 0.15%, and rms FPN with digital CDS is 0.027%. When operated at low frame rates, on-chip power management circuits permit complete powerdown between each frame conversion and readout. The digitized pixel data is read out over a 64-bit (8-pixel) wide bus operating at 167 MHz, i.e., over 1.33 GB/s. The chip is suitable for general high-speed imaging applications as well as for the implementation of several still and standard video rate applications that benefit from high-speed capture, such as dynamic range enhancement, motion estimation and compensation, and image stabilization.

Adaptation of the Nomophobia Questionnaire (NMP-Q) to Spanish in a sample of adolescents. <=SEP=> INTRODUCTION
Nomophobia is the fear of being out of mobile phone contact. People suffering from this anxiety disorder have feelings of stress and nervousness when access to their mobiles or computers is not possible. This work is an adaptation and validation study of the Spanish version of the Nomophobia Questionnaire (NMP-Q).


METHODOLOGY
The study included 306 students (46.1% males and 53.9% females) with ages ranging 13 to 19 years (Md=15.41±1.22).


RESULTS
Exploratory factor analysis revealed four dimensions that accounted for 64.4% of total variance. The ordinal α-value was 0.95, ranging from 0.75 to 0.92 across factors. Measure of stability was calculated by the testretest method (r=0.823). Indicators of convergence with the Spanish versions of the “Mobile Phone Problem Use Scale” (r=0.654) and the “Generalized Problematic Internet Use Scale” (r=0.531) were identified. Problematic mobile phone use patterns were examined taking the 15P, 80P and 95P percentiles as cut-off points. Scores of 39, 87 and 116 on NMP-Q corresponded to occasional, at-risk and problematic users, respectively.


CONCLUSIONS
Psychometric analysis shows that the Spanish version of the NMP-Q is a valid and reliable tool for the study of nomophobia.

Novel air blowing control for balancing a unicycle robot <=SEP=> This paper presents the implementation of a novel control method of using air for balancing the roll angle of a unicycle robot. The unicycle robot is designed and built. The roll angle of the unicycle robot is controlled by blowing air while the pitch angle is also controlled by DC motors. Successful balancing performances are demonstrated.

REMAINING LIFE PREDICTION OF ELECTRONIC PRODUCTS USING LIFE CONSUMPTION MONITORING APPROACH <=SEP=> Various kinds of failures may occur in electronic products because of their life cycle environmental conditions including temperature, humidity, shock and vibration. Failure mechanism models are available to estimate the time to failure for most of these failures. Hence if the life cycle environment of a product can be determined, it is possible to assess the amount of damage induced and predict when the product might fail. This paper presents a life consumption monitoring methodology to determine the remaining life of a product. A battery powered data recorder is used to monitor the temperature, shock and vibration loads on a printed circuit board assembly placed under the hood of an automobile. The recorded data is used in conjunction with physics-of-failure models to determine the damage accumulation in the solder joints due to temperature and vibration loading. The remaining life of the solder joints of the test board is then obtained from the damage accumulation information. 1. Reliability Prediction Reliability is defined as the ability of a product to perform as intended (i.e., without failure and within specified performance limits) for a specified time, in its life cycle application environment. Over time, technology improvements have lead to higher I/O counts in components and circuit cards of electronic products. This has resulted in significant downsizing of interconnect thicknesses, making them more vulnerable in field applications. At the same time, increase in warranties and severe liabilities of electronic product failure have compelled manufacturers to predict the reliability of their products in field applications. An efficient reliability prediction scheme can be used for many purposes including [1]: • Logistics support (e.g., forecast warranty and life cycle costs, spare parts provisioning, availability)

You Can Run but You Can't Read: Preventing Disclosure Exploits in Executable Code <=SEP=> Code reuse attacks allow an adversary to impose malicious behavior on an otherwise benign program. To mitigate such attacks, a common approach is to disguise the address or content of code snippets by means of randomization or rewriting, leaving the adversary with no choice but guessing. However, disclosure attacks allow an adversary to scan a process - even remotely - and enable her to read executable memory on-the-fly, thereby allowing the just-in time assembly of exploits on the target site. In this paper, we propose an approach that fundamentally thwarts the root cause of memory disclosure exploits by preventing the inadvertent reading of code while the code itself can still be executed. We introduce a new primitive we call Execute-no-Read (XnR) which ensures that code can still be executed by the processor, but at the same time code cannot be read as data. This ultimately forfeits the self-disassembly which is necessary for just-in-time code reuse attacks (JIT-ROP) to work. To the best of our knowledge, XnR is the first approach to prevent memory disclosure attacks of executable code and JIT-ROP attacks in general. Despite the lack of hardware support for XnR in contemporary Intel x86 and ARM processors, our software emulations for Linux and Windows have a run-time overhead of only 2.2% and 3.4%, respectively.

TangiPaint: A Tangible Digital Painting System <=SEP=> TangiPaint is a digital painting application that provides the experience of working with real materials such as canvas and oil paint. Using fingers on the touchscreen of an iPad or iPhone, users can lay down strokes of thick, three-dimensional paint on a simulated canvas. Then using the Tangible Display technology introduced by Darling and Ferwerda [1], users can tilt the display screen to see the gloss and relief or "impasto" of the simulated surface, and modify it until they get the appearance they desire. Scene lighting can also be controlled through direct gesture-based interaction. A variety of "paints" with different color and gloss properties and substrates with different textures are available and new ones can be created or imported. The tangiPaint system represents a first step toward developing digital art media that look and behave like real materials. Introduction The development of computer-based digital art tools has had a huge impact on a wide range of creative fields. In commercial art, advertisements incorporating images, text, and graphic elements can be laid out and easily modified using digital illustration applications. In cinema, background matte elements can be digitally drawn, painted, and seamlessly integrated with live footage. In fine art, painters, printers, and engravers have also been embracing the new creative possibilities of computer-based art tools. The recent introduction of mobile, tablet-based computers with high-resolution displays, graphics processing units (GPUs) and multi-touch capabilities is also creating new possibilities for direct interaction in digital painting. However a significant limitation of most digital painting tools is that the final product is just a digital image (typically an array of RGB color values). All the colors, textures and lighting effects that we see when we look at the digital painting are “baked in” to the image by the painter. In contrast, when a painter works with real tools and media, the color, gloss, and textural properties of the work are a natural byproduct of the creative process, and lighting effects such as highlights and shadows are produced directly through interactions of the surface with light in the environment. In this paper we introduce tangiPaint, a new tablet-based digital painting system that attempts to bridge the gap between the real and digital worlds. tangiPaint is a tangible painting application that allows artists to work with digital media that look and behave like real materials. Figure 1 shows screenshots from the tangiPaint application implemented on an Apple iPad2. In Figure 1a an artist has painted a number of brushstrokes on a blank canvas. Note that in addition to color, the strokes vary in gloss, thickness, and texture, and run out just as if they were real paint laid down with a real brush. The paints also layer and mix realistically as they would on a real canvas. Figure 1. Screenshots of paintings created using the tangiPaint system. Note the gloss and relief of the brushstrokes and the texture of the underlying canvas. The system allows direct interaction with the “painted” surface both in terms of paint application and manipulation of surface orientation and

Estimating Data Integration and Cleaning Effort <=SEP=> Data cleaning and data integration have been the topic of intensive research for at least the past thirty years, resulting in a multitude of specialized methods and integrated tool suites. All of them require at least some and in most cases significant human input in their configuration, during processing, and for evaluation. For managers (and for developers and scientists) it would be therefore of great value to be able to estimate the effort of cleaning and integrating some given data sets and to know the pitfalls of such an integration project in advance. This helps deciding about an integration project using cost/benefit analysis, budgeting a team with funds and manpower, and monitoring its progress. Further, knowledge of how well a data source fits into a given data ecosystem improves source selection. We present an extensible framework for the automatic effort estimation for mapping and cleaning activities in data integration projects with multiple sources. It comprises a set of measures and methods for estimating integration complexity and ultimately effort, taking into account heterogeneities of both schemas and instances and regarding both integration and cleaning operations. Experiments on two real-world scenarios show that our proposal is two to four times more accurate than a current approach in estimating the time duration of an integration process, and provides a meaningful breakdown of the integration problems as well as the required integration activities. 1. COMPLEXITY OF INTEGRATION AND CLEANING Data integration and data cleaning remain among the most human-work-intensive tasks in data management. Both require a clear understanding of the semantics of schema and data – a notoriously difficult task for machines. Despite much research and development of supporting tools and algorithms, state-of-the-art integration projects involve significant human resource cost. In fact, Gartner reports that 10% of all IT cost goes into enterprise software c ©2015, Copyright is with the authors. Published in Proc. 18th International Conference on Extending Database Technology (EDBT), March 23-27, 2015, Brussels, Belgium: ISBN 978-3-89318-067-7, on OpenProceedings.org. Distribution of this paper is permitted under the terms of the Creative Commons license CC-by-nc-nd 4.0. for data integration and data quality, and it is well recognized that most of those expenses are for human labor. Thus, when embarking on a data integration and cleaning project, it is useful and important to estimate in advance the effort and cost of the project and to find out which particular difficulties cause these. Such estimations help deciding whether to pursue the project in the first place, planning and scheduling the project using estimates about the duration of integration steps, budgeting in terms of cost or manpower, and finally monitoring the progress of the project. Cost estimates can also help integration service providers, IT consultants, and IT tool vendors to generate better price quotes for integration customers. Further, automatically generated knowledge of how well and how easy a data source fits into a given data ecosystem improves source selection. However, “project estimation for [. . . ] data integration projects is especially difficult, given the number of stakeholders involved across the organization as well as the unknowns of data complexity and quality.” [14]. Any integration project has several steps and tasks, including requirements analysis, selection of data sources, determining the appropriate target database, data transformation specifications, testing, deployment, and maintenance. In this paper, we focus on exploring the database-related steps of integration and cleaning and automatically estimate their effort. 1.1 Challenges There are simple approaches to estimate in isolation the complexity of individual mapping and cleaning tasks. For the mapping, evaluating its complexity can be done by counting the matchings (i.e., correspondences) among elements. For the cleaning problem, a natural solution is to measure its complexity by counting the number of constraints on the target schema. However, as several integration approaches have shown, the interactive nature of these two problems is particularly complex [5, 11, 13]. For example, a data exchange problem takes as input two relational schemas, a transformation between them (a mapping), a set of target constraints, and answers two questions: whether it is possible to compute a valid solution for a given setting and how. Interestingly, to have a solution, certain conditions must hold on the target constraints, and extending the setting to more complex languages or data models bring tighter restrictions on the class of tractable cases [6, 12]. In our work, the main challenge is to estimate complexity and effort in a setting that goes beyond these ad-hoc studies while satisfying four main requirements: Generality: We require independence from the language used to express the data transformation. Furthermore, real cases often fail the existence of solution tests considered in formal frameworks http://www.gartner.com/ technology/research/ it-spending-forecast/ http://www.gartner.com/newsroom/ id/2292815 (e.g., weak acyclicity condition [11]), but an automatic estimation is still desirable for them in practice. Completeness: Only a subset of the constraints that hold on the data are specified over the schema. In fact, business rules are commonly enforced at the application level and are not reflected in the metadata of the schemas, but should nevertheless be considered. Granularity: Details about the integration issues are crucial for consumption of the estimation. For a real understanding and proper planning, it is important to know which source and/or target attributes are cause of problems and how (e.g., phone attributes in source and target schema have different formats). Existing estimators do not reason over actual data structures and thus make no statements about the causes of integration effort. Configurability and extensibility: The actual effort depends on subjective factors like the capabilities of available tools and the desired quality of the output. Therefore, intuitive, yet rich configuration settings for the estimation process are crucial for its applicability. Moreover, users must be able to extend the range of problems covered by the framework. These challenges cannot be tackled with existing syntactical methods to test the existence of solutions, as they work only in specific settings (Generality), are restricted to declarative specifications over the schemas (Completeness), and do not provide details about the actual problems (Granularity). On the other hand, as systems that compute solutions require human interaction to finalize the process [8, 13], they cannot be used for estimation purpose and their availability is orthogonal to our problem (Configurability). 1.2 Approaching Effort Estimation Figure 1 presents our view on the problem of estimating the effort of data integration. The starting point is an integration scenario with a target database and one or more source databases. The right-hand side of the figure shows the actual integration process performed by an integration specialist, where the goal is to move all instances of the source databases into the target database. Typically, a set of integration tools are used by the specialist. These tools have access to the source and target and support her in the tasks. The process takes a certain effort, which can be measured, for instance as amount of work in hours or days or in a monetary unit. Our goal is to find that effort without actually performing the integration. Moreover, we want to find and present the problems that cause this effort. To this end, we developed a two-phase process as shown on the left-hand side of Figure 1. The first phase, the complexity assessment, reveals concrete integration challenges for the scenario. To address generality, these problems are exclusively determined by the source and target schemas and instances; if and how an integration practitioner deals with them is not addressed at this point. Thus, this first phase is independent of external parameters, such as the level of expertise of the specialist or the available integration tools. However, it is aided by the results of schema matching and data profiling tools, which analyze the participating databases and produce metadata about them (to achieve completeness). The output of the complexity assessment is a set of clearly defined problems, such as number of violations for a constraint or number of different value representations. This detailed breakdown of the problems achieves granularity and is useful for several tasks, even if not interpreted as an input to calculate actual effort. Examples of application are source selection [9], i.e., given a set of integration candidates, find the source with the best ‘fit’; and support for data visualization [7], i.e., highlight parts of the schemas that are hard to integrate. Data

Bidirectional Decoder Networks for Attention-Based End-to-End Offline Handwriting Recognition <=SEP=> Recurrent neural networks that can be trained end-to-end on sequence learning tasks provide promising benefits over traditional recognition systems. In this paper, we demonstrate the application of an attention-based long short-term memory decoder network for offline handwriting recognition and analyze the segmentation, classification and decoding errors produced by the model. We further extend the decoding network by a bidirectional topology together with an integrated length estimation procedure and show that it is superior to unidirectional decoder networks. Results are presented for the word and text line recognition tasks of the RIMES handwriting recognition database. The software used in the experiments is freely available for academic research purposes.

A Piezoelectric Energy Harvester for Rotary Motion Applications: Design and Experiments <=SEP=> This paper investigates the analysis and design of a vibration-based energy harvester for rotary motion applications. The energy harvester consists of a cantilever beam with a tip mass and a piezoelectric ceramic attached along the beam that is mounted on a rotating shaft. Using this system, mechanical vibration energy is induced in the flexible beam due to the gravitational force applied to the tip mass while the hub is rotating. The piezoelectric transducer is used to convert the induced mechanical vibration energy into electricity. The equations of motion of the flexible structure are utilized along with the physical characteristics of the piezoelectric transducer to derive expressions for the electrical power. Furthermore, expressions for the optimum load resistance and maximum output power are obtained and validated experimentally using PVDF and PZT transducers. The results indicate that a maximum power of 6.4 mW at a shaft speed of 138 rad/s can be extracted by using a PZT transducer with dimensions 50.8 mm × 38.1 mm × 0.13 mm. This amount of power is sufficient to provide power for typical wireless sensors such as accelerometers and strain gauges.

Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis. <=SEP=> Virtual reality exposure therapy (VRET) is an increasingly common treatment for anxiety and specific phobias. Lacking is a quantitative meta-analysis that enhances understanding of the variability and clinical significance of anxiety reduction outcomes after VRET. Searches of electronic databases yielded 52 studies, and of these, 21 studies (300 subjects) met inclusion criteria. Although meta-analysis revealed large declines in anxiety symptoms following VRET, moderator analyses were limited due to inconsistent reporting in the VRET literature. This highlights the need for future research studies that report uniform and detailed information regarding presence, immersion, anxiety and/or phobia duration, and demographics.

Hair Follicle Miniaturization in a Woolly Hair Nevus: A Novel "Root" Perspective for a Mosaic Hair Disorder. <=SEP=> Woolly hair nevus is a mosaic disorder characterized by unruly, tightly curled hair in a circumscribed area of the scalp. This condition may be associated with epidermal nevi. We describe an 11-year-old boy who initially presented with multiple patches of woolly hair and with epidermal nevi on his left cheek and back. He had no nail, teeth, eye, or cardiac abnormalities. Analysis of plucked hairs from patches of woolly hair showed twisting of the hair shaft and an abnormal hair cuticle. Histopathology of a woolly hair patch showed diffuse hair follicle miniaturization with increased vellus hairs.

On Using Twitter to Monitor Political Sentiment and Predict Election Results <=SEP=> The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and we make a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content.

Generalized neural-network representation of high-dimensional potential-energy surfaces. <=SEP=> The accurate description of chemical processes often requires the use of computationally demanding methods like density-functional theory (DFT), making long simulations of large systems unfeasible. In this Letter we introduce a new kind of neural-network representation of DFT potential-energy surfaces, which provides the energy and forces as a function of all atomic positions in systems of arbitrary size and is several orders of magnitude faster than DFT. The high accuracy of the method is demonstrated for bulk silicon and compared with empirical potentials and DFT. The method is general and can be applied to all types of periodic and nonperiodic systems.

Robust Instance Recognition in Presence of Occlusion and Clutter <=SEP=> We present a robust learning based instance recognition framework from single view point clouds. Our framework is able to handle real-world instance recognition challenges, i.e, clutter, similar looking distractors and occlusion. Recent algorithms have separately tried to address the problem of clutter [9] and occlusion [16] but fail when these challenges are combined. In comparison we handle all challenges within a single framework. Our framework uses a soft label Random Forest [5] to learn discriminative shape features of an object and use them to classify both its location and pose. We propose a novel iterative training scheme for forests which maximizes the margin between classes to improve recognition accuracy, as compared to a conventional training procedure. The learnt forest outperforms template matching, DPM [7] in presence of similar looking distractors. Using occlusion information, computed from the depth data, the forest learns to emphasize the shape features from the visible regions thus making it robust to occlusion. We benchmark our system with the state-of-the-art recognition systems [9, 7] in challenging scenes drawn from the largest publicly available dataset. To complement the lack of occlusion tests in this dataset, we introduce our Desk3D dataset and demonstrate that our algorithm outperforms other methods in all settings.

Indoor Localization Using Camera Phones <=SEP=> Indoor localization has long been a goal of pervasive computing research. In this paper, we explore the possibility of determining user's location based on the camera images received from a smart phone. In our system, the smart phone is worn by the user as a pendant and images are periodically captured and transmitted over GPRS to a web server. The web server returns the location of the user by comparing the received images with images stored in a database. We tested our system inside the Computer Science department building. Preliminary results show that user's location can be determined correctly with more than 80% probability of success. As opposed to earlier solutions for indoor localization, this approach does not have any infrastructure requirements. The only cost is that of building an image database.

Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions <=SEP=> We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping, time-based compression, and finally classification with a recurrent neural network. We demonstrate the effectiveness of the approach in a case study with 279 participants. On the basis of sparse sensor events, the network continually predicts whether the participants would attend to a notification within 10 minutes. Compared to a random baseline, the classifier achieves a 40% performance increase (AUC of 0.702) on a withheld test set. This approach allows to forgo resource-intensive, domain-specific, error-prone feature engineering, which may drastically increase the applicability of machine learning to mobile phone sensor data.