nils-herrmann/clean_scirepeval huggingface dataset:
Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models
Venue: VISIGRAPP

Long wave infrared (LWIR) imaging is an imaging modality currently gaining increasing attention. Facial images acquired with LWIR sensors can be used for illumination invariant person recognition and the contactless extraction of vital signs such as respiratory rate. In order to work properly, these applications require a precise detection of faces and regions of interest such as eyes or nose. Most current facial landmark detectors in the LWIR spectrum localize single salient facial regions by thresholding. These approaches are not robust against out of plane rotation and occlusion. To address this problem, we therefore introduce a LWIR face tracking method based on an active appearance model (AAM) The model is trained with a manually annotated database of thermal face images. Additionally, we evaluate the effect of different methods for AAM generation and image preprocessing on the fitting performance. The method is evaluated on a set of still images and a video sequence. Results show that AAMs are a robust method for the detection and tracking of facial landmarks in the LWIR spectrum.
Hybridized Ant Colony System for Tasks to Workstations Assignment
Venue: 2016 IEEE Symposium Series on Computational Intelligence (SSCI)

Ant Colony System is a well known metaheuristic used to solve combinatorial optimization problems that is not intrinsically prepared to deal with precedence constraints. The work reported here is the continuation of the results presented in a previous paper that proposed an Ant System algorithm devoted to Tasks to Workstations Assignment problem. A special technique was developed in order to increase the effectiveness of precedence constraints treatment. On the one hand the contribution of this paper consists in the amelioration of this technique. On the other hand, the Ant System algorithm is hybridized with a local descent deterministic algorithm that contributes greatly to the avoiding of solutions bias. The results of the hybridized Ant System algorithm have proved the effectiveness of the proposed way to treat the precedence constraints
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Venue: ICLR

We introduce the "exponential linear unit" (ELU) which speeds up learning in deep neural networks and leads to higher classification accuracies. Like rectified linear units (ReLUs) leaky ReLUs (LReLUs) and parametrized ReLUs (PReLUs) ELUs alleviate the vanishing gradient problem via the identity for positive values. However, ELUs have improved learning characteristics compared to the units with other activation functions. In contrast to ReLUs, ELUs have negative values which allows them to push mean unit activations closer to zero like batch normalization but with lower computational complexity. Mean shifts toward zero speed up learning by bringing the normal gradient closer to the unit natural gradient because of a reduced bias shift effect. While LReLUs and PReLUs have negative values, too, they do not ensure a noise robust deactivation state. ELUs saturate to a negative value with smaller inputs and thereby decrease the forward propagated variation and information. Therefore, ELUs code the degree of presence of particular phenomena in the input, while they do not quantitatively model the degree of their absence. In experiments, ELUs lead not only to faster learning, but also to significantly better generalization performance than ReLUs and LReLUs on networks with more than 5 layers. On CIFAR 100 ELUs networks significantly outperform ReLU networks with batch normalization while batch normalization does not improve ELU networks. ELU networks are among the top 10 reported CIFAR 10 results and yield the best published result on CIFAR 100, without resorting to multi view evaluation or model averaging. On ImageNet, ELU networks considerably speed up learning compared to a ReLU network with the same architecture, obtaining less than 10% classification error for a single crop, single model network.
Preparing for the future of Artificial Intelligence
Venue: AI SOCIETY

In October 2016, the US National Science and Technology Council published a report on Artificial Intelligence (AI) (United States 2016) that summarised evidence from a wide variety of sources on how they expect AI to develop, what impact it would have and what actions it recommended the US Government to take. It built on several previous US Government reports, e.g. United States (2014, 2016) and consulted widely among AI experts in the USA, e.g. Horvitz and Selman (2009) and five workshops. A companion document has also been published: 'The National Artificial Intelligence Research and Development Strategic Plan' which lays out a strategic plan for federally funded research and development in AI. Overall, this is a comprehensive report, which I recommend to anyone wanting a balanced review of the state of the AI art, its potential impact and what ethical, economic and societal issues it presents. It does, of course, duck some of the more difficult issues or rather recommend that someone else considers them in detail. This concern about AI is, of course, triggered by the phenomenal recent successes of mainly statistical machine learning in games (Chess, Go and Jeopardy) self driving cars, automated assistants (Apple Siri, Amazon Alexa, Google Now and Microsoft Cortana) The other main driver is the worry that Artificial General Intelligence (AGI) will exceed human intelligence and supplant us as the dominant species on the Earth the, so called, Singularity. The report wisely ignores concerns about the Singularity, claiming that, if it occurs at all, it's a long way in the future, and that the immediate actions should be the same whether or not it occurs. It points out that the AI successes have been in, what it calls, narrow AI, i.e. often superhuman performance in a very narrow task, e.g. playing Go. I call such systems idiot savants. In contrast, progress in AGI has been disappointing, with no sign that this will improve in the foreseeable future. So the report focuses on the prospects for narrow AI. It claims that it has already brought 'major benefits to the public in fields as diverse as health care, transportation, the environment, criminal justice and economic inclusion' The report then explores its potential in autonomous vehicles, governance, education, cyber security and weapons. This exploration uncovers a number of important issues and concludes with 23 recommendations. These issues and recommendations include the following:
Rake Pmi Automated Keyphrase Extraction: An unsupervised approach for automated extraction of keyphrases
Venue: ICIA

Keyphrase extraction is a major step which is used in various applications such as document clustering, summarization. It can be solved using supervised as well as unsupervised approach. The unsupervised approach is based on the ranking of keyphrases and extracting the top few. For ranking, researchers either use statistical measures or language modeling. We propose a system called RAKE PMI, which uses both approaches for ranking of phrases. PointWise Mutual Information(PMI) score of adjacent words in a phrase is calculated through the language modeling. For calculation of PMI, we extract the scores from keywords extracted from the entire corpora. This helps in analyzing the term collocation of words within a phrase. We compared proposed approach with one of the popular systems called RAKE and achieved a precision of .41 as compared to the precision of .36 achieved by RAKE for the given data set.
Keep calm and react with foresight: strategies for low latency and energy efficient elastic data stream processing
Venue: PPoPP 2016

This paper addresses the problem of designing scaling strategies for elastic data stream processing. Elasticity allows applications to rapidly change their configuration on the fly (e.g. the amount of used resources) in response to dynamic workload fluctuations. In this work we face this problem by adopting the Model Predictive Control technique, a control theoretic method aimed at finding the optimal application configuration along a limited prediction horizon in the future by solving an online optimization problem. Our control strategies are designed to address latency constraints, using Queueing Theory models, and energy consumption by changing the number of used cores and the CPU frequency through the Dynamic Voltage and Frequency Scaling (DVFS) support available in the modern multicore CPUs. The proactive capabilities, in addition to the latency and energy awareness, represent the novel features of our approach. To validate our methodology, we develop a thorough set of experiments on a high frequency trading application. The results demonstrate the high degree of flexibility and configurability of our approach, and show the effectiveness of our elastic scaling strategies compared with existing state of the art techniques used in similar scenarios.
Leveraging data stream processing and weighted attack graph for real time bridge structural monitoring and warning
Venue: 2016 TRON Symposium (TRONSHOW)

Regarded as one of the physical aspects under societal and civil development and evolution, engineering structure is required to support growth of the nation. It also impacts life quality and safety of the civilian. Despite of carrying dead load (its own weight) and live load during operation, structural members are also significantly affected by disaster and environment. Proper inspection and detection are thus crucial both during regular and unsafe events. An Enhanced Structural Health Monitoring System Using Stream Processing and Artificial Neural Network Techniques (SPANNeT) has been developed and is described in this paper. SPANNeT applies wireless sensor network, real time data stream processing and artificial neural network based upon the measured bending strains. Major contributions include an effective, accurate and energy aware data communication and damage detection of the already built engineering structure. Strain thresholds have been defined according to computer simulation results and the AASHTO (American Association of State Highway and Transportation Officials) LRFD (Load and Resistance Factor Design) Bridge Design specifications for launching several warning levels. SPANNeT has been tested and evaluated by means of computer based simulation, test bed and on site levels. According to the measurements, the observed maximum values are 25 to 30 microstrains during normal operation. The given protocol provided at least 90% of data communication reliability. SPANNeT is capable of real time data report, monitoring and warning efficiently conforming to the predefined thresholds which can be adjusted regarding user's requirements and structural engineering characteristics.
Particle filter based phase estimation in digital holographic interferometry.
Venue: Journal of the Optical Society of America. A, Optics, image science, and vision

In this paper, we propose a particle filter based technique for the analysis of a reconstructed interference field. The particle filter and its variants are well proven as tracking filters in non Gaussian and nonlinear situations. We propose to apply the particle filter for direct estimation of phase and its derivatives from digital holographic interferometric fringes via a signal tracking approach on a Taylor series expanded state model and a polar to Cartesian conversion based measurement model. Computation of sample weights through non Gaussian likelihood forms the major contribution of the proposed particle filter based approach compared to the existing unscented Kalman filter based approach. It is observed that the proposed approach is highly robust to noise and outperforms the state of the art especially at very low signal to noise ratios (i.e. especially in the range of 5 to 20 dB) The proposed approach, to the best of our knowledge, is the only method available for phase estimation from severely noisy fringe patterns even when the underlying phase pattern is rapidly varying and has a larger dynamic range. Simulation results and experimental data demonstrate the fact that the proposed approach is a better choice for direct phase estimation.
The INTERSPEECH 2016 Computational Paralinguistics Challenge: Deception, Sincerity Native Language
Venue: INTERSPEECH

The INTERSPEECH 2016 Computational Paralinguistics Challenge addresses three different problems for the first time in research competition under well defined conditions: classification of deceptive vs. non deceptive speech, the estimation of the degree of sincerity, and the identification of the native language out of 11 L1 classes of English L2 speakers. In this paper, we describe these sub challenges, their conditions, and the baseline feature extraction and classifiers, as provided to the participants.
A Solar Irradiance Climate Data Record

AbstractWe present a new climate data record for total solar irradiance and solar spectral irradiance between 1610 and the present day with associated wavelength and time dependent uncertainties and quarterly updates. The data record, which is part of the National Oceanic and Atmospheric Administration's (NOAA) Climate Data Record (CDR) program, provides a robust, sustainable, and scientifically defensible record of solar irradiance that is of sufficient length, consistency, and continuity for use in studies of climate variability and climate change on multiple time scales and for user groups spanning climate modeling, remote sensing, and natural resource and renewable energy industries. The data record, jointly developed by the University of Colorado's Laboratory for Atmospheric and Space Physics (LASP) and the Naval Research Laboratory (NRL) is constructed from solar irradiance models that determine the changes with respect to quiet sun conditions when facular brightening and sunspot darkening features.
Short term solar irradiance forecasting using a mixed wavelet neural network

In modern smart grids and deregulated electricity markets, accurate forecasting of solar irradiance is critical for determining the total energy generated by PV systems. We propose a mixed wavelet neural network (WNN) in this paper for short term solar irradiance forecasting, with initial application in tropical Singapore. The key advantage of using wavelet transform (WT) based methods is the high signal compression ability of wavelets, making them suitable for modeling of nonstationary environmental parameters with high information content, such as short timescale solar irradiance. In this WNN, a combination of the commonly known Morlet and Mexican hat wavelets is used as the activation function for hidden layer neurons of a feed forward artificial neural network (ANN) To demonstrate the effectiveness of the proposed approach, hourly predictions of solar irradiance, which is an aggregate sum of irradiance value observed using 25 sensors across Singapore, are considered. The forecasted results show that WNN delivers better prediction skill when compared with other forecasting techniques.
Post processing techniques and principal component analysis for regional wind power and solar irradiance forecasting

This work explores a Principal Component Analysis (PCA) in combination with two post processing techniques for the prediction of wind power produced over Sicily, and of solar irradiance measured by Oklahoma Mesonet measurements' network. For wind power, the study is conducted over a 2 year long period, with hourly data of the aggregated wind power output of the Sicily island. The 0 72 h wind predictions are generated with the limited area Regional Atmospheric Modeling System (RAMS) with boundary conditions provided by the European Centre for Medium Range Weather Forecasts (ECMWF) deterministic forecast. For solar irradiance, we consider daily data of the aggregated solar radiation energy output (based on the Kaggle competition dataset) over an 8 year long period. Numerical Weather Prediction data for the contest come from the National Oceanic Atmospheric Administration Earth System Research Laboratory (NOAA/ESRL) Global Ensemble Forecast System (GEFS) Reforecast Version 2. The PCA is applied to reduce the datasets dimension. A Neural Network (NN) and an Analog Ensemble (AnEn) post processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post processing techniques leads to better results when compared to the implementation without the PCA reduction.
Magnitudes and timescales of total solar irradiance variability

The Sun's net radiative output varies on timescales of minutes to gigayears. Direct measurements of the total solar irradiance (TSI) show changes in the spatially and spectrally integrated radiant energy on timescales as short as minutes to as long as a solar cycle. Variations of ~0.01% over a few minutes are caused by the ever present superposition of convection and oscillations with very large solar flares on rare occasion causing slightly larger measurable signals. On timescales of days to weeks, changing photospheric magnetic activity affects solar brightness at the ~0.1% level. The 11 year solar cycle shows variations of comparable magnitude with irradiances peaking near solar maximum. Secular variations are more difficult to discern, being limited by instrument stability and the relatively short duration of the space borne record. Historical reconstructions of the Sun's irradiance based on indicators of solar surface magnetic activity, such as sunspots, faculae, and cosmogenic isotope records, suggest solar brightness changes over decades to millennia, although the magnitudes of these variations have high uncertainties due to the indirect historical records on which they rely. Stellar evolution affects yet longer timescales and is responsible for the greatest solar variabilities. In this manuscript I summarize the Sun's variability magnitudes over different temporal regimes and discuss the irradiance record's relevance for solar and climate studies as well as for detections of exo solar planets transiting Sun like stars.
Machine learning for solar irradiance forecasting of photovoltaic system

Photovoltaic generation of electricity is an important renewable energy source, and large numbers of relatively small photovoltaic systems are proliferating around the world. Today it is widely acknowledged by power producers, utility companies and independent system operators that it is only through advanced forecasting, communications and control that these distributed resources can collectively provide a firm, dispatchable generation capacity to the electricity market. One of the challenges of realizing such a goal is the precise forecasting of the output of individual photovoltaic systems, which is affected by a lot of factors. This paper introduces our short term solar irradiance forecasting algorithms based on machine learning methodologies, Hidden Markov Model and SVM regression. A series of experimental evaluations are presented to analyze the relative performance of the techniques in order to show the importance of these methodologies. The Matlab interface, the Weather Forecasting Platform, has been used for these evaluations. The experiments are performed using the dataset generated by Australian Bureau of Meteorology. The experimental results show that our machine learning based forecasting algorithms can precisely predict future 5 30 min solar irradiance under different weather conditions.
3D Quantification of Wall Shear Stress and Oscillatory Shear Index Using a Finite Element Method in 3D CINE PC MRI Data of the Thoracic Aorta
Venue: IEEE Transactions on Medical Imaging

Several 2D methods have been proposed to estimate WSS and OSI from PC MRI, neglecting the longitudinal velocity gradients that typically arise in cardiovascular flow, particularly on vessel geometries whose cross section and centerline orientation strongly vary in the axial direction. Thus, the contribution of longitudinal velocity gradients remains understudied. In this work, we propose a 3D finite element method for the quantification of WSS and OSI from 3D CINE PC MRI that accounts for both in plane and longitudinal velocity gradients. We demonstrate the convergence and robustness of the method on cylindrical geometries using a synthetic phantom based on the Poiseuille flow equation. We also show that, in the presence of noise, the method is both stable and accurate. Using computational fluid dynamics simulations, we show that the proposed 3D method results in more accurate WSS estimates than those obtained from a 2D analysis not considering out of plane velocity gradients. Further, we conclude that for irregular geometries the accurate prediction of WSS requires the consideration of longitudinal gradients in the velocity field. Additionally, we compute 3D maps of WSS and OSI for 3D CINE PC MRI data sets from an aortic phantom and sixteen healthy volunteers and two patients. The OSI values show a greater dispersion than WSS, which is strongly dependent on the PC MRI resolution. We envision that the proposed 3D method will improve the estimation of WSS and OSI from 3D CINE PC MRI images, allowing for more accurate estimates in vessels with pathologies that induce high longitudinal velocity gradients, such as coarctations and aneurisms.
Considerations for follow the leader motion of extensible tendon driven continuum robots
Venue: 2016 IEEE International Conference on Robotics and Automation (ICRA)

Path following and follow the leader motion is particularly desirable for minimally invasive surgery in confined spaces which can only be reached using tortuous paths, e.g. through natural orifices. While path following and followthe leader motion can be achieved by hyper redundant snake robots, their size is usually not applicable for medical applications. Continuum robots, such as tendon driven or concentric tube mechanisms, fulfill the size requirements for minimally invasive surgery, but yet follow the leader motion is not inherently provided. In fact, parameters of the manipulator's section curvatures and translation have to be chosen wisely a priori. In this paper, we consider a tendon driven continuum robot with extensible sections. After reformulating the forward kinematics model, we formulate prerequisites for follow the leader motion and present a general approach to determine a sequence of robot configurations to achieve follow the leader motion along a given 3D path. We evaluate our approach in a series of simulations with 3D paths composed of constant curvature arcs and general 3D paths described by B spline curves. Our results show that mean path errors ;0.4mm and mean tip errors ;1.6mm can theoretically be achieved for constant curvature paths and ;2mm and ;3.1mm for general B spline curves respectively.
Improving K means through better initialization and normalization
Venue: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

K means is still a popular clustering algorithm and active research area. The research is majorly focused at improving efficiency and effectiveness of the method. This paper proposes combined approach of a ranked initialization and normalization of data values with k means. Three variations of a score based initialization approach is proposed. Experiments are performed on normalized data to prove the superiority of the proposed algorithm.