nils-herrmann/clean_scirepeval huggingface dataset:
Face detection and recognition for smart glasses

Face detection and recognition are key components in multiple camera-based devices and applications. Smart glasses are a type of optical head mounted displays that integrate first-person cameras and hands free displays with immediate access to processing power able to analyze first person images in real time with hands free operation. In this context, we have constructed an application prototype that detects and recognizes faces in real-time, and runs independently on the device. We provide a description of the embedded implementation at a system-level where we highlight the application development challenges and trade-offs that need to be dealt with battery powered wearable devices. The implementation includes a parallel pipeline that reduces the latencies of the application.
An Optimized Illumination Normalization Method for Face Recognition

Differences in illumination conditions cause significant challenges for any 2-D face recognition algorithm. One of the methods to counter these effects is image preprocessing before feature extraction. In this paper we present a new preprocessing approach that uses custom filters obtained through an optimization procedure striving for most suitable preprocessing filters for the selected feature extractor and distance measure. We experiment with it using Local Binary Pattern texture features and X2 histogram distance metric. Results are provided for Face Recognition Grand Challenge (FRGC) 1.0.4 dataset.
HEp-2 Cell Classification via Combining Multiresolution Co-Occurrence Texture and Large Region Shape Information

Indirect immunofluorescence imaging of human epithelial type 2 (HEp-2) cell image is an effective evidence to diagnose autoimmune diseases. Recently, computer-aided diagnosis of autoimmune diseases by the HEp-2 cell classification has attracted great attention. However, the HEp-2 cell classification task is quite challenging due to large intraclass and small interclass variations. In this paper, we propose an effective approach for the automatic HEp-2 cell classification by combining multiresolution co-occurrence texture and large regional shape information. To be more specific, we propose to: 1) capture multiresolution co-occurrence texture information by a novel pairwise rotation-invariant co-occurrence of local Gabor binary pattern descriptor; 2) depict large regional shape information by using an improved Fisher vector model with RootSIFT features, which are sampled from large image patches in multiple scales; and 3) combine both features. We evaluate systematically the proposed approach on the IEEE International Conference on Pattern Recognition (ICPR) 2012, the IEEE International Conference on Image Processing (ICIP) 2013, and the ICPR 2014 contest datasets. The proposed method based on the combination of the introduced two features outperforms the winners of the ICPR 2012 contest using the same experimental protocol. Our method also greatly improves the winner of the ICIP 2013 contest under four different experimental setups. Using the leave-one-specimen-out evaluation strategy, our method achieves comparable performance with the winner of the ICPR 2014 contest that combined four features.
OuluVS2: A multi-view audiovisual database for non-rigid mouth motion analysis

Visual speech constitutes a large part of our nonrigid facial motion and contains important information that allows machines to interact with human users, for instance, through automatic visual speech recognition (VSR) and speaker verification. One of the major obstacles to research of non-rigid mouth motion analysis is the absence of suitable databases. Those available for public research either lack a sufficient number of speakers or utterances or contain constrained view points, which limits their representativeness and usefulness. This paper introduces a newly collected multi-view audiovisual database for non-rigid mouth motion analysis. It includes more than 50 speakers uttering three types of utterances and more importantly, thousands of videos simultaneously recorded by six cameras from five different views spanned between the frontal and profile views. Moreover, a simple VSR system has been developed and tested on the database to provide some baseline performance.
Learning local image descriptors using binary decision trees

In this paper we propose a unified framework for learning such local image descriptors that describe pixel neighborhoods using binary codes. The descriptors are constructed using binary decision trees which are learnt from a set of training image patches. Our framework generalizes several previously proposed binary descriptors, such as BRIEF, LBP and their variants, and provides a principled way to learn new constructions which have not been previously studied. Further, the proposed framework can utilize both labeled or unlabeled training data, and hence fits to both supervised and unsupervised learning scenarios. We evaluate our framework using varying levels of supervision in the learning phase. The experiments show that our descriptor constructions perform comparably to benchmark descriptors in two different applications, namely texture categorization and age group classification from facial images.
3D gait recognition using multiple cameras

Gait recognition is used to identify individuals in image sequences by the way they walk. Nearly all of the approaches proposed for gait recognition are 2D methods based on analyzing image sequences captured by a single camera. In this paper, video sequences captured by multiple cameras are used as input, and then a human 3D model is set up. The motion is tracked by applying a local optimization algorithm. The lengths of key segments are extracted as static parameters, and the motion trajectories of lower limbs are used as dynamic features. Finally, linear time normalization is exploited for matching and recognition. The proposed method based on 3D tracking and recognition is robust to the changes of viewpoints. Moreover, better results are achieved for sequences containing difficult surface variations than with 2D methods, which prove the efficiency of our algorithm
System Frequency Support Through Multi-Terminal DC (MTDC) Grids

Control of the converter stations in a multi-terminal DC (MTDC) grid to provide frequency support for the surrounding AC systems is the subject matter of this paper. The standard autonomous power sharing control loop for each converter is modified with a frequency droop control loop. The objective is to minimize the deviation from nominal AC system frequency and share the burden of frequency support among the converter stations of the MTDC grid. The effectiveness of the frequency support is demonstrated through nonlinear simulation of a test system consisting of three isolated AC systems interconnected through an MTDC grid with four converter stations. An averaged model of the MTDC grids is developed to carry out modal analysis of combined multi-machine AC-MTDC systems. Modal analysis is used to characterize and substantiate the time domain behavior in presence of frequency droop control. It is established that appropriate droop control loop for the MTDC grid converters could be effective in reducing the deviation from nominal AC system frequency provided the sensitivity of the system eigen-values to changes in control parameters (e.g., droop coefficients) is accounted for a priori through modal analysis.
A new approach to continuous latency compensation with adaptive phasor power oscillation damping controller (POD)

Latency or delay in remote feedback signals can adversely affect the closed-loop damping performance. Accurate time-stamp information at both (PMU location and control center) ends offers a possibility to continuously compensate for time-varying latency. In this paper, an adaptive phasor power oscillation damping controller (APPOD) is proposed wherein the rotating coordinates for phasor extraction are adjusted to account for the change in phase caused due to the delay. The oscillatory component of the original signal is thus retrieved out of the delayed signal received at the control center. Unlike conventional phasor POD, which uses a fixed phase shift to generate damping control signal, an adaptive phase shift algorithm is used here to suit varying signal locations and operating conditions. Case studies confirm the effectiveness of the proposed technique, both in terms of robustness and handling continuously varying delays. A comparison with a conventional gain scheduled POD (CGPOD) and a Unified Smith Predictor (USP) approach is also presented.
Object Detection With DoG Scale-Space: A Multiple Kernel Learning Approach

Difference of Gaussians (DoG) scale-space for an image is a significant way to generate features for object detection and classification. While applying DoG scale-space features for object detection/classification, we face two inevitable issues: dealing with high-dimensional data and selecting/weighting of proper scales. The scale selection process is mostly ad-hoc to present. In this paper, we propose a multiple kernel learning (MKL) method for both DoG scale selection/weighting and dealing with high-dimensional scale-space data. We design a novel shift invariant kernel function for DoG scale-space. To select only the useful scales in the DoG scale-space, a novel framework of MKL is also proposed. We utilize a 1-norm support vector machine (SVM) in the MKL optimization problem for sparse weighting of scales from DoG scale-space. The optimized data-dependent kernel accommodates only a few scales that are most discriminatory according to the large margin principle. With a 2-norm SVM, this learned kernel is applied to a challenging detection problem in oil sand mining: to detect large lumps in oil sand videos. We tested our method on several challenging oil sand data sets. Our method yields encouraging results on these difficult-to-process images and compares favorably against other popular multiple kernel methods.
Automatic blood cell classification based on joint histogram based feature and bhattacharya kernel

We propose a blood cell classification method with the aim of designing an automatic differential blood count system, which can help cancer diagnosis. The proposed system contains two automated steps: an active contour-based segmentation of blood cells from microscopy images and their classification. For classification we investigate several joint histogram-based features extracted from the segmented blood cells. We use support vector machine with a proposed kernel based on the Bhattacharya coefficient of joint histograms. Experimental results show the effectiveness of our system. Furthermore, comparative study illustrates that the proposed system outperforms other existing classification approaches in terms of classification accuracy.
Fast contingency screening and ranking for small signal stability assessment

Utilities today are in need of tools and techniques that will enable them to predict the dynamic stability and reliability of the grid in the real-time. The problem is challenging because of the large number of contingencies that are to be simulated. In this paper a fast method for power system contingency screening and ranking for small signal stability assessment is presented which essentially reduces the number of contingencies for detailed evaluation. The proposed method avoids repeated computation of eigenvalues for all possible post-contingency scenarios. Instead, the eigenvalues corresponding to critical modes for post-outage conditions are estimated based on first-order eigenvalue-sensitivity using just the nominal condition eigenvalues and post outage system state matrices. Since a critical outage condition can produce a large change in the eigenvalues, the first order prediction might not have acceptable accuracy. To overcome this issue, a second-order correction is applied which needs the computation of the eigenvectors corresponding to the eigenvalues of interest. A significant reduction in computation time for evaluation of post contingency eigenvalues using the proposed method is demonstrated. A case study on a 16-machine NETS-NYPS system shows promising results.
Damping Control in Power Systems Under Constrained Communication Bandwidth: A Predictor Corrector Strategy

Damping electromechanical oscillations in power systems using feedback signals from remote sensors is likely to be affected by occasional low bandwidth availability due to increasing use of shared communication in future. In this paper, a predictor corrector (PC) strategy is applied to deal with situations of low-feedback data rate (bandwidth), where conventional feedback (CF) would suffer. Knowledge of nominal system dynamics is used to approximate (predict) the actual system behavior during intervals when data from remote sensors are not available. Recent samples of the states from a reduced observer at the remote location are used to periodically reset (correct) the nominal dynamics. The closed-loop performance deteriorates as the actual operating condition drifts away from the nominal dynamics. Nonetheless, significantly better performance compared to CF is obtained under low-bandwidth situations. The analytical criterion for closed-loop stability of the overall system is validated through a simulation study. It is demonstrated that even for reasonably low data rates the closed-loop stability is usually ensured for a typical power system application confirming the effectiveness of this approach. The deterioration in performance is also quantified in terms of the difference between the nominal and off-nominal dynamics.
Online Levenberg-Marquardt algorithm for neural network based estimation and control of power systems

Levenberg-Marquardt (LM) algorithm, a powerful off-line batch training method for neural networks, is adapted here for online estimation of power system dynamic behavior. A special form of neural network compatible with the feedback linearization framework is used to enable non-linear self-tuning control. Use of LM is shown to yield better closed-loop performance compared to conventional recursive least square (RLS) approach. For successive disturbance use of LM in conjunction with non-linear neural network structure yields faster convergence compared to RLS. A case study on a test system demonstrates the effectiveness of the online LM method for both linear and nonlinear estimation over RLS estimation (linear).
Modeling and stability analysis of modular multilevel HVDC converters

A nonlinear averaged model of MMCs is proposed that considers the SM capacitor insertion dynamics and takes into account the current control loops that follow widely used vector control approach. A stability analysis framework is presented by exploiting the linear time-periodic (LTP) nature of this system. The averaged model, developed in MATLAB/Simulink, is benchmarked against a detailed model in EMTDC/PSCAD. The 1000-MW, ±320-kV INELFE VSC-HVDC link is considered in the case study.
Data packet-drop-resilient wide-area damping control using DFIG-based wind farm

In this paper a novel Observer-driven Reduced Copy (ORC) approach is proposed to deal with communication network data-dropouts in a smart power grid with large-scale deployment of distributed and networked Phasor Measurement Units (PMUs) and wind energy resources, which uses knowledge of the nominal system dynamics during data dropouts to improve the damping performance under large disturbances, where the conventional feedback would suffer. To that end a reduced order 16-machine 5-area dynamic equivalent model of the New England-New York power system with replacement of one existing synchronous generator and a power system stabilizer (PSS) by a DFIG-based wind farm (WF) is considered. The problem with electromechanical oscillation damping control through WFs using locally available signals is identified and a systematic approach for selection of control input and remote feedback signals through modal analysis is presented. The remote feedback signals sent through communication channels encounter data dropout which is represented by the Gilbert-Elliott model. Moreover, an expression for the bound on the error norm between the actual and the estimated states relating data dropout and model mismatch is also derived. Nonlinear time-domain simulations demonstrate that the ORC gives significantly better performance compared to the conventional feedback under higher data drop situations.
Convolutional gated recurrent networks for video segmentation

Semantic segmentation has recently witnessed major progress, but most of the previous work focused on improving single image segmentation. In this paper, we introduce a novel approach to implicitly utilize temporal data in videos for online segmentation. This design receives a sequence of consecutive video frames and outputs the segmentation of the last frame. Convolutional gated recurrent networks are used for the recurrent part to preserve spatial connectivities in the image. This architecture is tested for both binary and semantic video segmentation tasks. Experiments are conducted on the recent benchmarks in SegTrack V2, Davis, Camvid, and Synthia. Using recurrent fully convolutional networks improved the baseline network performance in all of our experiments. Namely, 5% and 3% improvement of F-measure in SegTrack2 and Davis respectively, 5.7% and 1.6% improvement in mean IoU in Synthia and Camvid. Thus, RFCN networks can be seen as a method to improve any baseline segmentation network by embedding them into a recurrent module that utilizes temporal data.
A comparative study on wide-area damping controllers using multiple DFIG-based wind farms under intermittent observations

In this paper specific attention is focused on wide-area oscillation damping control of a power grid with substantial penetration of large-scale inverter-interfaced wind energy resources using remote feedback signals. These signals sent through communication channels encounter data dropout, which is represented by the Gilbert-Elliott model. Performance of three different control techniques are compared: Conventional Feedback Control (CFC), an Observer-driven Reduced Copy (ORC) approach, and Modified Kalman Filtering (MKF). ORC uses the knowledge of the nominal system dynamics during data dropouts to improve the damping performance whereas MKF adapts the updation step according to data drop. An expression for the expectation of the bound on the error norm between the actual and the estimated states relating uncertainties in the cyber system due to data dropout and physical system due to change in operating conditions is also derived for ORC. Nonlinear timedomain simulations are carried out to compare the performance of CFC, MKF, and ORC under higher data drop scenarios.
Impact of wind penetration and HVDC upgrades on dynamic performance of future grids

Impact of DFIG based wind penetration on the dynamic performance of future grids is studied in this paper. Both AC and a mix of AC and HVDC transmission are considered. In general, DFIGs are found to contribute positively to system damping although the effect could be opposite depending on the operating condition. For deeper understanding, various effects contributing to the overall system damping (e.g. wind penetration at sending vs receiving end etc.) are studied in isolation keeping the other factors (e.g. tie-flow etc.) constant. Implications of supplying bulk of the future load growth by wind energy with only AC and both AC and HVDC are analyzed and compared. Observed trends are explained in terms of damping torque contribution of synchronous generators which is the primary source of damping in power systems. Power oscillation damping (POD) control action through supplementary modulation of DFIG rotor currents (both direct and quadrature axes) or active and reactive power order of VSC HVDC link is demonstrated.
MIMO model reduction of modern power grids with wind generation: Some new findings

This paper presents some new findings in model order reduction of multi input-multi output (MIMO) systems of modern power grid with wind generation. A widely-used model reduction technique called Balanced Truncation (BT) is compared with a relatively new Interpolatory approach of Iterative Rational Krylov Algorithm (IRKA). It is demonstrated that both BT and IRKA can produce an acceptable accuracy during model reduction for power grids with synchronous generators. Whereas, when modern grid with wind generation is considered, BT fails to produce a stable reduced order model (ROM) while IRKA is still able to derive accurate stable ROM retaining the poorly-damped inter-area modes of the system.
Face recognition using multi-modal low-rank dictionary learning

Face recognition has been widely studied due to its importance in different applications; however, most of the proposed methods fail when face images are occluded or captured under illumination and pose variations. Recently several low-rank dictionary learning methods have been proposed and achieved promising results for noisy observations. While these methods are mostly developed for single-modality scenarios, recent studies demonstrated the advantages of feature fusion from multiple inputs. We propose a multi-modal structured low-rank dictionary learning method for robust face recognition, using raw pixels of face images and their illumination invariant representation. The proposed method learns robust and discriminative representations from contaminated face images, even if there are few training samples with large intra-class variations. Extensive experiments on different datasets validate the superior performance and robustness of our method to severe illumination variations and occlusion.
A New Approach to Continuous Latency Compensation With Adaptive Phasor Power Oscillation Damping Controller (POD)

Latency or delay in remote feedback signals can adversely affect the closed-loop damping performance. Accurate time-stamp information at both (PMU location and control center) ends offers a possibility to continuously compensate for time-varying latency. In this paper, an adaptive phasor power oscillation damping controller (APPOD) is proposed wherein the rotating coordinates for phasor extraction are adjusted to account for the change in phase caused due to the delay. The oscillatory component of the original signal is thus retrieved out of the delayed signal received at the control center. Unlike conventional phasor POD, which uses a fixed phase shift to generate damping control signal, an adaptive phase shift algorithm is used here to suit varying signal locations and operating conditions. Case studies confirm the effectiveness of the proposed technique, both in terms of robustness and handling continuously varying delays. A comparison with a conventional gain scheduled POD (CGPOD) and a Unified Smith Predictor (USP) approach is also presented.
Mitigation and Recovery From Cascading Failures in Interdependent Networks Under Uncertainty

The interdependence of multiple networks makes today's infrastructures more vulnerable to failures. Prior works mainly focused on robust network design and recovery strategies after failures, given complete knowledge of failure location. Nevertheless, in real-world scenarios, the location of failures might be unknown or only partially known. In this paper, we focus on cascading failures involving the power grid and its communication network with imprecision in failure assessment. We consider a model where functionality of the power grid and its failure assessment rely on the operation of a monitoring system and vice versa. We address ongoing cascading failures with a twofold approach: first, once a cascading failure is detected, we limit further propagation by redispatching generation and shedding loads; and second, we formulate a recovery plan to maximize the total amount of load served during the recovery intervention. We performed extensive simulations on real network topologies showing the effectiveness of the proposed approach in terms of number of disrupted power lines and total served load.