nils-herrmann/clean_scirepeval huggingface dataset:
Camera-Based Fall Detection on Real World Data

Several new algorithms for camera-based fall detection have been proposed in the literature recently, with the aim to monitor older people at home so nurses or family members can be warned in case of a fall incident. However, these algorithms are evaluated almost exclusively on data captured in controlled environments, under optimal conditions (simple scenes, perfect illumination and setup of cameras), and with falls simulated by actors. In contrast, we collected a dataset based on real life data, recorded at the place of residence of four older persons over several months. We showed that this poses a significantly harder challenge than the datasets used earlier. The image quality is typically low. Falls are rare and vary a lot both in speed and nature. We investigated the variation in environment parameters and context during the fall incidents. We found that various complicating factors, such as moving furniture or the use of walking aids, are very common yet almost unaddressed in the literature. Under such circumstances and given the large variability of the data in combination with the limited number of examples available to train the system, we posit that simple yet robust methods incorporating, where available, domain knowledge (e.g. the fact that the background is static or that a fall usually involves a downward motion) seem to be most promising. Based on these observations, we propose a new fall detection system. It is based on background subtraction and simple measures extracted from the dominant foreground object such as aspect ratio, fall angle and head speed. We discuss the results obtained, with special emphasis on particular difficulties encountered under real world circumstances.
The Use of Fast Fourier Transform for the Estimation of Power Spectra : A Method Based on Time Aver . aging Over Short , Modified Periodograms

The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections.  In many instances this method involves fewer computations than other methods. Moreover, it involves the transformation of sequences which are shorter than the whole record which is an advantage when computations are to be performed on a machine with limited core storage. Finally, it directly yields a potential resolution in the time dimension which is useful for testing and measuring nonstationarity. As will be pointed out, it is closely related to the method of complex demodulation described Let X(j), j= 0, N-1 be a sample from a stationary , second-order stochastic sequence. Assume for simplicity that E(X) 0. Let X(j) have spectral density Pcf), I f \ 5%. We take segments, possibly overlapping, of length L with the starting points of these segments D units apart. Let X,(j),j=O, L 1 be the first such segment. Then Xdj) X($ and finally X&) X(j+ (K 1)D) j 0, ,L-1. We suppose we have K such segments; Xl(j), X,($, and that they cover the entire record, Le., that (K-1)DfL N. This segmenting is illustrated in Fig. 1. The method of estimation is as follows. For each segment of length L we calculate a modified periodo-gram. That is, we select a data window W(j), j= 0, L-1, and form the sequences Xl(j)W(j), X,(j) W(j). We then take the finite Fourier transforms A1(n), AK(~) of these sequences. Here ~k(n) xk(j) w(j)e-z~cijnlL 1 L-1 L j-0 and i= Finally, we obtain the K modified periodograms L U Ik(fn) I Ah(%) k 1, 2, K, where f n 0 , o-,L/2 n " L and 1 Wyj). L j=o The spectral estimate is the average of these periodo
Depression recognition based on dynamic facial and vocal expression features using partial least square regression

Depression is a typical mood disorder, and the persons who are often in this state face the risk in mental and even physical problems. In recent years, there has therefore been increasing attention in machine based depression analysis. In such a low mood, both the facial expression and voice of human beings appear different from the ones in normal states. This paper presents a novel method, which comprehensively models visual and vocal modalities, and automatically predicts the scale of depression. On one hand, Motion History Histogram (MHH) extracts the dynamics from corresponding video and audio data to represent characteristics of subtle changes in facial and vocal expression of depression. On the other hand, for each modality, the Partial Least Square (PLS) regression algorithm is applied to learn the relationship between the dynamic features and depression scales using training data, and then predict the depression scale for an unseen one. Predicted values of visual and vocal clues are further combined at decision level for final decision. The proposed approach is evaluated on the AVEC2013 dataset and experimental results clearly highlight its effectiveness and better performance than baseline results provided by the AVEC2013 challenge organiser.
Indoor positioning system based on sensor fusion for the Blind and Visually Impaired

There are over 1.2 million Australians registered as having vision impairment. In most cases, vision impairment severely affects the mobility and orientation of the person, resulting in loss of independence and feelings of isolation. GPS technology and its applications have now become omnipresent and are used daily to improve and facilitate the lives of many. Although a number of products specifically designed for the Blind and Vision Impaired (BVI) and relying on GPS technology have been launched, this domain is still a niche and ongoing R&D is needed to bring all the benefits of GPS in terms of information and mobility to the BVI. The limitations of GPS indoors and in urban canyons have led to the development of new systems and signals that bridge the gap and provide positioning in those environments. Although still in their infancy, there is no doubt indoor positioning technologies will one day become as pervasive as GPS. It is therefore important to design those technologies with the BVI in mind, to make them accessible from scratch. This paper will present an indoor positioning system that has been designed in that way, examining the requirements of the BVI in terms of accuracy, reliability and interface design. The system runs locally on a mid-range smartphone and relies at its core on a Kalman filter that fuses the information of all the sensors available on the phone (Wi-Fi chipset, accelerometers and magnetic field sensor). Each part of the system is tested separately as well as the final solution quality.
Parameter Design of a Two-Current-Loop Controller Used in a Grid-Connected Inverter System With LCL Filter

LCL filters offer a better choice of attenuating switching frequency harmonics. However, in a grid-connected system, an LCL filter may cause resonance which is a disaster for the system's stability. In order to solve the problem, a two-current-loop control strategy, which includes grid-current outer loop and filter-capacitor-current inner loop, is adopted here. The implementation of this strategy is easy, but the tuning procedure is complex since the outer and inner controllers cannot cooperate well if the parameters of the controllers are not suitable. There is no literature which mentions a method to help give out accurate parameters of the controller. The difficulty of designing the controller is that only two feedbacks cannot provide complete information of a three-order LCL filter. A specific method is proposed in this paper to design the controller. The advantage of this method is to provide a way to maximize the utilization of the two feedbacks to get good system performance through parameter determination. The practicability of the method is tested by using bode diagram, and the antidistortion ability of the system is analyzed. Finally, experimental results verify the availability and correctness of the proposed method.
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

The original ImageNet dataset is a popular large-scale benchmark for training Deep Neural Networks. Since the cost of performing experiments (e.g, algorithm design, architecture search, and hyperparameter tuning) on the original dataset might be prohibitive, we propose to consider a downsampled version of ImageNet. In contrast to the CIFAR datasets and earlier downsampled versions of ImageNet, our proposed ImageNet32x32 (and its variants ImageNet64x64 and ImageNet16x16) contains exactly the same number of classes and images as ImageNet, with the only difference that the images are downsampled to 32×32 pixels per image (64×64 and 16×16 pixels for the variants, respectively). Experiments on these downsampled variants are dramatically faster than on the original ImageNet and the characteristics of the downsampled datasets with respect to optimal hyperparameters appear to remain similar. The proposed datasets and scripts to reproduce our results are available at http://image-net.org/download-images and https://github.com/PatrykChrabaszcz/Imagenet32_Scripts
Triple-Band Microstrip-Fed Monopole Antenna Loaded With CRLH Unit Cell

This letter presents a novel metamaterial-inspired planar monopole antenna. The proposed structure consists of a monopole loaded with a composite right/left-handed (CRLH) unit cell. It operates at two narrow bands, 0.925 and 1.227 GHz, and one wide band, 1.56-2.7 GHz, i.e., it covers several communication standards. The CRLH-loaded monopole occupies the same Chu's sphere as a conventional monopole that operates at 2.4 GHz. The radiation patterns at the different operating frequencies are still quasi-omnidirectional. Measurements and EM simulations are in a good agreement with the theoretical predictions.
Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis

In the absence of medical diagnosis evidences, it is difficult for the experts to opine about the grade of disease with affirmation. Generally many tests are done that involve clustering or classification of large scale data. However many tests could complicate the main diagnosis process and lead to the difficulty in obtaining the end results, particularly in the case where many tests are performed. This kind of difficulty could be resolved with the aid of machine learning techniques. In this research, we present a comparative study of different classification techniques using three data mining tools named WEKA, TANAGRA and MATLAB. The aim of this paper is to analyze the performance of different classification techniques for a set of large data. A fundamental review on the selected techniques is presented for introduction purpose. The diabetes data with a total instance of 768 and 9 attributes (8 for input and 1 for output) will be used to test and justify the differences between the classification methods. Subsequently, the classification technique that has the potential to significantly improve the common or conventional methods will be suggested for use in large scale data, bioinformatics or other general applications.
Multi-Dialect Speech Recognition with a Single Sequence-to-Sequence Model

Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network. In this work, we look at one such sequence-to-sequence model, namely listen, attend and spell (LAS) , and explore the possibility of training a single model to serve different English dialects, which simplifies the process of training multi-dialect systems without the need for separate AM, PM and LMs for each dialect. We show that simply pooling the data from all dialects into one LAS model falls behind the performance of a model fine-tuned on each dialect. We then look at incorporating dialect-specific information into the model, both by modifying the training targets by inserting the dialect symbol at the end of the original grapheme sequence and also feeding a 1-hot representation of the dialect information into all layers of the model. Experimental results on seven English dialects show that our proposed system is effective in modeling dialect variations within a single LAS model, outperforming a LAS model trained individually on each of the seven dialects by 3.1~16.5% relative.
Video-audio driven real-time facial animation

We present a real-time facial tracking and animation system based on a Kinect sensor with video and audio input. Our method requires no user-specific training and is robust to occlusions, large head rotations, and background noise. Given the color, depth and speech audio frames captured from an actor, our system first reconstructs 3D facial expressions and 3D mouth shapes from color and depth input with a multi-linear model. Concurrently a speaker-independent DNN acoustic model is applied to extract phoneme state posterior probabilities (PSPP) from the audio frames. After that, a lip motion regressor refines the 3D mouth shape based on both PSPP and expression weights of the 3D mouth shapes, as well as their confidences. Finally, the refined 3D mouth shape is combined with other parts of the 3D face to generate the final result. The whole process is fully automatic and executed in real time.
 The key component of our system is a data-driven regresor for modeling the correlation between speech data and mouth shapes. Based on a precaptured database of accurate 3D mouth shapes and associated speech audio from one speaker, the regressor jointly uses the input speech and visual features to refine the mouth shape of a new actor. We also present an improved DNN acoustic model. It not only preserves accuracy but also achieves real-time performance.
 Our method efficiently fuses visual and acoustic information for 3D facial performance capture. It generates more accurate 3D mouth motions than other approaches that are based on audio or video input only. It also supports video or audio only input for real-time facial animation. We evaluate the performance of our system with speech and facial expressions captured from different actors. Results demonstrate the efficiency and robustness of our method.
An EScheduler-Based Data Dependence Analysis and Task Scheduling for Parallel Circuit Simulation

The sparse matrix solver has become the bottleneck in a Simulation Program with Integrated Circuit Emphasis circuit simulator. It is difficult to parallelize the sparse matrix solver because of the high data dependence during the numerical LU factorization. In this brief, a parallel LU factorization algorithm is developed on shared-memory computers with multicore central processing units, based on KLU algorithms. An Elimination Scheduler (EScheduler) is proposed to represent the data dependence during the LU factorization. Based on the EScheduler, the parallel tasks are scheduled in two modes to achieve a high level of concurrence, i.e., cluster mode and pipeline mode . The experimental results on 26 circuit matrices reveal that the developed algorithm can achieve speedup of 1.18-4.55&times; (on geometric average), as compared with KLU, with 1-8 threads. The result analysis indicates that for different data dependence, different parallel strategies should be dynamically selected to obtain optimal performance.
A Practical Roadside Camera Calibration Method Based on Least Squares Optimization

In this paper, we propose a more practical and accurate method for calibrating the roadside camera used in traffic surveillance systems. Considering the characteristics of the traffic scenes, we propose a minimum calibration condition that consists of two vanishing points and a vanishing line, which can be easily satisfied in most traffic scenes. Based on the minimum calibration condition, we provide a calibration method to estimate camera intrinsic parameters and rotation angles, which employs least squares optimization instead of closed-form computation. Compared with the existing calibration methods, our method is suitable for more traffic scenes and is able to accurately determine more camera parameters including the principal point. By making full use of video information, multiple observations of the vanishing points are available from different objects. For more accurate calibration, we present a dynamic calibration method using these observations to correct camera parameters. As for the estimation of the camera translation vector, known lengths in the road or known heights above the road are exploited. The experimental results on synthetic data and real traffic images demonstrate the accuracy, robustness, and practicability of the proposed calibration method.
Nonparametric Neural Networks

Automatically determining the optimal size of a neural network for a given task without prior information currently requires an expensive global search and training many networks from scratch. In this paper, we address the problem of automatically finding a good network size during a single training cycle. We introduce nonparametric neural networks, a non-probabilistic framework for conducting optimization over all possible network sizes and prove its soundness when network growth is limited via an `p penalty. We train networks under this framework by continuously adding new units while eliminating redundant units via an `2 penalty. We employ a novel optimization algorithm, which we term “Adaptive Radial-Angular Gradient Descent” or AdaRad, and obtain promising results.
Real-time face detection and tracking on mobile phones for criminal detection

In this paper a criminal detection framework that could help policemen to recognize the face of a criminal or a suspect is proposed. The framework is a client-server video based face recognition surveillance in the real-time. The framework applies face detection and tracking using Android mobile devices at the client side and video based face recognition at the server side. This paper focuses on the development of the client side of the proposed framework, face detection and tracking using Android mobile devices. For the face detection stage, robust Viola-Jones algorithm that is not affected by illuminations is used. The face tracking stage is based on Optical Flow algorithm. Optical Flow is implemented in the proposed framework with two feature extraction methods, Fast Corner Features, and Regular Features. The proposed face detection and tracking is implemented using Android studio and OpenCV library, and tested using Sony Xperia Z2 Android 5.1 Lollipop Smartphone. Experiments show that face tracking using Optical Flow with Regular Features achieves a higher level of accuracy and efficiency than Optical Flow with Fast Corner Features.
Knowledge-driven understanding of images in comic books

Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way.
Discovering personally meaningful places: An interactive clustering approach

The discovery of a person's meaningful places involves obtaining the physical locations and their labels for a person's places that matter to his daily life and routines. This problem is driven by the requirements from emerging location-aware applications, which allow a user to pose queries and obtain information in reference to places, for example, “home”, “work” or “Northwest Health Club”. It is a challenge to map from physical locations to personally meaningful places due to a lack of understanding of what constitutes the real users' personally meaningful places. Previous work has explored algorithms to discover personal places from location data. However, we know of no systematic empirical evaluations of these algorithms, leaving designers of location-aware applications in the dark about their choices.
 Our work remedies this situation. We extended a clustering algorithm to discover places. We also defined a set of essential evaluation metrics and an interactive evaluation framework. We then conducted a large-scale experiment that collected real users' location data and personally meaningful places, and illustrated the utility of our evaluation framework. Our results establish a baseline that future work can measure itself against. They also demonstrate that that our algorithm discovers places with reasonable accuracy and outperforms the well-known K-Means clustering algorithm for place discovery. Finally, we provide evidence that shapes more complex than “points” are required to represent the full range of people's everyday places.
Tables as Semi-structured Knowledge for Question Answering

Question answering requires access to a knowledge base to check facts and reason about information. Knowledge in the form of natural language text is easy to acquire, but difficult for automated reasoning. Highly-structured knowledge bases can facilitate reasoning, but are difficult to acquire. In this paper we explore tables as a semi-structured formalism that provides a balanced compromise to this tradeoff. We first use the structure of tables to guide the construction of a dataset of over 9000 multiple-choice questions with rich alignment annotations, easily and efficiently via crowd-sourcing. We then use this annotated data to train a semistructured feature-driven model for question answering that uses tables as a knowledge base. In benchmark evaluations, we significantly outperform both a strong unstructured retrieval baseline and a highlystructured Markov Logic Network model.
Long Short-Term Memory Network for Remaining Useful Life estimation

Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a critical role in Prognostics and Health Management(PHM). Data driven approaches for RUL estimation use sensor data and operational data to estimate RUL. Traditional regression based approaches and recent Convolutional Neural Network (CNN) approach use features created from sliding windows to build models. However, sequence information is not fully considered in these approaches. Sequence learning models such as Hidden Markov Models (HMMs) and Recurrent Neural Networks (RNNs) have flaws when modeling sequence information. HMMs are limited to discrete hidden states and are known to have issues when modeling long-term dependencies in the data. RNNs also have issues with long-term dependencies. In this work, we propose a Long Short-Term Memory (LSTM) approach for RUL estimation, which can make full use of the sensor sequence information and expose hidden patterns within sensor data with multiple operating conditions, fault and degradation models. Extensive experiments using three widely adopted Prognostics and Health Management data sets show that LSTM for RUL estimation significantly outperforms traditional approaches for RUL estimation as well as Convolutional Neural Network (CNN).