Reviewerly/exHarmony huggingface dataset:
Rainfall Threshold of Citarum River Flood for Early Warning on Urban Area

Abstract Floods in Bandung basin located in the upper Citarum river resulted in huge losses because they occurred in industrial areas and densely populated settlements. Disaster risk reduction can be done with flood early warning in residential areas. The purpose of this study is to determine the rainfall threshold that causes inundation in residential areas in seven districts located on the floodplain of the Citarum river. The method used is the analysis of the rainfall-runoff model to obtain the Citarum river unit hydrograph. Flows in the rivers are assumed to be one-dimensional unsteady flow whereas overflow is assumed to be overland flow. Threshold rainfall is determined based on the deconvolution method, with the drainage coefficient calculated based on the land use map. The results of the study are the capacity discharge and the load discharge of the Citarum river in the seven sub-districts, which the most upstream is the Majalaya sub-district and the downstream is Katapang sub-district. The lowest threshold rainfall has occurred in the sub-district Bojongsoang that is 54.34 mm that produced the flood water level as high as 662 m MSL. and the evacuation time for early warning is 5.5 hours.
Quantum entanglement criterion based on extended complex plane

Abstract The state space of quantum system is a Hilbert space with complex inner product. The two states quantum system is called a qubit. Its quantum state gives different geometric representations. The density matrix of a quantum state can describe all the information of a quantum system. In particular, the representation of quantum pure states by means of extended complex plane is given. On this basis, combined with the matrix representation of the quantum states of the composite system, the criteria for quantum entanglement and separability are studied. Finally, the measurement method of quantum pure state entanglement is given.
Multiple-manifold Generation with an Ensemble GAN and Learned Noise Prior

Generative adversarial networks (GANs) learn to map samples from a noise distribution to a chosen data distribution. Recent work has demonstrated that GANs are consequently sensitive to, and limited by, the shape of the noise distribution. For example, for a single generator to map continuous noise (e.g. a uniform distribution) to discontinuous output (e.g. separate Gaussians), it must generate off-manifold points in the discontinuous region with nonzero probability. While existing applications generally ignore these outliers, they nevertheless represent a hindrance to accurate modeling in current frameworks. We address this problem by learning to generate from multiple networks such that the generator’s output is an ensemble of distinct sub-generators. We contribute a novel formulation of multi-generator models where we learn a prior over the generators conditioned on the noise, parameterized by another neural network. Thus, this network not only learns the optimal rate to sample from each generator but also optimally shapes the noise received by each generator. The resulting Noise Prior GAN (NPGAN) achieves flexibility that surpasses both single generator models and previous multi-generator models even when the total number of parameters in the ensemble is the same as the single-generator models.
PU Learning for RPN Generalization

Region proposal is an important part of the two-stage object detection. RPNs pay more attention to the object within dataset, which has poor generalization and cannot generate objects that is unseen in the dataset. The traditional method does not rely on data but the performance is poor. Our work use the two-step method of PU learning to improve the generalization of RPN. In the first step, we identify reliable negative samples to train the basic RPN network. Then we consider rotation to extract more efficient pseudo-labels. Finally we use co-training to improve robustness about noise from pseudo-labeled. On Lvis dataset as an extension of COCO with more categories, the AR100 increase by 4%, and AR1000 can increase by 3%, which proves the reliability of the algorithm.
PERFORMANCE EVALUATION OF SELF-QUOTIENT IMAGE METHODS

Lighting Normalization is an especially important issue in the image recognitions systems since different illumination conditions can significantly change the recognition results, and the lighting normalization allows minimizing negative effects of various illumination conditions. In this paper, we are evaluating the recognition performance of several lighting normalization methods based on the Self-Quotient ImagE(SQI) method introduced by Haitao Wang, Stan Z. Li, Yangsheng Wang, and Jianjun Zhang. For evaluation, we chose the original implementation and the most perspective latest modifications of the original SQI method, including the Gabor Quotient ImagE(GQI) method introduced by Sanun Srisuk and Amnart Petpon in 2008, and the Fast Self-Quotient ImagE(FSQI) method and its modifications proposed by authors in previous works. We are proposing an evaluation framework which uses the Cropped Extended Yale Face Database B, which allows showing the difference of the recognition results for different illumination conditions. Also, we are testing all results using two classifiers: Nearest Neighbor Classifier and Linear Support Vector Classifier. This approach allows us not only to calculate recognition accuracy for each method and select the best method but also show the importance of the proper choice of the classification method, which can have a significant influence on recognition results. We were able to show the significant decreasing of recognition accuracy for un-processed (RAW) images with increasing the angle between the lighting source and the normal to the object. From the other side, our experiments had shown the almost uniform distribution of the recognition accuracy for images processed by lighting normalization methods based on the SQI method. Another showed but expected result represented in this paper is the increasing of the recognition accuracy with the increasing of the filter kernel size. However, the large filter kernel sizes are much more computationally expensive and can produce negative effects on output images. Also, we were shown in our experiments, that the second modification of the FSQI method, called FSQI3, is better almost in all cases for all filter kernel sizes, especially, if we use Linear Support Vector Classifier for classification.
Geomagnetic Navigation with Adaptive Search Space for AUV based on Deep Double-Q-Network

In order to solve the problem that the AUV uses geomagnetic navigation for multiple round trips between the two places without a geomagnetic database, a geomagnetic navigation algorithm with adaptive search space for AUV based on Deep Double-Q-Network is proposed. The algorithm takes multiple geomagnetic parameters as input and uses the DDQN reinforcement learning algorithm to predict the heading angle. In addition, a boundary function is constructed to limit the search space of the AUV, so that the AUV can search the target point faster. Under the effect of the DDQN navigation algorithm, as the number of AUV round trips increases, the number of search steps gradually decreases. Finally, AUV reaches the target position with fewer steps. Simulation experiments prove the effectiveness of the algorithm.
Design of Reversible JPEG Image Compression Scheme for Data Hiding

This paper presents a new Steganography technique, In this paper, we propose a new high-capacity reversible data hiding method for JPEG-compressed images. This method is based on modifying the quantization table and quantized discrete cosine transformation (DCT) coefficients. Some elements of the quantization table are divided by an integer while the corresponding quantized DCT coefficients are multiplied by the same integer and added by an adjustment value to make space for embedding the data. By analyzing the effect of each single quantized DCT coefficient on the image quality, an embedding sequence is chosen to help control the increase of file size after hiding the data meanwhile the PSNR value between the original uncompressed image and stego JPEG image is high. Experimental results show that the proposed method achieves both high capacity and high image quality. The simulations are taken for the MATLAB standard images of Lena, baboon, Barbara, Jet, peppers, with images size of 512x512 size, and the data hidden is of 1 Kb. The PSNR observe between 36 to 38 for those images and that is significantly better than base works.
Performance and Consistency Analysis for Distributed Deep Learning Applications

Accelerating the training of Deep Neural Network (DNN) models is very important for successfully using deep learning techniques in fields like computer vision and speech recognition. Distributed frameworks help to speed up the training process for large DNN models and datasets. Plenty of works have been done to improve model accuracy and training efficiency, based on mathematical analysis of computations in the Con-volutional Neural Networks (CNN). However, to run distributed deep learning applications in the real world, users and developers need to consider the impacts of system resource distribution. In this work, we deploy a real distributed deep learning cluster with multiple virtual machines. We conduct an in-depth analysis to understand the impacts of system configurations, distribution typologies, and application parameters, on the latency and correctness of the distributed deep learning applications. We analyze the performance diversity under different model consistency and data parallelism by profiling run-time system utilization and tracking application activities. Based on our observations and analysis, we develop design guidelines for accelerating distributed deep-learning training on virtualized environments.
Quantifiers in Justification Logic

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Viability of Azure IoT Hub for Processing High Velocity Large Scale IoT Data

We utilize the Clemson supercomputer to generate a massive workload for testing the performance of Microsoft Azure IoT Hub. The workload emulates sensor data from a large manufacturing facility. We study the effects of message frequency, distribution, and size on round-trip latency for different IoT Hub configurations. Significant variation in latency occurs when the system exceeds IoT Hub specifications. The results are predictable and well-behaved for a well-engineered system and can meet soft real-time deadlines.
Power System Fault Diagnosis Technology Based on IGWO Algorithm

In order to improve the accuracy and optimization efficiency of analog, an improved classifier based on (igwo) algorithm is proposed by introducing nonlinear convergence factor, dynamic weight and boundary mutation strategy. The fault samples are collected in the saln key band-pass filter and four op amp double quadratic high pass filter circuit, and the fault samples are extracted by wavelet packet feature; then, the extracted samples are divided into training samples and test samples, and the igwo algorithm in SVM, and the optimal SVM classifier model is obtained, Compared with the SVM classifier optimized by other algorithms, the results show that igwo SVM classifier can prevent the population from falling into local optimum, and the convergence speed has been greatly improved.
A Hybird Learning-based Framework for Estimating Teaching Ability of Ideological and Political Courses in Modern Chinese University

This paper proposes a hybrid learning-based two-stage framework consisting of an RNNs-based architecture for subjective textual sentiment analysis and a deep decision forest for teaching ability estimation. According to the annotated self-collected dataset and SnowNLP corpus, the connectivity of the subjective items' answers is enhanced to be fed into fixed-sized RNNs with LSTM units for distribution of soft labels. The outputted labels are merged with the objective items' answers to form the learnable vectors for the deep global decision forest framework. We use the top-5 features' variance to weigh the random forests in each level of the cascade forest structure for a fitness voting result. On the self-collected dataset, the F1 score of 0.974 and the CPM score of 0.925 have reached, which are prior to those of the compared methods.
Does individual stereo acuity affect performance using stereo 3D in a helmet mounted display?

A pilot’s Helmet-Mounted Display (HMD) is now a critical part of the aircraft system. Next generation HMDs will be able to display information and imagery binocularly, with stereoscopic depth. Stereo 3D (S3D) can potentially be used to enhance situational awareness and improve performance. The degree to which performance is improved may be linked to individual visual capabilities of the user, in particular, stereo acuity. Stereo acuity varies tremendously in the general population with up to 30% being classed as ‘stereo blind’. For most military aviators there is a minimum stereo acuity standard, however current test methods are crude and fallible. Many previous S3D studies do not accurately characterize individual stereo acuity, and in some cases do not even screen for its presence, making their results difficult to interpret. The Operational Based Vision Assessment (OBVA) laboratory has developed a flight simulation platform using an SA Photonics SA-62 HMD to display stereoscopic symbology and five 85-inch displays to provide the “out the window” view. After completing a battery of vision tests, participants fly various mission profiles while responding to a combination of navigational instructions and warning alerts displayed in the HMD. The warning alerts are displayed in 2D (flashing at 1 Hz), intermittent S3D (flashing on and off at depth), persistent S3D (alternating between 2 depth planes), and dynamic S3D (motion in depth). We present preliminary data examining whether a stereoscopic HMD could be used to improve performance when responding to a critical warning alert, and discuss potential implications for military aviator vision standards as well as HMD requirements
A Multi-angle Improved Small Sample Clustering Algorithm

Abstract The random selection of initial clustering centers, outliers, and the differences between attributes will affect the clustering effect of k-means. This article first uses the elbow method to determine the number of categories and then uses the square difference radius method to select the cluster seed center to optimize the cluster center’s reselection. Finally, the entropy method is used to calculate the difference between attributes. The results show that when the number of categories remains the same and abnormal data is added, the improved clustering algorithm from multiple perspectives is more accurate and stable for small sample data with small dimensions and large differences between categories.
A System to Detect the Faces in Unconstrained Environment

We proposed work on face detection using Normalized Pixel Difference (NPD). Here we used histogram of Gradients features. The extracted features are invariant and able to reconstruct the original image. In this work we shows that using NPD function it is good enough to detect the faces easily and scaled well. In addition we also used Principal component analysis as reduction method. Further experimentation is conducted on public datasets. Results are encouraging.
Analysis of Emotion Recognition with Gesture Analysis Through the Machine Learning and Fuzzy Concepts

Human facial expressions play a very important role to develop any interactive system, especially in artificial intelligence and computer vision field. Human-computer interaction (HCI) based applications are working on the same issues. HCI applications nowadays play a very major, interesting as well as challenging role in the present scenario of the world. To develop such a system where it can understand human expressions, emotions, intentions, body language with the help of gesture and posture will benefit greatly in the society. Being a human, many times we do not get actual intension of other person. So, for a machine, it is a big challenge. Since many approaches, techniques are developed to recognize and predict human emotions. So, in this regard, this paper presents a method to identify human emotions through hand gesture. This work uses the upper part of the body. This combined approach is based on the machine learning and fuzzy logic concepts.
Evaluation of a Gait Analysis Tool Using Posture Estimation Technology in Clinical Rehabilitation

This paper describes the development of a gait analysis tool and its usefulness in clinical rehabilitation. The developed gait analysis tool generates a video of the skeletal image superimposed on the gait video, obtained using OpenPose, a posture estimation technology. The therapist can provide the most appropriate treatment based on the video and analysis obtained using the tool. The experimental result showed that this tool could detect gait abnormalities in patients and must be useful in clinical rehabilitation. The problem with gait analysis based on visual inspection is that the individual clinician makes a diagnostic judgment based on subjectivity. However, since this tool can detect abnormalities in gait, it will provide adequate medical treatment to patients and promote a rapid return to society.
Inversion Within Information Architecture: A Journey into the Micro–Meso–Macro–Meta

The chapter discusses the impact of increasing amounts of information; the limitations of micro- and macro-models; the benefits of the levels of analysis framework introducing additional levels to manage information complexity; inversion as a mechanism to leverage complexity.
Real-Time Estimation of Parameter Maps

System parameters might have a distinct operating point dependency that is unknown. Nonlinear state observers or Kalman Filters can be applied to estimate such parameters in real-time, revealing the unknown parameter value in the vicinity of the current operating point. Commonly, these methods are prone to forget the revealed dependence continuously when a different operating point is approached. This paper provides a procedure to preserve past estimates and reveal the hidden parameter map during operation of the system. Parameter dependencies are approximated via adjustable interpolants. In particular, ready-to-use formulae for piecewise linear and cubic Hermite interpolants are provided. An existing approach as well as a newly derived approach to embed these interpolants within an Unscented Kalman Filter are presented and discussed. While the first approach utilizes the parameter map estimation directly within the Kalman Filter scheme, the new approach expands the Kalman Filter steps by a recursive map adaption scheme and is thereby far less computationally expensive. Both methods are compared and validated via numerical simulations, where a superior performance is achieved compared to the standard parameter estimation within the Kalman Filter approach.
Adversarial Attack Algorithm based on Saliency Detection

In the development process of today's artificial intelligence system, many text, voice and image recognition systems have been born. In the process of development and application, it has been found that the artificial intelligence system will suffer from classification errors and other problems after adding a small disturbance to the recognized objects. We call such objects with small perturbations adversarial examples, and the process of generating adversarial examples is called adversarial attacks. At present, there are FGSM, I-FGSM and MI-FGSM adversarial attacks algorithms for image. These algorithms are able to carry out global disturbance on image examples. Although they have good adversarial attack effect, in real life, the adversarial examples is often used to cause the failure of artificial intelligence system with smaller local disturbance. Therefore, we hope to study the influence of small-scale disturbance on artificial intelligence system. In this paper, an adversarial algorithm based on saliency detection, LC-MIFGSM, is proposed to maintain the adversarial attack effect and at the same time compress the adversarial attack range, improve the attack concealment, and make the generation of adversarial attack examples more oriented to real scenes.
Image Dehazing by Image Enhancement and Multi-scale Laplacian Pyramid Fusion

When atmospheric particles, such as dust, smoke or water droplets, accumulate in the air, a haze phenomenon occurs. The inclement weather conditions diminish the visibility, reduce the contrast and change the color of an image. The poor visibility may cause a serious problem to various computer vision applications such as object recognition in surveillance systems, transportation systems, etc. Therefore, image dehazing is an essential task, used to remove the influence of weather factors from the image. In this paper, a novel single image dehazing method is introduced which improves the visibility and contrast of the hazy image. To achieve a haze-free image, the original hazy image is processed through basic image enhancement-based operations such as log transformation, inverse log transformation and guided filtering. The resulting set of multi-exposure images is then merged into a single high-quality haze-free image through a multi-scale Laplacian fusion. The experimental evaluation is performed on a large set of challenging hazy images in terms of both qualitative and quantitative parameters. The results obtained through the proposed fusion-based method not only remove the haze effectively but also able to highlight the micro details of the objects in the image.
The Application of Japanese Electronic Media in Japanese Learning

With the development of internet technology, language learning had become different from the past. A variety of electronic media are used as a means of learning Japanese. This paper makes a questionnaire survey of Japanese majors in a university in China, and clarifies the students’ application of Japanese electronic media, and analyzes the problems faced by students. This paper makes suggestions on how to use Japanese electronic media correctly to help to learn Japanese. Using learners’ love of Japanese electronic media, stimulating their motivation to learn Japanese, making full use of online resources, encouraging learners to use time outside the classroom to learn scientifically and effectively is our research topic.
Computing Touch-Point Ambiguity on Mobile Touchscreens for Modeling Target Selection Times

Finger-Fitts law (FFitts law) is a model to predict touch-pointing times, modified from Fitts' law. It considers the absolute touch-point precision, or a finger tremor factor σa, to decrease the admissible target area and thus increase the task difficulty. Among choices such as running an independent task or performing parameter optimization, there is no consensus on the best methodology to measure σa. This inconsistency could be detrimental to HCI studies such as pointing technique evaluations and user group comparisons. By integrating the results of our 1D and 2D touch-pointing experiments and reanalyses of previous studies' data, we examined the advantages and disadvantages of each approach to compute σa. We found that the parameter optimization method is a suboptimal choice for predicting the performance.
Das polyphone Spiel mit dem Signifikanten: Klangrepräsentationen und Bildevokationen bei Raymond Queneau

No AccessDas polyphone Spiel mit dem Signifikanten: Klangrepräsentationen und Bildevokationen bei Raymond QueneauIsabel ZollnaIsabel ZollnaSearch for more papers by this authorhttps://doi.org/10.14220/9783737009904.97SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book cover 1. AuflageISBN: 978-3-8471-0990-7 eISBN: 978-3-7370-0990-4HistoryPublished online:April 2021 PDF download