Reviewerly/exHarmony huggingface dataset:
Design a short-time backscanning profile for IRST application

Backscaning step and stare imaging is a popular method for widening the coverage of Electro-Optical/Infra-Red (EO/IR) system. Meanwhile the high fresh rate of thermal panoramic scene is a significant factor for the false alarm rate of system in early warning, the precise motion of steering optical component in this short time as some micro seconds is a challenge, especially in high disturbance environment. The synchronization between optical movement and the integrating point demands the strict time and fine motion for speed-up, back-can and fly-back process of fast steering mirror (FSM). The paper presents the high-order trajectory scanning profile design method for FSM, thereby optimizing the required performance of systems component such as voice coil motor and also improving the overall quality of fly-back process. Next, the linearization feedback controller and linear state observer are used to control the system in both tracking follow trajectory and rejecting disturbance. Finally, a prototype version of fast steering system is built to apply new controller algorithms. The simulation and experimental results show the good agreement that RMS error of Line of Sight (LOS) can be achieved smaller than one hundred micro radian during back-scan process
A Comparison of Learning Approaches to Dialogue Management in Conversational Systems

Dialogue systems have an increasingly higher number of applications and so their development is raising interest both in academic and industrial setting. Dialogue management is a key aspect for the development of these systems, as it is in charge of the decision making processes and the identification of the most appropriate responses to the user inputs. In this paper we compare different statistical techniques to train dialogue managers using generally available corpora at the disposal for the scientific community. Our results show that the use of generative models and in particular an intent classifier with Neural Networks and Seq2Seq using GRU cells attain the best accuracy for dialogue management.
The discrete system of Fibonacci functions for the constructions of a global automated system for monitoring objects littering the environment using remote sensing

The article explores the detection of unauthorized landfills based on remote sensing data. We have developed a mathematical approach that translates ordinary images to the 2-D manifold or 3-D manifolds in stereo images. At the same time, the posed task when developing algorithms is the transformation (convolution) of these manifolds into a one-dimensional sample. The developed method corresponds to the following two conditions: 1) preservation of the topological proximity of the elements of the original and expanded spaces, 2) preservation of correlations between the elements of the original and transformed spaces. The basic idea that underlies the mathematical support of the developed automated system is the concept of fractal sets. The concept of continuous orthogonal transformations, including the Fibonacci transform, is used as a mathematical basis for determining anomalous signal structures in the surrounding background. The problem of monitoring and decoding space images from the point of view of the synthesis of orthogonal systems with predetermined properties (speed of calculations, order of transformation, etc.) is presented. Examples of processing by the proposed algorithm are presented on the satellite images of the Moscow Region territories of the Russian Federation.
Deep Learning in Image Signal Processing for Minimal Method by Using Kernel DBN

The use of profound certification networks in advanced vision applications has the potential to be beneficial. Deep learning accelerator in-sensor is energy efficient. However, their adverse impact was severely underestimated by precision. The conventional vision pipeline undermines the accuracy of standard post-ISP datasets-trained machine learning algorithms. For example, in a car detection case, the off-the-shelf Faster RCNN algorithm’s detection accuracy is decreased by 59%. Our approach increases accuracy by 24–59% for the problem of vehicle recognition. Combine the kernel process with the deep conviction network by the researcher. It is an algorithm to preserve their advantages and compensate for their disadvantages, and add deep learning to the kernel to improve performance. The PolSAR Image Classification has recently been applied to a deep belief network (DBN) that can use several unmarked pixels in the image data model. Relative to the conventional edition Improve Image Signal Processing ISP, energy usage and reaction time 28% and 32%.
[Paper] Color Reproduction of Glass Dry Plate Scanned by a Linear Sensor Camera Using Filter Coefficient

A method for color reproduction of old glass dry plates digitized by a linear sensor camera is proposed. A high-resolution scanner based on linear sensor camera was developed for digitizing glass plates, which is beneficial for high-quality color reproduction. The color reproduction uses filter coefficient calculated from color information of multiband filters. An approach for the calculation of filter coefficient used for color reproduction is addressed in detail. The method was applied to the digitization and color reproduction of a wall painting belonging to World Cultural Heritage Horyuji Kondo, Japan. A verification experiment is conducted to evaluate the accuracy of color reproduction results. Experimental results show that the proposed method is capable of high-quality color reproduction of image details, and there is merit to using this high-resolution digitization techniques for cultural heritage.
Deep Learning-Enabled Low-light Image Enhancement In Maritime Video Surveillance

With the rapid development of artificial intelligence technology and computer vision technology, the maritime camera is gradually applied to monitoring the inland river bridge area. However, the monitoring images of inland river bridge area obtained under low light imaging conditions usually have the characteristics of low brightness, low contrast, and low resolution, which brings many restrictions on the supervision and management of inland river traffic. To further solve the problem of the poor imaging effect of the low light images, this paper proposes a low light image enhancement method based on deep learning. Four typical neural network structures (i.e., Attention-guided CNN, DnCNN, DenseNet, and Noise2Noise) are applied to mapping the low light image to the positive light image, and then an EN-Net (Enhancement Network) is used to balance and enhance the output results of these four networks, to get a final output image that has been splined and enhanced. EN-Net will adopt U-Net's basic structure and integrate the local residual and global residual modules to increase the diversity of features and speed up training. L1 norm is used as the loss function to improve the quality of enhanced images further. The experimental results show that the depth network proposed in this paper improves the brightness and contrast with the monitoring images of the inland river bridge area and further improves the monitoring effect of the inland river bridges area, thus providing a guarantee of water traffic safety in the bridge area to a certain extent.
Deep Unitary Convolutional Neural Networks

Deep neural networks can suffer from the exploding and vanishing activation problem, in which the networks fail to train properly because the neural signals either amplify or attenuate across the layers and become saturated. While other normalization methods aim to fix the stated problem, most of them have inference speed penalties in those applications that require running averages of the neural activations. Here we extend the unitary framework based on Lie algebra to neural networks of any dimensionalities, overcoming the major constraints of the prior arts that limit synaptic weights to be square matrices. Our proposed unitary convolutional neural networks deliver up to 32% faster inference speeds and up to 50% reduction in permanent hard disk space while maintaining competitive prediction accuracy.
Models and filtering of circular images with harmonic components of the covariance function

Nowadays, image processing problems are becoming increasingly important due to development of the aerospace Earth monitoring systems, medical devices etc. But the most of the image processing works deal with images defined on rectangular two-dimensional grids or grids of higher dimension. In some practical situations, images are set on a circle. The peculiarity of such images requires its consideration in their models. In the early works of the authors, several autoregressive models of such images were proposed. However, these models can generate images with positive covariation functions. Sometimes the impact of the external environment and internal features of the object lead to the presence of harmonic components. For example, facies, eye, virus, sunflower, etc. To represent such images, complex roots are necessary in characteristic equation of autoregressive model. In the present paper, autoregressive models of circular quasiperiodic images are considered. The expressions of their covariance functions are given. The pseudo-gradient algorithms for model identification, and images forecasting and filtering are proposed.
CONDUCTING THE MATHEMATICS OLYMPIAD IN THE CONDITIONS OF DISTANCE LEARNING

The paper presents the experience of conducting a student Olympiad in Mathematics using Internet testing technology as a part of the distance learning process.
Exploring loss functions for optimising the accuracy of Siamese neural networks in re-identification applications

Re-Identification (Re-ID) is becoming more and more common in today’s world, the need for more optimized algorithms also becomes more wanted. This is due to the importance of high accuracy as the consequences of an incorrect match can mean security issues, if used to gain access or result in incorrect findings in science due to wrong data. This paper explores enhancing the performance of Siamese Neural Networks by exploring the performance of loss functions to better suit the user’s Re-IDing needs. These loss functions are Triplet loss, Triplet Hard loss and Quadruplet loss. Results show that the Triplet hard loss function performs better than the two others. The functions were tested on a human dataset as well as on animal datasets.
Employee Engagement and Job Performance in Pasikuda Hotels: The Mediating Role of Creative Behavior of Employees

The hotel industry in Sri Lanka has been growing significantly in the past years. The need for creative behavior among the hotel employees is inevitable. Inducing creative behavior among the hotel employees is considered as very important strategy element in hotels. This study investigates the relationship between employee engagement and job performance and the mediating role of creative behavior of employees of selected Pasikuda hotels. This study is implemented with of three dimensions of employee engagement, namely vigor, dedication and absorption and job performance is as the dependent variable. Job performance is measured using two dimensions, task performance and contextual performance. Creative behavior functioned as a mediator between the two variables. Creative behavior included with two dimensions, namely idea exploration and idea generation. This study eliminates the empirical knowledge gap in the Pasikuda area regarding mediating role of creative behavior between employee engagement and job performance. Using questionnaire, the data were collected from 148 employees of 10 selected hotels in Pasikuda. The collected data were analyzed by using descriptive statistics, correlation, regression and mediating analyses. The questionnaire included established measures relating to employee engagement, job performance and creative behavior in addition to various demographic questions. The findings reveal that the employee engagement, creative behavior and job performance are in high level among the respondents. The analyses stated the strong positive relationship between the employee engagement and job performance, the strong positive relationship between the employee engagement and creative behavior and the strong positive relationship between job performance and creative behavior. Creative behavior has been found to cast a partial mediating effect on the relationship between employee engagement and job performance.&#x0D; Keywords: Employee Engagement (EE), Job Performance (JP), Creative Behavior (CB)
Fall Recognition in Open Scenes

Falls would cause harm to the fall-prone group, including the elderly, children and the disabled people. Fall behavior recognition is important to protect them from being injured. In order to improve the accurancy of the fall behavior recognition, a two-stream neural network model based on MobileNetV2, a lightweight deep neural network, is proposed in this paper. Experiments are conducted on the following three datasets, UR fall detection dataset, Multiple cameras fall dataset and Le2i fall detection dataset. The performances of the presented model are compared with those of single-stream model, 3D-CNN, and two-stream model combining CNN and optical stream. The effectiveness of the proposed method is indicated.
Applying Extreme Gradient Boosting for Surface EMG Based Sign Language Recognition

Sign languages are used as a non-verbal form of communication that helps people to exchange information. Various sign language recognition systems have been developed to assist healthy and differently-abled people in understanding and conveying messages using sign languages. These recognition systems are mostly developed using computer vision-based algorithms, which have a limitation that their efficiency depends heavily upon surrounding lighting conditions. On the other hand, surface Electromyography is least affected by surrounding lightning conditions, so can be used as an alternative to computer vision based methods. In this paper, we have focused on surface Electromyography signal’s ability to build a reliable sign language recognition system. We have applied boosting based algorithm to build an accurate and reliable classifier to recognize American Sign Language (ASL) using surface Electromyography signals. For, this a new data-set was made by collecting surface Electromyography signals from ten adult-subjects. The classification model was trained using Extreme gradient boosting (XGBoost) algorithm and we obtain an accuracy of 99.09%.
Static and Dynamic Hand Gesture Recognition for Indian Sign Language

Sign language recognition offers better types of assistance to the hard of hearing as it avoids the gap of communication between the deaf and mutes and the remaining people in the society. Hand signals, the essential mode of communication via gestures correspondence, plays a critical part in improving communication through gestures. Approaches for image detection, analysis and classification are available in glut, but the distinction between such approaches continues to be esoteric. It is essential that proper distinctions between such techniques should be interpreted and they should be analyzed. Standard Indian Sign Language (ISL) images of a person’s hand photographed under several different environmental conditions are taken as the dataset. In this work, the system has been designed and developed which can recognize gestures in front of a web camera. The main aim is to acknowledge and classify hand gestures to their correct which means with the most accuracy doable. A unique approach for same has been planned and a few different wide standard models have compared with it. The novel model is made using canny edge detection, dilation, threshold and ORB. The preprocessed information is passed through many classifiers to draw effective results. The accuracy of the new models has been found considerably higher than the prevailing model.
Domestic Waste Sorting System Based on Deep Learning

In this paper, an automatic garbage sorting robot system based on deep learning is designed and implemented. Firstly, a garbage classification network is designed to learn relevant garbage features, and then a garbage target detection network is constructed by introducing attention mechanism and local feature fusion. The domestic garbage image of “Huawei cloud Cup” 2020 Shenzhen open data application innovation competition is used as the dataset of target detection. The experimental results show that the target detection system can accurately and quickly identify the domestic waste objects in the image, and realize the capture successfully.
Automatic Generation of High-Performance Inference Kernels for Graph Neural Networks on Multi-Core Systems

Graph neural networks are powerful in learning from high-dimensional graph-structured data, for which a number of frameworks such as DGL and Pytorch-geometrics have been developed to facilitate the construction, training, and deployment of such models. Unfortunately, existing systems underperform when inferring on huge graph data on multi-core CPUs. Furthermore, traditional graph processing systems are struggling with complexity issues due to their low-level programming interfaces. In this paper, we present a new compiler-based software framework Gin optimized for graph neural network inference, which offers a user-friendly interface, via an intuitive programming model, for defining graph neural network models. Gin builds high-level dataflow graphs as intermediate representations, which are transformed into highly efficient codes and then compiled into binary inference kernels. Our evaluation shows that Gin significantly accelerates the inference on billion-edge graphs, beating three state-of-the-art solutions i.e., DGL, Tensorflow, and Pytorch-geometrics, by 31.44×on average, with much higher CPU and memory bandwidth utilization. In addition, Gin is able to achieve considerable speedup (up to 7.6×) over traditional graph processing system Ligra.
Improved Gradient Vector Flow over Manifold for Image Segmentation Using Active Contours

In this article, an improved gradient vector on manifold (IGVFOM) model was proposed for active contours. In this proposed IGVFOM model, two components and gradient of image coupled together during the diffusion by generalizing the Laplacian operator from flat space to manifold. Since coupling terms could fuse information of image during diffusion, the IGVFOM snake behaves better than GVF snake on weak edge preserving, noise robustness, objects separation, narrow and deep concavity convergence while keep advantages in terms of capture range enlarging and initialization insensitivity. Experiments on synthetic and real images manifest these properties of the IGVFOM snake.
Sketching for Digital Human Work

This chapter summarizes and concludes on HWID as a form of socio-technical HCI design under the heading ‘Sketching for digital human work’. It lists the main insights in one display. These include that theorizing for socio-technical HCI should avoid the technological determinism trap, that the HWID platform is a solution to avoid this, and that it should be used as a multi-sided platform, open for interpretation and change, to design and valorize local theory relevant to the local community. Furthermore, it includes four types of socio-technical relation artefacts associated with psychological need finding, socio-technical ideation sketching, socio-technical hypothesis prototyping, and action and design interventions. These insights should be particularly useful to any HCI researcher interested in action-oriented approaches to HCI in organizations and work settings. With the HWID platform, they should be able to involve most stakeholder groups in theorizing about socio-technical HCI and do this in a context and culturally sensitive way. Furthermore, the HWID approach supports consultancy and policy work of importance to many people. The ideal is that workers will be the designers of their own (work) world. Hence, in contrast to some work design methodologies, sketching for human work with the HWID platform is an open and accessible approach.
Color Texture Image Segmentation Using Histogram-Based CV Model Driven by Local Contrast Pattern

This paper proposes a novel texture descriptor called local contrast pattern (LCP) to drive a histogram-based Chan-Vese(CV) model for color texture image segmentation. The LCP has two features: differential contrast and orientation. The former measures the variation of local intensity and the latter extracts the texture orientation information. In order to enhance the localization, a truncated Gaussian kernel function is also incorporated that determines a positive correlation between weight and distance. Then, a novel histogram-based CV model is established which is guided by a combination of the LCP feature maps and the kernel to obtain color texture segmentation. The effectiveness of the LCP descriptor in color texture segmentation was prove to be true by many single variable validation experiments. Comparisons with many color-texture image segmentation models demonstrate that our proposed model can not only successfully partition all types of images but also bear strong robustness for illumination, noise and curve initialization.
Study on the Rain Removal Algorithm of Single Image

In this paper, we propose a new method to remove raindrop and rain streak from a single image. We know that raindrop and rain streak on the image can severely hamper the visibility of a background scene and degrade an image considerably. By analysing the rainy image, we conclude that raindrop and the rain streak are the nature noise, at the same time, we find that compared to the background scene, the spectrum of the raindrops and rain streaks are mainly the high-frequency component, especially to the rain streaks. So we propose a new method to remove raindrop and rain streak from a single image. First we use the median filter to filter the salt and pepper noise in the nature noise. Secondly, we use a group of low-pass filters to filter the high-frequency component of the image. Thus, we remove a lot of raindrops and rain streaks on the image, but after filtering, there is something like mist or fog on the image, then we use the haze removal method to remove them. Finally, we use the sharpening technology to show off details of the image. In the end, we use our method to experiments, we find the result is significantly, so we can conclude our approach is effectiveness and quantitatively.
Linear Optimal Filter for Descriptor Systems With Time-Correlated Measurement Noise

This paper is concerned with the linear optimal filtering problem for descriptor systems with correlated process and measurement noises. The process noise is a one-step auto-correlated noise and the measurement noise described by a first order Gauss-Markov process is time-correlated. Using a canonical form of descriptor and measurement differencing, the linear optimal filter in the linear minimum variance (LMV) sense is designed based on an innovation analysis method. It is computed by the solutions of some auto- and cross-covariance matrices. The simulation example verifies the effectiveness of the proposed algorithm.