Reviewerly/exHarmony huggingface dataset:
Improvement of Three Days Course with Student Surveys using Google® Forms - A Case Study of "Introduction to Environmental Science" in Hokkaido University

To conduct and implement a cycle of quality assurance in the short-termed lecture, we need to get hold of students' status quickly. This study reports introducing Google® forms, which enable to get hold of students' status in real time, into the course for three days composed of six modules with more than 100 students. Pre-/Post-surveys of the entire course and end-of-module student survey clarified that percentage of students who gave good self-evaluation to their team discussions had increased gradually with modules and that detailed students' responses to each slide used in teacher's explanations served improving quality of the specific slide. As a part of cycle of quality assurance, we can also give students a supplemental comment in the next module, based on the students' answers to the end-of-module student survey.
Improved Adaptive Bilateral Filter

Bilateral filtering is a non-linear filtering method that combines spatial proximity and pixel similarity of an image. It is good to preserve the high-frequency details in the image and smooth the number while preserving the strong edges. The traditional bilateral filtering algorithm needs to preset the spatial standard deviation and the gray scale deviation according to experience, the versatility is poor, the algorithm operation time is long and the effect is not ideal. Therefore, we proposed an adaptive bilateral filtering algorithm based on genetic algorithm. However, since the bilateral filtering preserves too much high-frequency information during denoising, the filtering effect on high-frequency noise is not ideal. Therefore, based on the bilateral filtering algorithm combined with wavelet threshold decomposition and least squares filtering method, an adaptive bilateral filtering algorithm based on wavelet threshold is proposed. Firstly, the low-frequency and high-frequency coefficients of the image are obtained by wavelet decomposition. Then the low-frequency part is processed by the adaptive bilateral filtering algorithm based on the genetic algorithm, and the least-squares filtering method is used to process the high-frequency part in this paper. The experimental results show that the proposed method has a good effect on the removal of random stationary noise.
A Method to Velocity Measurement of Clouds Based on Genetic Algorithm Threshold Segmentation

Aiming at the problem of cloud coverage over the heliostats field of solar power tower plant, a velocity measurement method for predicting the moving cloud over the heliostats field is proposed. The image of the moving cloud layer is taken by the camera, which is treated by a series of image processing methods such as threshold segmentation, morphological operation and rectangular contour marking to obtain the central coordinate of the moving target. The speed and direction of the moving cloud layer are gotten according to the coordinates of the acquired center point through the calculation formula. The experimental results show that the method is effective with higher accuracy and less calculation, providing an effective and reasonable application scheme for solar thermal power generation technology.
Research on the Construction Method of Chinese - Vietnamese Parallel Corpus

The Chinese-Vietnameseparallel corpus is the basic research problem in the fields of natural language processing. The traditional methods use the DOM tree or element anchors in HTML extract parallel sentences with low accuracy and slow alignment speed. Therefore, this paper proposes a new Web-based Chinese-Vietnamese parallel corpus construction scheme. The scheme will determine the parallel web page through the LDA (Latent Dirichlet Allocation) and Gibbs Sampling. And the BeautifulSoup and regular expression will be used to crawl the webpage text and clean the corpus. The DOM tree and the element anchors in HTML are used to optimize the extraction of parallel sentence pairs. Combined with the sentence length and Champollion algorithm, the dynamic programming algorithm is adopted to improve the correct rate and recall rate of sentence alignment. The program successfully established a million-level Chinese-Vietnamese parallel corpus.
Variational Bayesian Adaptive Embedded Cubature Kalman Filter Algorithm for Initial Alignment of SINS with Uncertain Observations

In the paper, a new variational bayesian based adaptive embedded cubature Kalman filter (VB-AECKF) for joint estimation of the dynamic state and measurement noise is proposed under a swing base. By constructing fully symmetric embedded integration rules for multidimensional integrals with a Gaussian weight function, a fifth-degree AECKF based filter with the estimated free parameter of ECKF based on maximum likelihood criterion is introduced in the initial alignment. The results of turntable experiment show that the proposed filter has better robustness to resist the uncertainties of measurement noise. What's more, the alignment accuracy and convergence rate of the VB- AECKF is much better than that of CKF.
Errors Detection Mechanism in Big Data

The rapid growth of information technologies has not been exempt from errors in the transmission and storage of data, which has affected the services, offered to customers on big data platforms. In these platforms, it has been particularly difficult to identify the causes given their complex nature, the large size and the growth of the data set. The detection and correction of errors in the processing of information in big data platforms are essential to guarantee the integrity of the stored data and thus give users confidence that the information is correctly stored without alterations. However, the options that exist to detect errors of this type lack efficiency in the methods used or use third party software that does not always allow determining the cause of the errors. In this research, big data analysis models are used to find bottlenecks of data processing management. Further, it is developed an alternative error detection mechanism that is based on analyzing error-log file with a variation of Fuzzy clustering mechanism. This approach, based on text mining, helps to look for error frequency and entity that had caused it. Moreover, it is shown how the errors that are presented are distributed according to the source.
A QoS Value Prediction for Web Services Using Ethical User Identification

When you use a web service, Our primary Consideration is the assurance we can gain by using the web service. Therefore the user should select the relevant web services, which can be easily managed. The Quality Of Service (QoS) value of web services determines the quality services up to a limit. QoS relates to multiple web service quality elements such as response time, throughput, packet loss etc. Assessing QoS have become a significant feature in building ethical service application. In our approach we first checks for missing fields in the dataset and by different levels of calculation we predict the missing values and fills the dataset and produces the output. It is evaluated based on QoS values given by the ethical users, who are identified by calculating the Prominence value of users. The system attempts to relegate the impact of unreliable feedbacks provided by dishonest users which affects the accuracy of QoS prediction. The predicted results proved to be accurate as the error rate is less, when the result is tested with Mean Absolute Error (MAE).
Quick-look methods

Quick-look methods, by definition, allow the log analyst to find useful petrophysical formation properties without having to make any numerical calculations. This chapter summarizes several such methods as they were used in the pre-digital era. Many are based on visual comparison of analog curves on a paper log print. Some of these methods are still in use today, others have been forgotten but are worthy of being documented here because they will show up in the files of the pre-digital archives. Compatible overlays of analog curves mirror the philosophy of taking ratios to eliminate unknowns and were made use of widely in times gone by—but they still have their place today.
End-to-end Physical Layer Optimization Scheme Based on Deep Learning Autoencoder

5G large-scale commercial enterprises will provide high-speed and low-latency communication services, but it also challenges optimization of system. In particular, the conventional block-by-block design communication model has less interpretability and increases the complexity of physical layer optimization. Fortunately, deep learning has an inherent advantage in structured information representation and data extraction. Therefore, in this paper, based on deep learning autoencoder scheme is proposed to perform end-to-end physical layer optimization. Firstly, a two-stage training mode is proposed to improve the generalization of the neural network. Secondly, in order to decrease the system overhead caused by channel state information (CSI) feedback, utilizing autoencoder to reconstruct CSI by its compression feature. The simulation results show that the phased training can effectively improve the convergence rate, the compression and quantization of CSI alleviates the system loads.
Robust Edge Extraction for Detecting Discontinuities

Edge extraction process is affected when it is exposed to noise. Edges play a vital role in obtaining stability in image processing. Various edge detection operators determine confined gray scale variations among the neighboring pixels. Since noisy pixels occur even after the final edge extraction process, edge detection is prone to suffer from degradation. Hence the edge detection operator must be developed such that it has the capability to counteract the occurrence of noise. The detection operator uses Gaussian filter for performing filtering, and then gradient is computed in horizontal and vertical direction. Morphological operation involves dilation and erosion. Hysteresis thresholding and non-maximum suppression is performed to attain a better edge. The obtained result reveals that it attains better performance when corrupted by noise.
Enhancement of Micro-texture Images Using Bi-histogram Equalization Based on Arcsine Distribution

Histogram equalization is the most used among enhancement methods. However, over enhancement is a major issue and shifts mean brightness of images, which creates an unnatural look. In this paper, proposed a method which controls over major issues of histogram equalization to enhance micro texture image by splitting each color channel histogram into two sub histograms based on mean as the threshold and replacing their cumulative distribution with arcsine distribution. Proposed a method tested on color micro texture images collected from IICBU Biological Image Repository. Experimental results shows that the proposed method performs better than other state-of-the-art methods.
Blind Color Image Denoising Using Low Rank Matrix Minimization

This paper presents a simple approach for reducing the Additive White Gaussian Noise (AWGN) on noisy color image. The proposed approach exploits the effectiveness of Low Rank Matrix Minimization (LRMM) technique for suppressing the occurred noise. However, the LRMM has a slight limitation on the image denoising process. It requires the prior knowledge about the noise level. To overcome this aforementioned problem, we develop a simple noise level estimation for color image using the Singular Value Decomposition (SVD). Our proposed method performs blind image denoising task over LRMM setting and SVD-based estimated noise level. As documented in the experimental section, the proposed method yields a promising result for the blind image denoising task.
Multi-UAV Multi-target Tracking in the Presence of Doppler Blind Zone

Generalized Covariance Intersection (GCI) is state-of-the-art distributed fusion method, however, its fusion accuracy is affected by low detection probability targets. The presence of the inherent Doppler blind zone (DBZ) of the airborne radar seriously affects the detection probability of the targets as well as the performance of distributed multi-target tracking (DMTT). The traditional GCI fusion tends to remain only the targets that detected by all nodes. For tracking the targets hidden in the DBZ, based on multi-model (MM) Gaussian mixture cardinalized probability hypothesis density (GM-CPHD) filter, an efficient fusion strategy is proposed. Numerical experiment clearly shows the effectiveness of the proposed fusion algorithm.
Software platform for analysis of data in the field of waste management

This article is dedicated modern problem of soft poor in recycling in Russia. In our time we have a big problem with ecology and need automatization process of exchange waste. In first time it will be an account in which every people may be write information about own waste, which he wanted utilizationing. In net, another client matter may see this information and may be -it want buy these waste, for business, for example. So, we need analyzed all program, which take part in performance , Russian plants, because every day produced hundred tons of waste. About this soft, and another analogical program discussing in this article.
Efficient algorithm for object detection using saliency feature in machine vision systems

Object detection and recognition is the important application of the machine vision systems. Saliency is modern modes which find the visible items from rushed scenes in daily routines. But mania forecast model in tangled surrounding is harsh to put into action. Contour based segmentation technique is now used in extracting these structures from scenes. But some objects cannot be easily identified with this method. In this paper, a novel technique is presented for the segmentation of the salient objects from both complex and simple scenes. The technique search the local and global information of object features in a random manner. Multilevel thresholding with YCbCr color space is used to segment the salient objects from the image. The depth of the salient object is also estimated to categorize the objects within the scenes. Texture analysis is also done to This scheme is successfully tested with MSRA, ECSSD dataset and acquired an accuracy of 94%.This algorithm is also tested in the detection of traffic sign board and in the tracking of human.
The Feeling of Presence

Immersion, as a concept, has been used for centuries not merely for enhancing the entertainment experience, but as a powerful mechanism to distract from reality. The purpose of this literature review is to present different approaches, definitions, main applications and features that induce immersion. We found a dichotomy between views of immersion (system-centered approach versus human-centered approach) and focused the discussion on immersive systems embracing games, applications, technology, and virtual reality. We proposed a didactic explanation of our point of view of immersion and listed the main benefits of its utilization. This theme is relevant for future research of a 3D interactive framework development. We intend to prove a relation between immersion and the enhancement of a 3D design review process.
Translation line UAV inspection Image quality assessment based on Salient edge Blur Metric

The recent development of UAV in translation line inspection leads to more efficient inspection than human. The image quality of UAV is an import rule to judge the UAV inspection effectiveness. A global and local features fusion based method is presented for UAV image quality evaluation in this paper. For the evaluation of overall quality of image, it combines sharpness index in frequency domain and the distribution density and orientation features of edge lines in space domain to judge the existence of the camera shake blur. And the average width of salient edges is analyzed to determine the blur degree of local details in image. Finally, this evaluation method is verified effectively through many inspection images. It shows its effectiveness and robustness under various complex background and different contents of the image, and the evaluation result is consistent with 5 standards of subjective evaluation. So the method will be suitable to the intelligent recognition and analysis.
Actived Edge Strength for Image Quality Assessment

These years has witnessed the success of deep learning methods in computer vision. The approximation capabilities of neural networks is partly responsible for these success, and active function is crucial for the approximation capability. Motivated by the success of deep learning, this paper presents an Actived Edge Strength Similarity (AESSIM) based image quality assessment algorithm. Numerical experiments on the public datasets indicates that AESSIM is quite competitive in assessing performance.
Earthing Electronic Art

In this short paper I offer a richly illustrated ecocritical review of a prize-winning* art exhibition called 'Arte Eletrônica Indígena' [Indigenous Electronic Art] that took place in Salvador da Bahia in August 2018. The exhibition was the fruit of a project run by the NGO Thydêwá to host artistic residencies in a number of different indigenous communities in the North East of Brazil, bringing together an international line-up of artists with interested community members to collaborate in the creation of works of electronic art. My review of the exhibition concentrates on the degree to which the works exhibited engage with the indigenous communities' relationship to the natural environment, and the different ways in which they do this, ranging from more didactic approaches to more poetic ones. Overall, I argue for the important role performed by this project and the resultant collection of artworks in 'earthing' electronic art and enhancing its social function for a wider cross-section of society.
Technology of acquiring statistical information in library information systems and open archives

The author reviews the experience of RNPLS&amp;T in developing the Open Archive and in particular, in building the system of statistical information acquiring and analysis. The author emphasizes that the RNPLS&amp;T’s main goal is to increase the standards of user library and information services, which is accomplished through building, maintaining, analyzing its collections, databases and databanks, including its e-catalogs, specialized topical dayabases and periodical databases in sciences. For this purpose, the system of acquiring and analyzing statistical information is being built. This system, at a new level, enables to acquire a different segment of data essential for further optimization of information systems and for user services, on the whole. The new technologies enable to keep track of user flows, efficient order processing, and demand for certain subjects and individual publications. The author concludes that this makes a powerful and universal instrument and technology for analyzing the RNPLS&amp;T’s technologies and information systems and forecasting their development with actual information on user behavior.
Triplet Deep Hashing with Joint Supervised Loss for Fast Image Retrieval

In recent years, the emerging hashing techniques have been successful in large-scale image retrieval. Due to its strong learning ability, deep hashing has become one of the most promising solutions and achieved good results in practice. However, existing deep hashing methods had some limitations, for example, most methods consider only one kind of supervised loss, which leads to insufficient utilization of supervised information. To address this issue, we proposed a Triplet Deep Hashing method with Joint supervised Loss based on convolution neural network (JLTDH) in this work. The proposed JLTDH method combine triplet likelihood loss and linear classification loss, moreover, the triplet supervised label is adopted, which contains richer supervised information than that of pointwise and pairwise label. At the same time, in order to overcome the cubic increase in the number of triplets and make triplet training more effective, we adopt a novel triplet selection method. The whole process is divided into two stages, in the first stage, taking the triplets generated by the triplet selection method as the input of CNN, the three CNNs with shared weights are used for image feature learning, the last layer of the network outputs a preliminary hash code. In the second stage, relying on the hash code of the first stage and the joint loss function, the neural network model is further optimized so that the generated hash code has higher query precision. We perform extensive experiments on three public benchmark datasets CIFAR-10, NUS-WIDE, and MS-COCO. Experimental results demonstrate that the proposed method outperforms the compared methods, the method is also superior to all previous deep hashing methods based on triplet label.
Mirage: A Protocol for Decentralized and Secured Communication of IoT Devices

Internet of Things (IoT) is rapidly emerging as the manifestation of the networked society vision. But its centralized architecture will lead to a single point of failure. On the other hand, it will be difficult to handle communications in the near future considering the rapid growth of IoT devices. Along with its popularity, IoT suffers from a lot of vulnerabilities, which IoT developers are constantly working to mitigate. This paper proposes a new protocol called Mirage which can be used for secure and decentralized communication of IoT devices. This protocol is built based on security principles. Out of which Mirage mainly focuses on authentication, integrity, and non-repudiation. In this protocol, devices are authenticated via secret keys known only to the parties involved in the communication. These secret keys are not static and will be constantly changing for every communication. For ensuring integrity, an intermediary is asked to exchange the hash of the messages. As the intermediary nodes are lending their computing and networking powers, they should be rewarded. To ensure non-repudiation, instead of going for trusted third parties, blockchain technology is used. Every node in the network needs to spend a mirage token for sending a message. Mirage tokens will be provided only to those nodes, who help in exchanging the hashes as a reward. In the end, a decentralized network of IoT devices is formed where every node contribute to the security of the network.
Self-tuning Incremental Kalman Filter under Poor Observation Condition

Under poor observation condition, a single-sensor self-tuning incremental Kalman filter is proposed for the incremental system with unknown model parameters, and its convergence is proved by the dynamic error system analysis (DESA) method. As an application in signal processing, three algorithms for estimating the parameters of autoregressive (AR) signal model based on incremental model are proposed for AR signals with white noise. Based on this, a self-tuning incremental Kalman filter for AR signals is proposed. A simulation example proves its effectiveness and feasibility.
Quasi-optimal Fusion Incremental Kalman Filter with Correlated Noises

In this paper, the local incremental Kalman filter with correlated noises is firstly presented, which solves the state estimation problem for the systems with unknown measurement errors. Furthermore, a quasi-optimal fusion incremental Kalman filter with correlated noises is proposed. They effectively solve the state estimation problem for the multi-sensor systems under poor observation condition. The proposed algorithms have the advantages of simple form and small computational burden so that they can be easily applied in engineering practice. Two simulation examples show the effectiveness and feasibility of these algorithms.