Reviewerly/exHarmony huggingface dataset:
Visual Deep Learning-Based Mobile Robot Control: A Novel Weighted Fitness Function-Based Image Registration Model

The recent development of faster and more accurate deep learning models has enabled researchers to utilize the potential of deep learning in robotics. Convolutional neural networks used for the process of semantic segmentation are being applied to improve the traditional robotic tasks by adding an additional level of intelligence, through the execution of context-aware tasks. Having that in mind, visual servoing can now be performed in a completely new manner, by exploiting only semantic and geometric knowledge about the environment. To carry out visual servoing, the mathematical model of the error between the images generated at the current and the desired mobile robot pose (i.e. position and orientation) in the image space needs to be adequately defined. In this paper, we propose the novel mathematical model for the weighted fitness function evaluation, which is utilized for the image registration process within the visual servoing framework. By weighting the classes by their importance in the desired image, the convergence domain of the initial error in the visual servoing process can be greatly extended. The experimental evaluation is carried out on the mobile robot RAICO (Robot with Artificial Intelligence based COgnition), where it is shown that weighted fitness function enables more robust intelligent visual servoing systems with a lower possibility of failure, easier real-world implementation, and feasible object driven navigation.
Visual Analysis and Prediction of Teen Hammer Throw Athletes’ Performance Using Data Mining Techniques

Hammer throw is a difficult yet important event in track and field. However, in China, there exist some problems in improving teenagers’ hammer throw performance. In this paper, we conducted research to explore the factors related to hammer throw in order to tackle this problem. By utilizing athletes’ physical test data and FMS (Functional movement screen) data, we first visualized to explore and interpret factors related to hammer throw. Then several state-of-the-art regression models were performed to predict athletes’ scores. The experimental results indicate that hammer throw has high correlation with function movement screen and also has connection with weight and height. For hammer throw prediction, ELM model outperforms the other two models. The promising results will undoubtedly promote athletes’ performance and assist their coaches for better training programs.
Groups versus covers revisited: Structured pluralities and symmetric readings

A number of natural language constructions seem to provide access to structured pluralities — that is, pluralities of pluralities. A body of semantic work has debated how to model this additional structure and the extent to which it depends on pragmatics. In this article, after controlling for the distinction between ambiguity and underspecification, we present new data showing that structured pluralities are sometimes but not always available, depending on the form of the plural noun phrase used. We show that these results challenge two longstanding theories of plurality. We sketch two different ways to account for these data and describe some of the diverging predictions they make.
Results of an Independent Assessment of the Quality of the Conditions for the Implementation of Educational Activities by Educational Organizations of General, Secondary Vocational and Additional Education in 2020

The article presents the results of a study conducted in 2020, commissioned by the Ministry of Education of the Russian Federation, on an independent assessment of the quality of conditions for the implementation of educational activities by organizations carrying out educational activities in basic general education programs, educational programs of secondary vocational education, basic vocational training programs, additional general education programs.
A linkable Ring Signature Electronic Cash Scheme Based on Blockchain

Aiming at the problem of repetitive spending in the current blockchain e-cash transactions, a linkable ring signature scheme is proposed for blockchain. Based on the linkability of the signature, it can judge whether two signatures are generated by the same signer, which solves the double blossom problem in the e-cash transaction and ensures the anonymity of the transaction. In order to solve the problem of too long signature length in ring signature, the aggregate signature technology is introduced to generate short signature with fixed length. After security analysis, the scheme has anonymity and unforgeability, and can resist the at-tack of double blossom. Compared with the equivalent scheme, this scheme has lower storage cost and higher processing efficiency.
Analytical Literature Survey on Existing Task Allocation Techniques in Distributed System

Task allocation is an important activity to achieve the high performance in distributed computing systems. Various techniques and algorithms have been developed by various researchers in different scenarios using numerous mathematical models. This paper will discuss about various task allocation techniques, developed by various researchers and their performance. Paper will also present comparative analysis of the performance of all the techniques present in the literature.
Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing

Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.
WebEvo: Taming Web Application Evolution via Semantic Structure Change Detection

In order to prevent information retrieval (IR) and robotic process automation (RPA) tools from functioning improperly due to website evolution, it is important to develop web monitoring tools to monitor changes in a website and report them to the developers and testers. Existing monitoring tools commonly make use of DOM-tree based similarity and visual analysis between different versions of web pages. However, DOM-tree based similarity suffers are prone to false positives, since they cannot identify content-based changes (i.e., contents refreshed every time a web page is retrieved) and GUI widget evolution (e.g., moving a button). Such imprecision adversely affect IR tools or test scripts. To address this problem, we propose approach, WebEvo, that first performs DOM-based change detection, and then leverages historic pages to identify the regions that represent content-based changes, which can be safely ignored. Further, to identify refactoring changes that preserve semantics and appearances of GUI widgets, WebEvo adapts computer vision (CV) techniques to identify the mappings of the GUI widgets from the old web page to the new web page on an element-by-element basis. We evaluated WebEvo on 10 real-world websites from 8 popular categories to demonstrate the superiority of WebEvo over the existing work that relies on DOM-tree based detection or whole-page visual comparison.
Efficient correlation filter tracking with adaptive training sample update scheme

Visual tracking serves as a significant module in many applications. However, the heavy computation and low speed of many recent trackers restrict their applications in some computing power restricted scenarios. On the other hand, the simple update scheme of most correlation filter based trackers limits their robustness during target deformation and occlusion. In this paper, we explore the update scheme of correlation filter based trackers and propose an efficient and adaptive training sample update scheme. Training sample extracted in each frame is updated to the training set according to its distance between existing samples measured with difference hashing algorithm(DHA) or discarded according to tracking result reliability. Experiments on OTB-2015, Temple Color 128 and UAV123 demonstrate our tracker performs favourably against state-of-the-art trackers with light computation and runs over 100 fps on desktop computer with Intel i7-8700 CPU(3.2GHz).
An Efficient (<i>t</i>,<i>n</i>) Threshold Secret Image Sharing Scheme Based on Thien-Lin Secret Image Sharing

In a ( t , n ) threshold secret image sharing scheme (TSIS), a secret image is first encoded into n pieces, normally referred as shares or shadow images. Those share-images can be stored or transferred safely as individually a share-image cannot reveal any detail about the secret image. The receiver or user of the share-images referred as participants. If at least t (≤ n ) share-images are submitted by the participants, the secret image can be recomputed. One of the most popular secret image sharing schemes was proposed by Thien-Lin (2002). The authors had proposed two versions of the scheme. The first version of SIS by Thien-Lin is efficient, but lossy as the pixel values more than 250 (&lt; 256) are truncated to 250 . The second one is lossless, but it increases the size of the secret image during preprocessing. In this paper, we propose a secret image sharing scheme based on Thien-Lin scheme. We consider a little loss in the reconstructed image which is maximum one per pixel (the loss can be one to five in Thien-Lin scheme) and keep the size of the secret image same. Although buffer pixels may be added for the sake of programming, after reconstruction of the secret image the buffer pixels are easily identifiable and can be safely removed.
A New Modified Approach to Improve PSNR, NC and MSE via Invisible Embedding of Binary Information using HAAR Transformation

A new modified approach of digital watermarking using HAAR wavelet function is presented in this paper. An invisible watermark or message is embedded into original data for protection. At the user end image segmentation approach is used to separate the object of interest from message and cover. The message is inserted in watermark on choosing the odd square of LH and even square of HL band and proposed embedding rule set. A set of images, two of family members and 4 already existing are as used as cover image to improve PSNR, NC and MSE.
Geochemical provenance studies of basalt vessel preforms from the Iron Age workshop at Tel Hazor, Israel, and potential geological sources

Abstract The discovery of an Iron Age basalt vessel workshop at Tel Hazor revealed numerous discarded preforms in different production stages. Provenance analyses allow us to reconstruct the vessels' journey from the mining of raw material off site to production in the workshop. To determine the extraction sites, the geochemical compositions of the artifacts were compared to an extensive set of geochemical data on basaltic rocks in Israel. Combined with petrographic features and insights from field surveys on the relevant locations, the results show that the raw material was extracted most probably from two different locations, each several kilometres distant from Tel Hazor.
A Non-local Low-rank and Sparsity based Framework for Depth Map Inpainting

Depth is an important cue along with RGB data in many computer vision applications. Depth maps captured by most of the mainstream depth sensors are noisy with a lot of missing depth information causing annoying visual artifacts. In this work, we proposed a joint framework for color guided depth map inpainting. We exploit the redundancy of RGBD data by integrating non-local low-rank patch regularization along with local structural information in an optimization framework. The proposed non-local patch-based regularization prior in addition to a complementary local smoothness prior facilitate robust recovery of the sharp depth discontinuities and the missing depth information.
Use of MARC21 control field data in University Library OPACs in Karnataka: A study

This study discusses about importance of MARC21 control field and its effective implementation in university libraries OPACs in Karnataka. The catalogue records available through library web OPACs of universities under study formed the data source for this study. The university library web OPACs which we examined in this study have used Koha ILMS. One thousand and eighty-eight (1088) records were randomly selected from the nine university library web OPACs for the study. The study examined the proper implementation of 008 control field of MARC21, which is an important data element in information retrieval. The study found that average length of a cataloguing record was 1572.78 characters. The study as also revealed that libraries web OPACs under study have given scant attention for control fields, 001 and 003 were the two tag numbers that have been used extensively. Overall the university libraries have neglected control fields data element in their OPACs. This adversely affect the data retrieval from the user point of view. The study strongly recommend that libraries should fill up the data required for control fields while cataloguing their records.
Accuracy Comparison of Home Security Face Recognition Model in The Several Lighting Condition Using Some Kinect Produced Image

As technology advances, security systems and the development of artificial intelligence systems around the world continue to be developed. Artificial intelligence is very often used to automate parts of a system. In terms of data security and physical security, the use of artificial intelligence to be able to recognize a person's face plays a very important role in increasing the efficiency and management of knowledge. This paper was written with the aim of proposing a comprehensive comparison of accuracy, in recognizing faces using the features found on the Kinect camera (depth images, 3-dimensional images, and infrared images), compared to features found on ordinary cameras (RGB images), using techniques digital image processing in several different light conditions (dark room conditions and bright / standard room lighting). This research was conducted by comparing how reliably the facial recognition system classifies images that are part of the data class, as well as fake class images in the home security system. From this research, the Support Vector Machine (SVM) algorithm is used for classification, resulting in 20% better accuracy for images produced by Kinect cameras than for standard RGB cameras.
The Efficiency of Learning Methodology for Privacy Protection in Context-aware Environment during the COVID-19 Pandemic

When the COVID-19 coronavirus hit, the context-aware application users were willing to relax their context privacy preferences during the lockdown to cope their lives while staying home. Such disturbance in the privacy behavior affected the performance of Machine Learning (ML) algorithm that is trained on normal behavior. In this paper, we present the impact of the pandemic on the efficiency of the learning algorithm implementation of a privacy protection system. The system is composed of three modules, in this work we focus on Privacy Preferences Manager (PPM) module which is implemented using hybrid methodology based on a Statistical Model (SM) and Logistic Regression (LR) learning algorithm. The efficiency of the hybrid methodology is assessed using two real-world datasets collected prior and during the COVID-19 pandemic. The results show that the pandemic significantly impacted the efficiency of the hybrid methodology by 13.05% and 15.22% for the accuracy and F1 score respectively.
Detecting the Nuclei in Different Pictures Using Region Convolutional Neural Networks

Recognizing the cells’ nuclei at the initial stage is the most crucial process for most examinations. Reason for this being the fact that a human body comprises about 30 trillion cells and each of them encompasses a nucleus, which is brimming with DNA, the hereditary code that programs every cell. Recognizing nuclei enables researchers to distinguish every individual cell under investigation, and by estimating how cells respond to different medicines, the scientist can comprehend the fundamental biological procedures at work. Envision accelerating research for pretty much every infection, from lung malignant growth and coronary illness to uncommon disorders, by automating the detection of nuclei this vision can be achieved and we can speed up the entire process. We propose to use image segmentation technique to achieve this using Region convolution neural network algorithm. We will be improving this algorithm specifically for detecting microscopic nuclei in images which varies in size and modality. Our dataset contains large number of nuclei images which were gained under varied conditions such as different imaging methodology (brightfield versus fluorescence), type of cells and magnification level. Training data is selected in a way that maximizes algorithms ability to detect nuclei from these generalized images.
Research on embedded Braille learning machine based on STM32

At present, China has the largest number of blind people in the world. The low level of education for visually impaired people is mainly due to the insufficient popularity of Braille. In order to help visually impaired people and special education people learn Braille, this paper designs an embedded Braille learning machine based on STM32. The Braille learning machine uses GB2312 font library, and the Braille and Chinese characters are exchanged according to the structure of the Braille. The Braille dot matrix touch module is designed, and the Braille is displayed in the dot matrix touch module according to the learning content. The user completes the Braille learning through the touch of the dot matrix touch module. In the learning process, voice broadcast and display on the LCD screen can be carried out to assist users in learning. This learning machine Braille learning machine has a high cost performance ratio, with SD card external storage can also update the font at any time. This study can better serve society and bring convenience to the visually impaired.
On Black-Hole Numbers of Iteration of Digital Biquadratic Sum in Base 7, 11, 13 or 14

There are different periodic and fixed points under the iteration of biquadratic sum of digits of positive integer in different carry systems. There are one fixed point, one 3-circle, one 4-circle, one 6-circle, one 8-circle and one 12-circle in base 7; three fixed points, one 2-circle, one 7-circle and one 18-circle in base 11; two fixed pointsk, two 2-circles, one 5-circle, one 7-circle and one 11-circle in base 13; one fixed point, one 2-circle and one 13-circle in base 14.
An Algorithm for User Interest Modeling Using Facial Expression Recognition

research-article An Algorithm for User Interest Modeling Using Facial Expression Recognition Share on Authors: Zejun Huan Nanjing University of Science and Technology Nanjing University of Science and TechnologyView Profile , Yuan He Nanjing University of Science and Technology Nanjing University of Science and TechnologyView Profile , Fen Chen Nanjing University of Science and Technology Nanjing University of Science and TechnologyView Profile , Chengzhi Zhang Nanjing University of Science and Technology Nanjing University of Science and TechnologyView Profile Authors Info & Claims CONF-CDS 2021: The 2nd International Conference on Computing and Data ScienceJanuary 2021 Article No.: 137Pages 1–9https://doi.org/10.1145/3448734.3450867Published:28 January 2021 0citation9DownloadsMetricsTotal Citations0Total Downloads9Last 12 Months9Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Improving Event detection in Cricket Videos Using Audio Feature Analysis

This paper discusses an event detection approach based on audio, which proves to be effective when applied to the audio component of cricket television broadcaster’s video. In this approach, both crowed noise levels which is in the background and commentator voice which is in focus are considered correlated to key events in the time frame. Classifiers related to Hidden Markov Model (or HMM) are utilized as it is an efficient tool for modeling processes varying with time and broadly used in the area of speech recognition. This experiment done using cricket television broadcaster videos, was successful thus this method can be used for automatic indexing of audio (or video containing audio) for quick searching or segmentation.
Weakly supervised mitosis detection using ellipse label on attention Mask R-CNN

research-article Weakly supervised mitosis detection using ellipse label on attention Mask R-CNN Share on Authors: Xiaoxue Liu Chongqing University of Posts and Telecommunications Chongqing University of Posts and TelecommunicationsView Profile , Xinwei Li Chongqing University of Posts and Telecommunications Chongqing University of Posts and TelecommunicationsView Profile , Wei Zhang Chongqing University of Posts and Telecommunications Chongqing University of Posts and TelecommunicationsView Profile , Peng Ran Chongqing University of Posts and Telecommunications Chongqing University of Posts and TelecommunicationsView Profile , Bingyu Zhang Chongqing University of Posts and Telecommunications Chongqing University of Posts and TelecommunicationsView Profile , Zhangyong Li Chongqing University of Posts and Telecommunications Chongqing University of Posts and TelecommunicationsView Profile Authors Info & Claims CONF-CDS 2021: The 2nd International Conference on Computing and Data ScienceJanuary 2021 Article No.: 34Pages 1–7https://doi.org/10.1145/3448734.3450486Published:28 January 2021 0citation20DownloadsMetricsTotal Citations0Total Downloads20Last 12 Months20Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access