Reviewerly/exHarmony huggingface dataset:
3-D Mesh Watermarking Based on Optimized Multiple Histograms

In this paper, we discuss a 3-D mesh watermarking scheme with optimized multiple histograms. Watermarking algorithms exist as one of technologies used to protect a copyright of digital data. The conventional methods choose embedded bins in the histogram without accounting for the number of elements, and lead to decrease a robustness or increase a visual change.Our method introduces multiple histograms from a host 3-D mesh for selecting bins with a smaller number of elements in the multiple histograms. In the proposed approach, we utilize a cylindrical coordinate system instead of the Cartesian coordinate to generate two histograms. This modification reduces the total distance of moved vertices by watermark embedding. As a results, the proposed watermarking gives a superior visual quality to other previous watermarking with a single histogram.
Design and Development of a Hexacopter for the Search and Rescue of a Lost Drone

Search and rescue with an autonomous robot is an attractive and challenging task within the research community. This paper presents the development of an autonomous hexacopter that is designed for ...
Basic Review of Different Strategies for Classification of Sentiment Analysis in Social Networks

The growth of different online networks such as MySpace, Twitter, LinkedIn, and Face book has been increased in recent years, high amount of data outsource via social media into data sources. This huge amount of data analyzed for research on different types of real-time applications. So that analysis of sentiment and mining user opinion is one of aggressive concepts to explore meaning of outsourced data. While different types of approaches are implemented to identifying sentiment and opinion in social networks like pattern-based classification with respect to parts of speech, emotions, and batch model learning while analyzing huge amount of data. So that in this paper description of different machine learning methodologies to describe utilizes sentiment of huge amount data in social networks. We give survey of different approaches with respect to sentiment exploration from online social network. Also describe comparative analysis of different methods used for analysis of sentiment and mining of user opinion in online social networks.
Los desafíos que enfrenta la educación virtual en Colombia

For several years the world has been evolving in its way of perceiving and doing things according to all the changes that are taking place, changes that are determining a trend of progress or reinvestment to continue advancing. Its impact is so strong because it unfolds in any type of organization, forcing in many scenarios to be attentive to the opportunities of those external variables that require a dynamic movement so as not to be left behind. Education is no stranger to all those constant events and variables that the environment goes through and that resembles a differential need in today's people. It is an emerging component of the new learning model that must be developed using new teaching tools that diversify the autonomous and responsible knowledge of each individual. Although it is not a radical step in the new education, it should be understood that virtual education refers to a pedagogical model connected to smart devices that help and facilitate education in any environment other than a classroom. For this reason, it is necessary to understand the possible events or discrepancies that may be detected in the educational framework, teaching, participation, self-learning and the willingness to enter this new methodological process of knowledge.
Gesture-Based Interaction of Humans with Dual Arm Robot

In the near future, it is expected that robot programming methods will be more intuitive and user-friendly. Currently, the industrial robot programming is mainly based on the use of teach pendants and offline programming tools. The way of editing a program is currently not user friendly and it mainly requires an expert to find the robot position to edit and replace it, without considering higher level details. This chapter sets out a method for simplifying industrial robot programming by using visual sensors that detect human gestures. A vocabulary of movements of body and hand is described, allowing the robot to move in various directions. For the transformation between human and robot motions, an external controller application is used. A decoder application is developed on the robot side to translate human messages into robot motions. The system is incorporated into an open Robot Operating System (ROS)-based communication architecture, allowing easy extensibility with new functionalities. An automotive industry case study demonstrated the method, including the di-rectification of a dual arm robot for single and bi-manual motions.
Error Attention Interactive Segmentation of Medical Image Through Matting and Fusion

Deep learning-based interactive segmentation has attracted research interest recently since it can smartly utilize user interactions to refine a coarse automatic segmentation to get higher accuracy for clinical use. Current methods usually transform user clicks to geodesic distance hint maps as guidance, then concatenate them with the raw image and coarse segmentation, and feed them into a refinement network. Such methods are insufficient in refining error region, which is a key capability required for interactive segmentation. In this paper, we propose Error Attention Interactive network with Matting and Fusion to auto-extract guide information of mis-segmentation region from two branches and transfer it into main segmentor. We first design Region Matting to obtain foreground and background mattings from coarse segmentation. And then we adopt the features extracted by two branches trained on above mattings as guidance. Attention-Fusion is further proposed to transfer the guidance to main segmentor effectively based on attention mechanism and feature concatenation. Experimental results on BraTS 2015 and our Neuroblastoma datasets have shown that our method significantly outperforms state-of-the-art methods, with the advantage of fewer interactions.
A Method for Evaluating the Level of Shooting that Attenuates the Effect of Small Arms on Accuracy

This paper is based on the related theory of probability theory and mathematical statistics through the study of small arms shooting accuracy, fire intensity test, and characterization methods and establishes the relation model among shooting performance, small arms accuracy, and shooting level. The shooting performance data were incorporated into the calculation process of shooting accuracy and shooting intensity of small arms, and then the shooting intensity of small arms was used to evaluate the stability level of the shooter in vertical and horizontal directions. The shooting accuracy of small arms is used to modify the shooting performance data so as to form a shooting level evaluation method that can reduce the influence of the accuracy of small arms from the shooting performance data.
Online Parameter-Free Learning of Multiple Low Variance Tasks.

We propose a method to learn a common bias vector for a growing sequence of low-variance tasks. Unlike state-of-the-art approaches, our method does not require tuning any hyper-parameter. Our approach is presented in the non-statistical setting and can be of two variants. The one updates the bias after each datapoint, the one updates the bias only at the end of each task. We derive an across-tasks regret bound for the method. When compared to state-of-the-art approaches, the aggressive variant returns faster rates, the lazy one recovers standard rates, but with no need of tuning hyper-parameters. We then adapt the methods to the statistical setting: the aggressive variant becomes a multi-task learning method, the lazy one a meta-learning method. Experiments confirm the effectiveness of our methods in practice.
Overview of Autonomous Unmanned Systems

Since the beginning of the 21st century, research in the intelligent systems had gained tremendous popularity as various technologies such as the micro-electronics technology, the intelligent sensor technology, the advanced intelligent control technology and other information technology, especially with the recent breakthrough in the field of deep learning, neural network and other artificial intelligence, had seen incredible advancement. It is worthwhile to note that the development of the intelligent technology is closely related to the current low-cost and high-speed data computing and storage capabilities. For instance, the network communication technology that had been applied in the military, industrial, medical and other relevant fields, had proven outstanding performance which result in the increased trust, and hence, attention on the intelligent unmanned systems. It can be predicted that the intelligent unmanned systems will become the commanding point of science and technology in the near future and an indicator of a country’s scientific and technological international status.
Bounding the expected run-time of nonconvex optimization with early stopping.

This work examines the convergence of stochastic gradient-based optimization algorithms that use early stopping based on a validation function. The form of early stopping we consider is that optimization terminates when the norm of the gradient of a validation function falls below a threshold. We derive conditions that guarantee this stopping rule is well-defined, and provide bounds on the expected number of iterations and gradient evaluations needed to meet this criterion. The guarantee accounts for the distance between the training and validation sets, measured with the Wasserstein distance. We develop the approach in the general setting of a first-order optimization algorithm, with possibly biased update directions subject to a geometric drift condition. We then derive bounds on the expected running time for early stopping variants of several algorithms, including stochastic gradient descent (SGD), decentralized SGD (DSGD), and the stochastic variance reduced gradient (SVRG) algorithm. Finally, we consider the generalization properties of the iterate returned by early stopping.
Integrating less represented countries into the mainstream of European geosciences

&amp;lt;p&amp;gt;In order to build a diverse, inclusive community of geoscientists within Europe, a statistical study is carried out based on participation statistics of different conferences in Europe over the past five years. Data of geoscience conferences (EGU, IUGG), planetary (EPSC) and solar-terrestrial science conferences (ESWW, ESPM) are investigated. Special focus is given to the historical division between Eastern and Western Europe and senior and junior scientists. The aim is to show that the geographical division continues to exist and does not show a general improving trend, while the position of the younger generation seems to improve. Some &amp;amp;#8220;success cases&amp;amp;#8221; defying the usual trend are shown and analysed in detail. We suggest some reasons behind the statistics and draw some lessons that can help integrating less represented researchers into the mainstream of European geosciences.&amp;lt;/p&amp;gt;
Student’s folk music band: technology of group interpretation of musical composition

The article deals with the problem of collaboration and group interaction during the production of a musical composition. The terms “music band”, “pedagogic technology” and “interpretation” are revealed. Based on author’s experience, the technological dimensions in the activity of student’s folk music band are shown. These dimensions are aimed at performance of musical compositions during collective interactions. According to this problem the four main stages are market out, such as familiarization with a musical composition, technical mastering, realization of artistic content and music performance. It is stressed, that performance interpretation covers all possibilities of musician. Thus for the sake of realization owns ideas it is necessary to have professional skills. Among them we point out creative thinking, performance skills, including ensemble mastering, possibilities, specifically good ear for music, and psychological stability, i.e. the high level of self-regulation during public performances. The main part of the article shows theoretical grounds, methodological recommendation and practical advice as for formation of collective thinking and common masterly performance. It is stressed, that necessary condition of good activity of student group is implementation of methods and tasks directed at development of creative individuality of every folk music band member, improvement of masterly performance and increasing of psychological stability during the performances. It is pointed out that team work during permanent cooperation creates band performance style and makes the conditions for generalization of masterly performance and development of creative potential, intuition and artistic feeling.
LibGuides: Return to Library 2021: Booking a Study Seat

This guide provides information for library users on returning to the library in the academic year 2020/2021.
Research on the method of cross-cluster migration of bank’s massive valuable data

This paper starts with the problems faced by the big data platform of a bank, and takes a large commercial bank as an example, analyzes the urgent security management problems that bank needs to solve, clarifies the tasks that different types of Hive tables need to be migrated across clusters, puts forward the methods of seamless migration of banking data across clusters and components, and then focuses on data migration across clusters. Finally, it summarizes the implementation process from the implementation effect and impact, and introduces a tool to improve the efficiency of data synchronization, which paves the way for the technical upgrading of commercial banks.
Research on Precision Evaluation Model of College English Teaching Based on Big Data

With the continuous development of the big data era, the process of global economic integrationhas been pushed forward, and the demand for contemporary talents has also changed from a single type to acomprehensive one. Employers are paying more and more attention to the students' comprehensive Englishproficiency and practical ability. It can be seen that colleges and universities must pay more attention toEnglish teaching in order to output more comprehensive talents for the society, and how to improve Englishsubject accomplishment is one of the problems that college English teachers must be consider. At present,due to the one-sided English teaching in many colleges and universities, it is very difficult for students toimprove their interest in the learning process. However, once their interest drops, it will directly lead to thedecline of teaching quality and learning effect. Therefore, this paper analyzes the problems and causes ofcollege students' lack of interest in English, and by analyzing the key points of college English teaching precision assessment, based on the development background of the big data era, this paper discusses how toconstruct the college English teaching precision assessment. Some effective suggestions and strategies areput forward.
Mean Field Theory for Deep Dropout Networks: Digging up Gradient Backpropagation Deeply.

In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural network structures, including CNNs, RNNs, Residual networks, and Batch normalization. Inevitably, recent work has also covered the use of dropout. The mean field theory shows that the existence of depth scales that limit the maximum depth of signal propagation and gradient backpropagation. However, the gradient backpropagation is derived under the gradient independence assumption that weights used during feed forward are drawn independently from the ones used in backpropagation. This is not how neural networks are trained in a real setting. Instead, the same weights used in a feed-forward step needs to be carried over to its corresponding backpropagation. Using this realistic condition, we perform theoretical computation on linear dropout networks and a series of experiments on dropout networks. Our empirical results show an interesting phenomenon that the length gradients can backpropagate for a single input and a pair of inputs are governed by the same depth scale. Besides, we study the relationship between variance and mean of statistical metrics of the gradient and shown an emergence of universality. Finally, we investigate the maximum trainable length for deep dropout networks through a series of experiments using MNIST and CIFAR10 and provide a more precise empirical formula that describes the trainable length than original work.
Reasoning the necessity of study basis of computer design by future bachelors in computer sciences

The article is devoted of the value of graphic competence as an important component of the professional formation of future bachelor's in computer science taking into account modern social processes.
The narrative program

The narrative program (NP), developed by Greimas, is an abstract formula used to represent an action. Doing (action) is defined as a temporal succession from one state to the opposite state, effected by any agent (S1: the subject of doing). A state may be broken down into a subject of state (S2) and an object of state (O) and the junction between them, which is either a conjunction (n: the subject with the object) or a disjunction (u: the subject without the object). The long formula of the narrative program is: NP = F {S1 → [(S2 u O) → (S2 n O)]} (the conjunctive NP) or NP = F {S1 → [(S2 n O) → (S2 u O)]} (the disjunctive NP). For example, in the fable “The crow and the fox”, we have: NP = F {Fox → [(Fox u Cheese) → (Fox n Cheese)]}. The abbreviated formula, which is the most commonly used, gives only the second state: NP = F {S1 → (S2 n O)} (the conjunctive NP) or NP = F {S1 → (S2 u O)} (the disjunctive NP). This would give us, for example: NP = F {Fox → (Fox n Cheese)}. An array of NPs is composed of at least two NPs between which at least one temporal relation (succession, simultaneity) or one presential relation (presupposition, mutual exclusion, etc.) is described. For example, the previous NP describing appropriation by the Fox and the (short form) NP of dispossession, NP = F {Fox → (Crow u Cheese)}, presuppose each other reciprocally, in that one does not exist without the other; the temporal relation is simultaneity.
The Effects of Cracks and Fluids on Post-Seismic Healing

&amp;lt;p&amp;gt;It is now well established that large seismic events change the surrounding velocities, and that these velocities slowly recover over time. &amp;amp;#160;Precisely which mechanisms control the recovery process are less well understood. &amp;amp;#160;We present the results of laboratory experiments to better characterise what properties of the underlying material control the recovery process. &amp;amp;#160;We do this by mixing two waves, one which perturbs the velocity of the sample (as an earthquake does in field data) and one which senses the change in velocity (as in changing noise correlations). &amp;amp;#160;This is an inherently nonlinear experiment as we mix two waves and measure the effects of this wave mixing. &amp;amp;#160;Within our experiments, we vary the properties of the samples to understand which are most important in controlling the nonlinear response. &amp;amp;#160;We focus on two mechanisms. &amp;amp;#160;The first is fractures and how changes in fracture properties change the nonlinear response. &amp;amp;#160;The second is fluids, in particular the effect of low saturations on the nonlinear response. &amp;amp;#160;By changing the fluids and fractures we can turn on and off the nonlinear mechanism, helping us to move toward a better understanding of the underlying mechanisms of these wave-wave interactions.&amp;lt;/p&amp;gt;
SE(3) multimotion estimation through occlusion

Visual motion estimation is an integral and well- studied challenge in autonomous navigation. It is significantly more challenging in highly dynamic environments with multiple moving objects. This paper introduces an approach to this multimotion estimation problem capable of estimating the full SE(3) trajectory of every motion in a scene, even when motions become occluded. The Multimotion Visual Odometry (MVO) pipeline employs multilabeling techniques and continuous motion models to estimate all motions simultaneously, including the camera egomotion. Motion closure is used to recognize when trajectories become unoccluded, and the motion models are used to interpolate the occluded estimates. The estimation performance of the pipeline is demonstrated on real-world trajectory data from the Oxford Multimotion Dataset.
LibGuides: Percussion: ALLIES

This is a guide for students and faculty of percussion. This page will tell you how to request a book from another library.
Automated Detection of Cervical Pre-Cancerous Lesions Using Regional-Based Convolutional Neural Network.

The Cervical Colposcopy image is an image of woman’s cervix taken with a digital colposcope after application of acetic acid. The captured cervical images must be understood for diagnosis, prognosis and treatment planning of the anomalies. This Cervix image understanding is generally performed by skilled medical professionals. However, the scarcity of human medical experts and the fatigue and rough estimate procedures involved with them limit the effectiveness of image understanding performed by skilled medical professionals. This paper, the model uses Regional Based Convolutional Neural Network (R-CNN) to effectively visualize of pre-cancerous lesions and to aid in diagnosis of the disease. The model was trained, on a dataset comprising of 10,383 cervical images samples. The datasets were derived from public dataset repositories. The training samples comprised of type class 1, 2 and 3 traits of cervical precancerous traits. The performance was evaluated using K-nearest -neighbor model over R-CNN. With an accuracy rate of 86%, this approach heralds a promising development in the detection of cervical precancerous lesions. This study findings established that the proposed model in provision of the better accuracy and misclassifications performance than various testing algorithms.