Reviewerly/exHarmony huggingface dataset:
Analysis and research of news gathering and editing process based on data Mining

In recent years, with the continuous development and progress of China's society, the speed of social data storage is gradually improving, while the types of data are also more and more. With the advent of the era of big data, these data will bring huge economic benefits. More and more enterprises adopt data mining technology to screen the data from production, sales and other links, so as to help enterprise managers to make decisions. In this paper, how to make use of the era of big data to mine and analyze data, so as to change the way of news analysis and research, and in order to better meet the needs of people to obtain information, the news business is reformed under the background of big data. In this paper, data mining technology is applied in news gathering and editing business, and data analysis is carried out on news business process, news productivity, communication influence and other contents according to the news business process. At the same time, the efficiency of news communication and the work efficiency of the news collection and editing department are improved to improve the news communication influence. This paper mainly studies the working principle of news data mining and the data analysis method in the process of news gathering and editing business process.
Towards Universal Adversarial Examples and Defenses

Adversarial examples have recently exposed the severe vulnerability of neural network models. However, most of the existing attacks require some form of target model information (i.e., weights/model inquiry/architecture) to improve the efficacy of the attack. We leverage the information-theoretic connections between robust learning and generalized rate-distortion theory to formulate a universal adversarial example (UAE) generation algorithm. Our algorithm trains an offline adversarial generator to minimize the mutual information between the label and perturbed data. At the inference phase, our UAE method can efficiently generate effective adversarial examples without high computation cost. These adversarial examples in turn allow for developing universal defenses through adversarial training. Our experiments demonstrate promising gains in improving the training efficiency of conventional adversarial training.
Enabling Robot-assisted Motion Capture with Human Scale Tracking Optimization

Motion tracking systems with viewpoint concerns or whose marker data include unreliable states have proven difficult to use despite many impactful benefits. We propose a technique inspired by active vision and using a customized hill-climbing approach to control a robot-sensor setup and apply it to a magnetic induction system capable of occlusion-free motion tracking. Our solution reduces the impact of displacement and orientation issues for markers which inherently present a dead-angle range that disturbs usability and accuracy. The resulting interface is successful in stabilizing previously unexploitable data while preventing sub-optimal states for up to hundreds of occurrences per recording and featuring an approximate 40% decrease in tracking error.
Human Action Recognition as part of a Natural Machine Operation Framework

The reliability of systems that use machine learning to recognize the human working in an industrial environment is of high importance for the employee safety. we present a framework which is capable of recognizing the person's natural interaction with an industrial machine. We focus on the application of human action recognition in the context of machine operation by skilled workers in industrial or commercial environments. We propose a framework that includes action recognition as part of a software component for understanding behavior. For our use case, we defined an exemplary machine operation workflow which we use to compare five different neural networks in terms of prediction accuracy and real-time capabilities. Moreover, we compare different input shapes as the resolution of input images and the size of the possible 3D-volume in order to study the robustness of the models. For our evaluation, we created our own custom dataset containing six action classes. Our analysis shows that the best model is the I3D with color images, a resolution of 112 × 112 pixels and 16 consecutive frames. The I3D also exhibited the best run-time performance for real-time applications.
A year of great achievements

Metabolism, formerly Arquivos Brasileiros de Endocrinologia e Metabologia, first published 70 years ago.That's also my seventh year as Editor-in-Chief.During this period, the AE&M, official scientific journal of The Brazilian Society of Endocrinology and Metabolism (SBEM), increased its impact factor from 0.6 to 2.309 (1), even considering the COVID-19 pandemics which strongly affected our regular life.The number of submitted papers from outside Brazil also increased.In fact, in 2015 the Journal received 165 Brazilian manuscripts and 191 from other countries (46.3% vs. 53,7%).In 2020 the numbers went to: 190 (33%) vs. 387 (67%).Also, the amount of countries increased significantly encompassing all world continents.This improvement could not be achieved without the collaboration of our Associated Editors, Reviewers from Brazil and abroad, editorial assistants, and the support of SBEM's board of Directors.Recently our site was renewed, becoming more attractive and interactive.Therefore, I will begin my eighth year as Editor aiming to continuously improve AE&M and project it more and more in the international scientific scenario.For this purpose, I count on the outstanding collaboration of all people who are hearty involved with their time and expertise with our Journal and ask all the endocrinology community around the world to submit their manuscripts to us.
Liquid-Based Pap Test Analysis Using Two-Stage CNNs

According to the World Health Organization (WHO) cervix cancer is a real threat for women at earthly level. A practice to avoid those losses is an early diagnosis of the disease, generally done with the Papanicolaou or Pap test. This requires for a pathologist to check pap smear images in an arduous assignment, to determine the existence of suspicious or cancer cells. In third world countries doctors checks pap smear manually with microscopes, creating an enormous deficit of service. This paper proposes a TensorFlow ambient where the analysis of digital pap smears is carry out as a two-stage process. First, the sample is scanned using a ROI of \(150\times 150\) pixels and two versions of the resulting image are stored in separated lists; one of low resolution (\(20\times 20\) pixels) and one of high resolution (\(250\times 250\) pixels). Then for the analysis, the first stage quickly evaluates the low-resolution images using a neural network that detects cells shapes saving their index (coordinates). In the second stage a specialized deep network uses this index to locate the high resolution images of the detected cells for zooming and recognition, being finally able to make high-resolution classifications. The software uses liquid-based pap smear equivalent to 460 patients with a 40x magnification. The trained system successfully classifies cells into normal and abnormal and could be big help to overloaded pathologists.
Research on Software Development Project Implementation Effect Evaluation Based on Support Vector Machine

The software development project implementation effect evaluation is important for assessing the software development. From the aspects of progress, cost and performance, the software development project implementation effect evaluation indicator was constructed, and the software development project implementation effect evaluation process based on support vector machine was proposed, and three support vector machine classifiers (excellent/qualified, excellent/unqualified, qualified/unqualified) were used to train and test the model with sample sets. The accuracy of the tested model was 92%, and a case analysis was carried out. The research conclusions provide effective methods for carrying out the implementation effect evaluation of software development projects.
Research on Synchronization Mechanism of Artificial Intelli-gence Model Based on Belief Measure and Its Application in Pumped Storage Power Station

Abstract For the security monitoring of pumped storage power station, a model synchroniza-tion mechanism for cloud edge cooperation framework is proposed. The method uses the belief function to describe the threshold and uses the ping-pong operation strategy to update the model alternately, which solves the problem of artificial intelligence model synchronization and update of edge equipment. The cloud is based on Baidu BML platform, the edge uses customized servers, and the average model update cycle is about three months.
Research on text retelling based on Seq2Seq

With the development of deep learning, natural language processing technology has gradually been applied to various fields, such as information extraction, machine translation, and question answering systems. Among them, the task of text retelling is to let the machine use different words and expressions to retell the original text. This process needs to retain the core semantics of the original text to the greatest extent. First, the relevant background and research status are introduced. Second, it introduces the acquisition of retelling resources and the research of retelling generation method based on deep learning. Then, the existing data sets and related evaluation indicators are introduced. Finally, the summary and Prospect of current text retelling technology are given.
Control System Actuator Risk Assessment Based on ITOPSIS Method

In order to improve the reliability of actuators and ensure the stable operation of control system, a hierarchical structure model of actuator faults of control system was established using AHP firstly; Then, the improved fuzzy analytic hierarchy process (IFAHP) was used to obtain the composite weight of evaluation criteria by the dynamic combination of subjective and objective weighting methods; Afterwards, the Improved Technique for Order Preference by Similarity to an Ideal Solution (ITOPSIS) was obtained by introducing grey correlation degree, and the final sequence of each failure mode was calculated. Finally, the numerical analysis of the pneumatic actuator in a gas turbine power plant was carried out. The results show that the evaluation results of the proposed method are basically consistent with the actual situation, and compared with the traditional TOPSIS method, it can deal with the uncertain factors in the evaluation process, reduce the subjective sensitivity, ensure the comprehensiveness of decision rules.
E2E Visual Analytics: Achieving &gt;10X Edge/Cloud Optimizations

As visual analytics continues to rapidly grow, there is a critical need to improve the end-to-end efficiency of visual processing in edge/cloud systems. In this paper, we cover algorithms, systems and optimizations in three major areas for edge/cloud visual processing: (1) addressing storage and retrieval efficiency of visual data and meta-data by employing and optimizing visual data management systems, (2) addressing compute efficiency of visual analytics by taking advantage of co-optimization between the compression and analytics domains and (3) addressing networking (bandwidth) efficiency of visual data compression by tailoring it based on analytics tasks. We describe techniques in each of the above areas and measure its efficacy on state-of-the-art platforms (Intel Xeon), workloads and datasets. Our results show that we can achieve >10X improvements in each area based on novel algorithms, systems, and co-design optimizations. We also outline future research directions based on our findings which outline areas of further performance and efficiency advantages in end-to-end visual analytics.
Ground-truth information agnostic deep dehazing network for C919 aircraft image

Aircraft images captured by a third-party camera during take-off and landing can be used for monitoring and aircraft pose measurement. Hazy weather would severely affect the aircraft image quality and incur the worse visual perception. Haze removal from the aircraft image has become an important task for practical industrial applications. Existing deep learning algorithms need the hazy image and corresponding hazy-free ground-truth image simultaneously for the same scene and time, to learn the dehazing process. However, the ground-truth aircraft images are difficult to obtain, which hinders those approaches from addressing the actual aircraft image dehazing problem. In this paper, we present an endto- end ground-truth information agnostic deep dehazing network for single C919 aircraft image dehazing problem. Instead of the requirement of ground-truth image, we train the network only by utilizing the pair of hazy and predehazed images. The pre-dehazed image can be easily obtained by the conventional dehazing manner without deep learning, and the Natural Image Quality Evaluator (NIQE) is introduced to find the best dehazing model. Compared to existing dehazing algorithms, the proposed algorithm can be capable of addressing real-world hazy C919 aircraft images effectively and achieve the best dehazed performance on our collected aircraft dataset.
Comparable Study on Readability of Machine Generated News and Human News

Traditional news publishing and transmission are ever changing with the development of integration of artificial intelligence and journalism. The quality of machine generated news has gained wide attention in both journalism and engineering. We build an English comparative corpus containing machine generated news and human written news about several topics. An empirical study is carried on the readability of machine generated articles and news written by journalists. The news topics cover the range of financial, politics and sports. This study compares the distribution of machine news and human news on eight widely used readability indices. The results of hypothesis-testing indicate that the readability of machine generated news can vary with different news themes but still has a significant gap comparing with readability of human news.
Shaping AI as the Tool for Subconscious Design

The whole process of human evolution is dynamic in the interaction with tools, as a designer, there is no doubt that we benefit from the digital tool - CAD (computer aid design) in the design process. But most of the tools are only the extension of our hand which reduce the repeating job, increase the efficiency, and help us create diverse outcome as quick as possible. This way of tools making bring us into the era of Mass-production and laid the foundation of modern design.There is always a limitation for increasing the efficiency based on the First Principles [], because the physical workflow is tangible. But the value of a designer is never about efficiency, it’s about creativity and perception about the humanity. When the “design one and fit all” model hit its ceiling, we need to improve with another direction, which is the perception a deeper perception of the people and merging it into the early design process. Therefore, a new generation of tools will be required. Artificial Intelligent, especially the Neural Networks is a viable tool to generate the potential vision and form that driven by the data from the customers.In this thesis, we take the machine learning model training project as an example. Proposing how to create Artificial intelligence tools/assistants, which helping bring the conscious/unconscious need of the customer into the beginning of the kitchen design and make the Mass-customization becoming a real-time experience. The outcome is a Machine Learning model which is suggestive assistant for the customer/designer to explore the form of the kitchen.
Discontinuity Preserving Optical Flow Based on Anisotropic Operator

Variational models are more popular approaches in the estimation of optical flow between two image frames and yield the most accurate flow fields. More fidelity terms in the variational model makes the estimation robust. This paper proposed an anisotropic operator which is designed using the greatest integer and an exponential function to estimate average flow velocity. This will help to preserve discontinuity in the optical flow and provides a significant smooth flow over a uniform region. The design of this operator is motivated from an isotropic operator that is based on the intensity differences of the pixels. This is employed in the controlling of flow propagation. The validation of the accuracy and robustness of our algorithm is provided in terms of qualitative and quantitative results on a variety of spectrum datasets.
Optimization of Human Pose Detection Based on Mask RCNN

This paper briefly describes the similarities and differences of the mainstream models of deep learning target detection box, analyzes the characteristics and advantages of Mask RCNN, a universal target detection box, and focuses on the application of Mask RCNN in human posture detection in multi- person human posture task. Through the analysis, it is considered that the advantage of Mask RCNN in multi-person human posture detection task is the accuracy, while the bottleneck is the detection speed. To solve this problem, an optimization of Mask RCNN model based on MobileNet was proposed to accelerate the inference calculation speed of Mask RCNN. At the same time, in order to further improve the detection accuracy of Mask RCNN, a method of using pixel segmentation results to assist the detection of human body key points is proposed. Experimental results show that compared with the original algorithm, it improves the reasoning speed and reduces the false detection rate caused by the environment.
The Application of Big Data Technology in Police Tactics Teaching

The advent of the era of big data has brought us many shocks. Especially for police teaching, it is both a challenge and an opportunity. In the current police tactics teaching, due to the peculiarities of the troops and the necessity of teaching methods, uniformity is necessary. In the implementation of teaching, some teachers walked into a teaching misunderstanding. At the same time, big data technology has strong application capabilities and promotion power for the ever-increasing scale of police data, which can better improve work efficiency and serve the masses. The purpose of this article is to study the application of big data technology in police tactics teaching. In this paper, by clarifying the police reform brought by big data and responding to the challenges of the big data era, researching data mining technology and algorithms, using outbound data and case data in police data as data sources, the data is preprocessed and multi-dimensional data modeling can be applied to police tactics teaching. This article starts from the actual work of police activities, with both theoretical accumulation and practical exploration. Theory and practice are closely integrated, and from the perspective of practical applications, focusing on the current organic combination of information and communication technology and police work, and finally come up with a solution to the shortcomings of the traditional police work system. Provide ideas for the information integration work carried out by police work. Experimental research shows that the biggest improvement is the risk-avoidance action. Before the use of big data technology, the learning efficiency was only 70.6%, and after the improvement, it was as high as 85.7%, an improvement of 15.1%. Generally speaking, after the improvement, both the basic theory teaching of police tactics and the basic movement teaching have made great progress.
EyeFIX: An Interactive Visual Analytics Interface for Eye Movement Analysis

Eye movements are closely related to the cognitive processes and often act as a window to the brain and mind. To facilitate a window to the brain using eye movements, we propose EyeFIX, an interactive visual analytics interface. Our interface currently focuses on processing gaze movements to generate the two most prominent events, fixations, and saccades which are used in a large part of the eye movement literature. Our interface has multiple interactive widgets that allow the users to choose the algorithm and tune its hyper parameters that control how fixations and saccades are generated. It has four major visualizations: (1) A Gaze Plot to showcase the gaze movement of each participant, (2) a Fixation Plot that helps to identify regions of the stimulus which are of interest to a particular participant, (3) a Heat Map to study the denser regions of fixation and regions which the participant frequently visited, and (4) a Timeline Visualization to assist in having a closer look at temporal regions of interest. We design a set of brushing and linking operators that allow users to interactively control all the visualizations to dig deeper in both, the spatial and temporal domains. We demonstrate the applicability of our interface with datasets obtained by human subjects viewing naturalistic stimuli while performing various viewing tasks, including visual exploration, visual search, and prolonged visual fixation.
EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning

Advances in reinforcement learning (RL) have achieved significant success in many areas. However, RL typically requires a large amount of computation and memory. Often RL implemented in Python is too heavy to run on a resource-limited edge device. Therefore, making the RL model lighter is very important for on-device machine learning. In this paper, we propose a lightweight C/C++ RL framework aiming for RL on edge devices. The proposed RL framework is designed to run on a single-core processor that is typically included in a resource-limited embedded platform. The evaluation using OpenAI Gym’s CartPole demonstration shows that the model can be trained on an edge device in real-time.
Teaching Reform and Research of Java Programming with the Objective of Cultivating Innovative Practice Ability under OBE Concept

The current international competition is actually a competition for talents. The most important thing is the competition for innovative talents; the key to cultivating innovative talents is to cultivate students' innovative ability. In order to better achieve the goal of cultivating innovative and top-notch talents in Java programming, a series of teaching methods are proposed to help cultivate students' Java practice and innovation ability under the guidance of OBE education mode, which can effectively promote the improvement of students' innovative practice awareness and ability.
Investigation on Error-Tolerability Enhancement of Videos via Re-Encoding for Computer Vision: A Case Study on Object Detection

Run-time video re-encoding via adjusting the Group of Pictures (GOP) size has been shown to be able to effectively enhance the error-tolerability of videos. However, this study was done by focusing on the human visual system’s perception to video errors. In this paper we investigate on this issue by taking machine’s tolerability of errors into consideration. Objection detection carried out by yolov3 is employed as a case study. In this study, errors are injected into a benchmark video to generate more than 100,000 erroneous videos, and then yolov3 is employed to evaluate the quality (acceptability) of these videos. The results demonstrate that video re-encoding can have higher application efficiency (e.g., larger GOP size) for computer vision, mainly because that the error-tolerability of machines is larger than that of humans. This can lead to better video encoding efficiency (smaller video size) and lower implementation cost for re-encoding.