Reviewerly/exHarmony huggingface dataset:
The Use of Audiovisual Media in Writing Skills Learning During Pandemic

This study aims to describe the conditions of learning media needed by teachers and students. The data are in the form of an analysis of the conditions and needs of teachers and students on audiovisual media powtoon based in learning writing skills. The data were obtained through interview, observation, and documentation. Data analysis with interactive analysis model consists of three activity lines, namely data reduction, data presentation, and conclusion drawing/verification. From the results of the analysis, it is known that during the pandemic the learning was carried out boldly. Bold learning is carried out with Whatsapp group media, power point, Google Workspace. Learning that often makes students feel bored. The use of audiovisual media powtoon based in the learning process can be used as an alternative learning media that pleases students in following the learning process.Keywords: Audiovisual Media, Writing, Powtoon
An application of information and communication technologies upon professional training of primary school teachers

A critical factor in improving the quality of education, according to researchers, is an increase in the special pedagogical and digital competence of primary school teachers. The novelty of the research lies in the study of the practical results of changes in the quality of knowledge when using a specialized dedicated course of IST in early school education for students of a teacher training college. The study was conducted on a representative sample of 355 College of teacher students at Hunan R&D Center for Applied Talent Cultivation in a Practical Setting (China) based on the results of three examinations after a third and fourth semester of study in a homogeneous group. The chi-squared study showed relevance levels of χ2 = 0.00018, 0.00022, and 0.00011 for the three exams, which rejected the null hypothesis that there was no statistically significant association between the test scores of the control and treatment groups in which the specialized course was used. The practical results of the research can be widely used for the introduction of specialized courses for increasing digital competence and pedagogical competence in the application of ICT for future primary school teachers.
Research on coal gangue recognition of GF-5 hyperspectral image

Coal gangue is one of the main pollution sources in coal mining area, which can cause air, water, land and vegetation pollution. Therefore, in order to protect the ecological environment, coal gangue is usually buried underground. However, with the influence of soil erosion and other human factors, coal gangue from historical landfills gradually exposed to the surface, mixed with sand, and became the source of pollution again. In this study, through the analysis of X-ray powder diffraction and spectrum of coal gangue samples in Ordos coal mining area, it is found that the coal gangue in this area contains kaolinite. Kaolinite has unique spectral characteristics in the short wave infrared spectrum, which can be recognized by hyperspectral remote sensing. China launched the GF-5 satellite on May 9, 2018, equipped with a hyperspectral imager called the Advanced Hyperspectral Imager (AHSI). It has a spectral range of 400-2500 nm, with spectral resolutions of higher than 5 nm in the VNIR and 10 nm in the SWIR, respectively. On the basis of data processing, kaolinite information is extracted from hyperspectral reflectance data. Based on the field sample validation and image spectral analysis, it is concluded that hyperspectral data of GF-5 can effectively identify kaolinite information. At the same time, it is found that the spectrum of kaolinite in coal gangue is obviously different from that of kaolinite in clay mining, and they can be distinguished. Therefore, the distribution of coal gangue in mining area can be indicated by the identified kaolinite information.
On the Construction of Online Open Courses of Japanese in Colleges and Universities under the Background of "Computer Internet +"

The 3 characteristics of the computer A. High-speed calculation (numerical calculation and logical calculation), B. Storage memory and run according to the program C. Processing massive data. The nature of the Internet is a huge network covering the whole world. In other words, the characteristics of the computer Internet determine its functional advantages and capabilities, and it is irreplaceable and indispensable. The Internet changes education. Through analysis, it can be found that, as an open teaching mode, the "computer Internet +" teaching model can be used in the construction of open Japanese classrooms. With the "online open course" model, students can complete their own choice of learning content according to their own interests, and teachers can complete targeted teaching content arrangements according to students' learning conditions. Through the interaction between teachers and students, students and students, students can also better complete the learning and application of Japanese knowledge, which can not only improve the classroom teaching effect but also improve the teaching quality of colleges and universities.
Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution

Small ships in optical remote sensing images are hard to detect due to the lack of sufficient detail information. In this paper, we adopt the image super-resolution technology to solve this problem. Specifically, an effective super-resolution network is designed to generate clear super-resolution ship images from small blurry ones produced by the ship detector. Inspired by the idea of generative adversarial network (GAN), the super-resolution network is trained together with a discriminator network in an adversarial way, aiming at generating more realistic super-resolution images. Moreover, to eliminate false detections, the discriminator network is also used to distinguish ship and non-ship images via an additional classification branch. Experimental results demonstrate the effectiveness of the proposed method.
“Through the looking glass: envisioning new library technologies” the scale of Facebook part 1: misinformation

Purpose This column aims to explore the implications of Facebook’s size and reach to understand the role of massive social networks in our world, in general, and libraries, in particular. Design/methodology/approach This column uses the example of misinformation to underscore the challenges endemic to connecting so many people under Facebooks particular business model. Findings Facebooks massive size, combined with the nature of the parts of our social lives it connects to, means that it currently stands alone and raises unique questions about what it would take to make meaningful changes to it or any similar competitor that arises. Originality/value Only by grappling with the dynamics of network effects on an organization of this size is it possible for informational professionals to understand the challenges and potential futures of massive social networks.
TIARA 2.0: an interactive tool for annotating discourse structure and text improvement

Abstract Discourse structure annotation aims at analysing how discourse units (e.g. sentences or clauses) relate to each other and what roles they play in the overall discourse. Several annotation tools for discourse structure have been developed. However, they often only support specific annotation schemes, making their usage limited to new schemes. This article presents TIARA 2.0, an annotation tool for discourse structure and text improvement. Departing from our specific needs, we extend an existing tool to accommodate four levels of annotation: discourse structure, argumentative structure, sentence rearrangement and content alteration. The latter two are particularly unique compared to existing tools. TIARA is implemented on standard web technologies and can be easily customised. It deals with the visual complexity during the annotation process by systematically simplifying the layout and by offering interactive visualisation, including clutter-reducing features and dual-view display. TIARA’s text-view allows annotators to focus on the analysis of logical sequencing between sentences. The tree-view allows them to review their analysis in terms of the overall discourse structure. Apart from being an annotation tool, it is also designed to be useful for educational purposes in the teaching of argumentation; this gives it an edge over other existing tools.
Discussion on College English Blended Teaching under MOOC

As an advanced way of education, MOOC (Massive Open Online Courses) is gradually introduced into traditional education in order to promote the quality of education. MOOC is also playing an important role in College English teaching. This paper firstly analyzes the meaning of MOOC and advantages of hybrid teaching, then it focuses on the implementation strategies of the mixed English teaching based on MOOC according to the current situation of College English Teaching in China. We hope that it provides a new way for College English teaching through this study.
Characterizing AI Model Inference Applications Running in the SGX Environment

Intel Software Guard Extensions (SGX) is a set of extensions built into Intel CPUs for the trusted computation. It creates a hardware-assisted secure container, within which programs are protected from data leakage and data manipulations by privileged software and hypervisors. With the trend that more and more machine learning based programs are moving to cloud computing, SGX can be used in cloud-based Machine Learning applications to protect user data from malicious privileged programs.However, applications running in SGX suffer from several overheads, including frequent context switching, memory page encryption/decryption, and memory page swapping, which significantly degrade the execution efficiency. In this paper, we aim to i) comprehensively explore the execution of general AI applications running on SGX, ii) systematically characterize the data reuses at both page granularity and cacheline granularity, and iii) provide optimization insights for efficient deployment of machine learning based applications on SGX. To the best of our knowledge, our work is the first to study machine learning applications on SGX and explore the potential of data reuses to reduce the runtime overheads in SGX.
Hand Gesture Recognition in Complex Background Based on improved Deep Residual Learning network

Although deep learning-based hand gesture recognition techniques have achieved amazing performance in recent years, it is challenges as hand gesture is prone to interference, such as light changes and gesture shadows on gesture recognition. In this paper, we propose a recognition model based on ellipse skin color, and improved Deep Residual Learning network for gesture recognition. By utilizing the clustering effect of skin color in YCbCr color space, we segment CbCr two-dimensional space based on the elliptical skin color model to hand gesture. To further improve performance, the mathematical morphology with logic operation is proposed to assist hand gesture segmentation. Lastly, the Deep Residual Learning architecture is used to increase the width of the network and the channel attention mechanism. The experimental results show that the proposed method can obtain a high recognition rate compared with existing methods.
A Central Opinion Extraction Framework for Boosting Performance on Sentiment Analysis

With the rapid development of the Internet, mining opinions and emotions from the explosive growth of user-generated content is a key field of social media analysis. However, the expression forms of the central opinion which strongly expresses the essential points and converges the main sentiments of the overall document are diverse in practice, such as sequential sentences, a sentence fragment, or an individual sentence. Previous research studies on sentiment analysis based on document level and sentence level fail to deal with this actual situation uniformly. To address this issue, we propose a Central Opinion Extraction (COE) framework to boost performance on sentiment analysis with social media texts. Our framework first extracts a span-level central opinion text, which expresses the essential opinion related to sentiment representation among the whole text, and then uses extracted textual span to boost the performance of sentiment classifiers. The experimental results on a public dataset show the effectiveness of our framework for boosting the performance on document-level sentiment analysis task.
The Common Law and the Self-Driving Car

In law, as in life, change is a constant. From the horse and buggy, to the locomotive, and now to the car, the way we move on land keeps changing. And in days and years to come, it is set to change even more. We are on the cusp of a new era—the self-driving car era. The self-driving car era will remove the driver from the driving enterprise. With that change comes hard questions. For one, who will we look to for liability for self-driving car collisions? Our present car-collision-liability framework is driver-centered. We focus on whether one (or more) of the drivers was at-fault. This driver-centric framework answers many (if not all) liability questions from fender-benders, traffic infractions, and the like. The impending driverless future, however, puts this well-worn liability framework in question. In a driverless future, who will be liable and why? This paper seeks to answer those questions. While some states have authorized self-driving cars, we still do not have a robust legislative liability framework in place. And yet, self-driving cars are already on American roads and more are on the way. Self-driving cars now deliver pizzas, groceries, give rides, and take over the driving function from willing drivers. But some self-driving cars have been involved in fatal accidents and serious traffic infractions. It takes little imagination to foresee that those incidents won’t be the last. As a result, until legislatures enact comprehensive liability frameworks, the common law will have to perform its limited function to provide answers in real cases and controversies based on existing rules. E.g., OKLA. STAT. TIT. 12, §2 (West 2021). The way of the common law, Oliver W. Holmes Jr. and Benjamin N. Cardozo told us, is reasoning by analogy, informed by tradition. Just as the common law analogized from longstanding rules about defective products generally and made them applicable to defective cars, it will likely have to apply those same rules to another form of car, the self-driving car in real cases and controversies. This paper outlines an analytical blueprint based on longstanding common law rules to address this new driverless phenomenon. This paper considers the common law liability continuum starting with the person in the driver’s seat who engages the self-driving feature, to the owner who uses her self-driving car as a common carrier, to the manufacturer of the vehicle in case of a product defect. Drawing examples from (and analogizing from the common law’s response to) the automated elevator, cruise control, autopilot, runaway and unmanned vehicles, and ships, this paper shows that the common law has longstanding answers to even self-driving cars. This paper also analyzes whether the common law rules that extend legal personality (the right to sue and be sued) to ships, for example, could also apply to another form of conveyance—i.e., the self-driving car.
PP-NAS: Searching for Plug-and-Play Blocks on Convolutional Neural Network

Multi-scale features are of great importance in modern convolutional neural networks and show consistent performance gains on many vision tasks. Therefore, many plug-and-play blocks are introduced to upgrade existing convolutional neural networks for stronger multi-scale representation ability. However, the design of plug-and-play blocks is getting more complex and these manually designed blocks are not optimal. In this work, we propose PP-NAS to develop plug-and-play blocks based on neural architecture search. Specifically, we design a new search space and develop the corresponding search algorithm. Extensive experiments on CIFAR10, CIFAR100, and ImageNet show that PP-NAS can find a series of novel blocks that outperform manually designed ones. Transfer learning results on representative computer vision tasks including object detection and semantic segmentation further verify the superiority of the PP-NAS over the state-of-the-art CNNs (e.g., ResNet, Res2Net). Our code will be made avaliable at https://github.com/sbl1996/PP-NAS.
Similarity Model using Gradient Images to Compare Human and AI Agents

The objective is to determine how close an Artificial Intelligence agent is, in comparison to a human player, using only game play images. Identifying Artificial Intelligence agents during game play is typically done through the analysis and collection of bio-metric data, such as keyboard, mouse and other controller interfaces. This document presents a model of an Auto Encoder architecture, with Long Short Term Memory layers. Gradient and Non-Augmented Images have been evaluated and compared. Three distinct personalities of agents are evaluated, human players, a trained Artificial Intelligence agent, and a basic Artificial Intelligence agent. Through testing the Gradient Image augmentation sets present promising results, with the model successfully identifying the human as the closest similarity to the baseline, followed by the trained Artificial Intelligence agent.
Research on Online Teaching Satisfaction Based on Big Data and COVID-19

Under the background of COVID-19, online teaching has become the focus of the whole society. Based on this, this paper takes Shaanxi higher vocational colleges of finance and economics as the research object and conducts a questionnaire survey on the satisfaction of online teaching during the epidemic period. The results show that the offline students' homework performance, final exam scores and teaching satisfaction are better than the online students. Therefore, this paper proposes to enhance the interaction between teachers and students, stimulate the motivation of students; Provide environmental support and strengthen learning motivation; Improve the online course design to reduce students' learning pressure. This study not only helps to solve the urgent needs of online teaching, but also provides a reference path for the healthy and orderly development of online teaching in the new era.
Research on single image super resolution enhancement algorithm based on deep learning

To solve the problem of low edge protection index in traditional enhancement algorithm, a single image super-resolution enhancement algorithm based on deep learning is proposed. The super-resolution feature of a single image is extracted by the sinusoidal two-dimensional transform function modulated by Gaussian function. The local Laplacian filter is used to preprocess the super-resolution of a single image, and the deep learning method is introduced to enhance the super-resolution of a single image. The experimental results show that the improved method has higher edge protection index, can effectively improve the enhancement accuracy, and has certain advantages.
Differential Gaze Estimation with Ocular Counter-Rolling Compensation

The main challenges in the gaze estimation task are the variations in head poses, eye shapes and inner eye structures amongst individuals. Conventional methods have proposed many techniques to reduce these variations. One of the most adopted techniques is the normalization method which realigns the head pose to remove the roll component of head pose. However, this global transformation, which forces the eye to move with the other part of the face together, violates the human anatomy as eyeballs are controlled separately by the surrounding muscles. These muscles restrain the eyeball to be horizontally stable even when the head is tilted in a natural pose. In this work, we propose a novel method to model this counter-rolling effect and use it to remove the error from the normalization step. Experiments validate our approach, which outperforms state-of-the-art eye-image-only gaze estimation methods.
Siamese Differential AE-Net: Noisy Patch Comparison and Its Application in NLM

The structural similarity of image patches is widely used in image processing because of its simple calculation and effectiveness. However, the calculation of image patch similarity is often affected by noise, which makes it difficult to adjust the parameters for image processing tasks, such as the image denoising, classification etc. In this paper, we propose a Siamese differential Auto-Encoder network (AE-net), which applies the deep features of the noise patches to the noise patches comparison. The self-learning characteristic of the AE-net helps to adjust the parameters and extract the deep features of similar patches according to the cost function. The proposed cost function can effectively reduce the difference between the deep features of the noisy and noise-free images. We apply our scheme to the Non Local Means (NLM) denoising method. The experimental results show that our method outperforms the traditional NLM in terms of the PSNR and SSIM values and can better preserve the texture detail of the image while denoising.
Performance Evaluation of Classification and Verification with Quadrant IQ Transition Image

This paper presents a physical layer wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. This work introduces classification and detection tasks in one process. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, a technology to identify wireless devices by using unique variations in analog signals. We propose a quadrant IQ image technique to reduce the size of CNN while maintaining accuracy. The CNN utilizes the IQ transition image, which image processing cut out into four-part. An over-the-air experiment is performed on six Zigbee wireless devices to confirm the proposed identification method's validity. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 weight parameters in serial use and 146,000 in parallel use. Furthermore, the proposed threshold algorithm can verify the authenticity using one classifier and achieved 80% accuracy for further secured wireless communication. This work also introduces the identification of expanded signals with SNR between 10 to 30dB. As a result, at SNR values above 20dB, the proposals achieve classification and detection accuracies of 87% and 80%, respectively.
Research on Artificial Potential Field Route Planning Method Based on Threat Level Assessment

This paper first studies the uncertain probabilistic inference model based on Bayesian networks, constructs a threat level evaluation algorithm, and combines fuzzy inference technology to evaluate the relative threat levels of different threat sources in the battlefield environment. Then the threat level assessment is combined with the path planning method, the two-dimensional Gaussian probability density function is used to model each threat source, and the artificial potential field method is combined to establish a UAV trajectory planning method based on threat situation assessment. Finally, simulations verify the effectiveness of the proposed method.