Reviewerly/exHarmony huggingface dataset:
Genesis, essence and structure of pedagogical technology “Cooperative learning”

The emergence of pedagogical technologies and their mass introduction dates back to the 1960s. Reformation of the American and European schools was provoked by reinterpretation of the learning goals. However, the historical roots of some pedagogical technologies are much older than studies of J. Carroll and D. Bruner – renowned authorities in this area of research. One of these technologies is cooperative learning. Initially recognized as a key component of humanistic pedagogy of J. Dewey, this technology has been further developed in works of many Soviet and foreign scholars. In the 1920s, the works by J. Dewey had a serious impact on the reformation of the Soviet education system, which aimed to educate the entire population of the Soviet Union. However, for some reasons, the gradual introduction of cooperative learning into learning process took a break in the 1930s. Since the late 1990s, a serious pedagogical crisis has emerged in the Russian Federation, which cannot be mended by traditional education system; it encourages many teachers to look at the well-studied pedagogical technologies from a different perspective. Today the social order sets new requirements concerning a major breakthrough in training a person. Teamwork and analytical thinking skills, the ability to lifelong self-education and self-develop-ment require fundamental changes in the traditional education system. At the same time, for the successful implementation of pedagogical technology, it is necessary to resolve a number of issues related to the essence of the concept of cooperative learning and the definition of components. Research relevance is indicated, the historical roots and essence of the concept of pedagogical technology of cooperative learning are determined, examples of the practical application of models of this pedagogical technology are exemplified.
PUGCQ: A Large Scale Dataset for Quality Assessment of Professional User-Generated Content

Recent years have witnessed a surge of professional user-generated content (PUGC) based video services, coinciding with the accelerated proliferation of video acquisition devices such as mobile phones, wearable cameras, and unmanned aerial vehicles. Different from traditional UGC videos by impromptu shooting, PUGC videos produced by professional users tend to be carefully designed and edited, receiving high popularity with a relatively satisfactory playing count. In this paper, we systematically conduct the comprehensive study on the perceptual quality of PUGC videos and introduce a database consisting of 10,000 PUGC videos with subjective ratings. In particular, during the subjective testing, we collect the human opinions based upon not only the MOS, but also the attributes that could potentially influence the visual quality including face, noise, blur, brightness, and color. We make the attempt to analyze the large-scale PUGC database with a series of video quality assessment (VQA) algorithms and a dedicated baseline model based on pretrained deep neural network is further presented. The cross-dataset experiments reveal a large domain gap between the PUGC and the traditional user-generated videos, which are critical in learning based VQA. These results shed light on developing next-generation PUGC quality assessment algorithms with desired properties including promising generalization capability, high accuracy, and effectiveness in perceptual optimization. The dataset and the codes are released at https://github.com/wlkdb/pugcq_create.
An improved ant colony optimization algorithm in mobile robot path planning

In order to promote the convergence speed and global optimization effect, an improved ant colony optimization algorithm (IACO) is proposed. Firstly, an initial pheromone unequal rule is constructed, which is based on the starting and ending positions to avoid the blindness of early search. Secondly, the heuristic information function is improved by considering the distance between the starting position and the target position. Thirdly, the state transition pseudo random strategy is utilized to select the next node and the state transition probability coefficient is adaptively adjusted. Finally, the pheromone update rules have been revised, that is, the pheromone volatilization coefficient can be dynamically adjusted and the pheromone threshold can also be set. Simulation results show that compared with traditional ant colony method, the proposed algorithm is valid and effective to settle path planning problems of mobile robot in different contraint environments.
Infrared image enhancement algorithm based on seagull optimized Otsu and BEEPS algorithm in NSST domain

Aiming at the problems of fuzzy, noisy, and low contrast of infrared images in detection of power equipment. This paper designs an improved Otsu segmentation threshold based on seagull optimization and BEEPS filter algorithm in NSST domain. First, the original infrared image is decomposed by NSST to high and low frequency components. Low-frequency component is divided into two parts, the foreground and background, and the enhancement processing is performed separately to each part. BEEPS algorithm is used in this paper for high-frequency component processing. Finally, the processed low-frequency components and high-frequency components are subjected to NSST inverse transformation to obtain the final enhanced image. The algorithm in this paper is compared with the other three algorithms to verify its superiority: it has improved the accuracy of infrared image threshold segmentation and strengthened the depth of field, and increase the brightness of power equipment. The overall contrast of the image is enhanced and the noise part is also effectively filtered out, improving the overall visual effect of the image which is conducive to use thermal effects to determine the operating status of power equipment and the detection and fault location of thermal fault detection.
Research on Path Planning for Mobile Robot Based on Improved Ant Colony Algorithm

Path planning of mobile robots is an important research content in mobile robot design. In view of the characteristics of classic ant colony algorithm, such as long distance, low efficiency and easy to fail into dead zone, a new pheromone updating algorithm and global/local pheromone distribution models are proposed. The grid method is used to construct two-dimensional plane space with obstacles and the simulation experiment is carried out. The simulation results show that under the improved algorithm, the robot path planning efficiency is high and the path distance is short, which verifies the validity of the model.
Imitation Learning with Inconsistent Demonstrations through Uncertainty-based Data Manipulation

Aleatoric uncertainty estimation, based on the observed training data, is applied for the detection of conflicts in a demonstration data set. The particular focus of this paper is the resolution of conflicting data resulting from scenarios with equivalent action choices, such as obstacle avoidance, path planning or multiple joint configurations. In terms of the estimated uncertainty, the proposed algorithm aims to decrease this otherwise irreducible value through direct alteration of the accrued data set and to provide data that a policy-learning neural network is able to fit appropriately. The proposed algorithm was validated with real robot scenarios while learning from inconsistent demonstrations, where the resulting policies consistently achieved their prescribed objectives. A video showing our method and experiments can be found at: https://youtu.be/oGYnzlW9Ncw.
Content Moderation of Speech in Political Discussions

Abstract Social media platforms have been hailed as “politically disruptive communication technologies” (Hong &amp; Nadler, 2012). Individuals express opinions and engage with politicians, the press, and each other on social media, sometimes using offensive language (Rossini et al., 2020). Content moderation has been adopted by many social media platforms to screen and evaluate offensive speech. In the present study we trained offensive speech classifiers to analyze offensive speech examples by integrating three archival datasets. We then used the trained classifier to examine a large body of comments about YouTube videos posted during the 2018 midterm election cycle. This provided information on the prevalence of various kinds of offensive comments and the pattern of content moderation used by YouTube. We also examined comment negativity using offensive speech lexicons. Our results showed systematic variance in the prevalence of offensive speech topics depending upon the political orientation of the content. Language use was significantly different between left and right‐leaning videos for comments related to sexism.
CppSig

For decades, the C programming language proved to be a cornerstone of system-software ecosystems, leaving us with billion lines of existing source code. From today's perspective of object-oriented and functional languages, C itself seems rather limited in its expressiveness and abstractive power. However, with the C preprocessor (CPP) as its companion, macros, which operate on the raw token stream, allow for abstractions that are impossible to achieve within the language itself. While its flexibility and its ease of use make CPP attractive for programmers, its potential undisciplined usage makes it problematic for static source-code analysis and can slow down the on-boarding of new developers.
Security Concepts and Principles

Our subject area is computer and Internet security—the security of software, computers and computer networks, and of information transmitted over them and files stored on them. We first consider the primary objectives or fundamental goals of computer security. Many of these can be viewed as security services provided to users and other system components. Later in this chapter we consider a longer list of design principles for security, useful in building systems that deliver such services.
Participants’ Characteristics at Iser-Lab in 2020

We summarize the experimentally measured characteristics of the registered participants of the experiments conducted at the Institute of Social and Economic Research, Osaka University. Measured characteristics include fluid intelligence, risk preference (risk aversion, prudence, and temperance), social value orientation, theory of mind, personality (Big Five and Grit), ability to backward induct, as well as their general trust. We discuss reliability of these measures and correlation among them.
OCR-based Inventory Management Algorithms Robust to Damaged Images

Accurate and fast inventory management algorithms are essential in the modern distribution industry. However, the configuration process of inventory management algorithms is very expensive, and the direct comprehensive management of inventory procedures is labor intensive and inaccurate. Therefore, in this paper, we propose an optical character recognition (OCR)-based inventory management algorithm to resolve these practical issues. The main purpose of our inventory management algorithm is to automatically inspect whether a list of items and the actual items match. To this end, our method consists of three steps, namely, text detection, text recognition, and text matching. In addition, to expand our algorithm to real-world applications, we propose adversarial training to ensure robustness against various damaged images, including corruption, blur, and inappropriate viewpoints. To train the network, we construct a new inventory management dataset (IMD) consisting of 10,000 sheets in real retail store environments. We verify our algorithm on the public dataset and our new IMD. As a result, we experimentally demonstrate that our method is not only robust against various damaged images but is also easily applicable in both large and small scale distributions stores at a low cost. Our code and dataset is available at https://blog.airlab.re.kr/Deform-and-Recover/.
Recognition of Warhead by Range-Profile Matching

A novel algorithm is presented for warhead recognition in the defense of ballistic missiles. The deviation of a range profile from a standard range profile is measured by the 2-norm of the difference between the two range profiles. A target is recognized as a warhead if the deviation of its range profile from the range profile of the warhead is less than a threshold. The threshold is chosen such that the detection rate is constant. Since the threshold is automatically calculated according to the signal- to- noise ratio and the detection rate, this algorithm has a larger applicability than the traditional methods based on range-profile matching.
Electromyographic Signal Based Dynamic Hand Gesture Recognition Using Transfer Learning

Recent years, the research of gesture recognition based on surface EMG signal has become an active topic. The conventional methods mainly focus on feature engineering, but the sEMG signal is non-stationary temporally, which makes proper feature design and selection very complicated. To tackle this problem, this paper proposes a novel dynamic gesture recognition method by employing Convolutional Neural Networks. Short-time Fourier transform (STFT) and continuous wavelet transform (CWT) are introduced to model the time-frequency features of a single channel and the relationship between different channels. Meanwhile, the performance of deep learning-based methods relies on a large amount of training data. In the context of sEMG based gesture recognition, one user cannot be expected to generate tens of thousands of examples at a time. Hence, transfer learning (TL) techniques is utilized to alleviate the data generation burden imposed on a single individual and enhance the performance of the Convolutional Neural Networks. Experiment results on the sEMG data set recorded with Myo Armband indicate that the transfer learning augmented ConvNet can achieve an accuracy of 99.41%.
Augmenting TV Shows via Uncalibrated Camera Small Motion Tracking in Dynamic Scene

To augment the TV show in post-production, we propose a novel solution to uncalibrated camera small motion tracking in a dynamic scene that simultaneously reconstructs the sparse 3D scene and computes camera poses and focal lengths of each frame. The critical elements of our approach are a robust image feature tracking strategy in dynamic scenes followed by automatic local-window frames slicing, local and global bundle adjustment optimization initialized by a homography-based uncalibrated relative rotation solver. The proposed method allows us to add the virtual objects (elements) into the reconstructed 3D scene, then composite them back into the original shot while perfectly matched perspective and appear seamless.
Fall Detection and Protection System Based on Characteristic Areas Algorithm

Hip fracture caused by falls and its complications is one of the greatest threats to disability and death of the elderly. To reduce physical damage from falls in the elderly, the current solution to achieve effective protection is detecting fall trends and turning on protective devices. However, the existing products have the problems of low accuracy and poor real-time. In this paper, a high accuracy and high real-time human fall detection and protection system based on characteristic areas algorithm is designed, which can detect the trend of falls within 400 ms after the human body begins to fall and is filled with the airbag in the 400 ms later, realizing effective protection of the human hip. The system got 95.33% accuracy, with an average airbag opening time of 70 ms.
Deep Evidential Fusion Network for Image Classification

Evidential deep learning (EDL) has been proposed to estimate the uncertainty and the prediction confidence of neural networks. In this paper, we investigate the fusion method based on the EDL model and Dempster's rule of combination. For fusion models, a better uncertainty estimation may be more helpful than high accuracy. To this end, we propose a deep evidential fusion method to best utilize the belief assignment and uncertainty estimation by improving the objective function and introducing the approximation of the base rate distributions. The experimental results show that our proposed method achieves a more reliable fusion result. We also explore the application of belief function and evidence theory in the field of medical image analysis, where multi-modality well fits the framework of belief functions.
Forensic Camera Identification in Social Networks via Camera Fingerprint

Image-related crimes cause the urgent demand for tracing the origin of digital images. The breakthrough is a passive detection method via photo response non-uniformity (PRNU) analysis proposed by Lukáš et al. Recently, digital images are often shot with handheld devices (such as smartphones) and transmitted using social media (such as LINE). Most of the images are distorted (such as compressed and resized) during transmission. Previous studies are less focused on the impact of transmission compression through social networks. Thirty-one different Apple mobile phones were used to capture digital images in the experiment. Images were uploaded to the photo album via LINE software and then downloaded. The modified signed peak correlation energy (MSPCE) statistics is used to evaluate the correlation between the PRNU values of the disputed images and the pattern noise of the experimental devices. Experimental results show that the PRNU analysis method can effectively trace the source of the shot device using the distorted images which are compressed and resized during the transmission in LINE.
A Quantum Circuit Optimization Framework Based on Pattern Matching

In the NISQ era, quantum computers have insufficient qubits to support quantum error correction, which can only perform shallow quantum algorithms under noisy conditions. Aiming to improve the fidelity of quantum circuits, it is necessary to reduce the circuit depth as much as possible to mitigate the coherent noise. To address the issue, we propose PaF , a Pattern matching-based quantum circuit rewriting algorithm Framework to optimize quantum circuits. The algorithm framework finds all sub-circuits satisfied in the input quantum circuit according to the given external pattern description, then replaces them with better circuit implementations. To extend the capabilities of PaF , a general pattern description format is proposed to make rewriting patterns in existing work become machine-readable. In order to evaluate the effectiveness of PaF , we employ the BIGD benchmarks in QUEKO benchmark suite to test the performance and the result shows that PaF provides a maximal speedup of [Formula: see text] by using few patterns.
On some batch code properties of the simplex code

The binary k-dimensional simplex code is known to be a $$2^{k-1}$$ -batch code and is conjectured to be a $$2^{k-1}$$ -functional batch code. Here, we offer a simple, constructive proof of a result that is “in between” these two properties. Our approach is to relate these properties to certain (old and new) additive problems in finite abelian groups. We also formulate a conjecture for finite abelian groups that generalizes the above-mentioned conjecture.
A Fast and Robust Matching System for Multimodal Remote Sensing Image Registration

The rapid and explosive growth of remote sensing image dataset (e.g., optical, SAR, LiDAR) promotes the development of the aerospace industry. However, images with complex coverage scenes are usually captured by either different sensors from different perspectives or the same sensor in different periods [1]. These factors have brought a great challenge to precision image co-registration, and it is difficult to identify a fully universal method to cope with all registration cases. Any kind of image registration algorithm needs to consider the imaging principle, radiometric and geometric distortions, noise interference, and so on. To date, numerous efforts have been made to overcome these challenges and improve the performance of multimodal remote sensing image registration, which can be classified into three categories: area-based methods, feature-based methods and a joint of previous two categories [2].
Keystroke Dynamics Based Authentication System

Nowadays, many companies use biometric technologies for the security of critical systems as well as username-password methods. In the literature, biometric systems are the most commonly used systems among the two-factor authentication systems. There are two different approaches to biometric systems: physical and behavioral biometric systems. In the last decade, the accuracy of behavioral biometric systems has significantly increased with the use of machine learning methods in these systems. For this reason, the usage areas of the studies in this field have expanded. In this study, we focus on keystroke dynamics based on behavioral methods. Firstly, we make a web application to collect keystroke data from 54 employees in a company. Then, we use the benchmark database and our database to train-test machine learning algorithms, which have the highest accuracy in this field in the literature. Among them, tree-based algorithms have the highest accuracy score, with an average of 0.94.
Making Sense of the Prefix de- with an English–Chinese Parallel Corpus

This study uses a large-scale English–Chinese parallel corpus to examine the senses of the prefix de- in English. Our investigation was designed in three main steps. The most commonly used de- verbs were first identified in the British National Corpus, their corresponding Chinese lexical items were manually collected from the English–Chinese parallel corpus, and using these Chinese items, we were able to identify the recurring Chinese characters and words that correspond to de- verbs in English, i.e. their translation candidates. The Chinese characters and words served as effective means to tease out five major sense groups entailed by the prefix de-. The results underscore the value of parallel corpora not only for sense disambiguation and translation candidate retrieval but also for contrasting typologically distant languages in terms of their morphological as well as alternative means for conveying meaning.
End-to-end Quality of Experience Evaluation for HTTP Adaptive Streaming

Exponential growth in multimedia streaming traffic over the Internet motivates the research and further investigation of the user's perceived quality of such services. Enhancement of experienced quality by the users becomes more substantial when service providers compete on establishing superiority by gaining more subscribers or customers. Quality of Experience (QoE) enhancement would not be possible without an authentic and accurate assessment of the streaming sessions. HTTP Adaptive Streaming (HAS) is today's prevailing technique to deliver the highest possible audio and video content quality to the users. An end-to-end evaluation of QoE in HAS covers the precise measurement of the metrics that affect the perceived quality, eg. startup delay, stall events, and delivered media quality. Mentioned metrics improvements could limit the service's scalability, which is an important factor in real-world scenarios. In this study, we will investigate the stated metrics, best practices and evaluations methods, and available techniques with an aim to (i) design and develop practical and scalable measurement tools and prototypes, (ii) provide a better understanding of current technologies and techniques (eg. Adaptive Bitrate algorithms), (iii) conduct in-depth research on the significant metrics in a way that improvements of QoE with scalability in mind would be feasible, and finally (iv) provide a comprehensive QoE model which outperforms state-of-the-art models.