Reviewerly/exHarmony huggingface dataset:
Towards Improving Adversarial Training of NLP Models

Adversarial training, a method for learning robust deep neural networks, constructs adversarial examples during training.However, recent methods for generating NLP adversarial examples involve combinatorial search and expensive sentence encoders for constraining the generated instances.As a result, it remains challenging to use vanilla adversarial training to improve NLP models' performance, and the benefits are mainly uninvestigated.This paper proposes a simple and improved vanilla adversarial training process for NLP models, which we name Attacking to Training (A2T ).The core part of A2T is a new and cheaper word substitution attack optimized for vanilla adversarial training.We use A2T to train BERT and RoBERTa models on IMDB, Rotten Tomatoes, Yelp, and SNLI datasets.Our results empirically show that it is possible to train robust NLP models using a much cheaper adversary.We demonstrate that vanilla adversarial training with A2T can improve an NLP model's robustness to the attack it was originally trained with and also defend the model against other types of word substitution attacks.Furthermore, we show that A2T can improve NLP models' standard accuracy, cross-domain generalization, and interpretability. 1
Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning

Information sharing is one of the huge topics in social media platform regarding the daily news related to events or disasters happens in nature or its human-made.The automatic urgent need identification and sharing posts and information delivery with a short response are essential tasks in this area.The key goal of this research is developing a solution for management of disasters and emergency response using social media platforms as a core component.This process focuses on text analysis techniques to improve the process of authorities in terms of emergency response and filter the information using the automatically gathered information to support the relief efforts.Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to extract real-time content related to the emergency events to comfort the fast response in a critical situation.Similarly, the blockchain framework used in this process for trust verification of the detected events and eliminating the single authority on the system.The main reason of using the integrated system is to improve the system security and transparency to avoid sharing the wrong information related to an event in social media.
PDBL: Improving Histopathological Tissue Classification With Plug-and-Play Pyramidal Deep-Broad Learning

Histopathological tissue classification is a simpler way to achieve semantic segmentation for the whole slide images, which can alleviate the requirement of pixel-level dense annotations. Existing works mostly leverage the popular CNN classification backbones in computer vision to achieve histopathological tissue classification. In this paper, we propose a super lightweight plug-and-play module, named Pyramidal Deep-Broad Learning (PDBL), for any well-trained classification backbone to improve the classification performance without a re-training burden. For each patch, we construct a multi-resolution image pyramid to obtain the pyramidal contextual information. For each level in the pyramid, we extract the multi-scale deep-broad features by our proposed Deep-Broad block (DB-block). We equip PDBL in three popular classification backbones, ShuffLeNetV2, EfficientNetb0, and ResNet50 to evaluate the effectiveness and efficiency of our proposed module on two datasets (Kather Multiclass Dataset and the LC25000 Dataset). Experimental results demonstrate the proposed PDBL can steadily improve the tissue-level classification performance for any CNN backbones, especially for the lightweight models when given a small among of training samples (less than 10%). It greatly saves the computational resources and annotation efforts. The source code is available at: https://github.com/linjiatai/PDBL.
How University Teachers View the Digital Transformation of Higher Education

The absolute majority of publications about changes in higher education resulting from the COVID-19 pandemic focus on the problems faced by students. They fail to articulate the position of university faculty members who are concerned about their dwindling role as a result of the digital transformation of education. Since 2020, the Institute of Social Analysis and Forecasting at the Russian Presidential Academy of National Economy and Public Administration with the support of the Russian Ministry of Science and Higher Education has conducted a monitoring study of the attitude of university faculty members to the changes taking place in higher education. The present article is based on the results of three research waves (non-random, administrative, two-stream samples) conducted in April 2020 (N = 33,987), June-July 2020 (N = 27,484) and April-May 2021 (N = 26,334). An overall positive trend is observed in teacher attitudes: the peak of discontent about the introduction of distance education has passed, and the attitude to online learning has become calmer and more level-headed. Most teachers continue to express unconditional support for traditional in-person learning, however. The article takes a close look at the attitude of teachers to the digital transformation of higher education and analyzes their narratives. Teachers believe that the most promising aspect of the digital transformation of universities is the use of blended learning technologies that combine the benefits of classical and innovative teaching methods. The article identifies risk factors and further opportunities for digital innovations in higher education.
Efficient and Robust MultiView Clustering With Anchor Graph Regularization

Multi-view clustering has received widespread attention owing to its effectiveness by integrating multi-view data appropriately, but traditional algorithms have limited applicability to large-scale real-world data due to their high computational complexity and low robustness. Focusing on the aforementioned issues, we propose an efficient and robust multi-view clustering algorithm with anchor graph regularization (ERMC-AGR). In this work, a novel anchor graph regularization (ARG) is designed to improve the quality of the learned embedded anchor graph (EAG), and the obtained EAG is decomposed by nonnegative matrix factorization (NMF) under correntropy criterion to acquire clustering results directly. Different from the traditional graph regularization that needs to construct a large-scale Laplacian matrix pertaining to the all-sample graph, our lightweight AGR, constructed from the perspective of anchors, can reduce the computational complexity significantly while improving the EAG quality. Moreover, a factor matrix of NMF is constrained to be the cluster indicator matrix to omit additional k-means after optimization. Subsequently, correntropy is utilized to improve the effectiveness and robustness of ERMC-AGR owing to its promising performance to complex noises and outliers. Extensive experiments on real-world datasets and noisy datasets show that ERMC-ARG can improve the clustering efficiency and robustness while ensuring comparable or even better effectiveness.
MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration

Abstract Robust automatic event detection and location is central to real-time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both the methods, we propose a new workflow—MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ-Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short-term average over long-term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.
Up to 100x Faster Data-Free Knowledge Distillation

Data-free knowledge distillation (DFKD) has recently been attracting increasing attention from research communities, attributed to its capability to compress a model only using synthetic data. Despite the encouraging results achieved, state-of-the-art DFKD methods still suffer from the inefficiency of data synthesis, making the data-free training process extremely time-consuming and thus inapplicable for large-scale tasks. In this work, we introduce an efficacious scheme, termed as FastDFKD, that allows us to accelerate DFKD by a factor of orders of magnitude. At the heart of our approach is a novel strategy to reuse the shared common features in training data so as to synthesize different data instances. Unlike prior methods that optimize a set of data independently, we propose to learn a meta-synthesizer that seeks common features as the initialization for the fast data synthesis. As a result, FastDFKD achieves data synthesis within only a few steps, significantly enhancing the efficiency of data-free training. Experiments over CIFAR, NYUv2, and ImageNet demonstrate that the proposed FastDFKD achieves 10x and even 100x acceleration while preserving performances on par with state of the art. Code is available at https://github.com/zju-vipa/Fast-Datafree.
Educational direction of STEM in the system of realization of blended teaching of physics

Today's requirements for the training of specialists encourage the modernization of education through the introduction of new educational technologies, in particular the introduction of STEM (Science, Technology, Engineering and Mathematics). The article analyzes the aspects of the concept of STEM implementation in the educational sector of Ukraine. The analysis of scientific works on the development of STEM education allowed to establish the features of teaching physics, taking into account current trends in education. The components of STEM teaching of physics include means of blended learning in combination with cloud-based technologies. This concept is especially relevant in the context of the COVID-19 pandemic. Seven models of blended learning, which are the most common in educational practice in Ukraine, are analyzed. The concepts of compositional combination of full-scale experiment with the use of digital laboratories, cloud services and BYOD (Bring Your Own Device) technologies as tools for the implementation of blended learning in the STEM system are outlined. Guided by the recommendations of the state program to improve the quality of natural and mathematical education, the emphasis is on the use of modern experimental tools and digital laboratories. The use of digital laboratories makes it possible to organize a physical experiment at a fundamentally new level. An example of a complex study of mechanics using a digital laboratory, cloud services and BYOD technology is given. The results of the pedagogical experiment convincingly prove that the technologies of blended learning with the use of cloud services and BYOD tools are a powerful tool in the work of teachers.
A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning

Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is often difficult or even impossible. In such cases, we can easily face the problem of imbalanced learning. There are many methods in the literature for solving the imbalanced learning problem, so it has become a serious question how to compare the performance of the imbalanced learning methods. Inadequate validation techniques can provide misleading results (e.g., due to data shift), which leads to the development of methods designed for imbalanced data sets, such as stratified cross-validation (SCV) and distribution optimally balanced SCV (DOB-SCV). Previous studies have shown that higher classification performance scores (AUC) can be achieved on imbalanced data sets using DOB-SCV instead of SCV. We investigated the effect of the oversamplers on this difference. The study was conducted on 420 data sets, involving several sampling methods and the DTree, kNN, SVM, and MLP classifiers. We point out that DOB-SCV often provides a little higher F1 and AUC values for classification combined with sampling. However, the results also prove that the selection of the sampler–classifier pair is more important for the classification performance than the choice between the DOB-SCV and the SCV techniques.
A Study of "Churn" in Tweets and Real-Time Search Queries

The real-time nature of Twitter means that term distributions in tweets and in search queries change rapidly: the most frequent terms in one hour may look very different from those in the next. Informally, we call this phenomenon "churn". Our interest in analyzing churn stems from the perspective of real-time search. How do we "correctly" compute term statistics, considering that the underlying distributions change rapidly? In this paper, we present an analysis of tweet and query churn on Twitter, as a first step to answering this question. Analyses reveal interesting insights on the temporal dynamics of term distributions on Twitter and hold implications for the design of search systems.
D-STC: Deep learning with spatio-temporal constraints for train drivers detection from videos

Video-based train driver operation monitoring is one of the emerging requirements for train safety management and driving operation regularization. Recent years, deep learning methods such as Faster R-CNN have achieved excellent detection performance on images. However, they are not specially designed for object detection from videos, especially for those train drivers who often perform tiny moving in the monitoring video. Spatial and temporal information of videos are not fully explored together to solve this problem. In this paper, a new framework D-STC is proposed to handles the complex situations in train cab and detect train drivers from videos in a more reliable way. The proposed framework first utilizes fine tuning Faster R-CNN framework to detect the train drivers as the initial detection results. Then, the initial detection results of each frame is processed further to suppress false detection results by using the customized spatial constraints. Finally, an optimal threshold adjustment mechanism is presented to improve detection accuracy for the whole video sequence. The D-STC framework improves the accuracy of train driver detection and fully guarantees the detection speed for videos. Experimental results demonstrate the effectiveness of the proposed framework.
Interactive Genetic Algorithm Oriented toward the Novel Design of Traditional Patterns

To create alternative complex patterns, a novel design method is introduced in this study based on the error back propagation (BP) neural network user cognitive surrogate model of an interactive genetic algorithm with individual fuzzy interval fitness (IGA-BPFIF). First, the quantitative rules of aesthetic evaluation and the user’s hesitation are used to construct the Gaussian blur tool to form the individual’s fuzzy interval fitness. Then, the user’s cognitive surrogate model based on the BP neural network is constructed, and a new fitness estimation strategy is presented. By measuring the mean squared error, the surrogate model is well managed during the evolution of the population. According to the users’ demands and preferences, the features are extracted for the interactive evolutionary computation. The experiments show that IGA-BPFIF can effectively design innovative patterns matching users’ preferences and can contribute to the heritage of traditional national patterns.
A Weighted Threshold Secret Sharing Scheme for Remote Sensing Images Based on Chinese Remainder Theorem

The recent advances in remote sensing and computer techniques give birth to the explosive growth of remote sensing images. The emergence of cloud storage has brought new opportunities for storage and management of massive remote sensing images with its large storage space, cost savings. However, the openness of cloud brings challenges for image data security. In this paper, we propose a weighted image sharing scheme to ensure the security of remote sensing in cloud environment, which takes the weights of participants (i.e., cloud service providers) into consideration. An extended Mignotte sequence is constructed according to the weights of participants, and we can generate image shadow shares based on the hash value which can be obtained from gray value of remote sensing images. Then we store the shadows in every cloud service provider, respectively. At last, we restore the remote sensing image based on the Chinese Remainder Theorem. Experimental results show the proposed scheme can effectively realize the secure storage of remote sensing images in the cloud. The experiment also shows that no matter weight values, each service providers only needs to save one share, which simplifies the management and usage, it also reduces the transmission of secret information, strengthens the security and practicality of this scheme.
Research Challenges at the Intersection of Big Data, Security and Privacy

As reports from McKinsey Global Institute Mckinsey (2011) and the World Economic Forum Schwab2 (2016) suggest, capturing, storing and mining “big data” may create signiﬁcant value in many industries3 ranging from health care to location based services. For example, McKinsey estimates that capturing4 the value of big data can create $300 billion dollar annual value in the US healthcare sector and $6005 billion dollar annual consumer surplus globally Mckinsey (2011). Still, several important issues need to be6 addressed to capture the full potential of big data. Among them, security and privacy as well as incentives7 to share data are identiﬁed as critical Mckinsey (2011). As big data becomes the new oil for the digital8 economy, realizing the beneﬁts that big data can bring requires considering many different security and9 privacy issues. Basically, the entire big data pipeline needs to be considered with security and privacy in10 mind. For example, while the big data is stored and recorded, appropriate privacy-aware access control11 policies need to be enforced so that the big data is only used for legitimate purposes. On the other hand,12 while linking and sharing data across organizations, privacy/security issues need to be considered. Below,13 we provide an overview of research challenges that are at the intersection of cybersecurity, privacy and big14 data.
Exploring autonomy in the design of an intelligent health assistant for older adults

In the last few years, interest in intelligent voice assistants (IVAs) to aid older adults in living healthier and longer at home has seen a drastic increase. As these devices become more integrated into the lives of older adults to assist with health and wellness tasks, it is rapidly becoming important to understand how to design systems that also match older adults' goals for how they want to govern their personal health tasks. As a first step in understanding older adults' needs for autonomy in intelligent health assistant (IHA) design, we conducted a Wizard of Oz (WOZ) study that included a semi-structured interview with 10 older adults to understand their needs and concerns about IHAs for consumer health. We present our findings as opportunities to design IHAs that help inform users in order to meet their needs for autonomy.
Quantum filtering for a two-level atom driven by two counter-propagating photons

The purpose of this paper is to propose quantum filters for a two-level atom driven by two continuous-mode counter-propagating photons and under continuous measurements. Two scenarios of multiple measurements are discussed: (1) homodyne detection plus photodetection and (2) two homodyne detections. Filtering equations for both cases are derived explicitly. As demonstration, the two input photons with rising exponential and Gaussian pulse shapes are used to excite a two-level atom under two homodyne detection measurements. Simulations reveal scaling relations between atom-photon coupling and photonic pulse shape for maximum atomic excitation.
A simplified and versatile calibration method for multi-camera optical systems in 3D particle imaging

This article describes a stereoscopic multi-camera calibration method that does not require any optical model. It is based on a measure of the light propagation within the measurement volume only instead of modeling its entire path up to the sensors. The calibration uses simple plane by plane transformations which allow us to directly link pixel coordinates to light rays. The appeal of the proposed method relies on the combination of its simplicity of implementation (it is particularly easy to apply in any sophisticated optical imaging setup), its versatility (it can easily handle index-of-refraction gradients, as well as complex optical arrangements), and its accuracy {we show that the proposed method gives better accuracy than commonly used techniques, based on Tsai’s simple pinhole camera model [R. Tsai, J. Rob. Autom. 3, 323 (1987)], while its numerical implementation remains extremely simple}. Based on ideas that have been available in the fluid mechanics community, this method is a compact turn-key algorithm that can be implemented with open-source routines.