Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Telegram App in Learning English

There have been enormous improvements in every field in the modernized and technological period. When human understanding advances, several new developments have come to light and Such innovations contribute to human development. Because of the emergence of this modern technology and internet, the field of education is evolving a lot. The usual, blackboard books are replaced by smart boards and e-books. In addition, there is a behavioral shift in the learning of the learners and they rely more on modern technologies. Education, rather than teacher dependence. While teaching English, the teachers bring the new technology into their classrooms using the technology available, they change their methods and techniques to teach the EFL/ESL learners English. In their EFL / ESL classrooms even some teachers encourage the learners to use mobile phones to make their learning simple. This paper focuses on the studies done on the use of mobile phones in the classrooms. Moreover, the pros of using mobile apps for the learners are discussed.
The concept of a digital transformation of a traditional university to a “digital university”

One of the priorities of the government program “Digital economy of the Russian Federation” is the progressive development of the existing education system, which should provide the economy with competent personnel. In addition, this document declares the need to create a system of motivation for the development of the necessary competencies and participation of personnel in the development of the digital economy of Russia. All this requires the transformation of the classical educational system, which ultimately implies the creation of a “digital university”. According to the concept, proposed by the Agency for Strategic Initiatives together with Skolkovo University, the “digital university” will allow us to change the form and content of education in accordance with the requirements for new digital economy personnel. At the same time, the main emphasis should be placed on the individual trajectory of education, that is, collecting information not only according to the teacher’s assessment, but also collecting information in the student’s online course (what subjects the student was interested in, how he passed the intermediate tests, etc.). In addition, you need to create a specific set of services at the university. They should work in a similar way to the super services that are already being created in the Ministry of Digital Development, Communications and Mass Media of the Russian Federation for receiving public services online. So, the student will be able to get help and get registered in the military enlistment office in a single window mode, without having to go to the offices. In addition, changes are needed related to the content of education. So, along with the already developing segment of mass online courses, courses using AR and VR and virtual simulators should be introduced in universities. With the help of modern technologies, students will be able to learn the profession more effectively in practice. The article attempts to generalize the minimum set of necessary transformations of a classical university into a “digital” one and present their description.
An Invocation Chain Test and Evaluation Method for Fog Computing

In this paper, an invocation chain technology is used to test the security of fog computing systems. Atomic attacks based on the attack graph are combined according to the type, time sequence, and causal relationship. Different test sequences have gone through according to the principle of depth-first. In addition, the vulnerability assessment based on an invocation chain is evaluated to verify whether it can detect existing or unknown vulnerability in fog computing systems. Finally, the experimental results show that the invocation chain test and evaluation method based on the attack graph can evaluate the system risk quantitatively rather than qualitatively by calculating comprehensive probability on all test sequences.
Deep RP-CNN for Burst Signal Detection in Cognitive Radios

This article proposes a convolutional neural network (CNN)-based signal detection scheme using image encoding techniques for burst signals in wireless networks.The conventional signal detection approach based on energy measurement performs poorly when detecting burst signals owing to the short signal length and relatively long sensing duration.To detect the presence of a burst signal, the proposed scheme encodes the received time-series signal into an image that is further fed to a CNN model.For image encoding techniques, recurrence plot algorithms are adopted in the proposed scheme with a CNN.In particular, the proposed scheme achieves the correct detection probability of 99% even in the presence of a short burst signal at SNR= -10 dB.
A Novel (2, 3) Reversible Secret Image Sharing Based on Fractal Matrix

This article first proposes two types of three-dimensional (3D) fractal reference matrices.Then, a new reversible (2, 3) secret image sharing scheme based on a fractal matrix is presented.With the help of the fractal reference matrix, the secret data can be embedded in an original image to generate three meaningful shadow images.It is worth mentioning that these two types of fractal matrices are designed for different hiding capacity, meanwhile the characteristics of the fractal matrix can guarantee good visual quality of the generated shadow images.Moreover, we provide two authentication mechanisms according to different situations, one of the authentication mechanisms can achieve 100% ability cheat detection.When the authentication is passed, we only need two of the three shadow images to extract the correct secret data and perfectly restore the original image.Experimental results show that our proposed scheme provides excellent authentication ability and good visual quality.
Detecting Asymmetric Patterns and Localizing Cancers on Mammograms

One in eight women develops invasive breast cancer in her lifetime. The frontline protection against this disease is mammography. While computer-assisted diagnosis algorithms have made great progress in generating reliable global predictions, few focus on simultaneously producing regions of interest (ROIs) for biopsy. Can we combine ROI-oriented algorithms with global classification of cancer status, which simultaneously highlight suspicious regions and optimize classification performance? Can the asymmetry of breasts be adopted in deep learning for finding lesions and classifying cancers? We answer the above questions by building deep-learning networks that identify masses and microcalcifications in paired mammograms, exclude false positives, and stepwisely improve performance of the model with asymmetric information regarding the breasts. This method achieved a co-leading place in the Digital Mammography DREAM Challenge for predicting breast cancer. We highlight here the importance of this dual-purpose process that simultaneously provides the locations of potential lesions in mammograms.
Chatbot Experiences of Informal Language Learners

In 2016, a number of language applications released chatbots to complement their programmes. Used primarily in informal learning settings, chatbots enable language learners to engage in conversational speaking practice, which can be perceived as less threatening than face-to-face interactions with native speakers. This study takes a closer look at four second language (L2) chatbots—Duolingo, Eggbun, Memrise, and Mondly—and analyses the experiences which informal language learners expressed on various online platforms (e.g., Duolingo forum, Memrise community, Reddit). Results indicate a degree of curiosity and a willingness to engage in conversation with chatbots. However, learners expressed frustration if the dialogues did not correspond to their learning goals or if they were excluded from using the bots because of technical or payment issues, or discontinuation of services.
Assessment of threat and negativity bias in virtual reality

Abstract Negativity bias, i.e., tendency to respond strongly to negative stimuli, can be captured via behavioural and psychophysiological responses to potential threat. A virtual environment (VE) was created at room-scale wherein participants traversed a grid of ice blocks placed 200 m above the ground. Threat was manipulated by increasing the probability of encountering ice blocks that disintegrated and led to a virtual fall. Participants interacted with the ice blocks via sensors placed on their feet. Thirty-four people were recruited for the study, who were divided into High (HN) and Low (LN) Neuroticism groups. Movement data were recorded alongside skin conductance level and facial electromyography from the corrugator supercilii and zygomaticus major. Risk-averse behaviours, such as standing on ‘safe’ blocks and testing blocks prior to movement, increased when threat was highest. HN individuals exhibited more risk-averse behaviour than the LN group, especially in the presence of high threat. In addition, activation of the corrugator muscle was higher for HN individuals in the period following a movement to an ice block. These findings are discussed with respect to the use of room-scale VE as a protocol for emotion induction and measuring trait differences in negativity bias within VR.
Evolutionary Algorithm Enhanced Neural Architecture Search for Text-Independent Speaker Verification

State-of-the-art speaker verification models are based on deep learning techniques, which heavily depend on the handdesigned neural architectures from experts or engineers. We borrow the idea of neural architecture search(NAS) for the textindependent speaker verification task. As NAS can learn deep network structures automatically, we introduce the NAS conception into the well-known x-vector network. Furthermore, this paper proposes an evolutionary algorithm enhanced neural architecture search method called Auto-Vector to automatically discover promising networks for the speaker verification task. The experimental results demonstrate our NAS-based model outperforms state-of-the-art speaker verification models.
Can Speaker Augmentation Improve Multi-Speaker End-to-End TTS?

Previous work on speaker adaptation for end-to-end speech synthesis still falls short in speaker similarity. We investigate an orthogonal approach to the current speaker adaptation paradigms, speaker augmentation, by creating artificial speakers and by taking advantage of low-quality data. The base Tacotron2 model is modified to account for the channel and dialect factors inherent in these corpora. In addition, we describe a warm-start training strategy that we adopted for Tacotron2 training. A large-scale listening test is conducted, and a distance metric is adopted to evaluate synthesis of dialects. This is followed by an analysis on synthesis quality, speaker and dialect similarity, and a remark on the effectiveness of our speaker augmentation approach. Audio samples are available online.
3D Self-Supervised Methods for Medical Imaging

Self-supervised learning methods have witnessed a recent surge of interest after proving successful in multiple application fields. In this work, we leverage these techniques, and we propose 3D versions for five different self-supervised methods, in the form of proxy tasks. Our methods facilitate neural network feature learning from unlabeled 3D images, aiming to reduce the required cost for expert annotation. The developed algorithms are 3D Contrastive Predictive Coding, 3D Rotation prediction, 3D Jigsaw puzzles, Relative 3D patch location, and 3D Exemplar networks. Our experiments show that pretraining models with our 3D tasks yields more powerful semantic representations, and enables solving downstream tasks more accurately and efficiently, compared to training the models from scratch and to pretraining them on 2D slices. We demonstrate the effectiveness of our methods on three downstream tasks from the medical imaging domain: i) Brain Tumor Segmentation from 3D MRI, ii) Pancreas Tumor Segmentation from 3D CT, and iii) Diabetic Retinopathy Detection from 2D Fundus images. In each task, we assess the gains in data-efficiency, performance, and speed of convergence. Interestingly, we also find gains when transferring the learned representations, by our methods, from a large unlabeled 3D corpus to a small downstream-specific dataset. We achieve results competitive to state-of-the-art solutions at a fraction of the computational expense. We publish our implementations for the developed algorithms (both 3D and 2D versions) as an open-source library, in an effort to allow other researchers to apply and extend our methods on their datasets.
Stochastic Optimization for Performative Prediction

In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions. We initiate the study of stochastic optimization for performative prediction. What sets this setting apart from traditional stochastic optimization is the difference between merely updating model parameters and deploying the new model. The latter triggers a shift in the distribution that affects future data, while the former keeps the distribution as is. Assuming smoothness and strong convexity, we prove rates of convergence for both greedily deploying models after each stochastic update (greedy deploy) as well as for taking several updates before redeploying (lazy deploy). In both cases, our bounds smoothly recover the optimal $O(1/k)$ rate as the strength of performativity decreases. Furthermore, they illustrate how depending on the strength of performative effects, there exists a regime where either approach outperforms the other. We experimentally explore the trade-off on both synthetic data and a strategic classification simulator.
Word associations and the distance properties of context-aware word embeddings

What do people know when they know the meaning of words?Word associations have been widely used to tap into lexical representations and their structure, as a way of probing semantic knowledge in humans.We investigate whether current word embedding spaces (contextualized and uncontextualized) can be considered good models of human lexical knowledge by studying whether they have comparable characteristics to human association spaces.We study the three properties of association rank, asymmetry of similarity and triangle inequality.We find that word embeddings are good models of some word associations properties.They replicate well human associations between words, and, like humans, their context-aware variants show violations of the triangle inequality.While they do show asymmetry of similarities, their asymmetries do not map those of human association norms.
The workweek is the best time to start a family – A Study of GPT-2 Based Claim Generation

Argument generation is a challenging task whose research is timely considering its potential impact on social media and the dissemination of information.Here we suggest a pipeline based on GPT-2 for generating coherent claims, and explore the types of claims that it produces, and their veracity, using an array of manual and automatic assessments.In addition, we explore the interplay between this task and the task of Claim Retrieval, showing how they can complement one another.
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events

In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations.Our proposed models receive a pair of events within a span of text as input and they identify temporal relations (Before, After, Equal, Vague) between them.Given that a key challenge with this task is the scarcity of annotated data, our models rely on either pretrained representations (i.e.RoBERTa, BERT or ELMo), transfer and multi-task learning (by leveraging complementary datasets), and self-training techniques.Experiments on the MATRES dataset of English documents establish a new state-of-the-art on this task.
Super-resolution reconstruction of a digital elevation model based on a deep residual network

Abstract The digital elevation model (DEM) is an important basic data tool applied in geoscience applications. Because of its high cost and long development cycle of enhancing hardware performance, designing the related models and algorithms to improve the resolution of DEM is of considerable significance. At present, there is little research on DEM super-resolution based on deep learning, and the results of the reconstructed DEMs obtained by existing methods are inaccurate. Therefore, deepening of the network layers is utilized to improve the accuracy of a reconstructed DEM. This paper designs a neutral network model with 30 convolutional layers to learn the feature mapping relationship between a low- and high-resolution DEM. To avoid the problem of network degradation caused by increasing the number of convolutional layers, residual learning is introduced to accelerate the convergence speed of the model, thereby preferably realizing the DEM super-resolution process. The results show that DEM super-resolution based on a deep residual network is better than that obtained using a neural network with fewer convolutional layers, and the reconstructed result of the DEM based on a deep residual network is remarkably improved in terms of the peak signal to noise ratio and visual effect.
Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.
Look Out for Apple's AR Glasses: With head-up displays, cameras, inertial sensors, and lidar on board, Apple's augmented-reality glasses could redefine wearables

Apple didn't invent the portable music player, although I challenge you to name one of the approximately 50 digital-music gadgets that preceded the iPod. Apple didn't invent the smartphone either—it just produced the first one that made people line up overnight to buy it.
Mitigating the Security Intention-Behavior Gap: The Moderating Role of Required Effort on the Intention-Behavior Relationship

Although users often express strong positive intentions to follow security policies, these positive intentions fail to consistently translate to behavior. In a security setting, the inconsistency between intentions and behavior—termed the intention-behavior gap—is particularly troublesome, as a single failure to enact positive security intentions may make a system vulnerable. We address a need in security compliance literature to better understand the intention-behavior gap by explaining how an omnipresent competing intention—the user’s desire to minimize required effort—negatively moderates the relationship between positive intentions and actual security behavior. Moreover, we posit that this moderating effect is not accounted for in extant theories used to explain behavioral information security, introducing an opportunity to broadly impact information security research to more consistently predict behavior. In three experiments, we found that high levels of required effort negatively moderated users’ intentions to follow security policies. Controlling for this moderating effect substantially increased the explained variance in security policy compliance. The results suggest that security researchers should be cognizant of the existence of competing intentions, such as the desire to minimize required effort, which may moderate the security intention-behavior relationship. Otherwise, such competing intentions may cause unexpected inconsistencies between users’ intentions to behave securely and their actual security behavior.
Capturing Users’ Reality: A Novel Approach to Generate Coherent Counterfactual Explanations

The opacity of Artificial Intelligence (AI) systems is a major impediment to their deployment.Explainable AI (XAI) methods that automatically generate counterfactual explanations for AI decisions can increase users' trust in AI systems.Coherence is an essential property of explanations but is not yet addressed sufficiently by existing XAI methods.We design a novel optimizationbased approach to generate coherent counterfactual explanations, which is applicable to numerical, categorical, and mixed data.We demonstrate the approach in a realistic setting and assess its efficacy in a humangrounded evaluation.Results suggest that our approach produces explanations that are perceived as coherent as well as suitable to explain the factual situation.
Novel Case-Based Reasoning System for Public Health Emergencies

Purpose: Several threatening infectious diseases, including influenza, Ebola, SARS, and COVID-19, have affected human society over the past decades. These disease outbreaks naturally inspire a demand for sustained and advanced safety and suppression measures. To protect public health and safety, further research developments on emergency analysis methods and approaches for effective emergency treatment generation are urgently needed to mitigate the severity of the pandemic and save lives. Methods: To address these issues, a novel case-based reasoning (CBR) system is proposed using three phases. In the first phase, the similarity between the current case and the historical cases is calculated under a variety of heterogeneous information. In the second phase, a filter approach based on grey clustering analysis is created to retrieve relevant cases. In the third phase, the cases retrieved are taken as initial host nests in a cuckoo search (CS) algorithm, and our system searches an optimal solution through iteration of this algorithm. Results: The proposed model is compared with a CBR method improved by particle swarm optimization (PSO) and a CBR method improved by a differential evolution algorithm (DE), to confirm the efficiency of our CS algorithm in adapting solutions for public health emergencies. The results show that the proposed model is better than the existing algorithms. Conclusion: The proposed model improves the speed of case retrieval using grey clustering and increases solution accuracy with CS algorithms. The present research can contribute to government, CDC, and infectious disease emergency management fields with regard to the implementation of fast and accurate public biohazard prevention and control measures based on a variety of heterogeneous information. Keywords: case-based reasoning, grey clustering, cuckoo search algorithm, public health emergencies