Reviewerly/exHarmony huggingface dataset:
Foreign Language Teacher Perceptions of the Virtual Classroom

This study investigated foreign language (FL) teachers’ experiences of the virtual classroom during COVID-19 lockdown. The sample consisted of 405 FL teachers who participated in the survey which aimed to measure FL teachers’ perceptions of TPACK, virtual classroom activities, online self-efficacy, student engagement and encountered obstacles. The findings indicate that teachers working in remote areas practice fewer virtual classroom activities and thus perceive low student online engagement. The article discusses the differences between FL teachers regarding the FL they teach, i.e., differences in student online engagement exist between the FL taught, and differences in virtual classroom activities depend on the FL taught. In addition, school stage predicts FL teachers’ online self-efficacy and the relationship between teachers’ perceptions of TPACK and school size exists.
Stick: An End-to-End Encryption Protocol Tailored for Social Network Platforms

End-to-End Encryption (E2EE) has become a de facto standard in messengers, especially after the development of the secure messaging protocol – Signal. However, the adoption of E2EE has been limited to messengers, and has not yet seen a noticeable trace in social network platforms, despite the increase in users’ privacy violations. In this paper, we propose, verify, implement and evaluate a novel E2EE protocol – Stick. Stick is a Signal-based protocol tailored for social network platforms. We believe our protocol is the first to support re-establishable encryption sessions in an asynchronous multi-device setting while preserving forward secrecy and introducing backward secrecy. Stick includes several innovative features, including a new session concept, multiple pairwise sessions and refreshing identity keys. We verified Stick using Verifpal - a formal verification tool in the symbolic model. Our security analysis shows our protocol does achieve a form of post-compromise security in many-to-many communications – the trait most group protocols lack. Most importantly, the Stick protocol can re-establish encryption sessions while ensuring authentication and confidentiality. We implemented our protocol as a stand-alone open-source API. Our evaluation shows the Stick protocol can be used in a real-world social network app with no noticeable compromise on usability or performance.
FT-DeepNets: Fault-Tolerant Convolutional Neural Networks with Kernel-based Duplication

Deep neural network (deepnet) applications play a crucial role in safety-critical systems such as autonomous vehicles (AVs). An AV must drive safely towards its destination, avoiding obstacles, and respond quickly when the vehicle must stop. Any transient errors in software calculations or hardware memory in these deepnet applications can potentially lead to dramatically incorrect results. Therefore, assessing and mitigating any transient errors and providing robust results are important for safety-critical systems. Previous research on this subject focused on detecting errors and then recovering from the errors by re-running the network. Other approaches were based on the extent of full network duplication such as the ensemble learning-based approach to boost system fault-tolerance by leveraging each model&#x2019;s advantages. However, it is hard to detect errors in a deep neural network, and the computational overhead of full redundancy can be substantial.We first study the impact of the error types and locations in deepnets. We next focus on selecting which part should be duplicated using multiple ranking methods to measure the order of importance among neurons. We find that the duplication overhead for computation and memory is a trade-off between algorithmic performance and robustness. To achieve higher robustness with less system overhead, we present two error protection mechanisms that only duplicate parts of the network from critical neurons. Finally, we substantiate the practical feasibility of our approach and evaluate the improvement in the accuracy of a deepnet in the presence of errors. We demonstrate these results using a case study with real-world applications on an Nvidia GeForce RTX 2070Ti GPU and an Nvidia Xavier embedded platform used by automotive OEMs.
Magnetic-Field-Inspired Navigation for Robots in Complex and Unknown Environments

Over the course of the past decade, we have witnessed a huge expansion in robotic applications, most notably from well-defined industrial environments into considerably more complex environments. The obstacles that these environments often contain present robotics with a new challenge - to equip robots with a real-time capability of avoiding them. In this paper, we propose a magnetic-field-inspired navigation method that significantly has several advantages over alternative systems. Most importantly, 1) it guarantees obstacle avoidance for both convex and non-convex obstacles, 2) goal convergence is still guaranteed for point-like robots in environments with convex obstacles and non-maze concave obstacles, 3) no prior knowledge of the environment, such as the position and geometry of the obstacles, is needed, 4) it only requires temporally and spatially local environmental sensor information, and 5) it can be implemented on a wide range of robotic platforms in both 2D and 3D environments. The proposed navigation algorithm is validated in simulation scenarios as well as through experimentation. The results demonstrate that robotic platforms, ranging from planar point-like robots to robot arm structures such as the Baxter robot, can successfully navigate toward desired targets within an obstacle-laden environment.
Unsupervised Cross-Domain Adaptation for Response Selection Using Self-Supervised and Adversarial Training

Recently, many neural context-response matching models have been developed for retrieval-based dialogue systems. Although existing models achieve impressive performance through learning on a large amount of in-domain parallel dialogue data, they usually perform worse in another new domain. How to transfer a response retrieval model trained in high-resource domains to other low-resource domains is a crucial problem for scalable dialogue systems. To this end, we investigate the unsupervised cross-domain adaptation for response selection when the target domain has no parallel dialogue data. Specifically, we propose a two-stage method to adapt a response selection model to a new domain using self-supervised and adversarial training based on pre-trained language models (PLMs). To efficiently incorporate domain awareness and target-domain knowledge to PLMs, we first design a self-supervised post-training procedure, including domain discrimination (DD) task, target-domain masked language model (MLM) task and target-domain next sentence prediction (NSP) task. Based on this, we further conduct the adversarial fine-tuning to empower the model to match the proper response with extracted domain-shared features as much as possible. Experimental results show that our proposed method achieves consistent and significant improvements on several cross-domain response selection datasets.
A Stereo Calibration Method of Multi-Camera Based on Circular Calibration Board

In the application of 3D reconstruction of multi-cameras, it is necessary to calibrate the camera used separately, and at the same time carry out multi-stereo calibration, and the calibration accuracy directly affects the effect of the 3D reconstruction of the system. Many researchers focus on the optimization of the calibration algorithm and the improvement of calibration accuracy after obtaining the calibration plate pattern coordinates, ignoring the impact of calibration on the accuracy of the calibration board pattern coordinate extraction. Therefore, this paper proposes a multi-camera stereo calibration method based on circular calibration plate focusing on the extraction of pattern features during the calibration process. This method preforms the acquisition of the subpixel edge acquisition based on Franklin matrix and circular feature extraction of the circular calibration plate pattern collected by the camera, and then combines the Zhang’s calibration method to calibrate the camera. Experimental results show that compared with the traditional calibration method, the method has better calibration effect and calibration accuracy, and the average reprojection error of the multi-camera is reduced by more than 0.006 pixels.
A Calculus for Multi-language Operational Semantics

We present $$\lambda ^{{\mathrm {\mathcal {L}}}}$$ , a statically typed lambda-calculus with languages as first-class citizens. In $$\lambda ^{{\mathrm {\mathcal {L}}}}$$ , languages can be defined and used to execute programs and, furthermore, they also can be bound to variables, passed to functions, and returned by functions, among other operations. Moreover, code of different languages can safely interact thanks to pre- and post-conditions that are checked at run-time. We provide a type system and an operational semantics for $$\lambda ^{{\mathrm {\mathcal {L}}}}$$ . We observe that Milner's type safety is too strong a property in this context, as programmers may intentionally execute unsafe languages. We then identify a type safety theorem that is specific to the domain of multi-language programming, and we prove that $$\lambda ^{{\mathrm {\mathcal {L}}}}$$ satisfies this theorem.
Application of Thermography and Adversarial Reconstruction Anomaly Detection in Power Cast-Resin Transformer

Dry-type power transformers play a critical role in the power system. Detecting various overheating faults in the running state of the power transformer is necessary to avoid the collapse of the power system. In this paper, we propose a novel deep variational autoencoder-based anomaly detection method to recognize the overheating position in the operation of the dry-type transformer. Firstly, the thermal images of the transformer are acquired by the thermal camera and collected for training and testing datasets. Next, the variational autoencoder-based generative adversarial networks are trained to generate the normal images with different running conditions from heavy to light loading. Through the pixel-wise cosine difference between original and reconstructed images, the residual images with faulty features are obtained. Finally, we evaluate the trained model and anomaly detection method on normal and abnormal testing images to demonstrate the effeteness and performance of the proposed work. The results show that our method effectively improves the anomaly accuracy, AUROC, F1-scores and average precision, which is more effective than other anomaly detection methods. The proposed method is simple, lightweight and has less storage size. It reveals great advantages for practical applications.
Adaptive Redirection: A Context-Aware Redirected Walking Meta-Strategy

Previous research has established redirected walking as a potential answer to exploring large virtual environments via natural locomotion within a limited physical space. However, much of the previous work has either focused on investigating human perception of redirected walking illusions or developing novel redirection techniques. In this paper, we take a broader look at the problem and formalize the concept of a complete redirected walking system. This work establishes the theoretical foundations for combining multiple redirection strategies into a unified framework known as adaptive redirection. This meta-strategy adapts based on the context, switching between a suite of strategies with a priori knowledge of their performance under the various circumstances. This paper also introduces a novel static planning strategy that optimizes gain parameters for a predetermined virtual path, known as the Combinatorially Optimized Pre-Planned Exploration Redirector (COPPER). We conducted a simulation-based experiment that demonstrates how adaptation rules can be determined empirically using machine learning, which involves partitioning the spectrum of contexts into regions according to the redirection strategy that performs best. Adaptive redirection provides a foundation for making redirected walking work in practice and can be extended to improve performance in the future as new techniques are integrated into the framework.
DeepRelease: Language-agnostic Release Notes Generation from Pull Requests of Open-source Software

The release note is an essential software artifact of open-source software that documents crucial information about changes, such as new features and bug fixes. With the help of release notes, both developers and users could have a general understanding of the latest version without browsing the source code. However, it is a daunting and time-consuming job for developers to produce release notes. Although prior studies have provided some automatic approaches, they generate release notes mainly by extracting information from code changes. This will result in language-specific and not being general enough to be applicable. Therefore, helping developers produce release notes effectively remains an unsolved challenge. To address the problem, we first conduct a manual study on the release notes of 900 GitHub projects, which reveals that more than 54% of projects produce their release notes with pull requests. Based on the empirical finding, we propose a deep learning based approach named DeepRelease (Deep learning based Release notes generator) to generate release notes according to pull requests. The process of release notes generation in DeepRelease includes the change entries generation and the change category (i.e., new features or bug fixes) generation, which are formulated as a text summarization task and a multi-class classification problem, respectively. Since DeepRelease fully employs text information from pull requests to summarize changes and identify the change category, it is language-agnostic and can be used for projects in any language. We build a dataset with over 46K release notes and evaluate DeepRelease on the dataset. The experimental results indicate that DeepRelease outperforms four baselines and can generate release notes similar to those manually written ones in a fraction of the time.
Word Embeddings Reveal How Fundamental Sentiments Structure Natural Language

Central to affect control theory are culturally shared meanings of concepts. That these sentiments overlap among members of a culture presumably reflects their roots in the language use that members observe. Yet, the degree to which the affective meaning of a concept is encoded in the way linguistic representations of that concept are used in everyday symbolic exchange has yet to be demonstrated. The question has methodological as well as theoretical significance for affect control theory, as language may provide an unobtrusive, behavioral method of obtaining EPA ratings complementary to those heretofore obtained via questionnaires. We pursue a series of studies that evaluate whether tools from machine learning and computational linguistics can capture the fundamental affective meaning of concepts from large text corpora. We develop an algorithm that uses word embeddings to predict EPA profiles available from a recent EPA dictionary derived from traditional questionnaires, as well as novel concepts collected using an open-source web app we have developed. Across both a held-out portion of the available data as well as the novel data, our best predictions correlate with survey-based measures of the E, P, and A ratings of concepts at a magnitude greater than 0.85, 0.8, and 0.75 respectively.
Survey on Homomorphic Encryption

The process of transforming plain text data into a secret code known as cipher text is known as encryption. The decryption procedure can be used to transform cypher text back to plain text. Homomorphic encryption is a technique which enables users to perform manipulations or computations on encrypted data itself without ever revealing the plaintext to anyone. The decryption can only be performed using private keys owned by the data owner only. In homomorphic encryption the resulting ciphertext has the same structure or form as the result generated without using HE on the same data. The structure or form of data remains the same, this property is called homomorphism. Many researchers have identified various applications of HE in different fields, mostly it is used in privacy preserving areas or where data is outsourced to third parties. The aim of this paper is to present a literature survey on different homomorphic encryption techniques and classifications. HE applications in cloud computing, electronic voting, internet of things are also reviewed with their limitations and future scope.
SimuMan: A Simultaneous Real-Time Method for Representing Motions and Emotions of Virtual Human in Metaverse

Metaverse is the next generation gaming Internet, and virtual humans play an important role in Metaverse. The simultaneous representation of motions and emotions of virtual humans attracts more attention in academics and industry, which significantly improves user experience with the vivid continuous simulation of virtual humans. Different from existing work which only focuses on either the expression of facial expressions or body motions, this paper presents a novel and real-time virtual human prototyping system, which enables a simultaneous real-time expression of motions and emotions of virtual humans (short for SimuMan). SimuMan not only enables users to generate personalized virtual humans in the metaverse world, but also enables them to naturally and simultaneously present six facial expressions and ten limb motions, and continuously generate various facial expressions by setting parameters. We evaluate SimuMan objectively and subjectively to demonstrate its fidelity, naturalness, and real-time. The experimental results show that the SimuMan system is characterized by low latency, good interactivity, easy operation, good robustness, and wide application.
Mixed sessions

Session types describe patterns of interaction on communicating channels. Traditional session types include a form of choice whereby servers offer a collection of options, of which each client selects exactly one. Mixed choices blur the distinction between servers and clients (that is, external and internal choice) by allowing options to be both offered and selected in the same choice. We introduce mixed choices in the context of session types and argue that they increase the flexibility of program development at the same time that they reduce the number of synchronisation primitives down to exactly one. We present a type system incorporating subtyping and prove preservation and absence of runtime errors for well-typed processes. We further show that classical (conventional) sessions can be faithfully and tightly embedded in mixed choices, and conversely that there is a minimal encoding from mixed choices to classical sessions. Finally, we discuss algorithmic type checking and a runtime system built on top of a conventional (choice-less) message-passing architecture.
Side-channel attacks on mobile and IoT devices for Cyber–Physical systems

The attacks that leverage the side-channels produced by processes running on mobile and IoT devices are a concrete threat for cyber–physical systems. This special issue is focused on the most recent research work that investigates novel aspects of this topic. This editorial summarizes the contributions of the seven accepted papers for this special issue.
Intelligent Clinical Decision Support

Early recognition of pathologic cardiorespiratory stress and forecasting cardiorespiratory decompensation in the critically ill is difficult even in highly monitored patients in the Intensive Care Unit (ICU). Instability can be intuitively defined as the overt manifestation of the failure of the host to adequately respond to cardiorespiratory stress. The enormous volume of patient data available in ICU environments, both of high-frequency numeric and waveform data accessible from bedside monitors, plus Electronic Health Record (EHR) data, presents a platform ripe for Artificial Intelligence (AI) approaches for the detection and forecasting of instability, and data-driven intelligent clinical decision support (CDS). Building unbiased, reliable, and usable AI-based systems across health care sites is rapidly becoming a high priority, specifically as these systems relate to diagnostics, forecasting, and bedside clinical decision support. The ICU environment is particularly well-positioned to demonstrate the value of AI in saving lives. The goal is to create AI models embedded in a real-time CDS for forecasting and mitigation of critical instability in ICU patients of sufficient readiness to be deployed at the bedside. Such a system must leverage multi-source patient data, machine learning, systems engineering, and human action expertise, the latter being key to successful CDS implementation in the clinical workflow and evaluation of bias. We present one approach to create an operationally relevant AI-based forecasting CDS system.
LiveSR: Enabling Universal HD Live Video Streaming With Crowdsourced Online Learning

The high-definition (HD) live video streaming has gained significant popularity due to the rapid growth of 4 G/5 G and social media. However, for devices with constrained bandwidth, they still have no sufficient bandwidth to support HD live video streaming. In this paper, we propose a neural-enhanced HD live video streaming framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveSR</i> to provide universal HD live video streaming for both bandwidth-constrained and bandwidth-rich devices. For bandwidth-constrained devices, LiveSR delivers low-quality video streams and then boosts video quality at the device side with super-resolution (SR) techniques. The difficulty lies in how to train the SR model with low cost and conduct quality enhancement in real time. To address these challenges, we design a crowdsourced online training method by exploiting computation resources and HD video data on bandwidth-rich devices in the same video channel. We also propose an imitation learning-based decision making algorithm to make downloading decisions for video chunks and SR models under limited bandwidth. We implement and evaluate our proposed LiveSR framework using real network traces, and the experiment results show that LiveSR outperforms all the other baseline approaches, with 65.5% improvement in terms of the average QoE and 5.7% in terms of video quality (i.e., PSNR), and the achieved frame rate can be as high as 30 frames per second.