Reviewerly/exHarmony huggingface dataset:
Machine Learning-Based Intelligent Scoring System for English Essays under the Background of Modern Information Technology

This work is to reduce the workload of teachers in English teaching and improve the writing level of students, so as to provide a way for students to practice English composition scoring independently and satisfy the needs of college teachers and students for intelligent English composition scoring and intelligently generated comments. In this work, it firstly clarifies the teaching requirements of college English classrooms and expounds the principles and advantages of machine learning technology. Secondly, a three-layer neural network model (NNM) is constructed by using the multilayer perceptron (MLP), combined with the latent Dirichlet allocation (LDA) algorithm. Furthermore, three semantic representation vector technologies, including word vector, paragraph vector, and full-text vector feature, are used to represent the full-text vocabulary of English composition. Then, a model based on the K-nearest neighbors (kNN) algorithm is proposed to generate English composition evaluation, and a final score based on the extreme gradient boosting (XGBoost) model is proposed. Finally, a model dataset is constructed using 800 college students' English essays for the CET-4 mock test, and the model is tested. The research results show that the semantic representation vector technology proposed can more effectively extract the lexical semantic features of English compositions. The XGBoost model and the kNN algorithm model are used to score and evaluate English compositions, which improves the accuracy of the scores. This makes the management of the entire scoring model more efficient and more accurate. It means that the model proposed is better than the traditional model in terms of evaluation accuracy. This work provides a new direction for the application of artificial intelligence technology in English teaching under the background of modern information technology.
Digital Inequities and Digital Inclusion in Education

COVID-19 exposed a wide range of challenges hidden unnoticed in the promise of digital education. Digital education was once promised as the grand equalizer of access and inclusion in education. However, the massive deployment of digital tools in the educational realm during COVID-19 provided significant counterevidence to this promise. If education is a fundamental right and if digital technologies are the only way to gain access to education, it is important that these technologies be made available to everyone for effective use. However, as COVID-19 would demonstrate to us, this has not been the case. There have been stark and widespread inequities in the availability and quality of digital technologies for education, and the need for purposeful efforts to bridge the gap was felt prominently. In the backdrop of COVID-19, this chapter identifies some of the key equity issues and proposes solutions to address them.
Generating Content-Preserving and Semantics-Flipping Adversarial Text

Natural Language Processing (NLP) models are often vulnerable to semantics-preserving adversarial attacks. That is, they make different semantic predictions on input instances with similar content and semantics. However, it remains unclear to which extent modern NLP models are vulnerable to content-preserving and semantics-flipping (CPSF) adversarial attacks. That is, they would make the same semantic prediction on input instances with similar content but flipped semantics. Attackers can use either semantics-preserving or CPSF adversarial examples to create misunderstanding between humans and models, and incur severe consequences in real-world applications. However, this equally important problem on CPSF adversarial examples has not been studied by researchers yet. In this paper, we perform the first study to investigate CPSF adversarial examples and propose CPSF adversarial attacks to reveal this new type of vulnerability of NLP models. We develop a two-stage approach to generate CPSF adversarial examples. Our experiments on two types of NLP tasks, sentiment analysis and textual entailment, demonstrate that CPSF adversarial examples can successfully fool victim models while preserving the same content with flipped semantics to humans. We further validate the good transferability of CPSF adversarial examples on NLP services of Microsoft and Google. Moreover, we demonstrate that adversarial training can to a meaningful extent mitigate CPSF adversarial attacks. Overall, our work implies that researchers need to improve NLP models' robustness against CPSF adversarial attacks that uniquely exploit the blind spots where NLP models are too insensitive to even big changes in semantics.
Using Ontologies for Knowledge-Based Monitoring of Building Energy Systems

Many developments and research have led in last decade to enhanced building automation systems embedding data analytics algorithms and performing energy-optimized building systems control. The presented approach aims at providing a complementary analysis layer compared to such systems by the means of semantic modeling and knowledge reuse. For that purpose, it relies on a semantic description of a building energy system from which energy conservation measures can be derived. Starting from an ontological description of the building energy system, an expert system is proposed that provides a monitoring plan and operation advices to building users and managers. This system consumes for a part real-time building data gathered during its operation. For another part, it relies on information about the built-in technical systems, which is usually available in initial design models. The resulting software application might be used in the future as an add-on to existing building management systems and for easing their configuration.
Digital Support for Bridge Inspectors through Mixed Reality BIM Data Visualizations

We describe an interactive augmented and virtual reality (AR/VR) system that supports bridge inspectors during in-office preparation, on-site inspection, and follow-up work back in the office using building information modeling (BIM) data. Originating from the process steps required to perform a digitally supported bridge inspection, we focus on our prototypes’ implementation, a combination of VR and AR methods in a prototypical development. Our goal is thus to present one possible realization of these processes on a technological level. Therefore, the collaborative aspect between on-site inspection and in-office preparation and debriefing plays an important role. Several issues of data synchronization and communication techniques need consideration and proper integration into the entire system. It is essential to consider the bridge inspectors’ needs, requirements, and working environment to avoid losing track of the target group of such an application. Low initial hurdles and user-friendliness require seamless integration of different tools and additional resources. For that reason, we aim to link engineering tools and game engine environments in an operationally and reliable application. We believe that our application may serve as a starting point for further discussions on user-friendly digital support and broader usage of innovative technologies bearing in mind the evolution and tradition of structural inspections.
FedRME: Federated Road Markings Extraction from Mobile LiDAR Point Clouds

Road markings extraction (RME) from 3D point clouds acquired by mobile LiDAR systems has been widely used for road safety and autonomous driving. However, due to the increasing awareness of personal data protection and national informat ion security regulations, most autonomous driving companies are not willing to share their private point clouds data with the community. Therefore, such restriction of centralized training might inevitably inhibit the effectiveness of RME procedure. Federated learning (FL) is a distributed machine learning architecture that could address the aforementioned privacy-accuracy dilemma to collaboratively learn a global RME model from multiple clients without sharing raw data. In this paper, we propose a novel FedRME, a federated road markings extraction system to collaboratively learn a global RME model with multiple privacy-preserved local models from 3D mobile LiDAR point clouds. FedRME adopt the classical FedAvg model to construct a generalizable global feature embedding model without accessing local data. Moreover, to tackle data heterogeneity problem that local models vary in point clouds volumes and categories, we design a dynamic weighting mechanism to optimize the cooperative training effectiveness before server aggregation. Experimental results on three real-world mobil e LiDAR point clouds datasets with federated learning settings demonstrate that FedRME not only achieves superior performance but also reduces computation by up to 25%.The source code is available at https://github.com/WwZzz/easyFL#FedRME.
A Deinterleaving Method for Mechanical-Scanning Radar Signals Based on Deep Learning

Radar signal deinterleaving has been deeply studied in the field of electronic reconnaissance. In this work, a deinterleaving method for mechanical-scanning radar signals based on deep learning is proposed. In this method, the pulse repetition interval (PRI) and pulse amplitude (PA) are used to deinterleave mechanical-scanning radar signals. A bidirectional gated recurrent unit (BGRU) is employed, and the difference of time of arrival (DTOA) and PA of the pulse stream are input into the BGRU. Based on the PA variation features of different radars, each pulse in the obtained pulse stream is classified, and the radar signals are deinterleaved. Compared to the PRI-based deinterleaving methods, the proposed method utilizes the two-dimensional information of radar signals. As a result, signal deinterleaving of different radars with the same PRI is achieved. Compared to other existing radar signal multiparameter-based deinterleaving methods, the proposed method is applicable to radar signals with complex features and to complex environments, and can utilize multiparameter in one step.
Veksel

We propose Veksel, a simple generic paradigm for constructing efficient non-interactive coin mixes. The central component in our work is a concretely efficient proof π1-many that a homomorphic commitment c* is a rerandomization of a commitment c ∈ {c1, …,cℓ} without revealing c. We formalize anonymous account-based cryptocurrency as a universally composable functionality and show how to efficiently instantiate it using π1-many in a straightforward way. We instantiate and implement π1-many from Strong-RSA, DDH and random oracles targeting ≈ 112 bits of security. The resulting NIZK has constant size (|π1-many| = 5.3 KB) and constant proving/verification time (≈ 90 ms), on an already accumulated set. Compared to ZCash---which offers comparable marginal verification cost and an anonymity set consisting of every existing transaction---our transactions are larger (6.2 KB) and verification is slower. On the other hand, Veksel relies on better studied assumptions, has no expensive trusted setup for proofs and is arguably simpler to implement. Additionally we think that π1-many might be interesting in other applications, e.g. proving possession of some credential posted on-chain. The efficiency of our concrete NIZK relies on a new Ristretto-friendly elliptic curve, Jabberwock, that is of independent interest: it can be used to efficiently prove statements on "committments on commitments'' in Bulletproofs.
Depth, Breadth, and Complexity: Ways to Attack and Defend Deep Learning Models

Deep Learning is rapidly evolving to the point that it can be used in crucial safety and security applications, including self-driving vehicles, surveillance, drones, and robots. However, these deep learning models are vulnerable to attacks based on adversarial samples that are undetectable to the human eye but cause the model to misbehave. There is an increasing demand for comprehensive and in-depth analysis of behaviors of various attacks and the possible defenses against common deep learning models under several adversarial scenarios. In this study, we conducted four separate investigations. First, we examine the relationship between the model's complexity and its robustness against the studied attacks. Second, the connection between the performance and diversity of models is examined. Third, the first and second experiments were tested across different datasets to explore the impact of the dataset on the performance of the model. Four, throughout the defense strategies, the model behavior is extensively investigated. The code, trained models, and detailed settings and results are available at: https://github.com/InfoLab-SKKU/ML-Adversarial-Attacks-Analysis
State Recognition of Isolating Switch in Traction Substation Based on Dual Network

Aiming at the problem that it is difficult to locate and identify the isolating switch in complex scenes, this paper studies a method of isolating switch state recognition based on dual network, which is divided into C-R<sup>2</sup>CNN isolating switch detection and SqueezeNet state recognition. Firstly, C-R<sup>2</sup>CNN is a rotating box detection network based on R<sup>2</sup>CNN, in which the channel attention module is utilized to effectively eliminate noise interference and extract features, improve the detection accuracy of the model, and realize the precise positioning of the isolating switch. Secondly, the isolating switch detection network is used to locate the isolating switch sub-region in the original image, then the target sub-region is cut out and sent to SqueezeNet network for state recognition, thus realizing the state recognition of the isolation switch. Experiments show that the method proposed in this paper can effectively identify the state of isolating switch.
Multi-View Convolutional Neural Networks Crowd Counting Model Based on YOLOX

Crowd counting refers to estimating the number of crowds and crowd distribution in images or videos, which can effectively manage pedestrian volume and observe the degree of crowd congestion in time. Single-view crowd counting has received a lot of attention in recent years and achieved remarkable performance on many public datasets. However, it is not suitable for wide-area occluded scenes due to field-of-view limitations. Multi-view crowd counting sets up multiple cameras in the same scene from multiple angles to complete crowd counting task. This paper proposes a multi-view convolutional neural networks crowd counting model based on YOLOX. Experiments are conducted on two public datasets (PETS2009, CityStreet). Results show that this method can achieve good counting accuracy and fast training speed.
Application of Convolutional Neural Networks in Visual Feedback of Movable Camera Mounting Control

The aim of this work is to present an automatic solution to control the surveillance camera merely by the movements of the operator’s head. The method uses convolutional neural networks that work in a course-to-fine manner to estimate head orientation in image data. First, the image frame of the operator’s head is acquired from the camera on the operator’s side of the system. The exact position of a head, given by its bounding box, is estimated by a Multitask Cascaded Convolutional Network. Second, the customized network for a given scenario is used to classify the orientation of the head-on image data. In particular, the dedicated image dataset was collected for training purposes and was given a discrete set of possible orientations in the vertical and horizontal planes. The accuracy of the estimators is higher than 80%, with an average of 4.12 fps of validation time. Finally, the current head orientation data are converted into a control signal for two degrees of freedom surveillance camera mounting. The feedback response time is 1.5 s, which is sufficient for most real-life surveillance applications.
Research on Augmented Reality Technology in the Training of Pre-flight Safety Inspect Process

Traditional pre-flight safety training mainly relies on paper manuals or manual teaching, which is inefficient and of low quality, cannot meet the current trend of urgent introducing aircraft inspection personnel. In the inspect process, there is no real-time information support and guidance, and the difficult problems on the site cannot be solved in time, resulting in low inspect efficiency, but also easy to induce technical errors caused by human factors, such as the inspect personnel carelessly missed a certain step during the inspection process, that will result in many adverse consequences. Based on the current situation of civil aviation inspection industry, the pre-flight inspect process work is complexity, training technicians to efficiently perform new skills is challenging. fortunately, training of this type can be supported by Augmented Reality, a powerful industrial training technology that directly links instructions on how to perform the service tasks to the aircraft components that require processing. Meanwhile, because of the increasing complexity of inspection tasks, it is not sufficient to train the technicians in task execution. It is not sufficient to train the technicians in task execution. an effective solution with the help of visualization technology is proposed. It is mainly to digitize paper manual materials into text information, pictures, videos, 3D models and other multimedia means, combined with human computer interaction development, efficient inspection training services and real-time reliable inspection guidance services. Based on Unity 3D engine and with the help of Microsoft Hololens2, the augmented reality aircraft per-flight inspection training system pursues high-precision process guidance.
Design of Interactive Landscape Virtual Tour System Based on 3D Virtual Reality Technology

Under the background of the rapid development of the triple play and the Internet-of-Everything (IoE) model in the new era, it is of practical significance to promote the rapid development of digital landscape design. The effect simulation of traditional landscape design is based on images and videos, which has its limitations. Therefore, this research proposes a new method that breaks through the realisation of traditional landscape design schemes for the three stages of "design-implementation-experience", which is known as "virtual tour design". The novelty of this design is computing a simulated environment through virtual reality (VR) technology, which combines the design of the architectural garden landscape with the ecological environment. This achieves the purpose of improving the interactivity and interest in the tour design, as well as reducing the communication costs between the designer and participants. VR technology has many advantages such as the freedom of experience, diverse perspectives, supply of information, and strong interactivity. Therefore, the realism of landscape simulation based on VR technology can reach more than 95% and has strong applicability in the landscape field, and is a simple and convenient way to achieve the desired effect.
Privacy Guarantees of BLE Contact Tracing for COVID-19 and Beyond: A Case Study on COVIDWISE

Google and Apple jointly introduced a digital contact tracing technology and an API called "exposure notification,'' to help health organizations and governments with contact tracing. The technology and its interplay with security and privacy constraints require investigation. In this study, we examine and analyze the security, privacy, and reliability of the technology with actual and typical scenarios (and expected typical adversary in mind), and quite realistic use cases. We do it in the context of Virginia's COVIDWISE app. This experimental analysis validates the properties of the system under the above conditions, a result that seems crucial for the peace of mind of the exposure notification technology adopting authorities, and may also help with the system's transparency and overall user trust.
An Image Denoising and Enhancement Approach for Dynamic Low-light Environment

Image enhancement technology can greatly improve the visual recognition accuracy of robots in low-light environments. In the past few years, there have been several effective low light enhancement algorithms. However, a supervised deep-learning algorithm requires paired data, which is difficult to collect in dynamic scenes. Meanwhile, there are problems of color distortion and noise amplification in the enhanced image. In this paper, we train an effective image denoise and enhancement model. Furthermore, our method only uses low-light images as training data without ground truth to solve the difficulty of data collection. Extensive experiments on a variety of benchmarks have demonstrated that proposed model is qualitatively and quantitatively better than state-of-the-art methods.
Enhance Model Stealing Attack via Label Refining

With machine learning models being increasingly deployed, model stealing attacks have raised an increasing interest. Extracting decision-based models is a more challenging task with the information of class similarity missing. In this paper, we propose a novel and effective model stealing method as Label Refining via Feature Distance (LRFD), to re-dig the class similarity. Specifically, since the information of class similarity can be represented by the distance between samples from different classes in the feature space, we design a soft label construction module inspired by the prototype learning, and transfer the knowledge in the soft label to the substitution model. Extensive experiments conducted on four widely-used datasets consistently demonstrate that our method yields a model with significantly greater functional similarity to the victim model.
Automatic Calibration of the Adaptive 3D Scanner-Based Robot Welding System

An advanced automatic calibration procedure and its versatile usage in the context of the adaptive robot welding technology are presented. The 3D scanner-based robot welding system calibration is composed of the measurement of the reference plate and numerical optimization of the hand-eye and intrinsic parameters by minimizing the deviation between the measured and reference plate. The measurements of the reference plate are acquired from various robot poses (typically 15). The shape features of the reference plate are then detected, and finally, the calculation of hand-eye and intrinsic parameters is performed using Powell's optimization algorithm, where the merit function presents an average deviation between the measured and reference geometry. Validation experiments show appropriate system accuracy which is better than 0.06 mm perpendicular to the scanning direction. This calibration procedure's important features are complete automation and fast execution times (approximately 90 s). This enables its implementation into a regular daily robot self-maintenance and monitoring plan. The universal use of such a robot welding system is demonstrated in multi-layer heavy-duty welding of thick pipes on cast machined hollow parts and in precise laser welding of thin sheet metal parts.