Reviewerly/exHarmony huggingface dataset:
Design of an interactive platform for online audio-visual teaching of English based on augmented reality technology

In order to overcome the problems of high teaching cost, low efficiency of implementation and low utilisation of teaching resources, this paper proposes an interactive platform of English audio-visual online teaching based on augmented reality technology. According to the characteristics of the current online audio-visual teaching class of English, the user needs are analysed. The platform hardware is divided into web server, teacher client and touch integrator based on MVC framework, the connection between teachers and students is realised through hardware design. The augmented reality technology is applied to the interactive application of online audio-visual teaching of English. Through AR technology, an educational background is created, classroom teaching is strengthened, and makes the task cooperation between teachers and students smoother. The experimental results show that the platform can reduce the teaching cost and improve the efficiency of the system and the utilisation of teaching resources.
Traffic Light Detection and Recognition in Autonomous Vehicles (AVs)

Autonomous Vehicles (AVs) face the challenge of recognising active traffic lights under harsh environmental conditions. Standard cameras and computer vision algorithms also face the same challenge. In this paper, we built a small-scale system to mitigate this challenge. First, we developed a light controller and a dataset builder script. The light controller and dataset builder script were then used to build a dataset of traffic lights with different lights activated. Bounding boxes were annotated on the traffic light dataset using dlib's imglab software. The dataset uses the HOG with Linear SVM object detector. An RGB histogram approach is adopted to train a logistic regression model on the feature vector data to recognise which light is "on" among the training images. Finally, a robot control script is developed and tested. The script uses both the object detector and colour recogniser for its detection and recognition. Our results show 89% accuracy in identifying a red-yellow-green traffic light under extreme environmental conditions.
Classifying Incoming Customer Messages for an e-Commerce Site Using Supervised Learning

Throughout the world, the provision of online goods and services has increased significantly over the last few years. We consider the case of Tango Discos, a small company in Colombia that sells entertainment products through an e-commerce website and receives customer messages through various channels, including a webform, email, Facebook and Twitter. This dataset comprises 29,970 messages collected from 2019 to 2021. Each message can be categorized as being either being a sale, request or complaint. In this work we evaluate different supervised classification models to automate the task of classifying the messages, viz. decision trees, Naive Bayes, linear Support Vector Machines and logistic regression. As the data set is unbalanced, the different models are evaluated in combination with various data balancing approaches to obtain the best performance. In order to maximize revenue, the management is interested in prioritizing messages that may result in potential sales. As such, the best model for deployment is one that minimizes false positives in the sales category, so that these are processed in a timely fashion. As such, the best performing model is found to be the Linear Support Vector Machine using the Random Over Sampler balancing technique. This model is deployed in the cloud and exposed using a RESTful interface.
Multiple-Beam Two-Dimensional Scanning Apparatus

A multiple-beam two-dimensional steering apparatus built with two orthogonal one-axis scanning mirror arrays (1 × N) and cylindrical lenses is presented in this study. The merits of this scanning system include (1) low cost, as the support structures are all homemade using fused deposition modeling (FDM) 3D printing, (2) a clever system design, which shows a likeness to a 4-f optical system, and (3) no demand for two-axis mirrors. The maximum angular coverages by a sub-beam in the x and y directions are 4.48° and 3.12°, respectively. This multiple-beam steering apparatus can potentially be used for laser trackers, 3D shape measurement, light detection, and ranging (LiDAR).
Blind Perceptual Quality Assessment for Single Image Motion Deblurring

Single image deblurring is a typical ill-posed problem. Although a lot of effective algorithms have been proposed, there is a lack of blind evaluation metrics for the perceptual quality of deblurred images. In this paper, we introduce a new, low-cost and lightweight dataset, called Deblurred image Quality Assessment (DeblurQA). Next, we design an extendable model named DeBlurred Quality Assessment NETwork (DBQA-NET) based on multi-resolution deep feature aggregation, and train it by a two-stage training method of classification combined with quality prediction, along with a joint loss function of ranking and regression. Finally, we demonstrate the superiority of the method and show that it can assist the existing deblurring algorithms: in the hyperparameter selection experiment, it can find the best and worst results that match human perception; when applied to the training of deep-learning-based methods, it can significantly improve the abnormal results. The model, code and the dataset are available at https://github.com/weidelongdongqiang/deblurQA/tree/IQA .
Detecting Unexpected Behaviors in Scenario-Based Specification: Asystematic Literature Review

Scenarios are effective means for requirement elicitation. However, scenarios represent a partial story of a system and combining multiple scenarios for the complete system behavior may introduce unexpected behaviors within the system. Detecting and fixing these unwanted flaws in the early design phase can substantially reduce the deployment cost as well as any run-time hazards. The goal of this paper is to summarize the state-of-the-art of detection approaches for unexpected behaviours in scenario-based specifications. We provide an overview of the research area by searching for and categorizing relevant studies, along with discussing gaps and trends of the field. We investigated different types of scenario specifications, approaches used for behavioral modeling and types of unexpected behaviors identified by the researchers. We performed a systematic literature review (SLR) using the PRISMA framework. We studied 64 primary studies from multiple sources and found that the area of research is dominated by solution proposals. Most of the studies use MSC and UML SD as specification types. The automata based behavioral modeling are prevailing in the studies while new technologies like probabilistic modelsand ML models are introduced recently. Moreover, tool support for detecting EB is not matured enough for industry level acceptance.
AMA-Det: Enhancing Shared Head of One-Stage Object Detection With Adaptation, Merging, and Alignment

Feature adaptation (FA) and result alignment(RA) are critical issues in one-stage object detection, since FA amends feature misalignment problem by adapting sampling points to semantically significant locations, and RA corrects result misalignment problem by estimating the localization quality.For FA, the aligned and consistent prediction of sampling points is important.Previous studies directly inherit cascaded ''proposal-refine'' philosophy from multi-stage detectors and predict sampling points by approximating their minimal external rectangle to ground-truth bounding boxes.This manner generates poorly aligned and consistent sampling points and induces irrelevant features aggregated.Moreover, their sampling points for classification are conventionally generated through the localization branch, without feeding the corresponding features of the classification branch.For RA, previous studies have verified the superiority of utilizing the predicted edge distribution to estimate localization quality; however, their directly regressed distribution is incompatible with the cascaded regression framework.To solve these problems, we firstly propose a focused feature adaptation method by softening the supervision of the proposal points.This method can predict sampling points focused above the assigned objects with excellent alignment and consistency.Subsequently, inner-branch and cross-branch merging were investigated to promote feature sharing from the classification branch.Finally, cascaded distribution-guided result alignment is advanced and verified to predict accurate localization quality.After integrating our proposed adaptation, merging, and alignment, we created AMA-Det with an enhanced shared head, which impressively reaches 43.9 mAP with ResNet50 as the backbone.AMA-Det also achieves a 54.5 mAP by multi-scale testing on MSCOCO test-dev and outperforms all existing CNN-based one-stage counterparts.
A SQL Blind Injection Method Based on Gated Recurrent Neural Network

Security is undoubtedly the most serious problem for Web applications, and SQL injection (SQLi) attacks are one of the most damaging. The detection of SQL blind injection vulnerability is very important, but unfortunately, it is not fast enough. This is because time-based SQL blind injection lacks web page feedback, so the delay function can only be set artificially to judge whether the injection is successful by observing the response time of the page. However, brute force cracking and binary search methods used in injection require more web requests, resulting in a long time to obtain database information in SQL blind injection. In this paper, a gated recurrent neural network-based SQL blind injection technology is proposed to generate the predictive characters in SQL blind injection. By using the neural language model based on deep learning and character sequence prediction, the method proposed in this paper can learn the regularity of common database information, so that it can predict the next possible character according to the currently obtained database information, and sort it according to probability. In this paper, the training model is evaluated, and experiments are carried out on the shooting range to compare the method used in this paper with sqlmap (the most advanced sqli test automation tool at present). The experimental results show that the method used in this paper is more effective and significant than sqlmap in time-based SQL blind injection. It can obtain the database information of the target site through fewer requests, and run faster.
How Image Retrieval and Matching Can Improve Object Localisation on Offshore Platforms

Deep learning is gaining popularity in the realm of object localization. Existing deep learning methods have shown good accuracy and inference runtime, but they require a lot of training data. This needs a major investment in resources, especially for offshore industrial sites that lack huge datasets. Furthermore, because the inference set should contain the same types of objects as the training set, deep learning solutions are highly sensitive to object types. To address these two challenging issues, we proposed a novel framework based on image retrieval and matching algorithms. The set of relevant images to the object query is first retrieved using the Bag of Words. Furthermore, we developed two alternative image matching algorithms to localize the object query on the relevant images. The first one is based on generate and test, and the second one is based on geometric verification. Extensive simulation has been carried out to validate the suggest methodology, and the results are highly promising in terms of computing time and accuracy.
A Quick Review and Performance Analysis of Custom and Transfer Learning CNN Architectures for Event Detection in Videos

Event/Action detection in videos is a growing research interest as it has numerous applications such as patient monitoring in health care, anomaly detection in surveillance systems, retrieval of video, human and computer interactions, gaming environment, entertainment environment etc. This is all because of one and only Deep learning due to its capability to outperform the conventional hand-crafted feature extraction algorithms. Transfer learning is significant in training deep neural networks with small datasets. The objective of this paper is to compare popular pretrained CNN models (in Top-1 accuracy) with custom CNN, built from scratch to detect human actions in UCF11 dataset.
Dynamic Target Monitoring of Load Balancers in Cloud Computing

Cloud computing enables users to run applications and web services without having to worry about servers. However, certain occurrences, such as a power outage or a connection issue, might cause such servers to stop functioning. Hence, it is vital to address such issues to determine the root cause of server failure and how such issues may be rectified. As a result, we employed AWS services. In this article, we critically investigate the target failure on the load balancer caused by different difficulties such as power failure, port number discrepancy, inability to build connection, and so on, which can further impair the operation of an application. In addition, we have highlighted the use of the alarm metric to identify the severity.
Physical Layer Authentication Based on Continuous Channel Polarization Response in Low SNR scenes

In this paper, we propose a physical layer authentication scheme based on channel polarization response (CPR) in low SNR scenes, such as IoT, 5G, and 6G communication systems. We use the transmitting polarization state as the “pilot” to estimate CPR. Then, using the property that the polarization state of the signal is independent of the transmission content, the coherent superposition is used to stack the signal piecewise to improve the SNR. We derive the false alarm probability, detection probability, optimal threshold, and computational complexity of the proposed scheme, and give the solution method of the optimal stacking numbers under specific SNR and authentication performance. Besides, we analyze the effectiveness of the proposed scheme through numerical simulation. Simulation results show that the proposed scheme can still achieve good authentication performance under ultra low SNR, such as when SNR = -20dB, the detection probability is 99.6%, and the false alarm probability is 2%.
Self-Supervised Text Style Transfer with Rationale Prediction and Pretrained Transformers

Sentiment transfer involves changing the sentiment of a sentence, such as from a positive to negative sentiment, while maintaining the informational content. Given the dearth of parallel corpora in this domain, sentiment transfer and other text rewriting tasks have been posed as unsupervised learning problems. In this paper we propose a self-supervised approach to sentiment or text style transfer. First, sentiment words are identified through an interpretable text classifier based on the method of rationales. Second, a pretrained BART model is fine-tuned as a denoising autoencoder to autoregressively reconstruct sentences in which sentiment words are masked. Third, the model is used to generate a parallel corpus, filtered using a sentiment classifier, which is used to fine-tune the model further in a self-supervised manner. Human and automatic evaluations show that on the Yelp sentiment transfer dataset the performance of our self-supervised approach is close to the state-of-the-art while the BART model performs substantially better than a sequence-to-sequence baseline. On a second dataset of Amazon reviews our approach scores high on fluency but struggles more to modify sentiment while maintaining sentence content. Rationale-based sentiment word identification obtains similar performance to the saliency-based sentiment word identification baseline on Yelp but underperforms it on Amazon. Our main contribution is to demonstrate the advantages of self-supervised learning for unsupervised text rewriting.
Unstructured Text Data Rule Generalization in Attribute-Oriented Induction

With the advancement of science and technology, algorithms for data mining are constantly being introduced but most of the algorithms focus on solving the problem of how to reduce the dimension of variables. Nowadays, the amount of data is accumulating and increasing rapidly, and how to compress the amount of data becomes an important topic. Attribute-oriented induction (AOI) is a method of longitudinally compressing data. At present, there are many derivative methods to solve the problem of multi-value-attributes and ordered data. However, few researchers have focused on unstructured text data. Nowadays, the most common way to process text data for classification and induction is to use Latent Dirichlet Allocation (LDA) and generate attributes of the topic. However, the attributes of the topic do not consider whether they exist or not a potential hierarchical relationship. As the topics of text data are hierarchical, we focus on unstructured data such as comment content, transform the comment content into concept datasets, and use AOI to construct a reasonable hierarchical concept. Through induction cost, the final level of induction is determined, too. Finally, the induction rate of the topics generated by the LDA and AOI methods is evaluated to solve the problem of data compression with multiple events and hierarchical relationships among concepts.
Training Digital Competences of Educators in Continuing Education: A Three-Level Approach

The Centre for Continuing and Distance Education (CCDE) of the University of Geneva has initiated and experimented the segmentation of their supporting service in terms of digital transformation of continuing education programmes as well as staff training with a “Three-Level” solution, consisting of the “easy option”, the “intermediate option” and the “advanced option”. Guided by the Design-Based Research approach, we have not only redefined the objectives for each option but have also improved the staff training from only workshop-based format to self-assessment plus workshop format. In this article, we present the two design cycles of the “Three-Level” digital transformation as well as the digital competence training model in detail and share several concrete examples for staff training.
A Multimodal Interviewee Evaluation Approach for Candidates Facing Video Interviews

Automated video interview evaluation is an area that is becoming trending because of the significant usage of video interviews for candidates’ hiring processes. Though there is much research on video interviews, there are a smaller number of studies on the automated evaluation process in video interviews. Still, many entities use more human interaction for selecting candidates through video interviews. This study proposes a set of methodologies to evaluate the candidate by considering five aspects which are knowledge level, mindset, confidence, personality, behavior, and English language fluency. This paper proposes an answer evaluation using feature engineering and Siamese LSTM architecture where BERT is used as the encoding layer. The CNN-based approach is proposed for behavior and personality analysis. For language fluency and confidence, NLP techniques and machine learning methods are proposed in this study. Through the methodologies propose in this study, overall, all the evaluations yield around 85% - 90% accuracy. The approach suggested in this study will help organizations to do their talent acquisition process more smoothly.
Minimal Binary Linear Codes From Vectorial Boolean Functions

Recently, much progress has been made to construct minimal linear codes due to their preference in secret sharing schemes and secure two-party computation. In this paper, we put forward a new method to construct minimal linear codes by using vectorial Boolean functions. Firstly, we give a necessary and sufficient condition for a generic class of linear codes from vectorial Boolean functions to be minimal. Based on that, we derive some new three-weight minimal linear codes and determine their weight distributions. Secondly, by studying deeply the construction of linear codes in this paper, we find a necessary and sufficient condition of the linear codes to be minimal and to be violated the AB condition. As a result, we get three infinite families of minimal linear codes violating the AB condition. To the best of our knowledge, this is the first time that minimal liner codes are constructed from vectorial Boolean functions. Compared the parameters with other known ones, in general the minimal liner codes obtained in this paper have higher dimensions.
DeepShapeKit: accurate 4D shape reconstruction of swimming fish

In this paper, we present methods for capturing 4D body shapes of swimming fish with affordable small training datasets and textureless 2D videos. Automated capture of spatiotemporal animal movements and postures is revolutionizing the study of collective animal behavior. 4D (including 3D space + time) shape data from animals like schooling fish contains a rich array of social and non-social information that can be used to shed light on the fundamental mechanisms underlying collective behavior. However, unlike the large datasets used for 4D shape reconstructions of the human body, there are no large amounts of labeled training datasets for reconstructing fish bodies in 4D, due to the difficulty of underwater data collection. We created a template mesh model using 3D scan data from a real fish, then extracted silhouettes (segmentation masks) and key-points of the fish body using Mask R-CNN and DeepLabCut, respectively. Next, using the Adam optimizer, we optimized the 3D template mesh model for each frame by minimizing the difference between the projected 3D model and the detected silhouettes as well as the key-points. Finally, using an LSTM-based smoother, we generated accurate 4D shapes of schooling fish based on the 3D shapes over each frame. Our results show that the method is effective for 4D shape reconstructions of swimming fish, with greater fidelity than other state-of-the-art algorithms.
APPFed: A Hybrid Privacy-Preserving Framework for Federated Learning over Sensitive Data

In the era of Big Data, data silos have become a pressing problem due to the difficulty of secure data sharing. Federated learning provides a favorable solution by allowing data holders to collaborate in training a model without sharing local data. However, several existing inference attacks have led to the fact that a pure federated learning methodology is incapable of providing sufficient privacy protection. We propose an APPFed algorithm that combines differential privacy and homomorphic encryption based on federated learning, where exists an evaluation module that enables the privacy budget parameters to be adaptive according to different needs during the training. Trained with our proposed APPFed algorithm, the models are enabled to prevent inference attacks without drastic accuracy depletion. To verify the effectiveness of our proposed algorithm, we use the APPFed algorithm to train a set of sensitive data containing face images. The experimental results show that our approach can enhance privacy protection while balancing model accuracy.