Reviewerly/exHarmony huggingface dataset:
Contextualized Fairness for Recommender Systems in Premium Scenarios

Previous studies have demonstrated the potential bias and fairness issues in real recommender systems. Fairness issue is generally defined as equality of services between groups. However, fairness does not imply equality for all users/items in real scenarios, as premium users/items, who have paid for their services, are supposed to have better experiences. In this work, we focus on the fairness issue in recommender systems under premium scenario. Firstly, we design a new metric to measure the fairness issue when there are premium users. Then, our analyses demonstrate that premium users/items get better recommendations than standard users/items on average. While our further study shows that by controlling confounding factors, premium users are provided with similar system performances as standard users, which indicates current recommender systems can be unfair. Finally, we propose a flexible and contextual fairness-aware recommendation framework by considering a desired distribution to fit user (or item) groups' scores, and experimental results show that it is a solution to provide better services for premium users/items.
A Novel Approach for Semantic Web Application in Online Education Based on Steganography

Semantic Web technology is not new as most of us contemplate; it has evolved over the years. Linked Data web terminology is the name set recently to the Semantic Web. Semantic Web is a continuation of Web 2.0 and it is to replace existing technologies. It is built on Natural Language processing and provides solutions to most of the prevailing issues. Web 3.0 is the version of Semantic Web caters to the information needs of half of the population on earth. This paper links two important current concerns, the security of information and enforced online education due to COVID-19 with Semantic Web. The Steganography requirement for the Semantic web is discussed elaborately, even though encryption is applied which is inadequate in providing protection. Web 2.0 issues concerning online education and semantic Web solutions have been discussed. An extensive literature survey has been conducted related to the architecture of Web 3.0, detailed history of online education, and Security architecture. Finally, Semantic Web is here to stay and data hiding along with encryption makes it robust.
Improved U-Net-Based Novel Segmentation Algorithm for Underwater Mineral Image

Autonomous underwater vehicle (AUV) has many intelligent optical system, which can collect underwater signal information to make the system decision.One of them is the intelligent vision system, and it can capture the images to analyze.The performance of the particle image segmentation plays an important role in the monitoring of underwater mineral resources.In order to improve the underwater mineral image segmentation performance, some novel segmentation algorithm architectures are proposed.In this paper, an improved mineral image segmentation is proposed based on the modified U-Net.The pyramid upsampling module and residual module are bring into the U-Net model, which are called JPU-Net, JPMU-Net and ResU-Net.These models combined the power of the residual block and the pyramid upsampling in the encoder part and in the decoder part respectively.The proposed models are tested on the Electron Microscopy images (EM) dataset and the underwater mineral image dataset.The experimental results show that JPU-Net has superior performance on the EM dataset, and JPMU-Net has a better segmentation result than existing convolutional neural network on the underwater mineral image dataset.
Computing sparse cones with bounded distortion for conformal parameterizations

We propose a novel method to generate sparse cone singularities with bounded distortion constraints for conformal parameterizations. It is formulated as minimizing the ℓ 0 -norm of Gaussian curvature of vertices with hard constraints of bounding the distortion that is measured by the ℓ 2 -norm of the log conformal factor. We use the reweighted ℓ 1 -norm to approximate the ℓ 0 -norm and solve each convex weighted ℓ 1 minimization subproblem by the Douglas-Rachford (DR) splitting scheme. To quickly generate sparse cones, we modify DR splitting by weighting the ℓ 2 -norm of the proximal mapping to force the small Gaussian curvature to quickly approach zero. Accordingly, compared with the conventional DR splitting, the modified method performs one to two orders of magnitude faster. Besides, we perform variable substitution of log conformal factors to simplify the computation process for acceleration. Our algorithm is able to bound distortion to compute sparse cone singularities, so that the resulting conformal parameterizations achieve a favorable tradeoff between the area distortion and the number of cones. We demonstrate its effectiveness and feasibility on a large number of models.
A Performance Comparison of Balinese Carving Motif Detection and Recognition using YOLOv5 and Mask R-CNN

Balinese carving has various forms of unique motifs that adorn sacred buildings in Bali. As an effort of cultural object preservation, object detection and recognition method are needed to collect Balinese carving objects. However, the object detection and recognition task on Balinese carving is challenging due to the limited dataset available. This study proposed an object detection and recognition method with our currently developing dataset to detect and recognize Balinese carving motifs. We compared and evaluated the performance of YOLOv5 and Mask R-CNN to detect and recognize Balinese carving motifs based on mean average precision, training times, and visualization of detection results. The performance comparison of Mask R-CNN and YOLOv5 aims to produce the best detection and recognition models for Balinese carvings. Based on experiments, YOLOv5 outperformed Mask R-CNN with a mAP@[.5-.95] score of 0.987 and faster training times. However, the advantage of Mask R-CNN adds a mask to the final detection, thus showing better motif segmentation with a mask.
Output Thresholding for Ensemble Learners and Imbalanced Big Data

Class imbalance is a common problem in many real-world machine learning applications that has been shown to significantly degrade classification performance. This is especially true in the context of big data, where large volumes of data from the majority class dominate training processes and bias learning algorithms. Of the various methods for treating class imbalance, output thresholding is one technique that improves classification performance by tuning the decision threshold that is used to assign class labels to class probabilities. While thresholding techniques have been successful, systematic studies within big and imbalanced data applications are limited. In this study, we compare four popular thresholding strategies using two big and imbalanced fraud classification data sets. We focus specifically on tree-based ensemble learners, and employ four popular bagging and boosting ensemble learners that are well known for achieving state-of-the-art performance. Overall classification is measured using the Geometric Mean (G-Mean) and F-Measure metrics and class-wise performance tradeoffs are compared using the true positive rate (TPR) and true negative rate (TNR). The average threshold values of each strategy are compared, and statistical tests are provided to illustrate the importance of careful threshold tuning. Results show that the G-Mean and F-Measure metrics provide misleading results, and careful validation of TPR and TNR is necessary for selecting optimal thresholds. Furthermore, we show how small changes to decision thresholds yield significant changes to classification performance. Our comparison of popular thresholding techniques on both big and highly-imbalanced data makes this a unique contribution in the area of output thresholding with ensemble learners and big data.
Few-Shot Learning for Post-Earthquake Urban Damage Detection

Among natural disasters, earthquakes are recorded to have the highest rates of human loss in the past 20 years. Their unexpected nature has severe consequences on both human lives and material infrastructure, demanding urgent action to be taken. For effective emergency relief, it is necessary to gain awareness about the level of damage in the affected areas. The use of remotely sensed imagery is popular in damage assessment applications; however, it requires a considerable amount of labeled data, which are not always easy to obtain. Taking into consideration the recent developments in the fields of Machine Learning and Computer Vision, this study investigates and employs several Few-Shot Learning (FSL) strategies in order to address data insufficiency and imbalance in post-earthquake urban damage classification. While small datasets have been tested against binary classification problems, which usually divide the urban structures into collapsed and non-collapsed, the potential of limited training data in multi-class classification has not been fully explored. To tackle this gap, four models were created, following different data balancing methods, namely cost-sensitive learning, oversampling, undersampling and Prototypical Networks. After a quantitative comparison among them, the best performing model was found to be the one based on Prototypical Networks, and it was used for the creation of damage assessment maps. The contribution of this work is twofold: we show that oversampling is the most suitable data balancing method for training Deep Convolutional Neural Networks (CNN) when compared to cost-sensitive learning and undersampling, and we demonstrate the appropriateness of Prototypical Networks in the damage classification context.
Learning to Guide Human Attention on Mobile Telepresence Robots with 360° Vision

Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person’s true location. Thanks to the recent advances in 360° vision, many MTRs are now equipped with an all-degree visual perception capability. However, people’s visual field horizontally spans only about 120° of the visual field captured by the robot. To bridge this observability gap toward human-MTR shared autonomy, we have developed a framework, called GHAL360, to enable the MTR to learn a goal-oriented policy from reinforcements for guiding human attention using visual indicators. Three telepresence environments were constructed using datasets that are extracted from Matterport3D and collected from a real robot respectively. Experimental results show that GHAL360 outperformed the baselines from the literature in the efficiency of a human-MTR team completing target search tasks. A demo video is available: https://youtu.be/aGbTxCGJSDM
SPR: Similarity pairwise ranking for personalized recommendation

Bayesian personalized ranking (BPR) has been proposed as an effective method to model pairwise learning, and it is widely used in many personalized recommender systems. However, the effectiveness of BPR can be seriously affected by an imbalanced data distribution because it tends to rank popular items ahead of personalized items. As a result, the personalized needs of users cannot be well met. In this paper, we propose a novel personalized recommendation method called similarity pairwise ranking (SPR) to rank users' favorite items first. SPR eliminates the differences in the scores between popular and personalized items based on their similarity by using a new penalty. In such a way, the SPR-enhanced recommendation will render meaningful and personalized results that better meet the individual needs of users, and it overcomes the negative impact of imbalanced datasets. We design a model to illustrate the improvement of SPR: similarity pairwise ranking matrix factorization (SPRMF). Experimental results obtained using six datasets indicate the superiority in recommendation quality of SPRMF over the recent state-of-the-art methods.
Constructing, improving, and shortening tests for skill assessment

The work deals with the assessment of the set of skills an individual has available in a certain disciplinary domain (which is called the competence state) from the responses of this individual to test items. Competence-based test development is introduced as a novel approach for developing tests for skill assessment. Given a fixed collection of skills of interest in the disciplinary domain, the resulting tests are as informative as possible about individuals’ competence states. The development of conjunctive (all skills associated with an item are necessary for solving it) and disjunctive (each of the skills associated with a certain item is sufficient for solving it) tests is considered. Various applications of the proposed methodology are presented. They cover different scenarios, such as the construction of a test from scratch, or the improvement and shortening of an existing test.
Blockchain Opportunities and Issues in the Built Environment: Perspectives on Trust, Transparency and Cybersecurity

Blockchain as a form of distributed ledger enabling the renowned cryptocurrency Bitcoin has been in the spotlight since the late 2000s. The early discussions on its practical implementation were conducted mainly for the banking and finance industries with a promising narrative. Alongside the recent surge in the interest for the technology for a wide-range of possible applications such as smart contracts, crypto-assets, tokenization, secure transaction recording, peer-to-peer (P2P) trade, and transparent governance, its potential for the Built Environment (BE) has started to come more frequently into question. These discussions often contain a tone of caution, nevertheless, hinting the challenges for the technology in the BE context. This chapter is aimed at summarizing those opportunities and challenges for blockchain in the BE with a particular emphasis on its potential influence on trust, transparency and cybersecurity. Directions of future research are also provided contributing to the cyber-physical convergence in Construction 4.0.
A robust image segmentation framework based on total variation spectral transform

• The properties of total variation spectral transform are analyzed on the images without and with noise. • An image segmentation framework based on the TV spectral transform is presented. • High segmentation accuracy is shown in the proposed framework compared with existing state-of-the-art methods. • Robustness to noise can be reflected in the proposed framework. Image segmentation is a critical process that influences subsequent tasks in computer vision. Despite years of research, popular segmentation methods usually cause over-segmentation or under-segmentation when segmenting images with noises. To address the issue, this paper proposes an image segmentation framework with the help of total variation (TV) spectral transform technique. Firstly, we impose the TV spectral transform on an image with noises to get basic elements that preserve shape structure. Secondly, the object structure is roughly separated from the background by a separation surface, which is fitted using the maximal response time of each pixel. Thirdly, the roughly generated object structure is refined with a guided filter whose guidance image is calculated through a band-pass filter and inverse transform in the TV spectral domain. Finally, binary segmentation and morphological operations are conducted on the refined object structure to obtain the segmentation result. Experiment results indicate that our method can achieve high segmentation accuracy and be robust to noise compared with other classical approaches.
Surface Measurement of a Large Inflatable Reflector in Cryogenic Vacuum

The metrology of membrane structures, especially inflatable, curved, optical surfaces, remains challenging. Internal pressure, mechanical membrane properties, and circumferential boundary conditions imbue highly dynamic slopes to the final optic surface. Here, we present our method and experimental results for measuring a 1 m inflatable reflector’s shape response to dynamic perturbations in a thermal vacuum chamber. Our method uses phase-measuring deflectometry to track shape change in response to pressure change, thermal gradient, and controlled puncture. We use an initial measurement as a virtual null reference, allowing us to compare 500 mm of measurable aperture of the concave f/2, 1-meter diameter inflatable optic. We built a custom deflectometer that attaches to the TVAC window to make full use of its clear aperture, with kinematic references behind the test article for calibration. Our method produces 500 × 500 pixel resolution 3D surface maps with a repeatability of 150 nm RMS within a cryogenic vacuum environment (T = 140 K, P = 0.11 Pa).
Active Learning for Text Classification and Fake News Detection

Supervised classification of texts relies on the availability of reliable class labels for the training data. However, the process of collecting data labels can be complex and costly. A standard procedure is to add labels sequentially by querying an annotator until reaching satisfactory performance. Active learning is a process of selecting unlabeled data records for which the knowledge of the label would bring the highest discriminability of the dataset. In this paper, we provide a comparative study of various active learning strategies for different embeddings of the text on various datasets. We focus on Bayesian active learning methods that are used due to their ability to represent the uncertainty of the classification procedure. We compare three types of uncertainty representation: i) SGLD, ii) Dropout, and iii) deep ensembles. The latter two methods in cold- and warm-start versions. The texts were embedded using Fast Text, LASER, and RoBERTa encoding techniques. The methods are tested on two types of datasets, text categorization (Kaggle News Category and Twitter Sentiment140 dataset) and fake news detection (Kaggle Fake News and Fake News Detection datasets). We show that the conventional dropout Monte Carlo approach provides good results for the majority of the tasks. The ensemble methods provide more accurate representation of uncertainty that allows to keep the pace of learning of a complicated problem for the growing number of requests, outperforming the dropout in the long run. However, for the majority of the datasets the active strategy using Dropout MC and Deep Ensembles achieved almost perfect performance even for a very low number of requests. The best results were obtained for the most recent embeddings RoBERTa
Non-Intrusive Classroom Attention Tracking System (NiCATS)

This Innovative Practice Full-Paper presents a system for real-time accurate detection of classroom attentiveness using monitor-mounted webcams and eye trackers. Academic institutions and instructors cannot accurately assess the moment-to-moment attentiveness of students in classrooms where students' faces are obscured by computer monitors. This can cause the lectures of Computer Science, Information Technology, or other lab-based courses to be incorrectly paced, which leads to students having overall poorer grasps of the subject material. We present a system for accurate detection of classroom attentiveness using monitor-mounted webcams and eye trackers. To determine correlations for the attentiveness judging system, we compare an initial attentiveness score produced by trained labelers using an image of the student's face with a series of calculated eye metrics to determine a final attentiveness score. Because the student webcam images and eye coordinates are synchronously collected with the lecture, this final attentiveness score is used to provide post-hoc feedback to instructors on the status of their students via time-series graphs displayed on the instructor's computer monitor. The proposed system is invaluable for institutions seeking to improve student education, instructors striving to improve the flow of lectures, and students seeking a more accommodating learning environment. The primary source of innovation from this system comes from the correlation of extracted eye metrics with the face images labeled for attentiveness. Research exists about determining attentiveness using a convolutional neural network trained on face images and even determining attentiveness by correlating face-image-trained outputs, each of which we plan to incorporate to make our system real-time in the future. This novel research could prove helpful for the field of education.
Car Racing Line Optimization with Genetic Algorithm using Approximate Homeomorphism

In every timed car race, the goal is to drive through the racing track as fast as possible. The total time depends on selection of the racing line. Following a better racing line often decides who wins. In this paper, we solve the optimal racing line problem using a genetic algorithm. We propose a novel racing line encoding based on a homeomorphic transformation called Matryoshka mapping. We evaluate the fitness of racing lines by lap time estimation using a vehicle model suitable for F1/10 autonomous racing competition. By comparing to the former state-of-the-art, we show that our method is able to find racing lines with lower lap times. Specifically, on one of the testing tracks, we achieve 2.5% improvement.
Hand Gesture Recognition of Methods-Time Measurement-1 Motions in Manual Assembly Tasks Using Graph Convolutional Networks

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Human Motion Posture Detection Algorithm Using Deep Reinforcement Learning

To address problems of serious loss of details and low detection definition in the traditional human motion posture detection algorithm, a human motion posture detection algorithm using deep reinforcement learning is proposed. Firstly, the perception ability of deep learning is used to match human motion feature points to obtain human motion posture features. Secondly, normalize the human motion image, take the color histogram distribution of human motion posture as the antigen, search the region close to the motion posture in the image, and take its candidate region as the antibody. By calculating the affinity between the antigen and the antibody, the feature extraction of human motion posture is realized. Finally, using the training characteristics of deep learning network and reinforcement learning network, the change information of human motion posture is obtained, and the design of human motion posture detection algorithm is realized. The results show that when the image resolution is 384 × 256 px, the motion pose contour detection accuracy of this algorithm is 87%. When the image size is 30 MB, the recognition time of this method is only 0.8 s. When the number of iterations is 500, the capture rate of human motion posture details can reach 98.5%. This shows that the proposed algorithm can improve the definition of human motion posture contour, improve the posture detailed capture rate, reduce the loss of detail, and have better effect and performance.