Reviewerly/exHarmony huggingface dataset:
Should Digital Contact Tracing Technologies be used to Control COVID-19? Perspectives from an Australian Public Deliberation

Mobile phone-based applications (apps) can promote faster targeted actions to control COVID-19. However, digital contact tracing systems raise concerns about data security, system effectiveness, and their potential to normalise privacy-invasive surveillance technologies. In the absence of mandates, public uptake depends on the acceptability and perceived legitimacy of using technologies that log interactions between individuals to build public health capacity. We report on six online deliberative workshops convened in New South Wales to consider the appropriateness of using the COVIDSafe app to enhance Australian contact tracing systems. All groups took the position (by majority) that the protections enacted in the app design and supporting legislation were appropriate. This support is contingent on several system attributes including: the voluntariness of the COVIDSafe app; that the system relies on proximity rather than location tracking; and, that data access is restricted to local public health practitioners undertaking contact tracing. Despite sustained scepticism in media coverage, there was an underlying willingness to trust Australian governing institutions such that in principle acceptance of the new contact tracing technology was easy to obtain. However, tensions between the need to prove system effectiveness through operational transparency and requirements for privacy protections could be limiting public uptake. Our study shows that informed citizens are willing to trade their privacy for common goods such as COVID-19 suppression. But low case numbers and cautionary public discourses can make trustworthiness difficult to establish because some will only do so when it can be demonstrated that the benefits justify the costs to individuals.
A Smart Capacitive Sensor Skin with Embedded Data Quality Indication for Enhanced Safety in Human–Robot Interaction

Smart sensors are an integral part of the Fourth Industrial Revolution and are widely used to add safety measures to human-robot interaction applications. With the advancement of machine learning methods in resource-constrained environments, smart sensor systems have become increasingly powerful. As more data-driven approaches are deployed on the sensors, it is of growing importance to monitor data quality at all times of system operation. We introduce a smart capacitive sensor system with an embedded data quality monitoring algorithm to enhance the safety of human-robot interaction scenarios. The smart capacitive skin sensor is capable of detecting the distance and angle of objects nearby by utilizing consumer-grade sensor electronics. To further acknowledge the safety aspect of the sensor, a dedicated layer to monitor data quality in real-time is added to the embedded software of the sensor. Two learning algorithms are used to implement the sensor functionality: (1) a fully connected neural network to infer the position and angle of objects nearby and (2) a one-class SVM to account for the data quality assessment based on out-of-distribution detection. We show that the sensor performs well under normal operating conditions within a range of 200 mm and also detects abnormal operating conditions in terms of poor data quality successfully. A mean absolute distance error of 11.6mm was achieved without data quality indication. The overall performance of the sensor system could be further improved to 7.5mm by monitoring the data quality, adding an additional layer of safety for human-robot interaction.
3DVSR: 3D EPI volume-based approach for angular and spatial light field image super-resolution

Light field (LF) imaging, which captures both spatial and angular information of a scene, is undoubtedly beneficial to numerous applications. Although various techniques have been proposed for LF acquisition, achieving both angularly and spatially high-resolution LF remains a technology challenge. In this paper, a learning-based approach applied to 3D epipolar image (EPI) is proposed to reconstruct high-resolution LF. Through a 2-stage super-resolution framework, the proposed approach effectively addresses various LF super-resolution (SR) problems, i.e., spatial SR, angular SR, and angular-spatial SR. While the first stage provides flexible options to up-sample EPI volume to the desired resolution, the second stage, which consists of a novel EPI volume-based refinement network (EVRN), substantially enhances the quality of the high-resolution EPI volume. An extensive evaluation on 90 challenging synthetic and real-world light field scenes from 7 published datasets shows that the proposed approach outperforms state-of-the-art methods to a large extend for both spatial and angular super-resolution problem, i.e., an average peak signal to noise ratio improvement of more than 2.0 dB, 1.4 dB, and 3.14 dB in spatial SR ×2, spatial SR ×4, and angular SR respectively. The reconstructed 4D light field demonstrates a balanced performance distribution across all perspective images and presents superior visual quality compared to the previous works.
Distribution-Aware Margin Calibration for Semantic Segmentation in Images

The Jaccard index, also known as Intersection-over-Union (IoU), is one of the most critical evaluation metrics in image semantic segmentation. However, direct optimization of IoU score is very difficult because the learning objective is neither differentiable nor decomposable. Although some algorithms have been proposed to optimize its surrogates, there is no guarantee provided for the generalization ability. In this paper, we propose a margin calibration method, which can be directly used as a learning objective, for an improved generalization of IoU over the data-distribution, underpinned by a rigid lower bound. This scheme theoretically ensures a better segmentation performance in terms of IoU score. We evaluated the effectiveness of the proposed margin calibration method on seven image datasets, showing substantial improvements in IoU score over other learning objectives using deep segmentation models.
A Bidirectional Interpolation Method for Post-Processing in Sampling-Based Robot Path Planning

This paper proposes a post-processing method called bidirectional interpolation method for sampling-based path planning algorithms, such as rapidly-exploring random tree (RRT). The proposed algorithm applies interpolation to the path generated by the sampling-based path planning algorithm. In this study, the proposed algorithm is applied to the path created by RRT-connect and six environmental maps were used for the verification. It was visually and quantitatively confirmed that, in all maps, not only path lengths but also the piecewise linear shape were decreased compared to the path generated by RRT-connect. To check the proposed algorithm's performance, visibility graph, RRT-connect algorithm, Triangular-RRT-connect algorithm and post triangular processing of midpoint interpolation (PTPMI) were compared in various environmental maps through simulation. Based on these experimental results, the proposed algorithm shows similar planning time but shorter path length than previous RRT-like algorithms as well as RRT-like algorithms with PTPMI having a similar number of samples.
Multiclass Image Classification Using GANs and CNN Based on Holes Drilled in Laminated Chipboard

The multiclass prediction approach to the problem of recognizing the state of the drill by classifying images of drilled holes into three classes is presented. Expert judgement was made on the basis of the quality of the hole, by dividing the collected photographs into the classes: “very fine,” “acceptable,” and “unacceptable.” The aim of the research was to create a model capable of identifying different levels of quality of the holes, where the reduced quality would serve as a warning that the drill is about to wear down. This could reduce the damage caused by a blunt tool. To perform this task, real-world data were gathered, normalized, and scaled down, and additional instances were created with the use of data-augmentation techniques, a self-developed transformation, and with general adversarial networks. This approach also allowed us to achieve a slight rebalance of the dataset, by creating higher numbers of images belonging to the less-represented classes. The datasets generated were then fed into a series of convolutional neural networks, with different numbers of convolution layers used, modelled to carry out the multiclass prediction. The performance of the so-designed model was compared to predictions generated by Microsoft’s Custom Vision service, trained on the same data, which was treated as the benchmark. Several trained models obtained by adjusting the structure and hyperparameters of the model were able to provide better recognition of less-represented classes than the benchmark.
MonoScene: Monocular 3D Semantic Scene Completion

MonoScene proposes a 3D Semantic Scene Completion (SSC) framework, where the dense geometry and semantics of a scene are inferred from a single monocular RGB image. Different from the SSC literature, relying on 2.5 or 3D input, we solve the complex problem of 2D to 3D scene reconstruction while jointly inferring its semantics. Our framework relies on successive 2D and 3D UNets bridged by a novel 2D-3D features projection inspiring from optics and introduces a 3D context relation prior to enforce spatio-semantic consistency. Along with architectural contributions, we introduce novel global scene and local frustums losses. Experiments show we outperform the literature on all metrics and datasets while hallucinating plausible scenery even beyond the camera field of view. Our code and trained models are available at https://github.com/cv-rits/MonoScene.
Automatic Evaluations of Human Blood Using Deep Learning Concepts

Identification of human blood is very important to know before any blood transfusion. In emergency situations, if the blood group is not identified exactly, it causes many problems to the patient and may become fatal. Generally, the blood group is identified manually by the lab technicians performing blood tests. In all times, humans cannot be perfect, and in this pandemic, lab technicians need to handle large number of blood samples. So, the manual identification may undergo human errors. The idea of the present paper tends to solve the human errors using deep learning techniques. The proposed system identifies the blood group of each person and reduces the human errors. This would save the time consuming for testing and gives efficient results with good precision and accuracy. Deep learning obtains the blood sample from blood donation applications and have a trained model to predict the given image. Using the latest image processing models, the blood samples are detected. The result through the proposed system is a set of clustered blood samples. Thus, the proposed system benefits the society and efficiently affects the medical diagnosis.
Entanglement monotones connect distinguishability and predictability

Distinguishability and predictability are part of complementarity relations which apply to two different kinds of interference experiments, with and without a path-detector, respectively. In [Opt. Comm. 179, 337 (2000)], Englert and Bergou pointed out the possible connection between distinguishability, predictability, and entanglement. They even conjectured that an entanglement measure was hidden between the measures of distinguishability and predictability. Here, we push forward this conjecture. We start defining a new entropic distinguishability measure and suggesting an entanglement measure as the difference between this entropic distinguishability and an entropic predictability measure already defined in the literature. Besides, we prove that it is possible to define an entanglement monotone from the largest value of the distinguishability and the corresponding predictability, provided that the predictability satisfy the criteria already established in the literature. Thus, this result formally connects an entanglement monotone with distinguishability and the corresponding predictability, without appealing to specific measures.
Perspectives on Bayesian inference and their implications for data analysis.

Use of Bayesian methods has proliferated in recent years as technological and software developments have made Bayesian methods more approachable for researchers working with empirical data. Connected with the increased usage of Bayesian methods in empirical studies is a corresponding increase in recommendations and best practices for Bayesian methods. However, given the extensive scope of Bayes, theorem, there are various compelling perspectives one could adopt for its application. This paper first describes five different perspectives, including examples of different methodologies that are aligned within these perspectives. We then discuss how the different perspectives can have implications for modeling and reporting practices, such that approaches and recommendations that are perfectly reasonable under one perspective might be unreasonable when viewed from another perspective. The ultimate goal is to show the heterogeneity of defensible practices in Bayesian methods and to foster a greater appreciation for the variety of orientations that exist. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
IMPACT OF ARTIFICIAL INTELLIGENCE AND DIGITAL ECONOMY ON INDUSTRIAL REVOLUTION 4: EVIDENCE FROM BANGLADESH

This research investigates effects of artificial intelligence and digital economy on the 4th industry revolution from the perspective of Bangladesh. Artificial intelligence affecting the labor market both positively and negatively. Because of artificial intelligence, few existing jobs have been demolished and few new jobs have been emerged as well. Digital economy in Bangladesh is now an emerging issues with the blessings of artificial intelligence. Few employment opportunities will be created in Bangladesh because of emerging digital economy. The objective of this research is to analyze these opportunities and come out with few policy recommendations to implement towards industry revolution 4. Secondary data have been used along with recent relevant literatures to achieve the above stated objective as this is an empirical research. Ideas regarding revised national plan, financial tools, emerging digital economy in Bangladesh are the prominent outcome of this research. The policy recommendations of managing the gap between digital economy and industry revolution 4 regarding artificial intelligence could be helpful to The Government of the Peoples’ Republic of Bangladesh to implement policies.
Quantum amplitude-amplification operators

In this work, we show the characterization of quantum iterations that would generally construct quantum amplitude amplification algorithms with a quadratic speedup, namely, quantum amplitude amplification operators (QAAOs). Exact quantum search algorithms that find a target with certainty and with a quadratic speedup can be composed of sequential applications of QAAO: existing quantum amplitude amplification algorithms thus turn out to be sequences of QAAOs. We show that an optimal and exact quantum amplitude amplification algorithm corresponds to the Grover algorithm together with a single iteration of QAAO. We then realize 3-qubit QAAOs with the current quantum technologies via cloud-based quantum computing services, IBMQ and IonQ. Finally, our results find that fixed-point quantum search algorithms known so far are not a sequence of QAAOs, e.g. the amplitude of a target state may decrease during quantum iterations.
A two-step computer vision-based framework for bolt loosening detection and its implementation on a smartphone application

Bolt loosening detection is a labor-intensive and time-consuming process for field engineers. This paper develops a two-step computer vision-based framework to quickly identify bolt loosening angle from field images captured by unmanned aerial vehicle (UAV). In step one, a total of 1200 image samples of bolted structures were used to train faster region based convolutional neural network (Faster R-CNN) for bolt detection from UAV captured images. In step two, computer vision-based technologies, including Gaussian filter, perspective transform, and Hough transform (HT), were performed to quantify bolt loosening angle. The developed framework was then integrated into web server and an iOS application (app) was designed to enable fast data communication between field workplace (UAV captured images) and web server (bolt loosening angle quantification), so that field engineers can quickly view the inspection results on their phone screens. The proposed framework and designed smartphone app greatly help field engineers to improve the accuracy and efficiency for onsite inspection and maintenance of bolted structures.
A learning-based flexible autonomous motion control method for UAV in dynamic unknown environments

This paper presents a deep reinforcement learning (DRL)-based motion control method to provide unmanned aerial vehicles (UAVs) with additional flexibility while flying across dynamic unknown environments autonomously. This method is applicable in both military and civilian fields such as penetration and rescue. The autonomous motion control problem is addressed through motion planning, action interpretation, trajectory tracking, and vehicle movement within the DRL framework. Novel DRL algorithms are presented by combining two difference-amplifying approaches with traditional DRL methods and are used for solving the motion planning problem. An improved Lyapunov guidance vector field (LGVF) method is used to handle the trajectory-tracking problem and provide guidance control commands for the UAV. In contrast to conventional motion-control approaches, the proposed methods directly map the sensorbased detections and measurements into control signals for the inner loop of the UAV, i.e., an end-to-end control. The training experiment results show that the novel DRL algorithms provide more than a 20% performance improvement over the state-of-the-art DRL algorithms. The testing experiment results demonstrate that the controller based on the novel DRL and LGVF, which is only trained once in a static environment, enables the UAV to fly autonomously in various dynamic unknown environments. Thus, the proposed technique provides strong flexibility for the controller.
Observational Cosmology with Artificial Neural Networks

In cosmology, the analysis of observational evidence is very important when testing theoretical models of the Universe. Artificial neural networks are powerful and versatile computational tools for data modelling and have recently been considered in the analysis of cosmological data. The main goal of this paper is to provide an introduction to artificial neural networks and to describe some of their applications to cosmology. We present an overview on the fundamentals of neural networks and their technical details. Through three examples, we show their capabilities in the modelling of cosmological data, numerical tasks (saving computational time), and the classification of stellar objects. Artificial neural networks offer interesting qualities that make them viable alternatives for data analysis in cosmological research.
Joint Learning With BERT-GCN and Multi-Attention for Event Text Classification and Event Assignment

Government hotline is closely related to people’s lives and plays an important role in solving social problems and maintaining social stability in China. However, the event text of the hotline is inconsistent in length and unclear in elements, so it is a challenge for the operator to manually complete the assignment tasks of hotline event. To address these problems, we propose a joint learning method for event text classification and event assignment for Chinese government hotline. Firstly, graph convolution network (GCN) and BERT are used to process the event text respectively to obtain the corresponding representation vector. Then, the obtained two representation vectors are fused by the dynamic fusion gate to get fusion vector and classified the fusion vector through the text classification. Secondly, we use multi-attention mechanism to process the GCN result vector, BERT result vector and the “sanding” vector to obtain attentive “event-sanding” representation vector and calculate the corresponding department probability distribution. Finally, the historical prior knowledge based reorder model is used to sort the results of the “event-sanding” matching module and output the optimal assignment department of government hotline event. Experimental results show that our method can achieve better performance compared with several baseline approaches. The ablation experiments also demonstrate the validity of each proposed module in our model.
Fast Locality Discriminant Analysis With Adaptive Manifold Embedding

Linear discriminant analysis (LDA) has been proven to be effective in dimensionality reduction. However, the performance of LDA depends on the consistency assumption of the global structure and the local structure. Some work extended LDA along this line of research and proposed local formulations of LDA. Unfortunately, the learning scheme of these algorithms is suboptimal in that the intrinsic relationship between data points is pre-learned in the original space, which is usually affected by the noise and redundant features. Besides, the time cost is relatively high. To alleviate these drawbacks, we propose a Fast Locality Discriminant Analysis framework (FLDA), which has three advantages: (1) It can divide a non-Gaussian distribution class into many sub-blocks that obey Gaussian distributions by using the anchor-based strategy. (2) It captures the manifold structure of data by learning the fuzzy membership relationship between data points and the corresponding anchor points, which can reduce computation time. (3) The weights between data points and anchor points are adaptively updated in the subspace where the irrelevant information and the noise in high-dimensional space have been effectively suppressed. Extensive experiments on toy data sets, UCI benchmark data sets and imbalanced data sets demonstrate the efficiency and effectiveness of the proposed method.
Differential Privacy and Fairness in Decisions and Learning Tasks: A Survey

This paper surveys the recent work in the intersection of differential privacy (DP) and fairness. It focuses on surveying the work observing that DP systems may exacerbate bias and disparate impacts for different groups of individuals. The survey reviews the conditions under which privacy and fairness may be aligned or contrasting goals, analyzes how and why DP exacerbates bias and unfairness in decision problems and learning tasks, and reviews the available solutions to mitigate the fairness issues arising in DP systems. The survey provides a unified understanding of the main challenges and potential risks arising when deploying privacy-preserving machine learning or decisions making tasks under a fairness lens.