Reviewerly/exHarmony huggingface dataset:
YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications

Object detection algorithm such as You Only Look Once (YOLOv3 and YOLOv4) is implemented for traffic and surveillance applications. A neural network consists of input with minimum one hidden and output layer. Multiple object dataset (KITTI image and video), which consists of classes of images such as Car, truck, person, and two-wheeler captured during RGB and grayscale images. The dataset is composed (image and video) of varying illumination. YOLO model variants such as YOLOv3 is implemented for image and YOLOv4 for video dataset. Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset.
Visual Reasoning Strategies for Effect Size Judgments and Decisions

Uncertainty visualizations often emphasize point estimates to support magnitude estimates or decisions through visual comparison. However, when design choices emphasize means, users may overlook uncertainty information and misinterpret visual distance as a proxy for effect size. We present findings from a mixed design experiment on Mechanical Turk which tests eight uncertainty visualization designs: 95% containment intervals, hypothetical outcome plots, densities, and quantile dotplots, each with and without means added. We find that adding means to uncertainty visualizations has small biasing effects on both magnitude estimation and decision-making, consistent with discounting uncertainty. We also see that visualization designs that support the least biased effect size estimation do not support the best decision-making, suggesting that a chart user's sense of effect size may not necessarily be identical when they use the same information for different tasks. In a qualitative analysis of users' strategy descriptions, we find that many users switch strategies and do not employ an optimal strategy when one exists. Uncertainty visualizations which are optimally designed in theory may not be the most effective in practice because of the ways that users satisfice with heuristics, suggesting opportunities to better understand visualization effectiveness by modeling sets of potential strategies.
CM-BERT

Multimodal sentiment analysis is an emerging research field that aims to enable machines to recognize, interpret, and express emotion. Through the cross-modal interaction, we can get more comprehensive emotional characteristics of the speaker. Bidirectional Encoder Representations from Transformers (BERT) is an efficient pre-trained language representation model. Fine-tuning it has obtained new state-of-the-art results on eleven natural language processing tasks like question answering and natural language inference. However, most previous works fine-tune BERT only base on text data, how to learn a better representation by introducing the multimodal information is still worth exploring. In this paper, we propose the Cross-Modal BERT (CM-BERT), which relies on the interaction of text and audio modality to fine-tune the pre-trained BERT model. As the core unit of the CM-BERT, masked multimodal attention is designed to dynamically adjust the weight of words by combining the information of text and audio modality. We evaluate our method on the public multimodal sentiment analysis datasets CMU-MOSI and CMU-MOSEI. The experiment results show that it has significantly improved the performance on all the metrics over previous baselines and text-only finetuning of BERT. Besides, we visualize the masked multimodal attention and proves that it can reasonably adjust the weight of words by introducing audio modality information.
Between Subjectivity and Imposition

The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels. Previous human-centered investigations have largely focused on annotators? subjectivity as a major cause of biased labels. We propose a wider view on this issue: guided by constructivist grounded theory, we conducted several weeks of fieldwork at two annotation companies. We analyzed which structures, power relations, and naturalized impositions shape the interpretation of data. Our results show that the work of annotators is profoundly informed by the interests, values, and priorities of other actors above their station. Arbitrary classifications are vertically imposed on annotators, and through them, on data. This imposition is largely naturalized. Assigning meaning to data is often presented as a technical matter. This paper shows it is, in fact, an exercise of power with multiple implications for individuals and society.
Prediction intervals estimation of solar generation based on gated recurrent unit and kernel density estimation

With the increasing attention to the energy crisis and global warming, solar generation has become an important way to use clean solar energy and is playing an increasingly important role. Due to the highly-variable patterns of solar generation, the estimation of prediction intervals is receiving more attention, which is conducive to the safe and stable operation of the power system. In order to further improve the performance of prediction intervals of solar generation, this paper proposes a prediction intervals estimation method for solar generation based on gated recurrent unit (GRU) neural networks and kernel density estimation (KDE). GRU, a commonly used recurrent neural networks, is utilized to obtain the deterministic forecast of solar generation. In addition, according to the characteristics of solar generation, attention mechanism is designed on the GRU prediction model to further improve the prediction performance. Then, the KDE method is used to fit the prediction errors of solar generation obtained by the deterministic forecasting method. In order to verify the effectiveness of the proposed method, we have carried out a large number of experiments on freely available datasets. The experimental results show that the proposed method outperforms competing methods and can generate high-quality prediction intervals.
Assessing customer satisfaction of O2O takeaway based on online reviews by integrating fuzzy comprehensive evaluation with AHP and probabilistic linguistic term sets

In recent years, O2O takeaway has a rapid development with increasingly fierce market competitions. The online reviews of these takeaway platforms give restaurants an opportunity to better understand their customers and find their own shortages. However, how to dig customer satisfaction information from online reviews of restaurants is a challenge. Therefore, we establish an O2O takeaway customer satisfaction evaluation system and evaluate the satisfaction degree for restaurants by analyzing online reviews. First, we use European customer satisfaction index and Service performance-aware service quality measurement method as a logical framework. Meantime, we adopt content analysis to construct the evaluation system of customer satisfaction. Taking probabilistic linguistic term sets and the frequency into consideration, we improve analytic hierarchy process and develop a combination way to calculate the index weights of the evaluation system. The fuzzy comprehensive evaluation method is further designed to calculate the customer satisfaction level. Finally, we have an empirical analysis on Meituan and Ele.me platforms to validate our method.
Model-Agnostic Boundary-Adversarial Sampling for Test-Time Generalization in Few-Shot Learning

Few-shot learning is an important research problem that tackles one of the greatest challenges of machine learning: learning a new task from a limited amount of labeled data. We propose a model-agnostic method that improves the test-time performance of any few-shot learning models with no additional training, and thus is free from the training-test domain gap. Based on only the few support samples in a meta-test task, our method generates the samples adversarial to the base few-shot classifier’s boundaries and fine-tunes its embedding function in the direction that increases the classification margins of the adversarial samples. Consequently, the embedding space becomes denser around the labeled samples which makes the classifier robust to query samples. Experimenting on miniImageNet, CIFAR-FS, and FC100, we demonstrate that our method brings significant performance improvement to three different base methods with various properties, and achieves the state-of-the-art performance in a number of few-shot learning tasks.
The role of extended reality technology in healthcare education: Towards a learner-centred approach

<h3>ABSTRACT</h3> The use of extended reality (XR) technologies is growing rapidly in a range of industries from gaming to aviation. However, how this technology should be implemented in healthcare education is not well-documented in the literature. Learner-driven implementation of educational technology has previously been shown to be more effective than a technology-driven approach. In this paper we conduct a narrative literature review of relevant papers to explore the role of XR technologies in learner-driven approaches to healthcare educatio. This paper aims to evaluate the position of XR technologies in learner-centred pedagogical models, determine what functions of XR technologies can improve learner-centred approaches in healthcare education, and explore whether XR technologies can improve learning outcomes in healthcare education. We conclude that XR technologies have unique attributes that can improve learning outcomes when compared to traditional learning methods, but there is currently a shortfall in learner-centred implementation of XR technologies in healthcare education, where these technologies have the capacity to cause a paradigm shift.
Coverless Image Steganography Based on Multi-Object Recognition

Most of the existing coverless steganography approaches have poor robustness to geometric attacks, because these approaches use features of the entire image to map information, and these features are easy to be lost when being attacked. In order to improve the robustness against geometric attacks, we propose a coverless image steganography method based on multi-object recognition. In this scheme, we firstly use Faster RCNN to detect objects in the image data set, establish a mapping dictionary between object labels and binary sequence. Then we propose a novel mapping rule based on the filtered robust object labels for sequence generation. Therefore, an image can generate robust binary sequence through multi-objects recognition. In the transmission process, the transmitted image has not been modified, so our method can fundamentally resist steganalysis tools and avoid the attacker’s suspicions. In addition, the capacity and hiding rate of the proposed method are both considerable. Evaluations with under geometric attacks shows, on average, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.1\times $ </tex-math></inline-formula> robustness increase over other five coverless steganography methods. Moreover, evaluations under ten noise attacks shows, on average, the robustness of our method is also excellent, which reaches 83%.
The Unknown Potential of Blockchain for Sustainable Supply Chains

The growing attention to emerging technologies has raised several doubts regarding the sustainability aspects. Among the new technologies, blockchain has laid the foundations for a revolution in the financial field but also in the energy, tourism, medical, industrial and supply chains sectors. Through a systematic literature review, 37 documents were analyzed to describe the sustainable aspects generated using blockchain in supply chains. Therefore, the results achieved provide two levels of analysis. The former highlights the impact of the adoption of blockchain in supply chains based on the three dimensions of sustainability: environmental, economic and social. The latter shows the positive and negative impacts of each form of sustainability. The purpose of the work is to summarize the current state of the art in order to propose a future development agenda based on the need to simulate and define a measurability for each dimension of sustainability. Finally, the study provides a broad overview of practical and managerial implications for entrepreneurs and researchers. On the one hand, the work shows how the use of blockchain improves the profits and reputation of companies. On the other hand, it highlights several research gaps to be investigated and considered by researchers.
Blockchain in Accounting Research and Practice: Current Trends and Future Opportunities*

ABSTRACT This article provides a review of the accounting blockchain literature, with a focus on current trends and recommendations for future research opportunities. Our review identifies seven main areas: (i) future of blockchain technology, (ii) impact on the accounting function, (iii) auditing considerations, (iv) financial reporting for cryptoassets, (v) case studies, (vi) governance, and (vii) taxation. The article aims to bridge the gap between practitioners and academics by providing a review of both areas of literature and highlighting common ground between the two arenas. While academics have begun to explore how the accounting profession might change in response to blockchain, this research is limited primarily to the auditing field. Practitioners, for their part, have expanded their scope to also devote significant attention to the financial reporting and taxation of cryptoassets. Expanding the discussion of accounting and blockchains beyond their current concentrations in auditing and accounting information systems, we call for more research on the impact of blockchain technology in other areas such as corporate governance or the intersection of accounting and society.
Reinforcement learning algorithm for non-stationary environments

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, etc., one often encounters situations with non-stationary environments, and in these scenarios, RL methods yield sub-optimal decisions. In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward accrued when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. We illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. We further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem, and a traffic signal control problem.
Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019

Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of cancer tissue in whole slide imaging (WSI), using an annotated dataset of 150 training images and 50 test images from 200 patients. This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. All methods were evaluated using metrics using the precision, accuracy, sensitivity, specificity, and DICE coefficient (DC). The DC ranged from 0.7354±0.1149 to 0.8372±0.0858. The DC of the best method was close to the inter-observer agreement (0.8398±0.0890). All methods were based on deep learning and categorized into two groups: multi-model method and single model method. In general, multi-model methods were significantly better (p<; 0.01) than single model methods, with mean DC of 0.7966 and 0.7544, respectively. Deep learning based methods could potentially help pathologists find suspicious regions for further analysis of lung cancer in WSI.
Continuous-variable entropic uncertainty relations

Uncertainty relations are central to quantum physics.While they were originally formulated in terms of variances, they have later been successfully expressed with entropies following the advent of Shannon information theory.Here, we review recent results on entropic uncertainty relations involving continuous variables, such as position x and momentum p.This includes the generalization to arbitrary (not necessarily canonically-conjugate) variables as well as entropic uncertainty relations that take x-p correlations into account and admit all Gaussian pure states as minimum uncertainty states.We emphasize that these continuous-variable uncertainty relations can be conveniently reformulated in terms of entropy power, a central quantity in the information-theoretic description of random signals, which makes a bridge with variance-based uncertainty relations.In this review, we take the quantum optics viewpoint and consider uncertainties on the amplitude and phase quadratures of the electromagnetic field, which are isomorphic to x and p, but the formalism applies to all such variables (and linear combinations thereof) regardless of their physical meaning.Then, in the second part of this paper, we move on to new results and introduce a tighter entropic uncertainty relation for two arbitrary vectors of intercommuting continuous variables that takes correlations into account.It is proven conditionally on reasonable assumptions.Finally, we present some conjectures for new entropic uncertainty relations involving more than two continuous variables.
Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization

Here we explore which heuristic quantum algorithms for combinatorial optimization might be most practical to try out on a small fault-tolerant quantum computer. We compile circuits for several variants of quantum accelerated simulated annealing including those using qubitization or Szegedy walks to quantize classical Markov chains and those simulating spectral gap amplified Hamiltonians encoding a Gibbs state. We also optimize fault-tolerant realizations of the adiabatic algorithm, quantum enhanced population transfer, the quantum approximate optimization algorithm, and other approaches. Many of these methods are bottlenecked by calls to the same subroutines; thus, optimized circuits for those primitives should be of interest regardless of which heuristic is most effective in practice. We compile these bottlenecks for several families of optimization problems and report for how long and for what size systems one can perform these heuristics in the surface code given a range of resource budgets. Our results discourage the notion that any quantum optimization heuristic realizing only a quadratic speedup will achieve an advantage over classical algorithms on modest superconducting qubit surface code processors without significant improvements in the implementation of the surface code. For instance, under quantum-favorable assumptions (e.g., that the quantum algorithm requires exactly quadratically fewer steps), our analysis suggests that quantum accelerated simulated annealing would require roughly a day and a million physical qubits to optimize spin glasses that could be solved by classical simulated annealing in about four CPU-minutes.
Learning Robust and Multilingual Speech Representations

Unsupervised speech representation learning has shown remarkable success at finding representations that correlate with phonetic structures and improve downstream speech recognition performance.However, most research has been focused on evaluating the representations in terms of their ability to improve the performance of speech recognition systems on read English (e.g.Wall Street Journal and Lib-riSpeech).This evaluation methodology overlooks two important desiderata that speech representations should have: robustness to domain shifts and transferability to other languages.In this paper we learn representations from up to 8000 hours of diverse and noisy speech data and evaluate the representations by looking at their robustness to domain shifts and their ability to improve recognition performance in many languages.We find that our representations confer significant robustness advantages to the resulting recognition systems: we see significant improvements in out-of-domain transfer relative to baseline feature sets and the features likewise provide improvements in 25 phonetically diverse languages.
Learning to Detect Open Classes for Universal Domain Adaptation

Universal domain adaptation (UniDA) transfers knowledge between domains without any constraint on the label sets, extending the applicability of domain adaptation in the wild. In UniDA, both the source and target label sets may hold individual labels not shared by the other domain. A de facto challenge of UniDA is to classify the target examples in the shared classes against the domain shift. A more prominent challenge of UniDA is to mark the target examples in the target-individual label set (open classes) as “unknown”. These two entangled challenges make UniDA a highly under-explored problem. Previous work on UniDA focuses on the classification of data in the shared classes and uses per-class accuracy as the evaluation metric, which is badly biased to the accuracy of shared classes. However, accurately detecting open classes is the mission-critical task to enable real universal domain adaptation. It further turns UniDA problem into a well-established close-set domain adaptation problem. Towards accurate open class detection, we propose Calibrated Multiple Uncertainties (CMU) with a novel transferability measure estimated by a mixture of uncertainty quantities in complementation: entropy, confidence and consistency, defined on conditional probabilities calibrated by a multi-classifier ensemble model. The new transferability measure accurately quantifies the inclination of a target example to the open classes. We also propose a novel evaluation metric called H-score, which emphasizes the importance of both accuracies of the shared classes and the “unknown” class. Empirical results under the UniDA setting show that CMU outperforms the state-of-the-art domain adaptation methods on all the evaluation metrics, especially by a large margin on the H-score.
VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity

Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites, in particular against unseen phishing pages. This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN). VisualPhishNet learns profiles for websites in order to detect phishing websites by a similarity metric that can generalize to pages with new visual appearances. We furthermore present VisualPhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner. We show that our method outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks.