Reviewerly/exHarmony huggingface dataset:
A convolutional neural network based super resolution technique of CT image utilizing both sinogram domain and image domain data

In previous deep learning based super-resolution techniques for CT images, only image domain data is used for training. However, image blurring can occur in image domain method which disrupts accurate diagnosis. In this work, we propose using both sinogram and image domain data to resolve the blurring issue. To predict upsampled sinogram more accurately, we use a convolutional neural network (CNN) as an encoder, which maps an input image to feature map for decoder. For decoder, we use dual multi-layer perceptron (MLP) structure. Our proposed dual-MLP structure consists of modulator and synthesizer MLP. Synthesizer MLP predicts the output pixel value by using coordinate-based information as an input, and modulator MLP helps synthesizer to estimate the output value accurately by using feature map information as an input. This network structure preserves high frequency components better than simple CNN structure. Through our proposed sinogram upsampling network (SUN) at sinogram domain, upsampled sinogram was generated, and image was reconstructed by filtered backprojection. The reconstructed image from upsampled sinogram preserves detailed textures compared to LR image. However, residual artifacts and blur still remain. Therefore, we train CNN using image domain data to reduce residual artifacts and blur. For the dataset, we acquire projection data from Mayo Clinic image using Siddon's algorithm in fan-beam CT geometry applying 4x1 detector binning. The binned sinogram is then used as an input for the SUN. The results show that our proposed hybrid domain method outperforms image domain and sinogram domain method with higher quantitative evaluation results.
Subjective and Objective Quality Evaluation for Underwater Image Enhancement and Restoration

Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.
Protecting communities during the COVID-19 global health crisis: health data research and the international use of contact tracing technologies

Abstract Seen presently during the global COVID-19 global health crisis are the ways government agencies are enabled by digital data collection through the development of contact tracing technologies (CTT) and mobile-based tracking in their effort to limit the spread of the COVID-19 coronavirus. While recent research has focused on contact tracing and privacy (Simko et al. 2020. Contact tracing and privacy: studying opinion and preferences), contact tracing and data protection (Abeler et al. 2020. JMIR mHealth uHealth 8(4): e19359) contact tracing system and information security considerations (Gvili, 2020. Security analysis of the Covid-19 contact tracing specifications by Apple inc. and Google inc), CTT and the data collected and curated have not been framed to date via their intersections with health-datafication and the research participant. As this article outlines, each is strongly linked to public health, healthcare industries and to modalities of capturing and producing knowledge that is expected to help in addressing public health concerns. Where different countries and regions are implementing a range of social distancing and/or social isolation recommendations, some have introduced contact tracing and quick response (QR) barcodes on mobile device apps. To additionally explore access, uptake and use that accompany such technologies, the International Responses to COVID-19 Contact Tracing: COVID-19 APP Uptake and Use Survey was developed and used with participants living in Singapore, Australia, the United States and the United Kingdom. The article concludes that in the process and counter to the common good or public interest objective that all are kept safe, new forms of risk and exposure are being produced.
Trade-offs in Sampling and Search for Early-stage Interactive Text Classification

For many automated classification tasks, collecting labeled data is the key barrier to training a useful supervised model. Interfaces for interactive labeling tighten the loop of labeled data collection and model development, enabling a subject-matter expert to quickly establish the feasibility of a classifier to address a problem of interest. These interactive machine learning (IML) interfaces iteratively sample unlabeled data for annotation, train a new model, and display feedback on the model’s estimated performance. Different sampling strategies affect both the rate at which the model improves and the bias of performance estimates. We compare the performance of three sampling strategies in the “early-stage” of label collection, starting from zero labeled data. By simulating a user’s interactions with an IML labeling interface, we demonstrate a trade-off between improving a text classifier’s performance and computing unbiased estimates of that performance. We show that supplementing early-stage sampling with user-guided text search can effectively “seed” a classifier with positive documents without compromising generalization performance—particularly for imbalanced tasks where positive documents are rare. We argue for the benefits of incorporating search alongside active learning in IML interfaces and identify design trade-offs around the use of non-random sampling strategies.
A Comparative Analysis of Supervised Classifiers for Detecting Credit Card Frauds

Nowadays, Credit Card Frauds are one of the major fraudulent activities due to the vital increase of online payments. Credit Card Fraud generally happens when the card is stolen for any of the unauthorized purposes or even when the Credit Card information is used by fraudster for his use. Hence, we need a system which can notify when a transaction happens to be fraud. Credit Card Fraud Detection System detects given a transaction whether it is fraud or not. This paper mainly focuses on how frauds can be analyzed using Machine Learning approaches. The main objective of this paper focused on methods of handling imbalance in dataset of Credit Card transactions. Later, Credit Card transactions dataset is used on XGBoost and Random Forest Classifier. The results of the XGBoost and Random Forest Algorithms are compared based on the accuracy, precision, recall, and F1- score. The Algorithm that has higher F-1 score is considered to be the best algorithm that is used to detect the fraud.
Physical Exercise Monitoring System Based on Computer Somatosensory Technology

Aiming at the problems of poor convergence, slow acceleration, and low throughput in traditional physical exercise monitoring systems, a physical exercise monitoring system based on computer somatosensory technology is proposed and designed. The whole structure of the physical activity monitoring system was created from the hardware and software, according to the research. Human body posture monitoring sensor module, communication connection module, power supply module, main control unit module, sensor module, and other pieces make up the hardware section. To improve detection performance, computer somatosensory technology is used in the software design to gather physical activity posture data. Also, for experimental comparison and verification, we employ system convergence, acceleration, and throughput as indicators. Experimental results show that the improved method can effectively improve system convergence, increase system acceleration, and increase system throughput, which has certain advantages in use.
Analysis of a Kind of Harr Characteristic Big Data Algorithm for College Students’ Safety Education Method

In order to improve the effectiveness of safety education in colleges and universities and to maintain the long-term effectiveness of college students’ safety awareness, combined with the current safety problems in colleges and universities, by analyzing the relevance of campus safety accidents and safety education, a Harr characteristic big data algorithm for college students is proposed. Safety education methods have constructed a safety education system for college students. The status quo of safety education is analyzed for college students, a safety education system image acquisition module is designed based on Harr characteristic big data algorithm, Harr characteristic big data algorithm and Adaboost algorithm are used to shorten the training time of college students’ safety education image, learning resources are saved, and remote teaching is realized. A set of safety knowledge question banks based on recommendation algorithm has developed a set of practical safety knowledge online learning and testing systems, and targeted solutions to prevent and reduce campus safety accidents are proposed. The experimental results show that the number of student safety incidents in the school within 1 year after the application of this system is significantly reduced to 58, the system’s experimental response time is only 0.05 s on average, and the student’s satisfaction with the system reaches 93%. The application effect of the system is obvious, and it can effectively prevent and reduce the occurrence of campus safety accidents.
Exploring the Benefits of Social Presence Enabled Augmented Reality Platform for Online Learners

The effectiveness of Augmented Reality (AR)-based learning platform in providing the feeling of social presence to the online learners in the field of engineering education is yet to be studied intensively. This paper emphasizes the need for an application of AR in online STEM education as well as the effect of social presence on learners’ cognition. To fulfill this knowledge gap of exploring the effect of social presence on online learning students, the paper will present a framework of incorporating social presence elements into the AR-based learning platform, where students can talk/chat with each other and discuss the problem-solving techniques during online learning. The development of such a platform utilizing AR tools is discussed. It is expected that the result of the study will help provide further evidence as well as a theoretical framework to researchers and practitioners to develop a more advanced AR platform that is beneficial to online engineering learners.
One Type Context Is Not Enough: Global Context-aware Neural Machine Translation

How to effectively model global context has been a critical challenge for document-level neural machine translation (NMT). Both preceding and global context have been carefully explored in the sequence-to-sequence (seq2seq) framework. However, previous studies generally map global context into one vector, which is not enough to well represent the entire document since this largely ignores the hierarchy between sentences and words within. In this article, we propose to model global context for source language from both sentence level and word level. Specifically at sentence level, we extract useful global context for the current sentence, while at word level, we compute global context against words within the current sentence. On this basis, both kinds of global context can be appropriately fused before being incorporated into the state-of-the-art seq2seq model, i.e., Transformer . Detailed experimentation on various document-level translation tasks shows that global context at both sentence level and word level significantly improve translation performance. More encouraging, both kinds of global context are complementary. This leads to more improvement when both kinds of global context are used.
Enhancing horizontal gaze nystagmus test training through virtual human simulation

To maintain roadway safety, officers must be trained in the Standard Field Sobriety Test (SFST) which includes a Horizontal Gaze Nystagmus (HGN) component. HGN – a repetitive jerking movement in the eyes – is an effective impairment indicator, but can be subtle and difficult to observe, particularly for inexperienced officers. Therefore, hands-on HGN learning opportunities are important for SFST training. Current HGN training often focuses on wet labs with live participants or observational videos and lectures – both with limitations. This research shows that simulation training with a virtual human is a viable HGN training approach and provides hands-on experience in various situations. Study results from the Individual Nystagmus Simulation Training Experience (INSITE) system are presented with a summary of encouraging results in confidence gains, participant feedback and other areas, particularly for newer officers. A discussion of future potential for both INSITE and virtual human training techniques for law enforcement is included.
A Broad Learning System to Predict the 28-Day Mortality of Patients Hospitalized with Community-Acquired Pneumonia: A Case-Control Study

This study was to conduct a model based on the broad learning system (BLS) for predicting the 28-day mortality of patients hospitalized with community-acquired pneumonia (CAP). A total of 1,210 eligible CAP cases from Chifeng Municipal Hospital were finally included in this retrospective case-control study. Random forest (RF) and an eXtreme Gradient Boosting (XGB) models were used to develop the prediction models. The data features extracted from BLS are utilized in RF and XGB models to predict the 28-day mortality of CAP patients, which established two integrated models BLS-RF and BLS-XGB. Our results showed the integrated model BLS-XGB as an efficient broad learning system (BLS) for predicting the death risk of patients, which not only performed better than the two basic models but also performed better than the integrated model BLS-RF and two well-known deep learning systems-deep neural network (DNN) and convolutional neural network (CNN). In conclusion, BLS-XGB may be recommended as an efficient model for predicting the 28-day mortality of CAP patients after hospital admission.
How to Use Artificial Intelligence to Improve Entrepreneurial Attitude in Business Simulation Games: Implications From a Quasi-Experiment

Business simulation games (BSGs) have been widely used in entrepreneurship education with positive effects. However, there are still some deficiencies in the BSGs, such as limited guidance, low uncertainty and limited simulation environment, which make it impossible to exert the maximum effect. Artificial intelligence (AI) can solve the above shortcomings. The combination of AI and BSGs is the possible development direction of BSGs. But how to effectively combine BSGs with AI is still an open question. Using a quasi-experimental design, this study uses fuzzy-set qualitative comparative analysis to analyze how participants’ entrepreneurial attitude changes in BSGs. The results show that BSGs can effectively improve entrepreneurial attitude, and there are four types of promotion configurations. These four configurations consist of five antecedent conditions. According to the above conclusions, AI can improve entrepreneurial attitude in BSGs in various ways, such as simulating competitors, providing targeted feedback for failures, and improving game experience. The contribution of this paper is to highlight the possibility of combining AI with BSGs, and to provide suggestions on how AI can intervene in BSGs.
GASSER: A Multi-Objective Evolutionary Approach for Test Suite Reduction

Regression testing is a practice that ensures a System Under Test (SUT) still works as expected after changes have been implemented. The simplest approach for regression testing is Retest-all, which consists of re-executing the entire Test Suite (TS) on the changed version of the SUT. Retest-all could be expensive in case a SUT and its TS grow in size and, if resources are insufficient, its application could be impracticable. A Test Suite Reduction (TSR) approach aims to overcome these issues by reducing the size of TSs, while preserving their fault-detection capability. In this paper, we introduce and validate an approach for TSR based on a multi-objective evolutionary algorithm, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II). This approach seeks to reduce TSs by maximizing both statement coverage and diversity of test cases of the reduced TSs, while minimizing the size of the reduced TSs. We named this approach Genetic Algorithm for teSt SuitE Reduction (GASSER). To assess GASSER, we conducted an experiment on 19 versions of four software systems from a public dataset—i.e. Software-artifact Infrastructure Repository (SIR). We compared GASSER with nine baseline approaches. The comparison was based on the size of the reduced TSs and their fault-detection capability. The most important take-away result is that GASSER, as compared with the baseline approaches, reduces more the size of the TSs with a non-significant effect on their fault-detection capability. The results of our empirical assessment suggest that the application of multi-objective evolutionary algorithms and, in particular, NSGA-II might represent a viable means to deal with TSR.
A New Artificial Potential Field Based Global Path Planning Algorithm for Mobile Robot Navigation

This paper proposes a navigation algorithm for indoor mobile robots in unknown and unstructured environments using a modified Artificial Potential Fields (APF) approach. The proposed algorithm takes into consideration the well-known drawbacks of the APF such as local minima, U-Shaped traps, box canyon and the goal non-reachable with obstacles nearby (GNRON) and implies certain modifications to address those problems. The proposed method is tested in a series of simulations performed on actual lidar measurement data and taking into consideration the problems mentioned above. The results of the simulations confirm the performance of the proposed method yielding a robust and fast algorithm for indoor localization and navigation of mobile robots.
Multi-UAV Path Coordination through Generalized Potential Games

In this paper, the problem to safely and efficiently navigate multiple Unmanned Aerial Vehicles (UAVs) in complex environments is addressed. Each UAV aims at achieving an approximate optimal trajectory plan while considering constraints from the environment and other neighboring UAVs. The problem is formulated as a generalized potential game. An algorithm, following an iterative best response approach, is proposed. Experimental results verify the coordination of a team of UAVs for a given team goal description. Also, the time cost of computation for the proposed algorithm is investigated. The results show a high-level goal to navigate a team of UAVs can be automatically decomposed to safe and efficient trajectory plans for each UAV in the team by solving generalized potential games in a decentralized manner.
Efficiently tracking multi-strategic opponents: A context-aware Bayesian policy reuse approach

In Markov games, accurately detecting opponent policies and reusing optimal response policies is still a challenging problem. Most previous works assume that opponents switch their policies infrequently only at the end of an episode. However, the opponents may change their policies at high-frequency or even within an episode. Besides, the agent may achieve inconsistent optimal returns because of different opponent behaviors, which brings greater challenges to policy detection. This paper studies how to deal with the non-stationary opponent with abrupt policy changes through accurate policy detection and direct policy reuse. Specifically, we propose a context-aware Bayesian policy reuse (CABPR) algorithm to accurately identify and track the multi-strategic opponent. To continuously infer the opponent policy, an intra-episode belief is introduced taking advantage of opponent models. Within an episode, an inter-episode belief using Bayesian inference and the intra-episode belief are jointly used to detect the opponent type based on its behaviors and episodic rewards. Then the agent reuses the best response policies accordingly. We demonstrate the advantages of the proposed algorithm over several state-of-the-art algorithms in terms of episodic rewards, accumulated rewards, and detection accuracy in four competitive scenarios.
Off-axis aberration correction for a reflective coded aperture snapshot spectral imager

The reflective optical configuration of the coded aperture snapshot spectral imager (CASSI) based on a digital micromirror device (DMD) features both high quality and high flexibility of intensity modulation, but the image quality is always subject to off-axis aberration. This letter analyzes the formation of the off-axis aberration, and then proposes an optical-axis-shift (OAS) configuration to compensate for this adverse effect in the DMD-based reflective CASSI system. Experimental results demonstrate that the proposed design can effectively eliminate the off-axis aberration. The average peak signal-to-noise ratio of the reconstructed spectral images is improved by approximately 2.8 dB. In addition, other methods to alleviate the off-axis aberration are discussed, and the superiority of the proposed design is signified. The proposed OAS design is easy to implement with commercially available components, and provides an initial configuration of an optical design to implement a high-performance CASSI instrument.