Reviewerly/exHarmony huggingface dataset:
Estimation and Spatial Analysis of Heavy Metals in Metal Tailing Pond Based on Improved PLS With Multiple Factors

With the exploitation of mineral resources, pollution of the ecological environment in mines has garnered public attention. Particularly,erosion of the surrounding ecological environment re-sulting from heavy metals in tailings pond could be highly concerning. Instead of traditional field sampling and laboratory analysis method, remote sensing can be used to high-precisi es-timation soil heavy metal with less time and effort. soil heavy metal content is generally low, the spectral sensitivities of various heavy metals are insignificant, and the surface landscape is complex, there exist difficulties associated with heavy metal content estimation. Therefore, herein, we propose optimization of the commonly used partial least-square regression (PLS) method. In the optimized method, a variety of remote sensing indices and the modeled heavy metals were added as modeling factors to indirect estimation soil heavy metal. The method was validated via inversion experiments of heavy metals (Ni, Cu, and Zn) in the tailing pond and its surrounding environment,it improve the goodness-of-fit of Ni, Cu, and Zn by 0.0852,0.2291, and 0.2919 compared with traditional PLS. Spatia l analysis was then conducted on the entire studied area using the estimation model of the three heavy metals. It was shown that the results were essentially consistent with the actual heavy metal distribution in the area. Therefore, the indirect PLS model with multiple factors proves effective for the estimation of soil heavy metals. It also provides technical support for treatment and evaluation of ecological environments in mining areas.
A novel fruit fly optimization algorithm with Levi flight and challenge probability

Aiming at the problem of low convergence precision and premature convergence of fruit fly optimization algorithm (FOA), this paper proposes a novel fruit fly optimization algorithm with Levi flight and challenge probability (LCFOA). We introduce the Levi flight mechanism and challenge adjustment process to coordinate the global exploration and local exploitation capabilities of the algorithm. Then, we designed an adaptive challenge factor in the challenge adjustment process to improve the convergence precision of the algorithm. The convergence performance of the algorithm is verified by 6 different benchmark functions. The numerical experimental results comfirmed that the proposed LCFOA has better search precision and convergence speed.
Consistency Training with Virtual Adversarial Discrete Perturbation

Consistency training regularizes a model by enforcing predictions of original and perturbed inputs to be similar.Previous studies have proposed various augmentation methods for the perturbation but are limited in that they are agnostic to the training model.Thus, the perturbed samples may not aid in regularization due to their ease of classification from the model.In this context, we propose an augmentation method of adding a discrete noise that would incur the highest divergence between predictions.This virtual adversarial discrete noise obtained by replacing a small portion of tokens while keeping original semantics as much as possible efficiently pushes a training model's decision boundary.Experimental results show that our proposed method outperforms other consistency training baselines with text editing, paraphrasing, or a continuous noise on semi-supervised text classification tasks and a robustness benchmark 1 .
Unsupervised Sentiment Classification of Twitter Data using Emoticons

Twitter is a powerful social media where people share their opinion on various topics. Sentiment Analysis on twitter data gives the classification of opinion on a topic as positive, negative or neutral. Twitter messages are written informally and tweets are short. Hence, the classification of tweets by only considering the text part of the message does not give accurate results. To improve the classification accuracy we use Emotion Tokens like Emoticons or Emojis. Emotion Tokens are independent of language, grammar or size of the tweet. Considering Emotion tokens while classifying tweets will improve the accuracy of classification. In this paper, we propose unsupervised Sentiment classification on Twitter Data using Emoticons to improve the performance of classification.
Why flipping the classroom is not enough: Digital curriculum making after the pandemic

Abstract To slow down the proliferation of Covid-19, governments virtually shut down public life, temporarily closed schools, and forced teaching to be done exclusively on a remote basis. These measures offer an opportunity to reexamine conventional teaching and learning arrangements, test new digital and analogue concepts, and provide essential inspiration for curriculum making in the twenty-first century. This article addresses the historical development of schooling in the classroom as differentiated from “homeschooling”. On one hand, the question of how school closures and digitally supported teaching settings may affect an increase in educational inequalities is investigated using an international comparison. On the other hand, the pedagogical and didactical implications of distance learning and a digital teaching culture, which constitute the foundation for digital curriculum making, are examined.
Blockchain in Healthcare

The healthcare sector is a $3 trillion industry, and about $1 trillion of it goes to waste annually. Care coordination is becoming more complex as chronic conditions in the aging population continue to rise. In many instances, the technology available to healthcare providers is not adequate to capture all aspects of the care being provided. The result is a rugged transfer of information between parties that ultimately reduces the quality of care being provided to patients. This is largely due to providers’ having legacy systems, lack of integration with non-vendor-specific technologies, paper-based medical records, and a lack of horizontal transfer between allied healthcare professions. Hospitals are investing a considerable amount of resources into duplicating work that can be completed by a sophisticated technology infrastructure and amplifying the inefficiency of using poorly designed systems. In this use case, we discuss the payer–provider–patient model in the context of the incentives and services each provides and how this model is likely to change in the near future. We then introduce how blockchain can integrate into tracking the workflow of a patient from the first visit to the final diagnosis and treatment plan. We introduce two new features that blockchain integration can enable: hot switching of components and medical data curation. Then, we present a use case of physician credentialing using the blockchain. Finally, we conclude by discussing waste management in healthcare and efforts by Capital One + Gem to increase the economic output.
Three-Dimensional Submarine-to-Submarine Passive Target Tracking in the Presence of Non-Gaussian Noises

In underwater surveillance, three-dimensional target tracking is a challenging task. The angles-only measurements (i.e., bearing and elevation) obtained by hull mounted sensors are considered to appraise the target motion parameter. Due to noise in measurements and nonlinearity of the system, it is very hard to find out the target location. For many applications, UKF is best estimator that remaining algorithms. Recently, cubature Kalman filter (CKF) is also popular. It is proposed to use UKF (unscented Kalman filter) and CKF (cubature Kalman filter) algorithms that minimize the noise in measurements. So far, researchers carried out this work (target tracking) in Gaussian noise environment, whereas in this paper same work is carried out for non-Gaussian noise environment. The performance evaluation of the filters using Monte-Carlo simulation and Cramer-Rao lower bound (CRLB) is accomplished and the results are analyzed. Result shows that UKF is well suitable for highly nonlinear systems than CKF.
Sensation: Sonifying the Urban Running Experience

Following the need to promote physical activity as a part of a healthy lifestyle, in this study we focus on encouraging more physical activity by improving the experience, with running as an example of a popular outdoor activity. Running in nature is often described as more pleasant and relaxing than running in the city, yet in urban environments it is difficult to integrate true nature in one's running route. To bridge this gap we designed Sensation, a sonified running track that provides sensations of nature using audio feedback. Sensation senses the footsteps of runners and produces synced sounds of footsteps in several nature environments to augment the urban landscape. This way, Sensation aims to enhance the environmental factors that contribute to the positive feelings people experience during a run. We report on insights gathered during our Research-through-Design process, as well as a preliminary user test of this sonified running track.
General Game Heuristic Prediction Based on Ludeme Descriptions

This paper investigates the performance of different general-game-playing heuristics for games in the Ludii general game system. Based on these results, we train several regression learning models to predict the performance of these heuristics based on each game's description file. We also provide a condensed analysis of the games available in Ludii, and the different ludemes that define them.
The library has infinite streaming content, but are users infinitely content? The library catalog vs. vendor platform discovery

Despite the widespread adoption of streaming video by academic libraries, there has been little discussion about how video content is discovered and accessed through the library catalog as compared to vendor platforms. This article explores the current status of video resources in the library catalog to better understand how users find and access streaming content. MARC records were evaluated, and searches were performed to gauge issues related to metadata quality and its impact on discovery. The study reveals new trends in how users access streaming content for teaching, learning, and research.
A Systematic Review of Data-Driven Approaches to Item Difficulty Prediction

Assessment quality and validity is heavily reliant on the quality of items included in an assessment or test. Difficulty is an essential factor that can determine items and tests’ overall quality. Therefore, item difficulty prediction is extremely important in any pedagogical learning environment. Data-driven approaches to item difficulty prediction are gaining more and more prominence, as demonstrated by the recent literature. In this paper, we provide a systematic review of data-driven approaches to item difficulty prediction. Of the 148 papers that were identified that cover item difficulty prediction, 38 papers were selected for the final analysis. A classification of the different approaches used to predict item difficulty is presented, together with the current practices for item difficulty prediction with respect to the learning algorithms used, and the most influential difficulty features that were investigated.
Lossless Image Compression by Joint Prediction of Pixel and Context Using Duplex Neural Networks

This paper presents a new lossless image compression method based on the learning of pixel values and contexts through multilayer perceptrons (MLPs). The prediction errors and contexts obtained by MLPs are forwarded to adaptive arithmetic encoders, like the conventional lossless compression schemes. The MLP-based prediction has long been attempted for lossless compression, and recently convolutional neural networks (CNNs) are also adopted for the lossy/lossless coding. While the existing MLP-based lossless compression schemes focused only on accurate pixel prediction, we jointly predict the pixel values and contexts. We also adopt and design channel-wise progressive learning, residual learning, and duplex network in this MLP-based framework, which leads to improved coding gain compared to the conventional methods. Experiments show that the proposed method performs better than the conventional non-learning algorithms and also recent learning-based compression methods with practical computation time.
Optimization of selective two-photon absorption in cavity polaritons

We investigate optimal states of photon pairs to excite a target transition in a multilevel quantum system. With the help of coherent control theory for two-photon absorption with quantum light, we infer the maximal population achievable by optimal entangled vs separable states of light. Interference between excitation pathways as well as the presence of nearby states may hamper the selective excitation of a particular target state, but we show that quantum correlations can help to overcome this problem and enhance the achievable “selectivity” between two energy levels, i.e., the relative difference in population transferred into each of them. We find that the added value of optimal entangled states of light increases with broadening linewidths of the target states.
Reinforcement Learning Driven Adaptive VR Streaming with Optical Flow Based QoE

With the merit of containing full panoramic content in one camera, Virtual Reality (VR) and 360° videos have arisen in the field of industrial cloud manufacturing and training. Industrial Internet of Things (IoT), where many VR terminals needed to be online at the same time, can hardly guarantee VR's bandwidth requirement. However, by making use of users' quality of experience (QoE) awareness factors, including the relative moving speed and depth difference between the viewpoint and other content, bandwidth consumption can be reduced. In this paper, we propose Optical Flow Based VR(OFB-VR), an interactive method of VR streaming that can make use of VR users' QoE awareness to ease the bandwidth pressure. The Just-Noticeable Difference through Optical Flow Estimation (JND-OFE) is explored to quantify users' awareness of quality distortion in 360° videos. Accordingly, a novel 360° videos QoE metric based on Peak Signal-to-Noise Ratio and JND-OFE (PSNR-OF) is proposed. With the help of PSNR-OF, OFB-VR proposes a versatile-size tiling scheme to lessen the tiling overhead. A Reinforcement Learning (RL) method is implemented to make use of historical data to perform Adaptive BitRate (ABR). For evaluation, we take two prior VR streaming schemes, Pano and Plato, as baselines. Vast evaluations show that our system can increase the mean PSNR-OF score by 9.5-15.8% while maintaining the same rebuffer ratio compared with Pano and Plato in a fluctuate LTE bandwidth dataset. Evaluation results show that OFB-VR is a promising prototype for actual interactive industrial VR. A prototype of OFB-VR can be found in https://github.com/buptexplorers/OFB-VR.
Nowhere to Now-here: Empowering Children to Reimagine Bully Prevention at Schools Using Critical Design Fiction

Bullying is a problem concerning us all, especially our children. Human Computer Interaction (HCI) has invited children to tackle this problem through design and technology. However, yet there is limited research including critical engagement with the topic, even if the critical agenda is arousing increasing interest in HCI. Design fiction has shown potential in addressing critical concerns. However, albeit design fiction has entered HCI method repertoire already some time ago, it is an ambiguous concept with multiple meanings and usages. We map and articulate some of this variety: fascinating, future-oriented, and provocative studies are identified, while also a lack of critical design fiction, aiming at empowerment of the power-weak. We contribute by exploring such kind of design fiction in collaboration with children to tackle the problem of bullying. We scrutinize our participatory and empowering design fiction process and outcomes and discuss their implications for HCI research and design fiction practice.
The impact of digital learning technologies on functional and psychophysiological re-sponses of the organism: An analytical literature review

Introduction. The ambiguous attitude of professionals in the field of education, students and their parents to modernization based on ICT, differences of regional educational systems in their provision with digital tools have heightened the need for research on the relevant problem associated with the implementation of the federal project called “Digital School”. The purpose of this study is to analyze the vectors of the impact of digital learning technologies on functional and psychophysiological state of students' organisms. Materials and Methods. The authors have conducted a theoretical analysis of normative and legislative documents, the latest scholarly publications on the stated problem, mainly for the recent three years, using the methods of analogy, comparison, generalization and concretization. Results. The article summarizes the contradictory results of empirical studies confirming both positive and negative effects of digital educational gadgets that differ in technology and context on psychophysiological and somatic parameters of health and learning performance. The authors emphasize the idea that the positive vector of the “digital” school can dominate over the negative one only in those educational settings where the teaching staff creates a learning environment aimed at maintaining hygienic safety of children's life. A prerequisite for this is professional development of teachers who are ready to work in a digital environment. Conclusions. “School” gadgets are innovative tools, the adequate use of which contributes to improving the quality of education, without a negative impact on the psychological comfort and psychosomatic health of students.
NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer

Autoregressive models have been widely used in unsupervised text style transfer.Despite their success, these models still suffer from the content preservation problem that they usually ignore part of the source sentence and generate some irrelevant words with strong styles.In this paper, we propose a Non-Autoregressive generator for unsupervised text Style Transfer (NAST), which alleviates the problem from two aspects.First, we observe that most words in the transferred sentence can be aligned with related words in the source sentence, so we explicitly model word alignments to suppress irrelevant words.Second, existing models trained with the cycle loss align sentences in two stylistic text spaces, which lacks fine-grained control at the word level.The proposed non-autoregressive generator focuses on the connections between aligned words, which learns the word-level transfer between styles.For experiments, we integrate the proposed generator into two base models and evaluate them on two style transfer tasks.The results show that NAST can significantly improve the overall performance and provide explainable word alignments.Moreover, the nonautoregressive generator achieves over 10x speedups at inference.
Design of English hierarchical online test system based on machine learning

Abstract Large amount of data are exchanged and the internet is turning into twenty-first century Silk Road for data. Machine learning (ML) is the new area for the applications. The artificial intelligence (AI) is the field providing machines with intelligence. In the last decades, more developments have been made in the field of ML and deep learning. The technology and other advanced algorithms are implemented into more computational constrained devices. The online English test system based on ML breaks the shackles of the traditional paper English test and improves the efficiency of the English test. At the same time, it also maintains the fairness of English test and improves the marking speed. In order to realize an online English test system based on ML and facilitate the assessment of students’ college English courses, this paper mainly adopts relevant research and design on the main functional modules, key technologies, and functional realization of the online English test. The brand-new powerful teaching software and the online examination system can help schools to conduct more systematic and scientific management. The conclusion shows that as brand-new and powerful teaching software, the online examination system can help schools to conduct more systematic and scientific management.
A Differentially private hybrid decomposition algorithm based on quad-tree

Nowadays, spatial data provides users with more convenient services because of its wide application scenarios and rich content. At the same time, the collection and release of spatial data also bring risks of privacy disclosure. In this paper, a differentially private hybrid hierarchical decomposition algorithm named DP-HDAQT is proposed for privacy preserving spatial data query. Data dependent adaptive density grid decomposition is used for coarse sparse/dense region splitting at the first layer. Then for dense regions, an improved quad-tree decomposition way is adopted for further sub-region splitting by introducing bias count to eliminate the impact of recursion depth on a query result. Geometric budget allocation strategy and Laplace noise are used during the decomposition process for privacy preservation. Experimental results demonstrate that the proposed algorithm achieves better accuracy of the count queries in the condition of preserving data privacy well.
Defending IEEE Software Standards in Federal Criminal Court

As part of an alarming trend, IEEE Standard 1012 for independent software and hardware verification and validation is under attack in U.S. federal criminal court. If scientists and engineers do not engage, courts will continue to allow unreliable scientific evidence to deprive people of their rights.
Security of round-robin differential-phase-shift quantum-key-distribution protocol with correlated light sources

Among various quantum key distribution (QKD) protocols, the round-robin differential-phase-shift (RRDPS) protocol has a unique feature that its security is guaranteed without monitoring any statistics. Moreover, this protocol has a remarkable property of being robust against source imperfections assuming that the emitted pulses are independent. Unfortunately, some experiments confirmed the violation of the independence due to pulse correlations, and therefore the lack of a security proof without taking into account this effect is an obstacle for the security. In this paper, we prove that the RRDPS protocol is secure against any source imperfections by establishing a proof with the pulse correlations. Our proof is simple in the sense that we make only three experimentally simple assumptions for the source. Our numerical simulation based on the proof shows that the long-range pulse correlation does not cause a significant impact on the key rate, which reveals another striking feature of the RRDPS protocol. Our security proof is thus effective and applicable to wide range of practical sources and paves the way to realize truly secure QKD in high-speed systems.