Reviewerly/exHarmony huggingface dataset:
The Multimodal Driver Monitoring Database: A Naturalistic Corpus to Study Driver Attention

A smart vehicle should be able to monitor the actions and behaviors of the human driver to provide critical warnings or intervene when necessary. Recent advancements in deep learning and computer vision have shown great promise in monitoring human behavior and activities. While these algorithms work well in a controlled environment, naturalistic driving conditions add new challenges such as illumination variations, occlusions, and extreme head poses. A vast amount of in-domain data is required to train models that provide high performance in predicting driving related tasks to effectively monitor driver actions and behaviors. Toward building the required infrastructure, this paper presents the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multimodal driver monitoring</i> (MDM) dataset, which was collected with 59 subjects that were recorded performing various tasks. We use the Fi-Cap device that continuously tracks the head movement of the driver using fiducial markers, providing frame-based annotations to train head pose algorithms in naturalistic driving conditions. We ask the driver to look at predetermined gaze locations to obtain accurate correlation between the driver’s facial image and visual attention. We also collect data when the driver performs common secondary activities such as navigation using a smart phone and operating the in-car infotainment system. All of the driver’s activities are recorded with high definition RGB cameras and a time-of-flight depth camera. We also record the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">controller area network-bus</i> (CAN-Bus), extracting important information. These high quality recordings serve as the ideal resource to train various efficient algorithms for monitoring the driver, providing further advancements in the field of in-vehicle safety systems.
Orthogonal least squares based fast feature selection for linear classification

An Orthogonal Least Squares (OLS) based feature selection method is proposed for both binomial and multinomial classification. The novel Squared Orthogonal Correlation Coefficient (SOCC) is defined based on Error Reduction Ratio (ERR) in OLS and used as the feature ranking criterion. The equivalence between the canonical correlation coefficient, Fisher's criterion, and the sum of the SOCCs is revealed, which unveils the statistical implication of ERR in OLS for the first time. It is also shown that the OLS based feature selection method has speed advantages when applied for greedy search. The proposed method is comprehensively compared with the mutual information based feature selection methods and the embedded methods using both synthetic and real world datasets. The results show that the proposed method is always in the top 5 among the 12 candidate methods. Besides, the proposed method can be directly applied to continuous features without discretisation, which is another significant advantage over mutual information based methods.
Sentiment Analysis of Student Texts Using the CNN-BiGRU-AT Model

For most current sentiment analysis models, it is difficult to capture the complex semantic and grammatical information in the text, and they are not fully applicable to the analysis of student sentiments. A novel student text sentiment analysis model using the convolutional neural network with the bidirectional gated recurrent unit and an attention mechanism, called CNN-BiGRU-AT model, is proposed. Firstly, the text is divided into multiple sentences, and the convolutional neural network (CNN) is used to extract n-gram information of different granularities from each sentence to construct a sentence-level feature representation. Then, the sentences are sequentially integrated through the bidirectional gated recurrent unit (BiGRU) to extract the contextual semantic information features of the text. Finally, an attention mechanism is added to the CNN-BiGRU model, and different learning weights are applied to the model by calculating the attention score. The top-down text features of “word-sentence-text” are input into the softmax classifier to realize sentiment classification. Based on the weibo_senti_100 k dataset, the proposed model is experimentally demonstrated. The results show that the accuracy rate and recall rate of its classification mostly exceed 0.9, and the F1 value is not lower than 0.8, which are better than the results of other models. The proposed model can provide a certain reference for the related students’ text sentiment analysis research.
A Novel Learning-Based Binarization Scheme Selector for Swarm Algorithms Solving Combinatorial Problems

Currently, industry is undergoing an exponential increase in binary-based combinatorial problems. In this regard, metaheuristics have been a common trend in the field in order to design approaches to successfully solve them. Thus, a well-known strategy includes the employment of continuous swarm-based algorithms transformed to perform in binary environments. In this work, we propose a hybrid approach that contains discrete smartly adapted population-based strategies to efficiently tackle binary-based problems. The proposed approach employs a reinforcement learning technique, known as SARSA (State–Action–Reward–State–Action), in order to utilize knowledge based on the run time. In order to test the viability and competitiveness of our proposal, we compare discrete state-of-the-art algorithms smartly assisted by SARSA. Finally, we illustrate interesting results where the proposed hybrid outperforms other approaches, thus, providing a novel option to tackle these types of problems in industry.
Forecasting directional bitcoin price returns using aspect-based sentiment analysis on online text data

The emergence of cryptocurrency markets has drastically changed how online transactions are conducted and provide a new investment opportunity. This study contributes to the literature on directional cryptocurrency price returns prediction by expanding the set of meaningful features extracted from textual data with sentiment analysis and comparing their usefulness across multiple data sources. In contrast to previous studies, we use fine-grained topic-sentiment features. More specifically, aspect-based sentiment analysis models, JST and TS-LDA, are implemented to incorporate joint topical-sentiment features and the degree of text subjectivity. We collected, and make available, a dataset, which consists of data scraped from Reddit, Bitcointalk and CryptoCompare sources, to demonstrate that proposed features lead to interpretable topics and an improvement in predictive performance.
Automated Trouble: The Role of Algorithmic Selection in Harms on Social Media Platforms

Social media platforms like Facebook, YouTube, and Twitter have become major objects of criticism for reasons such as privacy violations, anticompetitive practices, and interference in public elections. Some of these problems have been associated with algorithms, but the roles that algorithms play in the emergence of different harms have not yet been systematically explored. This article contributes to closing this research gap with an investigation of the link between algorithms and harms on social media platforms. Evidence of harms involving social media algorithms was collected from media reports and academic papers within a two-year timeframe from 2018 to 2019, covering Facebook, YouTube, Instagram, and Twitter. Harms with similar casual mechanisms were grouped together to inductively develop a typology of algorithmic harm based on the mechanisms involved in their emergence: (1) algorithmic errors, undesirable, or disturbing selections; (2) manipulation by users to achieve algorithmic outputs to harass other users or disrupt public discourse; (3) algorithmic reinforcement of pre-existing harms and inequalities in society; (4) enablement of harmful practices that are opaque and discriminatory; and (5) strengthening of platform power over users, markets, and society. Although the analysis emphasizes the role of algorithms as a cause of online harms, it also demonstrates that harms do not arise from the application of algorithms alone. Instead, harms can be best conceived of as socio-technical assemblages, composed of the use and design of algorithms, platform design, commercial interests, social practices, and context. The article concludes with reflections on possible governance interventions in response to identified socio-technical mechanisms of harm. Notably, while algorithmic errors may be fixed by platforms themselves, growing platform power calls for external oversight.
Shortcutted Commonsense: Data Spuriousness in Deep Learning of Commonsense Reasoning

Commonsense is a quintessential human capacity that has been a core challenge to Artificial Intelligence since its inception.Impressive results in Natural Language Processing tasks, including in commonsense reasoning, have consistently been achieved with Transformer neural language models, even matching or surpassing human performance in some benchmarks.Recently, some of these advances have been called into question: so called data artifacts in the training data have been made evident as spurious correlations and shallow shortcuts that in some cases are leveraging these outstanding results.In this paper we seek to further pursue this analysis into the realm of commonsense related language processing tasks.We undertake a study on different prominent benchmarks that involve commonsense reasoning, along a number of key stress experiments, thus seeking to gain insight on whether the models are learning transferable generalizations intrinsic to the problem at stake or just taking advantage of incidental shortcuts in the data items.The results obtained indicate that most datasets experimented with are problematic, with models resorting to non-robust features and appearing not to be learning and generalizing towards the overall tasks intended to be conveyed or exemplified by the datasets.Question: What is something someone driving a car needs even to begin?
Improving Neural Machine Translation by Bidirectional Training

We present a simple and effective pretraining strategy -bidirectional training (BiT) for neural machine translation.Specifically, we bidirectionally update the model parameters at the early stage and then tune the model normally.To achieve bidirectional updating, we simply reconstruct the training samples from "src→tgt" to "src+tgt→tgt+src" without any complicated model modifications.Notably, our approach does not increase any parameters or training steps, requiring the parallel data merely.Experimental results show that BiT pushes the SOTA neural machine translation performance across 15 translation tasks on 8 language pairs (data sizes range from 160K to 38M) significantly higher.Encouragingly, our proposed model can complement existing data manipulation strategies, i.e. back translation, data distillation and data diversification.Extensive analyses show that our approach functions as a novel bilingual code-switcher, obtaining better bilingual alignment.
The Impact of Computer-based Test and Students’ Ability in Computer Self - Efficacy on Mathematics Learning Outcomes

The current era of digitalization has big impact on education, the use of technology and information is not only for learning but as an evaluation tool. This development raises problems that require the completion of high-level thinking, therefore students need to prepare the ability to think and be able to develop skills. This study aims to measure the self-efficacy of computer skills on student learning outcomes and interactions in mathematics subjects. This research method used quantitative approach in Quasi Experiment Design. Total sample were 64 students. CBT was used in evaluating mathematics subjects and using a questionnaire to determine the level of self-efficacy of computer abilities towards students learning outcomes. Analysis of research data was carried out using ANOVA, to test the hypothesis using two-way ANOVA (two-way ANOVA) using the SPSS version 25.0 for windows software program. The results showed that there was CBT of mathematics subjects towards student learning outcomes with a mean score of 85 in and a mean score of 80 with a criterion of 3.03, having impact and the criteria value 2.78. The interaction between computer-based tests with self-efficacy computer capabilities had the most correlation with a positive relationship. It can be stated that there is an impact of computer-based tests and self-efficacy of computer ability and has an interaction between CBT and self-efficacy on learning outcomes.
Sampling on NISQ Devices: "Who’s the Fairest One of All?"

Modern NISQ devices are subject to a variety of biases and sources of noise that degrade the solution quality of computations carried out on these devices. A natural question that arises in the NISQ era, is how fairly do these devices sample ground state solutions. To this end, we run five fair sampling problems (each with at least three ground state solutions) that are based both on quantum annealing and on the Grover Mixer-QAOA algorithm for gate-based NISQ hardware. In particular, we use seven IBM~Q devices, the Aspen-9 Rigetti device, the IonQ device, and three D-Wave quantum annealers. For each of the fair sampling problems, we measure the ground state probability, the relative fairness of the frequency of each ground state solution with respect to the other ground state solutions, and the aggregate error as given by each hardware provider. Overall, our results show that NISQ devices do not achieve fair sampling yet. We also observe differences in the software stack with a particular focus on compilation techniques that illustrate what work will still need to be done to achieve a seamless integration of frontend (i.e. quantum circuit description) and backend compilation.
Design and Implementation of Intelligent Educational Administration System Using Fuzzy Clustering Algorithm

The present work aims to solve the problems that the traditional educational administration management system has, such as low efficiency in analyzing big data, and the analysis results have low value, which is based on manual rules definition in big data analysis and processing. The work proposes a student achievement prediction model FCM-CF based on Fuzzy C-means (FCM) and Collaborative Filtering (CF). The work also introduces it into the research of educational administration management to construct an intelligent educational administration management system. At the beginning, the FCM-CF model is described in detail. Then, the system requirements and specific design methods are described in detail. Eventually, with the students’ performance prediction as an example, the performance of the system is tested by designed simulation experiments. The result shows that the students’ achievement in study is closely related to their daily study performance such as preparation before class, classroom performance, attendance, extracurricular study, and homework completion. Generally, the examination scores of students are significant to their daily performances. Under the same experimental conditions, the prediction error of the FCM-CF model proposed here is less than 10.8% of that of other algorithms. The model has better prediction performance and is more suitable for the prediction of middle school students’ examination scores in educational administration management system. The innovation of intelligent educational administration management system is that, in addition to the basic information management function, it also has two other functions: students’ performance prediction analysis and teacher evaluation prediction. It can provide data support for improving teaching quality. The research purpose is to provide important technical support for more intelligent educational administration and reduce the loss of human resources in educational administration.
Experimental Space-Division Multiplexed Polarization-Entanglement Distribution through 12 Paths of a Multicore Fiber

The development and wide application of quantum technologies highly depend on the capacity of the communication channels distributing entanglement. Space-division multiplexing (SDM) enhances data channel transmission capacity in classical telecommunication and bears the potential to transfer the idea to quantum communication using current infrastructure. Here, we demonstrate a SDM of polarization-entangled photons over a 411 m long 19-core multicore fiber distributing polarization-entangled photon pairs through up to 12 channels simultaneously. The quality of the multiplexed transfer is evidenced by high polarization visibility and Clauser–Horne–Shimony–Holt (CHSH) Bell inequality violation for each pair of opposite cores. Our distribution scheme shows high stability over 24 h without any active polarization stabilization and can be effortlessly adapted to a higher number of channels. This technique increases the quantum-channel capacity and allows the reliable implementation of quantum networks of multiple users based on a single entangled photon pair source.Received 18 August 2021Revised 14 October 2021Accepted 12 November 2021DOI:https://doi.org/10.1103/PRXQuantum.2.040356Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum correlations in quantum informationQuantum entanglementQuantum Information
Solar Potential in Saudi Arabia for Flat-Plate Surfaces of Varying Tilt Tracking the Sun

The objective of the present work is to investigate the performance of flat-plate solar panels in Saudi Arabia that continuously follow the daily motion of the sun. To that end, the annual energy sums are estimated for such surfaces at 82 locations covering all Saudi Arabia. All calculations use a surface albedo of 0.2 and another one with a near-real value. The variation of the solar energy sums on annual, seasonal, and monthly basis is given for near-real ground albedos; the analysis provides regression equations for the energy sums as function of time. A map of the annual inclined solar energy for Saudi Arabia is derived and presented. The annual energy sums are found to vary between 2159 and 4078 kWhm−2year−1. Finally, a correction factor, introduced in a recent publication, is used; it is confirmed that the linear relationship between the correction factor and the ground-albedo ratio is general enough to be graphically representable as a nomogram. A discussion regarding the differences among solar systems on horizontal, fixed-tilt, 1-axis, and 2-axis systems is presented.
Exploring the Effect of Digital Literacy Skill and Learning Style of Students on Their Meta-Cognitive Strategies in Listening

This study investigates the most dominant factors influencing Indonesian students' listening meta-cognitive strategies.The interaction of digital literacy skills research instruments and learning styles can improve the students' meta-cognitive strategies in listening skills.The role of digital literacy skills research instruments and learning styles is to enhance students' listening meta-cognitive strategies with a total sample of 242.This study uses quantitative research by applying the ex-postfacto method.Analysis of research data using descriptive and inferential statistics with a linear regression test.The results demonstrate that the pupils' digital literacy skills had the most dominant impact.The regression results disclosed that the Rvalue of the influence of the literacy variable on the meta-cognitive listening strategy was 0.468, meaning that it had an effect of 46.8%, while the learning style was only 19.20%.The interaction between digital literacy skills and learning styles could improve the students' meta-cognitive listening strategies, approximately by 50.40%.Digital literacy skills and learning styles have a positive role in improving students' meta-cognitive listening strategies.A student's meta-cognitive listening technique improves as his or her digital literacy skills and learning style improves.
LQGDNet: A Local Quaternion and Global Deep Network for Facial Depression Recognition

Recent visual-based depression recognition methods mostly use hand-crafted features with information lost in color channels, or deep network features with a limited performance from the finite data. In this paper, we propose a method called Local Quaternion and Global Deep Network (LQGDNet) which can combine advantages from hand-crafted and deep features. Specifically, the Quaternion XOR Asymmetrical Regional Local Gradient Coding (XOR-AR-LGC) is first designed, which encodes the facial images with local textures in the quaternion domain to keep the dependence of color channels, and integrated into the Quaternion Feature Extractor (QFE). To the best of our knowledge, it is the first attempt to use a quaternion-based method for facial depression recognition. Second, we design the Local Quaternion Representation Module (LQRM) composed of Local Deep Feature Extractor (LDFE) and QFE to output local quaternion facial features. Third, global deep facial features are encoded from the Global Deep Representation Module (GDRM) with the deep convolutional neural network. Finally, the LQGDNet integrates LQRM and GDRM with the local quaternion and global deep features and predicts the depression score. The experimental results on AVEC 2013 and AVEC 2014 show the superiority of our method compared to the state-of-the-art approaches.
An introduction to linear mixed effects modeling in R

This tutorial serves as both an approachable theoretical introduction to mixed effects modeling and a practical introduction to how to implement these models in R. The intended audience is any researcher who has some basic statistical knowledge, but little or no experience implementing mixed effects models in R using their own data. In an attempt to increase the accessibility of this paper, I deliberately avoid using mathematical terminology beyond what a student would learn in a standard graduate-level statistics course, but I reference articles and textbooks that provide more detail for interested readers. This tutorial includes snippets of R code throughout, as well as the data and R script used to build the models described in the text so readers can follow along if they wish. The goal of this practical introduction is to provide researchers with the tools they need to begin implementing mixed models in their own research.
Bilateral knowledge graph enhanced online course recommendation

Recommender system can provide users with items that meet their potential needs in mass information. Its development provides new ideas and supporting technologies for applications in online education scenarios. The previous recommendation methods usually only consider the enhancement of the item side, but ignore the importance of the user characteristics to the recommendation, and are not suitable for the online education scenario. To address this problem, we take knowledge graph as the auxiliary information source of collaborative filtering and propose an end-to-end framework using knowledge graph to enrich the semantics of the item representation. In particular, faced with the thorny problem of cold start, the framework makes use of the static features of users to personalize the modeling of new users. Experimenting with two public datasets and an industrial dataset, we demonstrate that the framework has significant performance improvements over the baseline and can maintain satisfactory performance with sparse user–item interactions.