Reviewerly/exHarmony huggingface dataset:
Automated Self-Supervised Learning for Graphs

Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designed from distinct perspectives. However, we observe that different pretext tasks affect downstream tasks differently cross datasets, which suggests that searching pretext tasks is crucial for graph self-supervised learning. Different from existing works focusing on designing single pretext tasks, this work aims to investigate how to automatically leverage multiple pretext tasks effectively. Nevertheless, evaluating representations derived from multiple pretext tasks without direct access to ground truth labels makes this problem challenging. To address this obstacle, we make use of a key principle of many real-world graphs, i.e., homophily, or the principle that "like attracts like," as the guidance to effectively search various self-supervised pretext tasks. We provide theoretical understanding and empirical evidence to justify the flexibility of homophily in this search task. Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks. By evaluating the framework on 7 real-world datasets, our experimental results show that AutoSSL can significantly boost the performance on downstream tasks including node clustering and node classification compared with training under individual tasks. Code is released at https://github.com/ChandlerBang/AutoSSL.
Seemingly Unrelated Multi-State Processes: A Bayesian Semiparametric Approach

Many applications in medical statistics and other fields can be described by transitions between multiple states (e.g. from health to disease) experienced by individuals over time. In this context, multi-state models are a popular statistical technique, in particular when the exact transition times are not observed. The key quantities of interest are the transition rates, capturing the instantaneous risk of moving from one state to another. The main contribution of this work is to propose a joint semiparametric model for several possibly related multi-state processes (Seemingly Unrelated Multi-State, SUMS, processes), assuming a Markov structure for the transitions over time. The dependence between different processes is captured by specifying a joint prior distribution on the transition rates of each process. In this case, we assume a flexible distribution, which allows for clustering of the individuals, overdispersion and outliers. Moreover, we employ a graph structure to describe the dependence among processes, exploiting tools from the Gaussian Graphical model literature. It is also possible to include covariate effects. We use our approach to model disease progression in mental health. Posterior inference is performed through a specially devised MCMC algorithm.
A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework

In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting.
F-UJI : An Automated Tool for the Assessment and Improvement of the FAIRness of Research Data

&amp;lt;p&amp;gt;Making research data FAIR (Findable, Accessible, Interoperable, and Reusable) is critical to maximizing its impact. However, since the FAIR principles are designed as guidelines and do not specify implementation rules, it is difficult to verify the practice of these principles. Therefore, metrics and associated tools need to be developed to enable the assessment of FAIR compliance of services and datasets. Such practical solutions are important for many stakeholders to assess the quality of data-related services. They are important for selecting such services, but can also be used to iteratively improve data offerings, e.g., as part of FAIR advisory processes. With the increasing number of published datasets and the need to test them repeatedly, there is a growing body of literature that recognizes this importance of automated FAIR assessment tools. Our goal is to contribute to this area of FAIR through the development of an open source tool called F-UJI.&amp;amp;#160; F-UJI supports programmatic FAIR assessment of research data based on a set of core metrics against which the implementation of FAIR principles can be assessed. This paper presents the development and application of F-UJI and the underlying metrics. For each of the metrics, we have designed and implemented practical tests based on existing standards and best practices for research data. The tests are important to our expanded understanding of how to test FAIR metrics in practice that have not been fully addressed in previous work on FAIR data assessment. We demonstrate the use of the tool by assessing several multidisciplinary datasets from selected trusted digital repositories, followed by recommendations for improving the FAIRness of these datasets. We summarize the experience and lessons learned from the development and testing.&amp;lt;/p&amp;gt;
Frustratingly Easy Transferability Estimation

Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer. Existing estimation algorithms either require intensive training on target tasks or have difficulties in evaluating the transferability between layers. To this end, we propose a simple, efficient, and effective transferability measure named TransRate. Through a single pass over examples of a target task, TransRate measures the transferability as the mutual information between features of target examples extracted by a pre-trained model and their labels. We overcome the challenge of efficient mutual information estimation by resorting to coding rate that serves as an effective alternative to entropy. From the perspective of feature representation, the resulting TransRate evaluates both completeness (whether features contain sufficient information of a target task) and compactness (whether features of each class are compact enough for good generalization) of pre-trained features. Theoretically, we have analyzed the close connection of TransRate to the performance after transfer learning. Despite its extraordinary simplicity in 10 lines of codes, TransRate performs remarkably well in extensive evaluations on 32 pre-trained models and 16 downstream tasks.
Research on New Approaches to the Reform of English Teaching Depend on Multimedia Technology

Abstract With the development of science and technology, big data technology has fully popularized the management of major enterprises, and big data technology has played a major role in the development of major companies. This article mainly analyzes and summarizes the characteristics of college English teaching in the context of big data, and it also explains the operating facilities of the new teaching model.
SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarization

In this paper, we present a conceptually simple while empirically powerful framework for abstractive summarization, SimCLS, which can bridge the gap between the learning objective and evaluation metrics resulting from the currently dominated sequence-to-sequence learning framework by formulating text generation as a reference-free evaluation problem (i.e., quality estimation) assisted by contrastive learning. Experimental results show that, with minor modification over existing top-scoring systems, SimCLS can improve the performance of existing top-performing models by a large margin. Particularly, 2.51 absolute improvement against BART and 2.50 over PEGASUS w.r.t ROUGE-1 on the CNN/DailyMail dataset, driving the state-of-the-art performance to a new level. We have open-sourced our codes and results: https://github.com/yixinL7/SimCLS. Results of our proposed models have been deployed into ExplainaBoard platform, which allows researchers to understand our systems in a more fine-grained way.
A Deep Metric Learning Approach to Account Linking

We consider the task of linking social media accounts that belong to the same author in an automated fashion on the basis of the content and metadata of their corresponding document streams.We focus on learning an embedding that maps variable-sized samples of user activity-ranging from single posts to entire months of activity-to a vector space, where samples by the same author map to nearby points.The approach does not require humanannotated data for training purposes, which allows us to leverage large amounts of social media content.The proposed model outperforms several competitive baselines under a novel evaluation framework modeled after established recognition benchmarks in other domains.Our method achieves high linking accuracy, even with small samples from accounts not seen at training time, a prerequisite for practical applications of the proposed linking framework.
Machine Learning Algorithms for Conversion of CVSS Base Score from 2.0 to 3.x

The Common Vulnerability Scoring System (CVSS) is the industry standard for describing the characteristics of software vulnerabilities and measuring their severity. However, not all publicly known vulnerabilities have criticality rating in CVSS 3.x, which is the latest and most advanced version of the standard. This is due to the large time gap between the publication of the CVSS 2.0 and CVSS 3.x standards, the large number of the detected and published vulnerabilities at the time, and significant differences in the method of determining vulnerability criticality and assigning vector properties to evaluation components. Consequently, organizations using CVSS to prioritize vulnerabilities use both CVSS versions and abandoned the full transition to CVSS 3.x standard. In this paper authors introduce machine learning algorithms for performing conversions from CVSS 2.0 to CVSS 3.x, scores, which should significantly facilitate the upgrade to CVSS 3.x standard for all stakeholders. The considered case corresponds to a real world application with a large potential impact of the research.
Evaluating the Performance of Extreme Learning Machine Technique for Ore Grade Estimation

Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of overestimating or underestimating the ore grade.These estimated grade results determine the profitability of mining the ore deposit or otherwise.In this study, five Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade.The motive is that the activation function has been identified to play a key role in achieving optimum ELM performance.Therefore, assessing the extent of influence the activation functions will have on the final outputs from the ELM has some scientific value worth investigating.This study therefore applied ELM as ore grade estimator which is yet to be explored in the literature.The obtained results from the five ELM variants were analysed and compared with the state-of-the-art benchmark methods of Backpropagation Neural Network (BPNN) and Ordinary Kriging (OK).The statistical test results revealed that the ELM with sigmoid activation function (ELM-Sigmoid) was the best among all the other investigated methods (ELM-Hard limit, ELM-Triangular basis, ELM-Sine, ELM-Radial Basis, BPNN and OK).This is because the ELM-sigmoid produced the lowest MAE (0.0175), MSE (0.0005) and RMSE (0.0229) with highest R 2 (91.93%) and R (95.88%) respectively.It was concluded that ELM-Sigmoid can be used by field practitioners as a reliable alternative ore grade estimation technique.
Learning Through a Social Gaming Platform

The COVID-19 pandemic that emerged at the beginning of 2020 has influenced all aspects of life, leading to adjustments limiting physical social interaction. One of the fields that has been affected significantly is the education system. Since the government closed all schools since March and has not decided yet when they will reopen, teachers and students have been required to run the learning process. Various new online learning media have played a significant role in supporting the learning process during the pandemic. However, the ongoing learning style causes stressful conditions for students during online learning. It is caused by the limited interaction with teachers, friends and their school environment. Thus, developing a game-based learning media could be an alternative to provide new learning media that is attractive for students. It is expected that using an online game will provide an interesting virtual interaction for students.&#x0D; Keywords: social game, learning, interactive
Attendance Management System using Face-Recognition

Nowadays Educational institutions are concerned about regularity of student attendance. Even in pandemic situation attendance is still a major issue in schools and colleges. Mainly there are two conventional methods of marking attendance which are calling out the roll call or by taking student sign on paper. They both were more time consuming and difficult. Hence, there is a requirement of computer-based student attendance management system which will assist the faculty for maintaining attendance record automatically. In this project we have implemented the automated attendance system using ‘TKINTER’ and ‘PYTHON’. We have projected our ideas to implement an “Automated Attendance System Based on Face Recognition”. The application includes face identification, which saves time as well as being purely softwere based it can be flagged as eco-friendly as it reduces the use of paper. This system also eliminates the chances of fake attendance because of the face being used as a biometric for authentication. Hence, this system can be implemented in a field where attendance plays an important role. The proposed system is designed in TKINTER platform supported with a script of PYTHON as well as SQL database. The algorithm used in the system is based on image comparison on the basis of the encoded values of the face from the image from database with the image recorded by the system in run time. The system has output in the form of excel sheet.
Learning Representations by Humans, for Humans

We propose a new, complementary approach to interpretability, in which machines are not considered as experts whose role it is to suggest what should be done and why, but rather as advisers. The objective of these models is to communicate to a human decision-maker not what to decide but how to decide. In this way, we propose that machine learning pipelines will be more readily adopted, since they allow a decision-maker to retain agency. Specifically, we develop a framework for learning representations by humans, for humans, in which we learn representations of inputs (‘advice’) that are effective for human decision-making. Representation generating models are trained with humans-in-the-loop, implicitly incorporating the human decision-making model. We show that optimizing for human decision-making rather than accuracy is effective in promoting good decisions in various classification tasks while inherently maintaining a sense of interpretability.
Building blocks of a task-oriented dialogue system in the healthcare domain

There has been significant progress in dialogue systems research.However, dialogue systems research in the healthcare domain is still in its infancy.In this paper, we analyse recent studies and outline three building blocks of a taskoriented dialogue system in the healthcare domain: i) privacy-preserving data collection; ii) medical knowledge-grounded dialogue management; and iii) human-centric evaluations.To this end, we propose a framework for developing a dialogue system and show preliminary results of simulated dialogue data generation by utilising expert knowledge and crowdsourcing.
Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation

Robustness and counterfactual bias are usually evaluated on a test dataset.However, are these evaluations robust?If the test dataset is perturbed slightly, will the evaluation results keep the same?In this paper, we propose a "double perturbation" framework to uncover model weaknesses beyond the test dataset.The framework first perturbs the test dataset to construct abundant natural sentences similar to the test data, and then diagnoses the prediction change regarding a single-word substitution.We apply this framework to study two perturbation-based approaches that are used to analyze models' robustness and counterfactual bias in English.(1) For robustness, we focus on synonym substitutions and identify vulnerable examples where prediction can be altered.Our proposed attack attains high success rates (96.0%-99.8%) in finding vulnerable examples on both original and robustly trained CNNs and Transformers.(2) For counterfactual bias, we focus on substituting demographic tokens (e.g., gender, race) and measure the shift of the expected prediction among constructed sentences.Our method is able to reveal the hidden model biases not directly shown in the test dataset.Our code is available at https://github.com/chong-z/nlp-second-order-attack.
DeepBlueAI at TextGraphs 2021 Shared Task: Treating Multi-Hop Inference Explanation Regeneration as A Ranking Problem

This paper describes the winning system for TextGraphs 2021 shared task: Multi-hop inference explanation regeneration.Given a question and its corresponding correct answer, this task aims to select the facts that can explain why the answer is correct for that question and answering (QA) from a large knowledge base.To address this problem and accelerate training as well, our strategy includes two steps.First, fine-tuning pre-trained language models (PLMs) with triplet loss to recall top-K relevant facts for each question and answer pair.Then, adopting the same architecture to train the re-ranking model to rank the top-K candidates.To further improve the performance, we average the results from models based on different PLMs (e.g., RoBERTa) and different parameter settings to make the final predictions.The official evaluation shows that, our system can outperform the second best system by 4.93 points, which proves the effectiveness of our system.Our code has been open source, address is https://github.com/DeepBlueAI/TextGraphs-15
Discuss on the Application of Computer Virtual Reality Technology in College Sports Training

Abstract With the computer technology slowly infiltrated into various industries, sports teaching also began to use the computer virtual reality technology for sports training. Cognition of sports teams to virtual reality training system with the computer software developers and investigate the cooperation between sports teams, and computer virtual reality technology is also being used in training such as diving or trampolining, the application is able to help athletes improve the motion precision, reduce the risk in daily training.
Sustainable Information Security Behavior Management: An Empirical Approach for the Causes of Employees’ Voice Behavior

As organizations’ interest in information resources expands, their investments in information security (IS), such as the introduction of IS policies and new technologies, are also expanding. Nevertheless, IS incidents and threats within the organization have not decreased. This study aims to protect organizations’ information assets by maintaining the level of continuous IS behavior of the organization insiders. Moreover, this study suggests a method to induce continuous security behavior of individuals by confirming the relationship between IS-related voice behavior and IS-related organizational justice, which is an action concept that provides continuous opinions to achieve security goals. This study derives research models and hypotheses through previous studies and tests hypotheses through structural equation modeling. The target subjects are members of the organization who introduced the IS policy. A total of 325 samples were secured through the questionnaire method, and hypotheses were verified. Results reveal that voice behavior related to IS is negatively influenced by work impediment and positively influenced by organizational identification. In addition, procedural and information justice that influence prior actions related to IS affect the cause of personal security behavior (work impediment and organizational identification). Additionally, justice sensitivity adjusted the impact relationship between IS-related organizational justice and the cause of security behavior. The study presents the importance of voice behavior in maintaining the level of IS within the organization continuously. Moreover, it has practical implications in that efforts to improve organizational justice and voice behaviors vary according to the level of individual justice sensitivity.
An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.
A Frame-Theoretic Model of Bayesian Category Learning

Abstract Bayesian models of category learning typically assume that the most probable categories are those that group input stimuli together around a maximally optimal number of shared features. One potential weakness of such feature list approaches, however, is that it is unclear how to weight observed features to be more or less diagnostic for a given category. In this theoretically oriented paper, we develop a frame-theoretic model of Bayesian category learning that weights the diagnosticity of observed attribute values in terms of their position within the structure of a frame (formalised as distance from the frame’s central node). We argue that there are good grounds to further develop and empirically test frame-based learning models, because they have theoretical advantages over unweighted feature list models, and because frame structures provide a principled means of assigning weights to attribute values without appealing to supervised training data.