Reviewerly/exHarmony huggingface dataset:
Direct and Indirect Effects of Virtual Reality Usage Toward Learning Performance Perception: A Case Study of Thai Undergraduate Students

Virtual reality (VR) has been used in diverse industries as a technological tool to improve communication. Many firms use VR to fulfill their purposes. In the educational field, VR technology has been used to improve educational outcomes, and many schools use VR as a learning tool. Previous studies found positive relationship between VR usage and learning experience. However, the details regarding this relationship are still unknown. The current study aims to find the direct and indirect benefits of familiarity with VR usage and the moderation effect of perception of VR as a learning tool. There were 431 respondents from undergraduates in two universities in Thailand that provided data through online questionnaires, using the multi-stage cluster random sampling method. The moderated mediator regression analysis was used to analyze the mechanism regarding the direct and indirect effects of VR usage. The empirical results show that familiarity with VR usage has both direct and indirect effects on learning performance in a positive way. Moreover, perception of VR usage as a learning tool acts as a moderator that increases the effect of VR usage as well as learning performance. The conclusion and limitations of this study are also discussed
Duality in Action (Invited Talk).

The duality between true and false is a hallmark feature of logic. We show how this duality can be put to use in the theory and practice of programming languages and their implementations, too. Starting from a foundation of constructive logic as dialogues, we illustrate how it describes a symmetric language for computation, and survey several applications of the dualities found therein.
A Note on the Interpretability of Machine Learning Algorithms

We are interested in the analysis of the concept of interpretability associated with a ML algorithm. We distinguish between the “How”, i.e., how a black box or a very complex algorithm works, and the “Why”, i.e. why an algorithm produces such a result. These questions appeal to many actors, users, professions, regulators among others. Using a formal standardized framework, we indicate the solutions that exist by specifying which elements of the supply chain are impacted when we provide answers to the previous questions. This presentation, by standardizing the notations, allows to compare the different approaches and to highlight the specificities of each of them: both their objective and their process. The study is not exhaustive and the subject is far from being closed.
On Robustness of the Normalized Subgradient Method with Randomly Corrupted Subgradients

Numerous modern optimization and machine learning algorithms rely on subgradient information being trustworthy and hence, they may fail to converge when such information is corrupted. In this paper, we consider the setting where subgradient information may be arbitrarily corrupted (with a given probability) and study the robustness properties of the normalized subgradient method. Under the probabilistic corruption scenario, we prove that the normalized subgradient method, whose updates rely solely on directional information of the subgradient, converges to a minimizer for convex, strongly convex, and weakly-pseudo convex functions satisfying certain conditions. Numerical evidence on linear regression and logistic classification problems supports our results.
ProGS: Property Graph Shapes Language (Extended Version).

Property graphs constitute data models for representing knowledge graphs. They allow for the convenient representation of facts, including facts about facts, represented by triples in subject or object position of other triples. Knowledge graphs such as Wikidata are created by a diversity of contributors and a range of sources leaving them prone to two types of errors. The first type of error, falsity of facts, is addressed by property graphs through the representation of provenance and validity, making triples occur as first-order objects in subject position of metadata triples. The second type of error, violation of domain constraints, has not been addressed with regard to property graphs so far. In RDF representations, this error can be addressed by shape languages such as SHACL or ShEx, which allow for checking whether graphs are valid with respect to a set of domain constraints. Borrowing ideas from the syntax and semantics definitions of SHACL, we design a shape language for property graphs, ProGS, which allows for formulating shape constraints on property graphs including their specific constructs, such as edges with identities and key-value annotations to both nodes and edges. We define a formal semantics of ProGS, investigate the resulting complexity of validating property graphs against sets of ProGS shapes, compare with corresponding results for SHACL, and implement a prototypical validator that utilizes answer set programming.
The Application of Computer Vision Technology in the Field of Industrial Automation

Computer vision technology, as the representative technology of the 21st century industry across the age, has had a significant impact on my country’s social and economic development and the quality of life of the people with the rapid development of my country’s modernization. The purpose of this article is to analyze the application of computer vision technology in industrial automation from the perspective of computer vision technology under the development of industrial vision. The method is based on the development and changes of the industrial foundation in the field of computing vision technology, and a deeper exploration of its practical application performance in the industrial automation industry.
SWOT Analysis as a Research Method in Pedagogy

SWOT analysis is an approach traditionally applied in the field of strategic planning and strategic management. Its essence finds expression in the analysis of internal and external factors which can influence over development of the organization, a given managerial technique, etc. In view of this in present article are analyzed the possibilities for application of SWOT analysis in the field of pedagogy. This thesis is based on two basic points of view: first – in every scientific research the analysis of the received information and data is required and although that the said method is a specific type of analysis it has potential to be applied not only in the field of management and marketing but in others scientific spheres one of which can be pedagogy. Second – SWOT analysis can be treated as a scientific method and the results obtained through it – used for solving scientific tasks and purposes.
Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning

Data annotation for training of supervised learning algorithms has been a very costly procedure. The aim of deep active learning methodologies is to acquire the highest performance in supervised deep learning models by annotating as few data points as possible. As the feature space of data grows, the application of linear models in active learning settings has become insufficient. Therefore, Deep Bayesian Active Learning methodology which represents model uncertainty has been widely studied. In this paper, a study has been conducted in order to increase the performance of Deep Bayesian Active Learning method. Feature dimensionality reduction is performed on data set by using Variational Autoencoder model. Low dimensional data is used to train a Bayesian Multi Layer Perceptron. The proposed method outperformed the Bayesian Multi Layer Perceptron model which is trained on entire feature space in terms of accuracy performance. The accuracy of the proposed method is tested on baseline datasets.
A Realistic Simulation Framework for Learning with Label Noise.

We propose a simulation framework for generating realistic instance-dependent noisy labels via a pseudo-labeling paradigm. We show that this framework generates synthetic noisy labels that exhibit important characteristics of the label noise in practical settings via comparison with the CIFAR10-H dataset. Equipped with controllable label noise, we study the negative impact of noisy labels across a few realistic settings to understand when label noise is more problematic. We also benchmark several existing algorithms for learning with noisy labels and compare their behavior on our synthetic datasets and on the datasets with independent random label noise. Additionally, with the availability of annotator information from our simulation framework, we propose a new technique, Label Quality Model (LQM), that leverages annotator features to predict and correct against noisy labels. We show that by adding LQM as a label correction step before applying existing noisy label techniques, we can further improve the models' performance.
Predicting fall in elderly people using machine learning

Fall is a serious health problem, it may threaten the life of many people in general and the life of the elderly in particular. That is why we tried very hard to develop a system to notify the mechanisms of their fall from the lane by monitoring their movement by means of wearable sensors in certain places on their bodies. This paper presents five supervised machine learning algorithms (SVM, Neural Network, Decision Tree, Random Forest, and Naïve Bayes) to predict fifteen falls in the elderly. We compared the five models in terms of performance measures (accuracy, precision and recall), and the Random Forest model achieved the best result with an accuracy of 95.91%. In the future, we plan to improve the results by pre-processing the data for better features and then classifying and predicting them using advanced algorithms in artificial intelligence techniques to build a system that will be able to predict with high speed and accuracy.
A preliminary study of identifying the visualization pattern of e-learning website for HEIS in Malaysia using card sorting method

Complex and complicated graphical user interface contributes to slow inter-action and lack of emotional engagement between users and user interface design of e-learning websites in Higher Education Institution (HEI) in Malaysia. Considering the emergence of the Industrial Revolution 4.0 concept within HEI is inevitable, the e-learning platform will be an integral component towards the realization of Universities 4.0 visions in Malaysia. This study is the extension of the previous research on the web objects visualization pattern of e-learning identified. A preliminary study is conducted to determine web objects placement of visualization pattern based on the standard web objects for e-learning websites using card sorting methods. Three different focus group; students, academicians, and IT administrators will be participating in this study. This study aims to build a guideline for developers in designing emotional engaging for the user interface for e-learning web-sites in HEI. © 2020 Mattingley Publishing. All rights reserved.
Multimodal Transportation Overview and Optimization Ontology for a Greener Future

Reducing the carbon footprint of our daily activities, as a race, is one of the most important priorities that we can have today. Optimizations play a key role in this process, as just swapping one fuel with another is not enough. An important part of managing and engineering a sustainable and economical transportation system, specifically in the field of multimodal transportation, is the complete overview of the entire system with it’s parts. With the growing complexity of such systems, along with the many constraints that they hold, means that manual design becomes impossible. For such purposes, the engineers need a complete database of all the aspects of the transportation system, complete with the existing constraints. In this paper, we present the structure and development of an ontology can be moulded on the specific needs of a company, even large ones, in order to understand and improve the system as a whole.
3D Visual Discomfort Prediction Based on Scene Structure and Depth Difference

Feature engineering under the stereoscopic images is widely used for visual discomfort prediction. Due to the complexity of the human visual system, extensive feature representation face major challenges, including a lack of inconsistent representation between the response of visual perception and the stimulation of stereoscopic display. To solve this problem, a saliency contrast feature representation method is proposed. Specifically, inspired by human attention mechanism, the stereo image is divided into saliency region and no-saliency region to estimate human fixations from complex natural scenes. The relative distance between saliency region and no-saliency region is obtained to express scene structure features, which combines the visual characteristic and scene structure factor. Besides, depth difference features are represented by combining subjectively perceived depth with objective simulated depth of each region. To evaluate the effectiveness of the proposed method, typical regression models are used to capture the relationship of the extracted features to visual discomfort scores. The experiments conducted on the benchmark IEEE-SA and IVY LAB S3D database show that the superiority of the proposed method and get the optimal solution with random forests as the regression model.
Applying Machine Learning and AI to Your Power BI Data Models

Having a mature enterprise business intelligence solution has become the norm for many organizations. A considerable number of firms have well-governed Enterprise Data Warehouses (EDWs) that are updated via sophisticated ETL processes. They use reporting tools such as SQL Server Report Services (SSRS) and Power BI to help the business gain valuable insights about their business. Organizations that fall in this category have done a great job of leveraging BI, and they are primed to introduce artificial intelligence (AI) to their data strategy. Here are a few ways AI can be used to enhance a mature business intelligence system:
Differentially Private Algorithms for 2020 Census Detailed DHC Race \& Ethnicity.

This article describes a proposed differentially private (DP) algorithms that the US Census Bureau is considering to release the Detailed Demographic and Housing Characteristics (DHC) Race & Ethnicity tabulations as part of the 2020 Census. The tabulations contain statistics (counts) of demographic and housing characteristics of the entire population of the US crossed with detailed races and tribes at varying levels of geography. We describe two differentially private algorithmic strategies, one based on adding noise drawn from a two-sided Geometric distribution that satisfies pure-DP, and another based on adding noise from a Discrete Gaussian distribution that satisfied a well studied variant of differential privacy, called Zero Concentrated Differential Privacy (zCDP). We analytically estimate the privacy loss parameters ensured by the two algorithms for comparable levels of error introduced in the statistics.
Adaptive Optimal Control of Baker’s Yeast Fermentation Process with Extreme Learning Machine and Recursive Least Square Technique

This paper presents combining extreme learning machine (ELM) and recursive least square (RLS) technique in modelling and optimisation of a fed-batch fermentation process. ELM has some characteristic features of fast training together with better generalisation capability. In order to cope with batch-to-batch variations due to unknown disturbances such as unknown process condition drift, the RLS algorithm is integrated with the ELM to update the output layer weights recursively from batch to batch. The offline trained output layer weights of the ELM are used as the initial parameter estimation in RLS. After updating the ELM model, optimisation is carried out to update the feeding policy for the next batch. The proposed method is applied to a simulated baker’s yeast fermentation process and the results obtained shows that the proposed method can cope with unknown disturbance and improve process operation from batch to batch.
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them

Making classifiers robust to adversarial examples is hard. Thus, many defenses tackle the seemingly easier task of detecting perturbed inputs. We show a barrier towards this goal. We prove a general hardness reduction between detection and classification of adversarial examples: given a robust detector for attacks at distance {\epsilon} (in some metric), we can build a similarly robust (but inefficient) classifier for attacks at distance {\epsilon}/2. Our reduction is computationally inefficient, and thus cannot be used to build practical classifiers. Instead, it is a useful sanity check to test whether empirical detection results imply something much stronger than the authors presumably anticipated. To illustrate, we revisit 13 detector defenses. For 11/13 cases, we show that the claimed detection results would imply an inefficient classifier with robustness far beyond the state-of-the-art.
APPLICATION OF RECURRENT NEURAL NETWORK IN RESEARCH OF INTELLIGENT WIND TUNNEL BALANCE

The shock tunnel ground test is vitally important to the research of the high-enthalpy aerodynamic characteristics of hypersonic vehicles, and the high-accuracy aerodynamic measurement is the key technology. When a force measurement test is conducted in an impulse shock tunnel, the flow field is established instantly after the starting process of shock tunnel, at this time, the great impact loads are acting on the force measurement system. The force measurement system is excited under the action of instantaneous impact, and the inertial vibration signal of the system cannot be rapidly attenuated during the short test time. The output signal of the balance will contain the interference due to the inertial vibration, which leads to a bottleneck in the further improvement of the accuracy of the transient force test. In order to improve the force measurement accuracy in the short-duration shock tunnel, the development of high-accuracy dynamic calibration technology is the key method to improve the performance of balance affected by inertial interference. Therefore, in this paper, recurrent neural network is used to train and intelligently process the balance dynamic calibration data, aiming to eliminate the vibration interference signals in the output dynamic signals. The error analysis of the current method is carried out, and the reliability of the current method is verified. The method is applied to the data processing of force test obtained in shock tunnel, and the effect of inertial vibration on the output signal of the balance is effectively reduced. According to the sample verification analysis of the intelligent model, the relative error of each component load is relatively small, where the case of high-frequency axial force component is about 1%. In the verification of wind tunnel force test data, the good results are also obtained, which are compared with those processed by the convolutional neural network model.
Blockchain and Production Agriculture

Abstract Consumers want to know more about where their food comes from, so fruit and vegetable growers and even some meat growers are implementing blockchains. But whether a blockchain system is necessary or even helpful for production agriculture is as yet undetermined. Earn 1.5 CEUs in Crop Management by reading this article and taking the quiz at www.certifiedcropadviser.org/education/classroom/classes/952 .
A Dynamic Program for Computing the Joint Cumulative Distribution Function of Order Statistics

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2021 SIAM Conference on Applied and Computational Discrete Algorithms (ACDA21)A Dynamic Program for Computing the Joint Cumulative Distribution Function of Order StatisticsRigel Galgana, Cengke Shi, Amy Greenwald, and Takehiro OyakawaRigel Galgana, Cengke Shi, Amy Greenwald, and Takehiro Oyakawapp.160 - 170Chapter DOI:https://doi.org/10.1137/1.9781611976830.15PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract We consider the problem of computing the joint cumulative distribution function of d ∊ ℕ select order statistics of n ≥ d independent random variables representing m ≤ n distinct populations. We present an efficient dynamic programming algorithm for this problem that is polynomial in both d and n, though exponential in m, hence most practical when m ≪ n. Previously, the fastest known algorithms were exponential in d, and either exponential in m or exponential in n, as well. Like others before us, we reduce the problem to a combinatorial one of tossing balls into bins, and then we calculate the probability of satisfying various bin conditions: i.e., of sufficiently many balls landing in bins. The key observation that leads to such a significant speedup is that one need not keep track of the exact configurations of balls in bins to determine if the requisite bin conditions are satisfied. Instead, the relevant information can be compressed by merging configurations that satisfy the same bin conditions. Previous chapter Next chapter RelatedDetails Published:2021eISBN:978-1-61197-683-0 https://doi.org/10.1137/1.9781611976830Book Series Name:ProceedingsBook Code:PRACDA21Book Pages:1-239
Demystifying Neural Language Models' Insensitivity to Word-Order

Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations have shown that the state-of-the-art models in several language tasks may have a unique way to understand the text that could seldom be explained with conventional syntax and semantics. In this paper, we investigate the insensitivity of natural language models to word-order by quantifying perturbations and analysing their effect on neural models' performance on language understanding tasks in GLUE benchmark. Towards that end, we propose two metrics - the Direct Neighbour Displacement (DND) and the Index Displacement Count (IDC) - that score the local and global ordering of tokens in the perturbed texts and observe that perturbation functions found in prior literature affect only the global ordering while the local ordering remains relatively unperturbed. We propose perturbations at the granularity of sub-words and characters to study the correlation between DND, IDC and the performance of neural language models on natural language tasks. We find that neural language models - pretrained and non-pretrained Transformers, LSTMs, and Convolutional architectures - require local ordering more so than the global ordering of tokens. The proposed metrics and the suite of perturbations allow a systematic way to study the (in)sensitivity of neural language understanding models to varying degree of perturbations.
Latent Event-Predictive Encodings through Counterfactual Regularization.

Author(s): Humaidan, Dania; Otte, Sebastian; Gumbsch, Christian; Wu, Charley M; Butz, Martin V. | Abstract: A critical challenge for any intelligent system is to infer structure from continuous data streams. Theories of event-predictive cognition suggest that the brain segments sensorimotor information into compact event encodings, which are used to anticipate and interpret environmental dynamics. Here, we introduce a SUrprise-GAted Recurrent neural network (SUGAR) using a novel form of counterfactual regularization. We test the model on a hierarchical sequence prediction task, where sequences are generated by alternating hidden graph structures. Our model learns to both compress the temporal dynamics of the task into latent event-predictive encodings and anticipate event transitions at the right moments, given noisy hidden signals about them. The addition of the counterfactual regularization term ensures fluid transitions from one latent code to the next, whereby the resulting latent codes exhibit compositional properties. The implemented mechanisms offer a host of useful applications in other domains, including hierarchical reasoning, planning, and decision making.