Miracle-dz/UniFAR-FTUs huggingface dataset:
Teachers' perceptions of the impact of COVID-19 on writing instruction in Australia

Education interruption can influence educational outcomes for students, particularly those already experiencing disadvantage. Little is known about how education interruption caused by COVID-19 has influenced the literacy learning of Australian students. This article provides insights into the impact of COVID-19 related education interruption on writing instruction of primary school aged children from the perspectives of their teachers. Drawing on data from a single-stage mixed-methods survey tool, it explores extent of perception of an impact of COVID-19 writing instruction in Australia, as well as capturing data on the nature of this perceived impact. As expected, the COVID-19 pandemic had an impact on writing instruction in the majority of cases, with impact typically perceived to be negative, though for some respondents, an unexpected benefit of the disruption was the collaborative response of educators and education systems. The diverse facets of the nature of this perceived impact were identified by respondents, some of which were related to context and home affordances, with findings highlighting how uneven levels of parental and technological resourcing at home can support or impede student learning of writing skills. The findings can be drawn upon to mitigate barriers to the teaching of writing during times of education interruption.
Speaker Recognition Based on 3DCNN-LSTM

 Abstract —The traditional speaker recognition method reduces the feature signal from high to low dimensions, but this often leads to some speaker information loss, resulting in a low speaker recognition rate. In response to this problem, this paper proposes a model based on the combination of a 3D convolutional neural network (3DCNN) and a long short-term memory neural network (LSTM). First, the model uses a fixed-step speech feature vector as the 3DCNN input, which converts the text-independent speaker recognition mode into a "semi-text"-related speaker recognition mode, which greatly preserves the speaker's speech features, and thus improving the difference between the characteristics of different speakers. Second, the 3D convolution kernel designed in this paper can extract the personality characteristics of speakers in different dimensions to further distinguish different speakers, connect the output signal to the LSTM network through a time series to enhance the contextual connection of the speaker's voice, and finally mark the classification output result to realize a complete speaker recognition system. The experimental results show that the model structure improves the speaker recognition rate on AISHELL-1 dataset in short-term speech compared with traditional algorithms and popular embedding features, and the system is more robust over time.
Evaluation of Feature Extraction Techniques for Punjabi Language in ASR system

This paper compares the performance of Speech recognition systems for Punjabi language. The Linear Predictive Coefficients, Cepstral Coefficients and Wavelet coefficients have been tested for speaker independent Automatic Speech recognition system for Punjabi language.The database is isolated digits of Punjabi language. The recognition performance of isolated speech recognition system with different feature extraction coefficients has been studied. The recognition performance of Speech recognition system with Perceptual Linear Prediction coefficients is better than Linear Prediction Coefficients and Linear Predictive Cepstral coefficient. The recognition performance of LPCC coefficients is 5.2% better than LPC. The recognition performance of PLP is 11.95% better than LPCC coefficient. MFCC coefficient gives 4.76% high performance than PLP. Wavelet Coefficients gives 16.79% high recognition performance than MFCC. Hidden Markov Model (HMM) classifier is used in all recognition systems and programming is done on MATLAB for training and testing of Punjabi digits.
Differentially private resilient distributed cooperative online estimation over digraphs

This article investigates resilient distributed online estimation (DOE) in unreliable directed networks with differential privacy requirements. In the network considered, some agents are subject to Byzantine attacks and thus could send arbitrary incorrect messages to their neighbors. The remaining agents aim to collaboratively estimate the value of an unknown vector parameter while protecting their private data. In this article, by adding private noises to mask the estimate, a stochastic approximation‐type resilient differentially private DOE algorithm is proposed to protect the privacy of sensitive information. A time‐varying step size is introduced to attenuate the divergence caused by the private noise, and furthermore, guarantees the convergence of the algorithm. When the directed graph is (2F$$ \Big(2F $$ +1)‐robust, the algorithm is shown to be both mean square and almost sure convergence in the sense of ϵ$$ \epsilon $$ ‐differential privacy. A simulation example is given to verify the effectiveness and superiority of the algorithm.
MEASURING THE COSMOLOGICAL LEPTON ASYMMETRY THROUGH THE COSMIC MICROWAVE BACKGROUND ANISOTROPY

A large lepton asymmetry in the Universe is still a viable possibility and leads to many interesting phenomena such as gauge symmetry nonrestoration at high temperature. We show that a large lepton asymmetry changes the predicted cosmic microwave background (CMB) anisotropy in a dramatic way. Confusion with other cosmological parameters limits our ability to constrain the lepton asymmetry with current data. However, any degeneracy in the relic neutrino sea may be measured to a precision of a few percent when the CMB anisotropy is measured at the accuracy expected to result from the planned satellite missions MAP and Planck. {copyright} {ital 1999} {ital The American Physical Society}
Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities

There is a lack of data on the location, condition, and accessibility of sidewalks across the world, which not only impacts where and how people travel but also fundamentally limits interactive mapping tools and urban analytics. In this paper, we describe initial work in semi-automatically building a sidewalk network topology from satellite imagery using hierarchical multi-scale attention models, inferring surface materials from street-level images using active learning-based semantic segmentation, and assessing sidewalk condition and accessibility features using Crowd+AI. We close with a call to create a database of labeled satellite and streetscape scenes for sidewalks and sidewalk accessibility issues along with standardized benchmarks.
A Proposal to Generate Planning Problems with Graph Neural Networks

In the field of Automated Planning, much effort has been devoted to learning planning domains from data. On the other hand, the task of generating planning problems for a given domain has received less attention. In most cases, these problems need to be created by hand or produced by hard-coded, domain-specific generators, which is a time-consuming endeavor. Having a large set of planning problems is useful for two main reasons. Firstly, they provide data to train Machine Learning methods (e.g., for learning planning heuristics and HTN domains) and, secondly, they can be used as benchmark problems in planning competitions. In this preliminary work, we propose a domain-independent method to generate planning problems for any typed STRIPS domain. We formulate problem generation as a Markov Decision Process and pro-pose the use of Graph Neural Networks, which are trained with Reinforcement Learning in order to generate valid, quality (i.e., difficult to solve) and diverse problems. We hope our approach will enable the effortless generation of large sets of planning problems with the qualities desired by the user, thus providing an alternative to existing problem generation meth-ods.
I know what you did on Venmo: Discovering privacy leaks in mobile social payments

Venmo is a US-based mobile social payments platform. Each Venmo transaction requires a “payment note”, a brief memo. By default, these memos are visible to all other Venmo users. Using three data sets of Venmo transactions, which span 8 years and a total of 389 M transactions with over 22.5 M unique users, we quantify the extent of private data leaks from public transaction notes. To quantify the leaks, we develop a classification framework SENMO, that uses BERT and regular expressions to classify public transaction notes as sensitive or non-sensitive. We find that 41 M notes (10.5%) leak some sensitive information such as health condition, political orientation and drug/alcohol consumption involving 8.5 M (37.8%) users. We further find that users seek privacy by making their notes private, inconspicuous or cryptic. However, the large increase in Venmo’s user base means that the number of users whose privacy is publicly exposed has grown substantially. Finally, the privacy of a user who transacts with a group on Venmo can be reduced or eliminated through the actions of other users. We find that this happens to around half of Alcoholics Anonymous, gambling and biker gang group members. Our findings strongly suggest that public-by-default payment information puts many users at risk of unintended privacy leaks.
Performance comparison of homomorphic encryption scheme implementations

— Homomorphic Encryption allows third party to receive encrypted data and perform arbitrarily computations on that data while it remains encrypted, despite not having the secret decryption key. This enables many new secure applications in cloud environments. For a long time, a key issue with the homomorphic encryption was its low performance which made it unusable in production environments. Advances in the last ten years in the field of homomorphic encryption resulted in several new schemes and software libraries which implement them. These homomorphic schemes have improved performance, but there is still a question whether the improvements would justify their use in production environments. In this paper we evaluated features and performances of several new homomorphic encryption mechanisms: BGV, BFV and CKKS.
Exploring immune-related signatures for predicting immunotherapeutic responsiveness, prognosis, and diagnosis of patients with colon cancer

The present study focused on identifying the immune-related signatures and exploring their performance in predicting the prognosis, immunotherapeutic responsiveness, and diagnosis of patients with colon cancer. Firstly, the immunotherapeutic response-related differential expressed genes (DEGs) were identified by comparing responders and non-responders from an anti-PD-L1 cohort using the edgeR R package. Then, the immunotherapeutic response related DEGs was intersected with immune-related genes (IRGs) to obtain the immunotherapeutic response and immune-related genes (IRIGs). Then, an immunotherapeutic response and immune-related risk score (IRIRScore) model consisting of 6 IRIGs was constructed using the univariable Cox regression analysis and multivariate Cox regression analysis based on the COAD cohort from the cancer genome atlas (TCGA) database, which was further validated in two independent gene expression omnibus database (GEO) datasets (GSE39582 and GSE17536) and anti-PD-L1 cohort. A nomogram with good accuracy was established based on the immune-related signatures and clinical factors (C-index = 0.75). In the training dataset and GSE39582, higher IRIRScore was significantly associated with higher TMN and advanced pathological stages. Based on the anti-PD-L1 cohort, patients who were sensitive to immunotherapy had significantly lower risk score than non-responders. Furthermore, we explored the immunotherapy-related signatures based on the training dataset. Kaplan-Meier curve revealed a high level of T cells regulatory (Tregs) was significantly related to poor overall survival (OS), while a high level of T cells CD4 memory resting was significantly related to better OS. Besides, the TMB value of patients in the high-risk group was significantly higher than those in a low-risk group. Moreover, patients in the high-risk group had significantly higher expression levels of immune checkpoint inhibitors. In addition, the immune-related signatures were applied to establish prediction models using the random forest algorithm. Among them, TDGF1 and NRG1 revealed excellent diagnostic predictive performance (AUC >0.8). In conclusion, the current findings provide new insights into immune-related immunotherapeutic responsiveness, prognosis, and diagnosis of colon cancer.
Low Adsorption UPLC Systems and Columns Based on MaxPeak High Performance Surfaces: The ACQUITY Premier Solution

CHALLENGES IN HPLC SEPARATIONS OF METAL-SENSITIVE ANALYTES Since the earliest development of high-performance liquid chromatography (HPLC), stainless steel has been the preferred material of construction for chromatographs and columns. However, it has long been observed that stainless-steel hardware can cause poor peak shapes and low recoveries for certain analytes.1–4 Compounds that show this behavior typically contain phosphate and/or carboxylate groups, although some analytes with other electron-rich functional groups have been reported to show similar issues.1 The severity of adsorption has been found to increase with the number of these functional groups in the analyte.3,4 These issues become highly problematic when the stainless steel is corroded,5 which can occur due to exposure to mobile phases that are highly acidic and/or contain chloride salts.6 Iron ions released from stainless-steel components have also been reported to adsorb on stationary phases, where they may interact with analytes, causing reactions including complexation,1 epimerization,7 and oxidation.8 Another issue associated with HPLC systems and columns constructed from stainless steel is the presence of iron adducts in mass spectra.9,10
RUNNING HEAD GENERATIONAL DIFFERENCES IN PC FULFILLMENT Psychological Contract Fulfilment and Its Impact on Employees’ Job Attitudes: Does Generations Make Any Difference?

In recent years, there has been, particularly among human resource management practitioners, a view that inter-generational differences exist among employees which provide challenges to employers in effectively managing diverse workforce. But academic research on inter-generational differences using psychological contract (PC) framework and its effects has been limited. Hence, this study aims to address the gap by exploring the moderating effects of generations on the relationships between PC fulfilment and employees’ job attitudes such as job satisfaction, organizational commitment and intention to quit. Data were collected using questionnaire survey on 356 full-time employees. Multiple regressions were used to analyse the data. The results suggest significant relationships between PC fulfilment and job attitudes but fail to establish significant moderating effect of generations.
BADF: BVH-Centric Adaptive Distance Field Computation for Deformable Objects on GPUs

We present a novel method (BADF) for accelerating the construction of ADFs (adaptive distance ﬁelds) of rigid and deformable models on GPUs. Our approach is based on constructing a bounding volume hierarchy (BVH) and we use that hierarchy to generate an octree-based ADF. We exploit coherence between successive frames and sort the grid points of the octree to accelerate the computation. Our approach is applicable to rigid and deformable models. Our GPU-based algorithm is about 20 − 50 X faster than CPU-based algorithms. Our BADF algorithm can construct the distance ﬁelds for deformable models with 60 K triangles at interactive rates on an NVIDIA GTX GeForce 1060 . Moreover, we observe 3 X speedup over prior GPU-based ADF algorithms.
Adopting Argumentation Mining for Claim Extraction from TED Talks

Engagement is critical for academic learning. It's commonly believed that motivating students to learn is crucial in education. We think that by providing students some interesting content based on what they are learning is a good idea. Since TED Talks share attractive new ideas, we are planning to motivate students by recommending TED Talks relevant to their learning content. Also, we found it's important to have some ``teasing texts'', which are used to convince students to watch TED Talks we recommended. to get these texts, we are going to adopt an argumentation mining technique called ``Claim Extraction'' on TED Talk subtitles.Claim extraction uses classifiers trained on a dataset to extract claim sentences from the given texts. And these claim sentences can be used as the ``teasing texts''. Due to the fact that there isn't any TED Talk based corpus and building one is extremely expensive, we have to train classifiers on the existing Wikipedia dataset. It means we have to deal with the cross-domain learning problem. This thesis will introduce our approach of building a TED Talk claim extraction system. This system will use classifiers trained on existing corpus and can extract claim sentences from TED Talk subtitles. Also, this thesis proposes using claims extracted from TED Talk subtitles can promote students to watch the recommended TED Talks.
Uncovering Latent Archetypes from Digital Trace Sequences: An Analytical Method and Empirical Example

The widespread availability of digital trace data provides new opportunities for researchers to understand human behaviors at a large scale. Sequences of behavior, captured when individuals interface with an information system, can be analyzed to uncover behavioral trends and tendencies. Rather than assume homogeneity among actors, in this study we introduce a method for identifying subsets of the population which demonstrate similar behavioral trends. The objective of this analysis would be to identify a finite set of behavioral archetypes, which we define as distinct patterns of action displayed by unique subsets of a population. This study makes a contribution to the literature by introducing a novel methodology for analyzing sequences of digital traces. We apply our technique to data from a lab experiment featuring thirty twenty-person teams communicating over Skype.
The Agave Fibers in Composite Materials

A new method to extract the agave fibers from the leaves is described. It consists of hydrolysing the leaf in 3N NaOH aqueous solution during 48 hours followed by a dioxane washing. The extracted fibers have medium performances compared to glass fibers (tensile modulus : 11.52GPa, tensile strength : 475.1MPa, deformation : 18.2%). We worked different composite test tubes using unsaturated polyester and polyepoxyde matrix reinforced by 1 to 5 Agave fibers. We compared their mechanical behaviour under one direction strength to composite test tubes reinforced by glass fibers and we have noticed that for the same fibers filling up, the performances of composites reinforced by agave fibers is near equal to those reinforced by glass fibers. We have also noticed that the agave fibers adhere to polyester and polyepoxyde matrix better than the glass fibers.
Design optimization method for roof-integrated TSSCs

6 This paper proposes a two-step design optimization method for roof-integrated two-stage solar 7 concentrators (TSSCs) as energy supply systems. The integration process of these systems with 8 buildings is complex as several conflicting and multi-disciplinary concerns need to be addressed. Thus, 9 the proposed approach is intended to be adaptable to informed decision-making processes in early 10 design stages, and yet to be collaborative where several key stakeholders are involved. The method 11 is an extension to our previously developed approach where the performance of roof-integrated TSSCs 12 in several design scenarios is accessed along with multiple performance indicators by developing a 13 parametric model and controlling a set of design inputs. In the current study, the proposed method is 14 combined with a multi-objective design optimization method, aiming to optimize, building and TSSCs 15 geometry. The method was validated in an illustrative case study of a single-family house (California) 16 for a number of conflicting objectives e.g., maximization of direct normal solar irradiance (DNI) and 17 annual average load match index (av.LMI), and minimization of covered roof area. The validation of 18 the method shows a number of interesting results. The method enables the generation of performance19 driven designs and searches for the most appropriate solutions, that can help to meaningfully support 20 the decision-making process. 21
Schedulater: Supporting Plant Operators in Scheduling Tasks by Visualizing Streaming Process Data and Model Predictions

We introduce Schedulater: A tool that visualizes production data and predictions to help plant operators schedule their tasks. The tool visualizes streaming data from the production system and a predictive model based on first principles. We follow a design study approach, collaborating with engineers and operators. We describe the tool, key tasks of operators, design goals, and discuss challenges in integrating predictions in the context of streaming data.
On Variational Autoencoders: Generative Models for Dimension Reduction

Variational Autoencoders (VAEs) are flexible, deep, generative models that have successfully been applied to modelling complex distributions. Over the past few years, VAEs have been applied to generating text, human faces, anime characters, handwritten digits, mesh models, and graphs. VAEs were first proposed as generative models [5]. This project motivates and analyzes VAEs as dimension reduction algorithms instead. VAEs can capture complex manifolds and produce flexible, interpretable representations making them an attractive option for dimension reduction despite being originally designed for generative modelling. We release the code to reproduce our experiments at https://github.com/AbdulSaleh/dimension-reduction-vae.
Automated visitor record system

As normally practiced, the information of visitor is recorded manually by either the visitor or the guard when the visitor arrives at the guard house. This manual process takes large amount of time and involves tedious works. Furthermore, it is prone to fraudulent information provided by visitors and occasionally interferes with guard’s actual job in securing the area. This paper aims to provide an automated solution to these aforementioned problems. We address these problems by replacing the existing manual way of recording visitor information by an automated and fully computerized system. The solution proposed uses Radio-Frequency Identification (RFID), smart card information retrieval as well as computer vision and image processing to record and manage visitors’ data. To evaluate the similarity between face images from camera and National Registration Identification Card (NRIC), we propose a method to find dissimilarity index between the faces. We found that the system minimizes the need for human interventions, improves the time required during recording of visitors’ information as well as efficiently manages and analyses visitors’ records. Additionally, our proposed method for finding face similarity yields a promising result of TPR = 0.914 and FPR = 0.140 when tested using publicly available face dataset called AR Dataset. The system is able to minimize the need for guard and visitor interventions, improves the time required during recording of visitors’ information especially for recurring visits and also capable of securely and efficiently manages and analyses visitors’ records.