Reviewerly/exHarmony huggingface dataset:
Establishing the DementiaBank Delaware Corpus: An Online Multimedia Database for the Study of Language and Cognition in Dementia

Abstract Background To better understand the progressive decline of language abilities in aging and dementia, we expanded the quality and quantity of resources in DementiaBank — an open‐access database of multimedia spoken language interactions for the study of speech and language abilities across the progression of dementia. This work builds from the success of the TalkBank Project with regard to data sharing, transcription, analysis, and web delivery. Specifically, this work collected connected speech and language data to develop the new “Delaware corpus” and to share resources for future analyses. Methods Cognitively unimpaired adults (n = 20) and adults with mild cognitive impairment (MCI) (n = 35) completed the DementiaBank discourse protocol and a cognitive‐linguistic battery in a 90‐minute session by Zoom with an administrator. The discourse protocol elicited four types of discourse: picture description, story narrative, procedural discourse, and personal narrative. Results The new Delaware corpus is available on the DementiaBank website. Data include spoken language CHAT transcripts linked to audio, test results, and medical/demographic information for 55 participants. CHAT is a standardized format for transcription and coding that is compatible with the CLAN language analysis software. CLAN provides methods for linking transcripts to media and automatic computation of a wide range of morphosyntactic features and other complex linguistic analyses. Additional resources include an automatic speech recognition pipeline code with tutorials to help streamline the transcription process and a collaborative commentary system that allows consortium members to insert comments or codes directly into online transcripts linked to media files. The collaborative commentary system is beneficial for training research assistants in coding techniques and for collaborative analysis of discourse samples. Conclusions Detailed digital measurement of speech and language abilities of older adults with and without mild cognitive impairment may help advance research and clinical practice by expanding the subtyping of MCI to include speech/language profiles. Open‐access databases, like DementiaBank, facilitate research collaborations, support advancements in computational analyses, and improve research dissemination to advance dementia science. The next steps for this project involve recruiting more researchers, clinicians, and students to use the new protocol and contribute their data to grow the database.
Shadowing as a universal method at the stage of working with interviews in teaching Russian as a foreign language

This article discusses the method of shadowing, its definition, features and stages of work in teaching Russian as a foreign language. Methods for implementing shadowing in the context of the interview method realization are given. The use of this method allows improving pronunciation, developing skills in speech perception and analysis, and independent work skills. In combination with the interview method, as the final stage of the lesson at the stage of creating your own text, shadowing is an effective pedagogical tool for mastering the Russian language.
Digital Twin of the Educational System as one of the Elements of the Budget System

The education of future generations is the most important strategic task of the state. During the pandemic in 2020, there was a black swan not only in the economic systems of the world stage, but also in the educational sphere. It is important to emphasize that this dramatic transition to digital education has had the double effect that we are seeing now. Convenience, without leaving home, to connect to lectures from both your university (or school) and various digital platforms of additional education, which has been an important factor in economic growth in recent years. The volume of the e-education market has grown 900 times since the beginning of 2000.
Super Pixel-level Contamination State Detection Model of Composite Insulator Specimens under Different Lighting Conditions Based on Hyperspectral Technology and Model Transfer

Accurate and real-time monitoring of the surface contamination status of insulators is of great significance for preventing pollution flashover accidents and maintaining system safety. This article introduces hyperspectral imaging technology to detect the contamination status of composite insulators. To address the impact of different outdoor lighting conditions on the detection of composite insulator contamination status, a composite insulator contamination status evaluation model is established based on the hyperspectral data collected indoors. Then, gradient spectral angle matching method is used to extract insulator images from the hyperspectral images collected under different outdoor lighting conditions, And the insulator image is segmented into multiple super-pixel regions using the energy driven sampling method. The super-pixel average spectral data was used instead of the pixel spectral data, and the contamination state was evaluated after the series processing of the two model transfer methods of correlation alignment method and piecewise direct standardization. The results show that the use of super-pixel segmentation method reduces the amount of data to be measured by 98% while ensuring the visualization of insulator pollution distribution, and the model transfer method improves the classification accuracy of the model from 50% to 90%, effectively reducing the impact of changes in lighting conditions on hyperspectral detection.
Federated Deep Learning for Intrusion Detection in Consumer-Centric Internet of Things

Consumer-centric Internet of Things (CIoT) will play a pivotal role in the fifth industrial revolution (Industry 5.0) but it exhibits vulnerabilities that can render it susceptible to various cyberattacks. Recent studies have explored the potential of Federated Learning (FL) for privacy-preserving intrusion detection in IoT. However, the development of the FL models relied on unrealistic and irrelevant network traffic data, while also exhibiting limitations in terms of covered attack types and classification scenarios. In this paper, we develop Federated Deep Learning (FDL) models using three recent and highly relevant datasets, covering a wide range of attack types as well as binary and multi-class classification scenarios. Our findings demonstrate that the FDL models not only achieve high classification performance, comparable to traditional Centralized Deep Learning (CDL) models, in terms of accuracy (99.60±0.46%), precision (92.50±8.40%), recall (95.42±6.24%), and F1 score (93.51±7.76%) but also exhibit superior computational efficiency compared to their CDL counterparts. The FDL approach reduces the training time by 30.52-75.87%. These classification performance and computational efficiency were achieved through multiple rounds of distributed local training in FDL. Therefore, the proposed FDL framework presents a robust security solution for designing and deploying a resilient CIoT.
Bootstrapped Gaussian Mixture Model-Based Data-Driven Forward Stochastic Reachability Analysis

We propose a data-driven forward stochastic reachability analysis algorithm for a system with unknown dynamics. In this paper, we assume a limited number of trajectory data is available and one cannot obtain additional data from the target system. The proposed algorithm learns the evolution of the state probability density function (pdf) as a Gaussian mixture model (GMM) from the given trajectory data and computes the pdf of the future state at a desired future time instance. We leverage the bootstrapping algorithm to account for the parameter estimation error of the GMM by computing the confidence interval of the estimated parameters. Then, the bootstrapped GMM is synthesized by selecting the optimal parameters within the confidence interval that yields the most informative model, thereby providing more reliable prediction results. The proposed algorithm is demonstrated via both numerical simulations and human subject experiments.
Towards Robust Image Denoising via Flow-based Joint Image and Noise Model

One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements. Existing denoising approaches generally focus on exploiting effective natural image priors to remove the noise. However, the utilization and analysis of the noise model are often ignored, although the noise model can provide complementary information to the denoising algorithms. As a result, they are very sensitive to different noise distributions. To tackle this issue and hence towards a robust image denoiser in practice, in this paper, we propose a novel Flow-based joint Image and NOise model (FINO) that distinctly decouples the image and noise in the latent space and losslessly reconstructs them via a series of invertible transformations. We further present a variable swapping strategy to align structural information in images and a noise correlation matrix to constrain the noise based on spatially minimized correlation information. Experimental results demonstrate FINO’s capacity to remove both synthetic additive white Gaussian noise (AWGN) and real noise. Furthermore, the generalization of FINO to the removal of spatially variant noise and noise with inaccurate estimation surpasses that of the popular and state-of-the-art methods by large margins.
CMI: Client-Targeted Membership Inference in Federated Learning

Membership inference is a popular benchmark attack to evaluate the privacy risk of a machine learning model or a learning scheme. However, in federated learning, membership inference is still under-explored due to several issues. For instance, some assumptions in prior works may not be practical in federated learning. Most existing membership inference methods stand on those impractical assumptions or lack generalization ability, which may misestimate the privacy risk. To address these issues, we propose CMI, an attack framework armed by a targeted poisoning method, to conduct a critical evaluation of client-targeted membership inference in federated learning. Under CMI, we consider a strong adversary, refine the prior impractical assumptions, and apply simple but generalizable attack methods. The evaluation results on multiple datasets demonstrate the efficacy of CMI under identically independently distributed (i.i.d.) and non-i.i.d. settings. In terms of the defenses, although differetially private stochatic gradient descent (DP-SGD) is effective under the i.i.d. setting, it does not provide satisfactory protection under label-biased non-i.i.d. settings. Thus, we propose RR-Label, a modified random response algorithm, to defend against membership inference. Compared to DP-SGD and Random Response Top-k (RRTop-k), RR-Label enables a better trade-off between model utility and defensive performance under label-biased non-i.i.d. settings.
Classification with Trust: A Supervised Approach based on Sequential Ellipsoidal Partitioning

Standard metrics of performance of classifiers, such as accuracy and sensitivity, do not reveal the trust or confidence in the predicted labels of data. While other metrics such as the computed probability of a label or the signed distance from a hyperplane can act as a trust measure, these are subjected to heuristic thresholds. This paper presents a convex optimization-based supervised classifier that sequentially partitions a dataset into several ellipsoids, where each ellipsoid contains nearly all points of the same label. By stating classification rules based on this partitioning, Bayes' formula is then applied to calculate a trust score to a label assigned to a test datapoint determined from these rules. The proposed Sequential Ellipsoidal Partitioning Classifier (SEP-C) exposes dataset irregularities, such as degree of overlap, without requiring a separate exploratory data analysis. The rules of classification, which are free of hyperparameters, are also not affected by class-imbalance, the underlying data distribution, or number of features. SEP-C does not require the use of non-linear kernels when the dataset is not linearly separable. The performance, and comparison with other methods, of SEP-C is demonstrated on the XOR-problem, circle dataset, and other open-source datasets.
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing

Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the local and global dependencies for volumetric 3D medical image segmentation. The proposed framework exploits local volume-based self-attention to encode the local dependencies at high resolution and introduces a novel volumetric MLP-Mixer to capture the global dependencies at low-resolution feature representations, respectively. The proposed volumetric MLP-mixer learns better associations among volumetric feature representations. These explicit local and global feature representations contribute to better learning of the shape-boundary characteristics of the organs. Extensive experiments on three different datasets reveal that the proposed method achieves favorable performance compared to state-of-the-art approaches. On the challenging Synapse Multi-organ dataset, the proposed method achieves an absolute 3.82% gain over the state-of-the-art approaches in terms of HD95 evaluation metrics while a similar improvement pattern is exhibited in MSD Liver and Pancreas tumor datasets. We also provide a detailed comparison between recent architectural design choices in the 2D computer vision literature by adapting them for the problem of 3D medical image segmentation. Finally, our experiments on the ZebraFish 3D cell membrane dataset having limited training data demonstrate the superior transfer learning capabilities of the proposed vMixer model on the challenging 3D cell instance segmentation task, where accurate boundary prediction plays a vital role in distinguishing individual cell instances. Our source code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Daniyanaj/vMixer</uri> .
The disruptive times of Covid-19: higher education leadership and management logistics in Arab nations

The COVID-19 Pandemic has changed many parts of the world, turning everything upside down in its path. Everything has changed, not just education, which has seen some unexpected changes in many places worldwide. There has been a rapid transition to online education in Arab nations due to COVID-19, which has both positives and cons. Most institutions can already adapt to education's digital teaching and learning-based future. In this study, a narrative non-systematic review methodology was used to examine the influence of COVID-19 on postsecondary education, the function of academic leaders, and the administration of Arab nations. For this investigation, a total of twenty-six pertinent scientific publications published by Arab countries were considered. Findings revealed that the psyche and emotions of students have been suffering from the limitations of the online higher education system. It was also noticed that students and teachers faced huge problems during this pandemic. The review also explored the strength of existing Middle Eastern countries' digital infrastructure facilities. Despite many limitations, instructors, institutions, and students learned many essential things during this critical Covid-19 period. Additionally, they learned how to adapt to a technology environment, which is crucial for professional success. The study's findings support the attitudes of students, teachers, leaders, managers, and other stakeholders toward online learning in challenging circumstances also in the field of logistics and transport.
The Effect of Using WhatsApp Groups on Elementary School Students Learning Motivation

ABSTRACT&#x0D; This study aimed to determine an overview of the use of the WhatsApp group at SDN 22 Soreang, Minasatene District, Pangkep Regency, and overview of the learning motivation of high grade students and the effect of using the whatsApp group on motivation study high class students at SDN 22 Soreang, Minasatene District, Pangkep Regency. This type of research is a quantitative descriptive research. The population in this study were all students, totaling 108 people. The sampling technique in this study was purposive sampling, so that the research sample was 56 people. The data collection technique used was the distribution of questionnaires and documentation techniques. The data analysis technique was carried out using descriptive analysis in the form of percentages and inferential analysis using analysis prerequisite tests and hypothesis testing (t-test). The results showed that the description of the use of the whatsApp group at SDN 22 Soreang, Minasatene District, Pangkep Regency tended to be in the fairly good category, where descriptively a percentage value was obtained of 41%, an overview of the learning motivation of high grade students at SDN 22 Soreang Minasatene Subdistrict, Pangkep Regency tends to be in the good category, where descriptively a percentage value of 32% was obtained and there was a significant effect of the use of the whatsApp group on the learning motivation of high class students at SDN 22 Soreang, Minasatene District, Pangkep Regency, where the probability value was smaller than the significant level (0.018 &lt;0.05).
Redundant Acronym Syndrome in Indonesian News Articles: A Corpus Analysis Approach

The aims of the study are to explore the occurrence of Redundant Acronym Syndrome (RAS) in Indonesian news articles and examine its implications by employing corpus analysis approach. The main source of the data from the Indonesian news corpus, which consists of 150,466 articles from Indonesian news websites over a six-month period spanned from July 2015 to December 2015. The data analysis was conducted by using Antconc software version 4.02. The analysis reveals a systematic and recognisable structure in acronym formation, highlighting cultural or organisational conventions such as initial phoneme retention, retention of syllables + letters, and retention of syllables and syllables, and retention of initial phonemes and letters. The findings of this study revealed a total of 40 Redundant Acronym with 5730 occurrences identified in the Indonesian news corpus, covering various fields such as the name of football clubs, banks, political parties, educational institutions, government-related terms, and commonly used phrases. This research also discussed that that RAS in news articles can have both negative and positive impacts. It negatively impact readability by making repetitions and potentially confusing the reader. In contrast, RAS can also improve clarity by emphasising certain aspects or reinforcing associations in acronyms, so that readers can instantly recognise and understand them. The findings provided valuable insights for news writers and editors, emphasising the importance of a balance between readability and clarity in news articles.
Empowering Self-Regulated Learning: A Case Study Using Edlink Application At PBA IAIBA Purwoasri Kediri

This study endeavors to enhance the learning independence of PBA Institut Agama Islam Badrus Sholeh Purwoasri by utilizing the edlink and WhatsApp applications. Employing a quantitative approach, the research adopts a Quasi-Experimental Design. Data is collected through questionnaires and documentation, employing saturated sampling and heterogeneous group division. The analysis involves descriptive and inferential statistics. Findings indicate that the learning independence of PBA Institut Agama Islam Badrus Sholeh Purwoasri in online learning, utilizing the edlink application (Experiment), was initially in the low category during the pretest and progressed to the high category in the posttest. In the control class, the pretest showed a low category, and the posttest showed a medium category. The study reveals significant differences in learning independence between the experimental and control classes, highlighting the edlink application's impactful role in improving the learning independence of PBA Institut Agama Islam Badrus Sholeh Purwoasri Kediri.
Manuscript Quality, Authorship Fraud, and Other Issues

&#x0D; &#x0D; &#x0D; Unlike in the case of verbal communication, prior to submitting a written work for publication authors have the opportunity to edit and revise it multiple times. They can revisit it again and again in order to enhance its content and polish its language. Once they submit their ‘final’ draft to a journal, editors and reviewers evaluate and assess its merit and suitability. If a manuscript is accepted, they help further revise, refine, and make it ready for production. In addition to this editorial concern for the quality of research, in this essay I shall explore the questions of authorship, authorship fraud, and other related concerns. The discussion here is primarily driven to stress the importance of maintaining ethical standards in publishing, especially those related to authorship credit.&#x0D; &#x0D; &#x0D;
The Perspective on the Implementation of Mobile Assisted Language Learning (MALL)

The study explores the pervasive use of mobile computing devices, such as laptops, smartphones, and tablets, since the emergence of mobile learning in 2005, notably within the educational sphere. Brooks (2016) conducted a comprehensive quantitative survey involving over 72,000 students from 12 countries, revealing staggering ownership statistics: 96 percent of students possess smartphones, while 93 percent own laptops. Moreover, an overwhelming majority, exceeding ninety-three percent, deem these devices essential for their academic achievements. Examining people's attitudes toward utilizing mobile technology for language learning reveals a diverse landscape of results, often skewed towards negative perspectives. However, these studies frequently neglect to encompass specific student-held viewpoints and overlook the potential of mobile technology in facilitating learning for individuals with diverse educational approaches. To address these gaps, this research spanned two years and involved surveys, interviews, pre-tests, and background checks, engaging 57 instructors across 46 Vietnamese institutions. The primary aim was to illuminate the trajectory of Mobile Assisted Language Learning (MALL). The study's findings delineate five distinct attitudes toward mobile technology-enabled self-directed learning, prompting the development of tailored strategies for different student cohorts.