Reviewerly/exHarmony huggingface dataset:
Effective corpus use in second language learning: A meta-analytic approach

Data-driven learning (DDL) refers to the use of corpora by second and foreign language (L2) learners to explore and inductively discover patterns of their target language use from authentic language data without interventions from others. Although previous meta-analyses have demonstrated the positive effects of DDL on L2 learning (Boulton and Cobb, 2017), the number of empirical studies has been increasing since then. Therefore, this study included more recent studies and used meta-analyses to examine the extent to which: (1) DDL exerts an effect on L2 learning; and (2) moderator variables affect DDL's influence on L2 learning. The results demonstrated small to medium effect sizes for experimental/control group comparisons and pre/post and pre/delayed designs. Moreover, the moderator analyses found that moderator variables, such as publication types, learners’ factors, and research designs, influence the magnitude of DDL effectiveness in L2 learning.
Biometric key generation and multi round AES crypto system for improved security

To improve the reliability and system performance many transformation works have been investigated using various contexts. In general, numerical global key models and pseudo-random numbers for public block cipher algorithms suffers from key leakages and insufficient key strength. To overcome all these challenges, an intellectual cryptosystem is proposed with hierarchical hardware pipelined structures. An additional goal of the proposed framework is to enhance the area efficiency, throughput and optimize the hardware structures. In this paper 256-bit AES algorithm proposed with highly optimized hardware units using hybrid resource sharing and modified Key schedule along with optimized Sub bytes blocks, for reduced complexity overhead. The path delay optimization is achieved by decomposing the internal operations into bytes and applied stage wise pipelining and compared with direct 128-bit AES operations without pipeline. The proposed implementation helps in re-using the same hardware in a pipelined fashion which results in improved area efficiency up to 2.8 times and energy efficiency of 22 % over conventional AES crypto system.
Formation and development of mathematical literacy in the context of evaluative – Study tasks of PISA

To identify the level of education of schoolchildren in different countries of the world, the OECD conducts international studies in the field of education. Comparative results of these studies make it possible to determine the effectiveness of national educational systems contributing to adapting programs for modernization and reform of educational systems. This study aims to develop mathematical literacy among 15-year-old students of the Republic of Kazakhstan by developing the basic skills of the 21st century in solving practice-oriented problems. An experimental study was conducted to determine teachers' attitudes toward the formation of mathematical literacy among schoolchildren. The study involved 32 teachers. The Analysis of the results of the survey revealed that the majority of teachers had a negative attitude towards the formation of mathematical literacy among schoolchildren, substantiating their points of view by the fact that the formation and development of mathematical knowledge and skills by itself contributed to the development of skills in the 21st century. Only 11% of teachers could accurately indicate those tasks, out of the seven tasks presented for consideration, in the solution of which schoolchildren had shown critical, systemic thinking. An analysis of geometry textbooks used in schools of the Republic of Kazakhstan showed that out of the total number of educational tasks studied in geometry lessons, the share of practice-oriented tasks comprised less than 3%. Based on the results of the experimental study, it is concluded that it is necessary to develop appropriate methodological approaches that contribute to the formation of mathematical literacy among schoolchildren using the learning tasks of the school geometry course.
Using social media platforms to prepare for examinations post Covid-19: The case of saudi university EFL learners

Mobile-Assisted Language Learning (MALL) applications have been increasingly used by learners and instructors after the government imposed social and educational restrictions in Saudi Arabia for almost two years due to the Covid-19 pandemic. This research investigates Saudi EFL university learners' use of MALL applications, specifically Social Media Platform (SMP) applications, in their preparation for achievement exams post the Covid-19 pandemic social and educational restrictions. A questionnaire consisting of questions, as well as a range of open-ended question was designed and administered to 125 English major female learners who were enrolled at King Faisal University (KFU) in two different educational modes viz., regular (66) and distance (59) participation. Seven SMP applications - WhatsApp, YouTube, Telegram, Twitter, Instagram, Snapchat and Facebook - were used in this study. Data were analyzed quantitatively using the MANOVA test in SPSS, and qualitatively. The results have shown that both Regular Learners (RLs) and Distance Learners (DLs) used SMPs to prepare for their exams. The data analysis revealed that there were significant differences between RLs and DLs in their frequency of use of SMPs in preparation for exams. WhatsApp was the most frequently used SMP by RLs, whereas Telegram was the most frequently used SMP by DLs. Facebook was the SMP least frequently used by both groups. There were also statistically significant differences in favor of RLs over DLs in the frequency of use of SMPs in preparation for exams due to the Covid-19 pandemic Most participants reported that SMPs have a positive effect on their academic achievements. During the pandemic this was the main reason for continuing to use SMPs. Other reasons that emerged from the qualitative analysis of the open-ended responses included educational, technical and cognition reinforcement. The study results suggested implications for learners, instructors and educational policy makers.
Enhancing privacy with optical element design for fall detection

Abstract Falling poses significant risks, especially for the geriatric population. In this study, the authors introduce an innovative approach to privacy‐preserving fall detection using computer vision. The authors’ technique leverages a deep neural network (DNN) to accurately identify falling events in input images, while simultaneously prioritizing privacy through the implementation of an optical element. The experimental results establish that the authors’ proposed method outperforms alternative hardware and software‐based privacy‐preserving approaches in terms of encryption level and accuracy. These results are derived from an extensive dataset encompassing diverse falling scenarios.
Optimal transport-based unsupervised semantic disentanglement: A novel approach for efficient image editing in GANs

The latent space of pre-trained generative adversarial networks (GANs) is rich in semantic information, which often becomes highly entangled. It is crucial to identify semantic directions within this latent space, as these directions correlate with image attributes and are vital for image editing tasks. Existing methods for semantic discovery usually involve labor-intensive procedures such as manual labeling and training attribute classifiers, which limits their practicality. In response to this issue, the paper proposes the Optimal Transport-based Unsupervised Semantic Disentanglement (OTUSD) algorithm. This novel method efficiently uncovers semantic directions in the latent space of GANs by utilizing the concepts of manifold learning and optimal transport (OT) theory. OTUSD applies singular value decomposition (SVD) to the OT matrix that links latent codes to generated images. This process yields singular vectors that correspond to semantically meaningful directions. Unlike traditional methods, OTUSD bypasses the need for time-consuming labeling and training processes, thus enhancing efficiency and revealing a wider array of semantically meaningful directions. Experimental results demonstrate the effectiveness of OTUSD in discovering semantic directions from several state-of-the-art GAN models, including StyleGAN, StyleGAN2, and BigGAN. This performance emphasizes the potential applicability of OTUSD to image editing and other related tasks, and illuminates its value in harnessing the manifold learning and OT mapping capabilities inherent in GANs for semantic disentanglement. The implementation code is available at https://github.com/LuckAlex/OTUSD.
Meta-analysis: Evaluation of convolutional neural networks (CNNs) in diagnosing schizophrenia using PANSS Scale

The aim of this meta-analysis was to evaluate the diagnostic accuracy of Convolutional Neural Networks (CNNs) in diagnosing schizophrenia using the Positive and Negative Syndrome Scale (PANSS).
Quintessence of Dust 2023 restart: the what

So, &lt;i&gt;Quintessence of Dust&lt;/i&gt; is back in business. (A few days ago, &amp;lt;a href="https://sfmatheson.blogspot.com/2023/01/quintessence-of-dust-2023-restart-why.html" target="_blank"&amp;gt;I wrote about why&amp;lt;/a&amp;gt;.) Yay!
Federated Learning Performance on Early ICU Mortality Prediction with Extreme Data Distributions

Federated Learning (FL) is a novel machine learning technique that allows multiple parties to collaboratively train a global model without sharing their local data, thus addressing data privacy and security concerns. These issues are of paramount importance to the healthcare domain, due to strict regulations regarding patient data. By utilizing data from multiple medical institutions, FL could lead to generalizable models for one of the most prominent medical tasks, the early prediction of mortality risk in the ICU setting, where patients in critical condition are treated. This paper evaluates the performance of various FL algorithms in a realistic FL scenario and, also, in the presence of FL clients with ‘extreme’ data distributions, using real world data from a collaborative research database. Overall, the FL models perform, in general, substantially better than the local models of the participating hospitals and slightly worse than the ‘ideal’ model, which is trained on the centralized data. FedProx, a client-side optimization FL algorithm, regulates more effectively the contribution of a large FL client, with ‘extreme’ bias against the underrepresented class, while the server-side optimization FL algorithms incorporate the beneficial information of a smaller FL client into the global model more efficiently.
Ensemble Learning Model for Medical Text Classification

Automatic text classification, in which textual data is categorized into specified categories based on its content, is a classic issue in the science of Natural Language Processing (NLP). These models have proven useful when applied to data with several dimensions, including sparse features. It would appear that machine learning and other statistical approaches, like those employed in medical text classification, are highly effective for these jobs. Yet a lot of manual labor is still needed to classify the massive dataset used for training. Pretrained language models, such as machine learning models, have been proven effective in recent studies, demonstrating their capacity to reduce the time and effort spent on feature engineering. Yet, there is no statistically significant improvement in performance when applying the machine learning model directly to the classification job. We present a RFSVM algorithm-based hybrid machine learning model to boost the accuracy of the machine learning prediction. The model has three steps: (1) medical text processing; (2) medical text feature extraction; and (3) ensemble learning model for text classification. Using the PubMed dataset, we conducted experiments demonstrating that the proposed strategy greatly improves the precision of the results.
TLEF: Two-Layer Evolutionary Framework for t-Closeness Anonymization

Data anonymization is a fundamental and practical privacy-preserving data publication (PPDP) method, while searching for the optimal anonymization scheme using traditional methods has been proven to be NP-hard. Some recent studies have introduced genetic algorithms (GA) into data anonymization to address this issue, revealing potential solutions. However, the discussions are restricted to a few privacy protection models and evolutionary algorithms (EAs). This paper extends this field by introducing differential evolution (DE) for the first time to optimize data anonymization schemes under the constraints of the t-closeness privacy model. To further enhance the algorithm’s performance, this paper designs a two-layer evolutionary framework (TLEF) that effectively explores optimal solutions by leveraging the unique properties of both GA and DE in a balanced manner. Experimental evaluations conducted on 16 test datasets highlight the advantages of DE in addressing optimal t-closeness anonymization and validate the effectiveness of the TLEF.
Empowering Vulnerability Prioritization: A Heterogeneous Graph-Driven Framework for Exploitability Prediction

With the increasing number of software vulnerabilities being disclosed each year, prioritizing them becomes essential as it is challenging to patch all of them promptly. Exploitability prediction plays a crucial role in assessing the severity of vulnerabilities and determining their prioritization. Most existing works on exploitability prediction focus on building predictive models based on features extracted from individual vulnerabilities, neglecting the relationships between vulnerabilities and their contextual information. Only a few studies have explored using homogeneous graph-based techniques to enhance performance in this domain. This paper proposes a novel heterogeneous graph-driven framework for enhancing vulnerability exploitability prediction. The framework comprises two heterogeneous graph feature extraction technique streams: topological feature concatenation and node embedding based on heterogeneous graph neural networks (HGNN). Experimental results demonstrate that both streams, leveraging heterogeneous graph-based features, significantly improve the performance of exploitability prediction compared with using features extracted from individual vulnerabilities. Specifically, the two streams achieve 5.44% and 2.06% improvement in the F1 score, respectively. The data and codes are available on GitHub ( https://github.com/happyResearcher/HG-VEP ) to facilitate reproducibility and further research in this field.
Self-attention Convolutional Neural Network for Sequential Recommendation

Sequential recommendation uses user-interaction history of preferred items to predict which items a user is most likely to interact with in future. To tackle this prediction problem, over the years, many researchers have developed different approaches such as Convolutional Neural Networks (CNN) to identify patterns in interaction history, Self-Attention recommendation systems to find the similarity of items in a sequence with each other to model the connections between items etc. Although these approaches provide promising results, there are still a lot of scope for improvements. One limitation of such approaches for Top-N recommendation is the inability to capture long range dependencies in the sequence of interactions. Another issue is the inability for the network to model hierarchical information. To mitigate such limitations of existing approaches, in this paper, we propose a new approach called Self-Attention Convolutional (SACORec) network which combines both CNNs and Self-Attention. The idea is to take advantage of the effectiveness of various methods while mitigating their limitations. To find the effectiveness of our proposed model, we ran our model on several public data sets and collected a variety of evaluation metrics. The empirical study shows that our proposed model significantly outperforms state-of-art architectures that implement only one paradigm to the sequential recommendation. The efficiency of our model has led us to believe that it will open more pathways for future research in this area.
Choosing Your Friends: Shaping Ethical Use of Anonymity Networks

Anonymity networks protect the metadata of communication between participants. This is an important privacy guarantee for whistleblowers, activists, journalists and others who rely on anonymity networks. However, these same guarantees can also help criminals and disruptive users evade the consequences of their actions. Existing literature and research has little to say on what designers and operators of such networks can do to maximize beneficial uses while minimizing harm. We build on lessons learned from the widespread deployment of another strong privacy technology, end-to-end encrypted messaging applications, as well as on existing examples from anonymity networks, to formulate a set of design methods which anonymity networks can use to discourage harmful use. We find better solutions exist when networks are specialized to particular application domains since such networks are then able to provide a better trade-off between benefits and harms. One drawback of such specialization is that it may lead to smaller numbers of users and therefore an increased risk of insufficient anonymity.
Multivariate Time Series Anomaly Detection Based on Graph Neural Network for Big Data Scheduling System

With the increasing complexity of tasks in a big data task scheduling system, how to effectively organize the data collected from each subtask of a big data task scheduling system and model the spatiotemporal dependence between them, and then perform the accurate anomaly detection of multivariate time series is of great significance. Existing methods always could not effectively capture the relationship between different time series clearly, which leads to the inevitable false alarm. To address this issue, we propose a new multivariate time series anomaly detection framework called GraphTransformerAD, which combines GraphTransformer and LSTM. Specifically, each univariate time series is an independent feature, and graph neural networks are introduced to learn the complex dependence of multivariate time series in both time and feature dimensions. Runtime performance data are collected from the actual big data workflow task scheduling system, which is used to demonstrate the effectiveness of our proposed method. The proposed method outperforms baseline models on real datasets.
An Integrated Interactive Framework for Natural Language to SQL Translation

Numerous web applications rely on databases, yet the traditional database interface often proves inconvenient for effective data utilization. It is crucial to address the significant demand from a vast number of end users who seek the ability to input their requirements and obtain query results effortlessly. Natural Language (NL) Interfaces to Databases (NLIDBs) with interactive query mechanisms make databases accessible to end users and simultaneously retain user confidence in the results. This paper proposes an approach called IKnow-SQL for building interactive NLIDBs. IKnow-SQL introduces a unified framework for translation models to improve accuracy and increase interactivity. Specifically, IKnow-SQL first employs an underlying translation model to parse the semantics of a given NL query. By evaluating the model behavior, IKnow-SQL then recognizes the parts of the model output that may require human intervention. Next, IKnow-SQL presents clarifying questions to solicit and memorize user feedback until a polished result is obtained. Extensive experiments are performed to study IKnow-SQL on the public benchmark. The results show that the translation models can be effectively improved using IKnow-SQL with less user feedback.
Determining an Economic Value of High Assurance for Commodity Software Security

Security measures that attempt to prevent breaches of commodity software have not used high assurance methods and tools. Instead, rational defenders have risked incurring losses caused by breaches because the cost of recovery from a breach multiplied by the probability of that breach was lower than the cost of prevention by high assurance, e.g., by formal methods. This practice may change soon since breach-recovery costs have increased substantially while formal methods costs have decreased dramatically over the past decade. We introduce the notion of selective high assurance and show that it is economically justified, as producers can easily recoup its cost even in very small commodity markets, and necessary for rational defenders to decrease their breach recovery costs below a chosen limit. However, these decreases depend on defenders’ risk aversion, which is difficult to assess since risk preferences cannot be anticipated. A challenge is to determine a lower bound on the economic value of selective high assurance independent of the defenders’ risk preferences; i.e., a value that depends only on the commodity software itself and the attacks it withstands. We propose an approach to determine such a value and illustrate it for SCION, a networking software system with provable security properties.
An Ensemble-Based Approach for Generative Language Model Attribution

Recently, Large Language Models (LLMs) have gained considerable attention due to their incredible ability to automatically generate texts that closely resemble human-written text. They have become invaluable tools in handling various text-based tasks such as content creation and report generation. Nevertheless, the proliferation of these tools can create undesirable consequences such as generation of false information and plagiarism. A variety of LLMs have been operationalized in the last few years whose abilities are heavily influenced by the quality of their training corpus, model architecture, pre-training tasks, and fine-tuning processes. Our ability to attribute the generated text to a specific LLM will not only help us understand differences in the LLMs’ output characteristics, but also effectively distinguish machine-generated text from human-generated text. In this paper, we study whether a machine learning model can be effectively trained to attribute text to the underlying LLM that generated it. We propose an ensemble neural model that generates probabilities from multiple pre-trained LLMs, which are then used as features for a traditional machine learning classifier. The proposed approach is tested on Automated Text Identification (AuTexTification) datasets in English and Spanish languages. We find that our models outperform various baselines, achieving macro $$F_{macro}$$ scores of 0.63 and 0.65 for English and Spanish texts, respectively.
Prompt-Learning for Semi-supervised Text Classification

In the Semi-Supervised Text Classification (SSTC) task, the performance of the SSTC-based models heavily rely on the accuracy of the pseudo-labels for unlabeled data, which is not practical in real-world scenarios. Prompt-learning has recently proved to be effective to alleviate the low accuracy problem caused by the limited label data in SSTC. In this paper, we present a Pattern Exploiting Training with Unsupervised Data Augmentation (PETUDA) method to address SSCT under limited labels setting. We first exploit the potential of the PLMs using prompt learning, convert the text classification task into a cloze-style task, and use the masked prediction ability of the PLMs to predict the categories. Then, we use a variety of data augmentation methods to enhance the model performance with unlabeled data, and introduce a consistency loss in the model training process to make full use of unlabeled data. Finally, we conduct extensive experiments on three text classification benchmark datasets. Empirical results show that PETUDA consistently outperforms the baselines in all cases.