Reviewerly/exHarmony huggingface dataset:
Constraint-Aware Federated Scheduling for Data Center Workloads

The use of data centers is ubiquitous, as they support multiple technologies across domains for storing, processing, and disseminating data. IoT applications utilize both cloud data centers and edge data centers based on the nature of the workload. Due to the stringent latency requirements of IoT applications, the workloads are run on hardware accelerators such as FPGAs and GPUs for faster execution. The introduction of such hardware alongside existing variations in the hardware and software configurations of the machines in the data center, increases the heterogeneity of the infrastructure. Optimal job performance necessitates the satisfaction of task placement constraints. This is accomplished through constraint-aware scheduling, where tasks are scheduled on worker nodes with appropriate machine configurations. The presence of placement constraints limits the number of suitable resources available to run a task, leading to queuing delays. As federated schedulers have gained prominence for their speed and scalability, we assess the performance of two such schedulers, Megha and Pigeon, within a constraint-aware context. We extend our previous work on Megha by comparing its performance with a constraint-aware version of the state-of-the-art federated scheduler Pigeon, PigeonC. The results of our experiments with synthetic and real-world cluster traces show that Megha reduces the 99th percentile of job response time delays by a factor of 10 when compared to PigeonC. We also describe enhancements made to Megha’s architecture to improve its scheduling efficiency.
Contrastive Multi-view Interest Learning for Cross-domain Sequential Recommendation

Cross-domain recommendation (CDR), which leverages information collected from other domains, has been empirically demonstrated to effectively alleviate data sparsity and cold-start problems encountered in traditional recommendation systems. However, current CDR methods, including those considering time information, do not jointly model the general and current interests within and across domains, which is pivotal for accurately predicting users’ future interactions. In this article, we propose a Contrastive learning-enhanced Multi-View interest learning model (CMVCDR) for cross-domain sequential recommendation. Specifically, we design a static view and a sequential view to model uses’ general interests and current interests, respectively. We divide a user’s general interest representation into a domain-invariant part and a domain-specific part. A cross-domain contrastive learning objective is introduced to impose constraints for optimizing these representations. In the sequential view, we first devise an attention mechanism guided by users’ domain-invariant interest representations to distill cross-domain knowledge pertaining to domain-invariant factors while reducing noise from irrelevant factors. We further design a domain-specific interest-guided temporal information aggregation mechanism to generate users’ current interest representations. Extensive experiments demonstrate the effectiveness of our proposed model compared with state-of-the-art methods.
Gradual OCR: An Effective OCR Approach Based on Gradual Detection of Texts

In this paper, we present a novel approach to optical character recognition that incorporates various supplementary techniques, including the gradual detection of texts and gradual filtering of inaccurately recognized texts. To minimize false negatives, we attempt to detect all text by incrementally lowering the relevant thresholds. To mitigate false positives, we implement a novel filtering method that dynamically adjusts based on the confidence levels of recognized texts and their corresponding detection thresholds. Additionally, we use straightforward yet effective strategies to enhance the optical character recognition accuracy and speed, such as upscaling, link refinement, perspective transformation, the merging of cropped images, and simple autoregression. Given our focus on Korean chart data, we compile a mix of real-world and artificial Korean chart datasets for experimentation. Our experimental results show that our approach outperforms Tesseract by approximately 7 to 15 times and EasyOCR by 3 to 5 times in accuracy, as measured using a Jaccard similarity-based error rate on our datasets.
Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.
“There was much new to grok”: an analysis of word coinage in science fiction literature

Abstract As can be witnessed in projects such as The Oxford Dictionary of Science Fiction (Prucher, Jeff. 2007. Brave new words: The Oxford dictionary of science fiction . Oxford: Oxford University Press), science fiction has been fertile ground for the creation of new words and concepts. Whereas the aforementioned dictionary was constructed by eliciting examples and citations from volunteers, this paper presents an initial foray into data-driven methods for uncovering lexis unique to science fiction. Words unique to science fiction texts are extracted by comparing a science fiction corpus against the British National Corpus (BNC Consortium. 2007. The British National Corpus, XML edition . Oxford Text Archive. http://hdl.handle.net/20.500.12024/2554 (accessed 29 June 2022)) to produce a list of 306 neologisms from 74 texts. In addition, this study seeks to examine the ways in which authors impart the meaning of such words to the reader, drawing on frameworks of semantic word relations and work in cognitive linguistics. This reveals the use of definitions and glosses by the authors, both in narration and direct speech, co-occurrence with synonyms, and the drip-feeding of attributes pertaining to the concept being referenced. In addition, characters can be shown to struggle with the concepts to which neologisms refer, allowing authors to explore themes of alienation and other-worldliness.
<b>Focusing on Africa</b>

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTFocusing on AfricaCara E. Schwarz*Cara E. SchwarzDepartment of Chemical Engineering, Stellenbosch University, Banghoekweg, Stellenbosch, 7600, South Africa*Email: [email protected]More by Cara E. Schwarzhttps://orcid.org/0000-0001-5513-2105 and Kuveneshan MoodleyKuveneshan MoodleyThermodynamics Research Unit, School of Engineering, University of KwaZulu-Natal, Howard College Campus, Durban, 4041, South AfricaMore by Kuveneshan Moodleyhttps://orcid.org/0000-0003-1544-3624Cite this: J. Chem. Eng. Data 2023, 68, 11, 2751Publication Date (Web):November 9, 2023Publication History Received16 October 2023Published online9 November 2023Published inissue 9 November 2023https://doi.org/10.1021/acs.jced.3c00620Copyright © Published 2023 by American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views203Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (910 KB) Get e-AlertscloseSUBJECTS:Chemical engineering and industrial chemistry,Equilibrium,Phase equilibria,Quality management,Students Get e-Alerts
Analysis of Atom against quantum attacks

Abstract A significant amount of study is being done to review the security promises made for the various ciphers now in use as a result of the development of quantum computing technology. A general attack against symmetric key cryptography primitives that can reduce search costs to the square root is Grover's search algorithm. To implement Grover's algorithm, it is necessary that the target cipher be implemented as a quantum circuit. Despite being relatively new, this area of study has received significant attention from the research community. The authors have estimated the cost of Grover's key search attack against the stream cipher Atom, for the first time, under circuit depth restrictions defined in National Institute of Standards and Technology (NIST) PQC standardisation process. The authors implement the quantum circuit of Atom in QISKIT, (open‐source software development kit for working with quantum computers running on IBM Quantum Experience). The results are also compared with other existing literature on LFSR‐based stream ciphers, such as Grain‐v1, Grain‐128‐AEAD, and Lizard. The authors also find that, to the best of their knowledge, in the existing literature on estimating the cost of Grover's attack on symmetric ciphers, Atom is the only 128‐bit key cipher that meets the threshold of ≈2 170 set by NIST for quantum security of 128‐bit key ciphers. The authors also analyse the security of Atom against quantum TMDTO attacks.
Toward Enhanced Geological Analysis: A Novel Approach Based on Transmuted Semicircular Distribution

This paper introduces a novel semicircular distribution obtained by applying the quadratic rank transmutation map to the stereographic semicircular exponential distribution, referred to as the transmuted stereographic semicircular exponential distribution (TSSCED). This newly proposed distribution exhibits enhanced flexibility compared to the baseline stereographic semicircular exponential distribution (SSCEXP). We conduct a comprehensive analysis of the model’s properties and demonstrate its efficacy in data modeling through the application to a real dataset.
Leveraging Computational Psychometrics for Language Testing

The recent surge in the popularity of Large Language Models (LLM) for language assessment underscores the growing significance of cost-effective language evaluation in our increasingly digitalized society. This paper posits that the application of computational psychometrics can enable the incorporation of technology into language assessment, enhancing test accessibility for learners while simultaneously elevating the precision of language proficiency evaluation. In this context, computational psychometrics is defined as a fusion of theory-based psychometrics and data-driven methodologies drawn from machine learning, artificial intelligence, natural language processing, and data science. This amalgamation offers a more robust and adaptable framework for analyzing intricate data, particularly within the contemporary landscape of learner-centric assessment. The paper concludes by emphasizing that the integration of computational psychometrics into language assessment opens up promising avenues for future research and practical applications, heralding an era of innovation in this field.
Mobile Learning and Culturally Situated Practices for Equity in Brazil

Growing economic instability and the digital divide have been affecting education, especially in the COVID-19 pandemic. Research has shown that students use mobile devices to study in Brazil. This study aims to understand how mobile devices are used in Brazilian educational environments to improve culturally situated learning. During research-training in cyberculture—a research methodology in doctoral research—we proposed activities in which participants (in-service teachers) would narrate their perceptions about mobile learning. From these data, we triangulated the analysis to reflect on narratives, theoretical studies, and my experience in the field, and listed five topics to support actions that improve equity.
Twitter (X), Fast Fashion and Backlash: Argumentation and Ethics on Social Media

Social media backlashes have emerged as important phenomena complicating how businesses communicate online and representing significant brand risk. This article demonstrates the value of content analysis and argumentation theory for understanding and responding to social media backlash events, using two examples from the UK fashion industry (hashtags #ThanksItsASOS and #boycottboohoo). The results provide lessons about the way backlashes operate in practice, how to analyze these effectively, and have implications for business approaches to communicating about Corporate Social Responsibility and managing social media. The authors conclude with suggestions for training on social media and CSR for businesses.
Small groups in multidimensional feature space: Two examples of supervised two-group classification from biomedicine

Some biomedical datasets contain a small number of samples which have large numbers of features. This can make analysis challenging and prone to errors such as overfitting and misinterpretation. To improve the accuracy and reliability of analysis in such cases, we present a tutorial that demonstrates a mathematical approach for a supervised two-group classification problem using two medical datasets. A tutorial provides insights on effectively addressing uncertainties and handling missing values without the need for removing or inputting additional data. We describe a method that considers the size and shape of feature distributions, as well as the pairwise relations between measured features as separate derived features and prognostic factors. Additionally, we explain how to perform similarity calculations that account for the variation in feature values within groups and inaccuracies in individual value measurements. By following these steps, a more accurate and reliable analysis can be achieved when working with biomedical datasets that have a small sample size and multiple features.
Literary mystifications and the authorial use of numerals

This study pertains to stylometry. There are cases when a writer who has achieved fame, for various reasons, begins to create under a different name, attempts to write in a different manner and sometimes achieves success again in a new incarnation. The aim of the study is to test the feasibility of intentionally making significant changes to an author’s literary style. Numerals present in the texts by a particular author are used as a style marker. Examples from English, French and Russian literature demonstrate that the use of numerals is a literary ‘fingerprint’ that manifests in all or most of sufficiently long texts by that author. The obtained results show that, contrary to an author’s attempts to write in a ‘new’ way, the usage of numerals is conservative and allows for the recognition of fictitious authorship. This conclusion is drawn based on the analysis of works by R. Gary and B. Akunin (G. Chkhartishvili), who are known for their literary hoaxes. The analysis of numerals usage is also applied to the issue of authorship regarding Harper Lee’s novel ‘To Kill a Mockingbird’. Conclusions about the similarity/difference of literary styles are made based on hierarchical cluster analysis and are supported by the Pearson chi-squared test. The scientific originality of the paper lies in taking a new approach to the search for a literary ‘fingerprint’ and text attribution.
DEEP LEARNING-BASED FEATURE FUSION AND TRANSFER LEARNING FOR APPROXIMATING pIC VALUE OF COVID-19 MEDICINE USING DRUG DISCOVERY DATA

The pandemic disease Coronavirus 2019 (COVID-19) caused thousands of infections and deaths globally. It is important to introduce new medicines to address the critical situation in the medical system. The determination of approximate pIC value is necessary for designing medicines based on molecular compounds. Generally, the approximation of pIC value is a lengthy process, so it is difficult and time-consuming. Hence it is essential to introduce a new technique for automatic approximation. In this research, a Convolutional Neural Network-based transfer learning (CNN-TL) is designed for approximating the pIC value. Initially, Simplified Molecular Input Line Entry System (SMILES) notation is extracted from SMILES string symbols using an entropy-based one-hot encoding matrix and the molecular formula-based encoding. The molecular features are then extracted from the input data using Lorentzian similarity and Deep Residual Network (DRN). The pIC value approximation is performed using the CNN-TL model, where the Visual Geometry Group Network-16 (VGGNet-16) is used to fetch hyperparameters used to initialize the CNN. The experimental results proved that the designed CNN-TL technique achieved minimum error rates with normalized values of 0.406 for R 2 , 0.516 for Root Mean Square Error (RMSE), 0.267 for Mean Square Error (MSE), and for 0.277 Mean Absolute Percentage Error (MAPE).
Analysis of Suitable Roof Designs for a Photovoltaic Panel: A Case Study of Housing Roof in Gorontalo

The demand for PV on roof installations in the household sector is increasing. In this paper, an investigation on optimizing the energy performance of PV on housing roofs is conducted. Three housing roof designs found in Gorontalo city are selected as the mounting planes for PV on roofs. The designs represent stacked gable roofs, complex gable roofs, and complex hip roofs. The purpose of the research is to find which roof shape is better for PV mounting in terms of sun radiation gain and access, mountable spaces, and orientation flexibility. This research employs Rhinoceros 3D to model the three roofs. The models are designed to face 12 directions, from 0° to 330°. Radiation analysis using Ladybug is utilized to study the roof’s performance in obtaining solar radiation in all 12 directions. It was found that the complex hip roof has more evenly distributed solar radiation on the roof planes, is flexible for PV mounting in any orientation, but has few mountable spaces. The stacked gable roof has two out of four suitable planes to gain solar radiation, but they are spacious. The complex gable roof has only one out of five suitable planes since they are narrow and prone to self-shading. Overall, a stacked gable roof provides a better option for PV installation compared to the other roof shapes.
Combining Human and Automated Scoring Methods in Experimental Assessments of Writing: A Case Study Tutorial

In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching one’s understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. The purpose of this article is to provide a pipeline for using machine-based text analytic and data mining tools to augment traditional text-based impact analysis by analyzing impacts across an array of automatically generated text features. In this way, we can explore what an overall impact signifies in terms of how the text has evolved due to treatment. Through a case study based on a recent field trial in education, we show that machine learning can indeed enrich experimental evaluations of text by providing a more comprehensive and fine-grained picture of the mechanisms that lead to stronger argumentative writing in a first- and second-grade content literacy intervention. Relying exclusively on human scoring, by contrast, is a lost opportunity. Overall, the workflow and analytical strategy we describe can serve as a template for researchers interested in performing their own experimental evaluations of text.
FedEem: a fairness-based asynchronous federated learning mechanism

Abstract Federated learning is a mechanism for model training in distributed systems, aiming to protect data privacy while achieving collective intelligence. In traditional synchronous federated learning, all participants must update the model synchronously, which may result in a decrease in the overall model update frequency due to lagging participants. In order to solve this problem, asynchronous federated learning introduces an asynchronous aggregation mechanism, allowing participants to update models at their own time and rate, and then aggregate each updated edge model on the cloud, thus speeding up the training process. However, under the asynchronous aggregation mechanism, federated learning faces new challenges such as convergence difficulties and unfair model accuracy. This paper first proposes a fairness-based asynchronous federated learning mechanism, which reduces the adverse effects of device and data heterogeneity on the convergence process by using outdatedness and interference-aware weight aggregation, and promotes model personalization and fairness through an early exit mechanism. Mathematical analysis derives the upper bound of convergence speed and the necessary conditions for hyperparameters. Experimental results demonstrate the advantages of the proposed method compared to baseline algorithms, indicating the effectiveness of the proposed method in promoting convergence speed and fairness in federated learning.
Anomaly detection and prediction evaluation for discrete nonlinear dynamical systems

Anomalies in dynamical systems mostly occur as deviations between measurement and prediction. Current anomaly detection methods in multivariate time series often require prior clustering, training data, or cannot distinguish local and global anomalies. Furthermore, no generalized metric exists to evaluate and compare different prediction functions regarding their amount of anomalous behavior. We propose a novel methodology to detect local and global anomalies in time series data of dynamical systems. For this purpose, a theoretical density distribution is derived assuming that only noise conceals the time series. If the theoretical and the empirical density distribution yield significantly different entropies, an anomaly is assumed. For a local anomaly detection, the Mahalanobis distance using the theoretical noise distribution’s covariance is applied to evaluate sequences of predictions and measurements. In addition, the Wasserstein metric enables a comparison of predictions using the distance between the noise and empirical distribution as a measure for selecting the best prediction function. The proposed method performs well on nonlinear time series such as logistic growth and enables a useful selection of a prediction model for satellite orbits. Thus, the proposed method improves anomaly detection in time series and model selection for nonlinear systems.