Reviewerly/exHarmony huggingface dataset:
Cross-Corpus Readability Compatibility Assessment for English Texts

Text readability assessment has gained significant attention from researchers in various domains. However, the lack of exploration into corpus compatibility poses a challenge as different research groups utilize different corpora. In this study, we propose a novel evaluation framework, Cross-corpus text Readability Compatibility Assessment (CRCA) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> , to address this issue. The framework encompasses three key components: (1) Corpus: CEFR, CLEC, CLOTH, NES, OSP, andRACE. Linguistic features, GloVeword vector representations, and their fusion features were extracted. (2) Classification models: Machine learning methods (XGBoost, SVM) and deep learning methods (BiLSTM, Attention-BiLSTM) were employed. (3) Compatibility metrics: RJSD, RRNSS, and NDCG metrics. Our findings revealed: (1) Validated corpus compatibility, with OSP standing out as significantly different from other datasets. (2) An adaptation effect among corpora, feature representations, and classification methods. (3) Consistent outcomes across the three metrics, validating the robustness of the compatibility assessment framework. The outcomes of this study offer valuable insights into corpus selection, feature representation, and classification methods, and it can also serve as a beginning effort for cross-corpus transfer learning.
Low Pixel Resolution Hyperspectral Image Mosaics Generation Using Learning-Based Feature Matching

Remote sensing has become a key component of precision agriculture in the recent decade. Hyperspectral imaging is one key technology that is predicted to be one of the primary decision-making tools in remote sensing-based precision agriculture. Unmanned Aerial Vehicle-based hyperspectral image acquisition is becoming viable due to the reduced cost and the form factor of recently introduced hyperspectral cameras. However, these advantages come at the cost of pixel resolution. Due to the factors such as uniform textured surfaces in farmlands, low-pixel resolution, and repeated patterns, traditional stitching methods are unsuccessful at identifying matched features that are needed in generating mosaics from these images. Generating mosaics is a key step in the decision-making process since it opens the ability to interpret information field-wide instead of per-image information interpretations. This paper proposes an image mosaic generation pipeline based on LoFTR - a local feature matching method using transformers as a feature matcher for the low-pixel resolution hyperspectral images. Furthermore, the GPS point-based optimization method is also presented in order to minimize the computational cost and allow the multicore processing capability. The proposed method was evaluated using several field datasets obtained using a low-resolution hyperspectral camera and an unmanned aerial platform. Results present successfully stitched image cubes that could be used in future analysis tasks in agriculture-related decision-making processes.
Toward Label-Efficient Emotion and Sentiment Analysis

Emotion and sentiment play a central role in various human activities, such as perception, decision-making, social interaction, and logical reasoning. Developing artificial emotional intelligence (AEI) for machines is becoming a bottleneck in human–computer interaction. The first step of AEI is to recognize the emotion and sentiment that are conveyed in different affective signals. Traditional supervised emotion and sentiment analysis (ESA) methods, especially deep learning-based ones, usually require large-scale labeled training data. However, due to the essential subjectivity, complexity, uncertainty and ambiguity, and subtlety, collecting such annotations is expensive, time-consuming, and difficult in practice. In this article, we introduce label-efficient ESA from the computational perspective. First, we present a hierarchical taxonomy for label-efficient learning based on the availability of sample labels, emotion categories, and data domains during training. Second, for each of the seven paradigms, i.e., unsupervised, semisupervised, weakly supervised, low-shot, incremental, domain-adaptive, and domain-generalizable ESA, we give the definition, summarize existing methods, and present our views on the quantitative and qualitative comparison. Finally, we provide several promising real-world applications, followed by unsolved challenges and potential future directions.
Usage frequency and application variety of research methods in library and information science: Continuous investigation from 1991 to 2021

The present study analyzed over 26,000 research articles published between 1991 and 2021 in twenty-one major LIS (Library and Information Science) journals, using the machine learning (ML) approach to categorize the research methods used by LIS scholars. The findings of this study are significant. Firstly, there has been a shift in the research strategy from conceptual research (e.g., "Theoretical approach") to empirical research (e.g., "Interview") in LIS investigations over the past 31 years. Secondly, the research topics explored by LIS scholars during this period have moved from system-centered issues (e.g., "Information retrieval/models and algorithms") to user-centered topics (e.g., "Information services "). Thirdly, the study revealed dynamic and revealing relationships between the 18 research topics identified in the study and the 16 research methods commonly adopted in the LIS field. These dynamic relationships can be visualized by year and longitudinally via an interactive map created in this study.
Hierarchical Bayesian calibration of regional moment magnitude predictive models

Onsite Earthquake Early Warning (EEW) applications require robust predictive models that can relate the early part of the recorded seismic waves to seismicity and/or ground shaking characteristics, the latter representing the variables used to make decisions about raising or not an alarm. The use of limited information to establish these models within a real-time estimation setting, typically corresponding to the first few seconds after the identification of the seismic wave arrival, introduces significant uncertainty (variability) in the predictions. To increase the number of historical observations available for the model calibration, data from multiple seismic stations within the same geographic region are utilized for the latter task. Unfortunately, this introduces additional sources of variability in the predictive model development, originating from local geographical effects and site conditions that affect differently the underlying physics at each station. This paper introduces a hierarchical Bayesian model updating framework to address this challenge. To better focus the research effort, the calibration of earthquake magnitude nonlinear regression models based on the seismic maximum predominant period is investigated, though underlying principles can be extended to other type of decision variables and intensity measures used in EEW applications. Within the hierarchical updating framework, the predictive models for each station are separately calibrated (first level), while sharing information across the stations by selecting common calibration hyper-parameters (second level). This facilitates a reduction of the variability of the estimates originating from regional wave propagation effects while simultaneously providing sufficient amount of data for calibrating the predictive models even for stations with scarce available records. To support computational efficiency in the hierarchical calibration, conjugate priors are utilized, while for accommodating nonlinear regression relationships samples from the posterior distribution, representing the model calibration, are obtained using Metropolis-within-Gibbs sampling. As illustrative case study, the application to the Sichuan region of Southwestern China is examined, with extensive comparisons performed for the accuracy and robustness of the resultant probabilistic predictive models.
Human–Machine Interactive Learning Method Based on Active Learning for Smart Workshop Dynamic Scheduling

In the field of dynamic scheduling, workers and scheduling models (SMs) play a crucial role in decision-making. Workers are able to help SM training by sample labeling, thereby enhancing the decision-making ability of SMs. However, existing supervised learning methods require a large number of labeled samples to train SMs, which limits the learning efficiency between workers and SMs. In this article, a human-machine interactive learning method based on active learning (HMILM/AL) is proposed. The method introduces active learning (AL) techniques to reduce labeling costs and improve learning efficiency. Referring to the AL framework, only a small subset of samples are selected from an unlabeled dataset and are labeled by workers, to train SMs. To further reduce labeling costs, sample selection, the key to the HMILM/AL, is improved by two strategies. First, a novel hybrid selection strategy (NHSS) is developed. By identifying and selecting more useful samples in an unlabeled dataset, the NHSS promotes efficient use of workers, and reduces labeling costs. Second, an enhanced NHSS (E-NHSS) is proposed, which considers both the difficulty of labeling samples and the usefulness of the samples. It reduces labeling costs by selecting easily labeled samples as much as possible. Finally, the proposed method is evaluated through experiments conducted in a real smart workshop. The results demonstrate that the HMILM/AL is very competitive compared with existing supervised learning methods. Moreover, both the NHSS and the E-NHSS can reduce labeling costs efficiently.
Uplink-Assist Downlink Remote-Sensing Image Compression via Historical Referencing

The traditional strategy of acquiring satellite images involves transmitting compressed satellite data to ground stations solely via the downlink, without utilizing the uplink. In this paper, we propose an enhanced remote sensing (RS) image compression approach that utilizes uplink assistance to improve compression efficiency. By leveraging the uplink, historical images from ground stations can serve as reference images for on-orbit compression, effectively eliminating spatio-temporal redundancy in RS images. However, due to radiation variations among RS images captured on different dates, pixel-wise referencing as employed in the prior codec paradigm is insufficient. To address this, we propose a novel dual-end referencing downsampling-based coding (RefDBC) framework. At the encoder, relevance embedding evaluates reconstructability and records information to restore texture details from the reference prior to downsampling. At the decoder, relevance-based super-resolution uses the identical reference and recorded relevance information to reconstruct the decoded low-resolution image. By incorporating relevance referencing, RefDBC effectively mitigates fake texture generation caused by downsampling and compression, achieving significant bitrate savings ranging from 35%-70% compared to standard, learning-based, and DBC compression baselines in experiments on Spot-5 and Luojia3 images. Code, data, and pretrained models are available online at https://github.com/WHW1233/RefDBC.
Enabling Anonymous Authorized Auditing Over Keyword-Based Searchable Ciphertexts in Cloud Storage Systems

Cloud storage provides Data Owners (DOs) with flexible data storage and management services, but simultaneously poses various security concerns, of which the integrity of outsourced data, as the most important security issue, determines the widespread use of cloud storage services. In this article, we present an <inline-formula><tex-math notation="LaTeX">$\mathbf {\overline{A}}$</tex-math></inline-formula> nonymous <inline-formula><tex-math notation="LaTeX">$\mathbf {\overline{A}}$</tex-math></inline-formula> uthorized <inline-formula><tex-math notation="LaTeX">$\mathbf {\overline{A}}$</tex-math></inline-formula> uditing scheme over <inline-formula><tex-math notation="LaTeX">$\mathbf {\overline{K}}$</tex-math></inline-formula> eyword-based <inline-formula><tex-math notation="LaTeX">$\mathbf {\overline{S}}$</tex-math></inline-formula> earchable <inline-formula><tex-math notation="LaTeX">$\mathbf {\overline{C}}$</tex-math></inline-formula> iphertexts (AAA-KSC) in cloud storage systems. In AAA-KSC, we devise an anonymous authorization mechanism, only the designated Third Party Auditor (TPA) could decrypt DO's identity and execute auditing tasks. In particular, by utilizing the verification metadata, without breaking the privacy of DO's concerned keyword, TPA could check the integrity of outsourced ciphertexts containing the specific keyword. More attractively, we design an identity-based homomorphic signature algorithm that makes use of the polynomial encapsulation mechanism to tackle with large scale data files, and thus AAA-KSC enables TPA to fulfil integrity verification with nearly constant computational costs, which are independent of the number of encrypted files that contain the same keyword. We provide the correctness, and prove that AAA-KSC is adequately secure. The performance evaluation experimentally demonstrates the high efficiency and feasibility of AAA-KSC in the real applications of cloud storage systems.
Archival Finding Aids in Linked Open Data between description and interpretation

The Semantic Web in general, and the LOD in particular, suppose that the knowledge conveyed by documents must be adequately modeled and represented to produce reliable and trustworthy data. Following this statement, we understand that in the archival domain the tricky and subtle transition from the traditional methodologies for data description to Linked Open Data must be delegated to agents able to skilfully read the content of cultural objects. The Digital Hermeneutics model aims to propose a layered architecture that allows, beyond the descriptive specificities of each domain, to formalize the data transformation from the native system to LOD. The idea is to guarantee, through context information, that each moment of the transformation workflow is documented, finally strengthening the trust of the resultant dataset.
From typewriter to bit: how finding aids evolve

If the only tool you have is a hammer, you tend to treat everything as if it were a nail. But is it really so? Some thoughts on what determined the evolution of the finding aids, of the publication of archival databases on the web and of archival information systems. In this article we will analyze several of the solutions adopted during the time, trying to understand if these actually respond to the needs of the users of the archives.
Postdigital Speculation

Postdigital speculation describes methods and theoretical approaches that emphasise the importance of process, emergence, and experimentation in attempting to understand and work with postdigital realities and futures. Speculative methods are characterised by their non-representational, complexity-informed and inventive qualities, and emerge from work in design, media production, literature and art history, including postdigital work in these areas. In educational literature, speculative fiction has been increasingly used as a method to investigate postdigital futures. Speculative theories applied to the postdigital focus on the ongoingness, or becomingness, of reality and experience. Across and between the domains of method and theory, postdigital speculation has a particular emphasis on mobility, indeterminacy, and the limits of intention in shaping what will be in educational research and in practice.
From institutional series to online inventories: what has changed in the Soprintendenza

In the panorama of the institutions of the Italian Archival Administration, the Umbria Soprintendenza archivistica e bibliografica can certainly boast a primacy: its conspicuous editorial production that started in the late 1990s and composed by the series “Segni di civiltà” and “Scaffali senza polvere”, in addition to the many exhibition catalogs. To date, in SIUSA and the SAN Portal - Online Research Tools, there are over 500 inventories of Umbrian archives, more than a quarter of the national production. What path has been taken from printed volumes to downloadable pdfs and browsing archival description software?
Postdigital Learning Spaces

Postdigital learning spaces foreground people’s co-presence at multiple spaces, for example as a student navigates a university library website, while seating at their kitchen table at home. These spaces are often characterized by a mix of digital and material elements which come together to influence learning activity. Postdigital thinking enables researchers to explore the nature of learning spaces, to see and reveal nuances, noticing that people’s experiences and interactions with multiple material and digital elements are often tangled up. A postdigital stance also alerts us to increasing inequalities and surveillance, raising questions about spatial privacy.
Analysis of learning styles in engineering students after the COVID-19 pandemic

The research was carried out with the objective of knowing aspects that are interesting for researchers about learning; such as the analysis of the learning styles currently in the post-pandemic period, manifested by students of different degrees. Identifying and analyzing learning styles and relating them to current teaching practices will allow applying teaching strategies that could reduce the current high dropout and failure rates. The results obtained show the trends by educational institution, by semester and by career. Significant differences were found in the results in the active style between schools; by semester, most of the students of the initial semesters are visual and sensitive, in later semesters, there is a tendency to be intuitive and by career they show a tendency to be sensitive and visual; As a conclusion, a student can have different learning styles, but in general they have a certain affinity for a learning profile. Objective. Identify and analyze the current learning styles in Engineering students, derived from the abrupt changes in study modalities that occurred due to the Covid 19 pandemic. Methodology. A validated survey (Cronbach's : 0.872) was applied to students of Electrical Engineering, Electronic Engineering and Biochemical Engineering. The following learning styles were considered to be identified and analyzed: active-reflexive, sensitive-intuitive, visual-verbal, and sequential-global. A scale of dichotomous variables was chosen, they were valued according to the proposal of Felder and Silverman (Felder and Silverman, 1988), the answers were set in the categories of learning styles proposed by the author in active or reflective, sensitive or revealing, visual or verbal, and sequential or global, were quantified, tabulated, and analysis of variance (ANOVA) for parametric samples. Contribution. This research made it possible to identify the learning styles of undergraduate students in the post-pandemic period, to find out the current learning preferences, which can be applied to propose, where appropriate, the necessary adjustments within the teaching strategies of teachers interested in reduce the current high rate of failure and dropout.
Designing self-tracking experiences: A qualitative study of the perceptions of barriers and facilitators to adopting digital health technology for automatic urine analysis at home

Self-tracking technologies open new doors to previously unimaginable scenarios. The diagnosis of diseases years in advance, or supporting the health of astronauts on missions to Mars are just some of many example applications. During the COVID-19 pandemic, a wide range of self-monitoring protocols emerged, revealing opportunities but also challenges including difficulties in understanding how to self-use monitoring systems, struggling to recognize the benefit of such systems and a high likelihood of abandonment. In this paper, we explore the role that design plays in the creation of a user experience of self-tracking, with a focus on urine analysis at home. We investigate adoption factors and forms of data expression to overcome the presented challenges. By combining insights from related work, semi-structured interviews and indicative user-tests, we show the potential of pairing a traditional numerical data representation (data quantification) with a qualitative expression of the data (data qualification). Indeed, qualitative expressions have the potential to convey the complexity of the phenomena tracked, enabling deep meaning-making and emotional connection to personal data. At the same time, we also identify issues with this approach, which can require a longer learning curve and lead to rejection by users more accustomed to traditional, numerical approaches. Based on the results, several recommendations have been converted into an experimental proposition, which also presents future plans for the continuation of the project. This article presents the first fundamental step in creating a meaningful experience of self-tracking, taking into consideration the needs and expectations of future users.
Analysis of Parental Involvement in Early Literacy in Malang, Indonesia

Parental involvement is a crucial thing that can improve children's early literacy skills. The purpose of this research is to analyze the involvement of parents in early literacy in Malang, Indonesia. This study used a descriptive quantitative method with a survey involving 122 parents (mean age 37.5 years) who had kindergarten children aged 4-6 years. This study uses a measuring tool developed by PIRLS-2011 Parental Involvement from the parent's perspective. The results show that in general parental involvement in developing early literacy skills is in the low category of 48% or as many as 58 people and in the medium category of 37% or as many as 45 people. Parental involvement at home shows 38.87%. This shows that parents are quite involved in developing children's literacy at home.
Understanding cybercrime from a criminal's perspective: Why and how suspects commit cybercrimes?

Cybercrime is a current and worsening problem all over the world. Various types of cybercrimes such as hacking, identity theft, harassment, and various forms of financial fraud are rampant. Further, although cybercrimes using information and communication technology (ICT) are widespread, effective measures and legislation to prevent or cope with them are in short supply. These circumstances warrant an analysis from a criminal's perspective of the reasons and methods for committing cybercrimes. Therefore, in this study cybercrimes are classified into three types—integrity-related, computer-related, and content-related—to identify major factors for each type and then propose measures to thwart them. We derived the main crime factors (i.e., cybercrime motives, enablers, methods, channels, suspects, and victims) for each type of cybercrime, then compared and analyzed them through interviews with police officers who specialize in combating them. This study contributes to the expansion of the literature on cybercrimes by deriving the main crime factors for each type of cybercrime from a criminal's perspective. Preventive measures for each type of cybercrime, based on the derived factors, can also contribute to the prevention of and response to actual cybercrimes.