Reviewerly/exHarmony huggingface dataset:
Machine Learning in the Construction of Library's Special Collection Document Information Resources

With the rapid development of Internet technology, more and more people begin to focus on the construction of library (CL) information resources (IR) in the field of mobile network and social media applications.In order not to let the unique culture disappear gradually, and to realize cultural exchange and inheritance, this paper intends to use modern technology to build IR of library related documents.This paper mainly uses the principal component analysis and survey methods to carry out relevant research on the CL special collection document IR.The survey results show that 70% of people think that the utilization rate of library resources is not high, while 68% of people think that the research on special collection culture should be strengthened.
Lightning Warning Methods Based on Machine Learning and Single Station Ground Meteorological Elements

Lightning is a natural phenomenon in nature, and its occurrence rules and effects are very complex.The analysis of meteorological elements plays an extremely important guiding role in lightning prediction.Therefore, this paper uses the method of machine learning to carry out early warning analysis on lightning approaching, in order to obtain weather information in advance and take action to avoid danger.In this paper, the analytic hierarchy process (AHP) and the survey method are mainly used to carry out the correlation analysis on the lightning approaching warning.The survey data shows that in this system, the accuracy rate of lightning early warning using decision tree algorithm can reach about 90%, indicating that machine learning has a good effect in lightning approaching early warning.
Random Forest in Image Segmentation

Random forest algorithm has the advantages of fast processing speed in image processing, and can be used in image segmentation.The aim of this paper is to study image segmentation based on random forests.The principles of random forest algorithm and feature extraction are described in detail, and the characteristics of RGB colour space and HSV colour space are analysed.The experimental procedure for segmenting annual rings images is described.Regions of interest are extracted and then, based on the differences in colour and texture features of early and late wood, the segmentation of annual rings images is achieved.The experimental results show that the random forest algorithm achieves better results in image segmentation.
Temporal explanations.

People can explain phenomena by appealing to temporal relations, for example, you might explain a colleague's absence at a meeting by inferring that their prior meeting ended late. Previous explanatory reasoning research shows that people construct causal explanations to resolve causal conflicts. Accordingly, temporal explanations may help reasoners resolve temporal conflicts, and we describe four experimental tests of the hypothesis (N = 240). Experiment 1 provided participants with conflicting or consistent temporal information and elicited natural responses about what followed. Participants spontaneously provided temporal explanations to resolve inconsistencies, and only a minority of them provided more conservative, direct refutations. Experiments 2 and 3 showed that participants preferred temporal explanations over simpler refutations to resolve conflicts, and Experiment 4 showed that participants judged temporal explanations more probable than refutations, and thereby yielded a novel class of conjunction fallacies. The research is the first to examine patterns in temporal explanatory reasoning. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Liquid crystal on silicon-based real time phase stepping interferometry by spatial multiplexing

In this paper, we present a method to perform phase-shifting interferometry in real time. The technique is based on the use of a parallel aligned liquid crystal on a silicon display as a customized reference mirror. In order to implement the four-step algorithm, a set of macropixels is programmed onto the display, and these are divided into four zones with the appropriate phase shifts. This way, by spatial multiplexing, it is possible to obtain the phase of the wavefront at a rate limited only by the integration time of the employed detector. The customized mirror is able to both compensate the initial curvature of the object under study and introduce the necessary phase shifts to perform phase calculation. Examples of the phase reconstruction of static and dynamic objects are shown.
Software Practice in Small Software Companies: Development Context Constraints on Process Adoption.

Small software companies (SSCs) interact with the immediate environment, exposing them to challenges that force the organization to undertake adjustments if it must survive and remain in business. These adjustments result into counterproductive practices and changes that create complexities in process adoption. This cross-sectional survey investigates the occurrences around the customer in the development context that affect the adoption of process in SSCs. To answer the research questions, we conducted a survey on 115 respondents and found out that although customer engagement has a significant relationship with reducing rework, inadequacies in the engagement due to the customer's lack of knowledge of software processes, triggers unstructured and ad-hoc methods in SSCs. The main contribution of this paper is a customer engagement framework that seeks to transform software processes by focusing on the customer as a pillar of achieving purpose and value to reduce development effort and time.
Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed.Firstly, the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task.In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.
Design da informação e sociedade: levantamento do estado da arte do CIDI

The present study presents the third stage of the research "State of the art of Information Design in Brazil", analyzing the thematic axis Society.To this end, 64 articles from 2015, 2017 and 2019 proceedings of the International Congress of Information Design were analyzed.To this end, the content analysis method was adopted, which tabulates textual data.In this sense, the most frequent terms and their concepts were identified.In the end, a semantic synthesis of the axis was elaborated, where it was possible to perceive that Information Design is a social interlocutor for having collective goals and for giving shapes to objects and interactions, exploring cultural elements such as identities and memories.In addition, Information Design is complex because it explores practical, economic, social, identity, and interactive dimensions.
Review of Bar-Asher Siegal (2020): The NP-strategy for Expressing Reciprocity: Typology, History, Syntax and Semantics

Abstract This article reviews The NP-strategy for Expressing Reciprocity: Typology, History, Syntax and Semantics
Involving older adults in the design process: a human-centric Design Thinking approach

A product’s acceptance depends on the experience that it provides to its users. To consider user’s contextualised specific needs an user-centred design process is recommended. Human-centric design considers human´s opinions as a design priority, and puts them in the “centre” of the iterative design process. To understand the end-users influence (adults 55+ experience) in product development, we conducted an empirical study with 25 participants, supported by a human-centric co-design thinking process (participatory design) with collection of qualitative data. In this article we report a Design Based Research (DBR) study, that compares the acceptance of a set of two audio-visual artefacts: designed with adults older than 55 and a design process supported only by the designer’s expertise. Overall we believe this study depicts evidence that audio-visual artefacts for the online platform ICTskills4All are more effective when co-designed and validated with end-users.
Harmony Search-Based Approaches for Fine-Tuning Deep Belief Networks

Harmony Search (HS) is a metaheuristic algorithm inspired by the musical composition process, precisely the composition of harmonies, i.e., the chain of different musical notes. The algorithm’s simplicity allows several points to improve to explore the entire search space efficiently. This work aims to compare different HS variants in image restoration using Deep Belief Networks (DBN). We compared standard HS against five variants: Improved Harmony Search (IHS), Self-adaptive Global Best Harmony Search (SGHS), Global-best Harmony Search (GHS), Novel Global Harmony Search (NGHS), and Global Harmony Search with Generalized Opposition-based learning (GOGHS). Experiments in public datasets for binary image reconstruction highlighted that HS and its variants obtained superior results than a random search used as a baseline. Also, it was found that the GHS variant is inferior to the others for some cases.
Searching by parts: Towards fine-grained image retrieval respecting species correlation

Most of the existing works on fine-grained image categorization and retrieval focus on finding similar images from the same species and often give little importance to inter-species similarities. However, these similarities may carry species correlations such as the same ancestors or similar habits, which are helpful in taxonomy and understanding biological traits. In this paper, we devise a new fine-grained retrieval task that searches for similar instances from different species based on body parts. To this end, we propose a two-step strategy. In the first step, we search for visually similar parts to a query image using a deep convolutional neural network (CNN). To improve the quality of the retrieved candidates, structural cues are introduced into the CNN using a novel part-pooling layer, in which the receptive field of each part is adjusted automatically. In the second step, we re-rank the retrieved candidates to improve the species diversity. We achieve this by formulating a novel ranking function that balances between the similarity of the candidates to the queried parts, while decreasing the similarity to the query species. We provide experiments on the benchmark CUB200 dataset and Columbia Dogs dataset, and demonstrate clear benefits of our schemes.
Target Signal Recognition Method for 5G Communication Supporting Machine Learning

The identification of 5G communication target signal type may be the premise and foundation of modulation mode identification, signal demodulation and other links.Therefore, the identification of 5G communication target signal type has become an indispensable technical link in the intelligent signal processing system.In order to solve the shortcomings of existing agricultural machine vision system research, this paper discusses the composition and key technologies of agricultural machine vision system and machine learning algorithm, and then simply discusses the hardware selection and software development environment of the system in which the algorithm proposed in this paper is applied.And the overall results of agricultural machine vision monitoring system are designed and discussed.RCNN and K-means algorithms in machine learning are used to study the identification and classification of seedlings in images.Finally, through the experimental analysis of selected samples, it is known that the accuracy of RCNN and K-means algorithm in image recognition detection in agricultural machine vision monitoring system is up to 94.25%.Therefore, it is verified that machine learning algorithm has high practical value in agricultural machine vision system.
Large language models can segment narrative events similarly to humans.

Humans perceive discrete events such as "restaurant visits" and "train rides" in their continuous experience. One important prerequisite for studying human event perception is the ability of researchers to quantify when one event ends and another begins. Typically, this information is derived by aggregating behavioral annotations from several observers. Here we present an alternative computational approach where event boundaries are derived using a large language model, GPT-3, instead of using human annotations. We demonstrate that GPT-3 can segment continuous narrative text into events. GPT-3-annotated events are significantly correlated with human event annotations. Furthermore, these GPT-derived annotations achieve a good approximation of the "consensus" solution (obtained by averaging across human annotations); the boundaries identified by GPT-3 are closer to the consensus, on average, than boundaries identified by individual human annotators. This finding suggests that GPT-3 provides a feasible solution for automated event annotations, and it demonstrates a further parallel between human cognition and prediction in large language models. In the future, GPT-3 may thereby help to elucidate the principles underlying human event perception.
Integrated creativity of the assessment practices: among routines and opportunities

The objective of this biannual conference is to strengthen cross-disciplinary perspectives in order to develop knowledge in the fields of learning, as well as on organizations and institutional systems concerned with training and education. Education policies can be understood in the broadest sense and refer as much to supranational strategies as to national, regional and local policies, as well as to strategies and choices of various actors in the education system (academic actors, institutions, educational teams, families or even pupils). Questions of strategy and choice also refer to the development of teaching professions in specific organizational contexts where a localised appropriation of public policies is defined.This conference thus pays particular attention to issues of interdisciplinary research. Cross-disciplinary perspectives in the field of education are closely linked to methodological and epistemological issues. Such debates imply a paradigmatic emergence, where theoretical statements in the field of education can be discussed at the boundaries of the various methodologies and disciplines.Education policies and strategies, as well as the methodological and interdisciplinary challenges related to these issues, are analysed through the four following intertwined themes:- Contributions, limitations and new challenges of educational research in the field of learning and teaching fundamental skills such as reading,
Education inequality in underserved regions: Exploring the role of technology to promote diversity and inclusivity

In underserved regions, the educational inequality gap between better-served children and under-served children is wide. At the same time, the digital inequality gap in such countries is narrowing; less endowed families are now able to access some digital technologies. Therefore, there is the need to use technologies for educational purposes to support less-endowed children in promoting diversity and inclusivity. This research is explorative research to highlight issues in the use of technologies for education, such as adoption, barriers, challenges and benefits in African context. In doing so, we seek to promote diversity and inclusivity for education in underserved regions and bridge the education inequality gap. This research will contribute to academic research relating to inclusivity to support under-served children in developing countries. It will push forward educational technology research and contribute to policymaking. The outcome of this research would prepare the foundation for a future large-scale implementation across Sub-Saharan Africa.
Reflections on Remixing Open Access Content into Open Educational Resources: A New Paradigm for Sustainable Data-Driven Language Learning Systems Design in Higher Education

This chapter presents a new paradigm for sustainable data-driven language learning systems design in higher education that draws on qualitative reflections spanning a decade (2012–2022) with stakeholders from an ongoing global research study with the FLAX (Flexible Language Acquisition) and F-Lingo projects at the University of Waikato in Aotearoa New Zealand (Fitzgerald (2019) A new paradigm for open data-driven language learning systems design in higher education; König et al. (2022) Smart CALL). Design considerations are presented for remixing domain-specific open access content into Open Educational Resources (OER) for academic English language provision across formal and non-formal higher education contexts. Primary stakeholders in the research collaboration include the following three groups: (1) Knowledge organisations that provide open access to academic content—libraries and archives, including the British Library and the Oxford Text Archive, universities in collaboration with MOOC providers and the CORE (COnnecting REpositories) open access aggregation service at the UK Open University; (2) Researchers who mine and remix academic content into corpora and open data-driven language learning systems—converging from the fields of open education, computer science and applied corpus linguistics; (3) Knowledge users who re-use and remix academic content into OER—English for Academic Purposes (EAP) practitioners from university language centres. Automated content analysis was carried out on a corpus of interview and focus discussion data with the three stakeholder groups in this research. We discuss themes arising from the research data that reflect the different stakeholders’ experiences of remixing open access research content that has been produced within the academy for re-use as open educational content for teaching and learning features of academic language within open data-driven language learning systems. These open learning systems have been specifically designed to scale with OER expansion and traction in mind for their sustainable uptake both within and beyond the brick and mortar of the traditional university. The new paradigm presented in this chapter challenges, as the OER movement must, established business models and deeply embedded cultural or institutional norms that present obstacles to OER expansion and traction and the sustainability of the movement. One persistent challenge concerns the lack of open education policy across the higher education sector for full open access (for use, modification, adaptation) via Creative Commons licensing to content produced within the academy. Thus, while this research has theoretical and practical implications in applied linguistics, computer science, language teaching and learning and open education, more generally, it also has significant cultural, business model and policy implications for higher education.
STUDENT'S RECEPTION TOWARD SHAKESPEARE’S SELECTED CONTEMPORARY PLAYS

Literary reception is a literary genre that examines literary texts by considering the reader as the giver of the welcome or response. Researchers choose to analyze literary receptions in Shakespeare's plays, namely "Romeo and Juliet" and "Macbeth" as an interesting source of data to be studied and analyzed more deeply because of the difficulty of readers in responding to a literary work. This study aims to determine the reader's response to the two plays by Shakespeare. The data source of this research is the play script of "Romeo and Juliet" and "Macbeth" by William Shakespeare. The method used in this study is a qualitative descriptive method. This study uses Iser’s Reception theory with instruments in the form of documentation and observation studies because this research is reviewed by noting and marking the parts that are considered important and useful for the reader. Based on the results of this study, it is clear that there are reader responses in Shakespeare's plays. Then the results of the research are in the form of a questionnaire containing nine questions with excellent answers or responses from readers in engaging the two plays by Shakespeare.
Coherence distillation through one-way LICC

Understanding the resource consumption in distributed scenario is one of the main goals in resource theory. Entanglement swapping is an important example in such scenario. It involves at least three parties. Initially two pairs are entangled and other is separable. Performing Bell measurement on common party, resource is swapped in the separable pair. In resource theory of coherence, the initial separable pair may be in free state or in resource state. This feature leads to development of the idea of coherence swapping and coherence concentration. The latter one is the generalization of the first one. Both the protocols have important role in enhancement of coherence as well as coherence fraction and in reveling resource from a noisy state.
DTW Threshold Determination for English Word Utterances in Filipino Accent using MFCC

In the conduct of this project, the proponents primarily aimed to explore the threshold for the deviations of the Filipino-accented utterances of selected English words using the MFCC and DTW concepts. The initial premise utilized by the proponents would be the speaker-dependent nature of the MFCC; hence, the calculations, measurements, and data-gathering methodologies were conducted by means of acquiring the said coefficients from the same individual verbally uttering selected words in that of the American accent and in their native Filipino accent and subjecting these results to a series of MA TLAB algorithms devised by the researchers. As such, the study was able to conclude that, upon preliminary calculations, the normalized DTW threshold between the Filipino-Accented English was calculated to be 4.91 with the designed system having an accuracy of 68.73 &#x0025; in correctly determining which Filipino-accented utterances correspond to their respective English word counterparts. While this was able to procure plausible results, one of the limitations observed in this implementation would be the presence of noise in the samples that may have caused deviations along with the limited number of participants that partook in the acquisition of data for this study. Thus, it is then highly suggested that a wider and more robust database be implemented in future studies involving this subject and relative methodologies.