Reviewerly/exHarmony huggingface dataset:
HAEE: Low-Resource Event Detection with Hierarchy-Aware Event Graph Embeddings

The event detection (ED) task aims to extract structured event information from unstructured text. Recent works in ED rely heavily on annotated training data and often lack the ability to construct semantic knowledge, leading to a significant dependence on resource. In this paper, we propose a hierarchy-aware model called HAEE by constructing event graph embeddings. We utilize two relations (cause and subevent) to help model events on two dimensions of polar coordinates, so as to distinguish events and establish event-event relations. Specifically, events under the cause relation are constructed at the same level of the hierarchy through rotation, while events under the subevent relation are constructed at different levels of the hierarchy through modulus. In this way, coexistence and interactions between relations in time and space can be fully utilized to enhance event representation and allow the knowledge to flow into the low-resource samples. The experiments show that HAEE has high performance in low-resource ED task, and the analysis of different dimensions of embeddings proves that HAEE can effectively model the semantic hierarchies in the event graph.
An Intelligent Analysis of Mobile Evidence Using Sentimental Analysis

Smartphones are compatible and easily accessible compared to computers irrespective of place and time. Smartphones merge with our routine which acts as a medium of communication in several ways such as messaging, voice and video calling, sharing of audio and video contact, and many multimedia contents. We express our emotions through words and these communications can be analyzed by a technique called Natural Language Processing (NLP). Many researchers in the field of text mining and NLP have analyzed the polarity of online product reviews, blogs, social media comments, tweets, and many more. Mobile forensics investigation will be more effective by providing the evidences based on sentimental analysis of the text where the suspected messages contain a negative content of having threats, harassments, text against the community guidelines, and some keywords related to the cases which may be the turning point during the forensics investigation. In this paper, a novel framework called Integrated Mobile Device Forensic Investigation Process (IMDFIP) with inclusion of sentimental analysis techniques in the text which is used to emotionally analyze the texts during the communication process. This framework provides additional information to forensic analysts which will trap the suspect during investigation. The proposed framework of IMDFIP identifies the important terms, emotions, and polarity of the text during the crime. IMDFIP capitulates results compared with the previous models.
OPTIMIZATION OF PREDICTION AND PREVENTION OF DEFECTS ON METAL BASED ON AI USING VGG16 ARCHITECTURE

Manufacturing is one of the most valuable industries in the world, it can be automated without limits but still stuck in traditional manual and slow processes. Industry 4.0 is racing to define a new era in digital manufacturing through the implementation of Machine Learning methods. In this era, Machine learning has been widely applied to various fields and will certainly be very good applied in the manufacturing world. One of them is used to predict and prevent defects in metal. The process of predicting and preventing defects in metal is one of the important efforts in improving and maintaining production quality. Accuracy in predicting and preventing defects in metal can be an innovation and competitiveness in technology, both in production methods, and improving product safety and its users. Human operators and inspectors without digital assistance generally can spend a lot of time researching visual data, especially in high-volume production environments. For this reason, there needs to be research in developing Machine Learning technology in an effort to prevent the occurrence of defects in metal. And one of the development of this technology by using Convolutional Neural Network (CNN) architecture Visual Geometry Group 16 layer (VGG16). As for the metal defect dataset with 10 classes with details for training data as many as 17221, and test dataset as many as 4311, From the use of methods and datasets available, has been done training model used and produce very good accuracy, that is equal to 89% and testing with accuracy equal to 76%. And also done Interpreter process against new input data, to know metal defect type, prediction accuracy and appropriate action to prevent and overcome metal defect type result of Interpreter process application.
Higher recognition for learning developers: NTF and PFHEA discussion session

Although it might sometimes seem as though PFHEA and NTF are high-level and strategic forms of recognition for those working in high-level and strategic positions, this is not necessarily the case, and both have been achieved by an ever-increasing number of Learning Developers. Join us in this session if either of these is something you’ve been mulling over or a goal you’ve set yourself, and we will outline what’s involved in writing an application, share with you the kinds of evidence we used in our own applications, explore how you can meet the requirements, and hopefully answer your questions about how you might proceed. Aim high!
Model Language Tests for the Writing Skill of the Beginner Level Based on CEFR (Common European Framework of Reference)

The Common European Framework of Reference for Language (CEFR), is a method and tool for measuring a person's language skills by using tests according to special procedures for classifying beginner, intermediate, and advanced-level abilities. Therefore, this measurement tool is one of the researchers' goals: to provide examples of various language tests of beginner's writing skills that are classified according to the CEFR measures at levels A1 and A2. This research will analyze examples of writing skill tests for beginners based on the CEFR method. This research was carried out using a desk study design with reading and writing data sources related to language testing forms based on the CEFR method and scoring techniques using desk research methods. The results of this research indicate that the CEFR method is effective for measuring students' abilities and that the writing skill test models can be classified for beginners.
The Relationship of Perceived Usefulness (PU) and Attitude Towards Using (ATU) in Students of Scola Users Learning Biology at Dedikasi Edukasi Kualiva Senior High School

The material on Biology subjects is broad and abstract concepts, so that online biology learning media such as e-learning are needed. Based on observations, Dedekadasi Edukasi Kualiva (DEK) SHS is one of the private schools in Padang that uses Scola as an e-learning platform in biology learning even though learning is done offline. However, it has never been analyzed the success of the use of Scola in biology learning in high school deck with any instrument so it is not known the relationship of PU with ATU in students using Scola biology learning in DEK high school. The purpose of this study was to determine the relationship of PU with ATU in students using Scola biology learning at DEK high school. This type of research is descriptive correlation with quantitative approach conducted at DEK high school. The sample consisted of 48 students, in Class X Phase E, XI science, and XII science, with sampling techniques, namely total sampling techniques. Analysis of the data used is Pearson product moment correlation test and t test after knowing the normal distribution of data. The results of the study using Pearson product Moment correlation test showed a positive correlation with the value of r = 0.7587 (strong correlation) and the value of the count (7.899) &gt; table (1.679). These results indicate that the hypothesis is accepted. Therefore, PU has a significant positive relationship to the Attitude towards Using (ATU) in students who use Scola biology learning at DEK high school.
Fostering belonging: an interdisciplinary journal club

This session explored the evolution of an interdisciplinary journal club open to undergraduate and Master’s students, the challenges faced, and how we plan to develop the project going forward.&#x0D; The aim of the club is to empower students through an initial staff-led workshop, followed by student-led peer-to-peer discussion sessions, developing their confidence in group working (and indirectly meeting new people), critical reading and analysis. The club also provides the opportunity to analyse and interpret statistical data, an area that can be daunting for students (Mezgebe, Chesson and Thurston, 2019). Articles for discussion in the club are chosen to be accessible to students from all disciplines, with a focus where possible on aspects of equity, diversity, and inclusion. Collaboration between different facets of our learning development team allow our Librarian, Writing, and Maths and Statistics advisers to bring their specific expertise to the initiative, resulting in a holistic approach additionally underpinned by students’ increased ownership.&#x0D; Impact is measured through pre- and post-participation surveys* with mostly positive comments, touching on benefits beyond obvious ‘skills development’, most notably participation being seen to instil a sense of connection and belonging with fellow students, contributing to students’ sense of wellbeing and the appreciation of having a space to explore learning outside of the core assessed curriculum.&#x0D; We hoped to tap into delegates’ experiences of any similar initiatives and explore possibilities for further developing our initiative, which could include embedding subject-specific clubs within course programmes (i.e., empowering academic staff to facilitate these), with the aim of further enhancing a sense of belonging across the institution. Equally, the model we have developed is adaptable to different contexts, so we would encourage delegates to consider the potential for adaptation to their institutional contexts.&#x0D; *Ethical approval has been obtained to use survey responses for research.
Report in Documentary Communication in Southern Region of Russia during First Half of 18th Century

This article examines the report as a new type of document in the business writing system of the first half of the 18th century. The aim is to identify common and distinctive features of reports from various institutions in the southern region of Russia. The materials used for this study are documents from the Don Army (State Archive of the Volgograd Region) and the Astrakhan Province (National Archive of the Republic of Kalmykia). Through a comparative analysis of their content, functions, and speech organization, it is revealed that all reports were submitted from subordinate subjects to superiors, served informative purposes, and occasionally served as accompanying documents. They shared a similar form (addressee, addresser, title, main text, date, place of creation), although there were variations in the placement of requisites, document titles, and addresser formulas. Differences were also identified, such as the larger number of reports in the documentation of the Don Army and their frequent use as accompanying documents, while Astrakhan reports often contained financial reports. In Don reports, the addresser was referred to in the final formula as “about this report,” while Astrakhan reports only included their name (or signature). It can be concluded that reports were increasingly integrated into official communication, becoming firmly established in the military sphere, and their form became more standardized, reflecting the development of common rules for document management.
Book Review

Book Review| November 01, 2023 Book Review Adam B. Bath Adam B. Bath Commonwealth Scientific Industrial Research Organisation (CSIRO), Kensington, Western Australia, Australia Search for other works by this author on: GSW Google Scholar Author and Article Information Adam B. Bath Commonwealth Scientific Industrial Research Organisation (CSIRO), Kensington, Western Australia, Australia Publisher: Mineralogical Society of America First Online: 01 Nov 2023 Online ISSN: 1945-3027 Print ISSN: 0003-004X Copyright © 2023 by the Mineralogical Society of AmericaMineralogical Society of America American Mineralogist (2023) 108 (11): 2151. https://doi.org/10.2138/am-2023-B108118 Article history First Online: 01 Nov 2023 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Adam B. Bath; Book Review. American Mineralogist 2023;; 108 (11): 2151. doi: https://doi.org/10.2138/am-2023-B108118 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyAmerican Mineralogist Search Advanced Search Formation of Gold Deposits is a remarkable exploration into the world of gold formation, methodically crafted (including high-quality illustrations, figures, and instructive tables) by an author who has devoted many years of his life to the study of key gold deposits, such as the Golden Mile and Witwatersrand, among others. This book caters to a wide audience, from novices with an interest in geology to seasoned exploration geologists and even professors seeking a deeper understanding of the complexities of gold mineralization. One of the book’s most commendable qualities is its accessibility. The author’s capacity to distil complex geological concepts into... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Exploring the Advantages of Layout Procedure with Fully‐Connected Quantum Computing Technologies

Abstract Even though quantum algorithms are designed using an idealised high‐level quantum circuit description, the execution on real devices must consider the backend physical properties. Contemporary noisy intermediate‐scale quantum (NISQ) computers are characterized by limited inter‐qubit connectivity and an exclusive set of native gates, whose execution is affected by non‐negligible errors and non‐idealities. Quantum compilation, consisting of logic and layout synthesis, aims to recast the original circuit description to the target hardware by solving the coupling‐constraint and to minimize the execution time and the error rate. While a previous article addresses the first phase, the second one is the aim of this manuscript. Currently available layout tools mainly target superconducting technology. This article tries to match the unmet need for a layout synthesis library supporting a wider range of technologies. The most promising algorithms already present in the state of the art are adapted to extend the compatibility to all the supported technologies, and new approaches, which exploit the awareness of the coupling strength in fully‐connected topologies, are proposed. The integrated procedures are benchmarked against IBM's Qiskit and Cambridge Quantum Computing's compilation toolchains, and even though these strategies can be further improved, the results are encouraging.
Solidarity-Based Content Sharing on Social Media: A Closer Look at the Language Style among Millennial Social Platform Users

Social Media platforms have shaped diverse linguistic issues through informal language use, language adaptation and change, linguistic creativity and global communication. The current study reveals the issues about investigating about language style and linguistic form of solidarity expression used by the millennial community on social media platforms. Thirty respondents who aged about 23-43 years old considered as millennial participated in this study and inquired through open-ended questionnaire. The data was probed by using grounded theory proposed by Creswell. The findings exposed that language style were realized for certain communication purposes and based-social context oriented, solidarity expression was shown through a different linguistic form to reveal ideas, feeling, empathy, caring, respect, moral justice and kinds of arguments. after deeply investigated, there were four types of language styles that are dominantly used by millennial on this social media platform, namely formal language style, informal language style, slang language style and colloquial language style, unfortunately, many people abuse social media as a platform for venting their anger and their disappointment by using disrespectful language and behaviors are frequently observed online. Consequently, this scholarly paper also provides advice on the user of social media platform must possess a range of skills including linguistic knowledge, sociocultural knowledge, and the ability to use language in appropriate ways depending on the specific communicative context.
Cluster analysis and its application in teaching resources of university curriculum: A personalized method

Abstract The current personalized recommendation methods for teaching resources of university courses suffer from poor recommendation effectiveness due to the absence of user tags. To address this issue, a new personalized recommendation method based on cluster analysis is proposed. The proposed method leverages web crawler technology to obtain user tags, followed by processing the tags to remove meaningless terms, normalize word forms, and perform data processing. The processed tags are used to calculate user interest preferences for each tag cluster generated by clustering. Based on this, a user interest model is built, and user similarity is calculated to determine the recommendation score of each resource. The recommended resources are then ranked according to their recommendation score and presented to the target user. Experimental results demonstrate that the proposed method achieves high accuracy, recall rate, and F1 value for personalized recommendation of teaching resources in colleges and universities. In comparison, the method proposed in this paper has a significantly shorter recommendation time of 10.65 s. Further, the proposed model not only takes less time but also has higher recommendation efficiency when compared with existing personalized recommendation methods.
Evolutionary Machine Learning for Space

The Venn diagram of evolutionary computation, machine learning and space applications shows some intriguing overlaps. As evolutionary algorithms are often resource-intensive, they have not yet been applied in space. Nevertheless, it has been decisively demonstrated that evolutionary machine learning (EML) is a valuable tool for space, specifically in fields such as trajectory optimisation, optimal controlOptimal control and neuroevolutionNeuroevolution for robot control, where high-dimensional, discontinuous, sparse and/or non-linear problems abound. In the following chapter, we introduce common problems faced by the space research and application community, together with EML techniques used for generating robust, performant and, sometimes indeed, state-of-the-art solutions. The often complex mathematics behind some problems (especially in trajectory optimisation and optimal control)Optimal control has been simplified to the minimum necessary to convey the essence of the challenge without encumbering the overview of the relevant EML algorithms. We hope that this chapter provides useful information to both the EML and the space communities in the form of algorithms, benchmarks and standing challenges.
Evolutionary Machine Learning in Robotics

In this chapter, we survey the most significant applications of EML to robotics. We first highlight the salient characteristics of the field in terms of what can be optimized and with what aims and constraints. Then we survey the large literature concerning the optimization, by the means of evolutionary computation, of artificial neural networks, traditionally considered a form of machine learning, used for controlling the robots: for easing the comprehension, we categorize the various approaches along different axes, as, e.g., the robotic task, the representation of the solutions, the evolutionary algorithm being employed. We then survey the many usages of evolutionary computation for optimizing the morphology of the robots, including those that tackle the challenging task of optimizing the morphology and the controllerController at the same time. Finally, we discuss the reality gapReality gap problem that consists in a potential mismatch between the quality of solutions found in simulations and their quality observed in reality.
Evolutionary Approaches to Explainable Machine Learning

Machine learning models are increasingly being used in critical sectors, but their black-box nature has raised concerns about accountability and trust. The field of explainable artificial intelligence (XAI) or explainable machine learning (XML) has emerged in response to the need for human understanding of these models. Evolutionary computing, as a family of powerful optimization and learning tools, has significant potential to contribute to XAI/XML. In this chapter, we provide a brief introduction to XAI/XML and review various techniques in current use for explaining machine learning models. We then focus on how evolutionary computing can be used in XAI/XML, and review some approaches which incorporate EC techniques. We also discuss some open challenges in XAI/XMLChallenges in XAI and opportunities for future research in this field using EC. Our aim is to demonstrate that evolutionary computing is well-suited for addressing current problems in explainabilityExplainability and to encourage further explorationExploration of these methods to contribute to the development of more transparent, trustworthy, and accountable machine learning models.
A Study on the Application and Effectiveness of K-Star+ Innovative Teaching Method to Speaking Education for University Students

This study was conducted to verify its effectiveness by applying the K-Star+ innovative teaching method to university speaking education. The research method applied the K-Star+ method to the K University
Fundamentals of Evolutionary Machine Learning

In this opening chapter, we overview the quickly developing field of evolutionary machine learning. We first motivate the field and define how we understand evolutionary machine learning. Then we take a look at its roots, finding that it has quite a long history, going back to the 1950s. We introduce a taxonomy of the field, discuss the major branches of evolutionary machine learning, and conclude by outlining open problems.