Reviewerly/exHarmony huggingface dataset:
Research on the construction of intelligent elderly care community by Internet technology

Smart elderly care services have become a key factor in the development of today's smart cities and play an important role in promoting the transformation and upgrading of the service industry. In the Internet era, the intelligent pension system has become a comprehensive solution, which organically combines computer networks, modern communications, intelligent management and service technologies. The development of intelligent technology is inseparable from advanced equipment and network structures. Through the establishment of intelligent resource management center and information management center, it provides efficient and safe elderly care services for community residents. Through the Internet of Things and mobile Internet technology, intelligent elderly care services can provide a more convenient and efficient service experience for the elderly in the community. Internet intelligent community elderly care shall follow the unified standards of the government, and establish a personal information database of the elderly under the guidance of the community elderly care management body. By establishing a smart elderly care service subsystem, suppliers and customers are included in it, and the supervision and evaluation of elderly care institutions are realized to ensure the efficiency and quality of elderly care services.
HPAR: deep neural network-based approach for human pose-activity recognition

Human pose activity recognition (HPAR) offers a wide range of applications due to the widespread use of collection devices such as smartphones and video cameras, as well as its capacity to gather human activity data. Electronic devices and applications continue to evolve, and breakthroughs in artificial intelligence (AI) have transformed the capacity to extract deeply buried information for accurate recognition and interpretation. We propose a systematic design for integrating conventional networks and constraints into the attention framework for learning long-range dependencies, thereby achieving end-to-end pose estimation with flexibility and scalability. The proposed method modifies the temporal receptive field using a multi-scale structure of dilated convolutions and can be adapted to a causal model for real-time performance. Our approach achieves state-of-the-art performance on the task of three-dimensional HPAR and outperforms previous methods while maintaining a lower complexity cost.
Assessing linguistic generalisation in language models: a dataset for Brazilian Portuguese

Much recent effort has been devoted to creating large-scale language models. Nowadays, the most prominent approaches are based on deep neural networks, such as BERT. However, they lack transparency and interpretability, and are often seen as black boxes. This affects not only their applicability in downstream tasks but also the comparability of different architectures or even of the same model trained using different corpora or hyperparameters. In this paper, we propose a set of intrinsic evaluation tasks that inspect the linguistic information encoded in models developed for Brazilian Portuguese. These tasks are designed to evaluate how different language models generalise information related to grammatical structures and multiword expressions (MWEs), thus allowing for an assessment of whether the model has learned different linguistic phenomena. The dataset that was developed for these tasks is composed of a series of sentences with a single masked word and a cue phrase that helps in narrowing down the context. This dataset is divided into MWEs and grammatical structures, and the latter is subdivided into 6 tasks: impersonal verbs, subject agreement, verb agreement, nominal agreement, passive and connectors. The subset for MWEs was used to test BERTimbau Large, BERTimbau Base and mBERT. For the grammatical structures, we used only BERTimbau Large, because it yielded the best results in the MWE task. In both cases, we evaluated the results considering the best candidates and the top ten candidates. The evaluation was done both automatically (for MWEs) and manually (for grammatical structures). The results obtained for MWEs show that BERTimbau Large surpassed both the other models in predicting the correct masked element. However, the average accuracy of the best model was only 52% when only the best candidates were considered for each sentence, going up to 66% when the top ten candidates were taken into account. As for the grammatical tasks, the results presented better prediction, but also varied depending on the type of morphosyntactic agreement. On the one hand, cases such as connectors and impersonal verbs, which do not require any agreement in the produced candidates, had precision of 100% and 98.78% among the best candidates. On the other hand, tasks that require morphosyntactic agreement had results consistently below 90% overall precision, with the lowest scores being reported for nominal agreement and verb agreement, both having scores below 80% in overall precision among the best candidates. Therefore, we identified that a critical and widely adopted resource for Brazilian Portuguese NLP presents issues concerning MWE vocabulary and morphosyntactic agreement, even if it is prolific in most cases. These models are a core component in many NLP systems, and our findings demonstrate the need of additional improvements in these models and the importance of widely evaluating computational representations of language.
PEDAGOGICAL MEDIATION OF CHEMISTRY TEACHERS: CONTRIBUTIONS TO TEACHER AND STUDENT TRAINING

Investigations ofteachers' pedagogical mediation can help to understand their conceptions of mediation and practices, enabling ways to improve teaching/learning in Chemistry.This investigation intended to know the conceptions of high school and higher education chemistry teachers in Iturama-MG about mediation and their teaching practices.The starting point was the study of the different meanings that the word mediation assumes in literature and in teachers' daily lives.The qualitative research used electronic questionnaires sent to public school teachers: data were transcribed, categorized and analyzed in order to know the teachers' conceptions of mediation and their practices.The mediation of students' learning carried out by the teacher is implicit in their professional skills, so we sought to contribute to the theoretical and practical advances on the importance of mediation and how teaching processes can be improved to improve learning in Chemistry.
Introduction to Generative AI

When Dave Rogenmoser started his first business, he hired a programmer to develop an application. The cost came to about $10,000.
Means of Controlling a Stepper Motor and Measuring Forces During the Study of the Folding Process of Sheet Material

The improvement of the equipment for the forming of the volumetric shape of packages and containers requires a detailed study of the phenomena in the folding process of sheet material with a complex structure (corrugated cardboard). A stand has been created to study process parameters, successful research on which should be provided by programmed control of tool movement and synchronous transfer of measured data to a computer for further processing. Moving tools (punches) are driven by a stepper motor. The movement of the stepper motor shaft is programmed to obtain both a uniform movement and movement according to a certain law, which can be different for the working and reverse moves. Forces are measured by a strain gauge clamp, the signal from which is also transmitted to external devices for visualization and processing. The basis of the motor control and measurement unit is the Arduino Uno microcontroller, which, together with the Arduino Nano controller and the analog-to-digital converter, significantly expands the functionality of the stand. The diagram and operation of the created control and measurement unit is described. A special program has been created to study the geometric, kinematic and power parameters of the process, the possibility of quickly changing the law of motion, providing various ratios and laws of motion to the working and reverse motion of punches. The laws of motion for the motor shaft are presented in it in the form of matrices of values, which are accessed before starting the measurement from the stand control panel. The parameters of the process of simultaneous folding of corrugated cardboard, which were investigated using a stand, are described. The developed measurement technique and means of controlling the movement of the stepper motor of the tool drive allowed to estimate the maximum effort, the nature of the change during the work cycle, to compare different types of material and folding modes. The result of determining the optimal parameters of the process of operational packaging &amp; container formation is the creation of recommendations necessary for the design of modern energy-saving equipment.
A systematic review of blockchain adoption in education institutions

There is increasing interest in blockchain technology in relation to its adoption by education institutions. Several studies discuss the adoption of blockchain technology. However, there is a need to systematically synthesise and analyse the diverse body of knowledge to understand the influence of blockchain adoption in education institutions at different levels. Thus, this study applies a systematic literature review (SLR) approach to produce a useful taxonomy of the existing research and organise knowledge related to the adoption of blockchain by education institutions. An analysis of 107 relevant studies revealed the prevalence of review works and conceptual frameworks for blockchain applications as well as a lack of empirical research and theoretical foundations to study blockchain adoption. Based on this, future research directions are suggested to enhance both theoretical and practical knowledge of blockchain adoption in education.
Machine learning approaches to intelligent sign language recognition and classification

Sign language is a great visual communication technique for those who have auditory or speech impairments. The deaf and dumb people have long relied on sign language recognition (SLR) to communicate and integrate into society. This research uses Indian Sign Language to identify elementary sign-language gestures in images/videos and compares machine language methods. Images are pre-processed and feature-extracted to improve the performance of deployed models. The goal is to create a system that uses an efficient classifier to deliver reliable hand sign-language gesture recognition. For the recognition of the Indian Sign Language dataset for the Sign Language Translation and Recognition ISL-CSLTR database, the accuracy and precision of classification methods are analysed and compared. When compared to the decision tree and KNN models, the Random Forest model had a greater accuracy of 84% and 83% precision. We also got 77% Recall and a 0.7 F Score according to this study. The tool used for evaluating our work in python.
Data Storage in Google Cloud

In this chapter, we will explore Google Cloud Storage (GCS) and how to use it to store and manage files in a Java application. GCS is a cloud-based object storage service provided by Google Cloud Platform (GCP), which allows users to store and access their data from anywhere in the world. It is a highly scalable and reliable storage solution that offers features such as data encryption, data durability, and data life-cycle management.
Using principal component analysis to explore managerial factors influencing lecturers' job performance: a perspective from Nigeria

Proper use of relevant managerial factors is fundamental to organisational performance. Employing the methodology of systematic review, we placed 92 English empirical articles on this subject into perspective. 26 managerial factors were found to be relevant to determining lecturers' performance in Nigerian universities. In furtherance to that, the study sent online survey to 322 lecturers in Nigerian tertiary institutions using Google form, soliciting the respondents to rank the identified managerial factors based on how they affect their lecturing job. Then, the study used the Principal Component Analysis technique to identify the managerial factors with the highest variance. Motivation, recruitment process, adoption of strategic management practices and organisational support to employees were found to contribute 73.6% of the total variance. This indicates that these four factors were the top predictors of job performance among lecturers in Nigerian public universities. Motivation was found to explain 58% of the total variance. The study can go a long in way by providing tertiary institution managers with a guide on formulating robust human resource policies. The policies when formulated are expected to yield good results, since the input of the policies originated from those the policies affect the most.
THEORETICAL SUBSTANTIATED OF THE PEDAGOGICAL SYSTEM MODEL FOR FORMING THE PROFESSIONAL READINESS OF FUTURE TEACHERS TO USE CLOUD TECHNOLOGIES IN THE EDUCATIONAL PROCESS © Khmil N.

The article specifies the concepts of «system», «pedagogical system», «model». Based on methodical approaches (systemic, synergistic, person-oriented, activity, competence, informational and environmental) a theoretical substantiation of the pedagogical system model for forming the professional readiness of future teachers to use cloud technologies in the educational process. Its structural components are characterized as target (goal, tasks, methodological approaches, and principles), substantive (learning content, areas of work for adequate preparation of future teachers for the use of cloud technologies in the educational process), technological (forms, methods, teaching tools, phasing formation of professional readiness), subject-object (subject - teachers of higher education institutions, object - students of higher education, their interaction, subject-subject relations) and environmental (cloud-oriented educational an information environment for training future teachers to use cloud technologies in the educational process). It was determined the professional training of future teachers for using cloud technologies in the educational process has five components: social-humanitarian, psychological-pedagogical, information-technological, systematic, and practical. And a special environment should implement a relationship of four components: program-methodical, communication-control, result-corrective, and technological. The proposed model of future teacher training has been improved following the requirements of the information society and the needs of general secondary education institutions in highly qualified teachers with a formed readiness to use modern ICT in the educational process, in particular cloud-based. The author assures that under such a system, it is possible to ensure the implementation of innovative approaches to the digitalization of the educational process of secondary education institutions. On the other hand, to provide a competence-oriented process of training future teachers.
ADVERT: Defending against Reactive Attention Attacks

Following the definition in Sect. 1.2.3.2 , phishing can be a typical class of reactive attention attacks that exploit inattention to evade detection. This chapter proposes ADVERT, a human-technical solution that generates adaptive visual aids in real-time to prevent users from inadvertence and reduce their susceptibility to phishing attacks. Based on eye-tracking data, we extract visual states and attention states as system-level sufficient statistics to characterize the user’s visual behaviors and attention status. By adopting a data-driven approach and two learning feedback of different time scales, this work lays out a theoretical foundation to analyze, evaluate, and particularly modify humans’ attention processes while they vet and recognize phishing emails. We corroborate the effectiveness, efficiency, and robustness of ADVERT through a case study based on the data set collected from human subject experiments conducted at New York University. The results show that the visual aids can statistically increase the attention level and improve the accuracy of phishing recognition from 74.6% to a minimum of 86%. The meta-adaptation can further improve the accuracy to 91.5% (resp. 93.7%) in less than 3 (resp. 50) tuning stages.
Software Application of Hospital Information System in the Cloud. The Approach of Incorporation in the Hospital “Korgialenio - Benakio”

The aim of this project is to study software applica- tions of cloud Hospital Information Systems. In an effort to cover the ever-growing need for computing resources, combined with the need to achieve economies of scale, cloud computing technologies can contribute to the creation of cloud computing infra- structure. In addition, the integration of fragmented medical information and access when needed, leads to the need for upgrading and evolving Hospital Informa- tion Systems using cloud computing technology. The main concern of this project is to emerge the benefits that will arise for healthcare professionals and citizens who are the final recipients of health e-services. The existing information systems of the Hospital “Kor- gialenio – Benakio” are presented and the problems on data security, availability of electronic services and the cost of supply and maintenance of electronic infrastruc- tures are identified. Next, an approach to adopt cloud computing technology from these information systems is made through a specific operational action plan. The material of the study was the Greek and inter- national literature, as well as the data being kept in the Informatics and Organization Department of the Hospi- tal “Korgialenio – Benakio”. According to the results, cloud computing incorpo- rates technologies that promises to drastically reduce the cost of operating and maintaining hardware and soft- ware, bringing economic and business benefits to the health sector. Data security and confidentiality issues con- cerns users, who seem reluctant to fully accept these new technologies. However, cloud computing service providers are constantly trying to confront security uncertainties and challenges in an attempt to make cloud computing the most secure operation platform of information infra- structures.
In the Lab: The Use of Recovered Metal Complexes in Catalysis : <i>Johnson Matthey Technology Review</i> features laboratory research

Research in the group led by James Wilton‐Ely focuses on catalysis, imaging and sensing. All of this work involves d - and f -elements and is underpinned by the group’s wide-ranging expertise in synthesis. Collaborations with researchers in engineering, medicine and bioimaging allow these discoveries to be used to address major healthcare and sustainability challenges. Professor Wilton-Ely is the author of more than 100 publications and was the recipient of the 2021 Sir Geoffrey Wilkinson Award from the Royal Society of Chemistry for his work on metal-based carbon monoxide sensing. He has a strong interest in sustainability, having directed the MRes Green Chemistry course at Imperial College for over 12 years (2010‐2022). He has published on the use of recovered metals in catalysis (1‐5), the catalytic transformation of biomass to platform chemicals (6‐11) and magnetically-recoverable nanoscale catalysts (12, 13). Elemental sustainability is a pressing concern for all researchers using precious metals and this has led Wilton-Ely to work with colleagues to explore the potential for using gold (3) and palladium (4, 5) complexes recovered from end-of-life products in catalysis.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 45 • Number of submissions sent for review: 34 • Number of submissions accepted: 22 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 48.8% • Average number of reviews per paper: 3 • Total number of reviewers involved: 8 • Contact person for queries: Name: Dr. I Dewa Made Frendika Septanaya Email: septanaya@its.ac.id Affiliation: Institut Teknologi Sepuluh November (ITS)
Pre-service English Language Teachers’ 21st Century Skills: A Mixed-methods Study on Digital Literacy

Despite an increasing number of studies on pre-service teachers' digital literacy, there are few studies on pre-service English teachers, especially regarding samples, methodology, and including variables. Hence, this study recruits more participants from different institutions and studies the effects of 5 variables. The participants were 186 pre-service teachers. The quantitative data were collected via the Digital Literacy Scale and analyzed by Statistical Package for the Social Sciences (SPSS) 25.0 via independent samples t-test and one-way variance analysis. The qualitative data were collected via semi-structured interviews and analyzed by Descriptive Analysis. The findings showed that the participants had a medium to high level of digital literacy, while they reported higher levels in the interviews. The analysis of the survey indicated that the most used device and year of digital platform use had a significant effect on the participants’ digital literacy levels. The interview results combined with the survey implied that the vital point in digital literacy training is to ensure "learning how to exploit digital skills" rather than learning all the available tools, applications, and resources.
A Mathematical Benchmark for Inductive Theorem Provers

We present a benchmark of 29687 problems derived from the On-Line Encyclopedia of Integer Sequences (OEIS). Each problem expresses the equivalence of two syntactically different programs generating the same OEIS sequence. Such programs were conjectured by a learning-guided synthesis system using a language with looping operators. The operators implement recursion, and thus many of the proofs require induction on natural numbers. The benchmark contains problems of varying difficulty from a wide area of mathematical domains. We believe that these characteristics will make it an effective judge for the progress of inductive theorem provers in this domain for years to come.
The construction of cross-regional cooperation innovation network governance system based on data fusion technology

Abstract In recent years, with the development of network computer science and the increasing maturity of artificial intelligence technology, data fusion technology has been applied more and more widely. In order to respond to real-life needs and prevent and resolve risks, it helps to further broaden the research path of network governance. This paper focuses on network governance in the era of Internet big data, and uses an algorithm based on data fusion technology to calculate the judgment data, calculation formula and posterior estimates by computing the feature vector X and the measurement value I. After finding the maximum posterior estimate, the probability of evidence under the hypothesis is calculated using the subjective probability as the prior probability of the hypothesis. A multi-degree-of-freedom model is established for simulation research, so as to build a diversified, collaborative and intelligent innovative network social governance system across regional cooperation. The experiment proves that when the threshold value c = 0.4, the performance and efficiency are the best, and it is more able to enhance the leading position of the mainstream value and the assessment mechanism of the relevant departments, which can accelerate the guarantee of the implementation of the network ecological governance system on the ground.