Reviewerly/exHarmony huggingface dataset:
Variation Language Content Analysis Book (A Need Analysis Study)

&lt;div class="WordSection1"&gt;&lt;p&gt;This research aims to analyse whether the “Contrastive Linguistic and Error Analysis Book” is considered relevant to the SDGs criteria. The success of the English teaching learning process will also be encouraged by the application of instructional material. Most language programs rely heavily on teaching materials. Created material is one of the materials in a university. In this research, the writers used qualitative descriptive method. Then, the students who learned Contrastive Linguistics and Error Analysis (CLEA) and the lecturers who have taught CLEA were chosen to be the participants. The instruments used in this research were questionnaires both from student’s perspective and lecturer’s perspective. Student’s questionnaire consisted of 20 questions, and lecturer’s questionnaire consisted of 12 questions. The data collection was taken from 55 students and 8 lecturers who were given the questionnaire. Based on the results and discussion, it shows that dimension of “suit environment” from the lecturer’s perception, that the text in CLEA book is no longer suitable for this global era. Considering with Sustainable Development Goals, the CLEA textbook syllabus needs a revisiting to insert the SDGs theme in its reading text that is related with the 17 goals, such as: quality education, no poverty, zero hunger, gender equality, climate action, and many more. Since syllabus is guidance in achieving the learning objectives, it needs revisiting gradually to pursue the developing situation. The revisiting syllabus should consider both student and teacher as the practitioners of teaching and learning process. Hopefully by inserting the SDGs theme in the CLEA syllabus, student and teacher can be engaged together in creating a better life in the community.&lt;/p&gt;&lt;/div&gt;
Development of DelTalk (an English learning application) using Agile Method

English has become a world language that must be mastered by many people because English has dominated the era of communication to connect and transfer knowledge to the entire world. When it comes to the development of mobile applications for language learning, previous research has tended to emphasize testing on various technologies such as multimedia, virtual and augmented reality, conversational agents and artificial intelligence-based systems. This research, therefore, aimed to develop a mobile-based English learning application that provided English learning in the form of stories called the Deltalk. This mobile-based English learning application provides features in which the users can practice their speaking in English. This app was developed by integrating ASR (Automatic Speech Recognition) technology provided by NOVO Learning. The ASR was integrated with Deltalk through the API (Application Programming Interface) and Websocket. Deltalk development adapted two agile frameworks, including Scrum and Lean Software Development by performing MVP process one time.
Mathematics Preservice Teachers’ Perceptions Regarding ICT Use in Teaching and Learning Practices

Background : Since we have been living in the digital age, the ways of interacting with information, data and other human beings have changed. Being so, teachers need to understand their new role in pedagogical environments, changing their perspectives, and, most of all, transforming their knowledge to be able to teach with digital Information and Communication Technologies (ICT). Concerns about this new kind of teacher knowledge led to discussions that culminated, in the beginning of the 21st century, with the development of the Technological Pedagogical Content Knowledge (TPACK) framework. Objectives : Knowing the importance of preparing teachers to work with ICT to enrich their pedagogical practices, we propose this qualitative study that aims to analyse perceptions of a group of preservice teachers about the use of ICT in mathematics teaching and learning practices. Design : This study has a qualitative approach and it can be understood as a case study. Setting and Participants : The participants are 10 preservice teachers from a Portuguese University. Data collection and analysis : Data were collected using a survey and the responses were analysed through Discursive Textual Analysis. Results : We understand that most participants have a well-developed TPACK and they can see several educational benefits from using ICT to teach and learn mathematics. Conclusions : The results of this study highlight the valuable activity of this preservice teachers’ course, which is committed with the goal of helping them to develop their own TPACK.
Achieving Goals Using Reward Shaping and Curriculum Learning

Real-time control for robotics is a popular research area in the reinforcement learning community. Through the use of techniques such as reward shaping, researchers have managed to train online agents across a multitude of domains. Despite these advances, solving goal-oriented tasks still requires complex architectural changes or hard constraints to be placed on the problem. In this article, we solve the problem of stacking multiple cubes by combining curriculum learning, reward shaping, and a high number of efficiently parallelized environments. We introduce two curriculum learning settings that allow us to separate the complex task into sequential sub-goals, hence enabling the learning of a problem that may otherwise be too difficult. We focus on discussing the challenges encountered while implementing them in a goal-conditioned environment. Finally, we extend the best configuration identified on a higher complexity environment with differently shaped objects.
The Holocaust Archival Material Knowledge Graph

Abstract Research into the Holocaust faces particular challenges due to the diversity and dispersal of its sources. The EHRI Portal, one of the main outputs of the EHRI project, is a platform for contextualising and integrating metadata about Holocaust-related archival material. In this work we undertake to deliver the EHRI Portal’s archival metadata as Linked Open Data in order to explore the benefits that this model can provide to the field in terms of decentralised data access and integration with the wider Semantic Web. We describe the process of transforming the existing data to a Knowledge Grah aligned with the new ICA conceptual model, Records in Contexts (RiC). As part of this process we describe the challenges and limitations of this alignment, along with future developments that could result in a better fit with our use-case. We envision this work as the first step in delivering Holocaust data to the Semantic Web, allowing partner institutions to evaluate its capabilities and potentially adopt it for their own solutions, making the field more interconnected.
Spatial Link Prediction with Spatial and Semantic Embeddings

Abstract Semantic geospatial applications, such as geographic question answering, have benefited from knowledge graphs incorporating information regarding geographic entities and their relations. However, one of the most critical limitations of geographic knowledge graphs is the lack of semantic relations between geographic entities. The most extensive knowledge graphs specifically tailored to geographic entities are extracted from unstructured sources, with these graphs often relying on datatype properties to describe the entities, resulting in a flat representation that lacks entity relationships. Therefore, predicting links between geographic entities is essential for advancing semantic geospatial applications. Existing neural link prediction methods for knowledge graphs typically rely on pre-existing entity relations, making them unsuitable for scenarios where such information is absent. In this paper, we tackle the challenge of predicting spatial links in sparsely interlinked knowledge graphs by introducing two novel approaches: supervised spatial link prediction (SSLP) and unsupervised inductive spatial link prediction (USLP). These approaches leverage the wealth of literal values in geographic knowledge graphs through spatial and semantic embeddings. To assess the effectiveness of our proposed methods, we conduct evaluations on the WorldKG geographic knowledge graph, which incorporates geospatial data extracted from OpenStreetMap. Our results demonstrate that the SSLP and USLP approaches substantially outperform state-of-the-art link prediction methods.
An Evaluation Method of English Composition Automatic Grading Based on Genetic Optimization Algorithm and CNN Model

In response to the problems of traditional genetic algorithms in evaluating English compositions, the stability of automatic grading of English compositions has been further enhanced. This article evaluates the teaching effectiveness of automatic grading of English compositions using an optimization fusion algorithm combined with genetic optimization algorithm and CNN model. By analyzing genetic content and optimization algorithms, a corresponding fusion optimization model was obtained, and the automatic evaluation of English compositions was analyzed and predicted through experimental verification. The results indicate that the curves corresponding to different parameters exhibit typical segmentation features through the variation curves of individual numbers under different scale factors. And through quantitative description and analysis of the curve, it can be seen that the change in proportion factor has an absolute advantage in the impact of genetic algorithm on the number of children. As the number of samples increases, the performance of genetic optimization algorithms under the f function shows an upward trend. Research has shown that the writing content index has the greatest impact on English writing, while the corresponding grammar errors have the smallest impact on English writing. Finally, the accuracy of the optimized model was verified by comparing the model curve with experimental data. This study provides theoretical support for the use of genetic optimization algorithms and CNN models in English, and provides ideas for the use of optimization algorithms in other fields.
Keyphrase Distance Analysis Technique from News Articles as a Feature for Keyphrase Extraction: An Unsupervised Approach

Due to the rapid expansion of information and online sources, automatic keyphrase extraction remains an important and challenging problem in the field of current study. The use of keyphrases is extremely beneficial for many tasks, including information retrieval (IR) systems and natural language processing (NLP). It is essential to extract the features of those keyphrases for extracting the most significant keyphrases as well as summarizing the texts to the highest standard. In order to analyze the distance between keyphrases in news articles as a feature of keyphrases, this research proposed a region-based unsupervised keyphrase distance analysis (KDA) technique. The proposed method is broken down into eight steps: gathering data, data preprocessing, data processing, searching keyphrases, distance calculation, averaging distance, curve plotting, and lastly, the curve fitting technique. The proposed approach begins by gathering two distinct datasets containing the news items, which are then used in the data preprocessing step, which makes use of a few preprocessing techniques. This preprocessed data is then employed in the data processing phase, where it is routed to the keyphrase searching, distance computation, and distance averaging phases. Finally, the curve fitting method is used after applying a curve plotting analysis. These two benchmark datasets are then used to evaluate and test the performance of the proposed approach. The proposed approach is then contrasted with different approaches to show how effective, advantageous, and significant it is. The results of the evaluation also proved that the proposed technique considerably improved the efficiency of keyphrase extraction techniques. It produces an F1-score value of 96.91% whereas its present keyphrases are 94.55%.
A Vision-based Human Posture Detection Approach for Smart Home Applications

Effective posture identification in smart home applications is a challenging topic for people to tackle in order to decrease the occurrence of improper postures. Vision-based posture identification has been used to construct a system for identifying people's postures. However, the system complexity, low accuracy rate, and slow identification speed of existing vision-based systems make them unsuitable for smart home applications. The goal of this project is to address these issues by creating a vision-based posture recognition system that can recognize human position and be used in smart home applications. The suggested method involves training and testing a You Only Look Once (YOLO) network to identify the postures. This Yolo-based approach is based on YOLOv5, which provides a high accuracy rate and satisfied speed in posture detection. Experimental results show the effectiveness of the developed system for posture recognition on smart home applications.
Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

Dynamic and flexible computing resources are offered by cloud computing (CC), which has gained popularity as a computing technology. Efficient task scheduling (TS) plays a critical role in CC by optimizing the distribution of tasks across available resources to achieve maximum performance. The allocation of computational tasks in a cloud environment is a complicated process that is affected by multiple factors, such as available network bandwidth, make span, and cost considerations. Therefore, it is crucial to optimize available bandwidth for efficient TS in CC. In the present research, a novel pelican-based approach is introduced to optimize TS in the CC environment. The newly developed method also utilizes a security approach called Polymorphic Advanced Encryption Standard (P-AES) to encode cloud information during scheduling. The study evaluates the proposed algorithm’s performance in terms of the make span, resource utilization, cost, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using the Python tool, and it effectively handles a wide range of tasks from 1000 to 5000. The proposed algorithm offers a new perspective on utilizing pelican algorithms to optimize task scheduling in CC. The hybrid optimization enables the proposed algorithm to provide efficient task scheduling by exploiting the strengths of entire algorithms. The proposed approach offers an innovative solution to the challenges of scheduling tasks in cloud environments and provides a more effective and secure way of optimizing cloud services. Overall, this study provides valuable insights into task scheduling optimization in CC and offers an effective approach for enhancing the performance of CC services.
PENGARUH KOMIK DIGITAL DALAM MENINGKATKAN PENGUASAAN KONSEP DAN DAYA BERPIKIR KRITIS SISWA SEKOLAH DASAR

This early learning media is really needed to support the achievement of learning goals by students at school. Learning media can allegedly be a forum for communication and equalization of perceptions of material between teachers and students. When Covid-19 occurred, teachers were required to develop innovations in learning, both learning models and learning media. Especially in learning media, during Covid-19, teachers were encouraged to be creative and innovate in making digital-based learning media. This is because digital learning media can make it easier for students at home to access it. Recently, the digital media that has been popularly used, especially during the pandemic, is digital-based comic visual learning media. Digital comics are a novelty from conventional comic forms which are usually found in the form of books or sheets of paper. Digital comics are designed with an attractive appearance and are equipped with relevant learning material content so that they are believed to be able to facilitate students' mastery of the material as best as possible, even though the learning is carried out virtually outside of school. The focus of the research was to determine the effect of using digital comics in an effort to improve the quality of learning outcomes in the form of mastery of a concept and critical thinking skills of phase C class V students in integrated learning of human digestion science and science. The research adopted an experimental research method by implementing a One-Group Pretest-Posttest design. The pretest and posttest were carried out with the aim of finding out whether there was an influence, even the size of the influence, of using comics in communicating the material being taught. The results of the research found that there was an influence of the use of digital comics in increasing the mastery of concepts and critical thinking skills of class V students using human digestion as material even though learning was carried out face-to-face in class
Impelementasi Metode MOORA Pada Sistem Pendukung Keputusan Pemilihan Pegawai Teladan

Employees are a human resource asset for a company in carrying out the operational activities of an organization or company in achieving the main goals of the organization or company. The purpose of this study is to select exemplary employees in the company using the MOORA method, so that the company will quickly evaluate exemplary employees once every 6 months. The results of the ranking of exemplary employees who get rank 1 are exemplary employees, namely Zaenal Yuswantoro with a total score of 0.179431073, for rank 2 exemplary employees, namely Yolanda Prasasti with a total score of 0.175703146, and 3rd rank exemplary employees, namely Ahmad Budianto with a total value of 0.174341166. The results of manual calculations and by using a web application based on the Laravel framework show no difference in the results of the grades and ratings of the employees being assessed
Conversation Diary in EFL Class: A Perspective, Problem and Solution

Conversation needs two people or more to be conducted. In doing conversation, diary is needed to help students to speak during conversation. Therefore, this study aimed to know the students’ perception on conversation diary, problems and solution. A mixed method was employed to achieve the research objectives. Thirty five students of English Education Study Program were the sample of this resarch. The instruments used were questionnaire and interview. The data were quantitatively and qualitatively analyzed by the researcher. The quantitative data were analyzed by using descriptive statistics with calculating mean scores and percentage, while the qualitative one used several stages namely reduction, displaying, and concluding the data. The results showed that majority students had a positive perspective on using diary in conversation class because based on the questionnaire, many students opted strongly agree and agree to all items. However, students faced several barriers namely speaking and psychologycal factors. In speaking skill, several students were lack of vocabulary, pronunciation, and grammar, while the psychological factors covered anxiety, self confidence and motivation. Another finding displayed that improving motivation and joining public speaking can be alternative ways to deal with those barriers. In conclusion, conversation diary had benefits for students to support them in speaking.
Revisiting TENT for Test-Time Adaption Semantic Segmentation and Classification Head Adjustment

Test-time adaption is very effective at solving the domain shift problem where the training data and testing data are sampled from different domains. However, most test-time adaption methods made their success on classification tasks while object detection and segmentation tasks usually have more applications in the real world. Meanwhile, methods that update the model at test-time which is a main branch in test-time adaption (e.g., TENT [1], a typical method of this branch) only update the backbone, and the classification head remains unchanged. Though the classification head trained by the training data behaves well on the source domain, it is not guaranteed to be effective for a new domain and a new backbone. In our work, we re-weight the entropy of pixels in an image and adopt SAR [2] to overcome the instability in online adaption. Experiment results show that the segmentation method in TENT becomes more efficient and stable thanks to these improvements. For the classification task, we propose to use T3A [3] to update the backbone and finetune the classification head in the meantime based on TENT, which boosts the classification accuracy by a large margin.
Why and how you matter: learning development as everyday leadership

For the learning developers, operating within the third space in higher education (between academic-faculty positions, and professional/learning services and administration) brings its rewards through supporting students and staff to succeed. However, well-documented challenges, such as uncertainty around own professional identity; own place in the institutional culture and infrastructure; entitlement to CPD; and career progression trajectory (Silvey, Pejcinovic and Snowball, 2018; Obexer, 2020; Clark, 2021). Learning Developers who often are employed as both academics and professional services may find themselves grappling with finding ways to capture and evidence impact of their activity on their students’ academic and broader experience and proving their worth to their institution and the sector on the whole (Riggio, 2020). Our institutions may feel like worlds of academic standards and managerial constraints, neither of which helps reinforce the feeling of empowerment and belonging among learning developers.&#x0D; Everyday leadership, as a position and behaviour of choice, can help navigate these challenges, at individual, institutional and LD community levels. Everyday leadership has links with a stronger sense of empowerment, self-fulfilment and belonging – to the collective LD community, and to the institution (Drury, Evripidou, and Van Zomeren, 2015). Everyday leadership implies influence, and therefore, impact. In this session, we applied our thinking from Cashman (2017) and Whitelaw (2020) and define leadership as extending your energy into “things that matter” (Whitelaw, 2020. NP), to create value for others. Everyday leaders are all individuals, notwithstanding of their status, who “influence others to achieve shared objectives for the good of the collective” (Riggio, 2020, NP). Our participants also explored, and reframed their activity from the angle of everyday leadership – such as influencing peers and students, mentoring peers, initiating or leading a project, speaking up, acting as an active bystander. Finally we referred to broad themes from the ALDinHE Leadership Community of Practice. Participants shared practice and came up with a range of everyday leadership ‘expressions’ which are meaningful to them, within the context of their value and purpose; empowerment and belonging; influence and impact. Participants were encouraged to commit to one new everyday leadership action/ behaviour.