Reviewerly/exHarmony huggingface dataset:
Evaluation of the Archivio Vi.Vo Architecture: A Case Study on the Reuse of Legacy Data for Linguistic Purposes

The object of this paper is the validation of the Archivio Vi.Vo. architecture, developed within the CLARIN-IT consortium for the preservation and accessible consultation of historical oral archives. Following the first case study employing the Caterina Bueno archive, the goal is to now show how this innovative architecture is also suitable for conducting research investigations on the archival data and hosting different types of archives. The real use case study presented in this contribution, aims at employing the Angela Spinelli archive for conducting a sociophonetic investigation on Tuscan vernacular.
FineTuning-based BERT for Spam Emails Identification

In this world of information explosion, people require more effective ways to filter useful information from millions of data. Email as one of the most frequently used form of communication, carries important messages, yet along with messages of fake news, misinformation and scams known as spam emails. Manually categorizing them from non-spam emails requires a lot of time and money and other human along with material resources. In order to deal with this, deep learning, or natural language processing models in particular, is introduced to categorize emails faster and cheaper. The Natural Language Processing model used here is called Bidirectional Encoder Representations from Transformers (BERT). Since BERT is already a pre-trained model, the main task is to do the Fine-Tune part on it, with a dataset that contains around 5000 emails (85% spam emails and 15% non-spam ones). After that the model is tested on a group of 5 emails including 3 commercials/spams and 2 non-spam emails. The result shows that this model could separate them by giving commercials scores closer to 1 (spread from 0.5 to 0.7) and non-spam emails scores close to 1(spread from 0 to 0.1). Therefore, it can be concluded that this model works on small sets of data.
Impact of Employee Motivation on Maintaining Firm Performance

Employees are considered as key assets in an organization to ensure the attainment of sustainable business performance and long-term growth. The role of employee motivation in maintaining prolonged firm productivity is critically evaluated in this study. Relevant secondary articles are gathered in this study as a secondary qualitative strategy and an explanatory research design is adopted in this paper. Based on the relevant articles different factors that contribute in maintaining an optimum level of employee motivation are assessed here. It is found that the development of a positive work environment by maintaining team cooperation and communication plays a significant role in encouraging the workforce. Along with that, suitable leadership inclusion is also required to enhance employee motivation. An optimized connection with the top management contributes to resolve conflicts and issues within the workforce. Employee involvement in decision-making, providing adequate rewards and remuneration along with maintaining transparency within the organizational performance results in enhancing employee morale. It also helps the associated workforce in efficiently performing complex jobs. The retention of a talented workforce is also maintained in Keywords : Communication, Communication, Decision-Making, Employee Engagement, Employee Motivation, Job Performance, Job Satisfaction, Leadership, Organizational Growth, Team Cooperation, Teamwork
Understanding the Link of Knowledge Management and Organizational Performance

Purpose: Knowledge management nowadays becoming a huge area in terms of managing in any organization that closely related to human resources, organizational development, change management, information technology, brand and reputation management, performance measurement, and evaluation. The emergence of knowledge management in light of the very dynamic workforce in the hospitality industry especially in developing states ignites the need to understand its role. Thus, this study aims to discover the relationship of knowledge management and employee commitment in the hospitality industry so organizations can devise a good HR plan most especially for committed employees.&#x0D; Design/methodology/approach: This study was done in Davao Region, Philippines with 450 employees from the various sectors of the hospitality industry using a survey method.&#x0D; Findings: Empirical findings revealed that knowledge management and organizational commitment are of high levels are described by the respondents. Further, knowledge management and organizational commitment are significantly correlated with each other which implies significant role in the developing and molding skillful and committed employees in the hospitality industry. &#x0D; Paper type: Research paper
The impact of sets education on disaster education on student mitigation skills and resilience

Purpose: The high potential threat of disasters that will occur in Indonesia requires anticipation from various sectors including education. The concept of natural disasters will be easy to understand if applied in an integrated manner between natural disasters and science, environment, technology, and society. Therefore, it is very important for today's youth that it has a long-term impact and reaches a very wide population so that it is possible to minimize the risk in the event of a disaster. This study aims to 1) analyze the characteristics, 2) analyze the validity of the SETS-based disaster education model in disaster mitigation and resilience 3) analyze the effectiveness of the SETS-based disaster education model in natural disaster mitigation and resilience.&#x0D; Design/Methodology/Approach: Participants in this study were elementary school students. The method used is development research from Borg and Gall. According to professional judgement, this study followed five of the ten procedures outlined by Borg and Gall to develop a viable product. &#x0D; Finding: The results showed that 1) the SETS-based disaster education learning model has six stages; 2) the SETS-based disaster education model in disaster mitigation and resilience is valid and feasible to use; 3) The results of the effectiveness test show that the SETS-based disaster education model is effective in increasing students' fatigue and resilience skills.&#x0D; Conclusion: This research contributes to the body of knowledge by informing the public about the importance of SETS-based disaster education for individuals to improve their mitigation and resilience skills in the event of natural disasters. Furthermore, the research findings are enhanced by the literature on SETS-based disaster education, which is currently understudied. The limitation of this study is that it was only done up till professional validation was done. Therefore, it is necessary to carry out further research with more complete and extensive stages.
Egocentric Activity Recognition Using Two-Stage Decision Fusion

Abstract The usage of wearable devices in daily life has grown rapidly with the advancements in sensor technologies. These devices primarily rely on optical sensors to capture videos from an egocentric perspective, known as First Person Vision (FPV). FPV videos possess distinct characteristics compared to third-person videos, such as significant ego-motions and frequent scene changes. Consequently, vision-based methods designed for third-person videos, where the camera is positioned away from events and actors, cannot be directly applied to egocentric videos. Thus, it is essential to propose new approaches capable of effectively analyzing egocentric videos and accurately integrating inputs from various sensors to address specific tasks. In this study, we propose an audio-visual decision fusion framework for egocentric activity recognition. Our framework leverages deep features and incorporates a two-stage decision fusion mechanism. In addition, we introduce a new publicly available dataset, Egocentric Outdoor Activity Dataset (EOAD), which comprises 30 different egocentric activities and 1392 video clips with audio. Experimental evaluations show that combining audio and visual information enhances activity recognition performance, yielding promising results compared to using a single modality or equally weighted decisions from multiple modalities.
Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension

Multi-party multi-turn dialogue comprehension brings unprecedented challenges on handling the complicated scenarios from multiple speakers and criss-crossed discourse relationship among speaker-aware utterances. Most existing methods deal with dialogue contexts as plain texts and pay insufficient attention to the crucial speaker-aware clues. In this work, we propose an enhanced speaker-aware model with masking attention and heterogeneous graph networks to comprehensively capture discourse clues from both sides of speaker property and speaker-aware relationships. With such comprehensive speaker-aware modeling, experimental results show that our speaker-aware model helps achieves state-of-the-art performance on the benchmark dataset Molweni. Case analysis shows that our model enhances the connections between utterances and their own speakers and captures the speaker-aware discourse relations, which are critical for dialogue modeling.
Literacy in EFL Classroom: In-Service English Teachers’ Perceptions and Practices from Multiliteracies Perspective

The concept of literacy has evolved significantly over the years, with the advent of new technologies and the changing needs of society. In the context of ELT, the study of literacy conceptions and practices is still under-explored. Therefore, this study aims at exploring in-service English teachers’ perceptions and practices of literacy in ELT settings from a multiliteracies perspective. This research used a qualitative case study. The data were taken from in-service English teachers from one of the state high schools in Surabaya, Indonesia through interviews, observation, and document review. The data were then analyzed using thematic analysis. The result indicated that the in-service English teachers perceived a general conception of literacy, a skill-based conception of literacy, and literacy as social practice. In general, the English teachers lacked an understanding of the concept of literacy comprehensively and they associated literacy with skills. In addition, concerning literacy practices from a multiliteracy perspective covered the integration of multimodal in the use of media, the use of technology in teaching and learning activities, and a variety of literacy instructions. Despite some difficulties, the teachers utilized multiple modes of media and technology. This study might have implications for the understanding of the conception of literacy and teaching practices in an EFL setting.
The Effectiveness of Mobile Collaborative Learning on Students’ Writing Performance by Implementing Magnet Summary Strategy

The purpose of the study was to examine the effectiveness of using Mobile Collaborative Learning (MCL) and Magnet Summary Strategy (MSS) on students’ writing performance and their perceptions towards these strategies. This study utilized a pre-test and post-test control group quasi-experimental design. The results of the paired sample t-test revealed a statistically significant difference between the average pre-test score of 45.8000, indicating a lower level of knowledge among participants before the intervention, and the average post-test score of 68.2800, indicating a substantial improvement in knowledge following the interventions. The intervention used MCL and MSS to help students summarize and organize information, which resulted in improved writing performance. moreover, the significance level of 2-tailed test was less than 5% demonstrating that the difference in the pre-test and post-test scores was statistically meaningful. Thus, it is improbable that the variance in scores happened merely by chance, indicating that the intervention had considerable impact on the participants’ knowledge. The results also showed that the implementation of MCL and MSS helped students to improve their social skills. They appreciated the opportunity to collaborate and communicate with their peers, which helped them to develop their interpersonal skills and learn from each other. Moreover, the implementation of MCL and MSS resulted in a sense of group success when working on group activities, which contributed to a positive learning experience. Overall, the study suggests that using MCL and MSS can be an effective approach to improve students’ writing performance and their social skills.
SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers since they provide visual cues of an individual's emotions for a broad range of potential applications such as lie detection and criminal detection. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Different from other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. In contrast to other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which incorporates Shifted Patch Tokenization and Locality Self-Attention into the backbone network Swin Transformer, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach also performs well in the MEGC 2021 spotting task with an overall F1-score of 0.1824 and 0.1357 on CAS(ME)^2 and SAMM Long Videos, respectively. The code is publicly available on GitHub (https://github.com/eddiehe99/pytorch-expression-spotting).
Spark Computing Framework for Pet Medical Information Management System in Pet Healthcare

In recent years, the role of big data technology in various industries has become increasingly prominent. With the rise of the pet trend, the pet medical industry has been developing rapidly. However, the current application of big data in the pet medical industry is single and elementary. This study aims to improve the current situation of big data technology in the pet medical industry and build a pet medical information management system supported by Spark computing framework, HDFS, and HBase. This study uses descriptive research method and comparative analysis method to prove that big data analysis based on Spark framework can greatly improve the efficiency of treatment and reduce the time cost. The information management system based on Spark framework can realize the rapid storage and calculation of massive data, reduce the technical threshold of data application area in pet medical industry, and help to promote the accelerated development of big data industry and pet medical industry.
Prevalence of Hypertension and Diabetes Mellitus in Patients Thrombolysed for STEMI

Objective: The objective of this study is to evaluate the frequency of hypertension and diabetes in patients, thrombolysed for STEMI and explore the relationship between these comorbidities and patient outcomes. By examining the frequency of these comorbidities in thrombolysed STEMI patients, healthcare providers can develop personalized treatment plans to optimize patient outcomes. Furthermore, these findings have the potential to contribute to future research focused on preventing or managing hypertension and diabetes in STEMI patients. Study Design: cross sectional study Study Setting: Cardiology Department, Ayub Teaching Hospital, Abbottabad, Pakistan. Duration of Study: Six months from August 1st, 2022 to 31st Jan, 2023. Material and Methods:. A total of 170 patients, aged between 18 and 80 years, were included in the sample for analysis. The study focused specifically on patients who were thrombolysed using Streptokinase. Results: In this study, male to female ratio was nearly 3:1. Among the 170 patients, 96 (56%) were found to have hypertension, while 42 (24%) were diagnosed with diabetes. Among the hypertensive patients, which accounted for 96 (56%) of the total population, 60 (62.5%) were male and 36 (37.5%) were female. Among these diabetic patients, 24 (57.1%) were male, while 18 (42.9%) were female. Conclusion: This study highlights the high frequency of hypertension and diabetes in ST-elevation myocardial infarction (STEMI) patients treated with thrombolytic therapy. These findings suggest the importance of screening STEMI patients for these comorbidities and providing appropriate follow-up care to optimize patient outcomes. Keywords: Prevalence, Hypertension, Diabetes, STEMI.
METHODS OF IMPROVING PROFESSIONAL PEDAGOGICAL TRAINING OF THE FUTURE MUSIC TEACHER

This article discusses the scientific and theoretical foundations of technologies for improving the professional pedagogical training of future music teachers in the higher education system.
Improving the Ability of Writing Explanation Text Using the Problem-Based Learning Model in Class VIII Students of Prince Diponogoro Smpit Daarul 'Ilmi Bandarlampung

The low ability to write explanatory texts for class VIII students of Prince Diponogoro, SMPIT Daarul 'Ilmi Bandarlampung is the problem raised in the research. Therefore, researchers conducted research aimed at describing 1) lesson planning, 2) learning implementation, 3) learning assessment, and 4) increasing learning to write explanatory texts using the Problem Based Learning model. This research is classroom action research whose process consists of four stages, namely planning, implementing, observing, and reflecting. Classroom action research was carried out in two cycles. Each cycle consists of two meetings. Data collection techniques are carried out through observation and learning achievement tests. The results of the study showed an increase in learning. The increase that occurred was in 1) learning planning using the Problem Based Learning model, in cycle I got a score of 73.75 in the good category while the preparation of lesson plans in cycle II obtained an assessment result of 88.75 in the good category, 2) implementation of learning using the model Problem Based Learning in cycle I obtained a score of 75.2 in the sufficient category, while the implementation of learning in cycle II obtained a value of 89.95 in the good category, 3) assessment of learning using the Problem Based Learning model in cycle I obtained an average of 75.1 in the category complete while in cycle II an average score of 83.7 was obtained in the complete category, (4) learning activities as a whole when viewed from the results of the assessment of the preparation of learning plans, implementation of learning, and learning assessments of cycle II were better than pre-cycle I. Keywords: writing ability, explanatory text, Problem Based Learning
Abstract 5361: Deep learning models capture high dimensional features for cell morphology analysis

Abstract Many methods for imaging and sorting tumor cells require biomarker labels that perturb cells and may create a selection bias. The Deepcell platform can characterize and sort cells using only brightfield images of unlabeled single cells, thereby enabling more comprehensive and unbiased assessment of tumor cell morphology and heterogeneity. Cells are imaged with a high-speed camera during microfluidic flow and brightfield images are analyzed in real-time by deep learning models to generate quantitative embeddings, which are reproducible and multi-dimensional descriptions of cell morphology. One of the key technical challenges in building this platform was the development of an AI model to extract features from cell images from diverse human cells without prior knowledge of specific cell types, cell preparation, or other application-specific knowledge for an exploratory approach. Deepcell’s ‘Human Foundation Model’ (HFM) is a feature encoder that transforms cell images into 128 dimensional embedding vectors. The model backbone, responsible for extracting image features, is based on the ResNet18 convolutional deep neural network architecture. Training utilizes a multi-task semi-supervised training framework that combines the VicReg self-supervised learning model, which learns images features without labels, along with supervised auxiliary models using labeled data. These sub-models enable the backbone model to recognize specific cell attributes such as granulation, pigmentation, characteristics of malignancy, attributes related to cellular states like apoptosis and necrosis. We augment the deep learning embeddings with computer vision derived cellular features, such as area, perimeter, intensity, and texture features to improve model interpretability and accuracy. Here, we describe how the HFM self-supervised backbone model was trained, the discriminatory power added by the supervised tasks, and validation of the reproducibility and generalization capabilities of the resulting model. We also demonstrate how the resulting embeddings can be visualized using the Deepcell Cloud software and how clusters of cells in our embedding space can be related to cell images, interpretable morphological features, and actionable biological characteristics. Potential applications of the Deepcell platform are diverse and promising, such as label-free enrichment of malignant cells and discovery of morphologically distinct subpopulations of heterogeneous cancer cell samples. Citation Format: Kevin B. Jacobs, Kiran Saini, Nianzhen Li, Senzeyu Zhang, Ryan Carelli, Amy Wong-Thai, Stephane C. Boutet, Anastasia Mavropoulos, Andreja Jovic, Jeanette Mei, Mahyar Salek, Maddison Masaeli. Deep learning models capture high dimensional features for cell morphology analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5361.
A Machine Learning Approach to Identifying Facial Masks in Real Time

Face masks became a necessity since the outbreak of COVID-19 as a way to avoid the spread, especially in crowded places, and are counted as one of the most effective methods to control the spread. However, some people are frivolous and avoid wearing a face mask. But when we are in public places or places attracting a huge crowd, monitoring such people manually or identifying them through constant surveillance is a hectic task and requires strenuous efforts, which is why real-time face mask detection is in such high demand. The main objective of this paper is to introduce real-time face mask detection techniques, which are based on real-time analysis and detect whether someone is wearing a mask or not. It is quite challenging, as we have used different types of machine learning algorithms for face detection and then for face detection with a mask. It is important to have a good dataset that has less noise and minimum errors in order to train the model and have high accuracy in our results. This is difficult because the dataset must be cleaned, sorted, and data with incorrect labels removed. The Open-CV Python library, Keras, and TensorFlow are used in this paper for better results.