Reviewerly/exHarmony huggingface dataset:
BlueData: AI Assisted Primary Data Collection System for Conflict Zones

Primary data collection is key to achieve successful humanitarian governance in conflict-zones. Recent years witness a surge in works undertaking such surveys of data collection both of fieldwork and academia. Despite the proliferation of online and cloud-based survey services, several challenges should be addressed in order to unlock the full potential of primary data collection in conflict zones and in the global south. This paper presents BlueData, an AI-assisted Primary data collection system. BlueData provides high-quality data collection for monitoring and evaluation for humanitarian and non-humanitarian projects. The paper presents the architecture of the BlueData system, discuss its merits, outlines its limitations and identify future research perspectives.
Predicting Student Placements Using Machine Learning Algorithms

Predicting student placements is a critical task that can significantly impact the career trajectories of graduates and the efficiency of educational institutions' placement processes. In this research work, we investigate and study the use of machine learning techniques. to predict student placements using a comprehensive dataset containing attributes such as well as factors including age, gender, stream, internships, CGPA, lodging, and prior shortages. The study commences with data collection and preprocessing, ensuring data integrity and suitability for machine learning tasks. The process of enhancing features is used to extract meaningful insights from the data, and exploratory data analysis provides valuable visualizations and descriptive statistics, revealing underlying patterns and trends. Four machine learning algorithms, namely Decision Tree, Random Forest, XGBoost, and K Nearest Neighbors (KNN) classifiers, are employed for model training. The models are evaluated using various performance indicators to evaluate their ability to forecast, such as accuracy, precision, recall, and F1-score capabilities. Results indicate that the Random Forest Classifier stands out as the most effective model, achieving the highest accuracy and F1-score among the evaluated models. It demonstrates robust predictive capabilities and accurately classifies students into placed and not-placed categories. The study underscores the significance of predicting student placements, benefiting both educational institutions and students in making informed decisions. By understanding the factors influencing placement outcomes, institutions can optimize their placement programs, providing better career opportunities for students.
A Review on Text Summarization to Develop a Live Sports Match Journalism System Using NLP

The review paper details the process of converting live speech commentary into text and subsequently generating a text summary from this transcription. Natural Language Processing (NLP) serves as the most suitable technique for this task. Sporting events attract widespread global attention, with many individuals preferring news articles over live experiences. Consequently, there's a pressing need for rapid news production following the conclusion of any sporting event. Due to constraints such as time and budget, comprehensive reporting by commentators or journalists becomes impractical. Therefore, this project holds significant promise and effectiveness. The primary aim is to automate the creation of text summaries from live sports commentary. To achieve this, a system has been devised to generate summaries from live game commentary, an area that has seen minimal focus in current research. An audio recognition system will process provided audio data, transforming it into a lengthy text format. Subsequently, a text-summarising technique will condense this transformed text into a summary.
Real-Time Posture Estimation-Based Human Fall Detection System

Special attention should be given to those in vulnerable age groups, such as children and the elderly, who live in solitary living arrangements and have an increased likelihood of experiencing falls. The primary objective of a fall detector is to minimize the duration of time an elderly person remains on the ground subsequent to experiencing a fall event. The length of time that an individual remains on the floor subsequent to a fall is a determining factor in assessing the severity of the incident. The act of engaging in prolonged deceit has been shown to elevate the likelihood of experiencing adverse health outcomes such as hypothermia, dehydration, and pressure ulcers. The ultimate objective of the detector system is to identify the occurrence of a fall and expeditiously notify a carer. The potential for individuals to inadvertently trigger a fall detector by engaging in rapid movements such as standing up and sitting down underscores the need for a dependable detector that can effectively differentiate between instances of falling and other related occurrences. The primary aim of this research is to enhance the understanding of fall-related mechanisms among medical technologists in the field of public health. The assessment of an individual's physique alteration potential relies on the calculation of the ratio between their height and width. To validate a human fall, it is necessary to measure the height and centre of the rectangle that encloses the individual, and thereafter compare these measurements to a specified threshold. An alert system has been developed to notify persons who are connected to the network in the event of a catastrophe, provided that a state of inactivity is maintained for 100 consecutive frames.
BEYOND THE WORDS: THE ART AND SCIENCE OF TEACHING READING INTERACTIVELY

This paper explores the complex landscape of interactive reading practices, with a particular focus on pre-while-post reading stages, sub-reading skills, top-down and bottom-up processing, and intensive and extensive reading strategies. Together, these components are crucial in shaping effective reading pedagogy. The article reaffirms the multifaceted nature of interactive reading, highlighting the importance of a balanced and diverse approach to education. Insights gained from previous research underscore the transformative potential of this approach, providing a pathway to foster a generation deeply passionate about the literary realm. The findings emphasize the significance of a holistic engagement with various reading strategies. An ongoing exploration of these methodologies presents a promising trajectory, not only enriching English language teaching but also nurturing a profound appreciation for the power of reading among learners. As such, the study contributes to the constant conversation on effective reading instruction, providing a foundation for future r&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0011/a.php" alt="Hit counter" /&gt;&lt;/p&gt;esearch and practice in this area.
AN INVESTIGATION INTO READING STRATEGIES USED BY VIETNAMESE NON-ENGLISH MAJORED STUDENTS: THE CASE OF A UNIVERSITY IN VIETNAM

Reading comprehension is a critical feature in English language acquisition for all students since it serves as the foundation for teaching in all aspects of language learning (Mikulecky, 2008). Comprehension improves when the reader actively employs suitable reading methods (Brown, 1980). Furthermore, reading is a skill that receives the most focus in the typical English language school (Susser &amp;amp; Rob, 1990). Reading strategies should be used by students to plan how they will read and to improve their reading comprehension (Poole, 2010). The goal of this study was to look into how university students employ English as a Foreign Language (EFL) reading methods. There were 84 students from a university in the Mekong Delta that took part. Mokhtari and Reichard’s (2002) Survey of Reading Strategies (SORS) was utilized as the instrument to collect data on the participants’ use of reading strategies. The findings revealed that EFL high school students were well aware of the usage of reading strategies. Global reading strategies were preferred by students, followed by support strategies and problem-solving strategies. The implications of employing EFL reading skills were also highlighted.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0018/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Unlocking Insights from Medical Texts: Leveraging Natural Language Processing for Information Extraction in Clinical Notes

This research delves into the intersection of scientific inquiry and knowledge extraction, focusing on the application of Natural Language Processing (NLP) techniques in the medical domain. The introduction sets the stage by emphasizing the significance and relevance of the research, articulating a well-defined research question, and outlining the paper's structure. The methodology section meticulously describes the research framework, research methods and techniques, data collection process, and data analysis approach, emphasizing transparency and rigor. The results and discussion section presents key findings from a study on the efficacy of a drug (Drug X) in reducing blood pressure compared to a placebo. Demographic data, blood pressure reduction over time, adverse events, unexpected findings, and comparisons to previous research are detailed. Implications for clinical practice, future research directions, and study limitations are also addressed. The study concludes by summarizing its key findings related to NLP's efficiency in medical information extraction and its broader implications for healthcare efficiency, medical research, and global health impact. The research objective is restated, highlighting the success of NLP techniques in extracting valuable medical information from diverse text sources. Future research areas, such as multilingual NLP, semantic understanding, ethical considerations, and clinical validation, are identified as avenues for further exploration.
Analytic hierarchy process for evaluating the quality of courier services in e-commerce services from the perspective of Pos Indonesia Purwokerto

E-commerce became one of the technological developments in Indonesia in the fourth industrial revolution. Since then, the daily needs transaction process has become accessible through the e-commerce platform. The rapid development of e-commerce is predicted to increase and thus encourage the development of courier services over the past period, making courier services an essential element that affects customer satisfaction in e-commerce services. An analysis of customer satisfaction in several e-commerce service providers towards customer reviews on Google Play conducted by Sasmita and a statement by the Ministry of Trade through a consumer complaint report in 2021 stated that complaints about problems in the e-commerce sector have increased, which includes courier services incorporated with e-commerce service providers. Gulc argued that at least seven factors determine the quality of courier service based on the customer's perspective. The AHP method assists courier services affiliated with e-commerce services in recognizing which of these seven factors of courier service quality dimensions require attention to create a sustainable competitive advantage. This research aims to measure the priority level of criteria in determining the quality of courier services on e-commerce platforms, thus providing recommendations for the appropriate service priorities for courier service companies. The calculation results using AHP on the seven factors determining the quality of courier service according to the Manager Assistant of the Service Department at the Central Purwokerto Branch of the Kantor POS Indonesia show that the responsiveness criterion is more important than the other six criteria.
A Scene Boundary Detection Approach Using Audio Features

&lt;p&gt;Scene boundary detection is essential in video intelligence, video analytics, and video summarization. Cutting long videos into short meaningful and semantic scenes helps in applying video classification tasks and extracting useful information from the video. It’s a challenging task to extract meaningful scenes from the video. Previous work involved scene detection using camera angle change detection and subtitle text timestamps. This approach extracts tiny scenes that are irrelevant in many video intelligence applications that involve extracting long scenes such as video summarization video analytics and video content moderation. Here, we proposed an efficient scene-extracting mechanism using audio features that extract long and relevant scenes by identifying changes in audio features like speech, music, background audio, human voice, animal voice, etc. The proposed approach is efficient in terms of speed and extracting well-formed scenes.&lt;/p&gt;
Building Student Module For Large Language Models

&lt;p&gt;The current large language models (LLMs) mainly lacks this capability: autonomous learning capability. Also, we can not prepare all the data/knowledge in the world for LLMs. We propose Learning In Conversation (LIC) to solve the problem to let human teach machine to refresh/expand its data/knowledge automatically by natural language conversation. LIC system is an AI system that contains many deep learning tasks. LIC system provide the natural language interface to let machine to learn new data/knowledge in conversation interacting with human automatically. It is critical, but except for the developers of the AI system, others have no natural language interface to do this education for machine. Based on large language models (LLMs) conversation ability, we train an additional intent recognition model to determine when the AI system need to learn new data/knowledge. We add a module for editing the training dataset of LLMs. We propose the methods to implement our idea and discuss the reasons why we design these methods.&lt;/p&gt;
Sentiment analysis with adaptive multi-head attention in Transformer

&lt;p&gt;We propose a novel framework based on the attention mechanism to identify the sentiment of a movie review document. Previous efforts on deep neural networks with attention mechanisms focus on encoder and decoder with fixed numbers of multi-head attention. Therefore, we need a mechanism to stop the attention process automatically if no more useful information can be read from the memory.In this paper, we propose an adaptive multi-head attention architecture (AdaptAttn) which varies the number of attention heads based on length of sentences. AdaptAttn has a data preprocessing step where each document is classified into any one of the three bins small, medium or large based on length of the sentence. The document classified as small goes through two heads in each layer, the medium group passes four heads and the large group is processed by eight heads. We examine the merit of our model on the Stanford large movie review dataset. The experimental results show that the F1 score from our model is on par with the baseline model.&lt;/p&gt;
The Application of Kinemaster as a Learning Media for Indonesian Language Class IV in Covid-19 Period

Background. The kinemaster application can help someone or can make it easier for someone to produce good videos. Online learning really needs this application because students are now learning using cellphones and like to watch YouTube, so teachers can display this video creatively with physical learning material. At this time many people change their profession to become YouTubers, because this profession is very promising according to them and can guarantee their lives. Nowadays we find many youtubers. Many people today are competing to create their content, they are willing to play around and ruin people's moods. Purpose. This application aims to facilitate teachers in making various learning media. Almost everyone uses applications to work that function as the utilization of this sophisticated technology. Method. This article The method used in this research is a qualitative method, because qualitative methods are very flexible and accurate. Results. The result of this study is that the kinemaster application is very suitable for use as a learning media in class IV because the media is interesting and able to increase students' enthusiasm for learning. Conclusion. The use of kinemaster helps teachers in solving the problem of students who are reluctant to learn, because in general students really like to learn while doing. Hopefully in the future students will be more enthusiastic in learning.
Directivity of Radiating Quantum Source Systems

&lt;p&gt;We explore essential factors pertaining to the spatial directivity of quantum radiating source systems (QSSs), encompassing quantum antennas and quantum sensors. Our primary focus is on their capacity to control the emission of photons in specific spatial directions. We present a comprehensive definition of quantum directivity, drawing inspiration from Glauber’s photon detection theory. This definition closely parallels the framework of analogous concepts in classical antenna theory. By conducting thorough conceptual and mathematical analysis, we address the challenge of characterizing the directive properties of a general QSS. Essentially, our approach presents a computational model that relies solely on the radiation field operator’s density as input.&lt;/p&gt;
Generating Synthetic Data for Training Classifiers

&lt;p&gt;The absence of labeled datasets is one of the most significant challenges in machine learning. It requires both time and assets. Generative Adversarial Networks (GANs) have been tremendously successful at generating data. Although GANs can generate data, yet in standard setups, they are unable to produce the corresponding labels and the resulting data often lacks diversity. In this research, I will use conditional GANs to generate a synthetic data set based on Fashion-MNIST and compare the classification performance of models trained on each dataset. &lt;/p&gt;
Channel Coding Method Based on Weighted Probability Model

&lt;p&gt; X is a discrete memoryless binary source , Sequence X undergoes lossless transformation to satisfy the requirement “Each 1 is separated by one or more zeros", "Each 0 is separated by one or two 1s" or similar condition , These conditions are the decision conditions for error detection in channel transmission. A new channel coding method is proposed based on a weighted probability model for lossless coding , The encoding rate and encoding and decoding steps of different conversion methods are different , It is proved that the decoding error probability can reach 0 when the code length is long enough. In the simulation experiment of BPSK signal in AWGN channel, At 0.5 bit rate, the proposed method improves 1.1dB over Polar code when FER is 0.001, and 1.4dB over Polar code when FER is 0.0001. &lt;/p&gt;
BAHAN AJAR PENGELOMPOKAN HEWAN BERDASARKAN JENIS MAKANANYA BERBASIS MULTIMEDIA DI KELAS 5 SDN MEKARMUKTI 1

Education in the 21st century begins to integrate skills, knowledge, skills and attitudes, as well as mastery of information and communication technology. Innovative learning models and teaching materials such as multimedia-based learning systems play a very important role in the development of children's education. In accordance with these needs, this study aims to design a learning media about animal grouping for students in elementary schools that is interactive as well as a display that can attract students so that it can increase students' enthusiasm for learning and understanding efficiently. Provide learning media about the introduction of animal groupings with animation and animal cartoon characters making it easier for children to learn natural sciences, especially the introduction of various types of animals. This study uses a multimedia development methodology. The multimedia method according to Luther &amp; Sutopo consists of several stages, the first stage is the concept, this stage determines the learning objectives, user identification, application concepts and devices used. The second stage is design, this stage is carried out by designing pages, storyboards and navigation structures. The third stage is collecting material, this stage collects the materials needed in making the program. The fourth stage is assembly, in that stage the application begins to be made according to the design in the second stage and combines it with the materials that have been collected in the third stage. The fifth stage is testing, at this stage testing is carried out by application makers and users. The sixth stage is distribution, the last stage the application is saved to the storage media. The application of learning media for the introduction of animal grouping is made as attractive as possible by prioritizing visual uniqueness with animation and the addition of video features. The application of learning media for the introduction of animal grouping is made with materials that are in accordance with the competencies and learning materials
ControlFace: Feature Disentangling for Controllable Face Swapping

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, result in generated results failing to achieve a high identity similarity with the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllablely transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images and controllable face swapping.
Detecting Teacher Expertise in an Immersive VR Classroom: Leveraging Fused Sensor Data with Explainable Machine Learning Models

Currently, VR technology is increasingly being used in applications to enable immersive yet controlled research settings. One such area of research is expertise assessment, where novel technological approaches to collecting process data, specifically eye tracking, in combination with explainable models, can provide insights into assessing and training novices, as well as fostering expertise development. We present a machine learning approach to predict teacher expertise by leveraging data from an off-the-shelf VR device collected in a VirATec study. By fusing eye-tracking and controller-tracking data, teachers’ recognition and handling of disruptive events in the classroom are taken into account or considered. Three classification models were compared, including SVM, Random Forest, and LightGBM, with Random Forest achieving the best ROC-AUC score of 0.768 in predicting teacher expertise. The SHAP approach to model interpretation revealed informative features (e.g., fixations on identified disruptive students) for distinguishing teacher expertise. Our study serves as a pioneering effort in assessing teacher expertise using eye tracking within an interactive virtual setting, paving the way for future research and advancements in the field.