Reviewerly/exHarmony huggingface dataset:
Forecasting the Number of Tuberculosis Patients Using Automatic Clustering And Fuzzy Logical Relationship Method

Tuberculosis is an infectious disease caused by the bacterium Mycobacterium tuberculosis bacillus, which infects the lungs and can potentially cause death. This study aims to predict the number of tuberculosis sufferers in Kampar Regency in 2022. The method used is the automatic clustering and fuzzy logical relationship method. The data analyzed is secondary data obtained from the Kampar District Health Office from 2017 to 2021. From the results of the analysis carried out using the automatic clustering and fuzzy logical relationship method, it was obtained to forecast the number of tuberculosis patients in 2022, as many as 944 people with MAPE of 0.0882%, the accuracy of forecasting results of 99.9118%, and an increase in the number of tuberculosis sufferers from 2021 to 2022 as many as 4 people.
Analisis Metode Kalman Filter, Particle Filter dan Correlation Filter Untuk Pelacakan Objek

Object tracking is a challenging in computer vision. Object tracking is divided into two, which can be one object or several objects, depending on the object being observed. The process of tracking an object in the form of one object is to estimate the target in the next sequence based on information from the first frame given. In object tracking in the form of single object tracking, there are five steps that are often used in discriminatory methods, including motion models, feature extraction, observation models, model updates and integration methods. Although various algorithms of object tracking are proposed, there are still failures in the object tracking process caused by occlusion, non-rigid target deformation, and other factors. This study proposes the implementation of the Kalman filter, particle filter, and correlation filter methods for object tracking in video data. The results of the implementation of the three methods can track objects in traffic video data and the script circuit video. In object tracking calculations and method analysis, the kalman filter gets 96.89% where the kalman method is better in terms of accuracy compared to other methods. Meanwhile, in the average performance of computation time, the correlation method gets 26.69 FPS, where the correlation method is superior compared to other competitor methods.&#x0D; Keywords – Kalman Filter; Particle Filter; Correlation Filter; Object Tracking; Object Tracking in Video
IMPROVING STUDENTS’ READING COMPREHENSION BY USING PROBLEM-BASED LEARNING METHOD AT XI MIPA 5 SMA NEGERI 1 BADEGAN PONOROGO IN ACADEMIC YEAR 2021/2022

This research aimed to explain how the Problem-Based Learning Method improved the reading comprehension of XI MIPA 5 students at the SMAN 1 Badegan and to indentify what aspects of reading learning can be improved. The researcher used classroom action research. This research had 2 cycles consisting of planning, implementation, observation, and reflection. In this research, the researcher used 3 instruments: observations, questionnaires, and tests to measure student achievement and to know student responses after implementation of Problem-Based Learning in reading comprehension. The result of the study showed that students reading comprehension was improved. This was proven by the result of test which improved from 7% in cycle 1 to 100% in cycle 2. Then, the the result of the questionnaires which improved from 78% in cycle 1 to 98% in cycle 2. Furthermore, this study was also proven by the result of observation which improved from 65% in cycle 1 to 95% in cycle 2. Based on finding data from 3 instruments, the researcher concluded that the implementation of the Problem-Based Learning method was successful to improve students’ reading comprehension at X MIPA 5 SMA Negeri 1 Badegan in academic year 2021/2022.
Application of the K-Means Algorithm for Clustering Production of Capture Fisheries in Maluku Province

Maluku Province has large natural resources with various potentials, from the ocean floor to the mainland. Capture fishery products are one of the leading sectors that contribute greatly to the GRDP of Maluku Province. The K-Means clustering algorithm is a suitable algorithm for grouping data objects that have the same identity. The purpose of this study is to cluster districts/cities in Maluku Province based on capture fishery products. The type of data in this study is secondary data sourced from the Maluku Province Central Bureau of Statistics (BPS) Publication in 2022. The result is that there are 3 districts/cities clusters in Maluku Province based on capture fishery products. Cluster 1 with the category of sufficient capture fisheries products, namely the Districts of Tanimbar Islands, Buru, East Seram, West Seram, South Buru, Southwest Maluku, Ambon City and Tual City. Furthermore, Cluster 2 with the category of many capture fishery products, namely the Aru Islands Regency and Southeast Maluku Regency. Furthermore, for Cluster 3, the category of capture fishery products is very large, namely Central Maluku Regency.
String topology in three flavors

We describe two major string topology operations, the Chas–Sullivan product and the Goresky–Hingston coproduct, from geometric and algebraic perspectives. The geometric construction uses Thom–Pontrjagin intersection theory while the algebraic construction is phrased in terms of Hochschild homology. We give computations of products and coproducts on lens spaces via geometric intersection, and deduce that the coproduct distinguishes $3$-dimensional lens spaces. Algebraically, we describe the structure these operations define together on the Tate–Hochschild complex. We use rational homotopy theory methods to sketch the equivalence between the geometric and algebraic definitions for simply-connected manifolds and real coefficients, emphasizing the role of configuration spaces. Finally, we study invariance properties of the operations, both algebraically and geometrically.
Transformer Machine Translation Model Incorporating Word Alignment Structure

In the domain of machine translation (MT) processing, end-to-end neural machine translation (NMT) has emerged as a remarkable breakthrough, surpassing the conventional statistical MT approaches. Inspired by the Internet of Things (IoT) technology, some researchers are exploring how to integrate device-to-device communication patterns into NMT to enhance translation efficiency. However, the current state-of-the-art NMT models predominantly adopt sequence-based representations for both the source language and target language sentences. The lack of natural language sentence structure attributes leads to problems such as unfaithful translation in NMT. To enhance lexical alignment in NMT, the paper proposes a new transformer MT model that incorporates vocabulary alignment structure. The model receives external lexical alignment information during each step of the decoding process in the decoder design to alleviate the problem of missing lexical alignment structures. During the decoding phase of the model, the statistical MT system plays a crucial role by supplying relevant lexical alignment information derived from the decoding information obtained from the NMT. Additionally, the model suggests vocabulary recommendations based on this lexical alignment information. The experimental results provide evidence that this approach successfully integrates the vocabulary knowledge derived from statistical MT, leading to improved translation performance.
TravellingFL: Communication Efficient Peer-to-Peer Federated Learning

Peer-to-Peer federated learning is a distributed machine learning paradigm with a primary goal of learning a well-performing global model by collaboratively learning a shared model at different data hubs without the need of sharing data. Due to its immense practical applications, there is growing attention towards various challenges of efficient federated learning including communication efficiency, assumptions on connectivity, data heterogeneity, enhanced privacy, etc. In this paper, we address the problem of dynamic network topologies in federated learning. We present a technique to help new participants in Peer-to-Peer federated learning reach best possible accuracy by leveraging learning at other devices in a communication efficient manner. We model the costs in federated learning and apply a graph theoretical framework to show that one can draw from a range of graph-based algorithms to construct an efficient communication algorithm on a connected network, thereby matching the inference efficiency of centralized federated learning. We conduct experiments with varied graph formations and sizes to validate our claims.
Explainable Connectionist-Temporal-Classification-Based Scene Text Recognition

Connectionist temporal classification (CTC) is a favored decoder in scene text recognition (STR) for its simplicity and efficiency. However, most CTC-based methods utilize one-dimensional (1D) vector sequences, usually derived from a recurrent neural network (RNN) encoder. This results in the absence of explainable 2D spatial relationship between the predicted characters and corresponding image regions, essential for model explainability. On the other hand, 2D attention-based methods enhance recognition accuracy and offer character location information via cross-attention mechanisms, linking predictions to image regions. However, these methods are more computationally intensive, compared with the 1D CTC-based methods. To achieve both low latency and model explainability via character localization using a 1D CTC decoder, we propose a marginalization-based method that processes 2D feature maps and predicts a sequence of 2D joint probability distributions over the height and class dimensions. Based on the proposed method, we newly introduce an association map that aids in character localization and model prediction explanation. This map parallels the role of a cross-attention map, as seen in computationally-intensive attention-based architectures. With the proposed method, we consider a ViT-CTC STR architecture that uses a 1D CTC decoder and a pretrained vision Transformer (ViT) as a 2D feature extractor. Our ViT-CTC models were trained on synthetic data and fine-tuned on real labeled sets. These models outperform the recent state-of-the-art (SOTA) CTC-based methods on benchmarks in terms of recognition accuracy. Compared with the baseline Transformer-decoder-based models, our ViT-CTC models offer a speed boost up to 12 times regardless of the backbone, with a maximum 3.1% reduction in total word recognition accuracy. In addition, both qualitative and quantitative assessments of character locations estimated from the association map align closely with those from the cross-attention map and ground-truth character-level bounding boxes.
A Dual-Layer Weight-Leader-Vicsek Model for Multi-AGV Path Planning in Warehouse

Multiple automatic guided vehicles are widely involved in industrial intelligence. Path planning is crucial for their successful application. However, achieving robust and efficient path planning of multiple automatic guided vehicles for real-time implementation is challenging. In this paper, we propose a two-layer strategy for multi-vehicle path planning. The approach aims to provide fast computation and operation efficiency for implementation. The start–destination matrix groups all the vehicles, generating a dynamic virtual leader for each group. In the first layer, the hybrid A* algorithm is employed for the path planning of the virtual leaders. The second layer is named leader–follower; the proposed Weight-Leader-Vicsek model is applied to navigate the vehicles following their virtual leaders. The proposed method can reduce computational load and achieve real-time navigation by quickly updating the grouped vehicles’ status. Collision and deadlock avoidance is also conducted in this model. Vehicles in different groups are treated as dynamic obstacles. We validated the method by conducted simulations through MATLAB to verify its path-planning functionality and experimented with a localization sensor.
Multinational Visitors’ Framing of Al-Qarawiyyin University: An Analysis of Google Reviews

The present study investigated the viewpoints of visitors and their experiences with al-Qarawiyyin as conveyed through Google Reviews. The study aimed to determine the primary aspects that underlie the visitors’ experiences at al-Qarawiyyin and to examine these experiences through sentiment. This research examined both quantitative ratings and qualitative comments. The reviews were obtained from Google by utilizing the Google Review Downloader tool. The reviews underwent content analysis in order to categorize them into distinct aspects or categories. Subsequently, a sentiment analysis was performed to ascertain positive, negative, and neutral viewpoints about the institution. The results unveiled seven key aspects pertaining to visitor experiences: access, education, facilities, general aspects, location, national and international heritage, and serenity. The categories of national and international heritage, as well as facilities, were given greater emphasis, while the category of location received comparatively less attention. Based on the examination of star ratings and sentiment, it can be inferred that the majority of visitors were satisfied with their experience at al-Qarawiyyin. Nevertheless, the aspect that garnered the bulk of one-two-star ratings and negative reviews was the access category. The results of this study carry significant implications for scholarly investigations in the fields of higher education and tourism.
Analysis of S-shaped, U-shaped and V-shaped transfer functions in IoMT datasets using binary Aquila optimisation techniques

The Aquila optimisation (AO), a metaheuristic approach, is motivated by the Aquila's natural behaviour when collecting prey. However, while the method shines at several benchmark functions, it fails to solve the binary optimisation problem. We suggested a binary version of AO (BAO) for feature selection (FS) concerns in classification tasks in this research using internet of medical things (IoMT) datasets. We used 12 (S, U, and V-shaped) transfer functions (TF) to transform continuous data into binary values. The proposed TFs demonstrate that BAO techniques, particularly S2-BAO, outperform alternative transfer functions. According to the results, the suggested approach, as compared to traditional transfer functions, converges to the global minimum in multiple rounds based on the selection of optimal attributes, fitness values, and improved classification accuracy.
Determinants of entrepreneurial innovation: evidence from Kuwait

Innovation score of a country is imperative to the entrepreneurial culture of any country which subsequently affects the local economy. A high-income country like Kuwait is expected to be high on this rationale but literature suggests otherwise. This paper is based on the 14 pillars of global entrepreneurship index (GEI) to investigate the determinants of entrepreneurial innovation in Kuwait and analysis the favourable and unfavourable factors. Exploratory factor analysis and confirmatory factor analysis were used to investigate the causal relationships amongst the 14 pillars of entrepreneurship. We found that the government inefficiency is the main reason for the lack of entrepreneurship stimulus in the country. We found three explanatory latent variables: government efficiency, entrepreneurial risk, and skills/qualification which affect entrepreneur's innovation. Entrepreneur's qualification was observed with a direct link to innovation with significant direct and total influence.
One-Shot Simple Pattern Detection without Pre-Training and Gradient-Based Strategy

One-shot object detection has been a highly demanded yet challenging task since the early age of convolutional neural networks (CNNs). For some newly started projects, a handy network that can learn the target’s pattern using a single picture and automatically decide its architecture is needed. To specifically address a scenario in which a single or multiple targets are standing in relatively stable circumstances with hardly any training data, where the rough location of the target is required, we propose a one-shot simple target detection model that focuses on two main tasks: (1) deciding if the target is in the testing image, and (2) if yes, outputting the target’s location in the image. This model requires no pre-training and decides its architecture automatically; therefore, it could be applied to a newly started target detection project with unconventionally simple targets and few training examples. We also propose an architecture with a non-training parameter-gaining strategy and correlation coefficient-based feedforward and activation functions, as well as easy interpretability, which might provide a perspective on studies in neural networks. We tested this design on the data we collected in our project, the Brown–Yosemite dataset and part of the Mnist dataset. It successfully returned the target area in our project and obtained an IOU of up to 87.04%, reached 80.28% accuracy on the Brown–Yosemite dataset with disposable networks, and obtained an accuracy of up to 89.4% on part of the Mnist dataset in the detection task.
Cross-Project Fault Prediction using Artificial Intelligence

Software defect prediction project focuses on finding errors or flaws in software and aiming to improve accuracy which gives evolution batch with detectable results while adding to modern outcomes and advancement liability foretelling defective code regions can assist initiators with recognizing bugs and arrange their test activities. The percentage of groups providing the legitimate foretelling is fundamental for early identification.
Graph Alignment Neural Network Model With Graph to Sequence Learning

Network alignment aims at detecting the corresponding entities across multiple networks, which is an essential basis for the fusion and analysis of multiple network information. Moreover, embedding-based network alignment has gradually become one of the promising methods. However, existing methods ignore the confusing selection problem caused by the similarity-orientated principle of network embedding and over-dependence on the hypothesis of structural consistency. In this paper, we propose an end-to-end Graph Alignment Neural Network (GANN) model with graph-to-sequence learning. GANN mainly consists of two modules: Graph encoder and Sequence decoder. In graph encoder module, we present a restricted network embedding method, which can not only capture the local structure and attribute information of nodes but also realize the constraint of node embedding and space reconciliation. In sequence decoder module, we propose a graph-to-sequence learning model to address large graphs' structural consistency hypothesis problem. In this model, an attention-based LSTM mechanism is introduced to infer a node in the source network corresponding to the candidate node sequence in target networks. In this candidate sequence, the correct aligned node is placed at the top. We demonstrate that GANN outperforms the state-of-the-art methods in network alignment tasks on various real-world datasets.
Prognostic Analysis of Machine Learning Techniques for Breast Cancer Detection

Later lung cancer, breast cancer is the casual nosy cancer and it is the second dominant root of cancer demise in women. Cancer is when the mutations that occurs in genes regulate cell growth and mutations multiply and divide the cells in an undisciplined way. There are five stages of breast cancer. In each stage, the size of the tumor varies. Alcohol consumption, body weight, history of breast cancer, age, genetics, hormone treatments, etc. are the reasons for breast cancer. Two categories of breast cancer are Lobular and Ductal. Ultrasound, MRI, Mammogram are the several diagnosis methods. By employing Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), decision tree (DT), Random forest (RFA), Naïve Bayes ī (NB), gradient boosting (GB), Logistic regression (LR) and Support Vector Machine (SVM) breast cancer can be predicted. The model gives best results when the principal features are selected.
The RECS–A Successful Pandemic Learning Experiment

This piece explores the success of the Regional Enrichment Centers (RECs) created during the COVID-19 pandemic.
Pengaruh Penggunaan Media Roda Putar terhadap Hasil Belajar IPA Siswa Kelas V SDN Banyubiru 01

The background to this research is that the low science learning outcomes of class V students at SDN Banyubiru 01 are due to the teacher still using the lecture method so that students only compete with the teacher and students feel bored in the learning process. By using a rotary wheel media, students will have a high curiosity in the learning process. This study uses quantitative research. The data collection technique used is in the form of a test. The results of the study show that there is an effect of the use of rotary wheel media on science learning outcomes. This can be proven by the acquisition of hypotensive test results with the t test formula, namely with the calculated t value greater than t table (5.10504&gt; 2.042) then Ha is accepted and Ho is rejected.Keywords: rotating wheel media, science learning outcomes.
TOWARDS APPLYING NATURAL LANGUAGE APPROACH IN IMPROVING STUDENTS' WRITING SKILLS: LECTURER FEEDBACK TOOLS (LEFT)

Automatic text summarisation involves creating a concise and coherent description while preserving essential information and context. Time constraints faced by educators prompted this study. Writing is a crucial skill for students, especially in English; however, many need more feedback on their mistakes. Writing, akin to scaffolding, necessitates the involvement of proficient essay writers to assess and review students' work. Scaffolding facilitates student engagement and improves learning outcomes, making it an effective instructional strategy. To address this issue, Lecturer Feedback Tools (LeFT), a Natural Language Processing web application system, is introduced to educators to provide feedback to enhance students' writing skills. It utilises four summarisations SpaCy, Gensim, Natural Language Toolkit, and Sumy. What sets this tool apart is its ability to record all operations in a database, ultimately boosting educators' productivity and students' work. The evaluation results show a consensus among participants, with 66.7% expressing the system's usefulness and 83.3% finding it easy to use, reflecting their satisfaction with the study's outcomes.