Reviewerly/exHarmony huggingface dataset:
Measurement of Reading Level Index in Salatiga City

Abstract &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Background: Concerning the increase in reading interest in Salatiga City, a scientific study is needed, which can become the basis for a policy decision so that the index will increase. Purpose: To measure the level of interest in reading people in Salatiga in 2023 comprehensively and precisely. Method: Methods using quantitative. This research was conducted using sampling techniques and research instruments, which have been determined by the National Library of Indonesia and have become national standards. The location of the research was carried out in the city of Salatiga, and sampling was done by simple random technique. Respondents amounted to 399 people with criteria aged 10-69 years. Measurements were made using five key performance indicators, namely reading frequency per week, duration of reading per day, amount of reading material per quarter, frequency of internet access per week, and duration of internet access per day. Result: The study results show that the value of the reading enthusiasm of the people in Salatiga City in 2023 is in the High category (62.13). Conclusion: Measurement of reading level index in Salatiga City to improving community reading literacy. The index value for the level of fondness for reading from the results of the 2023 study has decreased from the 2022 measurement, which had an index of 64.08.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Keywords: Reading Frequency; Reading Duration; Reading Preferences; Reading Activity; Literacy Culture&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Abstrak &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Latar Belakang: Dalam kaitannya dengan peningkatan kegemaran membaca Kota Salatiga, diperlukan suatu kajian ilmiah yang dapat menjadi dasar dari suatu pengambilan kebijakan agar indeksnya semakin meningkat. Tujuan: Untuk mengukur tingkat kegemaran membaca masyarakat di Kota Salatiga tahun 2023 secara komprehensif dan tepat. Metode: Metode menggunakan kuantitatif. Penelitian ini dilakukan dengan menggunakan teknik sampling dan instrumen penelitian, yang sudah ditetapkan oleh Perpustakaan Nasional RI serta menjadi standar nasional. Lokasi penelitian dilakukan di Kota Salatiga. Pengambilan sampel dengan teknik acak sederhana. Responden berjumlah 399 orang dengan kriteria yang berusia 10-69 tahun. Pengukuran dilakukan dengan lima indikator kinerja kunci yaitu frekuensi membaca per minggu, durasi membaca per hari, jumlah bahan bacaan per triwulan, frekuensi akses internet per minggu, serta durasi akses internet per hari. Hasil: Hasil penelitian menunjukkan bahwa nilai tingkat kegemaran membaca masyarakat di Kota Salatiga tahun 2023 termasuk dalam kategori Tinggi (62,13). Kesimpulan: Pengukuran indeks tingkat gemar membaca di Kota Salatiga untuk meningkatkan literasi membaca masyarakat. Nilai indeks tingkat gemar membaca dari hasil kajian tahun 2023 ini mengalami penurunan dari pengukuran tahun 2022 yang indeksnya sebesar 64,08.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; Kata kunci: Frekuensi Membaca; Durasi Membaca; Preferensi Membaca; Aktivitas Membaca; Budaya Literasi&#x0D; &#x0D; &#x0D; &#x0D;
Safe physical interaction with cobots: a multi-modal fusion approach for health monitoring

Health monitoring is a critical aspect of personalized healthcare, enabling early detection, and intervention for various medical conditions. The emergence of cloud-based robot-assisted systems has opened new possibilities for efficient and remote health monitoring. In this paper, we present a Transformer-based Multi-modal Fusion approach for health monitoring, focusing on the effects of cognitive workload, assessment of cognitive workload in human-machine collaboration, and acceptability in human-machine interactions. Additionally, we investigate biomechanical strain measurement and evaluation, utilizing wearable devices to assess biomechanical risks in working environments. Furthermore, we study muscle fatigue assessment during collaborative tasks and propose methods for improving safe physical interaction with cobots. Our approach integrates multi-modal data, including visual, audio, and sensor- based inputs, enabling a holistic assessment of an individual's health status. The core of our method lies in leveraging the powerful Transformer model, known for its ability to capture complex relationships in sequential data. Through effective fusion and representation learning, our approach extracts meaningful features for accurate health monitoring. Experimental results on diverse datasets demonstrate the superiority of our Transformer-based multi- modal fusion approach, outperforming existing methods in capturing intricate patterns and predicting health conditions. The significance of our research lies in revolutionizing remote health monitoring, providing more accurate, and personalized healthcare services.
Enhanced multi-scale networks for semantic segmentation

Abstract Multi-scale representation provides an effective answer to the scale variation of objects and entities in semantic segmentation. The ability to capture long-range pixel dependency facilitates semantic segmentation. In addition, semantic segmentation necessitates the effective use of pixel-to-pixel similarity in the channel direction to enhance pixel areas. By reviewing the characteristics of earlier successful segmentation models, we discover a number of crucial elements that enhance segmentation model performance, including a robust encoder structure, multi-scale interactions, attention mechanisms, and a robust decoder structure. The attention mechanism of the asymmetric non-local neural network (ANNet) is merged with multi-scale pyramidal modules to accelerate model segmentation while maintaining high accuracy. However, ANNet does not account for the similarity between pixels in the feature map channel direction, making the segmentation accuracy unsatisfactory. As a result, we propose EMSNet, a straightforward convolutional network architecture for semantic segmentation that consists of Integration of enhanced regional module (IERM) and Multi-scale convolution module (MSCM). The IERM module generates weights using four or five-stage feature maps, then fuses the input features with the weights and uses more computation. The similarity of the channel direction feature graphs is also calculated using ANNet’s auxiliary loss function. The MSCM module can more accurately describe the interactions between various channels, capture the interdependencies between feature pixels, and capture the multi-scale context. Experiments prove that we perform well in tests using the benchmark dataset. On Cityscapes test data, we get 82.2% segmentation accuracy. The mIoU in the ADE20k and Pascal VOC datasets are, respectively, 45.58% and 85.46%.
Application of Natural Language Processing Techniques in Agile Software Project Management: A Survey

Software effort estimation has long been an important task for better software management. Most of the constructed effort estimation models were based on data collected from software projects that had been developed using traditional software development processes. The structure of this data is usually in the form of tabulated data. Recently, the Agile management framework invaded software production lines as an effective and productive management method. It helps software development teams to complete their tasks in a highly effective way. One of the main components of this success is predicting accurate story points from textual user stories. User stories and story points have become an essential component over which the project planning process is built. Machine learning, artificial intelligence, and deep learning are used to enhance the process of using user story context to put a close estimate of the required resources to finish the project. Using these models has become popular and remarkable in the field of effort estimation. The textual nature of user stories directed the research into the natural language processing path. Natural language processing models can be used to understand textual user story context in order to produce effort estimates. This study reviews the usage of natural language processing methodology in the context of Agile project effort estimation based on the contextual content of user stories.
SteamVis: A Tool for Collecting and Analyzing Data of the Game Distribution Platform Steam

In recent years, the gaming industry has experienced rapid growth, further bolstered by the pandemic. Today, games are being utilized beyond entertainment, finding applications in education, health, training, and various industries. Understanding trends and analyzing games and their user base is therefore crucial for game developers, stakeholders, business analysts, and researchers alike. With over 60,000 games, Steam stands out as a major game distribution platform that collects valuable data about games and their users. To simplify the analysis of current trends in the gaming world, we have developed SteamVis: an analysis and visualization tool that efficiently crawls, displays, and analyzes game-related information from Steam. In this paper, we present the tool’s details, conducted case studies, and a qualitative evaluation by domain experts. SteamVis facilitates the study of games and game-related information by providing powerful analytical and visualization tools, enabling the discovery of important trends and insights.
A Mixed Scale Triplet Network Camouflage Segmentation Algorithm Based on Attention Layered Feature Fusion

In recent years, with the rapid development of artificial intelligence technology, the field of camouflage segmentation has received widespread attention. Unlike other datasets, disguised segmentation datasets require a higher level of integration between the target and background, which poses a challenge for existing segmentation models to achieve accurate detection results. In order to solve the problem of edge blurring and effective utilization of information in camouflage segmentation, this paper proposes an enhancement algorithm based on ZoomNet network, which combines a new attention based hierarchical feature fusion network to solve the problem of edge blurring in camouflage objects. By introducing attention mechanisms, the network pays more attention to the edge regions of the target, thereby improving the accuracy of edge detection and localization. In addition, a channel unification module called Translayer is reconstructed to minimize channel differences, so that the network can emphasize the characteristics of specific targets and integrate more effective information. The experimental results on the CAMO, COD10K, and NC4K datasets indicate that the enhanced ZoomNet network has achieved varying degrees of improvement. Taking the CAMO dataset as an example, the experimental results showed an improvement of 2.2%, 3.8%, 1.4%, and 0.5% on different indicators. These results validate the effectiveness of the proposed model in the field of camouflage segmentation.
Entity Relationship Extraction for Chinese Electronic Medical Records Based on Tightly Cascaded Binary Pointers

Relationship extraction, as a subtask of information extraction, converts unstructured medical data in electronic medical records into structured data, which lays the foundation for subsequent knowledge graph construction. The existing relationship extraction ignores the potential relationships of similar entities in a single sentence and fails to make full use of the relationships among the closer entities in the text. Thus, we propose a novel tightness mechanism that increases the correlation between head and tail entities to better determine the relationship between similar entities. The experiments of the proposed model on the public dataset demonstrate that the model can extract the triples more accurately and the F1 value is improved by 2.84% over the baseline model.
A Kidney Tumor Segmentation Method Based on the Combination of U-Net and Transformer

According to the characteristics of kidney tumours in abdominal multi-organ CT images, analyzing the CNN network to deal with long-distance connection defects and the shortage of Transformer structure detail processing, we designed a multimodal residual continuous convolution CNN and an improved multiscale dual-path multi-head attention Transformer combined with an encoder, deconvolution up-sampling, and learnable attention gating mechanism of the decoder with a jump junction introduced into the multiscale convolutional fusion module to form an asymmetric U-Net model framework for kidney tumour segmentation. Experimental comparison results show that the proposed method achieves an average dice coefficient of 0.9352, an average cross-merge rate of 0.9894, and a class-average pixel accuracy of 0.98235 for kidney segmentation metrics, which are more accurate and detailed than the existing and newer network models for segmentation.
Explainable Artificial Intelligence Bundles for Algorithm Lifecycle Management in the Manufacturing Domain

Lack of understanding for machine learning models’ inner workings by business users inevitably leads to lack of trust, particularly in critical operations. Recent advancements in artificial intelligence include the development of explainability methods and tools for machine learning and deep learning models. These explainable AI (XAI) techniques can significantly reduce the black box effect that often hinders direct inclusion and automated integration of ML outputs in decision making. The manufacturing sector is gradually increasing its adoption of AI-enabled systems, a process accelerated in the context of the Fourth Industrial Revolution, strengthening the need for explainability and robust XAI pipelines. This paper presents a framework of processes and tools designed and developed to bring the benefits of explainability in AI-enabled decision making in the manufacturing domain, providing the mechanisms to create production ready, trustful machine learning processes that foster collaboration among stakeholders coming from both business and technical backgrounds.
Evaluation of User Experience in Code Learning Platform Using User Experience Questionnaire

This research is motivated by the need to enhance the user experience (UX) of the Code Learning (Clearn) website as a programming course platform. The objective of this study is to develop a better user experience through the utilization of the User Experience Questionnaire (UEQ) method. The UEQ method measures and improves the quality of user experience based on aspects such as satisfaction, efficiency, learning, security, and stimulation. The results user experience questionnaire based on the comparison to Benchmark UEQ criteria shows “Good” results with a score of 2.23 in Attractiveness, 1.90 in Perspicuity, 1.38 in Efficiency, 1.16 in Dependability, and 2.28 in Stimulation. Thus, it can be concluded that the development of user experience on the CLearn website using the UEQ method has successfully improved the quality of user experience, providing positive benefits for website users.
Adaptive Learning and AI to Support Medication Management

Artificial Intelligence (AI) has proven to be very helpful in different areas, and its application in the medical field can help clinicians to make more informed decisions, especially in the Intensive Care Unit (ICU), where the COVID-19 pandemic exposed several challenges. If a model is correctly selected, AI can help improve healthcare systems by predicting future occurrences. This study analyzes Machine Learning (ML) techniques, discusses their application in an ICU environment and proposes a methodology to develop ML models to predict mean arterial pressure (MAP) values that can assist healthcare professionals’ decision making. The current study starts by doing a brief review of ML methods and identifying interesting models to train. The chosen models are then trained and the one considered to be the most appropriate is chosen to be tested in a controlled environment, in a system that can generate medical data. Data is then cleaned, classified, and fed to the model, to give a prediction related to future MAP values. Finally, the developed system is integrated into the ICU4Covid project, more precisely in the B-Health IoT Box, which allows for deployments of new applications for ICUs. The proposed system's integration into ICU4Covid enables remote medical operations and can improve the quality care of healthcare services in ICU environments.
Research and FPGA Implementation of Convolutional Neural Network Accelerator

Compared with CPU and GPU, FPGA has high performance, low power consumption, and flexible deployment characteristics. Nowadays, the convolutional neural network (CNN) is updated rapidly, and using the high-level synthesis (HLS) technology to design CNN image recognition accelerators has high efficiency. However, the operating speed and the energy efficiency ratio (EER) of the existing CNN accelerators designed by HLS technology are relatively low. We designed an FPGA-based CNN accelerator using HLS technology, which further improved acceleration efficiency through pipeline optimization, perfect loop, and multiplication split operation. We also realized the custom reusing of intellectual property (IP) cores. The CNN accelerator is verified on an 8-bit LeNet-5 model, and it has a recognition accuracy rate of 97.95% on the MNIST dataset, a single recognition time of 21.5ms, and a platform power consumption of 1.965W. And it reduces the recognition time by at least 5ms compared with other existing works. Its energy efficiency ratio is 23.67(s • W) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , which is 2.3 times and 4 times that of ARM and CPU respectively. These reflect the advantages of the FPGA-based CNN image recognition accelerator in that it can be flexibly deployed and has a high energy efficiency ratio under limited deployment power consumption.
Weak Signal Detection Method Based on Bayesian Neural Network

With the development of The Times, the high efficiency and privacy of modern communication become increasingly important. How to distinguish weak signal from noise in the case of insufficient prior information becomes particularly important. The traditional modulation recognition process requires manual extraction of signal features, and the process is complicated and the unstable factors are too large, which cannot meet the requirements of high precision and high time of modern communication. With the development of deep learning, machine learning has made great breakthroughs in the direction of radio signal processing due to its obvious classification and decision advantages, and signal recognition has a higher accuracy than before. More and more researches begin to use machine learning method to extract features directly from the original data of communication signals, which ensures the real-time and robustness of automatic modulation recognition of communication signals. Naive Bayes is one of the classical algorithms of machine learning and one of the few classification algorithms based on probability theory. This method is still effective in the case of less data and can deal with multiple kinds of problems. Based on Bayesian network algorithm, this paper studies the weak signal identification method of wireless telecommunication below 0db. Aiming at the problem that the generalization ability of traditional model is insufficient, this paper proposes a weak signal recognition method based on Bayesian network. The experimental results show that the recognition rate of this method is 40 percent higher than that of different Bayesian neural networks in the recognition of radio weak signals, which improves the recognition ability of the whole recognition model for weak signals not in training concentration.
Design a Method for Tracking Moving Object and Predicting its Trajectory in a Noisy Environment

Target tracking is significant due to the fact that detection and tracking methods are being developed in order to accommodate the system's requirements regarding accuracy and speed. This article will propose a method of target tracking that is intended to address the practical issues of signal loss or a significant amount of noise that affects the process of target detection. The procedure of predicting the course of the object is one of the most promising tasks at the present time, the value of the predictions is apparent in the use of robots, automation and smart tracking radars. The mathematical foundation of the process of predication is related to the previous behavior of the target, the preceding motion of the object conveys information about its general trajectory. This is similar to the Kalman Filter.We created a module that predicts the movement of an object, the module is a hybrid of a Kalman filter and a numerical algorithm that estimates the path of any trajectory in terms of polynomial. The results indicate that the supposed method is a viable solution to the predication problem with the absence of a signal.
Toward Optimal Play of Cribbage

Cribbage is a popular multi-player imperfect-information card game. But despite recent advances in computational methods for imperfect information games, there has been little work on intelligent agents for cribbage. We present an agent for the two-player game based on Monte Carlo Counterfactual Regret Minimization and we describe the simplifications we chose in order to deal with the large state space; the end result achieves results superior to the best agents described in the literature to date.
Large-parallax Image Stitching Based on MAGSAC++ and Image Content

The purpose of image stitching is to generate high-resolution panoramic images that are seamless, distortion-free, and free of artifacts while minimizing computational costs. In this paper, we propose a stitching algorithm for large-parallax image based on MAGSAC++ and image content. Specifically, this paper decomposes the problem into three steps: image registration, seam cutting, and image fusion. Firstly, we employ an image registration algorithm based on MAGSAC++ to align the input images and obtain two aligned images in the same coordinate system. Next, we propose a pixel-based energy function that integrates color difference, gradient difference, and texture complexity information. We also introduce the LBP operator to better represent the underlying texture information of the images. Based on this energy function, we search for the perceptually optimal seams located in contiguous regions with high similarity. Finally, we use the Laplace pyramid fusion algorithm to eliminate visible seams and achieve natural color transitions. Experimental results demonstrate that the proposed method achieves seamless stitching of large-parallax images effectively and efficiently, outperforming other classical stitching algorithms.