Reviewerly/exHarmony huggingface dataset:
Self-supervised representation learning using feature pyramid siamese networks for colorectal polyp detection

Colorectal cancer is a leading cause of cancer-related deaths globally. In recent years, the use of convolutional neural networks in computer-aided diagnosis (CAD) has facilitated simpler detection of early lesions like polyps during real-time colonoscopy. However, the majority of existing techniques require a large training dataset annotated by experienced experts. To alleviate the laborious task of image annotation and utilize the vast amounts of readily available unlabeled colonoscopy data to further improve the polyp detection ability, this study proposed a novel self-supervised representation learning method called feature pyramid siamese networks (FPSiam). First, a feature pyramid encoder module was proposed to effectively extract and fuse both local and global feature representations among colonoscopic images, which is important for dense prediction tasks like polyp detection. Next, a self-supervised visual feature representation containing the general feature of colonoscopic images is learned by the siamese networks. Finally, the feature representation will be transferred to the downstream colorectal polyp detection task. A total of 103 videos (861,400 frames), 100 videos (24,789 frames), and 60 videos (15,397 frames) in the LDPolypVideo dataset are used to pre-train, train, and test the performance of the proposed FPSiam and its counterparts, respectively. The experimental results have illustrated that our FPSiam approach obtains the optimal capability, which is better than that of other state-of-the-art self-supervised learning methods and is also higher than the method based on transfer learning by 2.3 mAP and 3.6 mAP for two typical detectors. In conclusion, FPSiam provides a cost-efficient solution for developing colorectal polyp detection systems, especially in conditions where only a small fraction of the dataset is labeled while the majority remains unlabeled. Besides, it also brings fresh perspectives into other endoscopic image analysis tasks.
Sistem Keamanan Kartu NFC Menggunakan Metode AES pada Sistem Pembayaran Elektronik

To maintain data security, a method is needed that can improve data security, one of which is by using cryptographic techniques. In an effort to meet the needs of data security, cryptography focuses on understanding and applying techniques to protect messages. There are several methods used in cryptography, one of which is the AES method which is recognized as the best encryption algorithm for several reasons, such as using a sufficiently secure key, high processing speed, and ensuring data integrity and confidentiality. In this study, the AES method is used to secure data in the form of nominal balances stored on NFC cards. Based on the test results, it can be seen that the NFC card can function as a balance storage with a maximum limit of IDR 1,000,000. Then based on the confusion matrix method, the security system on the NFC card has an accuracy value of 81,81%, 83.3% precision, and 93,75% recall.&#x0D;
Penerapan Metode Regresi Linier Untuk Prediksi Jumlah Orang Terlantar Di Provinsi Riau

Displaced people are residents who for some reason cannot meet their needs naturally, both spiritually, physically, and socially. The problem of displaced people occurs for various reasons and urbanization is one of them. Social Service is a government agency that plays a role in improving the quality of social welfare of individuals, groups, and communities. Many community services are carried out by Social Service and one of them is the repatriation of displaced persons. Based on data from the Riau Province Social Service, the number of displaced people in Riau Province from year to year has increased or decreased erratically. This is of course a problem that hinders the Riau Province Social Service in its internal processes such as determining strategies or policies to make decisions. Therefore, this research was conducted to overcome these problems. This research was conducted using the linear regression method with a MAPE result of 7.09% which will be implemented into a prediction application for the number of displaced people and aims to help the Riau Province Social Service get information on the number of displaced people in the next period. Based on the results of the Blackbox test, shows that all menus and features have run very well and obtained a User Acceptance Test (UAT) calculation value of 92%.&#x0D;
Biocomputing through EnGINeering Organoid Intelligence (NSF)

Federal Grants & ContractsVolume 48, Issue 1 p. 7-7 Grant Alerts Biocomputing through EnGINeering Organoid Intelligence (NSF) First published: 07 December 2023 https://doi.org/10.1002/fgc.33404Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume48, Issue17 December 2023Pages 7-7 RelatedInformation
Impact of Vector Embeddings on the Performance of Tolerance Near Sets-based Sentiment Classifier for Text Classification

In recent years, Natural Language Processing (NLP) has gained significant attention, and sentiment analysis is an essential subfield of NLP that deals with identifying the sentiment or emotion conveyed in the text. Tolerance near sets (TNS) is a mathematical framework that has shown promising results in sentiment analysis tasks. However, the choice of word embeddings can significantly impact the performance of TNS-based classifiers. This paper investigates the impact of using different embeddings on the performance of tolerance near sets-based sentiment classifiers. This paper compares the use of different embeddings, including DistilBERT, MiniLM, and Word Embeddings, and their combinations, to understand their impact on TNS-based sentiment analysis. The TSC 2.0 model proposed in this paper achieves a weighted F1 score of 92.1% in one of the datasets, an improvement due to the sentence embeddings used. Experimental results have led to the observation that tie-breaking and variance-based classification may have led to a noticeable improvement in cases with more than three.
Suspicious Activity Classification in Classrooms using Deep Learning

Video processing is attracting the attention of both research and industry. The existence of intelligent surveillance cameras with high processing power has paved the way for designing intelligent visual surveillance systems. Along with analyzing video for information recovery, it is nowadays used to analyze live surveillance video to detect activities. These systems are implemented in real time. The proposed work's goal is to create a method that can examine and discover suspicious behaviors in the lecture room environment. Video analytics offers the most efficient answer because it enables pointing an occasion and retrieves applicable statistics from the video recorded. The method aims to identify suspicious activities like fighting, sleeping, looking elsewhere, eating, etc. that the students might be doing. The proposed method involves breaking a video input into frames and converting it into image data because the model has been trained on images collected from the internet. Several models were tested and experimented with, including efficientnet_b2, spnasnet_100, efficientnet_b3, and mobilenetv3_large_100. Parameters such as the Learning Rate were optimized to find out the best method and create a system with the best results.
A Sense of Ambient Entrapment in Hito Steyerl’s Factory of the Sun

This article proposes the notion of ambient entrapment to conceptualize the affective experience of surveillance in the current age of ubiquitous computing and smart technologies. A sense of ambient entrapment is identified as a vague, yet pervasive feeling of a controlled environment saturated by surveillance and exploitation, where machine perception and algorithmic processes are hard at work. Arguing that artworks are particularly adept at expressing affective experiences and emerging cultural feelings of surveillance, the article offers a reading of German artist Hito Steyerl’s immersive video installation environment Factory of the Sun (2015) to further explore the theoretical argument. Inside the dark installation space, visitors are immersed in a blue LED grid environment and encouraged to recline in beach chairs facing a large screen. What is perceptible as time passes inside Factory of the Sun, the paper argues, is an indefinable yet all-encompassing sense of something working and conditioning in the background, of technologies extracting and exploiting personal data while we, at the same time, desire and feel the lure of said technologies and devices. The article concludes that artworks can make us aware of the often invisible and barely perceptible forces at work in our environments and everyday life and suggests we should turn to contemporary art as sites of knowledge of the affective experience of ambient surveillance.
Analysis of Research Development on the Use of Internet of Things (IoT) Technology in Health Monitoring

Health monitoring is one of the many industries that has been transformed by the Internet of Things' (IoT) explosive growth. A thorough bibliometric analysis, cluster analysis, and term occurrences analysis are used in this study to reveal the complex world of IoT technology in health monitoring. Thematic groups are shown by cluster analysis, essential insights are provided by highly cited publications, and key concepts are clarified by word occurrences. The multidisciplinary aspect of research is exemplified by collaborative networks, prominent contributions, and rising trends within the literature. The amalgamation of results offers a comprehensive comprehension of the present condition of IoT in health monitoring, steering forthcoming research directions and promoting sustained innovation at the nexus of technology and healthcare.
PERBANDINGAN PERFORMA MODEL MACHINE LEARNING SUPPORT VECTOR MACHINE, NEURAL NETWORK, DAN K-NEAREST NEIGHBORS DALAM PREDIKSI HARGA SAHAM

This study aims to analyze the performance of three prediction models, namely K-Nearest Neighbors (K-NN), Neural Network (NN), and Support Vector Machine (SVM), in predicting the stock price of PT Astra International Tbk (ASII.JK). The research encompasses the initial stages through evaluation using optimal parameters for these three algorithms. The research findings reveal that the K-NN prediction model has the lowest Root Mean Square Error (RMSE) value, with a value of 0.037, indicating the most accurate prediction compared to the other models. Despite the NN model having an RMSE of 0.048, which is higher than K-NN, it still provides reasonably accurate predictions. Meanwhile, the SVM model has an RMSE of 0.075, indicating a higher level of error in its predictions. Based on these results, the recommendation is to utilize the K-NN model as the preferred choice for predicting the ASII.JK stock price.
Analisis Association Rule Untuk Memprediksi Kelulusuan Mahasiswa F-Kip Universitas Graha Nusantara Padangsidimpuan

University administrators need graduation predictions to determine initial steps to avoid dropout. The length of a student's study period is determined by various factors. Therefore, it is important to know which students may not graduate on time. Data mining techniques can be used to explore new insights to predict student graduation. By using the association rule technique we can obtain information from large data such as data from universities. The aim of this research is to determine the pattern of study duration for Graha Nusantara University F-KIP students. by using the association rule data mining method and comparing a priori algorithms and hash-based algorithms. The data used is Graha Nusantara University F-KIP master data which is processed using association rule data mining techniques with a priori algorithms and hash-based algorithms with minimum support of 1% and minimum confidence of 1%. The results of data processing with the a priori algorithm are the same as the results of data processing with the hash-based algorithm, namely 49 2-itemset combinations. The pattern that was formed included 7.5% of graduates from the mathematics department studying for more than 5 years with a confidence value of 38.5%.
A Bibliometric Analysis of the Development of Research on Social and Economic Inequality in Developing Countries

This bibliometric analysis explores the landscape of research on social and economic inequality in developing countries, employing a comprehensive approach that integrates co-authorship networks, keyword co-occurrence patterns, journal mapping, and geographical distribution maps. The study draws on a vast repository of scholarly publications, shedding light on key themes, influential authors, and global research trends. Co-authorship networks reveal collaborative patterns, emphasizing the importance of interdisciplinary approaches in understanding inequality. Keyword co-occurrence patterns identify central themes, highlighting the multifaceted dimensions of social and economic disparities. Journal mapping showcases pivotal platforms shaping the discourse, aiding researchers in navigating the academic landscape. Geographical distribution maps unveil regional concentrations and gaps, prompting nuanced explorations of inequality within specific contexts. The synthesis of these results provides a comprehensive understanding of the field, guiding future research endeavors and informing evidence-based policy interventions for inclusive development.
Future Prevention Strategy Based on Lessons Learned from the 2019 Peatland Fire in Tumbang Nusa Area, Indonesia

The objective of this study is to formulate the concept of managing forest and land fires at the village level assuming that socio-economic, biophysical and equipment facilitation aspects greatly determine the success of fire prevention. Peatland has high capacity for carbon storage and also social-economic resource. The Tumbang Nusa area encompasses Tumbang Nusa village and Tanjung Taruna, together with a special study forest area that is managed by the Ministry of Environment and Forestry on a regional level. . The area is a peatland with peat soil depths ranging from 2 to 8 meters, which can illustrate the effects of peat fire episodes on areas with moderate to deep peat depths. The lessons learned from the 2019 fires in that area can be used to improve fire prevention tactics and management plans to prevent future surface and peat fires. Our research has demonstrated that minimizing the likelihood of a fire occurring necessitates an awareness of socioeconomic factors such as human behaviors that spark fires. This should be accompanied by an understanding of the underlying biophysical and social factors, such as peatland characteristics, involvement of fire departments, police, and the army, fuel management, local fire danger rating, fire equipment, institutional capacity, suppression response, and post-fire action. All of these learning aspects can help to strengthen village prevention efforts.
A Bayesian Neural Network-based Obstacle Avoidance Algorithm for an Educational Autonomous Mobile Robot Platform

Autonomous mobile robots belong to automatically controlled objects that are designed and produced for various demands. This study aimed to develop an inexpensive platform of autonomous mobile robots that can be used for educational and research purposes in technical universities. The robot was built based on popular ultrasonic sensors to detect obstacles and a Raspberry Pi 4, which is a Linux-embedded computer. An effective obstacle avoidance algorithm for the robot was developed using a Bayesian neural network for classification. Training a Bayesian neural network does not require a validation dataset separate from the available data. In addition, the Bayesian approach can effectively handle the uncertainty of the system and result in the best generation for the network when inferring the unseen data. Training data are generated using robot-to-obstacle distances and the corresponding navigation modes. The commands to control the left and right motors of the robot are generated by the pretrained Bayesian neural network for three modes of navigation, forward, left, and right. Finally, this system can be useful as it can be conveniently integrated with advanced robot control algorithms.
Sensor Enabled Proximity Detection with Hybridisation of IoT and Computer Vision Models to Assist the Visually Impaired

Proximity Detection Systems (PDS) are used to detect objects or persons close to Visually Impaired (VI) persons. Sensors are used to identify proximity based on the distance from objects. This study aimed to design a hybrid proximity detection framework for VI people using ultrasonic sensors embedded in a Raspberry Pi board to detect the proximity of a VI user in an office environment. Hybridization was based on the integration of IoT-enabled devices, ultrasonic proximity sensors, and computer vision algorithms to control the detection of objects or people and inform the user with a voice message. The model framework was implemented with 100 samples and tested with 10 analyses in each sample. The results showed significant improvement in detecting the proximity of the objects with an accuracy of 98.7%, outperforming current PDS with good results in precision, range, obstacle recognition, false positives and negatives, response time, usability, durability, reliability, etc.
Enabling Context-based AI in Chatbots for conveying Personalized Interdisciplinary Knowledge to Users

Adaptable chatbots have revolutionized user interactions by dynamically tailoring responses to users' knowledge and explainability preferences in interdisciplinary domains, such as AI in education and medicine. While strides have been made in model explainability, little attention has been paid to making models contextually aware and responsive to users' background knowledge. This study investigated interdisciplinary knowledge learning principles in a different domain, where the chatbot's contextual understanding is enhanced through dynamic knowledge graphs that capture users' past interactions to deliver up-to-date and relevant responses. By incorporating explainability features, chatbot responses become enriched, enabling users to understand the reasoning behind answers. This study proposed a model that showed superior chatbot performance and accurately addressed most queries, outperforming competitor chatbots DBpedia, ODC, and ODA. A 0.7 F-measure showcased its excellence, attributed to dynamic knowledge graphs and explainability checks. This study envisions a new era of conversational AI that not only meets user needs but also fosters a deeper understanding of AI decision-making, making it indispensable in delivering personalized, informed, and contextually aware interactions.
Compare Internal and External Perceptions

Special Events GaloreVolume 24, Issue 1 p. 3-3 Image Enhancement Compare Internal and External Perceptions First published: 07 December 2023 https://doi.org/10.1002/speg.32094Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume24, Issue1January 2024Pages 3-3 RelatedInformation
Advances in Quantum Computing

Advances in quantum computing have continued to accelerate over the course of this Special Issue's publication [...].
Learning Outcome of e-Learning with Cuteness in Science Teaching about Plants and Animals for Grade 4 Hearing Impaired Students with Different Learning Style

The four purposes of this study were: First, to develop e-Learning with cuteness in science teaching about plants and animals for grade 4 students with hearing hearing-impaired who have different learning styles. Second, to study learning styles via e-Learning with hearing-impaired students who have different learning styles. Third, to examine the learning achievement before and after this e-Learning with cuteness, and finally, to study students' learning development who had different learning and studied via e-Learning with cuteness. This study is experimental research using one group pre-test and post-test design. The research instruments used for this experiment were: 1) e-Learning with cuteness in science teaching for grade 4 under the topic “plants and animals," pre-test and post-test exams, and Kolb’s learning style questionnaire. The samples for this study were 15 grade 4 students with hearing impairments at Surin School for the Deaf under the office of special education administration. Statistics employed for data analysis were mean ( ), standard deviation (S.D.), t-test, and f-test. The findings revealed that:&nbsp; The efficiency of e-Learning with cuteness in science teaching for grade 4 hearing-impaired students with different learning styles was highly efficient, meeting the requirement of Meguigans' criteria.&nbsp; The students’ learning achievement after learning via this e-Learning was significantly higher at the 0.05 level, while there is no significantly different in the learning achievement of students with different learning at the 0.05 level. However, learning via this e-Learning students’ achievement yielded a mean score of the learning achievement lower than the criterion (50 percent).
Enhancing Phishing Awareness Strategy Through Embedded Learning Tools: A Simulation Approach

Phishing attacks pose a persistent threat to individuals and organizations, demanding effective strategies for enhancing awareness and response. This research investigates the use of a web-based application employing simulation-based training and embedded learning tools to bolster phishing awareness. The study creates a comprehensive system that includes user roles, training modules, simulated phishing scenarios, and a robust database structure. Through rigorous testing, bug fixing, and user training, the application is deployed for real-world use. Results show that the web-based application significantly improves users' ability to detect and respond to phishing attempts. Notably, the integration of embedded learning tools enhances engagement and knowledge retention. The research contributes to proactive cybersecurity practices by offering a practical solution for countering phishing attacks. While addressing limitations of prior studies, this work opens avenues for further research in real-world applicability, long-term effectiveness, scalability, and user motivation. Ultimately, the web-based application serves as a valuable tool in building robust phishing awareness and mitigating cybersecurity risks.