Reviewerly/exHarmony huggingface dataset:
Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

Authors:
raw_author_name: Rostyslav Tsekhmystro
raw_author_name: Oleksii Rubel
raw_author_name: Vladimir Lukin

Abstract: The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.
A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

Authors:
raw_author_name: Lahcen Idouglid
raw_affiliation_string: Ibn Tofail University Kenitra
raw_author_name: Said Tkatek
raw_affiliation_string: Ibn Tofail University Kenitra
raw_author_name: Azidine Guezzaz
raw_affiliation_string: Cadi Ayyad University, Essaouira

Abstract: In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity.
Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan

Authors:
raw_author_name: Hayu Wikan Kinasih
raw_affiliation_string: Universitas Dian Nuswantoro
raw_author_name: Cinantya Paramita
raw_affiliation_string: Universitas Dian Nuswantoro
raw_author_name: Fauzi Adi Rafrastara
raw_affiliation_string: Universitas Dian Nuswantoro

Abstract: Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.
Pengumpulan Informasi pada Situs Web Dengan Menyusun Kerangka Kerja Keamanan Siber NIST

Authors:
raw_author_name: Dega Surono Wibowo
raw_affiliation_string: Politeknik Harapan Bersama
raw_author_name: M. Nishom
raw_affiliation_string: Politeknik Harapan Bersama
raw_author_name: Taufiq Abidin
raw_affiliation_string: Politeknik Harapan Bersama

Abstract: In the current era, the rapid development of websites has made them one of the most significant modern information media. Website creation is not only focused on the design and information presented, but also focuses on security aspects. The presence of security on a website is very important, considering the need to protect the data and information contained therein. Information Gathering is one method used to test a website's security. This information gathering is the earliest stage to obtain ownership and other sensitive information. This research aims to conduct security testing of the oase.poltektegal.ac.id website using tools in the form of penetration testing software; then, the testing results are entered into the cybersecurity framework issued by N.I.S.T. The test results obtained and adjusted to N.I.S.T. Cybersecurity are that the oase.poltektegal.ac.id website has vulnerabilities in the form of CVE-2003-1418 (apache webserver vulnerability), CVE-2005-3299 (PHP vulnerability), CVE-2010-4344 ( Buffer Overflow Vulnerability), CVE-2007-6750 (XSS). The solution to this vulnerability is updating the software and closing unused ports. These results will be used as a benchmark in creating or improving similar websites to increase awareness and vigilance in achieving cyber resilience
Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Authors:
raw_author_name: None Mohammed Fadhil Ibrahim
raw_author_name: None Raed Kamil Naser

Abstract: Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.
Permainan bahasa dalam penggunaan bahasa Inggris oleh pedagang asongan di pantai Kuta Lombok

Authors:
raw_author_name: Sulistyowati Sulistyowati

Abstract: The playfulness of English is the result of language interference, which can be a source of creating new English variations. The purpose of this study is to describe the form of English playfulness by hawkers at Kuta Beach, Lombok, and the factors of English playfulness. The method is descriptive qualitative. The data collection by ‘simak libat cakap’ or involved conversation observation technique, recording, and taking notes technique. The data analysis method is reduction, presentation, and conclusion. Data analysis shows that the playfulness of English by hawkers comes from loan translation in the form of reduplication of Indonesian and Sasak words into English. There are two forms of loan translation: a change in the meaning of English in accordance with Sasak and Indonesian, and not experiencing a change in meaning but experiencing the addition of English meaning in accordance with Sasak and Indonesian. The factors causing the English playfulness are internal factors such as the difference in vocabulary structure from Sasak and Indonesian to English and the influence of Indonesian and Sasak as the dominant language. Factors external the language are influential in English playfulness, such as level of education, economic status, occupation, regional origin, and the situation of speech.
Storybook berbasis augmented reality (AR) meningkatkan hasil belajar bahasa Inggris siswa

Authors:
raw_author_name: Made D. K. Degeng
raw_author_name: Saida Ulfa

Abstract: This study focuses on the combination of digital media and students' literacy, utilizing an Augmented Reality (AR)-based storybook as a tool for English language subject. The aim of this research is to assess how the use of AR-based storybooks can influence and enhance students' learning outcomes in English language subject. Employing an experimental research design, two groups were exposed to different stimuli. The experimental group's test results showed a significant difference with a two-tailed significance value of 0.032, indicating an impact attributed to the utilization of AR-based storybooks. Additionally, the N-gain percentage results revealed a 60% improvement, categorizing the AR-based storybook as moderately effective in enhancing students' learning outcomes compared to conventional storybook media. The findings of this study indicate that the implementation of AR-based storybooks significantly and effectively enhances students' learning outcomes in English language subject.
Library Book Recommendation System Using Content-Based Filtering

Authors:
raw_author_name: Prita Dellia
raw_affiliation_string: Informatics Education Study Program, Universitas Trunojoyo Madura, Bangkalan Regency, East Java, Indonesia

Abstract: In today's digital era, libraries are developed to adapt to student needs. Students can easily search for books on digital library services. However, the large number of books sometimes makes it difficult for students to find the books they want. Overcoming this problem can be done by using a recommendation system. A recommendation system is a system used to provide suggestions to users. This research aims to develop a library book recommendation system at Darul Mustofa Bangkalan Vocational School using content-based filtering. The content-based filtering method offers recommendations based on user preferences according to the item description. The algorithms used are Term Frequency Inverse Document Frequency (TF-IDF) and Cosine similarity. The method used in this research is research and development with a waterfall model. The researcher's testing stage used black box testing. Black box testing results were obtained from validation by system experts, website experts, and user tests with the qualification "Very Eligible."
Conceding Sentiment Prognosis on Twitter Data

Authors:
raw_author_name: Anshu Malhotra
raw_affiliation_string: Department of Applied Sciences, The NorthCap University, Gurugram, India 122017
raw_author_name: Nishu Sethi
raw_affiliation_string: Department of Computer Science and Engineering, School of Engineering, The NorthCap University, Gurugram, Haryana, India 122017

Abstract: Twitter is the biggest micro-blogging website that gives people a platform to share their opinions about any new happenings around the world. The size of tweets is generally short which makes it very suitable for opinion mining. The key focus of the paper is to analyze the feelings and ideas. In this paper, analysis is done on the classification of tweets on a particular keyword. The tweets related to the given keyword are collected, analyzed, and the result is generated in the form of percentage of positive, neutral and negative sentiments, which gives us a sense of overall sentiment of the keyword. Further, Classification is done using supervised learning algorithms and the best among these will be found by calculating the accuracy of each.
1. THE MOST POPULAR ARTIFICIAL INTELLIGENCE APPLICATION USED BY AEROSPACE ENGINEERING STUDENTS’ TO LEARN MATHEMATICS

Authors:
raw_author_name: None Rindu A. Funny

Abstract: Artificial intelligence (AI) develops over time. Education is concurrently the AI evolution,sometimes in front, sometimes behind. Education underlies AI, and later on, AI is used in education. Therefore, the utilization of AI in education cannot be denied. There are many advantages, but also some limitations and ethical issues that arise from the use of AI in education. There are many applications of AI in mathematics scattered throughout society, but not all of them are useful enough. Thus, the purpose of this research is to gain the famous application AI in mathematics (app AI math) that is mostly used and recommended by the students’ experience in doing and learning mathematics. T-test independent samples were used to test the score of two groups, using the AI math app or not, in order to know whether it was affected or not. The result of this research is that the most popular app is Photomath, followed by ChatGPT and Mathway. But still, many students did not use this app. Relating to their score, the students who have used the AI math app to learn math before are not different from the students who do not use the AI math app.&#x0D;
Intersecting Minds and Machines

Authors:
raw_author_name: Cheryll Anne Augustine
raw_affiliation_string: Universiti Sains Malaysia, Malaysia
raw_author_name: Pantea Keikhosrokiani
raw_affiliation_string: Universiti Sains Malaysia, Malaysia & University of Oulu, Finland
raw_author_name: Nasuha Lee Abdullah
raw_affiliation_string: Universiti Sains Malaysia, Malaysia

Abstract: The integration of technology in the medical sector, particularly for data acquisition and analysis via the internet of health things (IoHT), offers significant potential for enhancing community health management by monitoring individual lifestyle patterns. A systematic literature review focusing on articles published between 2019 and 2021 explored the behavioral intention toward using IoHT-based healthcare systems. Analyzing 20 selected articles revealed a preference for theories such as the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT), and the theory of planned behaviour (TPB) to facilitate behavioral change. Key factors influencing behavioral intention toward these systems include performance expectancy, perceived usefulness, effort expectancy, social influence, and resistance to change. The findings highlight the importance of user-centric design in developing IoHT-based healthcare systems to effectively meet community health needs, emphasizing that healthcare solutions should be tailored to individual health behaviors and preferences.
Narrative Threads and Cinematic Connections Using Intelligent Systems to Enhance Movie Recommendations with Market Basket Analysis and Advanced Algorithms

Authors:
raw_author_name: Pantea Keikhosrokiani
raw_affiliation_string: University of Oulu, Finland
raw_author_name: Moussa Pourya Asl
raw_affiliation_string: University of Oulu, Finland

Abstract: Movie streaming services are businesses driven by data and strategies to predict future viewing patterns based on historical data. Without unsupervised learning techniques, industries like movie streaming services might face laborious tasks and issues in anticipating customer preferences and forecasting changes in customer behavior. In this chapter, market basket analysis (MBA) and recommender systems were implemented on MovieLens Data. In MBA, movie watching patterns were identified using two types of rule-generating algorithms, namely the apriori algorithm and the FP-growth algorithm. Three visualization idioms were generated to understand the association rules extracted in the MBA. Secondly, five types of recommender systems, namely memory-based collaborative filtering, model-based collaborative filtering, content filtering, context filtering, and the hybrid method, were implemented to suggest relevant movies to customers. Each recommender system was experimented with three different TopN configurations, and the results were evaluated using information retrieval metrics.
3D orientation field transform

Authors:
raw_author_name: Xiaohao Cai
raw_affiliation_string: School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK
raw_author_name: Byung-Ho Kang
raw_affiliation_string: State Key Laboratory for Agrobiotechnology School of Life Sciences, The Chinese University of Hong Kong, Shatin, NT, Hong Kong

Abstract: Abstract Vascular structure enhancement is very useful in image processing and computer vision. The enhancement of the presence of the structures like tubular networks in given images can improve image-dependent diagnostics and can also facilitate tasks like segmentation. The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in 3D images due to the extremely complicated orientation in 3D against 2D. Given the rising demand and interest in handling 3D images, we experiment with modularising the concept and generalise the algorithm to 3D curves. In this work, we propose a 3D orientation field transform. It is a vascular structure enhancement algorithm that can cleanly enhance images having very low signal-to-noise ratio, and push the limits of 3D image quality that can be enhanced computationally. This work also utilises the benefits of modularity and offers several combinative options that each yield moderately better enhancement results in different scenarios. In principle, the proposed 3D orientation field transform can naturally tackle any number of dimensions. As a special case, it is also ideal for 2D images, owning a simpler methodology compared to the previous 2D orientation field transform. The concise structure of the proposed 3D orientation field transform also allows it to be mixed with other enhancement algorithms, and as a preliminary filter to other tasks like segmentation and detection. The effectiveness of the proposed method is demonstrated with synthetic 3D images and real-world transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones. Extensive experiments and comparisons with existing related methods also demonstrate the excellent performance of the proposed 3D orientation field transform.
A novel multi-scale loss function for classification problems in machine learning

Authors:
raw_author_name: Leonid Berlyand
raw_affiliation_string: Department of Mathematics, The Pennsylvania State University, University Park, PA, USA
raw_author_name: Robert Creese
raw_affiliation_string: Department of Mathematics, The Pennsylvania State University, University Park, PA, USA
raw_author_name: Pierre-Emmanuel Jabin
raw_affiliation_string: Department of Mathematics, The Pennsylvania State University, University Park, PA, USA

Abstract: We introduce two-scale loss functions for use in various gradient descent algorithms applied to classification problems via deep neural networks. This new method is generic in the sense that it can be applied to a wide range of machine learning architectures, from deep neural networks to support vector machines for example. These two-scale loss functions allow to focus the training onto objects in the training set which are not well classified. This leads to an increase in several measures of performance for appropriately-defined two-scale loss functions with respect to the more classical cross-entropy when tested on traditional deep neural networks on the MNIST, CIFAR10, and CIFAR100 data-sets.
Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

Authors:
raw_author_name: Jianing Qiu
raw_affiliation_string: Department of Computing and the Hamlyn Centre, Imperial College London, London, U.K.
raw_author_name: Frank Po Wen Lo
raw_affiliation_string: Department of Surgery and Cancer and the Hamlyn Centre, Imperial College London, London, U.K.
raw_author_name: Xiao Gu
raw_affiliation_string: Department of Computing and the Hamlyn Centre, Imperial College London, London, U.K.
raw_author_name: Wenyan Jia
raw_affiliation_string: Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
raw_author_name: Megan A. McCrory
raw_affiliation_string: Department of Health Sciences, Boston University, Boston, MA, USA
raw_author_name: Edward Sazonov
raw_affiliation_string: Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USA
raw_author_name: Mingui Sun
raw_affiliation_string: Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA
raw_author_name: Benny Lo
raw_affiliation_string: Department of Surgery and Cancer and the Hamlyn Centre, Imperial College London, London, U.K.

Abstract: Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.