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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Diogo Steinke Deconto Erick Luiz Fontoura Valenga Carlos N. Silla Graduate Program in Computer Science (PPGIA) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil Computer Music Technology Laboratory (Onloop) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil School of Information Technology Universidade Positivo Curitiba Brazil

Learning a musical instrument is challenging in many different aspects. One important aspect is the motivation of the music students. One common practice to keep music students engaged is to use songs that the students like. However, identifying an appropriate music score for the music students that is compatible with the specific skill level and music taste of each student can be a challenging task. Defining the difficulty of each score manually is a complex task, as it requires theoretical knowledge and mastery of a particular musical instrument and music theory. In this context, the automatic music score difficulty classification task can help music teachers and students by automatically classifying music scores according to their difficulty. The main contribution of this work is to evaluate the use of two approaches based on the musicXML files and different classification algorithms for this task concerning three different music instruments, namely the violin, the piano and the acoustic guitar. Our experiments show interesting results for all the musical instruments.

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Modeling Atomistically Assembled Diffractive Optics in Solids

arXiv

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arXiv 2024年

作者： Kling, Trevor Na, Dong-Yeop Hosseini, Mahdi Department of Electrical and Computer Engineering Applied Physics Program Northwestern University EvanstonIL60208 United States Department of Electrical Engineering Pohang University of Science and Technology Pohang37673 Korea Republic of Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States

We develop a model describing long-range atom-atom interactions in a two-dimensional periodic or a-periodic lattice of optical centers inside a solid-state host material. We consider realistic environmental and technical conditions such as frequency and position broadening. Even when considering a significant frequency broadening in the ensemble (∼300 GHz), we observe up to a three-fold increase in directional scattering from the resonant lattice in a system. The model can be used to scalably design quantum optical elements, e.g. a quantum lens, harnessing atomistic engineering (e.g. via ion implantation) of collective interactions in materials to enhance quantum properties. © 2024, CC BY.

关键词： Nanocrystals

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Database management system design for COVID-19 symptom analysis 6

Database management system design for COVID-19 symptom analy...

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6th International Conference on Information Management and technology, ICIMTech 2021

作者： Rahutomo, Reza Darmalim, Ferdinan Cen, Andrew Brilianto, Jayaku Pardamean, Bens School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia PT. Mega Medica Pharmaceuticals IT Department Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Technology and Design Bunda Mulia University Jakarta Indonesia BINUS Graduate Program - Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Applying a digital strategy is proven to be one way to reduce the infection rate of COVID-19. As a fundamental part of it, database is capable to create a data repository to predict uncertain impacts of the pandemic. The research proposed a database design for project C-Beta that emphasized on creating an ideal system to record and monitor the daily health condition of people with COVID-19. Mainly, the research adopted Connolly and Begg principles of Database Software Development Life Cycle. As a result, the database design activities produced blueprints in the forms of data dictionary, ERD, and user views. The database design delivered in the research included with several features namely user verification, restricted research membership, and flexibility in survey creation. Based on analysis and design phase, C-Beta provides a method to collect data for COVID-19 clinical study. Researchers who utilize C-Beta is able to manage multiple surveys and extract all collected records at the same time. © 2021 IEEE.

关键词： COVID-19

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Artificial Intelligence Assisted IoT Data Intrusion Detection 4

Artificial Intelligence Assisted IoT Data Intrusion Detectio...

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4th International Conference on Computing and Communications Technologies, ICCCT 2021

作者： Shukla, Amogh Ahamad, Shahanawaj Rao, G.Nageswara Al-Asadi, Avein Jabar Gupta, Ankur Kumbhkar, Makhan Research Assistant Vellore Institute of Technology Vellore India College of Computer Science and Engineering University of Hail Hail City Saudi Arabia Lakireddy Bali Reddy College of Engineering Dept. of EEE Mylavaram India Computer Institute of Sulaimani Dept. of Computer Program Iraq Vaish College of Engineering Dept. of Computer Science and Engineering Rohtak India Christian Eminent College Dept. of Computer Science Indore India

ISBN: (纸本)9781665414470

It has become increasingly important since the introduction of Internet of Things (IoT) technology to ensure the security of IoT networks. Network anomalies and threats can be identified and predicted using a variety of intrusion detection systems (IDS). Despite the fact that the Internet of Things is vulnerable to attacks, early detection of malicious behavior can prevent data from being compromised. In this work, the primary goal is to develop machine artificial intelligence energy efficient models that can be used to detect attacks on the Internet of Things network. Normal and attack data from the IoT environment must be collected in order to construct a model. The Bayesian Network, the Artificial Neural Network, and the Support Vector Machine are considered to have the most promise. Using roundtrip time and power consumption data, a traditional three-layer Artificial Neural Network is put through its paces in the real world. © 2021 IEEE.

关键词： Internet of things

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Defect Detection of Agricultural Commodities using Image Processing and Artificial Neural Networks

Defect Detection of Agricultural Commodities using Image Pro...

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2022 International Seminar on Application for technology of Information and Communication, iSemantic 2022

作者： Suprijono, Herwin Kartikadarma, Etika Yusianto, Rindra Marimin, Marimin Dian Nuswantoro University Faculty of Engineering Department of Electrical Engineering Semarang53192 Indonesia Dian Nuswantoro University Study Program of Information Technology Faculty of Computer Science Semarang53192 Indonesia Dian Nuswantoro University Faculty of Engineering Department of Industrial Engineering Semarang53192 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology West Java Bogor16002 Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Production losses of agricultural commodities on agricultural land due to product defects depend on the level of pest and disease attacks. Defects cause the product not to be harvested or rejected by the market. Data from the Ministry of Agriculture of the Republic of Indonesia in 2021 shows the percentage of defective products for this commodity is 5%. This study aims to detect defects in agricultural products using image processing and artificial neural networks. We used the screening threshold method with potato research samples from Dieng, Indonesia. The study results for spot defects showed that from 100 training data, there were five incorrect identification data, so the accuracy of the training process was 95%. At the same time, the hole defect accuracy is better, which is 97%. It shows that defects in agricultural commodities can be detected using this method. For further research, the development of agricultural product sorting can combine sorting techniques with this method. © 2022 IEEE.

关键词： Defects

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Enhanced Prognostics and Health Management in Automated Driving Systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception

arXiv

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arXiv 2024年

作者： Shahzad, Sahibzada Adil Hashmi, Ammarah Peng, Yan-Tsung Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei Taiwan The Department of Computer Science National Chengchi University Taipei Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan The Institute of Information Science Academia Sinica Taiwan

Multimodal deepfakes involving audiovisual manipulations are a growing threat because they are difficult to detect with the naked eye or using unimodal deep learning-based forgery detection methods. Audiovisual forensic models, while more capable than unimodal models, require large training datasets and are computationally expensive for training and inference. Furthermore, these models lack interpretability and often do not generalize well to unseen manipulations. In this study, we examine the detection capabilities of a large language model (LLM) (i.e., ChatGPT) to identify and account for any possible visual and auditory artifacts and manipulations in audiovisual deepfake content. Extensive experiments are conducted on videos from a benchmark multimodal deepfake dataset to evaluate the detection performance of ChatGPT and compare it with the detection capabilities of state-of-the-art multimodal forensic models and humans. Experimental results demonstrate the importance of domain knowledge and prompt engineering for video forgery detection tasks using LLMs. Unlike approaches based on end-to-end learning, ChatGPT can account for spatial and spatiotemporal artifacts and inconsistencies that may exist within or across modalities. Additionally, we discuss the limitations of ChatGPT for multimedia forensic tasks. © 2024, CC BY.

关键词： Audiovisual

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Mobile Application Implementation as Agriculture 4.0 Strategy for Sugarcane Yield Prediction

Mobile Application Implementation as Agriculture 4.0 Strateg...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Hangger Gahara Mawandha Reza Rahutomo Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology Institut Pertanian Stiper Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350304084

ISBN: (纸本)9798350304091

Information engineering strategies play a significant and transformative role in shaping the landscape of digital agriculture. It drives innovation and efficiency in modern farming practices, with a specific focus on enhancing the value of agricultural commodities, such as sugarcane. The application of mobile technology plays a crucial part in achieving increased sugarcane productivity. Mobile applications, equipped with advanced developer features, offer rapid and user-friendly access to vital information. Traditional methods of estimating sugarcane production relied on manual data recording on paper, followed by data transfer to computer systems, typically managed by sugar factory junior plant officers. This conventional approach presents several inherent weaknesses, including time and effort intensiveness during data recording and entry. Additionally, the potential for errors in calculation and data input is a concern. The storage capacity for paper-based documents is finite, and farmers are often unable to autonomously assess their production potential. The objective of this research is to confront these obstacles by creating a sugarcane production application for Android. This application functions as an information engineering approach focused on forecasting sugarcane yields for plantation owners and their assistants. The development procedure adhered to the systematic waterfall method, following the principles of the Software Development Life Cycle (SDLC) model. Data collection was carried out through observations, while interviews with junior plant officers provided valuable insights into sugarcane estimation techniques. Analysis involved the synthesis of observational and interview data to inform the design of the application’s interface and algorithmic system. The resulting application significantly simplifies the process of estimating production potential for farmers, enabling them to access sugarcane productivity data during harvest. Consequently, sugar

关键词： Productivity Operating systems Estimation Production facilities Raw materials Mobile applications Recording

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The Impact of Middle and High School Students Participation in the Innovation Nation Competition on Their Education

The Impact of Middle and High School Students Participation ...

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Information technology (ACIT)

作者： Dheya Mustafa Samah Zriqat Intisar Ghazi Mustafa Qutaibah AlThebyan dept. of Computer Engineering College of Engineering The Hashemite University Zarqa Jordan dept. of Information Technology and Computer Science Yarmouk University Irbid Jordan dept. of Educational Sciences College of Education Qatar University Doha Qatar Cybersecurity Program Al Ain University Al Ain UAE Jordan University of Science and Technology Irbid Jordan

The Innovative Nation competition is a Jordanian initiative that uses workshops, training camps, and competitions to improve students' general and soft skills, problem-solving, creativity, and invention. The curriculum is divided into three parts, each of which has the potential to increase interest in science, technology, Engineering, and Mathematics (STEM). The goal of this research is to determine how much the STEM competition has influenced students' education and decision to pursue jobs in science and technology. Personal interviews with students and mentors were employed in this study to assess their skill sets, prospective career paths, and interests. The collected data was reviewed, and it was found that participation in the Innovation Nation competition increased students' interest in STEM vocations.

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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International Conference on ICT For Smart Society (ICISS)

作者： Desyra Sukma Dewanthi Hari Kristopo Nunung Nurul Qomariyah Muhammad Axel Management Department Business Management & Marketing Program BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18–40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business.

关键词： Data privacy Privacy Consumer behavior Focusing Electronic commerce Security Artificial intelligence Sustainable development Interviews Recommender systems

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