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SSRN 2022年

作者： Sunardi Yudhana, Anton Fahmi, Miftahuddin Department of Electrical Engineering Universitas Ahmad Dahlan Yogyakarta55191 Indonesia Master Program of Informatics Universitas Ahmad Dahlan Yogyakarta55191 Indonesia

Waste identification has been widely carried out using machine learning with various methods. One method that is often used is the Convolutional Neural Network (CNN). Waste identification has a function as machine learning that helps and accelerates the waste management process, especially the waste sorting process. This research was conducted by collecting data by downloading a dataset from Kaggle. The data were then be labeled in a folder according to their type. The dataset folder was entered into a Python machine learning system equipped with the TensorFlow module. The data was preprocessed so that it became simplified and the machine could read the data. The data was trained to be able to distinguish between the types of waste. After that, the CNN method was implemented in machine learning so that the machine could classify the waste image. The results of the CNN implementation show that the system can classify images with the greatest accuracy of 99.83% with a validation accuracy of 76.1% and the smallest loss percentage of about 0.04% at epoch 18. The results show that there was overfitting in the dataset classification process caused by the average accuracy value of the training data and validation data being too large, around 20%. © 2022, The Authors. All rights reserved.

关键词： Waste management

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Comprehensive Analysis and Remediation of Insecure Direct Object References (IDOR) Vulnerabilities in Android APIs

Comprehensive Analysis and Remediation of Insecure Direct Ob...

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Cryptography, informatics, and Cybersecurity (ICoCICs), IEEE International Conference on

作者： Semi Yulianto Roni Reza Abdullah Benfano Soewito Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Informatics Engineering National Institute of Science and Technology Jakarta Indonesia Computer Science Department Binus Graduate Program. Master of Computer Science Bina Nusantara University Jakarta Indonesia

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience.

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A Precision Public Health Study on the Divergence of Life Expectancies over Time in United States Counties

A Precision Public Health Study on the Divergence of Life Ex...

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2023 IEEE Conference on Technologies for Sustainability, SusTech 2023

作者： Sheth, Suraj Bettencourt, Luís Pritzker School of Medicine and Interdisciplinary Scientist Training Program United States University of Chicago Biomedical Informatics Precision Medicine and Global Health Mansueto Institute for Urban Innovation ChicagoIL United States University of Chicago Mansueto Institute for Urban Innovation Ecology and Evolution Faculty Dept. of Sociology ChicagoIL United States Santa Fe Institute External Complex Systems United States

ISBN: (纸本)9798350345483

Ensuring healthy lifespans and promoting equity in health outcomes is a critical component of the United Nations Sustainable Development Goals, and progress has largely been measured using national life expectancy averages. While national averages reveal broad trends over time, they also largely obscure differences and inequalities at the subnational level. In studies of sustainability and public health, divergence refers to the increasing difference between two values over time. A major topic of interest is how subnational units, such as counties, deviate from the national average over time. In this work, we examined how three factors critical to evaluating the health of communities and quality of life, namely life expectancy, education, and income, have deviated in the United States at the county level in comparison to the national average. We found that while deviations in income and education have remained largely constant, the life expectancies of some counties have increased significantly over the national average, while others have stalled or even decreased in comparison. There has been a systematic divergence in life expectancies in US counties over a 34-year period leading to increasing disparities between counties. Gains in life expectancy were primarily concentrated in metropolitan areas while rural counties lagged, increasing urban-rural disparities over time. Identifying the upward or downward trends in life expectancy at the county level and understanding the root causes and risk factors driving those trends is critical to formulating customized Precision Public Health interventions and policies to improve health outcomes and create sustainable communities. © 2023 IEEE.

关键词： Risk management

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Analysis of Perceptron Quantum Artificial Neural Networks to Classify the Feasibility of Prospective Debtors 1

Analysis of Perceptron Quantum Artificial Neural Networks to...

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1st International Conference on Advanced Information Scientific Development, ICAISD 2020

作者： Pujiastuti, Lise Wahyudi, Mochamad Solikhun STMIK Antar Bangsa Information System Study Program Banten Indonesia Universitas Bina Sarana Informatika Faculty of Technical and Informatics Information Technology Study Program DKI Jakarta Indonesia AMIK Tunas Bangsa Pematangsiantar Informatics Management Study Program North Sumatra Indonesia

Bank is a business entity whose activities are collecting funds from the public in the form ofdeposits and channeling them to the public in the form of loans or other forms to improve the livesof many people. This study aims to classify the feasibility data of prospective borrowers with the perceptron algorithm using quantum computing to facilitate the bank in determining the prospective debtor. The results of this study are an analysis of the feasibility of prospective borrowers usingthe perceptron algorithm with quantum computing. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Neural networks

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programmable multimode squeezed light at visible wavelengths with >400 modes

Programmable multimode squeezed light at visible wavelengths...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Presutti, Federico Wright, Logan G. Ma, Shi-Yuan Wang, Tianyu Malia, Benjamin K. Onodera, Tatsuhiro McMahon, Peter L. School of Applied and Engineering Physics Cornell University 142 Sciences Dr IthacaNY14850 United States NTT Physics and Informatics Laboratories NTT Research Inc. 940 Stewart Dr SunnyvaleCA94085 United States Intelligence Community Postdoctoral Research Fellowship Program 142 Sciences Dr IthacaNY14850 United States Kavli Institute Cornell for Nanoscale Science Cornell University 245 Feeney Way IthacaNY14850 United States

We demonstrate a source of highly multimode, high gain vacuum squeezed light, and demonstrate how ultra-broadband frequency conversion enables the use of visible-light detector arrays and programmable frequency-domain... 详细信息

ISBN: (纸本)9781957171395

关键词： Squeezed light

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Running Cooperative Tasks in a Multi-Robot System with Cyber-Physical Features

Running Cooperative Tasks in a Multi-Robot System with Cyber...

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IEEE Latin American Robotics Symposium, LARS

作者： Héber Renato Fadel de Morais João Paulo Lima Silva de Almeida Lúcia Valéria Ramos de Arruda Graduate Program in Electrical Engineering and Industrial Informatics (CPGEI) Federal University of Technology - Paraná Curitiba Brazil Federal Institute of Paraná Jacarezinho Brazil

ISBN: (数字)9781665462808

ISBN: (纸本)9781665462815

This paper develops a system based on cyber-physical concepts to support robots in tasks that require cooperative work and information sharing. The work's inspiration is the need of the 4.0 industry for cobots visiting different workstations to assist human workers, for tools and raw material transportation, and other tasks. As in any industrial environment, unexpected events may occur, and the cobots must face them, reducing their effects on the production line. In this work, multiple robots (MR) managed by a system with cyber-physical (CP) features cooperate in an experimental dynamic environment to safely accomplish several tasks. For this, the robots must visit targets (analogous to workstations) in an orderly way, while one support robot must deal with oil stains on the environment's floor detected along the robots' navigation (unexpected event). The proposed CP-MR system is evaluated through experimental results allowing a performance analysis.

关键词： Visualization Service robots Federated learning Information sharing Robot sensing systems Workstations Multi-robot systems

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REPRESENTATION SHATTERING IN TRANSFORMERS: A SYNTHETIC STUDY WITH KNOWLEDGE EDITING

arXiv

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arXiv 2024年

作者： Nishi, Kento Okawa, Maya Ramesh, Rahul Khona, Mikail Tanaka, Hidenori Lubana, Ekdeep Singh CBS-NTT Program in Physics of Intelligence Harvard University United States Computer and Information Science University of Pennsylvania United States Department of Physics Massachusetts Institute of Technology United States Physics and Informatics Lab NTT Research Inc United States

Knowledge Editing (KE) algorithms alter models’ weights to perform targeted updates to incorrect, outdated, or otherwise unwanted factual associations. To better identify the possibilities and limitations of these approaches, recent work has shown that applying KE can adversely affect models’ factual recall accuracy and diminish their general reasoning abilities. While these studies give broad insights into the potential harms of KE algorithms, e.g., via performance evaluations on benchmarks, we argue little is understood as to why such destructive failures occur. Is it possible KE methods distort representations of concepts beyond the targeted fact, hence hampering abilities at broad? If so, what is the extent of this distortion? Motivated by such questions, we define a novel synthetic task wherein a Transformer is trained from scratch to internalize a "structured" knowledge graph. The structure enforces relationships between entities of the graph, such that editing a factual association has "trickling effects" on other entities in the graph (e.g., altering X’s parent is Y to Z affects who X’s siblings’ parent is). Through evaluations of edited models and analysis of extracted representations, we show that KE inadvertently affects representations of entities beyond the targeted one, distorting relevant structures that allow a model to infer unseen knowledge about an entity. We call this phenomenon representation shattering and demonstrate that it results in degradation of factual recall and reasoning performance more broadly. To corroborate our findings in a more naturalistic setup, we perform preliminary experiments with pre-trained Llama and Mamba models, reproducing the representation shattering effect therein as well. Overall, our work yields a precise mechanistic hypothesis to explain why KE has adverse effects on model abilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Knowledge graph

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Automatic Identification and Reconstruction 3D Image of the Rhinosinusitis Mucus

Automatic Identification and Reconstruction 3D Image of the ...

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International Conference of Information and Communication Technology (ICoICT)

作者： Ondra Eka Putra Sumijan Muhammad Tajuddin Department of Informatics Technology Faculty of Computer Science Universitas Putra Indonesia YPTK Padang Padang Indonesia Doctoral Program in Information Technology Universitas Putra Indonesia YPTK Padang Padang Indonesia Department of Computer Science Bumigora University Mataram Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Rhinosinusitis Mucus is mucus produced by mucus glands found in the nose in the paranasal sinus area which indicates inflammation. Segmentation of rhinosinusitis mucus is very difficult to do because of the many characteristics of different objects in the image of the paranasal Sinus CT-Scan in separating the mucus object with its background before the extraction process. The purpose of this study was to accurately identify and measure the mucus area of rhinosinusitis from CT-scan images of rhinosinusitis patients. The test data used in this study is secondary data from CT-Scan of paranasal sinus sagittal view in radiology RSUP M. Djamil Padang, Padang City, West Sumatra, Indonesia, using the Philips Ingenuity CT CT-Scan model type MRC880 numbered Tube series 163889. The segmentation process is carried out to obtain the optimal multi threshold value from each slice of the test image using the Improved Adaptive Multi Threshold (IAMT) segmentation method which produces a binary image to separate the mucus object with its background before the extraction process. Furthermore, the results of IAMT are applied to the identification and extraction model automatically to obtain the area of the rhinosinusitis mucus object. The extraction results were used for 2D to 3D image reconstruction in calculating the volume of rhinosinusitis mucus objects. The test results showed that the smallest area of rhinosinusitis mucus was 0.516 cm 2 and the largest was 1.807 cm 2 , and the average volume was 0.95 cm 3 with Accuracy is 96,5%. The identified areas and volumes indicate inflammation in the area of the paranasal sinuses and require treatment. Thus, this study can help doctors in making the right decision to take further medical action.

关键词： Image segmentation Visualization Adaptation models Three-dimensional displays Computed tomography Urban areas Nose Radiology Object recognition Image reconstruction

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Real-time Vehicular Sensing System Design for Integrated Sensing as Service and IoT Platform in Smart Cities

Real-time Vehicular Sensing System Design for Integrated Sen...

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International Conference on ICT For Smart Society (ICISS)

作者： Mindit Eriyadi Fetty Fitriyanti Lubis Suhono Harso Supangkat Fadhil Hidayat Doctoral Program of Instiut Teknologi Bandung Indonesia School of Applied Science Universitas Telkom Bandung Indonesia School of Electrical Engineering and Informatics Instiut Teknologi Bandung Indonesia Smart City and Community Innovation Center Institut Teknologi Bandung Bandung Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This study focuses on the design and implementation of a Real-time Vehicular Sensing System (RVSS) aimed at enhancing air quality monitoring in smart cities. By integrating advanced sensors into moving vehicles, the system captures real-time environmental data across diverse urban areas. This research addresses the limitations of static monitoring stations, offering broader geographical coverage and greater data collection flexibility. The proposed system includes low-cost components, enhanced sensor calibration, and optimized data processing algorithms to ensure accurate and reliable data. It also features real-time data transmission and a user-friendly dashboard for dynamic monitoring. The primary contribution of this study is to establish a robust foundation for an effective Vehicular Sensing system, providing valuable insights for researchers and policymakers to improve air quality management in smart cities.

关键词： Smart cities Heuristic algorithms Air quality Real-time systems Sensor systems Reliability Vehicle dynamics Monitoring Intelligent sensors System analysis and design

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Streamlining CLABSI Diagnosis: An Innovative Process Modeling Approach Using UML Activity Diagrams

Streamlining CLABSI Diagnosis: An Innovative Process Modelin...

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2024 Global Digital Health Knowledge Exchange and Empowerment Conference, gDigiHealth.KEE 2024

作者： Daboor, Mohammad Kharbat, Faten Azzuni, Fathi mueen Sultan, Iyad Odeh, Mohammad King Hussein Cancer center Nursing Department Amman Jordan College of Engineering Al Ain University Abu Dhabi United Arab Emirates Kanad Hospital Pediatric Department Abu Dhabi AlAin United Arab Emirates King Hussein Cancer Center Department of Pediatrics Amman Jordan Cancer Care Informatics Program Amman Jordan College of Art Technology and Environment University of the West of England Bristol United Kingdom Global Academy for Digital Health Bristol United Kingdom

ISBN: (纸本)9798331531331

This paper presents the development and validation of a Unified Modeling Language (UML) Activity Diagram for diagnosing Central Line-Associated Bloodstream Infections (CLABSIs) based on guidelines from the Centers for Disease Control and Prevention (CDC) and the National Healthcare Safety Network (NHSN). The activity diagram translates complex guidelines into an accessible visual model, refined through iterative feedback from domain experts in healthcare and software engineering. This tool aims to enhance understanding, adherence, and education regarding CLABSI diagnosis, ultimately improving diagnostic accuracy and consistency. Additionally, the diagram serves as a foundation for developing software applications to automate the detection and diagnosis of CLABSIs, further enhancing clinical efficiency and patient outcomes. By providing a clear and structured process model, this study contributes significantly to the field of healthcare informatics and patient safety. © 2024 IEEE.

关键词： Application programs

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