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2023 Symposium on Internet of Things, SIoT 2023

作者： Matos, Saulo Neves Coletti, Otavio Ferracioli Filho, Fernando Ugucioni De Carvalho, Ricardo C. C. L. Pinto, Thomas V. B. De Barros, Luiz G. D. Ranieri, Caetano Mazzoni Lopes, Bruno Eduardo Ueyama, Jo Pessin, Gustavo Institute of Mathematics and Computer Science University of Sao Paulo Brazil Sao Carlos School of Engineering University of Sao Paulo Brazil Vale Institute of Technology Ouro Preto Brazil Universidade Federal of Minas Gerais Graduate Program in Electrical Engineering MG Belo Horizonte Brazil Vale -SA Vitoria Brazil

ISBN: (纸本)9798350329438

Conveyor belts are commonly used in the mining industry for efficient material transport. However, they are prone to failures such as idler anomalies, belt tears, and misalignment. Current monitoring systems only evaluate the conveyor or idle belt's condition at the installation point. This study proposes a method to detect faults in conveyor belts, specifically idler anomalies and belt deviation, using two IMUs installed at the lateral extremities of the belt to evaluate its health along its route. In a reduced-scale conveyor belt, we conducted experiments by collecting gyroscopes and accelerometer data in various scenarios, including normal conditions and anomaly situations. We modified the idlers' physical structure or changed the belt tracks to create these scenarios. We used similarity analysis techniques to compare the data collected and performed statistical hypothesis analysis to observe the influence of the anomaly scenarios on the gyroscope and accelerometer data. We used Dynamic Time Warping (DTW) for gyroscope data to compare ground truth with anomaly scenarios. For the acceleration data, we performed vibration analysis by converting the time domain data to the frequency using Fast Fourier Transform (FFT) and evaluating the difference in spectral density between the reference and anomaly data. Statistical hypothesis analysis revealed that anomaly scenar-ios influenced the gyroscope x-axis and z-axis more. According to the spectral density analysis of the accelerometer, it was revealed that belt misalignment on the y-axis may be detected. © 2023 IEEE.

关键词： Belt conveyors

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Fuzzy Logic for Assessing the Health of a Corn Crop within Family Farming

Fuzzy Logic for Assessing the Health of a Corn Crop within F...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Vanessa Mendonça Mikael Santos Daniel M. Muñoz Carlos Humberto Llanos Department of Computer Science Federal Institute of Brasilia Department of Mechanical Engineering Mechatronics Graduate Program Department of Agronomy Federal Institute of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as ’Very Favorable’, and an accuracy of 65% in Scenarios 2 and 4, categorized as ‘Unfavorable’. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production.

关键词： Fuzzy logic Accuracy Computational modeling Plantations Crops Production Color Nutrients Monitoring Fuzzy systems

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Design and Optimization of the Porous Metamaterial EM Absorbers in X-band

Design and Optimization of the Porous Metamaterial EM Absorb...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Kanat Anurakparadorn Alan Taub Eric Michielssen Macromolecular Science and Engineering Program Department of Material Science and Engineering University of Michigan Ann Arbor Michigan USA Department of Mechanical Engineering University of Michigan Ann Arbor Michigan USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan USA

A strategy that combines experiment and simulation to design and optimize electromagnetic (EM) metamaterial absorbers containing a periodic porous structure is described. The approach provides the ability to produce absorbers that meet multiple user-specified objectives. Using the measured intrinsic properties of the baseline materials as an input to EM-field based computational modelling and optimization, absorption by the studied metamaterials measured by their reflection loss (RL) increases significantly. The resulting metamaterials have the potential for lower cost and lighter weight while providing greater protection than traditional metal gaskets and foams.

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Model for Endometriosis Detection Using Machine Learning Algorithms

Model for Endometriosis Detection Using Machine Learning Alg...

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Software engineering and computer science (CSECS), International Conference on

作者： Alexis Bautista Jahir Tardillo José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Endometriosis is a chronic disease that affects a considerable percentage of women of reproductive age and is characterized by the presence of endometrial tissue outside the uterine cavity, leading to symptoms such as pelvic pain and dysmenorrhea. The aim of this study is to develop a predictive model for the classification of endometriosis using four Machine Learning algorithms: Random Forest, LASSO, SVM, and Naive Bayes. For this purpose, a dataset from the Global Health Data Exchange was utilized, consisting of 1,000 cases of patients with endometriosis. The methodology included data cleaning and preprocessing, as well as the evaluation of each algorithm's performance using four metrics: precision, recall, F1-Score, and accuracy. The findings revealed that the Random Forest algorithm was the most effective in identifying endometriosis, outperforming the other algorithms with a precision of 0.99 for the “endometriosis” class and an overall accuracy of 0.98.

关键词： Support vector machines Measurement Training Machine learning algorithms Accuracy Software algorithms Predictive models Bayes methods Random forests Software engineering

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DECKER: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification

arXiv

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arXiv 2023年

作者： Zou, Anni Zhang, Zhuosheng Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on grasping either unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. In light of this, we propose DECKER, a commonsense fact verification model that is capable of bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. Experimental results on two commonsense fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the effectiveness of our DECKER and further analysis verifies its capability to seize more precious information through reasoning. The official implementation of DECKER is available at https://***/Anni-Zou/Decker. © 2023, CC BY.

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Chinese Spelling Correction as Rephrasing Language Model

arXiv

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arXiv 2023年

作者： Liu, Linfeng Wu, Hongqiu Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

This paper studies Chinese Spelling Correction (CSC), which aims to detect and correct the potential spelling errors in a given sentence. Current state-of-the-art methods regard CSC as a sequence tagging task and fine-tune BERT-based models on sentence pairs. However, we note a critical flaw in the process of tagging one character to another, that the correction is excessively conditioned on the error. This is opposite from human mindset, where individuals rephrase the complete sentence based on its semantics, rather than solely on the error patterns memorized before. Such a counter-intuitive learning process results in the bottleneck of generalizability and transferability of machine spelling correction. To address this, we propose Rephrasing Language Model (ReLM), where the model is trained to rephrase the entire sentence by infilling additional slots, instead of character-to-character tagging. This novel training paradigm achieves the new state-of-the-art results across fine-tuned and zero-shot CSC benchmarks, outperforming previous counterparts by a large margin. Our method also learns transferable language representation when CSC is jointly trained with other tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Exploring Lightweight Interventions at Posting Time to Reduce the Sharing of Misinformation on Social Media

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Proceedings of the ACM on Human-computer Interaction 2021年第CSCW1期5卷 1–42页

作者： Jahanbakhsh, Farnaz Zhang, Amy X. Berinsky, Adam J. Pennycook, Gordon Rand, David G. Karger, David R. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States University of Washington Allen School of Computer Science and Engineering SeattleWA United States Massachusetts Institute of Technology Political Science CambridgeMA United States University of Regina Hill/Levene Schools of Business Regina Canada Massachusetts Institute of Technology Sloan School of Management/Brain and Cognitive Sciences CambridgeMA United States

When users on social media share content without considering its veracity, they may unwittingly be spreading misinformation. In this work, we investigate the design of lightweight interventions that nudge users to assess the accuracy of information as they share it. Such assessment may deter users from posting misinformation in the first place, and their assessments may also provide useful guidance to friends aiming to assess those posts themselves. In support of lightweight assessment, we first develop a taxonomy of the reasons why people believe a news claim is or is not true;this taxonomy yields a checklist that can be used at posting time. We conduct evaluations to demonstrate that the checklist is an accurate and comprehensive encapsulation of people's free-response rationales. In a second experiment, we study the effects of three behavioral nudges - -1) checkboxes indicating whether headings are accurate, 2) tagging reasons (from our taxonomy) that a post is accurate via a checklist and 3) providing free-text rationales for why a headline is or is not accurate - -on people's intention of sharing the headline on social media. From an experiment with 1668 participants, we find that both providing accuracy assessment and rationale reduce the sharing of false content. They also reduce the sharing of true content, but to a lesser degree that yields an overall decrease in the fraction of shared content that is false. Our findings have implications for designing social media and news sharing platforms that draw from richer signals of content credibility contributed by users. In addition, our validated taxonomy can be used by platforms and researchers as a way to gather rationales in an easier fashion than free-response. © 2021 Owner/Author.

关键词： Taxonomies

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Autonomous Electron Tomography Reconstruction Using Bayesian Optimization

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Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada 2023年第1期29卷 1915-1916页

作者： Millsaps, William Schwartz, Jonathan Di, Zichao Wendy Jiang, Yi Hovden, Robert Department of Nuclear Engineering Radiological Sciences University of Michigan Ann Arbor MI United States Department of Materials Science and Engineering University of Michigan Ann Arbor MI United States Mathematics and Computer Science Division Argonne National Laboratory Lemont IL United States Advanced Photon Source Facility Argonne National Laboratory Lemont IL United States Applied Physics Program University of Michigan Ann Arbor MI United States

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Development of a Phase-Locked Loop Circuit in Running Frequency Mode for Capacitance-to-Voltage Conversion

Development of a Phase-Locked Loop Circuit in Running Freque...

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International Electrical engineering Congress (iEECON)

作者： Tada On-khong Noppadon Sisuk Paramote Wardkein Ratchanoo Katman Kriangsak Prompak Department of Physics Faculty of Science Naresuan University Phitsanulok Thailand Program in Computer Engineering Faculty of Industrial Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand Department of Telecommunication School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Program of Physics Faculty of Science and Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Capacitance-to-voltage conversion is essential for capacitive sensors in various industries, including touch interfaces, medical devices, and moisture measurement. However, circuit design faces challenges like non-linearity, noise, and small capacitance variations affecting voltage signals. This article proposes a phase-locked loop in free-running oscillator mode with a frequency-to-voltage converter to enhance accuracy and stability. Test results show a 97.9% measurement accuracy, demonstrating reduced noise and improved stability. The proposed circuit is ideal for high-precision applications in diverse environments, overcoming limitations of traditional methods.

关键词： Accuracy Sensitivity Moisture measurement Noise Moisture Capacitance Frequency conversion Circuit stability Phase locked loops Standards

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Malicious code detection in android: the role of sequence characteristics and disassembling methods

arXiv

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arXiv 2023年

作者： Balikcioglu, Pinar G. Sirlanci, Melih Kucuk, Ozge A. Ulukapi, Bulut Turkmen, Ramazan K. Acarturk, Cengiz Cyber Security Department Middle East Technical University Ankara Turkey Computer Science and Engineering Department Ohio State University ColumbusOH United States Cognitive Science Department Jagiellonian University Krakow Poland

The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in the contingency tables in malware detection studies has become a popular and efficient method and enabled researchers to evaluate their methodologies comparatively. In this study, we wanted to investigate and emphasize the factors that may affect the accuracy values of the models managed by researchers, particularly the disassembly method and the input data characteristics. Firstly, we developed a model that tackles the malware detection problem from a Natural Language Processing (NLP) perspective using Long Short-Term Memory (LSTM). Then, we experimented with different base units (instruction, basic block, method, and class) and representations of source code obtained from three commonly used disassembling tools (JEB, IDA, and Apktool) and examined the results. Our findings exhibit that the disassembly method and different input representations affect the model results. More specifically, the datasets collected by the Apktool achieved better results compared to the other two disassemblers. © 2023, CC BY.

关键词： Android (operating system)

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