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A Novel Method of Evaluating the Harmonic Power Analyzer's Performance for PC-Based Testing and Measurement Applications Related to Electromagnetic Compatibility 2

A Novel Method of Evaluating the Harmonic Power Analyzer's P...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Adeline Sneha, J. Wilfred Blessing, N.R. Palarimath, Suresh Poorana Senthilkumar, S. Manigandansekar Sutherlin Subitha, G. School of Computing Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman College of Computing and Information Sciences-Salalah University of Technology and Applied Sciences Oman Dr. N. G. P Arts and Science College Department of Computer Applications Tamil Nadu Coimbatore India Newyork University Abudhabi United Arab Emirates Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India

ISBN: (纸本)9798331542573

This paper discusses the methods for performance analysis of a PC-based harmonic power analyzer used in EMC testing and measuring applications. According to IEC 61000-3-2, this is an easy-to-use, reasonably priced way to assess how well a harmonics emission measurement system is performing in real time. There is no mention of any alternative approach or calibration method to verify the performance analysis in the IEC 61000-3-2 standard. Different vendor equipment is used to create a single harmonic emission monitoring setup. Thus, getting calibrating services is a highly difficult task that also requires a significant financial investment. Using this straightforward, low-cost analysis technique, it is possible to monitor the operation of the measurement setup locally and address all of these problems. Overall, the paper provides a comprehensive overview of the new approach to performance analysis of a PC-based harmonics power analyzer for EMC test and measurement applications, emphasizing the importance of a simple, cost-effective, and reliable method for maintaining measurement system performance. © 2024 IEEE.

关键词： Cost effectiveness

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Self-supervised Prototype Learning for Spatio-Temporal Enhanced Ultrasound-based Prostate Cancer Detection 22

Self-supervised Prototype Learning for Spatio-Temporal Enhan...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Elghareb, Tarek Jamzad, Amoon Nhat To, Minh Nguyen Fooladgar, Fahimeh Wilson, Paul F.R. Sojoudi, Samira Reznik, Gabrielle Leveridge, Michael Siemens, Robert Chang, Silvia Black, Peter Mousavi, Parvin Abolmaesumi, Purang University of British Columbia Department of Electrical and Computer Engineering BC Canada Vector Institute Toronto Canada School of Computing Queen's University KingstonON Canada Vancouver General Hospital Vancouver Canada Kingston General Hospital Kingston Canada

ISBN: (纸本)9798331520526

Prostate cancer detection in ultrasound data presents significant challenges due to the highly heterogeneous nature of the cancer and its appearance in ultrasound images. In this work, we introduce a novel multi-modal framework that integrates spatial information in ultrasound images with temporal time-series signals across a sequence of ultrasound images for robust prostate cancer detection. Our framework employs a clustering-based self-supervised learning approach, which allows the model to learn shared, complementary representations from combined spatio-temporal data. This approach not only captures the unique properties of spatial and temporal information, but also enhances the model's ability to handle noisy labels due to imperfect alignment between imaging data and histopathology reports, resulting in improved classification performance. Our method achieves a 7% improvement in AUC and a 10% increase in balanced accuracy compared to models relying on spatial or temporal ultrasound data alone. Our code and visualizations can be found in https://***/DeepRCL/Multi-SWAV. © 2025 IEEE.

关键词： Self-supervised learning

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Towards Integrated Spatial Crowdsourcing: Online Privacy-Preserving Selection

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Wang, Hengzhi Ma, Minghe Cui, Laizhong Liu, Jiangchuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China Simon Fraser University School of Computing Science Burnaby Canada

We study an intriguing and practical scenario of online Spatial Crowdsourcing (SC), in which workers have the flexibility to perform tasks using various methods, such as walking, driving, or utilizing Unmanned Aerial Vehicles (UAVs). This results in workers having heterogeneous, arbitrary, and non-stationary utilities over time. We refer to this scenario as integrated SC. Unfortunately, existing studies are limited in addressing integrated SC settings due to two aspects: (1) these studies are based on the assumption that workers' utilities are independently and identically distributed and follow a stationary distribution like Gaussian, which does not hold in integrated SC;(2) their approaches fail to provide personalized privacy preservation for different workers. Motivated by these limitations, we closely investigate the heterogeneous utility and personalized privacy requirement in integrated SC and propose an Online Personalized Privacy-preserving Selection framework (OPPS). In this framework, we present an online selection policy that balances the exploration-exploitation trade-off given heterogeneous utilities and develop a built-in privacy policy that ensures differential privacy guarantee. We then demonstrate that our framework effectively addresses the trade-off by deriving a sublinear, privacy-related upper bound on regret that scales as O(√T). Extensive numerical simulations based on real-world drone datasets are conducted to validate the effectiveness of our framework compared with state-of-the-art approaches. © 2004-2012 IEEE.

关键词： Differential privacy

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Dynamic Stop-Loss Approach for Short Term Trades using Deep Learning

Dynamic Stop-Loss Approach for Short Term Trades using Deep ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Samarasekara, Iromie K. Mendis, Oshan K. Ahangama, Sapumal Atukorale, Ajantha S. University of Colombo University of Colombo School of Computing Sri Lanka University of Moratuwa Department of Computer Science & Engineering Sri Lanka

ISBN: (纸本)9781665480451

Stop-Loss strategies are often used by investors to combat negative returns by predetermining thresholds at which they should exit trades. Though existing traditional Stop-Loss mechanisms such as Fixed Stop-Loss and Trailing Stop-Loss are empirically proven to have the ability to minimize risks associated with trades, they still face serious challenges when it comes to achieving a balance between risk and return. In this study, we develop a Deep Learning model that combines the concept of Stop-Loss with the capabilities offered by Deep Neural Networks. The architecture is composed of three components where trend detection and price prediction components provide inputs to the stop price prediction component which predicts the variation of stop price. The study focuses on short term trades of minute frequency which involves analysing massive chunks of data that fluctuates rapidly within extremely short time intervals. Despite the advancements that has taken place in the context of Big Data analytics, intraday financial time series analysis has not received much academic attention. The model utilizes convolutional layers to capture spatial features along with Long Short Term Memory networks to capture temporal dependencies in price sequences. The proposed solution gives outstanding results for diverse market conditions and it works specifically well for trades that are downtrending. We evaluate our model for a portfolio that consists of five stock symbols from NASDAQ that are adequately liquid and two cryptocurrencies which are highly circulated. The model delivers a stable outcome across all symbols indicating its ability to be generalized over a range of symbols and its tolerance to diverse market conditions. Results also indicate that Stop-Loss mechanisms possess the potential to work well even in speculative markets such as the cryptocurrency market. The ability to reduce losses without compromising on opportunities to realize profits in dynamic and unstable market

关键词： Time series analysis

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BP-DDI: Drug-drug interaction prediction based on biological information and pharmacological text

BP-DDI: Drug-drug interaction prediction based on biological...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Dou, Mingliang Han, Han Chen, Genlang Guo, Fei Tang, Jijun Tianjin University College of Intelligence and Computing Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China NingboTech University School of Computer and Data Engineering Ningbo China Central South University School of Computer Science and Engineering Changsha China University of South Carolina College of Engineering and Computing United States

ISBN: (纸本)9781665468190

In the treatment of many diseases, combination drug therapy has been widely used and achieved good clinical efficacy. However, drug-drug interaction (DDI) may occur between multiple drugs and pose a huge threat to the health of patients. Therefore, predicting the presence or absence of DDI among multiple drugs is an important part of pharmacovigilance. Currently, various computational methods for DDI prediction usually use biological information such as molecular structures, targets and enzymes of drugs, or construct heterogeneous networks about drugs, diseases, and genes, so as to obtain abundant information related to drugs. In addition to biological data, pharmacology texts also contain a wealth of information about drug properties, but these texts have not yet been applied to DDI predictions. In this study, we first collect six types of pharmacology texts from DrugBank that can reflect properties of drugs, and propose a novel method named BP-DDI which can combine biological information and pharmacological text to realize DDI event prediction. BP-DDI first extracts biological features (chemical substructure features and target features) from biological data, and then extracts specific types of text features from the collected pharmacology text data. Finally, the biological features are fused with different types of pharmacological text features in order to predict DDI events. Our experiments demonstrate that BP-DDI outperforms existing methods on all three types of prediction tasks. BP-DDI achieves 0.9052 on ACC, and achieves 0.9612 on AUPR. © 2022 IEEE.

关键词： Drug interactions

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Enhancing Android Security: Static Analysis for Robust Protection and Resilient Defences using Deep Learning 7

Enhancing Android Security: Static Analysis for Robust Prote...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Abhishek, S. Anjali, T. Prakash, Prathiba Nair, Arun Karunakaran Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India University of Greenwich Old Navel College Department of Computer Science LondonSE10 9LS United Kingdom

ISBN: (纸本)9798350341485

Android has been a popular target for criminal actions due to its widespread use as a prominent mobile operating system, requiring the creation of sufficient security measures. Android has grown exposed to illegal activities due to its open-source design and ability to install third-party apps without centralized oversight. The suggested study thoroughly examines the rights that programs ask for to see whether there are any noticeable differences between the permissions used by malware samples and their benign equivalents. Based on complex authorization patterns, various machine learning methods are applied to distinguish between the two groups effectively. The discoveries spur the creation of solid security frameworks, eventually reducing the dangers of potential malware attacks. © 2023 IEEE.

关键词： Mobile security

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TextureMeDefect: LLM-based Defect Texture Generation for Railway Components on Mobile Devices

arXiv

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arXiv 2024年

作者： Ferdousi, Rahatara Hossain, M. Anwar Saddik, Abdulmotaleb El School of Electrical Engineering and Computer Science University of Ottawa ON Canada School of Computing Queen’s University KingstonON Canada

Texture image generation has been studied for various applications, including gaming and entertainment. However, context-specific realistic texture generation for industrial applications, such as generating defect textures on railway components, remains unexplored. A mobile-friendly, LLM-based tool that generates fine-grained defect characteristics offers a solution to the challenge of understanding the impact of defects from actual occurrences. We introduce TextureMeDefect, an innovative tool leveraging an LLM-based AI-Inferencing engine. The tool allows users to create realistic defect textures interactively on images of railway components taken with smartphones or tablets. We conducted a multifaceted evaluation to assess the relevance of the generated texture, time, and cost in using this tool on iOS and Android platforms. We also analyzed the software usability score (SUS) across three scenarios. TextureMeDefect outperformed traditional image generation tools by generating meaningful textures faster, showcasing the potential of AI-driven mobile applications on consumer-grade devices. © 2024, CC BY.

关键词： Industrial railroads

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Continuous-Time Prediction of Industrial Paste Thickener System With Differential ODE-Net

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 686-698页

作者： Zhaolin Yuan Xiaorui Li Di Wu Xiaojuan Ban Nai-Qi Wu Hong-Ning Dai Hao Wang the Beijing Advanced Innovation Center for Materials Genome Engineering Institute of Artificial IntelligenceBeijing Key Laboratory of Knowledge Engineering for Materials ScienceSchool of Computer and Communication EngineeringUniversity of Science and Technology Beijing the Department of ICT and Natural Science Norwegian University of Science and Technology IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and Technology the Department of Computing and Decision Sciences Lingnan University the Department of Computer Science Norwegian University of Science and Technology

It is crucial to predict the outputs of a thickening system,including the underflow concentration(UC)and mud pressure,for optimal control of the *** proliferation of industrial sensors and the availability of thickening-system data make this ***,the unique properties of thickening systems,such as the non-linearities,long-time delays,partially observed data,and continuous time evolution pose challenges on building data-driven predictive *** address the above challenges,we establish an integrated,deep-learning,continuous time network structure that consists of a sequential encoder,a state decoder,and a derivative module to learn the deterministic state space model from thickening *** a case study,we examine our methods with a tailing thickener manufactured by the FLSmidth installed with massive sensors and obtain extensive experimental *** results demonstrate that the proposed continuous-time model with the sequential encoder achieves better prediction performances than the existing discrete-time models and reduces the negative effects from long time delays by extracting features from historical system *** proposed method also demonstrates outstanding performances for both short and long term prediction tasks with the two proposed derivative types.

关键词： Industrial 24 paste thickener ordinary differential equation(ODE)-net recurrent neural network time series prediction

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Mitigation for cloud computing security risks and governance

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International Journal of Cloud computing 2022年第5-6期11卷 560-567页

作者： Jabez, J. Narmadha, R. Porkodi, S. Devi, L. School of Computing Sathyabama Institute of Science and Technology Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India College of Engineering and Computer Science Lebanese French University Erbil Iraq PGCS Department Muthayammal College of Arts and Science Rasipuram India

The recent prosperous of cloud computing has become the biggest revenue for originalities with the issues of intricate servers that need regular maintenance, security, power and cooling systems throughout. Hence, it is extremely significant for any cloud service earner is to offer dense security models and policies to all users involves. Although creating best suited security strategies might necessitate enormous amount of reserve and endurance, it will bounce the casual for end user to relay on a computerised security system throughout. The cloud environs supplementary strongly secure, qualifying security hazards, appropriate controls must be imposed. Major contest is to consider cloud security and governance explanation together. In this segment, the emphasis will be in the contests involved in preserving a dependable security policy computerisation in at the application layer, network layer and host level of cloud computing. Moreover, securities related to models will be discussed to recruit for those security applications which are more composite and active. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Security systems

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SOS: Systematic Offensive Stereotyping Bias in Word Embeddings 29

SOS: Systematic Offensive Stereotyping Bias in Word Embeddin...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Elsafoury, Fatma Wilson, Steven R. Katsigiannis, Stamos Ramzan, Naeem School of Physics Engineering and Computing The University of The West of Scotland United Kingdom Department of Computer Science and Engineering Oakland University United States Department of Computer Science Durham University United Kingdom

Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms. In this work, we introduce a quantitative measure of the SOS bias, validate it in the most commonly used word embeddings, and investigate if it explains the performance of different word embeddings on the task of hate speech detection. Results show that SOS bias exists in almost all examined word embeddings and that the proposed SOS bias metric correlates positively with the statistics of published surveys on online extremism. We also show that the proposed metric reveals distinct information compared to established social bias metrics. However, we do not find evidence that SOS bias explains the performance of hate speech detection models based on the different word embeddings. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Embeddings

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