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29th International Conference on Telecommunications, ICT 2023

作者： Yulianto, Semi Soewito, Benfano Gaol, Ford Lumban Kurniawan, Aditiya Binus Graduate Program Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Binus Graduate Program Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350361100

Rising cyber risks have compelled organizations to adopt better cyber-protection measures. This study focused on discovering crucial security metrics and assessing the function of red teaming in enhancing cybersecurity defenses against novel cyber hazards. The PRISMA standard considered nine core research works issued between 2014 and 2023. The inclusion of red teaming best practices can significantly enhance cybersecurity architecture. Accurate simulations of cyber threats during red teaming exercises help identify vulnerabilities, and actively embracing red teaming can amplify an organization's capacity to repel future cyber assaults. Researchers and practitioners can utilize the study's insights to pioneer novel security solutions. Combining red teaming methodologies with relevant metrics is essential for enhancing cybersecurity posture. The study's discoveries grant companies a priceless benefit in navigating the rapidly changing cyber threat environment and reinforcing their cyber protection mechanisms. © 2023 IEEE.

关键词： Cybersecurity

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LimitAccess: on-device TinyML based robust speech recognition and age classification

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Discover Artificial Intelligence 2023年第1期3卷 8页

作者： Maayah, Marina Abunada, Ahlam Al-Janahi, Khawla Ahmed, Muhammad Ejaz Qadir, Junaid Department of Computer Science and Engineering Qatar University Doha Qatar CSIRO’s Data61 Sydney Australia

Automakers from Honda to Lamborghini are incorporating voice interaction technology into their vehicles to improve the user experience and offer value-added services. Speech recognition systems are a key component of smart cars, enhancing convenience and safety for drivers and passengers. In the future, safety-critical features may rely on speech recognition, but this raises concerns about children accessing such services. To address this issue, the LimitAccess system is proposed, which uses TinyML for age classification and helps parents limit children’s access to critical speech recognition services. This study employs a lite convolutional neural network (CNN) model for two different reasons: First, CNN showed superior accuracy compared to other audio classification models for age classification problems. Second, the lite model will be integrated into a microcontroller to meet its limited resource requirements. To train and evaluate our model, we created a dataset that included child and adult voices of the keyword "open". The system approach categorizes voices into age groups (child, adult) and then utilizes that categorization to grant access to a car. The robustness of the model was enhanced by adding a new class (recordings) to the dataset, which enabled our system to detect replay and synthetic voice attacks. If an adult voice is detected, access to start the car will be granted. However, if a child’s voice or a recording is detected, the system will display a warning message that educates the child about the dangers and consequences of the improper use of a car. Arduino Nano 33 BLE sensing was our embedded device of choice for integrating our trained, optimized model. Our system achieved an overall F1 score of 87.7% and 85.89% accuracy. LimitAccess detected replay and synthetic voice attacks with an 88% F1 score. © The Author(s) 2023.

关键词： Speech recognition

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Vision Intelligence Assisted Lung Function Estimation Based on Transformer Encoder-Decoder Network With Invertible Modeling

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3336-3349页

作者： Chen, Liuyin Lu, Di Zhai, Jianxue Cai, Kaican Wang, Long Zhang, Zijun City University of HongKong School of Data Science Hong Kong Nanfang Hospital Southern Medical University Department of Thoracic Surgery Guangzhou510515 China University of Science and Technology Beijing Department of Computer Science and Technology Beijing100083 China University of Science and Technology Beijing Shunde Graduate School Foshan China

Lung function evaluation is important to many medical applications, but conducting pulmonary function tests is constrained by different conditions. This article presents a pioneer study of an integrated invertible deep learning method for lung function estimation via using computed tomography (CT) images. First, the projection method is proposed to flatten the three-dimensional (3-D) image onto a two-dimensional (2-D) plane, with preserving location information in 3-D. Next, the MBConv transformer-based encoder-decoder structure is developed to extract latent features. Finally, we develop an invertible normalizing flow (NF) model to infer lung function based on the extracted features and design two loss functions for two directions. The method enables both estimating the lung function based on CT images and metadata as well as generating the corresponding simulated CT image according to the lung function. Computational studies show that the proposed regression model outperforms all state-of-the-art image regression models. A comprehensive comparative analysis also demonstrates the effectiveness of using generated images and confirms the superiority of the proposed method. To the best of our knowledge, this work is the first of its kind in combining encoder-decoder network with NFs to ensure the effectiveness of the fully invertible framework, especially in lung CT image analysis. © 2020 IEEE.

关键词： Metadata

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Few-Shot Object Detection Based on the Transformer and High-Resolution Network

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computers, Materials & Continua 2023年第2期74卷 3439-3454页

作者： Dengyong Zhang Huaijian Pu Feng Li Xiangling Ding Victor S.Sheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411004China Department of Computer Science Texas Tech UniversityLubbock79409TXUSA

Now object detection based on deep learning tries different *** uses fewer data training networks to achieve the effect of large dataset ***,the existing methods usually do not achieve the balance between network parameters and training *** makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection *** improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the *** and spatial attention are used to make the network focus on features that are more useful to the *** addition,the recently popular transformer is used to fuse the features of the existing *** compensates for the previous network failure by making full use of existing object *** on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

关键词： Object detection few shot object detection transformer high-resolution

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Ransomware Resilience: Investigating Organizational Security Culture and Its Impact on Cybersecurity Practices against Ransomware Threats

Ransomware Resilience: Investigating Organizational Security...

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2023 International Conference on Informatics Engineering, science and Technology, INCITEST 2023

作者： Yulianto, Semi Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350317862

The increasing threat of ransomware attacks in an interconnected and digital world presents important hurdles for cybersecurity, prompting further examination into how organizational security culture affects their ability to withstand these malicious threats. This study examines the impact and importance of organizational security culture in ransomware threat mitigation. We have surveyed five organizations, giving points from 1 to 5 for each question on cultural factors, leadership support, employee awareness and involvement (compliance), communication, behavior change or BBS practice incorporation, learning/training delivery methodology, and Size and industry impact on their security culture. The results indicate that a robust security culture, with leadership and effective communication, builds stronger resilience to ransomware. Organizations with jointly accountable cybersecurity and training practices are highly efficient. Suggestions range from establishing a secure environment to increasing management buy-in and integrating compliance. This work helps advance our understanding of ransomware resilience and highlights the key role of organizational security culture. The resulting learnings can help to inform organizations' security posture, ultimately helping them better protect their defenses against ransomware threats. © 2023 IEEE.

关键词： Cybersecurity

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Interactive Physiotherapy Game Using React Three Fiber for Hand Motion Rehabilitation

Interactive Physiotherapy Game Using React Three Fiber for H...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Yadav, Ram Kumar Soni, Ashish Kumar Sakare, Apeksha Manipal University Department of Data Science and Engineering Jaipur India G H Raisoni College of Engineering Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331529833

This article describes an interactive physical activity system designed to use React Three Fiber to assist patients with hand rehabilitation. The game simulates a virtual environment where rods controlled by motion-sensing devices represent the patient's hand. Through the "Create"function, a sphere can be created from the previous position and the patient must hold their hand firmly within the sphere's boundaries. Adjustable sliders allow optimal control of hold time and sensitivity to adapt to the patient's growth and treatment needs. The game provides immediate visual feedback, encouraging patients to practice stability and mobility, which are essential elements of good physical therapy. The app simulates real-world movements in a virtual environment, creating a controlled environment that provides immediate visual feedback to encourage patients to take action. This technology uses React Three Fiber to create a 3D environment, including writing information for precise control of the stick. This interactive model is designed to not only develop motor skills through consistent, guided exercises, but also to improve overall patient compliance. Through this application, we explore the intersection of play and physical therapy by examining the potential for digital therapy tools to complement and enhance traditional practices This approach uses the appeal of interactive games to potentially increase treatment compliance, making it an important tool in digital therapy. This article discusses the use of React Three Fiber's techniques, methods, and therapeutic potential for physical therapy. © 2025 IEEE.

关键词： Virtual environments

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Enhanced U-Net with Transformer-Driven Encoder for Medical Image Segmentation

Enhanced U-Net with Transformer-Driven Encoder for Medical I...

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2024 International Conference on Brain computer Interface and Healthcare Technologies, iCon-BCIHT 2024

作者： Palaniappan, Ramya Surendran, Suseendran Balakrishnan, Saranya Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Madanapalle India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India KGiSL Institute of Technology Department of Artificial Intelligence and Data science Coimbatore India

ISBN: (纸本)9798331540067

The automatic segmentation of tumours or lesions from magnetic resonance imaging (MRI) pictures is a critical but difficult task in clinical settings, sometimes necessitating laborious and time-consuming techniques. Deep learning has made a significant advancement in the medical imaging field in recent years. U-Net has emerged as the predominant model used for segmenting medical images. Although the standard U-Net framework has shown excellent performance in segmenting multimodal medical pictures, it has been noticed that it lags in certain aspects when dealing with tough datasets. We propose specific improvements to the state-of-the-art U-Net model. We have introduced attention block in the skipped connections to highlight relevant features. Additionally, we have incorporated transformers in the encoding path to capture temporal dependencies in medical images. In this research work, we have introduced an innovative architecture that has the potential to replace the U-Net model for segmenting regions of interest (ROI) in medical images. This study utilizes a total of 1760 samples obtained from the ISBI 2015 and PH2 medical repositories. The experimental findings have demonstrated that our proposed model has obtained highly encouraging results of 78.02 Jaccard coefficient and IoU of 65.14. In addition, we conducted tests and compared the performance of the proposed model with the classical U-Net model using the same dataset. As a result, we observed a relative improvement in the performance of both 2.36 and 1.85, respectively. The proposed technology will function as a sophisticated toolkit for radiologists to precisely detect and diagnose anomalous tumour cells. © 2024 IEEE.

关键词： Semantic Segmentation

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Word-Diffusion: Diffusion-Based Handwritten Text Word Image Generation 27th

Word-Diffusion: Diffusion-Based Handwritten Text Word Image ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Gurav, Aniket Krishnan, Narayanan C. Chanda, Sukalpa Department of Computer Science and Communication Østfold University College Halden1757 Norway Department of Data Science Indian Institute of Technology Palakkad Kerala India

ISBN: (纸本)9783031784941

Generating realistic handwritten word images that closely resemble a target style remains a challenging task in document image analysis. In recent years, deep learning techniques, such as Latent Diffusion Models (LDM), have shown promise in generating styled handwritten text. However, these models face significant challenges when creating images for ‘Out of Vocabulary’ (OOV) words, impacting their overall effectiveness. In this paper, we introduce an extended diffusion-based Handwritten generation method that incorporates a novel conditioning mechanism. It is based on the Pyramidal Histogram of Shapes (PHOS) representation, which takes into account the spatial and structural characteristics of the target handwriting style. By conditioning the diffusion model on input text, PHOS vector, and writer ID, our approach enables the generation of handwritten word images. Notably, our approach outperforms the original diffusion model, which only uses text and writer ID as conditions, in generating both in-sample and out-of-sample. Furthermore, we have developed a faster inference method that significantly reduces the number of steps required for generating the output. Through qualitative and quantitative evaluations, we demonstrate the effectiveness of our proposed method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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PAC PREDICTION SETS UNDER LABEL SHIFT 12

PAC PREDICTION SETS UNDER LABEL SHIFT

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12th International Conference on Learning Representations, ICLR 2024

作者： Si, Wenwen Park, Sangdon Lee, Insup Dobriban, Edgar Bastani, Osbert Department of Computer & Information Science University of Pennsylvania United States Graduate School of AI and Computer Science & Engineering POSTECH Korea Republic of Department of Statistics and Data Science University of Pennsylvania United States

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift, which is precisely when reliable uncertainty quantification can be most useful. We propose a novel algorithm for constructing prediction sets with PAC guarantees in the label shift setting, where the probabilities of labels can differ between the source and target distributions. Our algorithm relies on constructing confidence intervals for importance weights by propagating uncertainty through a Gaussian elimination algorithm. We evaluate our approach on four datasets: the CIFAR-10 and ChestX-Ray image datasets, the tabular CDC Heart dataset, and the AGNews text dataset. Our algorithm satisfies the PAC guarantee while producing smaller prediction set sizes compared to several baselines. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Forecasting

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Harnessing QGIS and Random Forest Analysis for Precision Storm Drain Reinforcement 2

Harnessing QGIS and Random Forest Analysis for Precision Sto...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramachandran, Ramya Arthi, D. Sudha, D. Anil, Abhirami B Babu, Aravind Md, Haripriya KCG College of Technology Artificial Intelligence and Data Science Chennai India KCG College of Technology Department of Computer Science and Engineering India KCG College of Technology Science Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331534950

Amidst the backdrop of increased urbanization and shifting climate patterns, this research delves into the growing vulnerability of landscapes to floods. The focus is on storm drain placement, emphasizing the identification of suitable locations and the need to reinforce drainage networks. The analysis takes into account parameters linked to flood sensitivity, including total rainfall, decadal growth rate, and soil types in each district within a state. Leveraging Geographic Information Systems (GIS) and relevant Coordinate Reference Systems (CRS), the study integrates a diverse array of datasets to inform precise cartographic projections. Employing a Random Forest Classifier, the study dissects the influence of these parameters on storm drain placement. The findings offer valuable insights for decision-makers to improve stormwater management systems, underscoring the importance of ongoing maintenance. Overcoming the challenge of obtaining official government data for recommendations, the research advocates the adoption of new technologies and data integration methods. By combining various data sources, such as topography, rainfall patterns, historical flood records, land use, and infrastructure data, it lays the groundwork for more advanced urban water management strategies, fortifying cities against flooding. © 2024 IEEE.

关键词： Rain

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