检索结果-内蒙古大学图书馆

Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Ryan Saptarshi Ray Debendranath Das Aritro Sengupta Pankaj Kumar Arun Kumar Sahani Information Technology Govt. College Of Engineering & Ceramic Technology Kolkata India Cryptology and Security Research Unit Indian Statistical Institute Kolkata India Department of Computer Science & Engineering Lingaya's Vidyapeeth Faridabad Haryana India

Elliptic curve cryptography is a widely recognized method of using a public key cryptosystem for encrypting and decrypting data. The message must be transformed to points on an Elliptic Curve using a mapping algorithm in order to send the message. This process of mapping the message to the point on the elliptic curve is done by applying a well-known mapping algorithm. Elliptic curve cryptography is widely acknowledged to have simpler mathematical operations compared to other cryptographic methods. However, researchers in the field of cryptography have faced challenges in mapping plain messages onto points on an elliptic curve and reversing the process at the receiver's end for decryption. Recent advancements have led to the development of mapping techniques that are resistant to frequency analysis. In this paper, we have discussed a well-known mapping scheme that has been used to map plain text messages to points on elliptic curves. However, the time taken to map a message and encrypt the points takes a substantial amount of time. This latency can be reduced if we parallelize the mapping process. In this paper, we have parallelized the reverse mapping phase of the scheme at the receiver's end to reduce the total communication time. Performance results after parallelization have been recorded and provided in the later section of the paper in tabular and graphical form.

关键词：

来源：评论

学校读者我要写书评

暂无评论

High Accuracy Vibration Sensing by using Random Interval Forest Classifier Based Distributed Fiber Sensing

High Accuracy Vibration Sensing by using Random Interval For...

引用

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Brian Pamukti Wang Zi Shien-Kuei Liaw Ya-Mei Yang Totok Soehartanto Agus Muhamad Hatta Graduate Institute of Electro-Optical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan The Green Energy and Environment Research Laboratories Industrial Technology Research Institute Tainan Taiwan Departement of Instrumentation Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Engineering Physics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

We present a novel machine-learning technique for vibration detection, called random interval forest classifier (RIFC), using a single Mach-Zehnder interferometer based on single-mode fiber. Our method achieved 98% ac... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Vibrations Mach-Zehnder interferometers Accuracy Refining Forestry Machine learning Lasers and electrooptics

来源：评论

学校读者我要写书评

暂无评论

Construction of Malaria Disease Prediction System using Deep Learning 1

Construction of Malaria Disease Prediction System using Deep...

引用

1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Sivakumar, J. Sudhagar, D. Vithya, V.T. Jaichandran, R. Narasimha Raju, K. Sundar Ganesh, C.S. Department of Electronics and Communication Engineering St.Joseph's College of Engineering Tamil Nadu Chennai India Department of Information Technology Jerusalem College of Engineering Tamil Nadu Chennai600100 India Department of Computer Science and Engineering R.M.K. College of Engineering and Technology Tamil Nadu Chennai601206 India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Tamil Nadu Paiyanoor603104 India Department of Computer Science and Engineering Gayatri Vidya Parishad College of Engineering Andhra Pradesh Visakhapatnam530048 India Department of Electrical and Electronics Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India

ISBN: (纸本)9781665460842

Malarial fever is an infectious sickness that influences a large number of lives each year. Malarial fever is as yet a significant worldwide wellbeing trouble, with more than 3.2 billion individuals in 91 nations staying in danger of the infection. Precisely recognizing intestinal sickness from different illnesses, particularly simple malarial fever from non-malarial contaminations, stays a test. Customary analysis of malarial fever in lab requires an accomplished individual and cautious assessment to segregate solid and contaminated red platelets. Profound learning-based models have had extraordinary accomplishment in object recognition;however, the cutting-edge models have not yet been generally applied to natural picture informationIn this article, item location model recently utilized on normal pictures to distinguish cells is appliedand perceive their stages in brightfield microscopy pictures of malarial fever tainted blood This paper, is incorporated with Spotting of Malarial fever from the output images of tiny malign by employing CNN (Convolutional Neural Network) model. The datasets are collected from the Kaggle and used for testing and training. In this model, malarial parasites are discerned from infinitesimal output images with 97.76% of accuracy. In this paper, the CNN model is evaluated using Accuracy and Loss. © 2022 IEEE

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Continual Self-Supervised Learning: Towards Universal Multi-Modal Medical Data Representation Learning

Continual Self-Supervised Learning: Towards Universal Multi-...

引用

Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yiwen Ye Yutong Xie Jianpeng Zhang Ziyang Chen Qi Wu Yong Xia School of Computer Science and Engineering Northwestern Polytechnical University China Australian Institute for Machine Learning (AIML) The University of Adelaide Australia College of Computer Science and Technology Zhejiang University China Research & Development Institute of Northwestern Polytechnical University in Shenzhen China Ningbo Institute of Northwestern Polytechnical University China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Self-supervised learning (SSL) is an efficient pre-training method for medical image analysis. However, current research is mostly confined to certain modalities, consuming considerable time and resources without achieving universality across different modalities. A straightforward solution is combining all modality data for joint SSL, which poses practical challenges. Firstly, our experiments reveal conflicts in representation learning as the number of modalities increases. Secondly, multi-modal data collected in advance cannot cover all real-world scenarios. In this paper, we reconsider versatile SSL from the perspective of continual learning and propose MedCoSS, a continuous SSL approach for multi-modal medical data. Different from joint representation learning, MedCoSS assigns varying data modalities to separate training stages, creating a multi-stage pre-training process. We propose a rehearsal- based continual learning approach to manage modal conflicts and prevent catastrophic forgetting. Specifically, we use the k-means sampling to retain and rehearse previous modality data during new modality learning. Moreover, we apply feature distillation and intra-modal mixup on buffer data for knowledge retention, bypassing pretext tasks. We conduct experiments on a large-scale multi-modal unlabeled dataset, including clinical reports, X-rays, CT, MRI, and pathological images. Experimental results demonstrate MedCoSS's exceptional generalization ability across 9 downstream datasets and its significant scalability in inte- grating new modality data. The code and pre-trained model are available at https://***/yeerwen/MedCoSS.

关键词： Representation learning Continuing education Training Pathology Scalability Magnetic resonance imaging X-rays

来源：评论

学校读者我要写书评

暂无评论

Expression of Concern for: An Assessment of the Security Benefits of Secure Shell (SSH) in Wireless Networks

Expression of Concern for: An Assessment of the Security Ben...

引用

Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： M Preetha Dharmesh Dhabliya Zeeshan Ahmad Lone Sakshi Pandey Kalyan Acharjya Gowrishankar J Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Comfortable Shell (SSH) is a community protocol that provides relaxed communique over an unsecured community, such as the internet. This protocol is generally used for far flung login, file switch, and remote command execution, making it a vital device for managing and securing networks. Wi-Fi networks, which rely upon radio frequency indicators for verbal exchange, are prone to various safety threats such as eavesdropping, spoofing, and man-in-themiddle assaults. This makes using relaxed protocols like SSH critical for protective sensitive statistics and keeping the integrity of Wi-Fi networks. This technical assessment evaluates the safety advantages of utilizing SSH in wireless networks. First of all, SSH makes use of strong encryption algorithms consisting of AES and RSA to encrypt the whole communique session, ensuring that records is transmitted securely between devices. This prevents attackers from intercepting and reading touchy records exchanged among gadgets. Secondly, SSH makes use of a public-key infrastructure for authentication, that's more at ease than conventional password-based totally authentication. This is because the private key, which is used for authentication, is not transmitted all through the login system, making it greater tough for attackers to advantage get entry to the network.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Intelligent Kidney Abnormality Detection in CT Images Using GLCM and Neural Networks

Intelligent Kidney Abnormality Detection in CT Images Using ...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Chhavi Bajpai Anitha S Manish Gaur M. Raghini S. Kaliappan Imran Khan Department of Computer Science and Engineering Centre for Advanced Studies AKTU Lucknow India Department of CSE-Cyber Security ACS College of Engineering Bangalore India Department of Computer Science & Engineering Institute of Engineering and Technology AKTU Lucknow India Department of Artificial Intell igence and Data Science Velammal College of Engineering and Technology Madurai India Department of Research and Development Lovely Professional University Jalandhar India Department of Medical Surgical Nursing Sharda School of Nursing Science and Research Sharda University Greater Noida India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Kidney stone illness, one of the most common medical problems worldwide, relieves pain and drives people to the ER. Diagnoses often use imaging, but educated doctors must interpret them. computer-aided diagnostics (CAD) systems help doctors discover kidney stones more accurately. The suggested technique includes preprocessing, feature extraction, and model training. Ultrasound (US) image preprocessing includes restoration, smoothness, sharpness, and contrast enhancement. The Grey Level Co-occurrence Matrices (GLCM) analyze image texture to extract characteristics. Model training uses KELM. The KELM-based model outperforms Extreme Learning Machine (ELM) and K-Nearest Neighbours (KNN) with an average accuracy of 92.54%. This accuracy demonstrates the method can detect kidney stones in ultrasound images. This proposed shows that computer-aided kidney stone detection can be fast and accurate. The combination of GLCM's texture analysis and KELM's classification skills can help medical professionals diagnose kidney stones using imaging.

关键词： Training Solid modeling Accuracy Ultrasonic imaging Computational modeling Medical services Nearest neighbor methods Feature extraction Kidney Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

Performance Optimization in the Cascade of VAD and ASR Systems: A Study on Evaluation and Alignment Strategies

Performance Optimization in the Cascade of VAD and ASR Syste...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Zhentao Lin Zihao Chen Bi Zeng Leqi Chen Jia Cai Computer Science and Engineering Guangdong University of Technology School China Electronic Product Reliability and Environmental Testing Research Institute Key Laboratory of MIIT for Intelligent Products Testing and Reliability

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

Voice Activity Detection (VAD) is a critical pre-processing step in Automatic Speech Recognition (ASR) systems, tasked with distinguishing speech from non-speech segments in audio signals. The accuracy of this segmentation is essential, as errors can significantly impair ASR performance. This study integrates multiple VAD algorithms with ASR systems and examines their interplay through a chapter-level analysis, focusing on the Word Error Rate (WER) of ASR in relation to VAD’s recall and precision. The research reveals a strong correlation between ASR’s insertion and deletion errors and VAD’s performance metrics. Insertions are mainly due to VAD misclassifying non-speech as speech, while deletions stem from misclassifying speech as non-speech. To address these issues, we introduce the Aligned Token Word Error Rate (atWER), a novel metric based on forced alignment from the Connectionist Temporal Classification (CTC) framework. Our experiments show that atWER accurately reflects the impact of VAD on ASR performance, effectively reducing discrepancies between VAD annotations and classification outcomes.

关键词： Voice activity detection Measurement Accuracy Correlation Error analysis Focusing Information processing User experience Reliability Optimization

来源：评论

学校读者我要写书评

暂无评论

Tracking Objects and Activities with Attention for Temporal Sentence Grounding

Tracking Objects and Activities with Attention for Temporal ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zeyu Xiong Daizong Liu Pan Zhou Jiahao Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wangxuan Institute of Computer Technology Peking University

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets’ behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed.

关键词： Solid modeling Target tracking Three-dimensional displays Grounding Benchmark testing Signal processing Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization 15th

Introducing QuantConn: Overcoming Challenging Diffusion Acqu...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Newlin, Nancy Schilling, Kurt Koudoro, Serge Chandio, Bramsh Qamar Kanakaraj, Praitayini Moyer, Daniel Kelly, Claire E. Genc, Sila Yang, Joseph Yuan-Mou Wu, Ye Adluru, Nagesh Nath, Vishwesh Pathak, Sudhir Schneider, Walter Gade, Anurag Consagra, William Rathi, Yogesh Hendriks, Tom Vilanova, Anna Chamberland, Maxime Pieciak, Tomasz Ciupek, Dominika Vega, Antonio Tristán Aja-Fernández, Santiago Malawski, Maciej Ouedraogo, Gani Machnio, Julia Thompson, Paul M. Jahanshad, Neda Garyfallidis, Eleftherios Landman, Bennett Department of Computer Science Vanderbilt University NashvilleTN United States Department of Radiology and Radiological Sciences Vanderbilt University Medical Center Nashville United States Indiana University Bloomington BloomingtonIN United States Mark and Mary Stevens Neuroimaging and Informatics Institute Keck School of Medicine of USC Los AngelesCA United States Developmental Imaging Murdoch Children’ Research Institute Melbourne Australia Murdoch Children’s Research Institute Melbourne Australia Turner Institute for Brain and Mental Health School of Psychological Sciences Monash University Melbourne Australia Department of Neurosurgery Royal Children’s Hospital Melbourne Australia Neuroscience Research Murdoch Children’s Research Institute Melbourne Australia Department of Paediatrics University of Melbourne Melbourne Australia School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Waisman Center Department of Radiology University of Wisconsin Madison United States NVIDIA Santa Clara United States Learning Research and Development Center University of Pittsburgh Pittsburgh United States Brigham and Women’s Hospital Boston United States Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computer Science and Mathmatics Eindhoven University of Technology Eindhoven Netherlands LPI ETSI Telecomunicación Universidad de Valladolid Castilla y León Spain Sano Centre for Computational Medicine Kraków30-054 Poland Aix-Marseille Université Marseille France Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States

ISBN: (纸本)9783031869198

White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

来源：评论

学校读者我要写书评

暂无评论

Emog: Synthesizing Emotive Co-Speech 3d Gesture with Diffusion Model

SSRN

引用

SSRN 2024年

作者： Yin, Lianying Wang, Yijun He, Tianyu Zhao, Wei Jin, Xin Lin, Jianxin The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Eastern Institute of Technology Ningbo315000 China Microsoft Research Asia Beijing100000 China

Co-speech 3D human gesture synthesis has received extensive attention, which aims to generate realistic 3D animations of human motion using audio signals as input. However, generating highly vivid motion remains a challenge due to the one-to-many nature between the speech content and gestures. This intricate association often leads to the oversight of certain gesture properties, notably the delicate differences of gesture emotion. Moreover, the complexity inherent in audio-driven 3D human gesture synthesis is compounded by the interaction of joint correlation and the temporal variation intrinsic to human gesture, often causing plain gesture generation. In this paper, we present a novel framework, dubbed \textbf{EM}otive \textbf{G}esture generation (EMoG), to tackle the above challenges with denoising diffusion models: 1) To alleviate the one-to-many problem, we incorporate emotional representations extracted from audio into the distribution modeling process of the diffusion model, explicitly increasing the generation diversity;2) To enable the emotive gesture generation, we propose to decompose the difficult gesture generation into two sub-problems: joint correlation modeling and temporal dynamics modeling. Then, the two sub-problems are explicitly tackled with our proposed Temporal-dynamics and Joint-correlation aware Transformer (TJFormer). Through extensive evaluations, we demonstrate that our proposed method surpasses previous state-of-the-art approaches, offering substantial advantages in gesture synthesis. Furthermore, our approach exhibits the additional capabilities of enabling emotion transfer and affording fine-grained style control over specific body parts. This remarkable advancement paves the way for expressive gesture generation with heightened fidelity, opening up new possibilities for immersive experiences in the realm of 3D human gesture synthesis. © 2024, The Authors. All rights reserved.

关键词： Diffusion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：