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Feature-wise and Sample-wise Adaptive Transfer Learning for High-dimensional Linear Regression

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arXiv 2024年

作者： He, Zelin Sun, Ying Liu, Jingyuan Li, Runze Department of Statistics Pennsylvania State University United States School of Electrical Engineering and Computer Science Pennsylvania State University United States MOE Key Laboratory of Econometrics Department of Statistics and Data Science School of Economics Wang Yanan Institute for Studies in Economics Fujian Key Lab of Statistics Xiamen University China

We consider the transfer learning problem in the high dimensional linear regression setting, where the feature dimension is larger than the sample size. To learn transferable information, which may vary across features or the source samples, we propose an adaptive transfer learning method that can detect and aggregate the feature-wise (F-AdaTrans) or sample-wise (S-AdaTrans) transferable structures. We achieve this by employing a fused-penalty, coupled with weights that can adapt according to the transferable structure. To choose the weight, we propose a theoretically informed, data-driven procedure, enabling F-AdaTrans to selectively fuse the transferable signals with the target while filtering out non-transferable signals, and S-AdaTrans to obtain the optimal combination of information transferred from each source sample. We show that, with appropriately chosen weights, F-AdaTrans achieves a convergence rate close to that of an oracle estimator with known transferable structure, and S-AdaTrans recovers existing near-minimax optimal rates as a special case. The effectiveness of the proposed method is validated using both synthetic and real data, demonstrating favorable performance compared to the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Transfer learning

The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption

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arXiv 2024年

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2024, CC BY.

关键词： Bamboo

A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables

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arXiv 2023年

作者： Jain, Pranay Ding, Cheng Rudin, Cynthia Hu, Xiao Department of Computer Science Duke University United States Department of Biomedical Engineering Georgia Institute of Technology Emory University United States Center for Data Science Nell Hodgson Woodruff School of Nursing Associated Faculty of Biomedical Informatics School of Medicine Associated Faculty of Computer Science College of Arts and Sciences Emory University United States

Smart watches and other wearable devices are equipped with photoplethysmography (PPG) sensors for monitoring heart rate and other aspects of cardiovascular health. However, PPG signals collected from such devices are susceptible to corruption from noise and motion artifacts, which cause errors in heart rate estimation. Typical denoising approaches filter or reconstruct the signal in ways that eliminate much of the morphological information, even from the clean parts of the signal that would be useful to preserve. In this work, we develop an algorithm for denoising PPG signals that reconstructs the corrupted parts of the signal, while preserving the clean parts of the PPG signal. Our novel framework relies on self-supervised training, where we leverage a large database of clean PPG signals to train a denoising autoencoder. As we show, our reconstructed signals provide better estimates of heart rate from PPG signals than the leading heart rate estimation methods. Further experiments show significant improvement in Heart Rate Variability (HRV) estimation from PPG signals using our algorithm. We conclude that our algorithm denoises PPG signals in a way that can improve downstream analysis of many different health metrics from wearable devices. Copyright © 2023, The Authors. All rights reserved.

关键词： Photoplethysmography

Optimal Privacy Preserving for Federated Learning in Mobile Edge Computing

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arXiv 2022年

作者： Nguyen, Hai M. Chu, Nam H. Nguyen, Diep N. Hoang, Dinh Thai Nguyen, Van-Dinh Hà, Minh Hoàng Dutkiewicz, Eryk Krunz, Marwan The School of Electrical and Data Engineering University of Technology Sydney Australia The College of Engineering and Computer Science VinUniversity Hanoi Viet Nam The ORLab Faculty of Computer Science Phenikaa University Hanoi Viet Nam The Department of Electrical and Computer Engineering University of Arizona United States

Federated Learning (FL) with quantization and deliberately added noise over wireless networks is a promising approach to preserve user differential privacy while reducing wireless resources. Specifically, an FL learning process can be fused with quantized Binomial mechanism-based updates contributed by multiple users to reduce the communication overhead/cost and to protect the privacy of participating users. However, optimizing quantization parameters, communication resources (e.g., transmit power, bandwidth, and quantization bits), and the added noise to guarantee the differential privacy requirement and performance of the learned FL model remains an open and challenging problem. This article aims to jointly optimize the level of quantization, parameters of the Binomial mechanism, and communication resources so as to maximize the convergence rate under the constraints of the wireless network and differential privacy (DP) requirement. To that end, we first derive a novel DP budget estimation of the FL with quantization and Binomial noise that is tighter than the state-of-the-art bound. We then analyze the relationship between the convergence rate and the transmit power, the bandwidth, the transmission time, and the quantization/noise parameters and provide a theoretical bound on the convergence rate. This theoretical bound is decomposed into two components, including the variance of the global gradient and an upper bound on the quadratic bias that can be minimized by optimizing the communication resources, quantization, and added noise parameters. The resulting optimization turns out to be a Mixed-Integer Non-linear Programming (MINLP) problem. To tackle it, we first transform this MINLP problem into a new problem whose solutions are proved to be the optimal solutions of the original one. We then propose an approximate algorithm to solve the transformed problem with an arbitrary relative error guarantee. Extensive simulations show that under the same wireless resour

关键词： Mobile edge computing

Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

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Plant Phenomics 2023年第4期5卷 905-916页

作者： Zixuan Teng Jiawei Chen Jian Wang Shuixiu Wu Riqing Chen Yaohai Lin Liyan Shen Robert Jackson Ji Zhou Changcai Yang Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou 350002China State Key Laboratory of Crop Genetics&Germplasm Enhancement academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China Ningxia Academy of Agriculture and Forestry Sciences Yinchuan 750002China College of Mechanical and Electrical Engineering Fujian Agriculture and Forestry UniversityFuzhou 350002China Cambridge Crop Research National Institute of Agricultural Botany(NIAB)Cambridge CB3 OLEUK Key Laboratory of Smart Agriculture and Forestry(Fujian Agriculture and Forestry University) Fujian Province UniversityFuzhou 350002China Center for Agroforestry Mega Data Science School of Future TechnologyFujian Agriculture and Forestry UniversityFuzhou 350002China

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate *** evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research ***,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological ***,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected *** facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial *** trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were ***,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice *** correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high ***,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice br

关键词： breeding cultivar hundreds

AUVs' Distributed Mission Planning System for Effective Underwater Chlorophyll Sampling

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AUVs' Distributed Mission Planning System for Effective Unde...

International Conference on Unmanned Vehicle Systems-Oman (UVS)

作者： Somaiyeh MahmoudZadeh Amirmehdi Yazdani Yashar Kalantari Bekir Ciftler College of Computing and IT UDST University Doha Qatar School of IT Deakin University Melbourne VIC Australia School of Engineering and Energy Murdoch University Perth WA Australia Department of Computer Science University-South Tehran Branch Tehran Iran Department of Data Science & AI College of Computing and IT UDST University Doha Qatar

This paper presents a distributed mission planning system that enables a team of autonomous underwater vehicles (AUVs) for effective underwater chlorophyll sampling. The proposed system is developed based on a distributed algorithm that optimizes the real-time task allocation and mission planning of AUVs in a cooperative fashion while considering environmental and vehicular constraints to ensure efficient and coordinated sampling efforts. A realistic case study of a chlorophyll sampling mission in Australia's Great Barrier is utilized to investigate the performance of the proposed system in a restricted mission time and in the exposure to the uncertainty of the mission. Simulation studies confirm the effectiveness and resilience of the proposed system and showcase its potential as a valuable tool for advancing underwater chlorophyll sampling missions.

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E-commerce Product Review Analysis based on Multi-class Support Vector Machine

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E-commerce Product Review Analysis based on Multi-class Supp...

Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Laith H. Alzubaidi Ibraheem Hatem Mohammed Gotte Ranjith kumar B Sarada N. Radha The Islamic University Najaf Iraq Department of Computer Communications Engineering Al Rafidain University College Bagdad Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE-Data Science Malla Reddy Engineering College for women Hyderabad India Department of CSE Annapoorana Engineering College (Autonomous) Salem India

Recently, an extensive number of consumers can choose their best options from automated retailers and compare products in online stores. Sentiment analysis is frequently employed in applications meant to demonstrate and provide client service as the voice of the user. On E-commerce websites, user reviews provide helpful information about the product. Sentiment analysis of the text reviews helps forecast product sales by assessing user sentiment regarding the product. The Word Embedding Attention approach assigns additional weight to words that have a strong association with a specific class. Sentiment analysis machine learning models provide a thorough depiction of capabilities and far surpass conventional feature-based methods. The obj ective of this research is to improve sentiment analysis performance by creating a weighted ensemble with a Multi-class Support Vector Machine (MSVM) model utilising novel word embedding techniques. Because of the weighted ensemble's higher generalisation skills, MSVM produces superior results. When comparing existing methods for sentiment analysis, the Weighted ensemble with MSVM has produced higher results in terms of accuracy, precision, f-score, and recall. The Weighted ensemble with an MSVM has achieved 99.87 % accuracy, 99.39% precision, 99.03% f-score, and 99.690/0 recall in sentiment analysis when comparing existing methods such as LSTM+FL, APSO-LSTM, ABCDM, SSentiA, and Hybrid CNN+LSTM.

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Communication-efficient distributed covariance sketch, with application to distributed PCA

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The Journal of Machine Learning Research

The Journal of Machine Learning Research 2021年第1期22卷 3643-3680页

作者： Zengfeng Huang Xuemin Lin Wenjie Zhang Ying Zhang School of Data Science Fudan University Shanghai China School of Computer Science and Engineering The University of New South Wales Sydney Australia School of Computer Science University of Technology Sydney Sydney Australia

A sketch of a large data set captures vital properties of the original data while typically occupying much less space. In this paper, we consider the problem of computing a sketch of a massive data matrix A ε ℝn × d that is distributed across s machines. Our goal is to output a matrix B ε ℝℓ × d which is significantly smaller than but still approximates A well in terms of covariance error, i.e., ∥AT A — BT B∥2. Such a matrix B is called a covariance sketch of A. We are mainly focused on minimizing the communication cost, which is arguably the most valuable resource in distributed computations. We show that there is a nontrivial gap between deterministic and randomized communication complexity for computing a covariance sketch. More specifically, we first prove an almost tight deterministic communication lower bound, then provide a new randomized algorithm with communication cost smaller than the deterministic lower bound. Based on a well-known connection between covariance sketch and approximate principle component analysis, we obtain better communication bounds for the distributed PCA problem. Moreover, we also give an improved distributed PCA algorithm for sparse input matrices, which uses our distributed sketching algorithm as a key building block.

关键词： matrix sketching PCA distributed streaming low rank approximation communication complexity

Investigating TrustZone: A Comprehensive Analysis

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Security and Communication Networks 2023年第1期2023卷

作者： Zhu, Qinyu Chen, Quan Liu, Yichen Akhtar, Zahid Siddique, Kamran Department of Information and Communication Technology School of Computing and Data Science Xiamen University Malaysia Sepang43900 Malaysia Department of Network and Computer Security State University New York Polytechnic Institute UticaNY13502 United States Department of Computer Science and Engineering University of Alaska Anchorage AnchorageAK99508 United States

The advent of the Internet and portable devices, including smartphones and watches, has brought unprecedented opportunities for embedded application systems developments. Along with these developments, there is an increasing need for embedded devices to handle important services, such as the ability to pay bills or manage bank accounts remotely via mobile phones. Such applications and developments have also highlighted the issues of cyberattacks and computing network security - these developments have made mobile phones a potential target for malware, trojans, and viruses, so it is critical to design a set of security technologies for embedded devices. In fact, security has become an essential requirement in the process of embedded system design. Thus, ARM has proposed system-level security solutions based on TrustZone technology. TrustZone technology is tightly integrated with Cortex™-A processors and extends the system through the AMBA® AXI bus and specific TrustZone system IP blocks to protect peripherals such as secure memory, encryption blocks, keyboards, and screens from software attacks. It divides the system into TEE (Trusted Execution Environment) and REE (Rich Execution Environment) by hardware and provides intrinsic software security services and interfaces. More precisely, it has built system security by combining hardware and software. It is worth noting that it does not influence performance, power consumption, and area as much as possible. Owing to such characteristics, the technology has gained the wide attention of researchers worldwide. There is lack of systematic documentation of the technology. Therefore, this paper documents the significant progress achieved in the field. In particular, this article mainly analyses the primary mechanism implementation, and how to build the Trusted Execution Environment in different environments. Then, this paper discusses the related research works in the academic field and business applications of the technolog

关键词： Cellular telephones