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arXiv 2024年

作者： Sim, Rachael Hwee Ling Zhang, Yehong Hoang, Trong Nghia Xu, Xinyi Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Peng Cheng Laboratory China School of Electrical Engineering and Computer Science Washington State University United States Dept. of Electrical Engineering and Computer Science MIT United States

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition’s model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition’s posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Wi-Fi RTT Range Offset and Multi-Band Analysis: Challenges and Opportunities for Indoor Localization

Wi-Fi RTT Range Offset and Multi-Band Analysis: Challenges a...

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IEEE Symposium on Position Location and Navigation (PLANS)

作者： Sultan Al Sulami Tarig Ballal Muhammad Moinuddin Tareq Al-Naffouri Electrical and Computer Engineering Dept King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering Dept King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (数字)9798331523176

ISBN: (纸本)9798331523183

Indoor localization has emerged as a critical research area, driven by the growing need for accurate positioning in environments where Global Navigation Satellite Systems (GNSS) are unavailable. Wi-Fi Round-Trip Time (RTT) has shown promise for precise indoor positioning, but the presence of systematic offsets across frequency bands poses significant challenges to its accuracy and reliability. These offsets, arise with factors such as device heterogeneity, multipath propagation, and environmental interference, not only degrade RTT performance but also complicate its integration with RTT multi-band and other measurement sources . This study presents a comprehensive analysis of RTT range offset across the 2.4 GHz, 5 GHz, and 6 GHz frequency bands, identifying it as limiting factor in achieving high-precision indoor positioning. Furthermore, we investigate the potential of multi-band fusion to mitigate these offsets and explore the associated challenges in realizing accurate localization systems. Our findings offer insights into integration of RTT-multi-band to overcome the practical limitations of RTT technology.

关键词： Location awareness Global navigation satellite system Time-frequency analysis Accuracy Systematics Limiting Correlation Calibration Frequency measurement Wireless fidelity

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A Dynamic Approach to Cyber Risk Assessment for Critical Information Infrastructure 3

A Dynamic Approach to Cyber Risk Assessment for Critical Inf...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Chadalawada, Rajendra Rao, K. Srinivas Khlaif, Abbas Mahdi Kumar, Bodla. Ashwin Babu, B. Sankara Saikumar, K. Narayana Engineering College Department of Cse Nellore India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Scientific Research Center Al-Ayen University Thi-Qar64001 Iraq Cvr College of Engineering Department of Computer Science and Engineering Hyderabad India Dept. of Cse Telangana Bachupally India Koneru Lakshmaiah Education Foundation Dept of Cse Telangana Hyderabad500075 India

ISBN: (纸本)9798350319125

In this study, a sophisticated cyberattack model was created for real-time DDoS & MITM attacks. Clouds, servers, big data, and networks are only a few of the modern infrastructures where cyber threats have proliferated. Threats to these platforms mean that private information may have been compromised within their applications. Cyber security experts could choose from a plethora of tried-and-true static models, but they all fell short when faced with dynamic attacks and other, more complicated threats. Furthermore, earlier cybersecurity models had problems with both high-ToC and target-attack scalability. Using machine learning methods, this study develops a sophisticated model for evaluating cyber risks. The recommended Lite RFO model was built using a top-notch Python programming utility. This architecture has been used to monitor cyberattacks on any network and locate the specific attack with a reduced Tree of Controls (ToC). There was a substantial improvement in performance metrics like detection rate (98.43% ), accuracy (98.23% ), sensitivity (96.89%), and recall (94.56%). © 2023 IEEE.

关键词： attacks critical infromation of cyber-attacks cyber risk cybersecurity

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Stealth: A Highly Secured End-to-End Symmetric Communication Protocol

Stealth: A Highly Secured End-to-End Symmetric Communication...

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2022 International Symposium on Networks, computers and Communications, ISNCC 2022

作者： Patgiri, Ripon Muppalaneni, Naresh Babu National Institute of Technology Silchar Dept. Computer Science & Engineering Assam Cachar788010 India

ISBN: (数字)9781665485449

ISBN: (纸本)9781665485449

Symmetric key cryptography is applied in almost all secure communications to protect all sensitive information from attackers, for instance, banking, and thus, it requires extra attention due to diverse applications. Moreover, it is vulnerable to various attacks, for example, cryptanalysis attacks. Cryptanalysis attacks are possible due to a single-keyed encryption system. The state-of-the-art symmetric communication protocol uses a single secret key to encrypt/decrypt the entire communication to exchange data/message that poses security threats. Therefore, in this paper, we present a new secure communication protocol based on Diffie-Hellman cryptographic algorithms, called Stealth. It is a symmetric-key cryptographic protocol to enhance the security of modern communication with truly random numbers. Additionally, it applies a pseudo-random number generator. Initially, Stealth uses the Diffie-Hellman algorithm to compute four shared secret keys. These shared secret keys are used to generate four different private keys to encrypt for the first block of the message for symmetric communication. Stealth changes its private keys in each communication, making it very hard to break the security protocol. Moreover, the four shared secret keys create additional complexity for the adversary to overcome, and hence, it can provide highly tight security in communications. Stealth neither replaces the existing protocol nor authentication mechanism, but it creates another security layer to the existing protocol to ensure the security measurement's tightness. © 2022 IEEE.

关键词： Cryptography

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Multi-View Brain Network Analysis with Cross-View Missing Network Generation

Multi-View Brain Network Analysis with Cross-View Missing Ne...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Luo, Gongxu Li, Chenyang Cui, Hejie Sun, Lichao He, Lifang Yang, Carl MBZUAI Department of Machine Learning Abu Dhabi United Arab Emirates University of Waterloo Dept. of Electrical and Computer Engineering WaterlooON Canada Emory University Department of Computer Science AtlantaGA United States Lehigh University Dept. of Computer Science and Engineering BethlehemPA United States

ISBN: (纸本)9781665468190

Parkinson's Disease (PD), one of the most common neurological disorders, has long been a challenge in public health clinical diagnosis as well as scientific understanding. Recently, there has been an upsurge of interest in brain network analysis which benefits the understanding of brain functions and early detection of neurological disorders extensively. Multi-view brain networks with different connectivity patterns among regions of interests (ROIs) can be constructed to reflect different and complementary perspectives of the brain connectivity profile. However, the extraction of such multi-view brain networks relies on the availability of multiple neuroimaging modalities and heavy data preprocessing, which often leads to severe missing data in either view. The cross-view missing issue hinders the pragmaticality of multi-view representation learning and downstream analysis. In this work, we formulate the novel problem of cross-view brain network generation and propose CroGen, a graph generative model that can generate the missing view when only one view is given. Specifically, GroGen leverages the potential correlation between diverse views of brain networks of the same individuals. Moreover, we design a pre-train schema to make full use of the labeled individuals with only single views of brain networks. Extensive experiments on real-life Parkinson's Progression Markers Initiative (PPMI) cohort demonstrate the supreme effectiveness of CroGen over baselines on both cross-view generation tasks and downstream PD classification. © 2022 IEEE.

关键词： Neuroimaging

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Dataset Construction and Classification Based on Pre-trained Models for Opinion Holder Detection 12

Dataset Construction and Classification Based on Pre-trained...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Al-Mahmud, Al Shimada, Kazutaka Kyushu Institute of Technology Dept. of Computer Science and Systems Engineering Fukuoka850-8502 Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

Nowadays, it is getting increased in the massive amount of internet users. People express subjective thinking (i.e., opinion) implicitly and explicitly on online platforms such as Facebook, Twitter, Amazon product reviews, etc. Opinion holders are the people or entities who express opinions implicitly and explicitly on the online platform. With the increasing trends of online opinion mass information, it is impossible to detect them manually. For this reason, an automatic approach for opinion holder detection is essential. Opinion holder detection is useful to detect specific person's/entity's concerns about a particular topic, product, or problem. Opinion holder detection consists of two steps: the presence of opinion holders in text and identification of opinion holders. In this paper, we focus on the first step, namely the presence of opinion holders in text. We handle this task as a binary classification problem: INSIDE or OUTSIDE. At first, we prepare a new English dataset for this task. Then, we apply two types of pre-trained models, BERT and DistilBERT, to the INSIDE/OUTSIDE classification task. BERT is a transformer-based pre-trained language model. DistilBERT is a small, fast, cheap, and light model based on knowledge distillation from the BERT architecture. As to the binary classification task, we employ a logistic regression model on the top layer of the pre-trained models. We compare the language models employed in our experiment in terms of the F1 score and accuracy. The experimental result shows that DistilBERT obtained superior performance among the models: 0.901 on the F1 score and 0.924 on the accuracy. © 2022 IEEE.

关键词： Distillation

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Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices 8

Deep Convolutional Neural Network Based Covid-19 Classificat...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Bhosale, Yogesh H. Zanwar, Shrinivas Ahmed, Zakee Nakrani, Mahendra Bhuyar, Devendra Shinde, Ulhas Dept. of Computer Science and Engineering Mesra Ranchi India Chh. Shahu College of Engineering Dept. of Artificial Intelligence and Data Science Csmss Aurangabad India WeAgile Software Solution Pvt. Ltd Dept. of Computer Vision Engineering Pune India

ISBN: (纸本)9781665408165

The Coronavirus Disease 2019 (COVID19) epidemic, which erupted at the end of 2019, continued rapidly throughout the nations from Wuhan, China. This highly contagious infectious disease is rapidly spreading among the public. Early research on COVID-19-affected patients has revealed distinctive anomalies in chest radiography images. As a result, it is now necessary to identify various risk factors that can move an infected person from a mild to a serious stage of sickness. In Deep Learning (DL), strategies as a subset of Artificial Intelligence (AI) are used to deal with many real-life glitches. This paper introduces a Deep Convolutional Neural Network (DCNN) to perform multiclass classification for COVID-19, Pneumonia, and Normal Patients from radiological imaging of the chest. Also, the work is implemented with an IoT framework, used for communicating user and DCNN model. This Deep Convolutional Neural Network (DCNN) classification mechanism achieved a perfect test accuracy of 94.95% for COVID-19. The used datasets are acquired from Kaggle and GitHub. © 2022 IEEE.

关键词： COVID-19

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Predicting the Price Direction of Bitcoin Using Twitter Data and Machine Learning 2

Predicting the Price Direction of Bitcoin Using Twitter Data...

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2nd IEEE International Conference on Data science and computer Application, ICDSCA 2022

作者： Kanji, Abdul Mannan Chaudhary, Ishita Shankar, Rithika Lakshmi Srinivasa, Gowri Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

Bitcoin is a decentralized digital currency that was intro- duced in 2009 and since then, has become increasingly popular as one of the most known and highly valued currencies. Contributing factors to its rise include crypto Twitter influencers. An engaged audience on Twitter seems to have an influence on the cryptocurrency market. In this paper, we analyze the impact that tweets have on the price of Bitcoin. Using word-clouds and candlestick plots, we gain insight into the factors that affect Bitcoin prices. We also use various machine learning techniques to automatically classify the sentiment in Tweets related to cryptocurrencies. We incorporate these and other relevant features to build and compare the performance of multiple machine learning models to predict the direction (increase or decrease) of the price of Bitcoin. © 2022 IEEE.

关键词： Forecasting

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Custom Hidden Markov Models for Effective Part-of-Speech Tagging 18

Custom Hidden Markov Models for Effective Part-of-Speech Tag...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Khalid, Hassan Bin Siddique, Abu Bakar Ali, Raja Hashim Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain. © 2023 IEEE.

关键词： Hidden Markov models

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Human age prediction from bone X-ray Scan images by using advanced Deep CNN 2

Human age prediction from bone X-ray Scan images by using ad...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Ashraf, Mohd Umair Choudhary, Jaytrilok Singh, Dhirendra Pratap Maulana Azad National Institute of Technology Dept. of Computer Science and Engineering Bhopal462003 India

ISBN: (纸本)9781665437899

The pace of innovation has enabled the development of computerized systems that do have a variety of uses in the medical field, including pediatrics. Fully automated bone age calculation from left-hand X-ray scanning has been one of the applications, which enables radiologists and pediatricians to make decisions about the skeletal bone growth status of youngsters. However, one of the most difficult aspects of building a computerized system is determining the best technique for getting more accurate estimation, especially in dealing with massive volumes of data. The earlier conventional techniques use hand skeletal bone scans and depend upon an optical examination of the bone picture scans. Conventional approaches like these have a lot of variation within observers, are exceedingly time-consuming, and depend upon an experienced domain expert who assesses skeletal bone age by taking an age annotated bone scan as a reference. Various automatic systems have been employed to reduce such variation. Neural network algorithms such as Convolution neural network (CNN) algorithms and their altered versions are employed to address this, resulting in much more accurate outcomes in significantly less time. Efficient Net is one such architecture that we use in this study. This design substantially replaces the usual Convolution architectures with the considerably more efficient Compound scaled architectures which optimize both accuracy and efficiency and result in drastically decreases in time it takes to generate a trained model. In this article, we have used a collection of left-hand scans provided by the RSNA. The testing results demonstrate a 6.03 month disparity in age in the bone estimation process. © 2022 IEEE.

关键词： Pediatrics

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