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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Butler, Kurt Waxman, Daniel Djurić, Petar M. Department of Electrical and Computer Engineering Stony Brook University United States

Causal discovery with time series data remains a challenging yet increasingly important task across many scientific domains. Convergent cross mapping (CCM) and related methods have been proposed to study time series that are generated by dynamical systems, where traditional approaches like Granger causality are unreliable. However, CCM often yields inaccurate results depending upon the quality of the data. We propose the Tangent Space Causal Inference (TSCI) method for detecting causalities in dynamical systems. TSCI works by considering vector fields as explicit representations of the systems' dynamics and checks for the degree of synchronization between the learned vector fields. The TSCI approach is model-agnostic and can be used as a drop-in replacement for CCM and its generalizations. We first present a basic version of the TSCI algorithm, which is shown to be more effective than the basic CCM algorithm with very little additional computation. We additionally present augmented versions of TSCI that leverage the expressive power of latent variable models and deep learning. We validate our theory on standard systems, and we demonstrate improved causal inference performance across a number of benchmark tasks. © 2024 Neural information processing systems foundation. All rights reserved.

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Blockchain Breakthrough: A Review of its Role in Modern Technology 2

Blockchain Breakthrough: A Review of its Role in Modern Tech...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

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Open Radio Access Network RIC Empowered Reconfigurable Intelligent Surface: A Physical-Layer Security Perspective

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 615-625页

作者： Lien, Shao-Yu Tseng, Chih-Cheng Hung, Wei-Cheng Tsai, Cheng-You Liu, Ting-Yu Deng, Der-Jiunn Lin, Yuan-Chun Lin, Shih-Cheng Chang, Chia-Chan Chang, Sheng-Fuh Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan711010 Taiwan Department of Electrical Engineering National Ilan University Ilan260007 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua50074 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621301 Taiwan

The reconfigurable intelligent surface (RIS) steering reflective beam directions toward a target mobile user equipment (UE) has been a promising technology for coverage enhancement and physical-layer (PHY) security to avoid signal eavesdroppers and jammers. However, tracking and predicting the moving trajectory of UE demand significant computing burdens unaffordable to the RIS. Performing beam steering computations on the radio access network (RAN) intelligent controller in the open RAN (O-RAN) architecture therefore emerges as a revolutionary paradigm. In this paper, we propose a PHY-secured beam steering design based on a pre-processing-based Kalman filter algorithm. Through a pre-processing phase to derive the radiation feature, the proposed design accurately tracks the position of the mobile UE and quickly recovers the position search while position tracking fails. Through practical field-try experiments, we justify the capability of O-RAN RIC-empowered RIS for PHY security. © 2013 IEEE.

关键词： Radio access networks

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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A new hybrid MobileNet-MLP model for secured data communication using signature verification and deep learning

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Iran Journal of computer science 2024年第1期8卷 233-252页

作者： Pandiyaraju, V. Ganapathy, Sannasi Thangaramya, K. Nahar, R. Raunak Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department Computer Science and Engineering Education National Institute of Technical Teachers Training and Research (NITTTR) Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an inability to effectively capture intricate patterns and relationships within the signature images. This paper introduces an innovative approach that addresses the drawbacks by integrating conventional machine learning methods with deep learning techniques. For this purpose, we design a new hybrid MobileNet model using deep learning to extract features from the signature images. Here, the Multilayer Perceptron (MLP) classifier is also used to carry out the binary classification over the extracted features. The experimental results of the proposed model demonstrate that the amalgamated technique surpasses the conventional methods and underscores the possibilities of merging traditional machine learning and deep learning in signature verification. Moreover, the proposed model overcomes the limitations of the traditional signature verification techniques by leveraging the strengths of both deep learning and traditional machine learning. Finally, it obtains a more robust and efficient solution and proves it by providing high security to the data in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Feature extraction MobileNet Multilayer perceptron Signature verification

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An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

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SN computer science 2024年第8期5卷 1140页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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An Eco-Friendly Approach for Reducing Carbon Emissions in Cloud Data Centers

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computers, Materials & Continua 2022年第8期72卷 3175-3193页

作者： Mohammad Aldossary Hatem A.Alharbi Department of Computer Science College of Arts and SciencePrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Engineering College of Computer Science and EngineeringTaibah UniversityMadinahSaudi Arabia

Based on the Saudi Green initiative,which aims to improve the Kingdom’s environmental status and reduce the carbon emission of more than 278 million tons by 2030 along with a promising plan to achieve netzero carbon by 2060,NEOM city has been proposed to be the“Saudi hub”for green energy,since NEOM is estimated to generate up to 120 Gigawatts(GW)of renewable energy by ***,the Information and Communication Technology(ICT)sector is considered a key contributor to global energy consumption and carbon *** data centers are estimated to consume about 13%of the overall global electricity demand by ***,reducing the total carbon emissions of the ICT sector plays a vital factor in achieving the Saudi plan to minimize global carbon ***,this paper aims to propose an eco-friendly approach using a Mixed-Integer Linear Programming(MILP)model to reduce the carbon emissions associated with ICT infrastructure in Saudi *** approach considers the Saudi National Fiber Network(SNFN)as the backbone of Saudi Internet ***,we compare two different scenarios of data center *** first scenario considers a traditional cloud data center located in Jeddah and Riyadh,whereas the second scenario considers NEOM as a potential cloud data center new location to take advantage of its green energy ***,we calculate the energy consumption and carbon emissions of cloud data centers and their associated energy *** that,we optimize the energy efficiency of different cloud data centers’locations(in the SNFN)to reduce the associated carbon emissions and energy *** results show that the proposed approach can save up to 94%of the carbon emissions and 62%of the energy cost compared to the current cloud physical *** savings are achieved due to the shifting of cloud data centers from cities that have conventional energy sources to a city that has rich in renewable energy ***,we

关键词： Cloud computing carbon emissions energy efficiency energy consumption energy costs eco-friendly data center

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Toward a smart multi-unmanned aerial vehicle system

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IEEE Potentials 2022年第1期41卷 22-25页

作者： Jaiswal, Shashwat Sidhanta, Subhajit Department of Electrical Engineering and Computer Science Indian Institute of Technology Bhilai492015 India

Drones, or unmanned aerial vehicles (UAVs) in general, have been increasingly gaining popularity and attention in both the commercial and nonprofit sectors. They have particularly been envisioned to be of critical importance in military and disaster zones for many purposes, including surveying, relief, and combat. © 1988-2012 IEEE.

关键词： Antennas

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Sample-based relationship for assisting diagnosis of pneumonia in medical care

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Multimedia Tools and Applications 2025年第1期84卷 267-288页

作者： Chen, Hongkang Lu, Huijuan Zhu, Wenjie Zhou, Ye Yao, Yudong Wang, Renfeng College of Information Engineering China Jiliang University Zhejiang Hangzhou China Drore Information and Technology Co. LTD Zhejiang Hangzhou China Department of Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ United States College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China

Deep neural networks have succeeded in learning balanced and imbalanced data in the field of pneumonia diagnosis. However, both require separate model designs in their respective domains. The pneumonia recognition methods have poor transfer performance between the two types of image distribution data. This paper proposes BeFormer, which focuses on sample relationships. BeFormer introduces two stages, feature extraction and establishing sample relationships, to solve the problem. Specifically, in the feature extraction stage, a new backbone network called PANet based on Multi-Scale Frequency Domain Attention (FPSA) is proposed. In establishing sample relationships, Deformable Transformer is used in parallel to model the relationships between partial regions of interest among the same and different samples in the pneumonia dataset. These two modules are connected in series to form BeFormer. Without making any changes to the model, this paper conducted experiments using BeFormer on both balanced and imbalanced pneumonia datasets. As compared to traditional pneumonia recognition models, the proposed method achieved significant improvements in the two different data distributions, demonstrating outstanding transfer performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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