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IEEE International Conference on Trust, Security and Privacy in computing and communications (TrustCom)

作者： Seyedsina Nabavirazavi Rahim Taheri Mohammad Shojafar Sundararaja Sitharama Iyengar School of Computing and Information Sciences Florida International University USA Faculty of Technology School of Computing University of Portsmouth UK 5G/6G Innovation Centre (5G/6GIC) Institute for Communication Systems University of Surrey UK

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Federated learning has gained significant attention as a privacy-preserving approach for training machine learning models across decentralized devices. However, this distributed learning paradigm is susceptible to adversarial attacks, particularly model poisoning attacks, where adversaries inject malicious model updates to compromise the integrity of the global model. In this paper, we investigate the impact of randomness on model poisoning attacks in federated networks, where the server employs two aggregation rules, Krum and Trimmed Mean, randomly in each federated round. We present three distinct adversaries: one targeting Krum throughout the entire learning process, another targeting Trimmed Mean entirely, and a third adversary employing a randomized strategy between Krum and Trimmed Mean for each round. Our objective is to evaluate their performance in reducing the overall accuracy of the federated network. We propose novel techniques to craft poisoned models and explore the efficacy of these attacks by exploiting the aggregation rules. We evaluated our proposed methods on Fashion-MNIST dataset. The experiments reveal the robustness of the federated network against the proposed adversarial scenarios, contributing to a better understanding of the vulnerabilities and defenses in federated learning systems.

关键词： Training Privacy Computer aided instruction Federated learning Distance learning Computational modeling Robustness

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Blockchain-based Data Sharing Framework for Malaysia Government Aid Management System

Blockchain-based Data Sharing Framework for Malaysia Governm...

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Digital Applications, Transformation & Economy (ICDATE), International Conference on

作者： Azman Azmi Farashazillah Yahya Ervin Gubin Moung Hasimi Sallehudin Rio Guntur Utomo Nur Afrina Azman Advanced Machine Intelligence Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Data Technologies and Application Research Lab Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Centre for Software Technology and Management Faculty of Information Science and Technology Universiti Kebangsaan Malaysia School of Computing Telkom University Bandung Indonesia Foundation Study and Language Institute University of Reading Malaysia Iskandar Puteri Johor Malaysia

The Covid-19 pandemic has impacted the world economy, and Malaysia is no exception. Due to the closure of businesses, the rate of unemployment has been recorded at an all-time high. The Malaysian Government through its agencies is assisting citizens through an online platform for incentives and an aid-giving platform. The current architecture of data sharing is using centralized, organization-oriented authentication and is not immutable for audit capability. In addition, the blockchain network is still a proof of concept in the digital government ecosystem. This paper studies the current aid data-sharing ecosystem and analyzes the potential of having a state government consortium blockchain framework that will be used in the Government Network Infrastructure for decentralized, transparent data sharing with audit capability. Several works involving Blockchain adoption based on domain, Blockchain consortium architecture and Government applications using blockchain are being presented. The need for a framework in implementing decentralized data sharing using consortium blockchain architecture for multi-agencies data sharing focusing on Malaysia Government Aid Management is discussed in this paper to provide the opportunity for the research domain. The findings in this paper will be used for future work on the requirement of a proposed government multi-layer consortium blockchain framework for data sharing in the Malaysian Government specifically for the Johore State Government ecosystem.

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Symbiotic Relationship Between Media technology and Artificial Intelligence: A Synthesis Analysis

Symbiotic Relationship Between Media Technology and Artifici...

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

作者： Miharaini Md Ghani Wan Azani Mustafa Aboothar mahmood Shakir Durratul Laquesha Shaiful Bakhtiar Ghazi Mohamad Ramadan Department of Communication Faculty of Modern Language and Communication Universiti Putra Malaysia Faculty of Electrical Engineering Technology Universiti Malaysia Perlis Padang Besar Perlis Malaysia Advanced Computing (AdvCOMP) Centre of Excellence Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Faculty of Engineering The Islamic University Najaf Iraq Faculty of Media and Communication Universiti Teknologi Mara Malaysia College of MLT Ahl Al Bayt University Karbala Iraq

The interaction between media technology and artificial intelligence (AI) has become a significant factor in today's technological environment. This review provides a thorough examination of the complex and mutually beneficial link between these two areas, clarifying important patterns, difficulties, and significant effects. The literature study consolidates current understanding by analysing the convergence of AI algorithms and improvements in media technology, and their impact on the landscape of content generation, distribution, and consumption. The concept of integration plays a vital role, since AI has a significant impact on media technologies and vice versa. We explore particular cases where AI-powered advancements redefine the creation of content, improve user experiences, and transform data-driven decision-making in media companies. The study also examines the significant influence of this symbiotic relationship on the wider media environment, including changes in audience participation, customized content distribution, and the merging of conventional and digital media platforms. Although the potential for transformation is significant, other problems arise, including ethical issues as well as algorithmic biases. The abstract also emphasizes the potential for AI to solve persistent difficulties in media production and consumption, while highlighting chances for innovation. The paper discusses probable future developments, such as the changing role of AI in immersive media experiences and the ethical considerations surrounding AI-generated content. Ultimately, this analysis offers a detailed comprehension of the interdependent connection between media technology and AI, presenting valuable perspectives for scholars, practitioners, and policymakers. As the field of technology advances, this investigation establishes the foundation for predicting and manoeuvring through the complex connections between media technology and artificial intelligence.

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Hybrid Energy Storage System interface for Electric Vehicles

Hybrid Energy Storage System interface for Electric Vehicles

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2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy, PESGRE 2020

作者： Arun Raj Kumar, K.P. Sreedevi, M.L. Mineeshma, G.R. Chacko, Renji V. Amal, S. A Scientific Society of the Ministry of Communication and Information Technology Govt. of India Power Electronics Group Centre for Development of Advanced Computing Thiruvananthapuram India

ISBN: (纸本)9781728142517

In conventional EV powertrain, Battery is the sole energy storage. This makes higher transient current stress on battery during operation, which shall reduce the life of the battery considerably. The size of the battery may required to be over rated to take care heavy transient demands with respect to its C rating. The regenerative braking capability of Electric power train is also gets limited due to the charging transient limitations in Battery. This eventually results in poor EV power train designs and vehicle performance. A hybrid energy storage system with Battery as primary energy source and Ultra capacitor as transient power manager is proposed as a viable solution. The combination shall provide better utilization of regenerative power, thus increase overall efficiency and range. The secondary storage can absorb all transients during regeneration and supply transients when required, during motoring. This can reduce the transient stress on battery increase the life. Cost effective Power train solution is also a major challenge in practical EV system design, and the proposed architecture is without dedicated DCDC converter for battery. The control logic can be formulated so as to meet the transient supplies of the load by ultra-capacitor and indirectly reduce that from battery. This provides low capacity DC-DC converter design, which could be appropriately overloaded for transient demands, thus reducing the overall size also. This can reduce the overall cost of EV power train. This paper discusses Hybrid Energy Storage System configuration for Battery Electric Vehicles and its Power Management strategy. The ultracapacitor is connected to DC bus using bidirectional converter in current control mode. Power split mechanism is implemented such that, drive current transient requirements, is met by the ultracapacitor depending on its voltage condition and remaining current requirement is met by battery. © 2020 IEEE.

关键词： Supercapacitor

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Skin Cancer Classification using Machine Learning Techniques

Skin Cancer Classification using Machine Learning Techniques

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BIT Conference (BITCON), International

作者： I. Sapthami Ch. Srilakshmi Batta Saranya N. Kumareshan Ramesh Kumar Ayyasamy P. Chinnasamy Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Information Technology VNR VJIET Bachupally Hyderabad India AIDS Department CBITGandipet Hyderabad India Department of ECE Professor/Centre for Advances in Signal Processing and Sensor Networks Sri Eshwar College of Engineering Coimbatore Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputtur TamilNadu

ISBN: (数字)9798331518394

ISBN: (纸本)9798331518400

The death rate from skin cancer has radically grown due to a lack of understanding about the indications and prevention measures. It is one of the most lethal types of cancers, and the mortality rate due to skin cancer has risen sharply. Cancer must be diagnosed as early as possible to halt its growth and prevent metastasis. Even though there are numerous varieties of skin cancer, melanoma, basal cell carcinoma, and squamous cell carcinoma are considered the three most dangerous types of skin cancer. This research, using the CNN image-processing technology and machine learning, tries to detect and classify into the many kinds of skin cancer. Artificial intelligence (AI) based algorithms for automated skin cancer classification have consistently outperformed human professionals in several clinical studies, laying the groundwork for a future in which AI will be widely used.

关键词： Squamous cell carcinoma Prevention and mitigation Mortality Melanoma Machine learning Data augmentation Skin Classification algorithms Convolutional neural networks Skin cancer

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PicToText: Text Recognition using Firebase Machine Learning Kit 2

PicToText: Text Recognition using Firebase Machine Learning ...

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2nd International Conference on Artificial Intelligence and Data Sciences, AiDAS 2021

作者： Neoh, Desmond Kim Seng Lock, Eric Chun Meng Lew, Kem Ho Tang, Hong Yip Tong, Dong Ling Tan, Tun Tai Tseu, Kwan Lee Artificial Intelligence Lab First City University College Faculty of Computing and Engineering Petaling Jaya Malaysia Centre of IoT Big Data Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Perak Malaysia Global Software House Sdn Bhd Petaling Jaya Malaysia

ISBN: (纸本)9781665417266

Back in the days, documents, payment receipts, and books are being published in printed format. To make a digital format, readers need to manually type them into a computer. However, things have change rapidly especially in the information technology (IT) industry. Optical character recognition (OCR) has been introduced to simplify the digitalization process for users. However, OCR not able to translate scanned text images into different human readable languages. Therefore, this paper proposed an Android-based application programming interface (API) developed using Firebase as an improvement text recognition program that able to translate the scanned text images into language of user preference. This proposed API processes text images, detects text from the scanned images, and translate the text into user-preferred languages. The used data in this paper is the research papers available in the university library. © 2021 IEEE.

关键词： Digital libraries

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Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

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Computer Systems Science & Engineering 2023年第10期47卷 553-574页

作者： Adnan Hasan Bdair Aighuraibawi Selvakumar Manickam Rosni Abdullah Zaid Abdi Alkareem Alyasseri Ayman Khallel Dilovan Asaad Zebari Hussam Mohammed Jasim Mazin Mohammed Abed Zainb Hussein Arif National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPenang11800Malaysia Baghdad College of Economic Sciences University BaghdadIraq School of Computer Sciences Universiti Sains MalaysPenang11800Malaysia Information Technology Research and Development Center University of KufaNajafIraq College of Engineering University of Warith Al-AnbiyaaKarbalaIraq Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Business Administration College of Administration and Financial SciencesImam Ja’afar Al-Sadiq UniversityBaghdad10001Iraq College of Computer Science and Information Technology University of AnbarRamadi AnbarIraq Department of Computer Science College of ScienceNawroz UniversityDuhok42001Kurdistan RegionIraq

Internet Protocol version 6(IPv6)is the latest version of IP that goal to host 3.4×10^(38)unique IP addresses of devices in the ***6 has introduced new features like Neighbour Discovery Protocol(NDP)and Address Auto-configuration ***6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol(ICMPv6).IPv6 is vulnerable to numerous attacks like Denial of Service(DoS)and Distributed Denial of Service(DDoS)which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial ***,an Intrusion Detection System(IDS)is a monitoring system of the security of a network that detects suspicious activities and deals with amassive amount of data comprised of repetitive and inappropriate features which affect the detection rate.A feature selection(FS)technique helps to reduce the computation time and complexity by selecting the optimum subset of *** paper proposes a method for detecting DDoS flooding attacks(FA)based on ICMPv6 messages using a Binary Flower PollinationAlgorithm(BFPA-FA).The proposed method(BFPA-FA)employs FS technology with a support vector machine(SVM)to identify the most relevant,influential ***,The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack *** results show that the proposed method BFPAFA achieved the best accuracy rate(97.96%)for the ICMPv6 DDoS detection with a reduced number of features(9)to half the total(19)*** proven proposed method BFPA-FAis effective in the ICMPv6 DDoS attacks via IDS.

关键词： IPv6 ICMPv6 DDoS feature selection flower pollination algorithm anomaly detection

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Convolutional Autoencoder based Deep Learning Model for Identification of Red Palm Weevil Signals

Convolutional Autoencoder based Deep Learning Model for Iden...

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

作者： S. R. Parvathy Deepak P. Jayan Nimmy Pathrose K. R. Rajesh Centre for Development of Advanced Computing A Scientific Society of the Ministry of Communication and Information Technology Govt. of IndiaThiruvananthapuramKeralaIndia

ISBN: (纸本)9781665441629

This paper presents a Convolutional Autoencoder based Deep Learning model for identification of Red Palm Weevil acoustic emissions from other background noise. Mel spectrogram of acoustic samples was chosen as the extracted feature for the proposed model. The designed Convolutional Autoencoder was trained using Mel spectrogram images of Red Palm Weevil acoustic activities which are regarded as the normal instances. Unbiased evaluation of the model was done with a test dataset composed of normal RPW acoustic emissions as well as anomalous acoustic samples. The model could achieve a very high classification accuracy of 95.85%. The results confirmed that the proposed method is highly efficient for the identification of Red Palm Weevil signals.

关键词： Deep learning Convolution information processing Acoustic emission Feature extraction Data mining Background noise

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Bandwidth Enhancement of Chamfered UWB Monopole Textile Antenna With Truncated Ground Plane for Breast Cancer Detection

Bandwidth Enhancement of Chamfered UWB Monopole Textile Ante...

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International Symposium on Antennas and Propagation (ISAP)

作者： Ayesha Wasim Qureshi Hasliza A Rahim Allan Melvin Mohd Fareq Abd Malek Samir Salem Al-Bawri Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Arau Perlis Malaysia Advanced Communication Engineering Centre of Excellence Universiti Malaysia Perlis Kangar Perlis Malaysia Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Arau Perlis Malaysia Faculty of Engineering and Information Sciences University of Wollongong in Dubai Dubai United Arab Emirates Space Science Centre Institute of Climate Change Universiti Kebangsaan Malaysia Bangi Malaysia

A bandwidth enhancement of chamfered square Ultrawideband (UWB) monopole textile antenna with truncated ground plane for breast cancer detection is presented in this paper. The overall dimensions of 28.5 x 28.5 x 3.34 mm 3 , covering a super bandwidth of range between 2.23 and 18.24 GHz is obtained. A total impedance bandwidth (<-10 dB) of 16.01 GHz is achieved with a total efficiency of 93.54% and realized gain of 12.95 dBi at 9.3 GHz.

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Compilation of algorithm-specific graph states for quantum circuits

arXiv

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arXiv 2022年

作者： Vijayan, Madhav Krishnan Paler, Alexandru Gavriel, Jason Myers, Casey R. Rohde, Peter P. Devitt, Simon J. Centre for Quantum Software and Information University of Technology Sydney SydneyNSW2007 Australia Aalto University Espoo02150 Finland Centre for Quantum Computation and Communication Technology Australia School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne MelbourneVIC3010 Australia Silicon Quantum Computing Pty Ltd. UNSW Sydney Level 2 Newton Building KensingtonNSW2052 Australia

We present a quantum circuit compiler that prepares an algorithm-specific graph state from quantum circuits described in high level languages, such as Cirq and Q#. The computation can then be implemented using a series of non-Pauli measurements on this graph state. By compiling the graph state directly instead of starting with a standard lattice cluster state and preparing it over the course of the computation, we are able to better understand the resource costs involved and eliminate wasteful Pauli measurements on the actual quantum device. Access to this algorithm-specific graph state also allows for optimisation over locally equivalent graph states to implement the same quantum circuit. The compiler presented here finds ready application in measurement based quantum computing, NISQ devices and logical level compilation for fault tolereant implementations. Copyright © 2022, The Authors. All rights reserved.

关键词： High level languages

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