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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Faisal Ali Garba Oluwakemi Oduwole Abubakar Balarabe Isa Abdulkadir Sarki Aliyu Rosemary Mrumun Dima Department of Computer Science Education Sa’Adatu Rimi University of Education Kano Nigeria Computer Science Programme National Mathematical Center Kwali-Sheda Abuja Nigeria Department of Software Engineering Federal University Dutsinma Katsina Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

The Routing Protocol for Low-Power and Lossy Networks (RPL), extensively applied in IoT applications, is susceptible to the Blackhole attack, wherein hostile nodes discard packets without transmission, resulting in considerable loss of network performance. This attack jeopardizes the reliability and usefulness of RPL-based networks, particularly in crucial applications like environmental monitoring and healthcare. Understanding the effects of Blackhole attacks in diverse network environments—static, mobile, and hybrid—is vital for creating personalized security solutions. Every scenario provides various issues, including the stability of static networks and the dynamic nature of mobile and hybrid setups, which determine the severity of the attack. This study performs a comparative investigation of the impact of Blackhole attacks on RPL-based networks in static, mobile, and hybrid scenarios. Simulations were done with varied node densities (10, 20, and 30 nodes) and different percentages of malicious nodes (10%, 20%, and 30%). Key performance indicators, such as Packet Delivery Ratio (PDR), Average End-to-End Delay (AE2ED), overhead packets, energy utilization, and throughput, were investigated across these conditions. The analysis suggests that static networks, however more robust to random attacks, are susceptible to disruption when Blackhole nodes interfere with important links. Mobile networks incur the highest performance deterioration due to frequent route changes and increasing energy demand. Hybrid networks, having both static and mobile nodes, demonstrate intermediate susceptibility but are more affected by mobile attackers. Overall, the statistics imply that mobility improves the impact of Blackhole attacks. The findings underscore the need for environment-specific security solutions in RPL-based networks. Static networks demand stronger detection systems for targeted attacks, while mobile and hybrid environments need adaptable, low-overhead security soluti

关键词： Energy consumption Network topology Throughput Propagation losses Hybrid power systems Stability analysis Routing protocols Topology Delays Reliability

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A review on regular clocking scheme in quantum dot cellular automata

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 8卷

作者： Goswami, Mrinal Sharma, Tonmoy Jyoti Boruah, Arpita Nath Programme of Computer Science and Engineering Faculty of Engineering Assam down town University Sankar Madhab Path Gandhi Nagar Panikhaiti Assam Guwahati India

Quantum-dot cellular automata (QCA) is a novel and emerging nanotechnology that explores the potential of using quantum dots as information carriers in computing devices. The information transfer, as well as timing synchronization of the QCA circuit, is controlled by clocks. Despite this, most of the work done in QCA does not consider the proper layout of the underlying clocking circuit required. Hence, it ends up with QCA circuits with irregular clock zones, which is not suitable for fabrication. However, for efficient design and to provide proper routing options within the clock zones, a regular, scalable clocking scheme is needed. Without a well-defined and regular clocking zone, the QCA circuit designs are incomplete and not a standard fabrication candidate. In this regard, several regular clocking schemes in QCA are discussed in the literature. In this survey, a systematic review of those clocking schemes will be investigated. A detailed comparison of these schemes is also discussed based on design architecture and fabrication point of view. © 2024 The Author(s)

关键词： QCA Clocking Quantum-dot cellular automata (QCA) Regular Clocking

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Hybrid Competitive Swarm Optimization Algorithm Based Scheduling in the Cloud Computing Environment 5

Hybrid Competitive Swarm Optimization Algorithm Based Schedu...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shrichandran, G.V. Narayana Tinnaluri, V.S. Senthil Murugan, J. Meeradevi, T. Dwivedi, Vijay Kumar Suma Christal Mary, S. B.Sc -CS Programme Srm Institute of Science and Technology Department of Computer Science & Applications Ramapuram Chennai89 India Koneru Lakshmaiah Education Foundation Department of Computer Science Vaddeswaram522025 India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Kongu Engineering College Department of Ece Perundurai Erode India V.E.C. Ambikapur Dist Surguja497001 India Panimalar Engineering College Poonamalle Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. Business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (TS) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the TS is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive Swarm Optimization Algorithm based Task Scheduling (HCSOA-TS) technique in the CC platform. The presented HCSOA-TS technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCSOA, Cauchy mutation operator is included in the CSO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCSOA-TS technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCSOA-TS technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCSOA-TS technique over recent approaches. © 2023 IEEE.

关键词： Cloud computing

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Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior

arXiv

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arXiv 2024年

作者： Yu, Yi Wang, Yufei Yang, Wenhan Guo, Lanqing Lu, Shijian Duan, Ling-Yu Tan, Yap-Peng Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Pengcheng Laboratory Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science Peking University Beijing China

Recent advancements in deep learning-based compression techniques have demonstrated remarkable performance surpassing traditional methods. Nevertheless, deep neural networks have been observed to be vulnerable to backdoor attacks, where an added pre-defined trigger pattern can induce the malicious behavior of the models. In this paper, we propose a novel approach to launch a backdoor attack with multiple triggers against learned image compression models. Drawing inspiration from the widely used discrete cosine transform (DCT) in existing compression codecs and standards, we propose a frequency-based trigger injection model that adds triggers in the DCT domain. In particular, we design several attack objectives that are adapted for a series of diverse scenarios, including: 1) attacking compression quality in terms of bit-rate and reconstruction quality;2) attacking task-driven measures, such as face recognition and semantic segmentation in downstream applications. To facilitate more efficient training, we develop a dynamic loss function that dynamically balances the impact of different loss terms with fewer hyper-parameters, which also results in more effective optimization of the attack objectives with improved performance. Furthermore, we consider several advanced scenarios. We evaluate the resistance of the proposed backdoor attack to the defensive pre-processing methods and then propose a two-stage training schedule along with the design of robust frequency selection to further improve resistance. To strengthen both the cross-model and cross-domain transferability on attacking downstream CV tasks, we propose to shift the classification boundary in the attack loss during training. Extensive experiments also demonstrate that by employing our trained trigger injection models and making slight modifications to the encoder parameters of the compression model, our proposed attack can successfully inject multiple backdoors accompanied by their corresponding triggers int

关键词： Semantic Segmentation

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The Response of EEG Signal on Brain Relaxation Meditation Using Different Type of Zikr

The Response of EEG Signal on Brain Relaxation Meditation Us...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Razak, N.N. Abd Sidek, K.A. Alaa, K.Y.D. Nasir, N.F. Mohd Department of Electrical and Computer Engineering Kulliyyah of Engineering International Islamic University Malaysia Department of Computer Engineering College of Computer Science and Engineering University of Ha’il Saudi Arabia Biomedical Electronic Engineering Programme Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Malaysia

Depression and stress are increasingly prevalent in today’s society, owing to people's hectic, competitive, and demanding lifestyles. These illnesses had become very common, particularly among young and middle-aged people, and suicidal ideation had been identified as one of the leading causes of death by the World Health Organization (WHO). Nature sound (sound of downpours or beach) has been linked to depression and anxiety in neurology research, and it has been shown to be an alternative to alleviate anxiety. The electroencephalogram (EEG) waveform has been discovered to possess the potential in identifying information from the brain signal as well as data from the past via Bluetooth communication. The waveform use in the study are the result of a few experiments. In this study, EEG data were collected from eight subjects, four males and four females, in between the age of 20 to 30 years old and in good health, using the BrainLink device. The participants were asked to listen to two playlists of zikr, Allah, Ya Allah, and SubhanAllah, during the experiments. To smooth the signal, the Butterworth filter was used. Later, the extracted features were Alpha, Beta, and Delta waves, which were segmented based on the filtered signal. To complete the decision-making stage, the average value of amplitude differences and the p-value test were performed in the final phase. Based on the results of the experiments, it is clear that zikr is dominant in Delta wave. In terms of data hypothesis analysis, the results of both experiment were differentiated to determine which brain signal was dominant, and p-value testing was performed. Furthermore, this research is an alternative to current methods because it suggest that zikr has the tendency to alter a person's brain state to be either in a relaxed or calm mode regardless of the type of zikr recitation. As an outcome, the study recommends the relationship of the reaction of EEG signal on brain relaxation with different types of zikr

关键词： Electroencephalography

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Use your INSTINCT: INSTruction optimization for LLMs usIng neural bandits coupled with transformers 24

Use your INSTINCT: INSTruction optimization for LLMs usIng n...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xiaoqiang Lin Zhaoxuan Wu Zhongxiang Dai Wenyang Hu Yao Shu See-Kiong Ng Patrick Jaillet Bryan Kian Hsiang Low Department of Computer Science National University of Singapore Institute of Data Science National University of Singapore and Integrative Sciences and Engineering Programme National University of Singapore LIDS and EECS Massachusetts Institute of Technology Department of Computer Science and Institute of Data Science National University of Singapore Guangdong Lab of AI and Digital Economy (SZ)

Large language models (LLMs) have shown remarkable instruction-following capabilities and achieved impressive performances in various applications. However, the performances of LLMs depend heavily on the instructions given to them, which are typically manually tuned with substantial human efforts. Recent work has used the query-efficient Bayesian optimization (BO) algorithm to automatically optimize the instructions given to black-box LLMs. However, BO usually falls short when optimizing highly sophisticated (e.g., high-dimensional) objective functions, such as the functions mapping an instruction to the performance of an LLM. This is mainly due to the limited expressive power of the Gaussian process (GP) which is used by BO as a surrogate to model the objective function. Meanwhile, it has been repeatedly shown that neural networks (NNs), especially pre-trained transformers, possess strong expressive power and can model highly complex functions. So, we adopt a neural bandit algorithm which replaces the GP in BO by an NN surrogate to optimize instructions for black-box LLMs. More importantly, the neural bandit algorithm allows us to naturally couple the NN surrogate with the hidden representation learned by a pretrained transformer (i.e., an open-source LLM), which significantly boosts its performance. These motivate us to propose our INSTruction optimization usIng Neural bandits Coupled with Transformers (INSTINCT) algorithm. We perform instruction optimization for ChatGPT and use extensive experiments to show that INSTINCT consistently outperforms baselines in different tasks, e.g., various instruction induction tasks and the task of improving zero-shot chain-of-thought instructions. Our code is available at https://***/xqlin98/INSTINCT.

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Envisioning Artificial Intelligence Possibilities in Tracking Eidolon Helvum Bats: A Review

SSRN

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SSRN 2023年

作者： Aworinde, Halleluyah O. Adebayo, Segun Ayokunle, Damilare I. Atobatele, Oluwatosin E. Computer Science Programme College of Computing and Communication Studies Bowen University Iwo Nigeria Mechatronics Engineering College of Agriculture Engineering & Science Bowen University Iwo Nigeria Biological Sciences College of Agriculture Engineering & Science Bowen University Iwo Nigeria

Artificial intelligence (AI) is fast playing important roles as it is useful in several ways to curate data on movement patterns, wildlife observations, monitor ecological population and as well, understand behaviours. Here, in this study, current trends of how artificial intelligence are used to understand several activities of the Eidolon helvum fruit bats. The study reviewed 122 publications that is related to bats and its environment, of which just 20 (16%) used machine learning to assess the several activities of bats ranging from specie classification to sound signal processing. We also identified the most popular machine-learning techniques, as well as probable explanations for why they are rarely used in ecology and workable remedies. Furthermore, the study identified other AI-based technology like Edge Computing and the Internet of Things (IoT) as new opportunities in ecological *** conclusion, the study anticipates that the joint use cutting edge technologies for ecological research and conservation and the need for innovative methods and AI-driven solutions to track wildlife demographics. © 2023, The Authors. All rights reserved.

关键词： Biodiversity

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Cloud-based fault prediction using IoT in office automation for improvisation of the health of employees 3

Cloud-based fault prediction using IoT in office automation ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shahdadpuri, Nakul Hari Krishna, Somanchi Reddy, SathiJyothirmaye Ganesh, C. Agarwal, Kirti Bhattacharya, Sourabh Iiitm Ipg Programme Gwalior India Vignana Bharathi Institute of Technology Department of Business Management Telangana Malkajigiri India Marwadi University Department of Management Gujarat Rajkot India Anna University Computer and Communication Engineering Sri Eshwar College of Engineering Chennai India Delhi India Haldia Institute of Technology Department of Management and Social Science West Bengal India

ISBN: (纸本)9798350399264

Modern technology is the best thing to be used in every commercial sector. The devices are well equipped to understand the problems of the users. The offices are now been maintained by technology the devices for daily monitoring even the electrical appliances are been monitored and maintained by technology. In previous times the best method of this work is to make it via cloud computing, but now as technology has evolved and evolved new adding devices are making system malfunction. The fault of cloud computing needs to be identified, So the best method is to automate the office with the introduction of IoT. it is a platform equipped with all the facilities for monitoring and maintaining the devices inside the office, even the working capacity of employees is also measured via this method. The study is showing the effectiveness of the IoT to identify the faults of cloud computing related to maintaining the working method of the office. The data collected for this study is secondary data like articles, books, journals and many more. Then gathered data is analyzed by qualitative data analysis. The results are quite positive about the use of it to automate the office and its working procedure. © 2023 IEEE.

关键词： Cloud computing

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Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uniform Motion

Robust e-NeRF: NeRF from Sparse & Noisy Events under Non-Uni...

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International Conference on computer Vision (ICCV)

作者： Weng Fei Low Gim Hee Lee The NUS Graduate School's Integrative Sciences and Engineering Programme (ISEP) Institute of Data Science (IDS) National University of Singapore Department of Computer Science National University of Singapore

Event cameras offer many advantages over standard cameras due to their distinctive principle of operation: low power, low latency, high temporal resolution and high dynamic range. Nonetheless, the success of many downstream visual applications also hinges on an efficient and effective scene representation, where Neural Radiance Field (NeRF) is seen as the leading candidate. Such promise and potential of event cameras and NeRF inspired recent works to investigate on the reconstruction of NeRF from moving event cameras. However, these works are mainly limited in terms of the dependence on dense and low-noise event streams, as well as generalization to arbitrary contrast threshold values and camera speed profiles. In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion. It consists of two key components: a realistic event generation model that accounts for various intrinsic parameters (e.g. timeindependent, asymmetric threshold and refractory period) and non-idealities (e.g. pixel-to-pixel threshold variation), as well as a complementary pair of normalized reconstruction losses that can effectively generalize to arbitrary speed profiles and intrinsic parameter values without such prior knowledge. Experiments on real and novel realistically simulated sequences verify our effectiveness. Our code, synthetic dataset and improved event simulator are public.

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BoTNetShield: Intelligent IoT Security Framework using Convolution Neural Network

BoTNetShield: Intelligent IoT Security Framework using Convo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： A. Sivasundari Pravin R. Kshirsagar Tan Kuan Tak Shrikant V. Sonekar Department of Computer Science and Engineering Misrimal Navajee Munoth Jain Engineering College Chennai India Department of Electronics & Telecommunication Engineering J D College of Engineering & Management Nagpur India Electrical Power Engineering Programme Singapore Institute of Technology Singapore J D College of Engineering & Management Nagpur India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The "Internet of Things (IoT)" sector has grown in recent years, penetrating many different markets. The diversity and diminutive size of Internet-connected gadgets render them vulnerable to attacks. Improving and managing network forensic processes to avoid the various attacks, key logging, and denial of service becomes challenging in big and heterogeneous networks. Implementing high-end safety systems is challenging in IoT networks due to their variety and resource restrictions. Using deep learning, botnet detection is made better. It often requires a great deal of internet engagement. Internet of Things devices with little RAM have difficulties when using deep learning. This study use Convolutional Neural Networks (CNNs) for model training and Similarity Associated Feature Selection (SAFS) to decrease the dimensionality of smaller Internet of Things (IoT) traffic networks in order to effectively classify attacks in devices with limited memory. The effectiveness of botnet attacks is evaluated by first collecting datasets from the internet. By improving attack detection data from acquired datasets, data cleaning aids in eliminating unnecessary information. To enhance attack detection, SAFS cleans the data and then extracts efficient deep features. To classify risks to Internet of Things devices based on retrieved deep data, an efficient Recurrent Neural Network method is employed. Research evaluations compare proposed methods against existing ones in order to determine whether one is more effective.

关键词： Deep learning Training Recurrent neural networks Botnet Random access memory Feature extraction Safety Internet of Things Security Informatics

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