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2024 Conference on Lasers and Electro-Optics/Pacific Rim, CLEO-PR 2024

作者： Umeki, Takeshi Kashiwazaki, Takahiro Inoue, Asuka Endo, Mamoru Furusawa, Akira NTT Device Technology Labs NTT Corporation 3-1 Morinosato Wakamiya Kanagawa Atsugi243-0198 Japan Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

We review the capabilities of optical parametric amplifiers based on periodically poled LiNbO3 (PPLN) waveguides for optical quantum computing. Specifically, we discuss the generation of squeezed and non-Gaussian states of light over a broad bandwidth and high-speed real-time detection of the quadrature-phase amplitude by using phase-sensitive amplifiers for continuous-variable optical quantum signal processing. © 2024 The Author(s)

关键词： Optical parametric amplifiers

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Computational Methods for Analysing Biometric Systems 3

Computational Methods for Analysing Biometric Systems

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3rd International Conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Multani, Fahim Bijlwan, Sheela Upadhyay, Viyat Varun Goyal, Bhawna Dogra, Ayush Lepcha, Dawa Chyophel Ies College of Technology Bhopal Department of Computer Science Engineering Madhya Pradesh India Uttaranchal School of Computing Sciences Uttaranchal Institute of Technology Uttaranchal University Dehradun248007 India Gla University Department of Mechanical Engineering Mathura Mathura India University Center for Research and Development Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department Electronics and Communication Engineering Punjab Mohali India

ISBN: (纸本)9798350342338

The use of biometric data, such as various anatomical and behavioural attributes in automatic identification and authentication systems for access control, surveillance, computer security, and law enforcement are increasing. Several nations are already or will soon begin to use biometric technology. In this paper, there is a in detail analysis of innovative biometric systems and also the research study of how the computational techniques used intelligent data system in order to analyse biometric system, The efficacy of biometric technologies will depend on how well they work. Biometric system evaluation examines how well biometric systems adapt to particular characteristics. Planning and carrying out biometric tests are frequently handled by an impartial third party. A measure of confidence in the validity of the claim might be one of the options the algorithm offers. Applications that validate a user's address or confirm their identity at the point of sale are examples of verification applications. © 2023 IEEE.

关键词： Attributes Biometric data Computational techniques Innovative Research technology

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Research on Enhanced Hybrid Whale Optimization Algorithm with Inverse Learning Strategy and Lévy Flight Mechanism

Research on Enhanced Hybrid Whale Optimization Algorithm wit...

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Computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Danyang Guo Yinghao Zheng Jiaxin Lu Sihua Liang Tuyin Chen Jialin Yang Chao Zhou School of Robotics Engineering Guangzhou City University of Technology China School of Materials Science and Engineering Guilin University of Electronic Technology China School of Big Data and Computing Guangdong Baiyun University China

In recent years, bio-inspired optimization algorithms have attained significant success in addressing complex global optimization issues. Nonetheless, a single bio-inspired search strategy may struggle to handle diverse and intricate problems. To surmount this constraint, this paper introduces an enhanced hybrid whale algorithm (MEHWOA) based on reverse learning strategy and Lévy flight mechanism improvement. This approach amalgamates the global search capabilities of the gray wolf algorithm with the local search prowess of the whale algorithm, further augmenting the convergence speed and optimization accuracy of MEHWOA by incorporating reverse learning strategy and Lévy flight mechanism. To assess the performance of MEHWOA, we conducted experiments on 23 general benchmark test functions and compared it with original gray wolf (GWO), whale (WOA), particle swarm (PSO), and sparrow search (SSA) optimization algorithms. The experimental outcomes reveal that MEHWOA exhibits faster convergence speed and superior accuracy across various test functions, including unimodal, multimodal, and composite benchmark test functions. These findings corroborate that MEHWOA possesses considerable potential for solving complex global optimization problems.

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Prompt Injection Detection Using Ensemble of BERT and LSTM with GloVe Embeddings

Prompt Injection Detection Using Ensemble of BERT and LSTM w...

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International Conference on Electrical and Computer engineering (ICECE)

作者： Md. Main Oddin Chisty Abu Noman Md Sakib Md Somir Khan Md. Badiuzzaman Shuvo Department of CSE Khulna University of Engineering and Technology Khulna Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA USA

ISBN: (数字)9798331531782

ISBN: (纸本)9798331507923

The main contribution of this work is a new approach to prompt injection detection in NLP, done by ensembling BERT with LSTMs on top of GloVe embeddings that enhance them. We first show the efficiency of our approach on DeepSet Prompt Injections by comparing the performances of the models used in our study to the state-of-the-art on this dataset. Indeed, we obtained a final accuracy of $92.24 \%$ for an AUC-ROC of 0.994. Our approach leverages the strength of transformer-based models, besides better RNNs and larger vocabulary coverage, backed by pre-trained word embeddings that enable the learning of semantic relationships. We further extend the performance evaluation of individual components and their synergistic effect in the ensemble for a deeper analysis of results showing our approach with the use of the ensemble method beating single models, hence providing a robust solution for prompt injection detection. We discuss the implications of our findings for improving the security of AI-driven conversational systems and indicate likely future directions of research in this important area of AI safety.

关键词： Vocabulary Accuracy Semantics Transformers Solids Safety Ensemble learning Artificial intelligence Long short term memory Research and development

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AI based Chatbots using Deep Neural Networks in Education 2

AI based Chatbots using Deep Neural Networks in Education

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Chempavathy., B. Prabhu, S. Nagendra Varshitha, D.R. Vinita Lokeswari, Yaratapalli New Horizon College of Engineering Department of Cse Bengaluru India Srm Institute of Science Technology Kattankulathur School of Computing Department of Computational Intelligence Chennai603203 India

ISBN: (纸本)9781665400527

The present era of technology had a tremendous influence on society. With the advancements in the artificial intelligence and machine learning domains, machines are trying to impersonate humans. Chabot's are gaining popularity in this aspect and have changed the patterns of conversations between humans and computers. With the creation of these virtual assistants, the conversational services have been improvised. Basically, a Chabot is a software program programmed to interact with humans using artificial intelligence in messaging platforms. Nowadays, Chabot's are not just restricted to one field, they can be used in numerous fields in different forms. The Chabot introduced in this paper mainly concerns itself with college activities. College campuses are vast in the area. If a particular person has a query about which he wants to inquire, the individual would have to shuttle from here and there in order to gather the information for the query. So, the main intention of this research is to develop a college friendly chatterbot sooner or later, servicing the scholars. The tools used are artificial intelligence, such as natural language input and deep learning methods like deep neural networks. This uses LSTM, an extension of RNN, in order to process the user input. It trains the Chabot using these techniques and gives a response. All the data of the Chabot, which is the input given by the human, will be stored in a database and the data will be saved every time a query is asked for future use. © 2022 IEEE.

关键词： Long short-term memory

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Three-dimensional path following control system for net cage inspection using bionic robotic fish

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Information Processing in Agriculture 2022年第1期9卷 100-111页

作者： Yuanrong Chen Jingfen Qiao Jincun Liu Ran Zhao Dong An Yaoguang Wei College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China National Innovation Centre for Digital Fishery China Agricultural UniversityBeijing 100083China Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture Beijing 100083China Key Laboratory of Agricultural Information Acquisition Technology Ministry of AgricultureBeijing 100083China School of Computing The Australian National UniversityCanberra2601Australia

With the increasing impacts of overfishing and environmental pollution,the deep-sea cage culture of marine fishes has become an important direction of *** this paper,a tuna-like robotic fish with a three-dimensional helix path-following control system is designed for deep-sea net cage *** mimic the flexibility of the fish’s movement,the kinematic model of the robotic fish adopts a tuna-like double-joint design with an addi-tional thruster device at the *** the descending interval control plays a critical role in deep-sea net cage inspection,the control system utilizes the proportion integration differ-entiation(PID)based fuzzy logic control method to control the descending interval and yaw angle during the helix path movement.A polar coordinate path definition method is also proposed to simplify the reference path definition during net cage *** experi-mental results demonstrates that the proposed three-dimensional path-following model can conduct net inspection task in an interferential environment and move along prede-fined reference path.

关键词： Net cage inspection Bionic robotic fish Path following Three-dimensional motion control

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Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset using ER-VEC

Cyber-Attacks and Anomaly detection on CICIDS-2017 dataset u...

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Disruptive Technologies (ICDT), International Conference on

作者： Ravi Shankar Jha Kushagra Ojha Aryan Mishra Riya Mishra Abha Kaushik School of Computing DIT University Dehradun India Computer Science and Engineering GL Bajaj Institute of Technology and Management Greater Noida India

Securing a network the preliminary defense mechanisms of applied Security Compliance along with IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) that are applied all over the networks to block the passage of malicious content into it. With the advent of modern technologies and innovations in every field, there has been a significant load on the security architecture to provide for faultless, secure, and robust infrastructure. In today's modern day, firewalls are capable of blocking a major portion of malicious activity from creeping into the network. Still, attackers have been successful in penetrating in the form of internal attacks and can perform their targeted exploitation, like denial of service, link failures, ransomware, and so on, which has disastrous consequences. Thus, such anomalies do occur that pose a threat to data security and the underlying structure. This study aims to propose a novel technique, including the use of ER-VEC (Extra-Tree Random Voting Ensemble Classifier), to classify various attack network packets into benign and suspicious categories. We also did comparisons with SVM(support vector machine), DT (decision tree), RF(random Foreset), and EXTC(extra tree extension) proposed by other researchers, to testify our models we have also tested and compared on the same datasets, and compared the results proved to be more effective and efficient than the previous methodologies. This article started with a section introduction then section two related work which included with problem statement, section three with the proposed predicted model, then we covered result analysis and comparative study with proposed strategies by other researchers and conclusion and future scope.

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Social Media and Stock Market Prediction: A Big Data Approach

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Computers, Materials & Continua 2021年第5期67卷 2569-2583页

作者： Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz School of Computing Faculty of EngineeringUniversity Teknologi MalaysiaJohorMalaysia Department of Software Engineering University of Management and TechnologyLahorePakistan Collage of Business Abu Dhabi UniversityAbu DhabiUnited Arab Emirates Oxford Center for Islamic Studies The University of OxfordOxfordUK The University of Liverpool Management School The University of LiverpoolLiverpoolUK Department of Computer Engineering National University of TechnologyIslamabadPakistan School of Computing UTM Big Data CentreUniversiti Teknologi MalaysiaJohorMalaysia

Big data is the collection of large datasets from traditional and digital sources to identify trends and *** quantity and variety of computer data are growing exponentially for many *** example,retailers are building vast databases of customer sales *** are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and *** of big data include volume and variety in both structured and unstructured *** this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional *** studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision *** implemented naive Bayes and logistic regression classification *** results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.

关键词： Big data analytics artificial intelligence machine learning stock market social media business analytics

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An Energy Efficient IPv6 Packet Delivery Outline for Industrial IoT Over G.9959 Protocol Based Wireless Sensor Network (WSN)

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SN Computer Science 2024年第7期5卷 813页

作者： Veena, R.S. Khambra, Geetanjli Sayal, Rishi Singh, Nagendra Dhandayuthapani, V. Bala Sar, Sumit Kumar Department of Information Science and Engineering Dayananda Sagar Academy of Technology and Management Kanakapura Road Karnataka Bengaluru India Department of Computer Science and Applications The Bhopal School of Social Sciences MP Bhopal India Department of Computer Science and Engineering Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telangana Hyderabad 501506 India Department of Electrical and Electronics Engineering Trinity College of Engineering and Technology Telangana Karimnagar 505001 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas campus Shinas Oman Department of Computer Science BIT Durg Chattisgarh Durg India

The world is witnessing a significant paradigm shift in the delivery and management of industrial services, driven by the need for automated manufacturing in industries such as mining. This transformation is pivotal as it leverages the Internet to interconnect people and devices, giving rise to a new field known as the Industrial Internet of Things (IIoT). IIoT represents the fusion of IoT’s computational capabilities with the principles of the industrial revolution, facilitating extensive scalability through the transition from IPv4 to IPv6. A crucial challenge within IIoT is the battery life of sensor nodes, which is directly influenced by the efficiency of their operating modules and protocols. Various protocols have been tested within IIoT-based Wireless Sensor Networks (WSNs) to address this issue. However, these protocols often fall short in terms of data rate performance, which is essential for supporting the diverse range of IIoT applications and services. This manuscript investigates the IPv6 packet delivery rate, focusing on energy efficiency and delay. The research identifies the G.9959 protocol as superior to IEEE 802.15.4 for IIoT adoption. Additionally, it examines the impact of bandwidth variation on energy efficiency. Through comprehensive simulations, the proposed scheme demonstrates superior performance compared to existing protocols, highlighting its potential for enhancing IIoT applications. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： G.9959 IIoT IPv6 OPD WSN

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Hybrid Spotted Hyena and Simulated Annealing Optimization Algorithm(HSHOSAA-1) for Efficient Task Scheduling in a Clustered Cloud Environment 3

Hybrid Spotted Hyena and Simulated Annealing Optimization Al...

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3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Sivakumar, T.B. Maria Michael Visuvasam, L. Sangeetha, V. Bhuvana, S. Sathish Kumar, K. Sachet, K. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology School of Computing Dept of CSE Tamil nadu Chennai India RMk College of Engineering and Technology Dept of AIDS Tamil nadu Chennai India Sri Sai Ram Engineering College Dept of AIDS Tamil nadu Chennai India Sriram Engineering College Dept of CSE Tamil nadu Chennai India Sri Sai Ram Engineering College Dept of CSBS Tamil nadu Chennai India

ISBN: (纸本)9798350391565

Efficient task scheduling in the cloud is vital as it maximises the utilisation of resources, guaranteeing optimal allocation of computing resources such as CPU and memory. It improves the efficiency of the system by reducing the amount of time it spends idle and maximising the amount of work that can be done by applications and services. This article presents a new way for optimization algorithms by combining two approaches: the Spotted Hyena Optimisation algorithm (SHO) and the Simulated Annealing algorithm (SA). SHO utilises the cooperative hunting behaviours of hyenas to investigate the issue space and discover the most efficient or nearly perfect solutions for optimising job scheduling. SA enhances SHO by repeatedly refining solutions, resulting in increased convergence rates and attaining superior task scheduling results. The problem involves the selection of appropriate virtual machines (VMs) and the allocation of executors (resources), like a Bin Packing problem, which is a computationally difficult optimization work known as NP-Hard. The objective of our proposed HSHOSA-1 is to evaluate the requirements of tasks and the resources that are available for task scheduling. The main goal is to optimize task scheduling by minimizing expenses, preventing virtual machine overload, and ensuring balanced resource utilization. By conducting simulations on the CloudSim platform, the experimental findings show that the approach effectively decreases the number of virtual machines (VMs) needed to deploy all executors. This methodology efficiently reduces the total expenditure on resources in comparison to conventional scheduling strategies. © 2024 IEEE.

关键词： Simulated annealing

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