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arXiv 2024年

作者： Catalano, GianCarlo A.P.I. Brownlee, Alexander E.I. Cairns, David McCall, John Ainslie, Russell University of Stirling Computing Science and Mathematics Stirling United Kingdom Robert Gordon University National Subsea Centre Aberdeen United Kingdom BT Technology Applied Research Ipswich United Kingdom

Genetic Algorithms have established their capability for solving many complex optimization problems. Even as good solutions are produced, the user’s understanding of a problem is not necessarily improved, which can lead to a lack of confidence in the results. To mitigate this issue, explainability aims to give insight to the user by presenting them with the knowledge obtained by the algorithm. In this paper we introduce Partial Solutions in order to improve the explainability of solutions to combinatorial optimization problems. Partial Solutions represent beneficial traits found by analyzing a population, and are presented to the user for explainability, but also provide an explicit model from which new solutions can be generated. We present an algorithm that assembles a collection of Partial Solutions chosen to strike a balance between high fitness, simplicity and atomicity. Experiments with standard benchmarks show that the proposed algorithm is able to find Partial Solutions which improve explainability at reasonable computational cost without affecting search performance. © 2024, CC BY.

关键词： Optimization algorithms

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Securing IEC60870-5-101 Communication Protocol using SCADA Cryptographic and Device Authentication Gateway 11

Securing IEC60870-5-101 Communication Protocol using SCADA C...

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2022 11th International Conference on Information and Electronics Engineering, ICIEE 2022

作者： Qassim, Qais Saif Jamil, Norziana Mahdi, Mohammed Najah Ahmad Zawawi, Md Nabil Jamaludin, Md Zaini Jidin, Razali Rusli, Mohd Ezanee University of Technology and Applied Sciences Ibri Oman College of Computing and Informatics Universiti Tenaga Nasional Selangor Malaysia Institute of Power Engineering Universiti Tenaga Nasional Selangor Malaysia College of Engineering Universiti Tenaga Nasional Selangor Malaysia

Since the renowned Stuxnet attack on the Iranian refinery planet in 2010, there have been many attempts to harden the security of these systems. One of the most significant challenges impacting these systems' security is the outdated and insecure communication protocols that have been used for many years in various sectors. This work presents a practical solution to protect the SCADA's critical communications' confidentiality, authenticity, and integrity. This work put forward a hardware-based general-purpose cryptographic device based on FPGA technology. The device contains a handful of cryptographic algorithms that can be customized to harden the security of devices operating on the IEC 60870-5-101 communication protocol. The experimental results show that the proposed solution enhances the secrecy and integrity of the IEC 60870-5-101 messages and ensures the source's authenticity. © Published under licence by IOP Publishing Ltd.

关键词： Authentication

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Data Analytics on Eco-Conditional Factors Affecting Speech Recognition Rate of Modern Interaction Systems

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Journal of Mobile Multimedia 2022年第4期18卷 1153-1176页

作者： Kaladevi, A.C. Saravanakumar, R. Veena, K. Muthukumaran, V. Thillaiarasu, N. Kumar, S. Satheesh Department of Computer Science and Engineering Sona College of Technology Salem India Department of CSE Dayananda Sagar Academy of Technology and Management Bangalore 560082 India Department of Computer Science J.K.K.Nataraja College of Arts & Science Namakkal Dt. Komarapalayam India Department of Mathematics School of Applied Sciences REVA University Karnataka Bengaluru India School of Computing and Information Technology REVA University Bengaluru India Department of Computer Science School of Applied Sciences REVA University Bengaluru India

Speech-based Interaction systems contribute to the growing class of contemporary interactive techniques (Human-Computer Interactive system), which have emerged quickly in the last few years. Versatility, multi-channel synchronization, sensitivity, and timing are all notable characteristics of speech recognition. In addition, several variables influence the precision of voice interaction recognition. However, few researchers have done a significant study on the five eco-condition variables that tend to affect speech recognition rate (SRR): ambient noise, human noise, utterance speed, and frequency. The principal strategic goal of this research is to analyze the influence of the four variables mentioned earlier on SRR, and it includes many stages of experimentation on mixed noise speech data. The sparse representation-based analyzing technique is utilized to analyze the effects. Speech recognition is not noticeably affected by a person's usual speaking pace. As a result, high-frequency voice signals are more easily recognized (∼98.12%) than low-frequency speech signals in noisy environments. By performing the experiments, the test results may help design the distributive controlling and commanding systems. © 2022 River Publishers.

关键词： ambient noise eco-conditional factors frequency human noise Interaction system recognition rate utterance speed

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Detection-Loss Tolerant Real-Time Amplitude Measurement for Ultra-Fast Optical Quantum Processors: 5-dB Squeezing from DC to 43 GHz

Detection-Loss Tolerant Real-Time Amplitude Measurement for ...

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Conference on Lasers and Electro-Optics Europe (CLEO EUROPE)

作者： Asuka Inoue Takahiro Kashiwazaki Taichi Yamashima Naoto Takanashi Takushi Kazama Koji Enbutsu Kei Watanabe Takeshi Umeki Mamoru Endo Akira Furusawa NTT Device Technology Labs NTT Corporation Kanagawa Japan Department of Applied Physics School of Engineering The University of Tokyo Tokyo Japan RIKEN Center for Quantum Computing Optical Quantum Computing Research Team Saitama Japan

ISBN: (纸本)9798350345995

Measurement-based optical quantum computers (MBOQCs) using continuous-variables (CVs) of light are a candidate for large-scale fault-tolerant quantum computing [1]. This form of processing has a high carrier frequency of light, so it can also perform ultra-fast quantum computing. To achieve ultra-fast MBOQCs, broadband squeezed light generation and ultra-fast realtime amplitude measurement are required. So far, over-4.5-dB squeezed light generation, which is required for preparing a two-dimensional cluster state, has been demonstrated in THz bandwidth by using noise intensity measurement with a narrow bandwidth [2], [3]. However, the realtime amplitude measurement frequency is limited to several GHz. Up to now, the highest frequency is still limited to 9 GHz with 0.1-dB squeezing even though specific detectors have been designed [4]. This is because there is a tradeoff relationship between the bandwidth and the quantum efficiency of homodyne detectors. As a result, we cannot use a fast balanced homodyne detector such as a detector for high-speed optical communications with a large optical loss, which collapses the quantum states of light. To achieve ultra-fast realtime amplitude measurement, we need a new measurement method to overcome the tradeoff limitations.

关键词： Frequency measurement Optical computing Optical signal processing devices Quantum computation Quantum efficiency Squeezed states

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A Cascaded Design of Best Features Selection for Fruit Diseases Recognition

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Computers, Materials & Continua 2022年第1期70卷 1491-1507页

作者： Faiz Ali Shah Muhammad Attique Khan Muhammad Sharif Usman Tariq Aimal Khan Seifedine Kadry Orawit Thinnukool Department of Computer Science COMSATS University IslamabadWah Campus47080Pakistan Department of Computer Science HITEC University TaxilaTaxila47040Pakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia National University of Science and Technology Islamabad IslamabadPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Research Group of Embedded Systems and Mobile Application in Health Science College of ArtsMedia and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Fruit diseases seriously affect the production of the agricultural sector,which builds financial pressure on the country’s *** manual inspection of fruit diseases is a chaotic process that is both time and cost-consuming since it involves an accurate manual inspection by an ***,it is essential that an automated computerised approach is developed to recognise fruit diseases based on leaf *** to the literature,many automated methods have been developed for the recognition of fruit diseases at the early ***,these techniques still face some challenges,such as the similar symptoms of different fruit diseases and the selection of irrelevant *** processing and deep learning techniques have been extremely successful in the last decade,but there is still room for improvement due to these ***,we propose a novel computerised approach in this work using deep learning and featuring an ant colony optimisation(ACO)based *** proposed method consists of four fundamental steps:data augmentation to solve the imbalanced dataset,fine-tuned pretrained deep learning models(NasNetMobile andMobileNet-V2),the fusion of extracted deep features using matrix length,and finally,a selection of the best features using a hybrid ACO and a Neighbourhood Component Analysis(NCA).The best-selected features were eventually passed to many classifiers for final *** experimental process involved an augmented dataset and achieved an average accuracy of 99.7%.Comparison with existing techniques showed that the proposed method was effective.

关键词： Fruits diseases data augmentation deep learning features fusion feature selection

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Navigating Towards Fairness with Data Selection 39

Navigating Towards Fairness with Data Selection

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yixuan Li, Zhidong Wang, Yang Chen, Fang Fan, Xuhui Zhou, Feng School of Statistics and Data Science Southeast University China Data Science Institute University of Technology Sydney Australia School of Computing Macquarie University Australia Center for Applied Statistics School of Statistics Renmin University of China China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing China

ISBN: (纸本)157735897X

Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier’s architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning algorithms

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification 5

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Kavin, Balasubramanian Prabhu Thirumalraj, Arunadevi Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Department of Applied Sciences - Physics Ring Road Bellandur Post Bengaluru India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Tamil Nadu Kattankulathur603 203 India K.Ramakrishnan College of Technology Department of Computer Science Engineering Tamil Nadu Trichy India Loyola College Department of Computer Science Chennai600034 India Delhi India Symbiosis Institute of Technology Pune Aiml Department Pune412115 India

ISBN: (纸本)9798350383522

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person's diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN's superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme's relatively strong computational mo

关键词： Deep Belief Network Fruits Classification Fruits Grading Handcrafted features Restricted Boltzmann Machine

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Comparing Ensemble Learning Techniques on Data Transmission Reduction for IoT Systems 1st

Comparing Ensemble Learning Techniques on Data Transmission...

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1st International Conference on Advances in computing Research, ACR’23

作者： Salah, Ahmad Bekhit, Mahmoud Alkalbani, Asma M. Mohamed, Mohamed A. Lestari, Nur I. Fathalla, Ahmed Computer Science Department Faculty of Computers and Informatics Zagazig University Zagazig Sharkia Egypt Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Ad Dhahirah516 Oman Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Kent Institute Australia SydneyNSW Australia 2P Perfect Presentation Riyadh Riyadh Saudi Arabia Computer Science Department Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (纸本)9783031337420

The Internet of Things (IoT) systems include a massive number of connected devices. The communication between these devices requires a huge amount of communication. Thus, it is very crucial for an IoT system to reduce communication volumes by minimizing the amount of data transmission. There are several approaches to achieving the goal of data transmission reduction, such as data compression and dual prediction. Dual prediction schemes received more attention in comparison to data compression. The mainstream techniques for proposing dual prediction schemes in the literature can be classified into two main groups, namely, filter-based and deep learning-based methods. The filter-based methods, such as the 1-D Kalman filter, are lightweight in terms of running time and the model’s memory requirements. On the other hand, deep learning-based methods require more memory space and training time, but deep learning models are more accurate as predictive models in comparison to filter-based methods in dual prediction schemes. There are very limited efforts to utilize machine learning methods in dual prediction schemes as a compromise between the aforementioned mainstream techniques. In this work, we extended one of these limited efforts, which utilizes boosting ensemble learning as a machine learning predictive method in a proposed dual prediction scheme. The current work proposes exploring the performance gap between the three main approaches to ensemble learning, namely, boosting, stacking, and bagging. The three proposed ensemble learning models are evaluated on a real dataset and compared against state-of-the-art methods. The obtained results show that among the ensemble learning models, boosting and bagging models are better than the stacking model, but the three proposed ensemble learning models outperformed the state-of-the-art methods of comparison. For instance, the average numbers of mispredictions for all of the experiments were 580, 551, and 1,106 for boosting, ba

关键词： Forecasting

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FluxProg 2.0 - Extended Version of a Visual Tool for Teaching Programming Logic Using Flowcharts with Real and Simulated Robots

FluxProg 2.0 - Extended Version of a Visual Tool for Teachin...

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2024 Brazilian Symposium on Robotics (SBR), and 2024 Workshop on Robotics in Education (WRE)

作者： Matheus Biscaya Gutierrez João Alberto Fabro André Cristiano Cordeiro Academic Department of Informatics (DAINF-CT) Federal University of Technology - Paraná (UTFPR) Curitiba PR Brazil Graduate Program on Applied Computing (PPGCA) DAINF-CT - UTFPR Curitiba PR Brazil

ISBN: (数字)9798350391084

ISBN: (纸本)9798350391091

This paper presents a new teaching tool with the goal of facilitating the learning of basic programming concepts among high school students and university freshmen. The tool incorporates a user-friendly visual interface and robotics, expanding on the existing Fluxprog project. New features were implemented, such as variables, logical and mathematical operations, conditional structures, and loops, to motivate students to engage in challenges that promote logical thinking. This paper presents the design, implementation and evaluation of a programming teaching tool using a visual language based on flowcharts, with subsequent data collection through a questionnaire and analysis of preliminary results to evaluate the user's experience while interacting with the tool.

关键词： Visualization Flowcharts Conferences Education Data collection User experience Logic Robots Programming profession

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Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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