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Intelligent Web Spam Detection in the Consumer Internet of Things

IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Wang, Rong Zhuang, Xu Zhu, Xiaogang Bashir, Ali Kashif Dabel, Maryam M. Al Yu, Keping College of Computer Science Sichuan Normal University Chengdu China OPPO Electronics Corporation Chengdu China School of Public Policy and Administration Nanchang University Nanchang Jiangxi China Department of Computing and Mathematics Manchester Metropolitan University UK Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Graduate School of Science and Engineering Hosei University Tokyo Japan

The Consumer Internet of Things (CIoT) integrates the advantage of Internet of Things (IoT) technologies to provide convenience in consumers’ daily lives. With the rapid development of the CIoT, data collected from consumer smart devices has increased exponentially. In the CIoT, web pages, as internet information carriers, offer spammers opportunities to conduct security attacks, which could harm the CIoT systems. Inspired by this challenge, this paper introduces an intelligent feature extraction method, page2vec, and a new classification algorithm, RFiRF, to detect web spam in the CIoT. page2vec is based on a score propagation model, which calculates each page’s goodness and badness scores through the links of a web graph. It is observed that different scoring functions can produce different web page features. Based on this observation, 20 to 30 scoring functions are designed and incorporated into page2vec. This way, page2vec can automatically extract web page features from a web graph. Unfortunately, page2vec also brings a high dimensionality problem when constructing binary classifiers for spam detection. To address this problem, a new classification algorithm, called Random Forest in Random Forest (RFiRF), is proposed. RFiRF replaces a random forest’s meta-classifier (decision tree) with a random forest. It divides the sparse data space into dense sub-spaces by randomly sampling training instances and features. Experiments on two benchmark datasets show that 1) page2vec features are much more predictive than the raw link-based and node2vec features, and 2) RFiRF outperforms some classification algorithms in most cases when facing the high dimensionality problem. We hope this paper can give peers valuable insights into the CIoT security. IEEE

关键词： Feature extraction

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An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

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Computer Systems science & engineering 2023年第9期46卷 3993-4006页

作者： ShahidMohammad Ganie Pijush Kanti Dutta Pramanik Majid BashirMalik Anand Nayyar Kyung Sup Kwak School of Business Woxsen UniversityHyderabadTelangana502345India School of Computing Science&Engineering Galgotias UniversityGreater NoidaUP 203201India Department of Computer Sciences Baba Ghulam Shah Badshah UniversityRajouri185234India Graduate School Faculty of Information TechnologyDuy Tan UniversityDa Nang50000Vietnam Department of Information and Communication Engineering Inha University22212Korea

Cardiovascular disease is among the top five fatal diseases that affect lives ***,its early prediction and detection are crucial,allowing one to take proper and necessary measures at earlier *** learning(ML)techniques are used to assist healthcare providers in better diagnosing heart *** study employed three boosting algorithms,namely,gradient boost,XGBoost,and AdaBoost,to predict heart *** dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML *** data analysis is performed to find the characteristics of data samples about descriptive and inferential ***,it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation *** were recorded before and after the data preprocessing techniques were *** of all the algorithms,gradient boosting achieved the highest accuracy rate of 92.20%for the proposed *** proposed model yielded better results with gradient boosting in terms of precision,recall,and *** attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

关键词： Heart disease prediction machine learning classifiers ensemble approach XGBoost AdaBoost gradient boost

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ChatGPT4PCG 2 Competition: Prompt engineering for science Birds Level Generation

ChatGPT4PCG 2 Competition: Prompt Engineering for Science Bi...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Pittawat Taveekitworachai Febri Abdullah Mury F. Dewantoro Yi Xia Pratch Suntichaikul Ruck Thawonmas Julian Togelius Jochen Renz Graduate School of Information Science and Engineering Ritsumeikan University Ibaraki Osaka Japan College of Information Science and Engineering Ritsumeikan University Ibaraki Osaka Japan NYU Tandon School of Engineering New York University New York USA School of Computing The Australian National University Canberra Australia

ISBN: (数字)9798350350678

ISBN: (纸本)9798350350685

This paper presents the second ChatGPT4PCG competition at the 2024 IEEE Conference on Games. In this edition of the competition, we follow the first edition, but make several improvements and changes. We introduce a new evaluation metric along with allowing a more flexible format for participants’ submissions and making several improvements to the evaluation pipeline. Continuing from the first edition, we aim to foster and explore the realm of prompt engineering (PE) for procedural content generation (PCG). While the first competition saw success, it was hindered by various limitations; we aim to mitigate these limitations in this edition. We introduce diversity as a new metric to discourage submissions aimed at producing repetitive structures. Furthermore, we allow submission of a Python program instead of a prompt text file for greater flexibility in implementing advanced PE approaches, which may require control flow, including conditions and iterations. We also make several improvements to the evaluation pipeline with a better classifier for similarity evaluation and better-performing function signatures. We thoroughly evaluate the effectiveness of the new metric and the improved classifier. Additionally, we perform an ablation study to select a function signature to instruct ChatGPT for level generation. Finally, we provide implementation examples of various PE techniques in Python and evaluate their preliminary performance. We hope this competition serves as a resource and platform for learning about PE and PCG in general 1 . 1 Source code and raw data: https://***/chatgpt4pcg/experiments2024

关键词： Measurement Procedural generation Codes Pipelines Education Buildings Chatbots

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Experimental demonstration of a high-fidelity virtual two-qubit gate

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Physical Review Research 2024年第1期6卷 013235-013235页

作者： Akhil Pratap Singh Kosuke Mitarai Yasunari Suzuki Kentaro Heya Yutaka Tabuchi Keisuke Fujii Yasunobu Nakamura Department of Applied Physics Graduate School of Engineering The University of Tokyo Bunkyo-ku Tokyo 113-8656 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka 560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino 180-8585 Japan RIKEN Center for Quantum Computing (RQC) Wako Saitama 351-0198 Japan

We experimentally demonstrate a virtual two-qubit gate and characterize it using quantum process tomography (QPT). The virtual two-qubit gate decomposes an actual two-qubit gate into single-qubit unitary gates and projection gates in quantum circuits for expectation-value estimation. We implement projection gates via midcircuit measurements. The deterministic sampling scheme reduces the number of experimental circuit evaluations required for decomposing a virtual two-qubit gate. We also apply quantum error mitigation to suppress the effect of measurement errors and improve the average gate fidelity of a virtual controlled-Z (CZ) gate to fav=0.9938±0.0002. Our results highlight a practical approach to implement virtual two-qubit gates with high fidelities, which are useful for simulating quantum circuits using fewer qubits and implementing two-qubit gates on a distant pair of qubits.

关键词： Quantum algorithms Quantum algorithms & computation Quantum benchmarking Quantum circuits Quantum computation Quantum gates Quantum nondemolition measurement Quantum simulation

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Performance upper bound of a Grover-mixer quantum alternating operator ansatz

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Physical Review A 2025年第1期111卷 012401-012401页

作者： Ningyi Xie Jiahua Xu Tiejin Chen Xinwei Lee Yoshiyuki Saito Nobuyoshi Asai Dongsheng Cai Graduate School of Science and Technology University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan School of Computing and Augmented Intelligence Arizona State University Tempe Arizona 85281 USA Graduate School of Systems and Information Engineering University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan Present address: Singapore Management University Singapore. Graduate School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima 965-8580 Japan School of Computer Science and Engineering University of Aizu Aizuwakamatsu Fukushima 965-8580 Japan Faculty of Engineering Information and Systems University of Tsukuba 1 Chome-1-1 Tennodai Tsukuba Ibaraki 305-8577 Japan

The quantum alternating operator ansatz (QAOA) represents a branch of quantum algorithms for solving combinatorial optimization problems. A specific variant, the Grover-mixer (GM) QAOA, ensures uniform amplitude across states that share equivalent objective values. This property makes the algorithm independent of the problem structure, focusing instead on the distribution of objective values within the problem. In this work we provide an alternative proof for the upper bound on the probability of measuring a computational basis state from a GM QAOA circuit with a given depth, which is a critical factor in QAOA cost. Using this, we derive the upper bounds for the probability of sampling an optimal solution and for the approximation ratio of maximum optimization problems, both dependent on the objective value distribution. Through numerical analysis, we link the distribution to the problem size and build the regression models that relate the problem size, QAOA depth, and performance upper bound. Our results suggest that the GM QAOA provides a quadratic enhancement in sampling probability and requires circuit depth that scales exponentially with problem size to maintain consistent performance.

关键词： Combinatorial optimization

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Dynamic Programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates

arXiv

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arXiv 2024年

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizu-Wakamatsu Japan School of Computing and Information Systems Singapore Management University 80 Stamford Rd Singapore Graduate School of Science and Technology University of Tsukuba Ibaraki Tsukuba Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Tsukuba Japan School of Computer Science and Engineering University of Aizu Fukushima Aizu-Wakamatsu Japan

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024, CC BY.

关键词： Inverse problems

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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RPC: a large-scale and fine-grained retail product checkout dataset

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science China(Information sciences) 2022年第9期65卷 289-290页

作者： Xiu-Shen WEI Quan CUI Lei YANG Peng WANG Lingqiao LIU Jian YANG School of Computer Science and Engineering Nanjing University of Science and Technology Graduate School of IPS Waseda University Megvii Technolody School of Computing and Information Technology University of Wollongong School of Computer Science University of Adelaide

Over recent years, emerging interest has occurred in integrating computer vision technology into the retail industry. Automatic checkout (ACO) is one of the critical problems in this area which aims to automatically generate the shopping list from the images of the products to purchase(Figure 1(a)). The main challenge of this problem comes from the large-scale and the fine-grained nature of the product categories as well as the difficulty for collecting training images that reflect the realistic checkout scenarios due to continuous updates of the products.

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Learning Soft Robot Dynamics Using Differentiable Kalman Filters and Spatio-Temporal Embeddings

Learning Soft Robot Dynamics Using Differentiable Kalman Fil...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Xiao Liu Shuhei Ikemoto Yuhei Yoshimitsu Heni Ben Amor School of Computing and Augmented Intelligence Arizona State University Department of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology

This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding process is discussed to handle observations with varying sensor placements and sampling frequencies. The efficacy of this approach is demonstrated on a tensegrity robot arm by learning end-effector dynamics from demonstrations with complex bending motions. The model is proven to be robust against missing modalities, diverse sensor placement, and varying sampling rates. Additionally, the proposed framework is shown to identify physical interactions with humans during motion. The utilization of a differentiable filter presents a novel solution to the difficulties of modeling soft robot dynamics. Our approach shows substantial improvement in accuracy compared to state-of-the-art filtering methods, with at least a 24% reduction in mean absolute error (MAE) observed. Furthermore, the predicted end-effector positions show an average MAE of 25.77mm from the ground truth, highlighting the advantage of our approach. The code is available at https://***/ir-lab/soft_robot_DEnKF.

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